feat(webui): add first-run AI setup flow

Co-authored-by: Claude Sonnet 5 <noreply@anthropic.com>
Co-authored-by: chengyongru <chengyongru.ai@gmail.com>

docs/cli-reference.md

@@ -139,7 +139,7 @@ Interactive mode exits with `exit`, `quit`, `/exit`, `/quit`, `:q`, or `Ctrl+D`.
 
 | Command | Description |
 |---|---|
-| `nanobot webui` | Create config/workspace if needed, enable the local WebUI channel after confirmation, start the gateway, and open `http://127.0.0.1:8765` |
+| `nanobot webui` | Create config/workspace if needed, enable the local WebUI channel after confirmation, start the gateway, open `http://127.0.0.1:8765`, and follow new gateway logs |
 | `nanobot webui --background` | Deprecated; prints the equivalent explicit `nanobot gateway --background` command and exits |
 | `nanobot webui --dev` | Start the gateway and Vite together at `http://127.0.0.1:5173`, with live frontend updates |
 | `nanobot webui --no-open` | Prepare and start the WebUI without opening a browser |
@@ -344,7 +344,7 @@ remain accepted as no-op compatibility aliases.
 | Command | Description |
 |---|---|
 | `nanobot provider login openai-codex --set-main` | Authenticate Codex and select its current default model |
-| `nanobot provider login xai-grok --set-main` | Authenticate an eligible X Premium / Grok subscription and select Grok 4.5; hosted X Search is enabled for models that advertise support |
+| `nanobot provider login xai-grok --set-main` | Authenticate an eligible X Premium / Grok subscription and select Grok 4.6; hosted X Search is enabled for models that advertise support |
 | `nanobot provider login github-copilot --set-main` | Authenticate GitHub Copilot and select its current default model |
 | `nanobot provider logout openai-codex` | Remove OpenAI Codex OAuth state |
 | `nanobot provider logout xai-grok --config <path>` | Remove the selected nanobot instance's xAI OAuth state |

docs/configuration.md

@@ -729,6 +729,11 @@ Then run:
 nanobot agent -m "Hello!"
 ```
 
+The WebUI model selector loads the models available to the signed-in account
+from Codex's online catalog. Context-window and reasoning-effort metadata come
+from that response; if discovery is unavailable, nanobot keeps a small built-in
+fallback instead of emptying the selector.
+
 Codex Fast mode can be enabled from the WebUI provider settings, or with:
 
 ```json
@@ -764,11 +769,14 @@ nanobot provider login xai-grok --set-main
 nanobot agent -m "Hello from Grok."
 ```
 
-The default model is `xai-grok/grok-4.5` with a 500,000-token context window.
+The default model is `xai-grok/grok-4.6` with a 500,000-token context window.
-The provider reads xAI's model catalog and includes the server-hosted `x_search`
+The provider reads and caches xAI's online model catalog for both WebUI model
-tool only when the selected model advertises `supportsBackendSearch`. Models
+selection and runtime capabilities. Newly available models appear automatically;
-without that capability continue normally without hosted X Search. When enabled,
+when discovery fails, the last successful catalog or built-in fallback remains
-searches run inside xAI's Responses API and citations arrive as inline links.
+available. The server-hosted `x_search` tool is included only when the selected
+model advertises support. Models without that capability continue normally
+without hosted X Search. When enabled, searches run inside xAI's Responses API
+and citations arrive as inline links.
 Hosted X Search is on by default to preserve this behavior. It can be turned off in the
 WebUI provider settings or with `providers.xaiGrok.extraBody.tools: []`.
 
@@ -805,6 +813,10 @@ a nanobot update.
 
 GitHub Copilot uses OAuth instead of API keys. Requires a [GitHub account with a plan](https://github.com/features/copilot/plans) configured. No `providers.github_copilot` block is needed in `config.json`; `nanobot provider login` stores the OAuth session outside config.
 
+After login, the WebUI loads the account-specific Copilot model catalog online.
+Only models compatible with nanobot's current chat-completions or Responses
+transport are shown.
+
 For GitHub Enterprise / Copilot for Business, set the endpoint overrides you need before login:
 ```bash
 export NANOBOT_GITHUB_COPILOT_CLIENT_ID="your-enterprise-client-id"
@@ -2256,16 +2268,12 @@ When a user is idle for longer than a configured threshold, nanobot **proactivel
 
 How it works:
 1. **Idle detection**: On each idle tick (~1 s), checks whether an idle-session scan is due. By default, the full scan runs at most once per minute.
-2. **Background compaction**: Idle sessions summarize the older live prefix via LLM and keep the most recent legal suffix (currently 8 messages).
+2. **Background compaction**: Older context is summarized while the most recent messages remain available.
-3. **Summary injection**: When the user returns, the summary is injected as runtime context (one-shot, not persisted) alongside the retained recent suffix.
+3. **Session preservation**: The complete session history remains stored for later inspection and reuse.
-4. **Restart-safe resume**: The summary is also mirrored into session metadata so it can still be recovered after a process restart.
+4. **Restart-safe resume**: The compacted context remains available after a process restart.
 
 > [!NOTE]
-> Mental model: "summarize older context, keep the freshest live turns, **and overwrite the session file with the compact form.**" It is not a full `session.clear()`, but it is a write — not a soft cursor move.
+> Auto compact shortens the context sent to the model without deleting the session's structured message history.
->
-> Concretely, auto compact rewrites `sessions/<key>.jsonl` in place: older messages (including their structured `tool_calls` / `tool_call_id` / `reasoning_content`) are replaced by just the retained recent suffix (currently 8 messages), while the archived prefix is preserved only as a plain-text summary appended to `memory/history.jsonl` (or a `[RAW] ...` flattened dump if LLM summarization fails). The original structured JSON of those turns is no longer recoverable from the session file.
->
-> This differs from the **token-driven soft consolidation** that fires when a prompt exceeds the context budget: that path only advances an internal `last_consolidated` cursor and leaves the session file untouched, so the raw tool-call trail stays on disk and can still be replayed or audited. If you rely on that trail for debugging or auditing, set `idleCompactAfterMinutes` to `0` and let only the token-driven path run.
 
 ## Timezone
 

docs/providers.md

@@ -572,15 +572,23 @@ For OpenAI Codex:
 nanobot provider login openai-codex --set-main
 ```
 
+The WebUI reads the account's Codex model catalog online, including current
+context-window and reasoning-effort metadata. A small compatible catalog remains
+available when the service cannot be reached.
+
 For an eligible X Premium / Grok subscription:
 
 ```bash
 nanobot provider login xai-grok --set-main
 ```
 
-This selects `xai-grok/grok-4.5`. The provider reads xAI's model catalog and
+This selects `xai-grok/grok-4.6`. The WebUI model selector reads xAI's online
-exposes the hosted `x_search` tool only when the selected model advertises
+model catalog, so newly available subscription models appear without a nanobot
-`supportsBackendSearch`; otherwise the model runs without hosted X Search.
+release. Online metadata is cached and enriched with nanobot's curated labels;
+if xAI is temporarily unavailable, nanobot uses the last successful catalog or
+a small built-in fallback instead of emptying the selector. The same catalog
+controls whether the provider exposes the hosted `x_search` tool; models that do
+not advertise support continue without hosted X Search.
 When enabled, Grok can search current X posts and return inline source links
 without invoking a local nanobot tool. Credentials are stored under the
 active instance's `auth/xai.json` (normally `~/.nanobot/auth/xai.json`), not in
@@ -599,6 +607,10 @@ For GitHub Copilot:
 nanobot provider login github-copilot --set-main
 ```
 
+The WebUI reads the models enabled for the signed-in Copilot account. nanobot
+lists entries that support its current Copilot chat-completions or Responses
+transport and hides models that it cannot route safely.
+
 Each command authenticates the selected provider and makes its current default model active. OpenAI Codex and eligible GitHub Copilot models participate in [Responses state retention](./configuration.md#responses-state-and-compaction), while native compaction remains provider-capability-specific. OAuth providers are not valid automatic fallbacks. See [`troubleshooting.md`](./troubleshooting.md#provider-and-model-problems) for proxy, headless-login, model-name, and config-key errors.
 
 ## Provider Resolution

docs/webui.md

@@ -23,7 +23,9 @@ one is missing, starts or joins the same on-demand gateway used by the native
 TUI, and opens the browser. With a fresh config,
 it can open before a model is configured so you can finish setup in **Settings
 → Models**. The first-run path binds the WebUI to `127.0.0.1` by default, so
-it is not available from other devices on your LAN.
+it is not available from other devices on your LAN. While the launcher remains
+attached, it mirrors new log output from that exact gateway instance in the
+terminal without replaying older logs.
 
 After model setup, explicitly promote the shared gateway when you do not want to keep a client open:
 

nanobot/agent/context.py

@@ -30,11 +30,7 @@ from nanobot.security.workspace_access import WorkspaceScopeResolver
 from nanobot.session.keys import last_channel_from_metadata
 from nanobot.session.manager import Session
 from nanobot.session.summary import SessionSummary
-from nanobot.utils.helpers import (
+from nanobot.utils.helpers import detect_image_mime, load_bundled_template
-    detect_image_mime,
-    load_bundled_template,
-    truncate_text_to_tokens,
-)
 from nanobot.utils.prompt_templates import render_template
 
 
@@ -75,14 +71,29 @@ class PersistedPromptContextResolver:
         return channel, scope.project_path
 
 
+@dataclass(frozen=True, slots=True)
+class TranscriptInput:
+    """Raw turn inputs from which ``ContextBuilder`` assembles a transcript."""
+
+    history: list[dict[str, Any]]
+    current_message: str | None
+    media: Sequence[str] | None = None
+    current_role: str = "user"
+    session_summary: SessionSummary | None = None
+    runtime_context_blocks: Sequence[RuntimeContextBlock] | None = None
+
+    @property
+    def message_count(self) -> int:
+        """Number of boundary-preserving messages in the assembled transcript."""
+        return 1 + len(self.history) + (self.current_message is not None)
+
+
 class ContextBuilder:
     """Builds the context (system prompt + messages) for the agent."""
 
     BOOTSTRAP_FILES = ["AGENTS.md", "SOUL.md", "USER.md"]
     _SKIPPABLE_DEFAULTS = {"AGENTS.md", "USER.md"}
     _RUNTIME_CONTEXT_TAG = RUNTIME_CONTEXT_TAG
-    _MAX_RECENT_HISTORY = 50
-    _MAX_HISTORY_TOKENS = 8_000  # hard cap on recent history section size (tokens)
     _RUNTIME_CONTEXT_END = RUNTIME_CONTEXT_END
 
     def __init__(self, workspace: Path, timezone: str | None = None, disabled_skills: list[str] | None = None):
@@ -98,9 +109,6 @@ class ContextBuilder:
         session_summary: SessionSummary | None = None,
         workspace: Path | None = None,
         include_memory: bool = True,
-        include_memory_recent_history: bool = True,
-        session_key: str | None = None,
-        unified_session: bool = False,
     ) -> str:
         """Build the system prompt from identity, bootstrap files, memory, and skills."""
         root = workspace or self.workspace
@@ -138,29 +146,6 @@ class ContextBuilder:
         if skills_summary:
             parts.append(render_template("agent/skills_section.md", skills_summary=skills_summary))
 
-        if include_memory_recent_history:
-            entries = self.memory.read_recent_history_for_prompt(
-                since_cursor=self.memory.get_last_dream_cursor(),
-                session_key=session_key,
-                unified_session=unified_session,
-            )
-            if entries:
-                capped = entries[-self._MAX_RECENT_HISTORY:]
-                capped = self._without_duplicate_session_summary(
-                    capped,
-                    session_key=session_key,
-                    session_summary=session_summary,
-                )
-                if capped:
-                    history_text = "\n".join(
-                        f"- [{e['timestamp']}] {e['content']}" for e in capped
-                    )
-                    history_text = truncate_text_to_tokens(
-                        history_text,
-                        self._MAX_HISTORY_TOKENS,
-                    )
-                    parts.append("# Recent History\n\n" + history_text)
-
         if session_summary:
             parts.append(
                 "[Archived Context Summary]\n\n"
@@ -170,25 +155,6 @@ class ContextBuilder:
 
         return "\n\n---\n\n".join(parts)
 
-    @staticmethod
-    def _without_duplicate_session_summary(
-        entries: list[dict[str, Any]],
-        *,
-        session_key: str | None,
-        session_summary: SessionSummary | None,
-    ) -> list[dict[str, Any]]:
-        """Drop the history entry already represented by the session summary."""
-        if not session_summary:
-            return entries
-        for index in range(len(entries) - 1, -1, -1):
-            entry = entries[index]
-            if (
-                entry.get("session_key") == session_key
-                and entry.get("content") == session_summary["text"]
-            ):
-                return [*entries[:index], *entries[index + 1:]]
-        return entries
-
     def _get_identity(self, channel: str | None = None, workspace: Path | None = None) -> str:
         """Get the core identity section."""
         root = workspace or self.workspace
@@ -278,46 +244,68 @@ class ContextBuilder:
         runtime_context_blocks: Sequence[RuntimeContextBlock] | None = None,
         workspace: Path | None = None,
         include_memory: bool = True,
-        include_memory_recent_history: bool = True,
-        session_key: str | None = None,
-        unified_session: bool = False,
     ) -> list[dict[str, Any]]:
-        """Build the complete message list for an LLM call."""
+        """Compatibility wrapper for callers that need merged adjacent roles."""
+        messages = self.build_transcript(
+            TranscriptInput(
+                history=history,
+                current_message=current_message,
+                media=media,
+                current_role=current_role,
+                session_summary=session_summary,
+                runtime_context_blocks=runtime_context_blocks,
+            ),
+            channel=channel,
+            workspace=workspace,
+            include_memory=include_memory,
+        )
+        current = messages[-1]
+        if len(messages) < 2 or messages[-2].get("role") != current.get("role"):
+            return messages
+
+        merged = dict(messages[-2])
+        merged["content"] = self._merge_message_content(
+            merged.get("content"),
+            current.get("content"),
+        )
+        current_meta = current.get("_meta")
+        if current.get("role") == "user" and isinstance(current_meta, dict):
+            internal_meta = dict(merged.get("_meta") or {})
+            internal_meta.update(cast(dict[str, Any], current_meta))
+            merged["_meta"] = internal_meta
+        return [*messages[:-2], merged]
+
+    def build_transcript(
+        self,
+        transcript: TranscriptInput,
+        *,
+        channel: str | None = None,
+        workspace: Path | None = None,
+        include_memory: bool = True,
+    ) -> list[dict[str, Any]]:
+        """Build a model transcript while preserving the fresh-turn boundary."""
         root = workspace or self.workspace
         messages: list[dict[str, Any]] = [
             {
                 "role": "system",
                 "content": self.build_system_prompt(
                     channel=channel,
-                    session_summary=session_summary,
+                    session_summary=transcript.session_summary,
                     workspace=root,
                     include_memory=include_memory,
-                    include_memory_recent_history=include_memory_recent_history,
-                    session_key=session_key,
-                    unified_session=unified_session,
                 ),
             },
-            *history,
+            *transcript.history,
         ]
-        current = self.build_current_message(
+        if transcript.current_message is None:
-            current_message,
-            media=media,
-            current_role=current_role,
-            runtime_context_blocks=runtime_context_blocks,
-        )
-        if messages[-1].get("role") == current_role:
-            last = dict(messages[-1])
-            last["content"] = self._merge_message_content(
-                last.get("content"),
-                current.get("content"),
-            )
-            current_meta = current.get("_meta")
-            if current_role == "user" and isinstance(current_meta, dict):
-                internal_meta = dict(last.get("_meta") or {})
-                internal_meta.update(cast(dict[str, Any], current_meta))
-                last["_meta"] = internal_meta
-            messages[-1] = last
             return messages
+
+        current = self.build_current_message(
+            transcript.current_message,
+            media=list(transcript.media) if transcript.media else None,
+            current_role=transcript.current_role,
+            runtime_context_blocks=transcript.runtime_context_blocks,
+        )
         messages.append(current)
         return messages
 

nanobot/agent/context_governance.py

@@ -13,6 +13,7 @@ from typing import TYPE_CHECKING, Any, cast
 
 from loguru import logger
 
+from nanobot.providers.base import LLMUsage
 from nanobot.utils.helpers import (
     estimate_message_tokens,
     estimate_prompt_tokens_chain,
@@ -27,12 +28,6 @@ if TYPE_CHECKING:
     from nanobot.providers.base import LLMProvider
 
 SNIP_SAFETY_BUFFER = 1024
-MICROCOMPACT_MIN_CHARS = 500
-INFLIGHT_COMPACT_TARGET_RATIO = 0.85
-COMPACTABLE_TOOLS = frozenset({
-    "read_file", "exec", "grep", "find_files",
-    "web_search", "web_fetch", "list_dir", "list_exec_sessions",
-})
 # read_file is the recovery path for persisted results; exempting it prevents persist->read->persist loops.
 TOOL_RESULT_OFFLOAD_EXEMPT_TOOLS = frozenset({"read_file"})
 BACKFILL_CONTENT = "[Tool result unavailable — call was interrupted or lost]"
@@ -41,6 +36,27 @@ PLACEHOLDER_TEXTS = frozenset({
 })
 
 
+class ContextWindowExceededError(RuntimeError):
+    """Raised before a locally fitted request that still exceeds its budget."""
+
+    def __init__(
+        self,
+        *,
+        session_key: str | None,
+        estimated_tokens: int,
+        input_budget: int,
+        source: str,
+    ) -> None:
+        self.session_key = session_key
+        self.estimated_tokens = estimated_tokens
+        self.input_budget = input_budget
+        self.source = source
+        super().__init__(
+            "Model input still exceeds the local context budget after request fitting "
+            f"for {session_key or 'default'}: {estimated_tokens}/{input_budget} via {source}"
+        )
+
+
 def _tool_call_name_is_valid(tool_call: Any) -> bool:
     """Whether a persisted OpenAI-style tool_call carries a usable name.
 
@@ -67,7 +83,6 @@ class ContextGovernanceConfig:
     context_window_tokens: int | None = None
     context_block_limit: int | None = None
     max_tokens: int | None = None
-    inflight_start_index: int = 0
 
 
 class ContextGovernor:
@@ -77,17 +92,85 @@ class ContextGovernor:
         self,
         config: ContextGovernanceConfig,
         messages: list[dict[str, Any]],
-        compacted_tool_call_ids: set[str],
     ) -> list[dict[str, Any]]:
         updated = self.strip_placeholder_assistant_messages(messages)
         updated = self.strip_malformed_tool_calls(updated)
         updated = self.drop_orphan_tool_results(updated)
         updated = self.backfill_missing_tool_results(updated)
-        updated = self.apply_tool_result_budget(config, updated)
+        return self.apply_tool_result_budget(config, updated)
-        updated = self.compact_inflight_overflow(config, updated, compacted_tool_call_ids)
+
-        updated = self.snip_history(config, updated)
+    def fit_to_budget(
+        self,
+        config: ContextGovernanceConfig,
+        messages: list[dict[str, Any]],
+        *,
+        tool_definitions: list[dict[str, Any]] | None,
+    ) -> list[dict[str, Any]]:
+        """Fit a model-facing copy while keeping the source transcript intact."""
+        updated = self.snip_history(
+            config,
+            messages,
+            tool_definitions=tool_definitions,
+            force=True,
+        )
         updated = self.drop_orphan_tool_results(updated)
-        return self.backfill_missing_tool_results(updated)
+        updated = self.backfill_missing_tool_results(updated)
+        if not config.context_window_tokens:
+            return updated
+        budget = self.input_budget(config)
+        estimated, source = estimate_prompt_tokens_chain(
+            config.provider,
+            config.model,
+            updated,
+            tool_definitions,
+        )
+        if budget > 0 and estimated <= budget:
+            return updated
+        raise ContextWindowExceededError(
+            session_key=config.session_key,
+            estimated_tokens=estimated,
+            input_budget=budget,
+            source=source,
+        )
+
+    def fit_request(
+        self,
+        config: ContextGovernanceConfig,
+        messages: list[dict[str, Any]],
+        usage: LLMUsage | None,
+        *,
+        usage_matches_messages: bool,
+        tool_definitions: list[dict[str, Any]] | None,
+        request_context_tokens: int | None = None,
+    ) -> tuple[list[dict[str, Any]], bool]:
+        """Fit the request when its measured or estimated input is pressured."""
+        if not config.context_window_tokens:
+            return messages, False
+        budget = self.input_budget(config)
+        if (
+            request_context_tokens is None
+            and usage_matches_messages
+            and usage is not None
+            and usage.context_tokens is not None
+        ):
+            pressured = budget <= 0 or usage.context_tokens >= budget
+        else:
+            estimated, _ = estimate_prompt_tokens_chain(
+                config.provider,
+                config.model,
+                messages,
+                tool_definitions,
+            )
+            if request_context_tokens is not None:
+                estimated = max(estimated, request_context_tokens)
+            pressured = budget <= 0 or estimated >= budget
+        if not pressured:
+            return messages, False
+        return self.fit_to_budget(
+            config,
+            messages,
+            tool_definitions=tool_definitions,
+        ), True
 
     @staticmethod
     def input_budget(config: ContextGovernanceConfig) -> int:
@@ -326,71 +409,13 @@ class ContextGovernor:
                 updated[idx]["content"] = normalized
         return updated
 
-    def compact_inflight_overflow(
-        self,
-        config: ContextGovernanceConfig,
-        messages: list[dict[str, Any]],
-        compacted_tool_call_ids: set[str],
-    ) -> list[dict[str, Any]]:
-        """Compact in-flight tool results only when the request would overflow."""
-        budget = self.input_budget(config)
-        if budget <= 0:
-            return messages
-
-        tools = config.tools.get_definitions()
-        updated = self._apply_recorded_compactions(messages, compacted_tool_call_ids)
-        estimate, source = estimate_prompt_tokens_chain(
-            config.provider,
-            config.model,
-            updated,
-            tools,
-        )
-        if estimate <= budget:
-            return updated
-
-        target = int(budget * INFLIGHT_COMPACT_TARGET_RATIO)
-        candidates = self._inflight_compaction_candidates(
-            config,
-            updated,
-            compacted_tool_call_ids,
-        )
-        if not candidates:
-            return updated
-
-        for candidate_idx, (idx, tool_call_id) in enumerate(candidates):
-            is_newest_candidate = candidate_idx == len(candidates) - 1
-            if is_newest_candidate and estimate <= budget:
-                break
-            if tool_call_id in compacted_tool_call_ids:
-                continue
-            if updated is messages:
-                updated = [dict(m) for m in messages]
-            compacted_tool_call_ids.add(tool_call_id)
-            self._compact_tool_result_at(updated, idx)
-            estimate, source = estimate_prompt_tokens_chain(
-                config.provider,
-                config.model,
-                updated,
-                tools,
-            )
-            if estimate <= target:
-                break
-
-        logger.debug(
-            "In-flight context compaction for {}: prompt={} budget={} target={} via {}, ids={}",
-            config.session_key or "default",
-            estimate,
-            budget,
-            target,
-            source,
-            len(compacted_tool_call_ids),
-        )
-        return updated
-
     def snip_history(
         self,
         config: ContextGovernanceConfig,
         messages: list[dict[str, Any]],
+        *,
+        tool_definitions: list[dict[str, Any]] | None,
+        force: bool = False,
     ) -> list[dict[str, Any]]:
         if not messages or not config.context_window_tokens:
             return messages
@@ -399,14 +424,13 @@ class ContextGovernor:
         if budget <= 0:
             return messages
 
-        tools = config.tools.get_definitions()
         estimate, _ = estimate_prompt_tokens_chain(
             config.provider,
             config.model,
             messages,
-            tools,
+            tool_definitions,
         )
-        if estimate <= budget:
+        if not force and estimate <= budget:
             return messages
 
         system_messages = [dict(msg) for msg in messages if msg.get("role") == "system"]
@@ -419,7 +443,7 @@ class ContextGovernor:
             config.provider,
             config.model,
             system_messages,
-            tools,
+            tool_definitions,
         )
         remaining_budget = max(0, budget - max(system_tokens, fixed_tokens))
         kept: list[dict[str, Any]] = []
@@ -434,16 +458,6 @@ class ContextGovernor:
 
         return system_messages + self._legal_history_tail(kept, non_system)
 
-    @staticmethod
-    def _tool_result_compaction_message(message: dict[str, Any]) -> str:
-        name = message.get("name", "tool")
-        return (
-            f"Error: The previous {name} result was compacted to fit context because it was too "
-            "large. Do not repeat the same call unchanged. Retry with a narrower path, query, "
-            "range, or result limit, use another tool, or tell the user the task cannot fit in "
-            "the available context."
-        )
-
     def _legal_history_tail(
         self,
         kept: list[dict[str, Any]],
@@ -462,50 +476,3 @@ class ContextGovernor:
             if messages[idx].get("role") == "user":
                 return messages[idx:]
         return []
-
-    def _apply_recorded_compactions(
-        self,
-        messages: list[dict[str, Any]],
-        compacted_tool_call_ids: set[str],
-    ) -> list[dict[str, Any]]:
-        if not compacted_tool_call_ids:
-            return messages
-        updated = messages
-        for idx, msg in enumerate(messages):
-            if msg.get("role") != "tool":
-                continue
-            tool_call_id = msg.get("tool_call_id")
-            if not tool_call_id or str(tool_call_id) not in compacted_tool_call_ids:
-                continue
-            compaction_message = self._tool_result_compaction_message(msg)
-            if msg.get("content") == compaction_message:
-                continue
-            if updated is messages:
-                updated = [dict(m) for m in messages]
-            updated[idx]["content"] = compaction_message
-        return updated
-
-    def _inflight_compaction_candidates(
-        self,
-        config: ContextGovernanceConfig,
-        messages: list[dict[str, Any]],
-        compacted_tool_call_ids: set[str],
-    ) -> list[tuple[int, str]]:
-        compactable: list[tuple[int, str]] = []
-        for idx, msg in enumerate(messages):
-            if idx < config.inflight_start_index:
-                continue
-            if msg.get("role") != "tool" or msg.get("name") not in COMPACTABLE_TOOLS:
-                continue
-            tool_call_id = msg.get("tool_call_id")
-            if not tool_call_id or str(tool_call_id) in compacted_tool_call_ids:
-                continue
-            content = msg.get("content")
-            if not isinstance(content, str) or len(content) < MICROCOMPACT_MIN_CHARS:
-                continue
-            compactable.append((idx, str(tool_call_id)))
-
-        return compactable
-
-    def _compact_tool_result_at(self, messages: list[dict[str, Any]], idx: int) -> None:
-        messages[idx]["content"] = self._tool_result_compaction_message(messages[idx])

nanobot/agent/loop.py

@@ -14,6 +14,7 @@ from collections.abc import Coroutine, Iterable, Mapping
 from contextlib import AbstractContextManager, ExitStack, nullcontext, suppress
 from dataclasses import dataclass, field
 from enum import Enum, auto
+from functools import partial
 from pathlib import Path
 from typing import TYPE_CHECKING, Any, Awaitable, Callable, TypeVar, cast
 
@@ -23,7 +24,7 @@ from nanobot.agent import context as agent_context
 from nanobot.agent import model_presets as preset_helpers
 from nanobot.agent.autocompact import AutoCompact
 from nanobot.agent.automation_turns import publish_next_deferred_turn
-from nanobot.agent.context import ContextBuilder, PersistedPromptContextResolver
+from nanobot.agent.context import ContextBuilder, PersistedPromptContextResolver, TranscriptInput
 from nanobot.agent.cron_turns import CronTurnCoordinator
 from nanobot.agent.hook import AgentHook, AgentTurnHookFactory
 from nanobot.agent.memory import Consolidator
@@ -135,7 +136,7 @@ class TurnContext:
     session: Session | None = None
 
     history: list[dict[str, Any]] = field(default_factory=list)
-    initial_messages: list[dict[str, Any]] = field(default_factory=list)
+    transcript_input: TranscriptInput | None = None
     provider_state: ProviderConversationState | None = field(default=None, repr=False)
     request_context: RequestContext | None = None
     runtime_context_blocks: list[RuntimeContextBlock] = field(default_factory=list)
@@ -443,7 +444,6 @@ class AgentLoop:
                 workspace_scopes=self.workspace_scopes,
                 unified_session=unified_session,
             ),
-            unified_session=unified_session,
         )
         self.auto_compact = AutoCompact(
             sessions=self.sessions,
@@ -723,22 +723,15 @@ class AgentLoop:
             return True
         return False
 
-    def _build_initial_messages(self, ctx: TurnContext) -> list[dict[str, Any]]:
+    def _build_transcript_input(self, ctx: TurnContext) -> TranscriptInput:
-        """Build the initial message list for the LLM turn."""
+        """Capture the persisted history and fresh input as separate transcript parts."""
         assert ctx.session is not None
-        scope = self.workspace_scopes.for_message(ctx.msg, ctx.session.metadata)
+        return TranscriptInput(
-        return self.context.build_messages(
             history=ctx.history,
             current_message=ctx.msg.content,
             media=ctx.msg.media if ctx.kind is TurnKind.USER and ctx.msg.media else None,
-            channel=ctx.delivery.route.channel,
             session_summary=ctx.pending_summary,
-            workspace=scope.project_path,
             runtime_context_blocks=ctx.runtime_context_blocks,
-            include_memory=ctx.session.policy.persist,
-            include_memory_recent_history=not ctx.ephemeral,
-            session_key=ctx.session.key,
-            unified_session=self._unified_session,
         )
 
     def _request_context_for_turn(self, ctx: TurnContext) -> RequestContext:
@@ -859,6 +852,22 @@ class AgentLoop:
             metadata={**metadata, "render_as": "text"},
         )
 
+    def _track_active_task(self, key: str, task: asyncio.Task[Any]) -> None:
+        """Track active session work until its task group becomes empty."""
+        tasks = self._active_tasks.setdefault(key, set())
+        tasks.add(task)
+        task.add_done_callback(partial(self._active_task_done, key, tasks))
+
+    def _active_task_done(
+        self,
+        key: str,
+        tasks: set[asyncio.Task[Any]],
+        task: asyncio.Task[Any],
+    ) -> None:
+        tasks.discard(task)
+        if not tasks and self._active_tasks.get(key) is tasks:
+            self._active_tasks.pop(key, None)
+
     async def _cancel_active_tasks(self, key: str) -> int:
         """Cancel and await all active work for *key*.
 
@@ -929,7 +938,7 @@ class AgentLoop:
 
     async def _run_agent_loop(
         self,
-        initial_messages: list[dict[str, Any]],
+        transcript_input: TranscriptInput,
         on_progress: Callable[..., Awaitable[None]] | None = None,
         on_stream: Callable[[str], Awaitable[None]] | None = None,
         on_stream_end: Callable[..., Awaitable[None]] | None = None,
@@ -1110,6 +1119,12 @@ class AgentLoop:
             message_metadata=request_metadata,
             session_metadata=session.metadata if session is not None else None,
         )
+        transcript_builder = partial(
+            self.context.build_transcript,
+            channel=request_ctx.channel,
+            workspace=effective_scope.project_path,
+            include_memory=session.policy.persist if session is not None else True,
+        )
         if request_context is None:
             request_ctx = dataclasses.replace(
                 request_ctx,
@@ -1156,11 +1171,13 @@ class AgentLoop:
                 run_extra_hooks_for_ephemeral=run_extra_hooks_for_ephemeral,
             ))
             result = await self.runner.run(AgentRunSpec(
-                initial_messages=initial_messages,
+                initial_messages=None,
                 tools=effective_tools,
                 runtime=runtime,
                 max_iterations=self.max_iterations,
                 max_tool_result_chars=self.max_tool_result_chars,
+                transcript_input=transcript_input,
+                transcript_builder=transcript_builder,
                 hook=hook,
                 concurrent_tools=True,
                 workspace=effective_scope.project_path,
@@ -1349,12 +1366,7 @@ class AgentLoop:
                 # Compute the effective session key before dispatching
                 # This ensures /stop command can find tasks correctly when unified session is enabled
                 task = asyncio.create_task(self._dispatch(msg))
-                active_tasks: set[asyncio.Task[Any]] = self._active_tasks.setdefault(
+                self._track_active_task(effective_key, task)
-                    effective_key,
-                    set(),
-                )
-                active_tasks.add(task)
-                task.add_done_callback(active_tasks.discard)
         finally:
             await self.aclose()
 
@@ -1878,6 +1890,13 @@ class AgentLoop:
                 session,
                 runtime=runtime,
             )
+            # Token consolidation may have committed a replacement checkpoint
+            # after the compact stage captured its summary for this request.
+            ctx.session, ctx.pending_summary = self.auto_compact.prepare_session(
+                session,
+                ctx.session_key,
+            )
+            session = ctx.require_session()
         is_subagent = ctx.kind is TurnKind.SYSTEM and ctx.msg.sender_id == "subagent"
 
         _hist_kwargs: dict[str, Any] = {
@@ -1968,7 +1987,7 @@ class AgentLoop:
             # Upgrade the replay-safe baseline to the resumable state before
             # prompt assembly and the first model checkpoint.
             self.sessions.save(session)
-        ctx.initial_messages = self._build_initial_messages(ctx)
+        ctx.transcript_input = self._build_transcript_input(ctx)
 
         if ctx.on_progress is None:
             ctx.on_progress = ctx.delivery.progress_callback()
@@ -1980,9 +1999,10 @@ class AgentLoop:
         if ctx.visible_run_started_at is None:
             ctx.visible_run_started_at = time.time()
         await ctx.delivery.running(started_at=ctx.visible_run_started_at)
+        assert ctx.transcript_input is not None
         with capture_message_deliveries() as message_sends:
             result = await self._run_agent_loop(
-                ctx.initial_messages,
+                ctx.transcript_input,
                 runtime=runtime,
                 on_progress=ctx.on_progress,
                 on_stream=ctx.on_stream,

nanobot/agent/memory.py

@@ -35,6 +35,7 @@ from nanobot.utils.helpers import (
     estimate_prompt_tokens_chain,
     strip_think,
     truncate_text,
+    truncate_text_to_tokens,
 )
 from nanobot.utils.prompt_templates import render_template
 from nanobot.utils.workspace_prompts import (
@@ -61,12 +62,6 @@ class MemoryStore:
     # Deliberately excludes memory/.dream_cursor so progress bookkeeping never
     # appears as a durable-memory edit in the audit record.
     _DREAM_CONTENT_PATHS = ("SOUL.md", "USER.md", "memory/MEMORY.md")
-    # Per-file cap when embedding current contents into the Dream prompt. The
-    # durable files are tiny in practice (~5 KB total), but a runaway file must
-    # not unbounded the prompt.
-    _DREAM_FILE_EMBED_CAP = 8000
-    _INTERNAL_HISTORY_SESSION_PREFIXES = ("cron:", "dream:")
-    _INTERNAL_HISTORY_SESSION_KEYS = {"heartbeat"}
     _LEGACY_ENTRY_START_RE = re.compile(r"^\[(\d{4}-\d{2}-\d{2}[^\]]*)\]\s*")
     _LEGACY_TIMESTAMP_RE = re.compile(r"^\[(\d{4}-\d{2}-\d{2} \d{2}:\d{2})\]\s*")
     _LEGACY_RAW_MESSAGE_RE = re.compile(
@@ -260,6 +255,29 @@ class MemoryStore:
 
     # -- history.jsonl — append-only, JSONL format ---------------------------
 
+    def _normalize_history_entry(
+        self,
+        entry: str,
+        *,
+        max_chars: int | None = None,
+    ) -> str:
+        """Return the exact bounded, model-safe text accepted by the journal."""
+        limit = max_chars if max_chars is not None else _HISTORY_ENTRY_HARD_CAP
+        raw = entry.rstrip()
+        content = strip_think(raw)
+        if len(content) > limit:
+            if not self._oversize_logged:
+                self._oversize_logged = True
+                logger.warning(
+                    "history entry exceeds {} chars ({}); truncating. "
+                    "Usually means a caller forgot its own cap; "
+                    "further occurrences suppressed.",
+                    limit,
+                    len(content),
+                )
+            content = truncate_text(content, limit)
+        return content
+
     def append_history(
         self,
         entry: str,
@@ -274,27 +292,16 @@ class MemoryStore:
         persisted. If the cleaned content is empty but the raw entry wasn't,
         the record is persisted with an empty string rather than falling back
         to the raw leak — otherwise `strip_think`'s guarantees would be
-        undone by history replay / consolidation downstream.
+        undone when Dream consumes the journal entry.
 
         A defensive cap (*max_chars*, default ``_HISTORY_ENTRY_HARD_CAP``) is
         applied as a final safety net: individual callers should cap their own
         content more tightly; this default only exists to catch unintentional
         large writes (e.g. an LLM echoing its input back as a "summary").
         """
-        limit = max_chars if max_chars is not None else _HISTORY_ENTRY_HARD_CAP
         ts = datetime.now().strftime("%Y-%m-%d %H:%M")
         raw = entry.rstrip()
-        if len(raw) > limit:
+        content = self._normalize_history_entry(entry, max_chars=max_chars)
-            if not self._oversize_logged:
-                self._oversize_logged = True
-                logger.warning(
-                    "history entry exceeds {} chars ({}); truncating. "
-                    "Usually means a caller forgot its own cap; "
-                    "further occurrences suppressed.",
-                    limit, len(raw),
-                )
-            raw = truncate_text(raw, limit)
-        content = strip_think(raw)
         # Cursor allocation and the append must be atomic: concurrent writers
         # could otherwise read the same current cursor and emit duplicates.
         with self._append_lock:
@@ -302,7 +309,7 @@ class MemoryStore:
             if raw and not content:
                 logger.debug(
                     "history entry {} stripped to empty (likely template leak); "
-                    "persisting empty content to avoid re-polluting context",
+                    "persisting empty content to avoid re-polluting Dream input",
                     cursor,
                 )
             record = {"cursor": cursor, "timestamp": ts, "content": content}
@@ -392,36 +399,6 @@ class MemoryStore:
         """Return history entries with a valid cursor > *since_cursor*."""
         return [e for e, c in self._iter_valid_entries() if c > since_cursor]
 
-    @classmethod
-    def _is_internal_history_session(cls, session_key: str | None) -> bool:
-        if not session_key:
-            return False
-        return (
-            session_key in cls._INTERNAL_HISTORY_SESSION_KEYS
-            or session_key.startswith(cls._INTERNAL_HISTORY_SESSION_PREFIXES)
-        )
-
-    def read_recent_history_for_prompt(
-        self,
-        since_cursor: int,
-        *,
-        session_key: str | None,
-        unified_session: bool = False,
-    ) -> list[dict[str, Any]]:
-        """Return unprocessed history entries safe to inject into a turn prompt."""
-        entries = self.read_unprocessed_history(since_cursor=since_cursor)
-        if session_key is None:
-            return entries
-        if not unified_session:
-            return [e for e in entries if e.get("session_key") == session_key]
-
-        return [
-            entry
-            for entry in entries
-            if (entry_session := entry.get("session_key")) == session_key
-            or not self._is_internal_history_session(entry_session)
-        ]
-
     def compact_history(self) -> None:
         """Drop oldest processed entries without discarding pending Dream input."""
         if self.max_history_entries <= 0:
@@ -568,9 +545,7 @@ class MemoryStore:
         Returns ``(prompt, last_cursor)`` or ``None`` if nothing to process.
 
         The current contents of the durable memory files (SOUL.md, USER.md,
-        memory/MEMORY.md) are embedded so the model edits the real files rather
+        memory/MEMORY.md) reach Dream through the normal agent system context.
-        than a stale mental model — eliminating a class of failed/out-of-bounds
-        edits that previously produced hallucinated audit records.
         """
         last_cursor = self.get_last_dream_cursor()
         entries = self.read_unprocessed_history(since_cursor=last_cursor)
@@ -583,35 +558,9 @@ class MemoryStore:
             for e in batch
         )
         template = self._dream_template()
-        files_section = self._render_current_memory_files()
+        prompt = f"{template}\n\n## Conversation History\n{history_text}"
-        prompt = (
-            f"{template}\n\n{files_section}\n\n"
-            f"## Conversation History\n{history_text}"
-        )
         return (prompt, batch[-1]["cursor"])
 
-    def _render_current_memory_files(self) -> str:
-        """Render the durable memory files' current contents for the Dream prompt.
-
-        Missing files render as ``(empty)``; oversized files are capped. The
-        section is the ground truth the model must edit against.
-        """
-        files = [
-            ("SOUL.md", self.soul_file),
-            ("USER.md", self.user_file),
-            ("memory/MEMORY.md", self.memory_file),
-        ]
-        blocks: list[str] = []
-        for label, path in files:
-            try:
-                content = path.read_text(encoding="utf-8") if path.exists() else ""
-            except OSError:
-                content = ""
-            if len(content) > self._DREAM_FILE_EMBED_CAP:
-                content = truncate_text(content, self._DREAM_FILE_EMBED_CAP) + "\n...[truncated]"
-            blocks.append(f"### {label}\n{content}" if content.strip() else f"### {label}\n(empty)")
-        return "## Current Memory Files\n" + "\n\n".join(blocks)
-
     def dream_content_diff(self) -> str:
         """Structured summary of uncommitted changes to the durable memory files.
 
@@ -718,21 +667,28 @@ class MemoryStore:
         *,
         max_chars: int | None = None,
         session_key: str | None = None,
-    ) -> None:
+    ) -> str:
-        """Fallback: dump raw messages to history.jsonl without LLM summarization."""
+        """Persist and return a bounded raw checkpoint when summarization degrades."""
-        limit = max_chars if max_chars is not None else _RAW_ARCHIVE_MAX_CHARS
+        checkpoint = self._build_raw_checkpoint(messages, max_chars=max_chars)
-        formatted = truncate_text(
+        self.append_history(checkpoint, session_key=session_key)
-            self._format_messages(public_history_messages(messages)),
-            limit,
-        )
-        self.append_history(
-            f"[RAW] {len(messages)} messages\n"
-            f"{formatted}",
-            session_key=session_key,
-        )
         logger.warning(
             "Memory consolidation degraded: raw-archived {} messages", len(messages)
         )
+        return checkpoint
+
+    def _build_raw_checkpoint(
+        self,
+        messages: list[dict[str, Any]],
+        *,
+        max_chars: int | None = None,
+    ) -> str:
+        """Build the same bounded checkpoint as :meth:`raw_archive` without writing it."""
+        limit = max_chars if max_chars is not None else _RAW_ARCHIVE_MAX_CHARS
+        checkpoint = (
+            f"[RAW] {len(messages)} messages\n"
+            f"{self._format_messages(public_history_messages(messages))}"
+        )
+        return self._normalize_history_entry(checkpoint, max_chars=limit)
 
     # ------------------------------------------------------------------
     # Dream helpers
@@ -787,12 +743,11 @@ class MemoryStore:
 # Memory ingestion and legacy context-pressure coordination
 # ---------------------------------------------------------------------------
 
-# Individual history.jsonl writers cap their own payloads tightly; the
+# Raw fallbacks use a tighter cap. Completed model summaries may scale with the
-# _HISTORY_ENTRY_HARD_CAP at append_history() is a belt-and-suspenders default
+# configured generation budget, while append_history() still enforces the
-# that catches any new caller that forgot to set its own cap.
+# emergency hard cap against pathological provider output.
-_RAW_ARCHIVE_MAX_CHARS = 16_000       # fallback dump (LLM failed)
+_RAW_ARCHIVE_MAX_CHARS = 16_000   # fallback dump (LLM failed)
-_ARCHIVE_SUMMARY_MAX_CHARS = 8_000    # LLM-produced consolidation summary
+_HISTORY_ENTRY_HARD_CAP = 64_000  # emergency cap in append_history
-_HISTORY_ENTRY_HARD_CAP = 64_000      # emergency cap in append_history
 
 
 class MemoryArchiver:
@@ -809,13 +764,45 @@ class MemoryArchiver:
         build_messages: Callable[..., list[dict[str, Any]]],
         get_tool_definitions: Callable[[], list[dict[str, Any]]],
         resolve_prompt_context: Callable[[Session], tuple[str | None, Path | None]] | None = None,
-        unified_session: bool = False,
     ) -> None:
         self.store = store
         self._build_messages = build_messages
         self._get_tool_definitions = get_tool_definitions
         self._resolve_prompt_context = resolve_prompt_context
-        self.unified_session = unified_session
+
+    def _raw_checkpoint(
+        self,
+        messages: list[dict[str, Any]],
+        *,
+        session_key: str,
+        previous_summary: str | None,
+        max_tokens: int,
+    ) -> str:
+        """Persist the failed chunk and return a bounded replacement checkpoint."""
+        raw = self.store.raw_archive(messages, session_key=session_key)
+        token_limit = max(1, max_tokens)
+        if not previous_summary:
+            return truncate_text_to_tokens(raw, token_limit)
+
+        combined = (
+            "[Previous archived context]\n"
+            f"{previous_summary}\n\n"
+            "[Newly archived raw context]\n"
+            f"{raw}"
+        )
+        bounded = truncate_text_to_tokens(combined, token_limit)
+        if bounded == combined:
+            return combined
+
+        # Keep evidence from both sides when their full concatenation cannot fit.
+        section_limit = max(1, (token_limit - 32) // 2)
+        return truncate_text_to_tokens(
+            "[Previous archived context]\n"
+            f"{truncate_text_to_tokens(previous_summary, section_limit)}\n\n"
+            "[Newly archived raw context]\n"
+            f"{truncate_text_to_tokens(raw, section_limit)}",
+            token_limit,
+        )
 
     async def archive(
         self,
@@ -825,48 +812,53 @@ class MemoryArchiver:
         session_key: str,
         request_messages: list[dict[str, Any]],
         request_tools: list[dict[str, Any]],
+        previous_summary: str | None = None,
     ) -> str | None:
         """Execute a prepared archive request and persist its result."""
         if not messages:
             return None
+
+        def raw_fallback() -> str:
+            return self._raw_checkpoint(
+                messages,
+                session_key=session_key,
+                previous_summary=previous_summary,
+                max_tokens=runtime.generation.max_tokens,
+            )
+
         try:
             with llm_usage_source("dream"):
                 response = await runtime.provider.chat_with_retry(
                     model=runtime.model,
                     messages=request_messages,
                     tools=request_tools,
-                    tool_choice="none",
                     temperature=runtime.generation.temperature,
                     max_tokens=runtime.generation.max_tokens,
                     reasoning_effort=runtime.generation.reasoning_effort,
                 )
         except Exception:
             logger.warning("Memory archive provider call failed, raw-dumping to history")
-            self.store.raw_archive(messages, session_key=session_key)
+            return raw_fallback()
-            return None
         if response.finish_reason in {"error", "length"}:
             logger.warning(
                 "Memory archive provider did not complete ({}), raw-dumping to history",
                 response.finish_reason,
             )
-            self.store.raw_archive(messages, session_key=session_key)
+            return raw_fallback()
-            return None
         if response.has_tool_calls is True:
             logger.warning("Memory archive provider returned tool calls, raw-dumping to history")
-            self.store.raw_archive(messages, session_key=session_key)
+            return raw_fallback()
-            return None
         summary = response.content
         if not summary or not summary.strip():
             logger.warning("Memory archive provider returned no summary, raw-dumping to history")
-            self.store.raw_archive(messages, session_key=session_key)
+            return raw_fallback()
-            return None
+        summary = self.store._normalize_history_entry(summary)
-        if summary.strip() == "(nothing)":
+        if not summary:
+            logger.warning("Memory archive provider summary was not safe to replay, raw-dumping")
+            return raw_fallback()
+        if summary == "(nothing)":
             return "(nothing)"
-        self.store.append_history(
+        self.store.append_history(summary, session_key=session_key)
-            summary,
-            max_chars=_ARCHIVE_SUMMARY_MAX_CHARS,
-            session_key=session_key,
-        )
         return summary
 
     async def archive_session(
@@ -881,13 +873,26 @@ class MemoryArchiver:
         messages = list(session.messages[session.last_archived:archive_end])
         if not messages:
             return None
+        session_summary = session_summary_from_metadata(
+            session.metadata,
+            fallback_last_active=session.updated_at,
+        )
+        previous_summary = session_summary["text"] if session_summary else None
+
+        def raw_fallback() -> str:
+            return self._raw_checkpoint(
+                messages,
+                session_key=session.key,
+                previous_summary=previous_summary,
+                max_tokens=runtime.generation.max_tokens,
+            )
+
         if input_token_budget <= 0:
             logger.debug(
                 "Memory archive has no safe input budget for {}; raw-dumping",
                 session.key,
             )
-            self.store.raw_archive(messages, session_key=session.key)
+            return raw_fallback()
-            return None
         prefix = Session(
             key=session.key,
             messages=list(session.messages[:archive_end]),
@@ -903,13 +908,8 @@ class MemoryArchiver:
                 "Memory archive cannot replay the full chunk for {}; raw-dumping",
                 session.key,
             )
-            self.store.raw_archive(messages, session_key=session.key)
+            return raw_fallback()
-            return None
+        prompt = render_template("agent/consolidator_archive.md", strip=True)
-        prompt = render_template(
-            "agent/consolidator_archive.md",
-            strip=True,
-            archive_count=len(archive_history),
-        )
         channel = session.key.split(":", 1)[0] if ":" in session.key else None
         workspace: Path | None = None
         if self._resolve_prompt_context is not None:
@@ -918,13 +918,8 @@ class MemoryArchiver:
             history=history,
             current_message=prompt,
             channel=channel,
-            session_summary=session_summary_from_metadata(
+            session_summary=session_summary,
-                session.metadata,
-                fallback_last_active=session.updated_at,
-            ),
             workspace=workspace,
-            session_key=session.key,
-            unified_session=self.unified_session,
         )
         tools = self._get_tool_definitions()
         estimated, source = estimate_prompt_tokens_chain(
@@ -941,14 +936,14 @@ class MemoryArchiver:
                 input_token_budget,
                 source,
             )
-            self.store.raw_archive(messages, session_key=session.key)
+            return raw_fallback()
-            return None
         return await self.archive(
             messages,
             runtime=runtime,
             session_key=session.key,
             request_messages=request_messages,
             request_tools=tools,
+            previous_summary=previous_summary,
         )
 
 
@@ -964,20 +959,16 @@ class Consolidator:
         build_messages: Callable[..., list[dict[str, Any]]],
         get_tool_definitions: Callable[[], list[dict[str, Any]]],
         resolve_prompt_context: Callable[[Session], tuple[str | None, Path | None]] | None = None,
-        unified_session: bool = False,
     ):
         self.store = store
         self.sessions = sessions
-        self.unified_session = unified_session
         self._build_messages = build_messages
         self._get_tool_definitions = get_tool_definitions
-        self._resolve_prompt_context = resolve_prompt_context
         self.archiver = MemoryArchiver(
             store=store,
             build_messages=build_messages,
             get_tool_definitions=get_tool_definitions,
             resolve_prompt_context=resolve_prompt_context,
-            unified_session=unified_session,
         )
         self._locks: weakref.WeakValueDictionary[str, asyncio.Lock] = (
             weakref.WeakValueDictionary()
@@ -1013,13 +1004,18 @@ class Consolidator:
             return []
         return session.get_history()
 
-    def _persist_last_summary(self, session: Session, summary: str | None) -> None:
+    @staticmethod
-        if summary and summary != "(nothing)":
+    def _set_last_summary(
+        session: Session,
+        summary: str,
+        *,
+        last_active: datetime | None = None,
+    ) -> None:
+        if summary != "(nothing)":
             session.metadata["_last_summary"] = {
                 "text": summary,
-                "last_active": session.updated_at.isoformat(),
+                "last_active": (last_active or session.updated_at).isoformat(),
             }
-            self.sessions.save(session)
 
     def estimate_session_prompt_tokens(
         self,
@@ -1039,8 +1035,6 @@ class Consolidator:
             current_message="[token-probe]",
             channel=channel,
             session_summary=summary,
-            session_key=session.key,
-            unified_session=self.unified_session,
         )
         return estimate_prompt_tokens_chain(
             runtime.provider,
@@ -1057,24 +1051,6 @@ class Consolidator:
             - self._SAFETY_BUFFER
         )
 
-    async def archive(
-        self,
-        messages: list[dict[str, Any]],
-        *,
-        runtime: LLMRuntime,
-        session_key: str,
-        request_messages: list[dict[str, Any]],
-        request_tools: list[dict[str, Any]],
-    ) -> str | None:
-        """Compatibility wrapper for the extracted MemoryArchiver."""
-        return await self.archiver.archive(
-            messages,
-            runtime=runtime,
-            session_key=session_key,
-            request_messages=request_messages,
-            request_tools=request_tools,
-        )
-
     async def archive_session(
         self,
         session: Session,
@@ -1101,26 +1077,23 @@ class Consolidator:
         The budget reserves space for completion tokens and a safety buffer
         so the LLM request never exceeds the context window.
         """
-        if runtime.context_window_tokens <= 0:
-            return
-
         lock = self.get_lock(session.key)
         async with lock:
             # Refresh session reference: AutoCompact may have replaced it.
             fresh = self.sessions.get_or_create(session.key)
             if fresh is not session:
                 session = fresh
+            if runtime.context_window_tokens <= 0:
+                return
             if not session.messages:
                 return
 
             budget = self._input_token_budget(runtime)
-            last_summary: str | None = None
             estimated, source = self.estimate_session_prompt_tokens(
                 session,
                 runtime=runtime,
             )
             if estimated <= 0:
-                self._persist_last_summary(session, last_summary)
                 return
             if estimated < budget:
                 unarchived_count = len(session.messages) - session.last_archived
@@ -1132,7 +1105,6 @@ class Consolidator:
                     source,
                     unarchived_count,
                 )
-                self._persist_last_summary(session, last_summary)
                 return
 
             end_idx = self.pick_consolidation_boundary(session)
@@ -1160,18 +1132,12 @@ class Consolidator:
                 archive_end=end_idx,
                 runtime=runtime,
             )
-            # Advance either way: archive_session raw-archives on degradation,
+            if summary is None:
-            # and replaying the same chunk would duplicate Memory material.
+                return
-            if summary:
+            self._set_last_summary(session, summary)
-                last_summary = summary
             session.last_archived = end_idx
             self.sessions.save(session)
 
-            # Persist the last summary to session metadata so it can be injected
-            # into the runtime context on the next prepare_session() call, aligning
-            # the summary injection strategy with AutoCompact._archive().
-            self._persist_last_summary(session, last_summary)
-
     async def compact_idle_session(
         self,
         session_key: str,
@@ -1209,12 +1175,10 @@ class Consolidator:
                 archive_end=archive_end,
                 runtime=runtime,
             )
+            if summary is None:
+                return None
 
-            if summary and summary != "(nothing)":
+            self._set_last_summary(session, summary, last_active=last_active)
-                session.metadata["_last_summary"] = {
-                    "text": summary,
-                    "last_active": last_active.isoformat(),
-                }
 
             # A turn can append while the provider call is in flight. Advance only
             # through the captured batch so new messages remain eligible next time.

nanobot/agent/runner.py

@@ -14,6 +14,7 @@ from typing import Any, cast
 
 from loguru import logger
 
+from nanobot.agent.context import TranscriptInput
 from nanobot.agent.context_governance import (
     ContextGovernanceConfig,
     ContextGovernor,
@@ -66,6 +67,7 @@ ContinuationCallback = Callable[[], str | None]
 RetryWaitCallback = Callable[[str], Awaitable[None]]
 CheckpointCallback = Callable[[dict[str, Any]], Awaitable[None]]
 InjectionCallback = Callable[..., Awaitable[Iterable[Any] | None]]
+TranscriptBuilder = Callable[[TranscriptInput], list[dict[str, Any]]]
 
 _DEFAULT_ERROR_MESSAGE = "Sorry, I encountered an error calling the AI model."
 _ARREARAGE_ERROR_MESSAGE = (
@@ -94,11 +96,13 @@ def _restore_outer_whitespace(content: str, original: str | None) -> str:
 class AgentRunSpec:
     """Configuration for a single agent execution."""
 
-    initial_messages: list[dict[str, Any]]
+    initial_messages: list[dict[str, Any]] | None
     tools: ToolRegistry
     runtime: LLMRuntime
     max_iterations: int
     max_tool_result_chars: int
+    transcript_input: TranscriptInput | None = None
+    transcript_builder: TranscriptBuilder | None = None
     hook: AgentHook | None = None
     error_message: str | None = _DEFAULT_ERROR_MESSAGE
     max_iterations_message: str | None = None
@@ -135,6 +139,17 @@ class AgentRunResult:
     provider_state: ProviderConversationState | None = field(default=None, repr=False)
 
 
+@dataclass(slots=True)
+class _ModelRequestState:
+    """Per-run state used to govern the next provider request."""
+
+    config: ContextGovernanceConfig
+    conversation: ProviderConversationStateController
+    usage: LLMUsage | None = None
+    messages: list[dict[str, Any]] | None = None
+    tool_definitions: list[dict[str, Any]] | None = None
+
+
 class AgentRunner:
     """Run a tool-capable LLM loop without product-layer concerns."""
 
@@ -410,7 +425,7 @@ class AgentRunner:
 
     async def run(self, spec: AgentRunSpec) -> AgentRunResult:
         hook = spec.hook or AgentHook()
-        messages = list(spec.initial_messages)
+        messages = self._initial_transcript(spec)
         context = AgentRunHookContext(messages=deepcopy(messages))
         llm_usage_source_token = bind_llm_usage_source(
             spec.llm_usage_source or source_from_session_key(spec.session_key)
@@ -462,6 +477,19 @@ class AgentRunner:
             finally:
                 reset_llm_usage_source(llm_usage_source_token)
 
+    @staticmethod
+    def _initial_transcript(spec: AgentRunSpec) -> list[dict[str, Any]]:
+        """Resolve exactly one supported source for the initial model transcript."""
+        if spec.transcript_input is not None:
+            if spec.initial_messages is not None:
+                raise ValueError("provide either transcript_input or initial_messages, not both")
+            if spec.transcript_builder is None:
+                raise ValueError("transcript_builder is required with transcript_input")
+            return list(spec.transcript_builder(spec.transcript_input))
+        if spec.initial_messages is None:
+            raise ValueError("initial_messages is required without transcript_input")
+        return list(spec.initial_messages)
+
     async def _run_core(
         self,
         spec: AgentRunSpec,
@@ -483,7 +511,6 @@ class AgentRunner:
         length_recovery_parts: list[str] = []
         had_injections = False
         injection_cycles = 0
-        compacted_tool_call_ids: set[str] = set()
         pending_stream_content: str | None = None
         conversation_state = ProviderConversationStateController(
             provider=spec.runtime.provider,
@@ -502,39 +529,29 @@ class AgentRunner:
             context_window_tokens=spec.runtime.context_window_tokens,
             context_block_limit=spec.context_block_limit,
             max_tokens=spec.runtime.generation.max_tokens,
-            inflight_start_index=len(spec.initial_messages),
+        )
+        request_state = _ModelRequestState(
+            config=governance_config,
+            conversation=conversation_state,
         )
 
         for iteration in range(spec.max_iterations):
-            # Keep the persisted conversation untouched. Context governance
-            # may repair or compact historical messages for the model, but
-            # those synthetic edits must not shift the append boundary used
-            # later when the caller saves only the new turn. A governance
-            # failure must stop the run instead of sending an ungoverned copy.
-            messages_for_model = self.context_governor.prepare_for_model(
-                governance_config,
-                messages,
-                compacted_tool_call_ids,
-            )
             context = AgentHookContext(
                 iteration=iteration,
                 messages=messages,
                 session_key=spec.session_key,
             )
             await hook.before_iteration(context)
-            provider_context = conversation_state.prepare_request(
-                messages,
-                context_window_tokens=spec.runtime.context_window_tokens,
-                model_messages=messages_for_model,
-            )
             response = await self._request_model(
                 spec,
-                messages_for_model,
+                messages,
                 hook,
                 context,
-                conversation_state=conversation_state,
+                request_state=request_state,
-                provider_context=provider_context,
+                transcript=messages,
             )
+            assert request_state.messages is not None
+            messages_for_model = request_state.messages
             conversation_state.observe_response(response, messages)
             context.response = response
             context.tool_calls = list(response.tool_calls)
@@ -546,7 +563,7 @@ class AgentRunner:
                 response.content,
             )
             response.content = cleaned_content
-            raw_usage = self._usage_or_estimate(spec, messages_for_model, response)
+            raw_usage = self._record_request_usage(spec, request_state, response)
             context.usage = raw_usage
             usage = self._merge_usage(usage, raw_usage)
             if reasoning_text and not context.streamed_reasoning:
@@ -620,7 +637,6 @@ class AgentRunner:
                     self.context_governor.prepare_for_model(
                         governance_config,
                         messages,
-                        compacted_tool_call_ids,
                     )
                     if response.provider_state is not None
                     else None
@@ -686,14 +702,13 @@ class AgentRunner:
                 )
                 if hook.wants_streaming():
                     await hook.on_stream_end(context, resuming=False)
-                retry_messages = self._finalization_retry_messages(messages_for_model)
                 response = await self._request_finalization_retry(
                     spec,
                     messages_for_model,
+                    request_state=request_state,
                     transcript=messages,
-                    conversation_state=conversation_state,
                 )
-                retry_usage = self._usage_or_estimate(spec, retry_messages, response)
+                retry_usage = self._record_request_usage(spec, request_state, response)
                 usage = self._merge_usage(usage, retry_usage)
                 raw_usage = self._merge_usage(raw_usage, retry_usage)
                 context.response = response
@@ -880,7 +895,7 @@ class AgentRunner:
                     hook,
                     messages,
                     usage,
-                    conversation_state,
+                    request_state=request_state,
                 )
             if terminal_content is None:
                 terminal_content = self._max_iterations_fallback(spec)
@@ -927,6 +942,60 @@ class AgentRunner:
         kwargs["reasoning_effort"] = generation.reasoning_effort
         return kwargs
 
+    def _prepare_model_request(
+        self,
+        state: _ModelRequestState,
+        messages: list[dict[str, Any]],
+        *,
+        tool_definitions: list[dict[str, Any]] | None,
+        transcript: list[dict[str, Any]] | None = None,
+    ) -> tuple[list[dict[str, Any]], ProviderCallContext | None]:
+        """Prepare, fit, and record the exact payload sent to a provider."""
+        prepared = self.context_governor.prepare_for_model(state.config, messages)
+        supplemental_messages = (
+            [prepared[-1]] if transcript is not None and tool_definitions is None else None
+        )
+        model_messages = None if supplemental_messages is not None else prepared
+        request_context_tokens = (
+            state.conversation.estimate_request_context_tokens(
+                transcript,
+                model_messages=model_messages,
+                supplemental_messages=supplemental_messages,
+                tool_definitions=tool_definitions,
+            )
+            if transcript is not None
+            else None
+        )
+        usage_matches_messages = (
+            state.messages is not None
+            and prepared == state.messages
+            and tool_definitions == state.tool_definitions
+        )
+        prepared, fitted = self.context_governor.fit_request(
+            state.config,
+            prepared,
+            state.usage,
+            usage_matches_messages=usage_matches_messages,
+            tool_definitions=tool_definitions,
+            request_context_tokens=request_context_tokens,
+        )
+        provider_context = (
+            state.conversation.prepare_request(
+                transcript,
+                context_window_tokens=state.config.context_window_tokens,
+                model_messages=model_messages,
+                supplemental_messages=supplemental_messages,
+                resume_state=not fitted,
+            )
+            if transcript is not None
+            else state.conversation.independent_request_context(
+                context_window_tokens=state.config.context_window_tokens,
+            )
+        )
+        state.messages = deepcopy(prepared)
+        state.tool_definitions = deepcopy(tool_definitions)
+        return prepared, provider_context
+
     async def _request_model(
         self,
         spec: AgentRunSpec,
@@ -934,21 +1003,29 @@ class AgentRunner:
         hook: AgentHook,
         context: AgentHookContext,
         *,
+        request_state: _ModelRequestState,
         malformed_retry: bool = False,
-        conversation_state: ProviderConversationStateController,
+        transcript: list[dict[str, Any]] | None,
-        provider_context: ProviderCallContext | None = None,
     ) -> LLMResponse:
         timeout_s = self._resolve_llm_timeout_s(spec)
+        tool_definitions = spec.tools.get_definitions()
+        messages, provider_context = self._prepare_model_request(
+            request_state,
+            messages,
+            tool_definitions=tool_definitions,
+            transcript=transcript,
+        )
 
         kwargs = self._build_request_kwargs(
             spec,
             messages,
-            tools=spec.tools.get_definitions(),
+            tools=tool_definitions,
         )
         wants_streaming = hook.wants_streaming()
 
         active_hosted_tools: dict[str, dict[str, Any]] = {}
         native_reasoning_open = False
+        native_reasoning_close_task: asyncio.Task[None] | None = None
         request_started_at = 0.0
         first_output_at: float | None = None
         generation_started_at: float | None = None
@@ -972,11 +1049,29 @@ class AgentRunner:
             generation_started_at = None
 
         async def _close_native_reasoning() -> None:
-            nonlocal native_reasoning_open
+            nonlocal native_reasoning_open, native_reasoning_close_task
-            if not native_reasoning_open:
+            if native_reasoning_close_task is None:
-                return
+                if not native_reasoning_open:
-            native_reasoning_open = False
+                    return
-            await hook.emit_reasoning_end()
+                native_reasoning_open = False
+                native_reasoning_close_task = asyncio.create_task(
+                    hook.emit_reasoning_end()
+                )
+
+            close_task = native_reasoning_close_task
+            cancellation: asyncio.CancelledError | None = None
+            while not close_task.done():
+                try:
+                    await asyncio.shield(close_task)
+                except asyncio.CancelledError as exc:
+                    cancellation = cancellation or exc
+            try:
+                close_task.result()
+            finally:
+                if native_reasoning_close_task is close_task:
+                    native_reasoning_close_task = None
+            if cancellation is not None:
+                raise cancellation
 
         async def _provider_tool_event(event: dict[str, Any]) -> None:
             if event.get("kind") != "hosted_tool":
@@ -1051,6 +1146,10 @@ class AgentRunner:
                 await coro if outer_timeout_s is None
                 else await asyncio.wait_for(coro, timeout=outer_timeout_s)
             )
+        except asyncio.CancelledError:
+            _pause_generation()
+            await _close_native_reasoning()
+            raise
         except asyncio.TimeoutError:
             if outer_timeout_s is None:
                 response = LLMResponse(
@@ -1098,11 +1197,9 @@ class AgentRunner:
             )
             return await self._request_model(
                 spec, retry_messages, hook, context,
+                request_state=request_state,
                 malformed_retry=True,
-                conversation_state=conversation_state,
+                transcript=None,
-                provider_context=conversation_state.independent_request_context(
-                    context_window_tokens=spec.runtime.context_window_tokens,
-                ),
             )
         if (
             all_dropped
@@ -1118,9 +1215,7 @@ class AgentRunner:
             return await self._request_no_tools(
                 spec,
                 fallback_messages,
-                provider_context=conversation_state.independent_request_context(
+                request_state=request_state,
-                    context_window_tokens=spec.runtime.context_window_tokens,
-                ),
             )
         return response
 
@@ -1188,21 +1283,17 @@ class AgentRunner:
         spec: AgentRunSpec,
         messages: list[dict[str, Any]],
         *,
+        request_state: _ModelRequestState,
         transcript: list[dict[str, Any]],
-        conversation_state: ProviderConversationStateController,
     ) -> LLMResponse:
         retry_messages = self._finalization_retry_messages(messages)
-        provider_context = conversation_state.prepare_request(
-            transcript,
-            context_window_tokens=spec.runtime.context_window_tokens,
-            supplemental_messages=[retry_messages[-1]],
-        )
         response = await self._request_no_tools(
             spec,
             retry_messages,
-            provider_context=provider_context,
+            request_state=request_state,
+            transcript=transcript,
         )
-        conversation_state.observe_response(
+        request_state.conversation.observe_response(
             response,
             transcript,
             adopt_candidate_state=False,
@@ -1221,16 +1312,15 @@ class AgentRunner:
         hook: AgentHook,
         messages: list[dict[str, Any]],
         usage: LLMUsage | None,
-        conversation_state: ProviderConversationStateController,
+        *,
+        request_state: _ModelRequestState,
     ) -> tuple[str | None, LLMUsage | None]:
         retry_messages = self._budget_exhausted_finalization_messages(messages)
         try:
             response = await self._request_no_tools(
                 spec,
                 retry_messages,
-                provider_context=conversation_state.independent_request_context(
+                request_state=request_state,
-                    context_window_tokens=spec.runtime.context_window_tokens,
-                ),
             )
         except Exception:
             logger.exception(
@@ -1239,7 +1329,7 @@ class AgentRunner:
             )
             return None, usage
 
-        raw_usage = self._usage_or_estimate(spec, retry_messages, response)
+        raw_usage = self._record_request_usage(spec, request_state, response)
         usage = self._merge_usage(usage, raw_usage)
         if response.finish_reason == "error" or response.has_tool_calls:
             logger.warning(
@@ -1268,8 +1358,15 @@ class AgentRunner:
         spec: AgentRunSpec,
         messages: list[dict[str, Any]],
         *,
-        provider_context: ProviderCallContext | None = None,
+        request_state: _ModelRequestState,
+        transcript: list[dict[str, Any]] | None = None,
     ) -> LLMResponse:
+        messages, provider_context = self._prepare_model_request(
+            request_state,
+            messages,
+            tool_definitions=None,
+            transcript=transcript,
+        )
         kwargs = self._build_request_kwargs(
             spec,
             messages,
@@ -1281,17 +1378,18 @@ class AgentRunner:
         )
         timeout_s = self._resolve_llm_timeout_s(spec)
         try:
-            return (
+            response = (
                 await coro
                 if timeout_s is None
                 else await asyncio.wait_for(coro, timeout=timeout_s)
             )
         except asyncio.TimeoutError:
-            return LLMResponse(
+            response = LLMResponse(
                 content=f"Error calling LLM: timed out after {timeout_s:g}s",
                 finish_reason="error",
                 error_kind="timeout",
             )
+        return response
 
     @staticmethod
     def _resolve_llm_timeout_s(spec: AgentRunSpec) -> float | None:
@@ -1333,33 +1431,53 @@ class AgentRunner:
         spec: AgentRunSpec,
         messages: list[dict[str, Any]],
         response: LLMResponse,
+        *,
+        tool_definitions: list[dict[str, Any]] | None,
     ) -> LLMUsage | None:
         usage = response.usage
         if response.finish_reason == "error":
             if usage is None or usage.total_tokens == 0:
                 usage = LLMUsage.empty_request()
         elif usage is None or usage.total_tokens == 0:
-            usage = self._estimate_response_usage(spec, messages, response)
+            usage = self._estimate_response_usage(
+                spec,
+                messages,
+                response,
+                tool_definitions=tool_definitions,
+            )
         return usage.with_timing(
             generation_ms=response.generation_ms,
             ttft_ms=response.ttft_ms,
         )
 
+    def _record_request_usage(
+        self,
+        spec: AgentRunSpec,
+        state: _ModelRequestState,
+        response: LLMResponse,
+    ) -> LLMUsage | None:
+        assert state.messages is not None
+        state.usage = self._usage_or_estimate(
+            spec,
+            state.messages,
+            response,
+            tool_definitions=state.tool_definitions,
+        )
+        return state.usage
+
     def _estimate_response_usage(
         self,
         spec: AgentRunSpec,
         messages: list[dict[str, Any]],
         response: LLMResponse,
+        *,
+        tool_definitions: list[dict[str, Any]] | None,
     ) -> LLMUsage:
-        try:
-            tools = spec.tools.get_definitions()
-        except Exception:
-            tools = None
         prompt_tokens, _ = estimate_prompt_tokens_chain(
             spec.runtime.provider,
             spec.runtime.model,
             messages,
-            tools,
+            tool_definitions,
         )
         assistant_message = build_assistant_message(
             response.content or "",

nanobot/agent/tools/execution.py

@@ -43,6 +43,13 @@ _WORKSPACE_VIOLATION_MARKERS: tuple[str, ...] = (
 )
 
 
+def _with_retry_hint(payload: str) -> str:
+    """Append the recovery hint exactly once."""
+    if payload.endswith(_RETRY_HINT):
+        return payload
+    return payload + _RETRY_HINT
+
+
 async def execute_tool_calls(
     tools: ToolRegistry,
     tool_calls: list[ToolCallRequest],
@@ -105,7 +112,7 @@ async def _execute_tool_call(
             "status": "error",
             "detail": "repeated external lookup blocked",
         }
-        return lookup_error + _RETRY_HINT, event
+        return _with_retry_hint(lookup_error), event
 
     prepare_call = cast(
         Callable[[str, Any], object] | None,
@@ -119,6 +126,7 @@ async def _execute_tool_call(
             if len(prepared_tuple) == 3:
                 tool, params, prep_error = cast(tuple[Any, Any, str | None], prepared_tuple)
     if prep_error:
+        payload = _with_retry_hint(prep_error)
         event = {
             "name": tool_call.name,
             "status": "error",
@@ -126,14 +134,14 @@ async def _execute_tool_call(
         }
         handled = _classify_violation(
             raw_text=prep_error,
-            soft_payload=prep_error + _RETRY_HINT,
+            soft_payload=payload,
             event=event,
             tool_call=tool_call,
             workspace_violation_counts=workspace_violation_counts,
         )
         if handled is not None:
             return handled
-        return prep_error + _RETRY_HINT, event
+        return payload, event
 
     await hook.before_execute_tool(context, tool_call, tool, params)
     try:
@@ -150,10 +158,9 @@ async def _execute_tool_call(
             "status": "error",
             "detail": str(exc),
         }
-        payload = f"Error: {type(exc).__name__}: {exc}"
+        payload = _with_retry_hint(f"Error: {type(exc).__name__}: {exc}")
         handled = _classify_violation(
             raw_text=str(exc),
-            # Preserve legacy exception payloads without the retry hint.
             soft_payload=payload,
             event=event,
             tool_call=tool_call,
@@ -165,6 +172,7 @@ async def _execute_tool_call(
 
     if is_tool_error_result(result):
         await hook.on_execute_tool_error(context, tool_call, tool, params, result)
+        payload = _with_retry_hint(result)
         event = {
             "name": tool_call.name,
             "status": "error",
@@ -172,14 +180,14 @@ async def _execute_tool_call(
         }
         handled = _classify_violation(
             raw_text=result,
-            soft_payload=result + _RETRY_HINT,
+            soft_payload=payload,
             event=event,
             tool_call=tool_call,
             workspace_violation_counts=workspace_violation_counts,
         )
         if handled is not None:
             return handled
-        return result + _RETRY_HINT, event
+        return payload, event
 
     await hook.after_execute_tool(context, tool_call, tool, params, result)
 

nanobot/agent/tools/filesystem.py

@@ -861,8 +861,10 @@ def _best_window(old_text: str, content: str) -> tuple[float, int, list[str], li
 @tool_parameters(
     tool_parameters_schema(
         path=StringSchema("The file path to edit"),
-        old_text=StringSchema("The text to find and replace"),
+        old_text=StringSchema("The text to find and replace; copy it from read_file."),
-        new_text=StringSchema("The text to replace with"),
+        new_text=StringSchema(
+            "The replacement text; must differ from old_text for an existing file."
+        ),
         replace_all=BooleanSchema(description="Replace all occurrences (default false)"),
         occurrence=IntegerSchema(
             description="Optional 1-based occurrence to replace when old_text appears multiple times.",
@@ -899,15 +901,9 @@ class EditFileTool(_FsTool):
     @property
     def description(self) -> str:
         return (
-            "Perform a small, exact replacement in one file by replacing "
+            "Perform a small, exact replacement in one file. "
-            "old_text with new_text. When replacing text in an existing file, "
+            "Prefer apply_patch for multi-file, structural, or generated edits. "
-            "old_text and new_text must be different. Use this for narrow text substitutions "
+            "occurrence, line_hint, and replace_all=true are mutually exclusive."
-            "with old_text copied from read_file. For multi-file, structural, "
-            "or generated code edits, prefer apply_patch. If old_text matches "
-            "multiple times, provide more context or set occurrence, line_hint, "
-            "replace_all, and expected_replacements. When editing from numbered "
-            "read_file output, set line_hint to the exact target line. "
-            "Shows closest-match diagnostics on failure."
         )
 
     @staticmethod

nanobot/agent/tools/session_messages.py

@@ -7,7 +7,7 @@ from __future__ import annotations
 import asyncio
 import json
 import time
-from collections import deque
+from collections import OrderedDict, deque
 from collections.abc import Callable
 from dataclasses import dataclass
 from typing import Any, Protocol
@@ -127,7 +127,7 @@ class SendSessionMessageTool(Tool):
         self._max_messages_per_minute = max_messages_per_minute
         self._schedule_later = schedule_later
         self._clock = clock or time.monotonic
-        self._sent_at: dict[str, deque[float]] = {}
+        self._sent_at: OrderedDict[str, deque[float]] = OrderedDict()
         self._pending_replies: dict[tuple[str, str], _PendingReply] = {}
         self._expiry_tasks: set[asyncio.Task[None]] = set()
         self._send_lock = asyncio.Lock()
@@ -240,8 +240,11 @@ class SendSessionMessageTool(Tool):
 
         async with self._send_lock:
             now = self._clock()
-            sent_at = self._sent_at.setdefault(source.session_key, deque())
             cutoff = now - _RATE_LIMIT_WINDOW_SECONDS
+            self._prune_expired_rate_limits(cutoff)
+            sent_at = self._sent_at.get(source.session_key)
+            if sent_at is None:
+                sent_at = deque[float]()
             while sent_at and sent_at[0] <= cutoff:
                 sent_at.popleft()
             if len(sent_at) >= self._max_messages_per_minute:
@@ -259,6 +262,8 @@ class SendSessionMessageTool(Tool):
                 input_role="user",
             ))
             sent_at.append(now)
+            self._sent_at[source.session_key] = sent_at
+            self._sent_at.move_to_end(source.session_key)
             self._cancel_pending_reply(reverse_wait_key)
             if timeout_seconds is not None:
                 self._cancel_pending_reply(wait_key)
@@ -271,6 +276,14 @@ class SendSessionMessageTool(Tool):
 
         return f"@{target.name}"
 
+    def _prune_expired_rate_limits(self, cutoff: float) -> None:
+        """Drop sources ordered by their most recent successful send."""
+        while self._sent_at:
+            _, sent_at = next(iter(self._sent_at.items()))
+            if sent_at[-1] > cutoff:
+                return
+            self._sent_at.popitem(last=False)
+
     @staticmethod
     def _validate_reply_timeout(
         expect_reply: bool,

nanobot/channels/dingtalk/runtime.py

@@ -182,6 +182,12 @@ class NanobotDingTalkHandler(_CallbackHandlerBase):
                 )
             )
 
+            if not self.channel._accepting_inbound_tasks:
+                self.channel.logger.debug(
+                    "Skipping DingTalk inbound dispatch during channel shutdown"
+                )
+                return AckMessage.STATUS_OK, "OK"
+
             self.channel.logger.info("Received message from {} ({}): {}", sender_name, sender_id, content)
 
             # Forward to Nanobot via _on_message (non-blocking).
@@ -196,7 +202,7 @@ class NanobotDingTalkHandler(_CallbackHandlerBase):
                 )
             )
             self.channel._background_tasks.add(task)
-            task.add_done_callback(self.channel._background_tasks.discard)
+            task.add_done_callback(self.channel._on_background_task_done)
 
             return AckMessage.STATUS_OK, "OK"
 
@@ -256,6 +262,17 @@ class DingTalkChannel(BaseChannel):
 
         # Hold references to background tasks to prevent GC
         self._background_tasks: set[asyncio.Task[None]] = set()
+        self._accepting_inbound_tasks = True
+
+    def _on_background_task_done(self, task: asyncio.Task[None]) -> None:
+        self._background_tasks.discard(task)
+        if task.cancelled():
+            return
+        exception = task.exception()
+        if exception is not None:
+            self.logger.opt(exception=exception).error(
+                "DingTalk inbound message task failed"
+            )
 
     async def start(self) -> None:
         """Start the DingTalk bot with Stream Mode."""
@@ -272,6 +289,7 @@ class DingTalkChannel(BaseChannel):
                 self.logger.error("client_id and client_secret not configured")
                 return
 
+            self._accepting_inbound_tasks = True
             self._running = True
             self._http = httpx.AsyncClient(
                 timeout=httpx.Timeout(10.0, connect=10.0, read=30.0, write=30.0, pool=10.0)
@@ -309,6 +327,7 @@ class DingTalkChannel(BaseChannel):
 
     async def stop(self) -> None:
         """Stop the DingTalk bot."""
+        self._accepting_inbound_tasks = False
         self._running = False
         await self._close_stream_client()
         start_task = self._start_task
@@ -326,8 +345,11 @@ class DingTalkChannel(BaseChannel):
             await self._http.aclose()
             self._http = None
         # Cancel outstanding background tasks
-        for task in self._background_tasks:
+        background_tasks = tuple(self._background_tasks)
+        for task in background_tasks:
             task.cancel()
+        if background_tasks:
+            await asyncio.gather(*background_tasks, return_exceptions=True)
         self._background_tasks.clear()
 
     async def _close_stream_client(self) -> None:

nanobot/channels/dingtalk/tests/test_dingtalk_channel.py

@@ -3,7 +3,7 @@ import json
 import zipfile
 from io import BytesIO
 from types import SimpleNamespace
-from unittest.mock import AsyncMock
+from unittest.mock import AsyncMock, MagicMock
 
 import httpx
 import pytest
@@ -402,6 +402,61 @@ async def test_handler_uses_voice_recognition_text_when_text_is_empty(monkeypatc
     assert msg.chat_id == "group:conv123"
 
 
+@pytest.mark.asyncio
+async def test_handler_retrieves_background_message_failure(monkeypatch) -> None:
+    bus = MessageBus()
+    channel = DingTalkChannel(
+        DingTalkConfig(client_id="app", client_secret="secret", allow_from=["user1"]),
+        bus,
+    )
+    handler = NanobotDingTalkHandler(channel)
+    failure = RuntimeError("inbound dispatch failed")
+    mock_logger = MagicMock()
+    channel.logger = mock_logger
+
+    class _FakeChatbotMessage:
+        text = SimpleNamespace(content="hello")
+        extensions = {}
+        sender_staff_id = "user1"
+        sender_id = "fallback-user"
+        sender_nick = "Alice"
+        message_type = "text"
+
+        @staticmethod
+        def from_dict(_data):
+            return _FakeChatbotMessage()
+
+    async def fail(*_args) -> None:
+        raise failure
+
+    monkeypatch.setattr(dingtalk_module, "ChatbotMessage", _FakeChatbotMessage)
+    monkeypatch.setattr(dingtalk_module, "AckMessage", SimpleNamespace(STATUS_OK="OK"))
+    monkeypatch.setattr(channel, "_on_message", fail)
+    event_loop = asyncio.get_running_loop()
+    previous_handler = event_loop.get_exception_handler()
+    loop_errors: list[dict[str, object]] = []
+    event_loop.set_exception_handler(lambda _loop, context: loop_errors.append(context))
+
+    try:
+        status, body = await handler.process(
+            SimpleNamespace(data={"conversationType": "1", "text": {"content": "hello"}})
+        )
+        for _ in range(10):
+            await asyncio.sleep(0)
+            if not channel._background_tasks:
+                break
+    finally:
+        event_loop.set_exception_handler(previous_handler)
+
+    assert (status, body) == ("OK", "OK")
+    assert not channel._background_tasks
+    assert not loop_errors
+    mock_logger.opt.assert_called_once_with(exception=failure)
+    mock_logger.opt.return_value.error.assert_called_once_with(
+        "DingTalk inbound message task failed"
+    )
+
+
 @pytest.mark.asyncio
 async def test_handler_processes_file_message(monkeypatch) -> None:
     """Test that file messages are handled and forwarded with downloaded path."""
@@ -451,6 +506,72 @@ async def test_handler_processes_file_message(monkeypatch) -> None:
     assert "/tmp/nanobot_dingtalk/user1/report.xlsx" in msg.content
 
 
+@pytest.mark.asyncio
+async def test_handler_does_not_spawn_message_task_after_stop_during_download(
+    monkeypatch,
+) -> None:
+    channel = DingTalkChannel(
+        DingTalkConfig(client_id="app", client_secret="secret", allow_from=["user1"]),
+        MessageBus(),
+    )
+    handler = NanobotDingTalkHandler(channel)
+    download_started = asyncio.Event()
+    release_download = asyncio.Event()
+    message_task_started = asyncio.Event()
+
+    class _FakeFileChatbotMessage:
+        text = None
+        extensions = {}
+        image_content = None
+        rich_text_content = None
+        sender_staff_id = "user1"
+        sender_id = "fallback-user"
+        sender_nick = "Alice"
+        message_type = "file"
+
+        @staticmethod
+        def from_dict(_data):
+            return _FakeFileChatbotMessage()
+
+    async def delayed_download(*_args):
+        download_started.set()
+        await release_download.wait()
+        return "/tmp/nanobot_dingtalk/user1/report.xlsx"
+
+    async def block_message(*_args) -> None:
+        message_task_started.set()
+        await asyncio.Future()
+
+    monkeypatch.setattr(dingtalk_module, "ChatbotMessage", _FakeFileChatbotMessage)
+    monkeypatch.setattr(dingtalk_module, "AckMessage", SimpleNamespace(STATUS_OK="OK"))
+    monkeypatch.setattr(channel, "_download_dingtalk_file", delayed_download)
+    monkeypatch.setattr(channel, "_on_message", block_message)
+
+    process_task = asyncio.create_task(handler.process(SimpleNamespace(data={
+        "conversationType": "1",
+        "content": {"downloadCode": "abc123", "fileName": "report.xlsx"},
+        "text": {"content": ""},
+    })))
+    await download_started.wait()
+
+    try:
+        await channel.stop()
+        release_download.set()
+        assert await process_task == ("OK", "OK")
+        await asyncio.sleep(0)
+
+        assert not message_task_started.is_set()
+        assert not channel._background_tasks
+    finally:
+        release_download.set()
+        if not process_task.done():
+            process_task.cancel()
+        pending = tuple(channel._background_tasks)
+        for task in pending:
+            task.cancel()
+        await asyncio.gather(process_task, *pending, return_exceptions=True)
+
+
 def _rich_text_message(rich_text_list):
     class _FakeRichTextChatbotMessage:
         text = None
@@ -650,6 +771,41 @@ async def test_stop_cancels_stream_client_after_sdk_swallows_first_cancel(monkey
     assert start_task.cancelled()
 
 
+@pytest.mark.asyncio
+async def test_stop_waits_for_background_message_tasks() -> None:
+    channel = DingTalkChannel(
+        DingTalkConfig(client_id="app", client_secret="secret", allow_from=["*"]),
+        MessageBus(),
+    )
+    mock_logger = MagicMock()
+    channel.logger = mock_logger
+    started = asyncio.Event()
+    cancelled = asyncio.Event()
+
+    async def wait_forever() -> None:
+        started.set()
+        try:
+            await asyncio.Future()
+        finally:
+            cancelled.set()
+
+    task = asyncio.create_task(wait_forever())
+    channel._background_tasks.add(task)
+    task.add_done_callback(channel._on_background_task_done)
+    await started.wait()
+
+    try:
+        await channel.stop()
+        assert task.done()
+        assert cancelled.is_set()
+        assert not channel._background_tasks
+        mock_logger.opt.assert_not_called()
+    finally:
+        if not task.done():
+            task.cancel()
+        await asyncio.gather(task, return_exceptions=True)
+
+
 @pytest.mark.asyncio
 async def test_download_dingtalk_file(tmp_path, monkeypatch) -> None:
     """Test the two-step file download flow (get URL then download content)."""

nanobot/channels/email/runtime.py

@@ -430,7 +430,13 @@ class EmailChannel(BaseChannel):
         skipped_uids: set[str],
         cycle_uids: set[str],
     ) -> list[dict[str, Any]] | None:
-        """Fetch messages by arbitrary IMAP search criteria."""
+        """Fetch messages by arbitrary IMAP search criteria.
+
+        Uses UID SEARCH so already-processed UIDs are recognized before any
+        FETCH at all, then fetches headers only to evaluate every filter — the
+        full body (and any attachments) is downloaded only for messages that
+        pass every check and are actually going to be delivered.
+        """
         mailbox = self.config.imap_mailbox or "INBOX"
 
         client = self._open_imap_client(mailbox=mailbox, missing_mailbox_ok=True)
@@ -438,29 +444,30 @@ class EmailChannel(BaseChannel):
             return messages
 
         try:
-            status, data = client.search(None, *search_criteria)
+            status, data = client.uid("SEARCH", None, *search_criteria)
-            if status != "OK" or not data:
+            if status != "OK" or not data or not data[0]:
                 return messages
 
-            ids = data[0].split()
+            uids = [raw.decode("ascii", errors="ignore") for raw in data[0].split()]
-            if limit > 0 and len(ids) > limit:
+            if limit > 0 and len(uids) > limit:
-                ids = ids[-limit:]
+                uids = uids[-limit:]
-            for imap_id in ids:
+
-                status, fetched = client.fetch(imap_id, "(BODY.PEEK[] UID)")
+            features: _ServerFeatures | None = None
+
+            for uid in uids:
+                if not uid or uid in cycle_uids:
+                    continue
+                if dedupe and uid in self._processed_uids:
+                    continue
+
+                status, fetched = client.uid("FETCH", uid, "(BODY.PEEK[HEADER])")
                 if status != "OK" or not fetched:
                     continue
-
+                header_bytes = self._extract_message_bytes(fetched)
-                raw_bytes = self._extract_message_bytes(fetched)
+                if header_bytes is None:
-                if raw_bytes is None:
                     continue
 
-                uid = self._extract_uid(fetched)
+                parsed = BytesParser(policy=policy.default).parsebytes(header_bytes)
-                if uid and uid in cycle_uids:
-                    continue
-                if dedupe and uid and uid in self._processed_uids:
-                    continue
-
-                parsed = BytesParser(policy=policy.default).parsebytes(raw_bytes)
                 sender = parseaddr(parsed.get("From", ""))[1].strip().lower()
                 if not sender:
                     continue
@@ -468,9 +475,8 @@ class EmailChannel(BaseChannel):
                     self.logger.info("From {} ignored: matches bot-owned address", sender)
                     self._remember_processed_uid(uid, dedupe, cycle_uids)
                     if mark_seen:
-                        client.store(imap_id, "+FLAGS", "\\Seen")
+                        features = self._mark_seen_uid(client, uid, features)
-                    if uid:
+                    skipped_uids.add(uid)
-                        skipped_uids.add(uid)
                     continue
 
                 # --- Anti-spoofing: verify Authentication-Results ---
@@ -482,8 +488,7 @@ class EmailChannel(BaseChannel):
                         sender,
                     )
                     self._remember_processed_uid(uid, dedupe, cycle_uids)
-                    if uid:
+                    skipped_uids.add(uid)
-                        skipped_uids.add(uid)
                     continue
                 if self.config.verify_dkim and not dkim_pass:
                     self.logger.warning(
@@ -492,18 +497,26 @@ class EmailChannel(BaseChannel):
                         sender,
                     )
                     self._remember_processed_uid(uid, dedupe, cycle_uids)
-                    if uid:
+                    skipped_uids.add(uid)
-                        skipped_uids.add(uid)
                     continue
 
                 if not self.is_allowed(sender):
                     self._remember_processed_uid(uid, dedupe, cycle_uids)
                     if mark_seen:
-                        client.store(imap_id, "+FLAGS", "\\Seen")
+                        features = self._mark_seen_uid(client, uid, features)
-                    if uid:
+                    skipped_uids.add(uid)
-                        skipped_uids.add(uid)
                     continue
 
+                # Passed every filter — only now fetch the full message body
+                # (and any attachments) for the message we're actually delivering.
+                status, full_fetched = client.uid("FETCH", uid, "(BODY.PEEK[])")
+                if status != "OK" or not full_fetched:
+                    continue
+                raw_bytes = self._extract_message_bytes(full_fetched)
+                if raw_bytes is None:
+                    continue
+                parsed = BytesParser(policy=policy.default).parsebytes(raw_bytes)
+
                 subject = self._decode_header_value(parsed.get("Subject", ""))
                 date_value = parsed.get("Date", "")
                 message_id = parsed.get("Message-ID", "").strip()
@@ -556,10 +569,19 @@ class EmailChannel(BaseChannel):
                 self._remember_processed_uid(uid, dedupe, cycle_uids)
 
                 if mark_seen:
-                    client.store(imap_id, "+FLAGS", "\\Seen")
+                    features = self._mark_seen_uid(client, uid, features)
         finally:
             self._close_imap_client(client)
 
+    def _mark_seen_uid(
+        self, client: Any, uid: str, features: _ServerFeatures | None
+    ) -> _ServerFeatures:
+        """Mark a single UID \\Seen, reusing session-learned STORE support."""
+        if features is None:
+            features = self._server_features(client)
+        self._uid_store_flag(client, uid, "\\Seen", features)
+        return features
+
     def _open_imap_client(self, mailbox: str, *, missing_mailbox_ok: bool = False) -> Any | None:
         if self.config.imap_use_ssl:
             client: Any = imaplib.IMAP4_SSL(self.config.imap_host, self.config.imap_port)
@@ -714,11 +736,14 @@ class EmailChannel(BaseChannel):
         return data[0].split()[0]
 
     def _uid_store_deleted(self, client: Any, uid: str, features: _ServerFeatures) -> bool:
+        return self._uid_store_flag(client, uid, "\\Deleted", features)
+
+    def _uid_store_flag(self, client: Any, uid: str, flag: str, features: _ServerFeatures) -> bool:
         # Optimistic path: try UID STORE first because UID is stable and avoids
         # sequence-number lookup. If this fails once for the session, remember it
         # and use the sequence STORE fallback directly for remaining UIDs.
         if features.uid_store is not False:
-            status, _ = client.uid("STORE", uid, "+FLAGS", "(\\Deleted)")
+            status, _ = client.uid("STORE", uid, "+FLAGS", f"({flag})")
             if status == "OK":
                 features.uid_store = True
                 return True
@@ -728,12 +753,12 @@ class EmailChannel(BaseChannel):
         # unreliable: resolve the current sequence number from UID and use STORE.
         imap_id = self._lookup_imap_id_by_uid(client, uid)
         if not imap_id:
-            self.logger.warning("Post-action skipped: UID {} not found", uid)
+            self.logger.warning("Could not locate UID {} to set flag {}", uid, flag)
             return False
 
-        status, _ = client.store(imap_id, "+FLAGS", "\\Deleted")
+        status, _ = client.store(imap_id, "+FLAGS", flag)
         if status != "OK":
-            self.logger.warning("Post-action failed: could not mark UID {} as deleted", uid)
+            self.logger.warning("Failed to set flag {} on UID {}", flag, uid)
             return False
         return True
 
@@ -773,16 +798,6 @@ class EmailChannel(BaseChannel):
                     return bytes(fetched_item[1])
         return None
 
-    @staticmethod
-    def _extract_uid(fetched: list[Any]) -> str:
-        for item in fetched:
-            if isinstance(item, tuple) and item and isinstance(item[0], (bytes, bytearray)):
-                head = bytes(item[0]).decode("utf-8", errors="ignore")
-                m = re.search(r"UID\s+(\d+)", head)
-                if m:
-                    return m.group(1)
-        return ""
-
     @staticmethod
     def _decode_header_value(value: str) -> str:
         if not value:

nanobot/channels/email/tests/test_email_channel.py

@@ -53,30 +53,7 @@ def _make_raw_email(
 def test_fetch_new_messages_parses_unseen_and_marks_seen(monkeypatch) -> None:
     raw = _make_raw_email(subject="Invoice", body="Please pay")
 
-    class FakeIMAP:
+    fake = _make_fake_imap(raw, uid=b"123")
-        def __init__(self) -> None:
-            self.store_calls: list[tuple[bytes, str, str]] = []
-
-        def login(self, _user: str, _pw: str):
-            return "OK", [b"logged in"]
-
-        def select(self, _mailbox: str):
-            return "OK", [b"1"]
-
-        def search(self, *_args):
-            return "OK", [b"1"]
-
-        def fetch(self, _imap_id: bytes, _parts: str):
-            return "OK", [(b"1 (UID 123 BODY[] {200})", raw), b")"]
-
-        def store(self, imap_id: bytes, op: str, flags: str):
-            self.store_calls.append((imap_id, op, flags))
-            return "OK", [b""]
-
-        def logout(self):
-            return "BYE", [b""]
-
-    fake = FakeIMAP()
     monkeypatch.setattr("nanobot.channels.email.runtime.imaplib.IMAP4_SSL", lambda _h, _p: fake)
 
     channel = EmailChannel(_make_config(), MessageBus())
@@ -86,38 +63,25 @@ def test_fetch_new_messages_parses_unseen_and_marks_seen(monkeypatch) -> None:
     assert items[0]["sender"] == "alice@example.com"
     assert items[0]["subject"] == "Invoice"
     assert "Please pay" in items[0]["content"]
-    assert fake.store_calls == [(b"1", "+FLAGS", "\\Seen")]
+    assert ("STORE", "123", "+FLAGS", "(\\Seen)") in fake.uid_calls
+    assert [call for call in fake.uid_calls if call[0] == "FETCH"] == [
+        ("FETCH", "123", "(BODY.PEEK[HEADER])"),
+        ("FETCH", "123", "(BODY.PEEK[])"),
+    ]
     assert skipped_uids == set()
 
     # Same UID should be deduped in-process.
     items_again, skipped_again = channel._fetch_new_messages()
     assert items_again == []
     assert skipped_again == set()
+    assert len([call for call in fake.uid_calls if call[0] == "FETCH"]) == 2
 
 
 def test_fetch_new_messages_returns_accepted_and_skipped_uids(monkeypatch) -> None:
     raw = _make_raw_email(subject="Invoice", body="Please pay")
 
-    class FakeIMAP:
+    fake = _make_fake_imap(raw, uid=b"123")
-        def login(self, _user: str, _pw: str):
+    monkeypatch.setattr("nanobot.channels.email.runtime.imaplib.IMAP4_SSL", lambda _h, _p: fake)
-            return "OK", [b"logged in"]
-
-        def select(self, _mailbox: str):
-            return "OK", [b"1"]
-
-        def search(self, *_args):
-            return "OK", [b"1"]
-
-        def fetch(self, _imap_id: bytes, _parts: str):
-            return "OK", [(b"1 (UID 123 BODY[] {200})", raw), b")"]
-
-        def store(self, _imap_id: bytes, _op: str, _flags: str):
-            return "OK", [b""]
-
-        def logout(self):
-            return "BYE", [b""]
-
-    monkeypatch.setattr("nanobot.channels.email.runtime.imaplib.IMAP4_SSL", lambda _h, _p: FakeIMAP())
 
     channel = EmailChannel(_make_config(post_action="delete"), MessageBus())
     items, skipped_uids = channel._fetch_new_messages()
@@ -130,26 +94,10 @@ def test_fetch_new_messages_returns_accepted_and_skipped_uids(monkeypatch) -> No
 def test_fetch_new_messages_rejected_returns_skipped_uid(monkeypatch) -> None:
     raw = _make_raw_email(from_addr="Nanobot <bot@example.com>", subject="Loop test")
 
-    class FakeIMAP:
+    monkeypatch.setattr(
-        def login(self, _user: str, _pw: str):
+        "nanobot.channels.email.runtime.imaplib.IMAP4_SSL",
-            return "OK", [b"logged in"]
+        lambda _h, _p: _make_fake_imap(raw, uid=b"123"),
-
+    )
-        def select(self, _mailbox: str):
-            return "OK", [b"1"]
-
-        def search(self, *_args):
-            return "OK", [b"1"]
-
-        def fetch(self, _imap_id: bytes, _parts: str):
-            return "OK", [(b"1 (UID 123 BODY[] {200})", raw), b")"]
-
-        def store(self, _imap_id: bytes, _op: str, _flags: str):
-            return "OK", [b""]
-
-        def logout(self):
-            return "BYE", [b""]
-
-    monkeypatch.setattr("nanobot.channels.email.runtime.imaplib.IMAP4_SSL", lambda _h, _p: FakeIMAP())
 
     channel_skip = EmailChannel(
         _make_config(from_address="bot@example.com", post_action="delete", post_action_ignore_skipped=True),
@@ -545,30 +493,7 @@ async def test_start_keeps_post_actions_for_successful_emails_when_later_deliver
 def test_fetch_new_messages_skips_self_sent_email_and_marks_seen(monkeypatch) -> None:
     raw = _make_raw_email(from_addr="Nanobot <bot@example.com>", subject="Loop test")
 
-    class FakeIMAP:
+    fake = _make_fake_imap(raw, uid=b"123")
-        def __init__(self) -> None:
-            self.store_calls: list[tuple[bytes, str, str]] = []
-
-        def login(self, _user: str, _pw: str):
-            return "OK", [b"logged in"]
-
-        def select(self, _mailbox: str):
-            return "OK", [b"1"]
-
-        def search(self, *_args):
-            return "OK", [b"1"]
-
-        def fetch(self, _imap_id: bytes, _parts: str):
-            return "OK", [(b"1 (UID 123 BODY[] {200})", raw), b")"]
-
-        def store(self, imap_id: bytes, op: str, flags: str):
-            self.store_calls.append((imap_id, op, flags))
-            return "OK", [b""]
-
-        def logout(self):
-            return "BYE", [b""]
-
-    fake = FakeIMAP()
     monkeypatch.setattr("nanobot.channels.email.runtime.imaplib.IMAP4_SSL", lambda _h, _p: fake)
 
     channel = EmailChannel(_make_config(from_address="bot@example.com"), MessageBus())
@@ -576,7 +501,7 @@ def test_fetch_new_messages_skips_self_sent_email_and_marks_seen(monkeypatch) ->
 
     assert items == []
     assert skipped_uids == {"123"}
-    assert fake.store_calls == [(b"1", "+FLAGS", "\\Seen")]
+    assert ("STORE", "123", "+FLAGS", "(\\Seen)") in fake.uid_calls
 
     # Same UID should still be deduped after being ignored.
     items_again, skipped_again = channel._fetch_new_messages()
@@ -614,37 +539,14 @@ def test_fetch_new_messages_skips_self_sent_across_identity_sources(
     imap_username matches, and must be case-insensitive."""
     raw = _make_raw_email(from_addr=from_header, subject="Loop test")
 
-    class FakeIMAP:
+    fake = _make_fake_imap(raw, uid=b"123")
-        def __init__(self) -> None:
-            self.store_calls: list[tuple[bytes, str, str]] = []
-
-        def login(self, _user: str, _pw: str):
-            return "OK", [b"logged in"]
-
-        def select(self, _mailbox: str):
-            return "OK", [b"1"]
-
-        def search(self, *_args):
-            return "OK", [b"1"]
-
-        def fetch(self, _imap_id: bytes, _parts: str):
-            return "OK", [(b"1 (UID 123 BODY[] {200})", raw), b")"]
-
-        def store(self, imap_id: bytes, op: str, flags: str):
-            self.store_calls.append((imap_id, op, flags))
-            return "OK", [b""]
-
-        def logout(self):
-            return "BYE", [b""]
-
-    fake = FakeIMAP()
     monkeypatch.setattr("nanobot.channels.email.runtime.imaplib.IMAP4_SSL", lambda _h, _p: fake)
 
     channel = EmailChannel(_make_config(**config_override), MessageBus())
     items, _ = channel._fetch_new_messages()
 
     assert items == []
-    assert fake.store_calls == [(b"1", "+FLAGS", "\\Seen")]
+    assert ("STORE", "123", "+FLAGS", "(\\Seen)") in fake.uid_calls
 
 
 def test_fetch_new_messages_retries_once_when_imap_connection_goes_stale(monkeypatch) -> None:
@@ -662,15 +564,16 @@ def test_fetch_new_messages_retries_once_when_imap_connection_goes_stale(monkeyp
         def select(self, _mailbox: str):
             return "OK", [b"1"]
 
-        def search(self, *_args):
+        def uid(self, command: str, *args):
-            self.search_calls += 1
+            if command == "SEARCH":
-            if fail_once["pending"]:
+                self.search_calls += 1
-                fail_once["pending"] = False
+                if fail_once["pending"]:
-                raise imaplib.IMAP4.abort("socket error")
+                    fail_once["pending"] = False
-            return "OK", [b"1"]
+                    raise imaplib.IMAP4.abort("socket error")
-
+                return "OK", [b"123"]
-        def fetch(self, _imap_id: bytes, _parts: str):
+            if command == "FETCH":
-            return "OK", [(b"1 (UID 123 BODY[] {200})", raw), b")"]
+                return "OK", [(b"1 (UID 123 BODY[] {200})", raw), b")"]
+            return "OK", [b""]
 
         def store(self, imap_id: bytes, op: str, flags: str):
             self.store_calls.append((imap_id, op, flags))
@@ -700,10 +603,7 @@ def test_fetch_new_messages_retries_once_when_imap_connection_goes_stale(monkeyp
 def test_fetch_new_messages_keeps_messages_collected_before_stale_retry(monkeypatch) -> None:
     raw_first = _make_raw_email(subject="First", body="First body")
     raw_second = _make_raw_email(subject="Second", body="Second body")
-    mailbox_state = {
+    mailbox_state = {"123": raw_first, "124": raw_second}
-        b"1": {"uid": b"123", "raw": raw_first, "seen": False},
-        b"2": {"uid": b"124", "raw": raw_second, "seen": False},
-    }
     fail_once = {"pending": True}
 
     class FlakyIMAP:
@@ -713,20 +613,18 @@ def test_fetch_new_messages_keeps_messages_collected_before_stale_retry(monkeypa
         def select(self, _mailbox: str):
             return "OK", [b"2"]
 
-        def search(self, *_args):
+        def uid(self, command: str, *args):
-            unseen_ids = [imap_id for imap_id, item in mailbox_state.items() if not item["seen"]]
+            if command == "SEARCH":
-            return "OK", [b" ".join(unseen_ids)]
+                keys = " ".join(sorted(mailbox_state.keys(), key=int))
-
+                return "OK", [keys.encode()]
-        def fetch(self, imap_id: bytes, _parts: str):
+            if command == "FETCH":
-            if imap_id == b"2" and fail_once["pending"]:
+                uid = args[0]
-                fail_once["pending"] = False
+                if uid == "124" and fail_once["pending"]:
-                raise imaplib.IMAP4.abort("socket error")
+                    fail_once["pending"] = False
-            item = mailbox_state[imap_id]
+                    raise imaplib.IMAP4.abort("socket error")
-            header = b"%s (UID %s BODY[] {200})" % (imap_id, item["uid"])
+                raw = mailbox_state[uid]
-            return "OK", [(header, item["raw"]), b")"]
+                header = f"{uid} (UID {uid} BODY[] {{200}})".encode()
-
+                return "OK", [(header, raw), b")"]
-        def store(self, imap_id: bytes, _op: str, _flags: str):
-            mailbox_state[imap_id]["seen"] = True
             return "OK", [b""]
 
         def logout(self):
@@ -1044,12 +942,13 @@ def test_fetch_messages_between_dates_uses_imap_since_before_without_mark_seen(m
         def select(self, _mailbox: str):
             return "OK", [b"1"]
 
-        def search(self, *_args):
+        def uid(self, command: str, *args):
-            self.search_args = _args
+            if command == "SEARCH":
-            return "OK", [b"5"]
+                self.search_args = args
-
+                return "OK", [b"999"]
-        def fetch(self, _imap_id: bytes, _parts: str):
+            if command == "FETCH":
-            return "OK", [(b"5 (UID 999 BODY[] {200})", raw), b")"]
+                return "OK", [(b"5 (UID 999 BODY[] {200})", raw), b")"]
+            return "OK", [b""]
 
         def store(self, imap_id: bytes, op: str, flags: str):
             self.store_calls.append((imap_id, op, flags))
@@ -1070,7 +969,7 @@ def test_fetch_messages_between_dates_uses_imap_since_before_without_mark_seen(m
 
     assert len(items) == 1
     assert items[0]["subject"] == "Status"
-    # search(None, "SINCE", "06-Feb-2026", "BEFORE", "07-Feb-2026")
+    # uid("SEARCH", None, "SINCE", "06-Feb-2026", "BEFORE", "07-Feb-2026")
     assert fake.search_args is not None
     assert fake.search_args[1:] == ("SINCE", "06-Feb-2026", "BEFORE", "07-Feb-2026")
     assert fake.store_calls == []
@@ -1080,11 +979,12 @@ def test_fetch_messages_between_dates_uses_imap_since_before_without_mark_seen(m
 # Security: Anti-spoofing tests for Authentication-Results verification
 # ---------------------------------------------------------------------------
 
-def _make_fake_imap(raw: bytes):
+def _make_fake_imap(raw: bytes, uid: bytes = b"500"):
     """Return a FakeIMAP class pre-loaded with the given raw email."""
     class FakeIMAP:
         def __init__(self) -> None:
             self.store_calls: list[tuple[bytes, str, str]] = []
+            self.uid_calls: list[tuple] = []
 
         def login(self, _user: str, _pw: str):
             return "OK", [b"logged in"]
@@ -1092,11 +992,16 @@ def _make_fake_imap(raw: bytes):
         def select(self, _mailbox: str):
             return "OK", [b"1"]
 
-        def search(self, *_args):
+        def capability(self):
-            return "OK", [b"1"]
+            return "OK", [b"IMAP4rev1"]
 
-        def fetch(self, _imap_id: bytes, _parts: str):
+        def uid(self, command: str, *args):
-            return "OK", [(b"1 (UID 500 BODY[] {200})", raw), b")"]
+            self.uid_calls.append((command, *args))
+            if command == "SEARCH":
+                return "OK", [uid]
+            if command == "FETCH":
+                return "OK", [(b"1 (UID " + uid + b" BODY[] {200})", raw), b")"]
+            return "OK", [b""]
 
         def store(self, imap_id: bytes, op: str, flags: str):
             self.store_calls.append((imap_id, op, flags))
@@ -1292,7 +1197,10 @@ def test_fetch_new_messages_ignores_unauthorized_sender_before_attachments(monke
 
     assert channel._fetch_new_messages() == ([], {"500"})
     assert called["attachments"] is False
-    assert fake.store_calls == [(b"1", "+FLAGS", "\\Seen")]
+    assert [call for call in fake.uid_calls if call[0] == "FETCH"] == [
+        ("FETCH", "500", "(BODY.PEEK[HEADER])")
+    ]
+    assert ("STORE", "500", "+FLAGS", "(\\Seen)") in fake.uid_calls
 
 
 def test_extract_attachments_saves_pdf(tmp_path, monkeypatch) -> None:

nanobot/channels/telegram/runtime.py

@@ -897,6 +897,68 @@ class TelegramChannel(BaseChannel):
             self.logger.debug("sendRichMessage failed: {}", exc)
             return False
 
+    async def _try_edit_rich(self, chat_id: int, message_id: int, content: str) -> bool:
+        """Upgrade an existing message to rich in place via editMessageText (Bot API 10.1).
+
+        Editing in place keeps the message identity, so the streaming preview is
+        upgraded without the delete-and-resend pattern that caused flickering and
+        dropped line breaks (issue #4470).
+
+        Returns True when the rich edit is in place (including the ambiguous
+        "message is not modified" retry outcome after a response timeout).
+        Returns False only when the legacy HTML path should take over:
+        capability errors (server older than Bot API 10.1, which also trip the
+        rich latch) and content-shaped BadRequest rejections. Transport,
+        rate-limit, and unexpected errors propagate so the final-edit retry
+        contract is preserved — ChannelManager retries the buffered send
+        instead of an immediate legacy edit doubling connection demand.
+        """
+        if not self._app:
+            return False
+
+        payload: dict[str, Any] = {
+            "chat_id": chat_id,
+            "message_id": message_id,
+            "rich_message": {
+                "markdown": content,
+            },
+        }
+        try:
+            await self._call_with_retry(
+                self._app.bot.do_api_request,
+                "editMessageText",
+                api_kwargs=payload,
+            )
+            return True
+        except BadRequest as exc:
+            if self._is_not_modified_error(exc):
+                # Ambiguous success: the rich edit was applied server-side but
+                # its response timed out, so the retry hit "message is not
+                # modified". Treat it as done rather than letting the legacy
+                # edit overwrite the already-successful rich result.
+                self.logger.debug("Rich stream edit already applied for {}", chat_id)
+                return True
+            # Before Bot API 10.1, editMessageText ignores rich_message and
+            # reports the absent text argument instead.
+            pre_rich_edit_server = (
+                bool(content)
+                and str(exc).strip().lower() == "message text is empty"
+            )
+            if self._is_rich_capability_error(exc) or pre_rich_edit_server:
+                self.logger.debug("editMessageText rich_message not available, disabling")
+                self._rich_send_disabled = True
+                return False
+            # Content-shaped rejections (invalid markdown, unsupported media in
+            # the rich payload, …) fall back to the legacy HTML edit.
+            self.logger.debug("editMessageText rich_message rejected: {}", exc)
+            return False
+        except Exception:
+            # Transport, rate-limit, and unexpected errors propagate so the
+            # final-edit retry contract stays intact: ChannelManager retries
+            # the buffered send instead of this handler doubling connection
+            # demand with an immediate legacy edit.
+            raise
+
     async def send(self, msg: OutboundMessage) -> None:
         """Send a message through Telegram."""
         app = await self._wait_for_app()
@@ -1136,26 +1198,16 @@ class TelegramChannel(BaseChannel):
                 thread_kwargs["message_thread_id"] = message_thread_id
             raw_text = buf.text
 
-            # Try sendRichMessage for final output (Bot API 10.1).
+            # Try upgrading the streaming preview to rich in place (Bot API 10.1:
-            # Skip when a streaming preview already exists to avoid the
+            # editMessageText gained a rich_message parameter). Editing in place
-            # delete-and-resend pattern that causes flickering and drops
+            # keeps the message identity, so there is no delete-and-resend and
-            # line breaks (issue #4470).
+            # none of the flickering / dropped line breaks from issue #4470.
-            if not buf.message_id and self.config.rich_messages and not getattr(self, "_rich_send_disabled", False):
+            # The previous branch here was unreachable: it was guarded by
-                reply_params = None
+            # ``not buf.message_id`` after an early return had already ensured
-                if reply_to_message_id := meta.get("message_id"):
+            # ``buf.message_id`` is set (issue #5516).
-                    reply_params = {"message_id": int(reply_to_message_id), "allow_sending_without_reply": True}
+            if self.config.rich_messages and not getattr(self, "_rich_send_disabled", False):
-                rich_ok = await self._try_send_rich(
+                rich_ok = await self._try_edit_rich(int_chat_id, buf.message_id, raw_text)
-                    int_chat_id, raw_text, reply_params, thread_kwargs, None,
-                )
                 if rich_ok:
-                    # Delete the streaming preview message
-                    try:
-                        await self._call_with_retry(
-                            app.bot.delete_message,
-                            chat_id=int_chat_id, message_id=buf.message_id,
-                        )
-                    except Exception:
-                        pass  # Preview stays if delete fails
                     self._stream_bufs.pop(chat_id, None)
                     return
 

nanobot/channels/telegram/tests/test_telegram_channel.py

@@ -2735,3 +2735,130 @@ def test_markdown_to_html_code_block_same_line_no_newline() -> None:
 
     stripped = _strip_md_block(text)
     assert stripped == "Use <tag> here"
+
+
+@pytest.mark.asyncio
+async def test_send_delta_stream_end_upgrades_preview_to_rich_in_place() -> None:
+    """Rich messages finally work with streaming: the preview is upgraded via
+    editMessageText rich_message (in place), not delete-and-resend (issue #5516)."""
+    from telegram.error import BadRequest
+
+    channel = TelegramChannel(
+        TelegramConfig(enabled=True, token="123:abc", allow_from=["*"], rich_messages=True),
+        MessageBus(),
+    )
+    _install_ready_app(channel)
+    channel._app.bot.do_api_request = AsyncMock()
+    channel._app.bot.edit_message_text = AsyncMock(side_effect=BadRequest("should not be reached"))
+    channel._stream_bufs["123"] = _StreamBuf(text="**hello**", message_id=7, last_edit=0.0)
+
+    await channel.send_delta("123", "", stream_end=True)
+
+    # editMessageText with rich_message payload, in place (same message_id)
+    channel._app.bot.do_api_request.assert_awaited_once()
+    args, kwargs = channel._app.bot.do_api_request.await_args
+    assert args[0] == "editMessageText"
+    assert kwargs["api_kwargs"]["chat_id"] == 123
+    assert kwargs["api_kwargs"]["message_id"] == 7
+    assert kwargs["api_kwargs"]["rich_message"] == {"markdown": "**hello**"}
+    # No delete-and-resend, no legacy HTML edit
+    channel._app.bot.edit_message_text.assert_not_awaited()
+    assert "123" not in channel._stream_bufs
+
+
+@pytest.mark.asyncio
+async def test_send_delta_stream_end_rich_capability_error_latches_and_falls_back() -> None:
+    """On a pre-10.1 Bot API server the rich edit fails, the latch trips, and the
+    legacy HTML edit handles the final output."""
+    from telegram.error import BadRequest
+
+    channel = TelegramChannel(
+        TelegramConfig(enabled=True, token="123:abc", allow_from=["*"], rich_messages=True),
+        MessageBus(),
+    )
+    _install_ready_app(channel)
+    # Before Bot API 10.1, editMessageText ignores rich_message and requires text.
+    channel._app.bot.do_api_request = AsyncMock(
+        side_effect=BadRequest("Message text is empty")
+    )
+    channel._app.bot.edit_message_text = AsyncMock()
+    channel._stream_bufs["123"] = _StreamBuf(text="hello", message_id=7, last_edit=0.0)
+
+    await channel.send_delta("123", "", stream_end=True)
+
+    channel._app.bot.do_api_request.assert_awaited_once()
+    # Latch tripped: subsequent sends skip the rich path entirely
+    assert channel._rich_send_disabled is True
+    # Legacy HTML edit handled the final message
+    channel._app.bot.edit_message_text.assert_awaited_once()
+    assert "123" not in channel._stream_bufs
+
+
+@pytest.mark.asyncio
+async def test_send_delta_stream_end_rich_disabled_uses_legacy_html() -> None:
+    """rich_messages=False (the default) keeps the legacy HTML path untouched."""
+    channel = TelegramChannel(
+        TelegramConfig(enabled=True, token="123:abc", allow_from=["*"]),
+        MessageBus(),
+    )
+    _install_ready_app(channel)
+    channel._app.bot.do_api_request = AsyncMock()
+    channel._app.bot.edit_message_text = AsyncMock()
+    channel._stream_bufs["123"] = _StreamBuf(text="hello", message_id=7, last_edit=0.0)
+
+    await channel.send_delta("123", "", stream_end=True)
+
+    channel._app.bot.do_api_request.assert_not_called()
+    channel._app.bot.edit_message_text.assert_awaited_once()
+    assert "123" not in channel._stream_bufs
+
+
+@pytest.mark.asyncio
+async def test_send_delta_stream_end_rich_network_error_propagates_for_retry() -> None:
+    """A transport failure on the rich edit must propagate so ChannelManager
+    retries the buffered send — not fall through to an immediate legacy edit
+    that doubles connection demand during pool exhaustion."""
+    from telegram.error import NetworkError
+
+    channel = TelegramChannel(
+        TelegramConfig(enabled=True, token="123:abc", allow_from=["*"], rich_messages=True),
+        MessageBus(),
+    )
+    _install_ready_app(channel)
+    channel._app.bot.do_api_request = AsyncMock(side_effect=NetworkError("pool exhausted"))
+    channel._app.bot.edit_message_text = AsyncMock()
+    channel._stream_bufs["123"] = _StreamBuf(text="hello", message_id=7, last_edit=0.0)
+
+    with pytest.raises(NetworkError):
+        await channel.send_delta("123", "", stream_end=True)
+
+    # No legacy fallback edit: the buffered state stays for the manager retry.
+    channel._app.bot.edit_message_text.assert_not_awaited()
+    assert "123" in channel._stream_bufs
+
+
+@pytest.mark.asyncio
+async def test_send_delta_stream_end_rich_not_modified_after_timeout_is_success() -> None:
+    """Ambiguous success: the rich edit applied server-side but its response
+    timed out, so the retry hit "message is not modified". That is a completed
+    rich upgrade — the legacy edit must not overwrite it."""
+    from telegram.error import BadRequest, TimedOut
+
+    channel = TelegramChannel(
+        TelegramConfig(enabled=True, token="123:abc", allow_from=["*"], rich_messages=True),
+        MessageBus(),
+    )
+    _install_ready_app(channel)
+    # First attempt (inside _call_with_retry) times out, retry reports the
+    # edit as already applied.
+    channel._app.bot.do_api_request = AsyncMock(
+        side_effect=[TimedOut(), BadRequest("Message is not modified")]
+    )
+    channel._app.bot.edit_message_text = AsyncMock(side_effect=AssertionError("must not overwrite rich result"))
+    channel._stream_bufs["123"] = _StreamBuf(text="hello", message_id=7, last_edit=0.0)
+
+    await channel.send_delta("123", "", stream_end=True)
+
+    assert channel._app.bot.do_api_request.await_count == 2
+    channel._app.bot.edit_message_text.assert_not_awaited()
+    assert "123" not in channel._stream_bufs

nanobot/channels/websocket/runtime.py

@@ -3,6 +3,7 @@
 from __future__ import annotations
 
 import asyncio
+import errno
 import ipaddress
 import json
 import socket
@@ -538,14 +539,32 @@ class WebSocketChannel(BaseChannel):
     # -- Server lifecycle and connection ingress ---------------------------
 
     @staticmethod
-    def _listener_is_serving(server: Server) -> bool:
+    def _socket_is_accepting(sock: socket.socket) -> bool:
+        """Return whether a bound socket still advertises a listen capability.
+
+        ``SO_ACCEPTCONN`` is not portable: macOS/BSD raise ``OSError`` with
+        ``ENOPROTOOPT`` ("Protocol not available") for this option even on a
+        perfectly healthy listening socket. Treating that as "not serving"
+        makes the listener look permanently degraded, so the caller retries
+        forever and the channel never reaches a ready state. When the option
+        is unavailable we fall back to the file-descriptor liveness check.
+        """
+        if sock.fileno() < 0:
+            return False
+        try:
+            return bool(sock.getsockopt(socket.SOL_SOCKET, socket.SO_ACCEPTCONN))
+        except OSError as exc:
+            if exc.errno in (errno.ENOPROTOOPT, errno.EOPNOTSUPP):
+                return True
+            raise
+
+    @classmethod
+    def _listener_is_serving(cls, server: Server) -> bool:
         """Return whether every bound socket still has a live listen capability."""
         try:
             sockets = server.sockets
             return bool(sockets) and server.is_serving() and all(
-                sock.fileno() >= 0
+                cls._socket_is_accepting(sock) for sock in sockets
-                and bool(sock.getsockopt(socket.SOL_SOCKET, socket.SO_ACCEPTCONN))
-                for sock in sockets
             )
         except OSError:
             return False

nanobot/cli/provider.py

@@ -29,7 +29,7 @@ _PROVIDER_DISPLAY: dict[str, str] = {
 
 _OAUTH_PROVIDER_DEFAULT_MODELS: dict[str, str] = {
     "openai_codex": "openai-codex/gpt-5.6-sol",
-    "xai_grok": "xai-grok/grok-4.5",
+    "xai_grok": "xai-grok/grok-4.6",
     "github_copilot": "github-copilot/gpt-5.4-mini",
 }
 
@@ -134,7 +134,10 @@ def _set_oauth_provider_as_main(
     config.agents.defaults.model_preset = None
     config.agents.defaults.provider = provider_name
     config.agents.defaults.model = selected_model
-    if provider_name == "xai_grok" and selected_model == "xai-grok/grok-4.5":
+    if provider_name == "xai_grok" and selected_model in {
+        "xai-grok/grok-4.5",
+        "xai-grok/grok-4.6",
+    }:
         config.agents.defaults.context_window_tokens = 500_000
     save_config(config, resolved_config_path)
 

nanobot/cli/webui_support.py

@@ -1,12 +1,14 @@
 """Shared WebUI setup, URL, health, and browser helpers."""
 
+import os
 import subprocess
 import sys
 import time
 import webbrowser
 from collections.abc import Callable
+from dataclasses import dataclass
 from pathlib import Path
-from typing import TYPE_CHECKING, Any
+from typing import TYPE_CHECKING, Any, BinaryIO
 
 import typer
 from pydantic import ValidationError
@@ -457,27 +459,104 @@ def _print_webui_foreground_lifecycle(*, attached: bool) -> None:
         console.print("[green]WebUI is attached to the shared gateway.[/green]")
     console.print("[dim]Closing the browser does not stop channels or automations.[/dim]")
     console.print(
-        "[dim]Press Ctrl+C to detach; the gateway stops only when the last local client exits.[/dim]"
+        "[dim]Following live gateway logs. Press Ctrl+C to detach; the gateway stops "
+        "only when the last local client exits.[/dim]"
     )
 
 
+_LOG_ANCHOR_BYTES = 64
+
+
+@dataclass
+class _GatewayLogCursor:
+    offset: int = 0
+    identity: tuple[int, int] | None = None
+    anchor: bytes = b""
+    pending: bytes = b""
+
+
+def _log_anchor(handle: BinaryIO, offset: int) -> bytes:
+    size = min(offset, _LOG_ANCHOR_BYTES)
+    handle.seek(offset - size)
+    return handle.read(size)
+
+
+def _start_gateway_log_cursor(log_path: Path) -> _GatewayLogCursor:
+    """Start following at the current end of *log_path*."""
+    try:
+        with log_path.open("rb") as handle:
+            stat = os.fstat(handle.fileno())
+            offset = stat.st_size
+            return _GatewayLogCursor(
+                offset=offset,
+                identity=(stat.st_dev, stat.st_ino),
+                anchor=_log_anchor(handle, offset),
+            )
+    except OSError:
+        return _GatewayLogCursor()
+
+
+def _read_new_gateway_logs(
+    log_path: Path,
+    cursor: _GatewayLogCursor,
+    *,
+    flush: bool = False,
+) -> list[str]:
+    """Read complete gateway log lines appended after *cursor*."""
+    try:
+        with log_path.open("rb") as handle:
+            stat = os.fstat(handle.fileno())
+            identity = (stat.st_dev, stat.st_ino)
+            reset = cursor.identity != identity or stat.st_size < cursor.offset
+            if not reset and cursor.offset:
+                reset = _log_anchor(handle, cursor.offset) != cursor.anchor
+            if reset:
+                cursor.offset = 0
+                cursor.pending = b""
+
+            handle.seek(cursor.offset)
+            chunk = handle.read()
+            cursor.offset = handle.tell()
+            cursor.identity = identity
+            cursor.anchor = _log_anchor(handle, cursor.offset)
+    except OSError:
+        return []
+
+    parts = (cursor.pending + chunk).split(b"\n")
+    cursor.pending = parts.pop()
+    if flush and cursor.pending:
+        parts.append(cursor.pending)
+        cursor.pending = b""
+    return [part.removesuffix(b"\r").decode("utf-8", errors="replace") for part in parts]
+
+
 def _attach_to_background_gateway(
     runtime: "GatewayRuntime",
     *,
     poll_hook: Callable[[], None] | None = None,
     sleep: Callable[[float], None] = time.sleep,
 ) -> None:
-    """Keep a WebUI launcher attached without taking ownership of the gateway."""
+    """Keep the launcher attached and mirror this gateway's new log output."""
+    status = runtime.status()
+    log_path = status.log_path
+    cursor = _start_gateway_log_cursor(log_path)
     _print_webui_foreground_lifecycle(attached=True)
     try:
-        while runtime.status().running:
+        while status.running:
+            for line in _read_new_gateway_logs(log_path, cursor):
+                console.print(line, markup=False, highlight=False)
             if poll_hook is not None:
                 poll_hook()
             sleep(0.5)
+            status = runtime.status()
     except KeyboardInterrupt:
+        for line in _read_new_gateway_logs(log_path, cursor, flush=True):
+            console.print(line, markup=False, highlight=False)
         console.print("\n[yellow]WebUI launcher detached.[/yellow]")
         return
 
+    for line in _read_new_gateway_logs(log_path, cursor, flush=True):
+        console.print(line, markup=False, highlight=False)
     console.print("[yellow]Gateway stopped.[/yellow]")
 
 

nanobot/cron/service.py

@@ -25,6 +25,7 @@ from nanobot.cron.types import (
     CronSchedule,
     CronStore,
 )
+from nanobot.runtime_context import RUNTIME_CONTEXT_INPUT_META
 from nanobot.utils.run_records import (
     write_run_record as write_automation_run_record,
 )
@@ -115,8 +116,21 @@ def _disable_malformed_legacy_job(job: CronJob) -> None:
     logger.warning("Cron: disabled malformed legacy job '{}' ({}): {}", job.name, job.id, reason)
 
 
+def _persistable_origin_metadata(metadata: dict[str, Any]) -> dict[str, Any]:
+    """Return a detached JSON-safe routing snapshot for a cron payload."""
+    snapshot: dict[str, Any] = {}
+    for key, value in metadata.items():
+        if key == RUNTIME_CONTEXT_INPUT_META:
+            continue
+        try:
+            snapshot[key] = json.loads(json.dumps(value, ensure_ascii=False, allow_nan=False))
+        except (TypeError, ValueError, RecursionError):
+            continue
+    return snapshot
+
+
 def _normalize_agent_turn_job(job: CronJob) -> bool:
-    """Migrate legacy user cron payloads into session-bound payloads.
+    """Make routing metadata persistable and migrate legacy user cron payloads.
 
     Pre-bound user cron jobs stored their delivery target in ``channel``/``to``.
     Normal user-created legacy jobs always have those fields; if they are
@@ -124,8 +138,12 @@ def _normalize_agent_turn_job(job: CronJob) -> bool:
     a runtime legacy execution path.
     """
     payload = job.payload
+    origin_metadata = _persistable_origin_metadata(payload.origin_metadata)
+    changed = origin_metadata != payload.origin_metadata
+    payload.origin_metadata = origin_metadata
+
     if payload.kind != "agent_turn" or not _has_legacy_delivery_context(payload):
-        return False
+        return changed
 
     if not payload.channel or not payload.to:
         _disable_malformed_legacy_job(job)
@@ -135,7 +153,7 @@ def _normalize_agent_turn_job(job: CronJob) -> bool:
     payload.origin_channel = payload.origin_channel or payload.channel
     payload.origin_chat_id = payload.origin_chat_id or payload.to
     if not payload.origin_metadata:
-        payload.origin_metadata = dict(payload.channel_meta or {})
+        payload.origin_metadata = _persistable_origin_metadata(payload.channel_meta or {})
 
     payload.deliver = False
     payload.channel = None

nanobot/providers/base.py

@@ -1029,6 +1029,20 @@ class LLMProvider(ABC):
         # Unknown 429 defaults to WAIT+retry.
         return True
 
+    @staticmethod
+    def _content_as_blocks(content: Any) -> list[dict[str, Any]]:
+        """Convert message content to blocks so mixed user content can be merged."""
+        if isinstance(content, list):
+            return [
+                dict(cast(dict[str, Any], item))
+                if isinstance(item, dict)
+                else {"type": "text", "text": str(item)}
+                for item in cast(list[object], content)
+            ]
+        if content is None:
+            return []
+        return [{"type": "text", "text": str(content)}]
+
     @staticmethod
     def _enforce_role_alternation(messages: list[dict[str, Any]]) -> list[dict[str, Any]]:
         """Merge consecutive same-role messages and drop trailing assistant messages.
@@ -1063,6 +1077,13 @@ class LLMProvider(ABC):
                 curr_content = msg.get("content") or ""
                 if isinstance(prev_content, str) and isinstance(curr_content, str):
                     prev["content"] = (prev_content + "\n\n" + curr_content).strip()
+                elif role == "user":
+                    combined = dict(msg)
+                    combined["content"] = [
+                        *LLMProvider._content_as_blocks(prev_content),
+                        *LLMProvider._content_as_blocks(curr_content),
+                    ]
+                    merged[-1] = combined
                 else:
                     merged[-1] = dict(msg)
             else:

nanobot/providers/conversation_state.py

@@ -11,6 +11,7 @@ from nanobot.providers.base import (
     ProviderCallContext,
     ProviderConversationState,
 )
+from nanobot.utils.helpers import estimate_prompt_tokens_chain
 
 _PROVIDER_STATE_OUTPUT_META = "provider_state_output"
 _PROVIDER_STATE_BOUNDARY_META = "provider_state_boundary"
@@ -69,6 +70,37 @@ class ProviderConversationStateController:
             session_id=self._session_id,
         )
 
+    def estimate_request_context_tokens(
+        self,
+        messages: list[dict[str, Any]],
+        *,
+        model_messages: list[dict[str, Any]] | None = None,
+        supplemental_messages: list[dict[str, Any]] | None = None,
+        tool_definitions: list[dict[str, Any]] | None = None,
+    ) -> int | None:
+        """Estimate resumed state plus the pending delta for the next request."""
+        state = self.checkpoint(messages, model_messages=model_messages)
+        if state is None:
+            return None
+        context_tokens = state.payload.get("context_tokens")
+        if (
+            isinstance(context_tokens, bool)
+            or not isinstance(context_tokens, int)
+            or context_tokens < 0
+        ):
+            return None
+        pending_messages = [
+            *state.pending_messages,
+            *(supplemental_messages or []),
+        ]
+        delta_tokens, _ = estimate_prompt_tokens_chain(
+            self._provider,
+            self._model,
+            pending_messages,
+            tool_definitions,
+        )
+        return context_tokens + max(0, delta_tokens)
+
     def prepare_request(
         self,
         messages: list[dict[str, Any]],
@@ -76,11 +108,20 @@ class ProviderConversationStateController:
         context_window_tokens: int | None,
         model_messages: list[dict[str, Any]] | None = None,
         supplemental_messages: list[dict[str, Any]] | None = None,
+        resume_state: bool = True,
     ) -> ProviderCallContext | None:
-        """Build typed context for the next request and remember its durable delta."""
+        """Build context for the next request and remember its durable delta.
+
+        ``resume_state=False`` abandons opaque history when local request
+        fitting has produced a new independent model-facing context.
+        """
         independent_context = self.independent_request_context(
             context_window_tokens=context_window_tokens,
         )
+        if not resume_state:
+            self._state = None
+            self._request_messages = []
+            return independent_context
         if self._state is None:
             self._request_messages = []
             return independent_context

nanobot/providers/github_copilot_provider.py

@@ -5,6 +5,7 @@
 from __future__ import annotations
 
 import asyncio
+import hashlib
 import os
 import time
 import webbrowser
@@ -17,7 +18,12 @@ from oauth_cli_kit.models import OAuthToken
 from oauth_cli_kit.storage import FileTokenStorage
 
 from nanobot.providers.base import LLMResponse, ProviderCallContext
+from nanobot.providers.oauth_model_catalog import (
+    OAuthModelCatalog,
+    OAuthModelCatalogSnapshot,
+)
 from nanobot.providers.openai_compat_provider import OpenAICompatProvider
+from nanobot.providers.registry import ProviderModelSpec, find_by_name
 
 DEFAULT_GITHUB_DEVICE_CODE_URL = "https://github.com/login/device/code"
 DEFAULT_GITHUB_ACCESS_TOKEN_URL = "https://github.com/login/oauth/access_token"
@@ -96,7 +102,9 @@ def login_github_copilot(
 
         device_code = str(payload["device_code"])
         user_code = str(payload["user_code"])
-        verify_url = str(payload.get("verification_uri") or payload.get("verification_uri_complete") or "")
+        verify_url = str(
+            payload.get("verification_uri") or payload.get("verification_uri_complete") or ""
+        )
         verify_complete = str(payload.get("verification_uri_complete") or verify_url)
         interval = max(1, int(payload.get("interval") or 5))
         expires_in = int(payload.get("expires_in") or 900)
@@ -180,8 +188,6 @@ class GitHubCopilotProvider(OpenAICompatProvider):
         *,
         provider_name: str = "github_copilot",
     ):
-        from nanobot.providers.registry import find_by_name
-
         self._copilot_access_token: str | None = None
         self._copilot_expires_at: float = 0.0
         self._copilot_token_lock: asyncio.Lock = asyncio.Lock()
@@ -217,7 +223,9 @@ class GitHubCopilotProvider(OpenAICompatProvider):
                 )
 
             timeout = httpx.Timeout(20.0, connect=20.0)
-            async with httpx.AsyncClient(timeout=timeout, follow_redirects=True, trust_env=True) as client:
+            async with httpx.AsyncClient(
+                timeout=timeout, follow_redirects=True, trust_env=True
+            ) as client:
                 response = await client.get(
                     _resolve("NANOBOT_COPILOT_TOKEN_URL", DEFAULT_COPILOT_TOKEN_URL),
                     headers=_copilot_headers(github_token.access),
@@ -296,3 +304,174 @@ class GitHubCopilotProvider(OpenAICompatProvider):
             on_tool_call_delta=on_tool_call_delta,
             provider_context=provider_context,
         )
+
+
+def get_github_copilot_model_catalog(
+    proxy: str | None = None,
+) -> OAuthModelCatalogSnapshot:
+    storage = get_storage()
+    token = storage.load()
+    account_key = _catalog_account_key(getattr(token, "account_id", None))
+    cache_key = (
+        f"{storage.get_token_path()}\0{account_key}\0"
+        f"{_resolve('NANOBOT_COPILOT_BASE_URL', DEFAULT_COPILOT_BASE_URL)}\0{proxy or ''}"
+    )
+    return _GITHUB_COPILOT_MODEL_CATALOG.get(cache_key=cache_key, proxy=proxy)
+
+
+def invalidate_github_copilot_model_catalog() -> None:
+    _GITHUB_COPILOT_MODEL_CATALOG.invalidate()
+
+
+def _fetch_github_copilot_models(proxy: str | None) -> tuple[ProviderModelSpec, ...]:
+    github_token = get_storage().load()
+    if not github_token or not github_token.access:
+        raise RuntimeError("GitHub Copilot is not logged in")
+
+    common_headers = {
+        "Accept": "application/json",
+        "User-Agent": USER_AGENT,
+        "Editor-Version": EDITOR_VERSION,
+        "Editor-Plugin-Version": EDITOR_PLUGIN_VERSION,
+    }
+    client_kwargs: dict[str, Any] = {"timeout": 20.0, "follow_redirects": True}
+    if proxy:
+        client_kwargs.update(proxy=proxy, trust_env=False)
+    with httpx.Client(**client_kwargs) as client:
+        exchange = client.get(
+            _resolve("NANOBOT_COPILOT_TOKEN_URL", DEFAULT_COPILOT_TOKEN_URL),
+            headers={**common_headers, "Authorization": f"token {github_token.access}"},
+        )
+        exchange.raise_for_status()
+        exchange_mapping = _catalog_mapping(exchange.json())
+        copilot_token = exchange_mapping.get("token")
+        if not isinstance(copilot_token, str) or not copilot_token:
+            raise RuntimeError("GitHub Copilot token exchange returned no token")
+        endpoint_base = _catalog_first_text(
+            _catalog_mapping(exchange_mapping.get("endpoints")),
+            "api",
+        )
+        base_url = endpoint_base or _resolve(
+            "NANOBOT_COPILOT_BASE_URL",
+            DEFAULT_COPILOT_BASE_URL,
+        )
+        models_url = (
+            base_url
+            if base_url.rstrip("/").endswith("/models")
+            else f"{base_url.rstrip('/')}/models"
+        )
+        response = client.get(
+            models_url,
+            headers={**common_headers, "Authorization": f"Bearer {copilot_token}"},
+        )
+    response.raise_for_status()
+    return _parse_github_copilot_models(response.json())
+
+
+def _parse_github_copilot_models(payload: Any) -> tuple[ProviderModelSpec, ...]:
+    rows = cast(dict[str, Any], payload).get("data") if isinstance(payload, dict) else None
+    if not isinstance(rows, list):
+        return ()
+
+    fallback_models = _oauth_fallback_models("github_copilot")
+    fallback_by_id = {model.id.split("/", 1)[-1]: model for model in fallback_models}
+    models: list[ProviderModelSpec] = []
+    seen: set[str] = set()
+    for value in cast(list[object], rows):
+        if not isinstance(value, dict):
+            continue
+        row = cast(dict[str, Any], value)
+        wire_id = _catalog_first_text(row, "id")
+        policy = _catalog_mapping(row.get("policy"))
+        endpoints = row.get("supported_endpoints")
+        if (
+            not wire_id
+            or wire_id in seen
+            or row.get("model_picker_enabled") is not True
+            or policy.get("state") == "disabled"
+            or not _copilot_transport_supported(wire_id, endpoints)
+        ):
+            continue
+        seen.add(wire_id)
+        capabilities = _catalog_mapping(row.get("capabilities"))
+        supports = _catalog_mapping(capabilities.get("supports"))
+        limits = _catalog_mapping(capabilities.get("limits"))
+        fallback = fallback_by_id.get(wire_id)
+        models.append(
+            ProviderModelSpec(
+                id=f"github-copilot/{wire_id}",
+                label=(
+                    _catalog_first_text(row, "name")
+                    or (fallback.label if fallback is not None else wire_id)
+                ),
+                description=(fallback.description if fallback is not None else ""),
+                owned_by="GitHub Copilot",
+                context_window=(
+                    _catalog_positive_int(limits, "max_context_window_tokens")
+                    or (fallback.context_window if fallback is not None else None)
+                ),
+                reasoning_efforts=_catalog_reasoning_efforts(supports.get("reasoning_effort")),
+            )
+        )
+    return tuple(models)
+
+
+def _copilot_transport_supported(wire_id: str, endpoints: object) -> bool:
+    if not isinstance(endpoints, list):
+        return True
+    supported = cast(list[object], endpoints)
+    if "/chat/completions" in supported:
+        return True
+    model = wire_id.lower()
+    return "/responses" in supported and any(
+        token in model for token in ("gpt-5", "o1", "o3", "o4")
+    )
+
+
+def _oauth_fallback_models(provider_name: str) -> tuple[ProviderModelSpec, ...]:
+    spec = find_by_name(provider_name)
+    assert spec is not None
+    return spec.builtin_models
+
+
+def _catalog_account_key(account_id: object) -> str:
+    value = account_id if isinstance(account_id, str) else ""
+    return hashlib.sha256(value.encode()).hexdigest()[:16] if value else "anonymous"
+
+
+def _catalog_mapping(value: Any) -> dict[str, Any]:
+    return cast(dict[str, Any], value) if isinstance(value, dict) else {}
+
+
+def _catalog_first_text(row: dict[str, Any], *keys: str) -> str:
+    for key in keys:
+        value = row.get(key)
+        if isinstance(value, str) and value.strip():
+            return value.strip()
+    return ""
+
+
+def _catalog_positive_int(row: dict[str, Any], *keys: str) -> int | None:
+    for key in keys:
+        value = row.get(key)
+        if isinstance(value, (int, float)) and not isinstance(value, bool) and value > 0:
+            return int(value)
+    return None
+
+
+def _catalog_reasoning_efforts(value: Any) -> tuple[str, ...]:
+    if not isinstance(value, list):
+        return ()
+    return tuple(
+        dict.fromkeys(
+            item.strip()
+            for item in cast(list[object], value)
+            if isinstance(item, str) and item.strip()
+        )
+    )
+
+
+_GITHUB_COPILOT_MODEL_CATALOG = OAuthModelCatalog(
+    fallback_models=_oauth_fallback_models("github_copilot"),
+    fetch=_fetch_github_copilot_models,
+)

nanobot/providers/oauth_model_catalog.py

@@ -0,0 +1,224 @@
+"""Shared cache seam for OAuth provider model discovery."""
+
+from __future__ import annotations
+
+import threading
+import time
+from collections.abc import Callable, Sequence
+from dataclasses import dataclass, replace
+from typing import Literal
+
+from loguru import logger
+
+from nanobot.providers.registry import ProviderModelSpec
+
+CatalogSource = Literal["remote", "cache", "stale", "fallback"]
+
+
+@dataclass(frozen=True, slots=True)
+class OAuthModelCatalogSnapshot:
+    """One usable catalog view, including where it came from."""
+
+    models: tuple[ProviderModelSpec, ...]
+    source: CatalogSource
+    fetched_at: float
+    message: str | None = None
+
+    def find(self, model: str) -> ProviderModelSpec | None:
+        wire_id = model.split("/", 1)[-1]
+        return next(
+            (item for item in self.models if item.id.split("/", 1)[-1] == wire_id),
+            None,
+        )
+
+
+@dataclass(frozen=True, slots=True)
+class _CacheEntry:
+    snapshot: OAuthModelCatalogSnapshot
+    stored_at: float
+
+
+class OAuthModelCatalog:
+    """Cache one provider's discovery behind a small failure-tolerant interface."""
+
+    def __init__(
+        self,
+        *,
+        fallback_models: Sequence[ProviderModelSpec],
+        fetch: Callable[[str | None], Sequence[ProviderModelSpec]],
+        fresh_ttl_s: float = 5 * 60,
+        stale_ttl_s: float = 24 * 60 * 60,
+        failure_ttl_s: float = 30,
+        max_entries: int = 8,
+        monotonic: Callable[[], float] = time.monotonic,
+        wall_clock: Callable[[], float] = time.time,
+    ) -> None:
+        if fresh_ttl_s < 0 or stale_ttl_s < fresh_ttl_s or failure_ttl_s < 0:
+            raise ValueError("catalog cache TTLs are invalid")
+        if max_entries < 1:
+            raise ValueError("catalog cache must allow at least one entry")
+        self._fallback_models = tuple(fallback_models)
+        self._fetch = fetch
+        self._fresh_ttl_s = fresh_ttl_s
+        self._stale_ttl_s = stale_ttl_s
+        self._failure_ttl_s = failure_ttl_s
+        self._max_entries = max_entries
+        self._monotonic = monotonic
+        self._wall_clock = wall_clock
+        self._condition = threading.Condition()
+        self._entries: dict[str, _CacheEntry] = {}
+        self._failures: dict[str, float] = {}
+        self._inflight: set[str] = set()
+        self._generation = 0
+
+    def get(self, *, cache_key: str, proxy: str | None = None) -> OAuthModelCatalogSnapshot:
+        """Return a fresh catalog, sharing concurrent work and retaining a fallback."""
+        with self._condition:
+            generation = self._generation
+            cached = self._cached_result(cache_key)
+            if cached is not None:
+                return cached
+            while cache_key in self._inflight:
+                self._condition.wait()
+                if generation != self._generation:
+                    return self._stale_or_fallback(None, self._monotonic())
+                cached = self._cached_result(cache_key)
+                if cached is not None:
+                    return cached
+            self._inflight.add(cache_key)
+
+        try:
+            models = tuple(self._fetch(proxy))
+            if not models:
+                raise ValueError("provider returned an empty model catalog")
+        except Exception as exc:
+            logger.warning("OAuth model catalog refresh failed: type={}", type(exc).__name__)
+            with self._condition:
+                result = (
+                    self._stale_or_fallback(None, self._monotonic())
+                    if generation != self._generation
+                    else self._failure_result(cache_key)
+                )
+        else:
+            now = self._monotonic()
+            result = OAuthModelCatalogSnapshot(
+                models=models,
+                source="remote",
+                fetched_at=self._wall_clock(),
+            )
+            with self._condition:
+                if generation != self._generation:
+                    result = self._stale_or_fallback(None, now)
+                else:
+                    self._store(cache_key, _CacheEntry(snapshot=result, stored_at=now))
+                    self._failures.pop(cache_key, None)
+        finally:
+            with self._condition:
+                self._inflight.discard(cache_key)
+                self._condition.notify_all()
+
+        return result
+
+    def invalidate(self) -> None:
+        """Drop cached work and prevent an older identity refresh from being stored."""
+        with self._condition:
+            self._generation += 1
+            self._entries.clear()
+            self._failures.clear()
+            self._condition.notify_all()
+
+    def _cached_result(self, cache_key: str) -> OAuthModelCatalogSnapshot | None:
+        now = self._monotonic()
+        entry = self._entries.get(cache_key)
+        if entry is not None and now - entry.stored_at < self._fresh_ttl_s:
+            return replace(entry.snapshot, source="cache")
+        failure_until = self._failures.get(cache_key)
+        if failure_until is not None and failure_until <= now:
+            self._failures.pop(cache_key, None)
+        elif failure_until is not None:
+            return self._stale_or_fallback(entry, now)
+        return None
+
+    def _failure_result(self, cache_key: str) -> OAuthModelCatalogSnapshot:
+        now = self._monotonic()
+        self._reserve(cache_key)
+        self._failures[cache_key] = now + self._failure_ttl_s
+        return self._stale_or_fallback(self._entries.get(cache_key), now)
+
+    def _stale_or_fallback(
+        self,
+        entry: _CacheEntry | None,
+        now: float,
+    ) -> OAuthModelCatalogSnapshot:
+        if entry is not None and now - entry.stored_at < self._stale_ttl_s:
+            return replace(
+                entry.snapshot,
+                source="stale",
+                message="Could not refresh the online model list; showing cached models.",
+            )
+        return OAuthModelCatalogSnapshot(
+            models=self._fallback_models,
+            source="fallback",
+            fetched_at=self._wall_clock(),
+            message="Could not load the online model list; showing built-in fallback models.",
+        )
+
+    def _store(self, cache_key: str, entry: _CacheEntry) -> None:
+        self._reserve(cache_key)
+        self._entries[cache_key] = entry
+
+    def _reserve(self, cache_key: str) -> None:
+        known = set(self._entries) | set(self._failures)
+        if cache_key in known or len(known) < self._max_entries:
+            return
+        oldest = min(
+            known,
+            key=lambda key: (
+                self._entries[key].stored_at
+                if key in self._entries
+                else self._failures[key] - self._failure_ttl_s
+            ),
+        )
+        self._entries.pop(oldest, None)
+        self._failures.pop(oldest, None)
+
+
+def get_oauth_model_catalog(
+    provider_name: str,
+    *,
+    proxy: str | None = None,
+) -> OAuthModelCatalogSnapshot:
+    """Discover models through the owning provider module."""
+    if provider_name == "openai_codex":
+        from nanobot.providers.openai_codex_provider import get_openai_codex_model_catalog
+
+        return get_openai_codex_model_catalog(proxy)
+    if provider_name == "xai_grok":
+        from nanobot.providers.xai_grok_provider import get_xai_grok_model_catalog
+
+        return get_xai_grok_model_catalog(proxy)
+    if provider_name == "github_copilot":
+        from nanobot.providers.github_copilot_provider import get_github_copilot_model_catalog
+
+        return get_github_copilot_model_catalog(proxy)
+    raise ValueError(f"OAuth model discovery is not available for {provider_name}")
+
+
+def invalidate_oauth_model_catalog(provider_name: str) -> None:
+    """Invalidate provider discovery after its OAuth identity changes."""
+    if provider_name == "openai_codex":
+        from nanobot.providers.openai_codex_provider import (
+            invalidate_openai_codex_model_catalog,
+        )
+
+        invalidate_openai_codex_model_catalog()
+    elif provider_name == "xai_grok":
+        from nanobot.providers.xai_grok_provider import invalidate_xai_grok_model_catalog
+
+        invalidate_xai_grok_model_catalog()
+    elif provider_name == "github_copilot":
+        from nanobot.providers.github_copilot_provider import (
+            invalidate_github_copilot_model_catalog,
+        )
+
+        invalidate_github_copilot_model_catalog()

nanobot/providers/openai_codex_provider.py

@@ -14,7 +14,10 @@ from typing import Any, cast
 import httpx
 from loguru import logger
 from oauth_cli_kit import get_token as get_codex_token
+from oauth_cli_kit.providers import OPENAI_CODEX_PROVIDER
+from oauth_cli_kit.storage import FileTokenStorage
 
+from nanobot import __version__
 from nanobot.providers.base import (
     LLMProvider,
     LLMResponse,
@@ -22,6 +25,10 @@ from nanobot.providers.base import (
     ProviderConversationState,
     resolve_stream_idle_timeout_s,
 )
+from nanobot.providers.oauth_model_catalog import (
+    OAuthModelCatalog,
+    OAuthModelCatalogSnapshot,
+)
 from nanobot.providers.openai_responses import (
     ResponsesStreamCapture,
     build_responses_state,
@@ -35,8 +42,11 @@ from nanobot.providers.openai_responses import (
     responses_state_items,
     responses_state_matches,
 )
+from nanobot.providers.registry import ProviderModelSpec, find_by_name
 
 DEFAULT_CODEX_URL = "https://chatgpt.com/backend-api/codex/responses"
+DEFAULT_OPENAI_CODEX_MODELS_URL = "https://chatgpt.com/backend-api/codex/models"
+OPENAI_CODEX_CATALOG_CLIENT_VERSION = "0.144.0"
 DEFAULT_ORIGINATOR = "nanobot"
 _COMPACTION_RETAINED_CHAR_BUDGET = 256_000
 
@@ -87,9 +97,7 @@ class OpenAICodexProvider(LLMProvider):
         model = model or self.default_model
         sanitized_messages = self._sanitize_empty_content(messages)
         sanitized_state = (
-            provider_context.conversation_state
+            provider_context.conversation_state if provider_context is not None else None
-            if provider_context is not None
-            else None
         )
         if sanitized_state is not None:
             sanitized_state = sanitized_state.with_pending_messages(
@@ -168,11 +176,7 @@ class OpenAICodexProvider(LLMProvider):
                     )
 
             compact_threshold = resolve_compact_threshold(
-                (
+                (provider_context.context_window_tokens if provider_context is not None else None),
-                    provider_context.context_window_tokens
-                    if provider_context is not None
-                    else None
-                ),
                 max_tokens,
             )
             if (
@@ -236,8 +240,12 @@ class OpenAICodexProvider(LLMProvider):
             return response
 
     async def chat(
-        self, messages: list[dict[str, Any]], tools: list[dict[str, Any]] | None = None,
+        self,
-        model: str | None = None, max_tokens: int = 4096, temperature: float = 0.7,
+        messages: list[dict[str, Any]],
+        tools: list[dict[str, Any]] | None = None,
+        model: str | None = None,
+        max_tokens: int = 4096,
+        temperature: float = 0.7,
         reasoning_effort: str | None = None,
         tool_choice: str | dict[str, Any] | None = None,
         provider_context: ProviderCallContext | None = None,
@@ -264,8 +272,12 @@ class OpenAICodexProvider(LLMProvider):
         )
 
     async def chat_stream(
-        self, messages: list[dict[str, Any]], tools: list[dict[str, Any]] | None = None,
+        self,
-        model: str | None = None, max_tokens: int = 4096, temperature: float = 0.7,
+        messages: list[dict[str, Any]],
+        tools: list[dict[str, Any]] | None = None,
+        model: str | None = None,
+        max_tokens: int = 4096,
+        temperature: float = 0.7,
         reasoning_effort: str | None = None,
         tool_choice: str | dict[str, Any] | None = None,
         on_content_delta: Callable[[str], Awaitable[None]] | None = None,
@@ -344,11 +356,7 @@ def _without_response_item_ids(
             sanitized_input.append(raw_item)
             continue
         item = cast(dict[str, Any], raw_item)
-        sanitized_input.append({
+        sanitized_input.append({key: value for key, value in item.items() if key != "id"})
-            key: value
-            for key, value in item.items()
-            if key != "id"
-        })
 
     body = dict(request_body)
     body["input"] = sanitized_input
@@ -444,15 +452,12 @@ async def _request_codex(
                 raw = text.decode("utf-8", "ignore")
                 retry_after = LLMProvider._extract_retry_after_from_headers(response.headers)
                 error_type, error_code = LLMProvider._extract_error_type_code(raw)
-                compaction_unsupported = (
+                compaction_unsupported = response.status_code in {400, 404, 422} and any(
-                    response.status_code in {400, 404, 422}
+                    marker in raw.lower()
-                    and any(
+                    for marker in (
-                        marker in raw.lower()
+                        "context_management",
-                        for marker in (
+                        "compact_threshold",
-                            "context_management",
+                        "compaction_trigger",
-                            "compact_threshold",
-                            "compaction_trigger",
-                        )
                     )
                 )
                 raise _CodexHTTPError(
@@ -461,7 +466,9 @@ async def _request_codex(
                     retry_after=retry_after,
                     error_type=error_type,
                     error_code=error_code,
-                    should_retry=_should_retry_status(response.status_code, error_type, error_code, raw),
+                    should_retry=_should_retry_status(
+                        response.status_code, error_type, error_code, raw
+                    ),
                     compaction_unsupported=compaction_unsupported,
                 )
             capture = ResponsesStreamCapture()
@@ -534,7 +541,9 @@ def _codex_error_response(exc: Exception) -> LLMResponse:
         default_detail = "HTTP request failed"
 
     if status_code is not None and should_retry is None:
-        retry_content = None if int(status_code) == 429 and isinstance(exc, _CodexHTTPError) else detail
+        retry_content = (
+            None if int(status_code) == 429 and isinstance(exc, _CodexHTTPError) else detail
+        )
         should_retry = _should_retry_status(
             int(status_code),
             getattr(exc, "error_type", None),
@@ -592,3 +601,139 @@ def _should_retry_status(
             )
         )
     return status_code in LLMProvider._RETRYABLE_STATUS_CODES or status_code >= 500
+
+
+def get_openai_codex_model_catalog(
+    proxy: str | None = None,
+) -> OAuthModelCatalogSnapshot:
+    storage = FileTokenStorage(token_filename=OPENAI_CODEX_PROVIDER.token_filename)
+    token = storage.load()
+    account_id = getattr(token, "account_id", None)
+    account_key = _catalog_account_key(account_id)
+    cache_key = f"{storage.get_token_path()}\0{account_key}\0{proxy or ''}"
+    return _OPENAI_CODEX_MODEL_CATALOG.get(cache_key=cache_key, proxy=proxy)
+
+
+def invalidate_openai_codex_model_catalog() -> None:
+    _OPENAI_CODEX_MODEL_CATALOG.invalidate()
+
+
+def _fetch_openai_codex_models(proxy: str | None) -> tuple[ProviderModelSpec, ...]:
+    token = get_codex_token(proxy=proxy)
+    account_id = getattr(token, "account_id", None)
+    if not isinstance(account_id, str) or not account_id:
+        raise RuntimeError("OpenAI Codex OAuth token has no account ID")
+    client_kwargs: dict[str, Any] = {"timeout": 10.0, "follow_redirects": False}
+    if proxy:
+        client_kwargs.update(proxy=proxy, trust_env=False)
+    with httpx.Client(**client_kwargs) as client:
+        response = client.get(
+            DEFAULT_OPENAI_CODEX_MODELS_URL,
+            params={"client_version": OPENAI_CODEX_CATALOG_CLIENT_VERSION},
+            headers={
+                "Authorization": f"Bearer {token.access}",
+                "chatgpt-account-id": account_id,
+                "originator": DEFAULT_ORIGINATOR,
+                "User-Agent": f"nanobot/{__version__} (python)",
+                "accept": "application/json",
+            },
+        )
+    response.raise_for_status()
+    return _parse_openai_codex_models(response.json())
+
+
+def _parse_openai_codex_models(payload: Any) -> tuple[ProviderModelSpec, ...]:
+    rows = cast(dict[str, Any], payload).get("models") if isinstance(payload, dict) else None
+    if not isinstance(rows, list):
+        return ()
+
+    fallback_models = _oauth_fallback_models("openai_codex")
+    fallback_by_id = {model.id.split("/", 1)[-1]: model for model in fallback_models}
+    parsed: list[tuple[int, ProviderModelSpec]] = []
+    seen: set[str] = set()
+    for value in cast(list[object], rows):
+        if not isinstance(value, dict):
+            continue
+        row = cast(dict[str, Any], value)
+        wire_id = _catalog_first_text(row, "slug", "id")
+        if not wire_id or wire_id in seen or row.get("visibility") in {"hide", "none"}:
+            continue
+        seen.add(wire_id)
+        fallback = fallback_by_id.get(wire_id)
+        priority = row.get("priority")
+        parsed.append(
+            (
+                priority if isinstance(priority, int) and not isinstance(priority, bool) else 2**31,
+                ProviderModelSpec(
+                    id=f"openai-codex/{wire_id}",
+                    label=(
+                        _catalog_first_text(row, "display_name", "name")
+                        or (fallback.label if fallback is not None else wire_id)
+                    ),
+                    description=(
+                        _catalog_first_text(row, "description")
+                        or (fallback.description if fallback is not None else "")
+                    ),
+                    owned_by="OpenAI Codex",
+                    context_window=(
+                        _catalog_positive_int(row, "context_window")
+                        or (fallback.context_window if fallback is not None else None)
+                    ),
+                    reasoning_efforts=(
+                        _catalog_reasoning_efforts(row.get("supported_reasoning_levels"))
+                        or (fallback.reasoning_efforts if fallback is not None else ())
+                    ),
+                ),
+            )
+        )
+    parsed.sort(key=lambda item: item[0])
+    return tuple(model for _, model in parsed)
+
+
+def _oauth_fallback_models(provider_name: str) -> tuple[ProviderModelSpec, ...]:
+    spec = find_by_name(provider_name)
+    assert spec is not None
+    return spec.builtin_models
+
+
+def _catalog_account_key(account_id: object) -> str:
+    value = account_id if isinstance(account_id, str) else ""
+    return hashlib.sha256(value.encode()).hexdigest()[:16] if value else "anonymous"
+
+
+def _catalog_first_text(row: dict[str, Any], *keys: str) -> str:
+    for key in keys:
+        value = row.get(key)
+        if isinstance(value, str) and value.strip():
+            return value.strip()
+    return ""
+
+
+def _catalog_positive_int(row: dict[str, Any], *keys: str) -> int | None:
+    for key in keys:
+        value = row.get(key)
+        if isinstance(value, (int, float)) and not isinstance(value, bool) and value > 0:
+            return int(value)
+    return None
+
+
+def _catalog_reasoning_efforts(value: Any) -> tuple[str, ...]:
+    if not isinstance(value, list):
+        return ()
+    efforts: list[str] = []
+    for item in cast(list[object], value):
+        if isinstance(item, str):
+            effort = item.strip()
+        elif isinstance(item, dict):
+            effort = _catalog_first_text(cast(dict[str, Any], item), "effort", "value", "id")
+        else:
+            effort = ""
+        if effort and effort not in efforts:
+            efforts.append(effort)
+    return tuple(efforts)
+
+
+_OPENAI_CODEX_MODEL_CATALOG = OAuthModelCatalog(
+    fallback_models=_oauth_fallback_models("openai_codex"),
+    fetch=_fetch_openai_codex_models,
+)

nanobot/providers/registry.py

@@ -20,12 +20,15 @@ from pydantic.alias_generators import to_snake
 
 @dataclass(frozen=True)
 class ProviderModelSpec:
-    """A curated model exposed by providers without a model-list endpoint."""
+    """Curated model metadata used for fixed catalogs or online fallback."""
 
     id: str
     label: str = ""
     description: str = ""
+    owned_by: str = ""
     context_window: int | None = None
+    reasoning_efforts: tuple[str, ...] = ()
+    supports_backend_search: bool = False
 
 
 @dataclass(frozen=True)
@@ -42,7 +45,7 @@ class ProviderSpec:
     keywords: tuple[str, ...]  # model-name keywords for matching (lowercase)
     env_key: str  # env var for API key, e.g. "DASHSCOPE_API_KEY"
     display_name: str = ""  # shown in `nanobot status`
-    model_catalog: str = "auto"  # WebUI model-list source
+    model_catalog: str = "auto"  # WebUI model-list source, including builtin/hybrid
     builtin_models: tuple[ProviderModelSpec, ...] = ()
     settings_alias_for: str = ""  # compatibility alias grouped under this provider in Settings
 
@@ -407,45 +410,56 @@ PROVIDERS: tuple[ProviderSpec, ...] = (
         keywords=("openai-codex",),
         env_key="",
         display_name="OpenAI Codex",
-        model_catalog="builtin",
+        model_catalog="hybrid",
         builtin_models=(
             ProviderModelSpec(
                 id="openai-codex/gpt-5.6-sol",
                 label="GPT-5.6-Sol",
                 description="Latest frontier agentic coding model.",
-                context_window=372000,
+                context_window=272_000,
+                reasoning_efforts=("low", "medium", "high", "xhigh", "max", "ultra"),
             ),
             ProviderModelSpec(
                 id="openai-codex/gpt-5.6-terra",
                 label="GPT-5.6-Terra",
                 description="Balanced agentic coding model for everyday work.",
-                context_window=372000,
+                context_window=272_000,
+                reasoning_efforts=("low", "medium", "high", "xhigh", "max", "ultra"),
             ),
             ProviderModelSpec(
                 id="openai-codex/gpt-5.6-luna",
                 label="GPT-5.6-Luna",
                 description="Fast and affordable agentic coding model.",
-                context_window=372000,
+                context_window=272_000,
+                reasoning_efforts=("low", "medium", "high", "xhigh", "max"),
             ),
             ProviderModelSpec(
                 id="openai-codex/gpt-5.5",
                 label="GPT-5.5",
                 description="Frontier model for complex coding, research, and real-world work.",
+                context_window=272_000,
+                reasoning_efforts=("low", "medium", "high", "xhigh"),
             ),
             ProviderModelSpec(
                 id="openai-codex/gpt-5.4",
                 label="GPT-5.4",
                 description="Strong model for everyday coding.",
+                context_window=272_000,
+                reasoning_efforts=("low", "medium", "high", "xhigh"),
             ),
             ProviderModelSpec(
                 id="openai-codex/gpt-5.4-mini",
                 label="GPT-5.4-Mini",
                 description="Small, fast, and cost-efficient model for simpler coding tasks.",
+                context_window=272_000,
+                reasoning_efforts=("low", "medium", "high", "xhigh"),
             ),
             ProviderModelSpec(
                 id="openai-codex/gpt-5.3-codex-spark",
                 label="GPT-5.3-Codex-Spark",
                 description="Ultra-fast coding model.",
+                context_window=128_000,
+                reasoning_efforts=("low", "medium", "high", "xhigh"),
             ),
         ),
         backend="openai_codex",
@@ -459,13 +473,19 @@ PROVIDERS: tuple[ProviderSpec, ...] = (
         keywords=("xai-grok", "xai_grok"),
         env_key="",
         display_name="xAI Grok",
-        model_catalog="builtin",
+        model_catalog="hybrid",
         builtin_models=(
+            ProviderModelSpec(
+                id="xai-grok/grok-4.6",
+                label="Grok 4.6",
+                description="Grok via xAI subscription; X Search is enabled when supported.",
+                context_window=500_000,
+            ),
             ProviderModelSpec(
                 id="xai-grok/grok-4.5",
                 label="Grok 4.5",
                 description="Grok via xAI subscription; X Search is enabled when supported.",
-                context_window=500000,
+                context_window=500_000,
             ),
         ),
         backend="xai_grok",
@@ -478,6 +498,19 @@ PROVIDERS: tuple[ProviderSpec, ...] = (
         keywords=("github_copilot", "copilot"),
         env_key="",
         display_name="Github Copilot",
+        model_catalog="hybrid",
+        builtin_models=(
+            ProviderModelSpec(
+                id="github-copilot/gpt-5.4-mini",
+                label="GPT-5.4 Mini",
+                description="GitHub Copilot Responses model.",
+            ),
+            ProviderModelSpec(
+                id="github-copilot/gpt-4.1",
+                label="GPT-4.1",
+                description="GitHub Copilot chat model.",
+            ),
+        ),
         backend="github_copilot",
         default_api_base="https://api.githubcopilot.com",
         strip_model_prefix=True,

nanobot/providers/xai_grok_provider.py

@@ -4,9 +4,9 @@ from __future__ import annotations
 
 import asyncio
 import base64
+import hashlib
 import json
 import re
-import time
 import uuid
 from collections.abc import Awaitable, Callable
 from typing import Any, cast
@@ -22,21 +22,24 @@ from nanobot.providers.base import (
     ToolCallRequest,
     resolve_stream_idle_timeout_s,
 )
+from nanobot.providers.oauth_model_catalog import OAuthModelCatalog, OAuthModelCatalogSnapshot
 from nanobot.providers.openai_responses import (
     consume_sse_with_reasoning,
     convert_messages,
     convert_tools,
 )
+from nanobot.providers.registry import ProviderModelSpec, find_by_name
 from nanobot.providers.xai_oauth import (
     XAI_CLIENT_VERSION,
-    XAIToken,
+    get_xai_oauth_login_status,
+    get_xai_oauth_storage_path,
     get_xai_oauth_token,
 )
 
+DEFAULT_XAI_GROK_MODEL = "xai-grok/grok-4.6"
 DEFAULT_XAI_GROK_URL = "https://cli-chat-proxy.grok.com/v1/responses"
 DEFAULT_XAI_GROK_MODELS_URL = "https://cli-chat-proxy.grok.com/v1/models"
-DEFAULT_XAI_GROK_MODEL = "xai-grok/grok-4.5"
+_HOSTED_SEARCH_MAX_TURNS = 5
-_MODEL_CAPABILITIES_TTL_S = 5 * 60
 _MAX_ERROR_BODY_CHARS = 1000
 _SENSITIVE_ERROR_KEYS = {
     "accesstoken",
@@ -63,6 +66,10 @@ def _is_named_x_search_tool(value: object) -> bool:
 class XAIGrokProvider(LLMProvider):
     """Call xAI's subscription proxy and expose supported hosted tools."""
 
+    # An incomplete hosted-tool stream can already have emitted answer text. Let the
+    # provider close that stream segment before its one bounded recovery attempt.
+    supports_stream_recover_callback = True
+
     def __init__(
         self,
         default_model: str = DEFAULT_XAI_GROK_MODEL,
@@ -75,37 +82,19 @@ class XAIGrokProvider(LLMProvider):
         self.default_model = default_model
         self.proxy = proxy or None
         self._extra_body = dict(extra_body or {})
-        self._model_capabilities: dict[str, bool] | None = None
-        self._model_capabilities_fetched_at = 0.0
 
-    async def _supports_backend_search(self, token: XAIToken, model: str) -> bool:
+    async def _supports_backend_search(self, model: str) -> bool:
-        now = time.monotonic()
+        catalog = await asyncio.to_thread(
-        capabilities = self._model_capabilities
+            get_xai_grok_model_catalog,
-        if (
+            self.proxy,
-            capabilities is None
+        )
-            or now - self._model_capabilities_fetched_at >= _MODEL_CAPABILITIES_TTL_S
+        if catalog.message:
-        ):
+            logger.warning(
-            try:
+                "xAI model catalog unavailable; hosted X Search disabled unless cached: {}",
-                capabilities = await _fetch_xai_model_capabilities(
+                catalog.message,
-                    DEFAULT_XAI_GROK_MODELS_URL,
+            )
-                    _build_model_headers(token),
+        info = catalog.find(model)
-                    proxy=self.proxy,
+        return bool(info and info.supports_backend_search)
-                )
-            except Exception as exc:
-                logger.warning(
-                    "xAI model capability lookup failed; hosted X Search disabled for model {}: "
-                    "type={} error={}",
-                    model,
-                    type(exc).__name__,
-                    str(exc).strip() or "unexpected error",
-                )
-                capabilities = {}
-                self._model_capabilities = capabilities
-                self._model_capabilities_fetched_at = now
-            else:
-                self._model_capabilities = capabilities
-                self._model_capabilities_fetched_at = now
-        return capabilities.get(model, False)
 
     async def _call_xai(
         self,
@@ -119,6 +108,7 @@ class XAIGrokProvider(LLMProvider):
         on_content_delta: Callable[[str], Awaitable[None]] | None = None,
         on_thinking_delta: Callable[[str], Awaitable[None]] | None = None,
         on_tool_call_delta: Callable[[dict[str, Any]], Awaitable[None]] | None = None,
+        on_stream_recover: Callable[[], Awaitable[None]] | None = None,
     ) -> LLMResponse:
         wire_model = _strip_model_prefix(model or self.default_model)
         system_prompt, input_items = convert_messages(messages)
@@ -128,17 +118,13 @@ class XAIGrokProvider(LLMProvider):
             token = await asyncio.to_thread(get_xai_oauth_token, proxy=self.proxy)
             configured_tools = self._extra_body.get("tools")
             tools_are_explicit = "tools" in self._extra_body
-            configured_hosted_search = (
+            configured_hosted_search = isinstance(configured_tools, list) and any(
-                isinstance(configured_tools, list)
+                _is_hosted_x_search_tool(tool) for tool in cast(list[object], configured_tools)
-                and any(
-                    _is_hosted_x_search_tool(tool)
-                    for tool in cast(list[object], configured_tools)
-                )
             )
             supports_backend_search = False
             if not tools_are_explicit:
                 stage = "model_capabilities"
-                supports_backend_search = await self._supports_backend_search(token, wire_model)
+                supports_backend_search = await self._supports_backend_search(wire_model)
             converted_tools = convert_tools(tools or [])
             if isinstance(configured_tools, list):
                 converted_tools.extend(cast(list[dict[str, Any]], configured_tools))
@@ -149,6 +135,8 @@ class XAIGrokProvider(LLMProvider):
             if supports_backend_search:
                 converted_tools.append({"type": "x_search"})
 
+            hosted_search_enabled = supports_backend_search or configured_hosted_search
+
             body: dict[str, Any] = {
                 "model": wire_model,
                 "store": False,
@@ -164,51 +152,65 @@ class XAIGrokProvider(LLMProvider):
                 "temperature": temperature,
                 "reasoning": _build_reasoning_options(reasoning_effort),
             }
+            if hosted_search_enabled:
+                # xAI's global default is intentionally unspecified. Five turns is
+                # their documented balanced setting and prevents a search from
+                # stopping after a single unsuccessful lookup.
+                body["max_turns"] = _HOSTED_SEARCH_MAX_TURNS
             if self._extra_body:
-                body.update({
+                body.update(
-                    key: value
+                    {key: value for key, value in self._extra_body.items() if key != "tools"}
-                    for key, value in self._extra_body.items()
+                )
-                    if key != "tools"
-                })
                 if tools_are_explicit and not isinstance(configured_tools, list):
                     body["tools"] = configured_tools
 
             headers = _build_headers(token.access, wire_model)
             stage = "xai_request"
-            try:
+            auth_retried = False
-                result = await _request_xai(
+            hosted_tool_retried = False
-                    DEFAULT_XAI_GROK_URL,
+            retry_usage: LLMUsage | None = None
-                    headers,
+            while True:
-                    body,
+                try:
-                    proxy=self.proxy,
+                    result = await _request_xai(
-                    on_content_delta=on_content_delta,
+                        DEFAULT_XAI_GROK_URL,
-                    on_thinking_delta=on_thinking_delta,
+                        headers,
-                    on_tool_call_delta=on_tool_call_delta,
+                        body,
-                )
+                        proxy=self.proxy,
-            except _XAIHTTPError as exc:
+                        on_content_delta=on_content_delta,
-                if exc.status_code != 401:
+                        on_thinking_delta=on_thinking_delta,
-                    raise
+                        on_tool_call_delta=on_tool_call_delta,
-                stage = "oauth_refresh"
+                    )
-                token = await asyncio.to_thread(
+                    break
-                    get_xai_oauth_token,
+                except _XAIHTTPError as exc:
-                    proxy=self.proxy,
+                    if exc.status_code != 401 or auth_retried:
-                    force_refresh=True,
+                        raise
-                )
+                    auth_retried = True
-                self._model_capabilities = None
+                    stage = "oauth_refresh"
-                self._model_capabilities_fetched_at = 0.0
+                    token = await asyncio.to_thread(
-                headers = _build_headers(token.access, wire_model)
+                        get_xai_oauth_token,
-                stage = "xai_request_retry"
+                        proxy=self.proxy,
-                result = await _request_xai(
+                        force_refresh=True,
-                    DEFAULT_XAI_GROK_URL,
+                    )
-                    headers,
+                    headers = _build_headers(token.access, wire_model)
-                    body,
+                    stage = "xai_request_after_oauth_refresh"
-                    proxy=self.proxy,
+                except _XAIIncompleteHostedToolError as exc:
-                    on_content_delta=on_content_delta,
+                    retry_usage = _combine_usage(retry_usage, exc.usage)
-                    on_thinking_delta=on_thinking_delta,
+                    cannot_recover_stream = exc.stream_output_emitted and on_stream_recover is None
-                    on_tool_call_delta=on_tool_call_delta,
+                    if hosted_tool_retried or cannot_recover_stream:
-                )
+                        exc.usage = retry_usage
+                        raise
+                    hosted_tool_retried = True
+                    stage = "hosted_tool_recovery"
+                    logger.warning(
+                        "xAI response ended with unfinished hosted tool(s): {}; retrying once",
+                        ", ".join(exc.tool_names),
+                    )
+                    if on_stream_recover is not None:
+                        await on_stream_recover()
+                    headers = _build_headers(token.access, wire_model)
 
             content, tool_calls, finish_reason, usage, reasoning_content = result
+            usage = _combine_usage(retry_usage, usage)
             return LLMResponse(
                 content=content,
                 tool_calls=tool_calls,
@@ -257,6 +259,7 @@ class XAIGrokProvider(LLMProvider):
         on_content_delta: Callable[[str], Awaitable[None]] | None = None,
         on_thinking_delta: Callable[[str], Awaitable[None]] | None = None,
         on_tool_call_delta: Callable[[dict[str, Any]], Awaitable[None]] | None = None,
+        on_stream_recover: Callable[[], Awaitable[None]] | None = None,
     ) -> LLMResponse:
         return await self._call_xai(
             messages,
@@ -269,6 +272,7 @@ class XAIGrokProvider(LLMProvider):
             on_content_delta,
             on_thinking_delta,
             on_tool_call_delta,
+            on_stream_recover,
         )
 
     def get_default_model(self) -> str:
@@ -288,6 +292,14 @@ def _build_reasoning_options(reasoning_effort: str | None) -> dict[str, str]:
     return options
 
 
+def _combine_usage(left: LLMUsage | None, right: LLMUsage | None) -> LLMUsage | None:
+    if left is None:
+        return right
+    if right is None:
+        return left
+    return left + right
+
+
 def _build_headers(token: str, model: str) -> dict[str, str]:
     conversation_id = str(uuid.uuid4())
     return {
@@ -308,44 +320,6 @@ def _build_headers(token: str, model: str) -> dict[str, str]:
     }
 
 
-def _build_model_headers(token: XAIToken) -> dict[str, str]:
-    headers = {
-        "Authorization": f"Bearer {token.access}",
-        "X-XAI-Token-Auth": "xai-grok-cli",
-        "x-grok-client-version": XAI_CLIENT_VERSION,
-        "x-grok-client-identifier": "nanobot",
-        "x-grok-client-mode": "headless",
-        "User-Agent": f"nanobot/{__version__} (python)",
-        "accept": "application/json",
-    }
-    claims = _decode_access_token_claims(token.access)
-    user_id = claims.get("sub")
-    if claims.get("principal_type") == "Team":
-        user_id = claims.get("principal_id") or user_id
-    if isinstance(user_id, str) and user_id:
-        headers["x-userid"] = user_id
-    email = claims.get("email")
-    if not isinstance(email, str) or "@" not in email:
-        email = token.account_id if token.account_id and "@" in token.account_id else None
-    if email:
-        headers["x-email"] = email
-    return headers
-
-
-def _decode_access_token_claims(token: str) -> dict[str, Any]:
-    """Read identity hints from the signed token; the server still authenticates it."""
-    parts = token.split(".")
-    if len(parts) < 2 or not parts[1]:
-        return {}
-    payload = parts[1]
-    try:
-        decoded = base64.urlsafe_b64decode(payload + "=" * (-len(payload) % 4))
-        claims = json.loads(decoded)
-    except (ValueError, TypeError):
-        return {}
-    return cast(dict[str, Any], claims) if isinstance(claims, dict) else {}
-
-
 class _XAIHTTPError(RuntimeError):
     def __init__(
         self,
@@ -367,65 +341,25 @@ class _XAIHTTPError(RuntimeError):
         self.response_body = response_body
 
 
-async def _fetch_xai_model_capabilities(
+class _XAIIncompleteHostedToolError(RuntimeError):
-    url: str,
+    """A nominally successful xAI stream ended before a hosted tool did."""
-    headers: dict[str, str],
-    *,
-    proxy: str | None = None,
-) -> dict[str, bool]:
-    client_kwargs: dict[str, Any] = {"timeout": 10.0, "follow_redirects": False}
-    if proxy:
-        client_kwargs.update(proxy=proxy, trust_env=False)
-    async with httpx.AsyncClient(**client_kwargs) as client:
-        response = await client.get(url, headers=headers)
-    if response.status_code != 200:
-        raw = response.content.decode("utf-8", "ignore")
-        raise _build_xai_http_error(response.status_code, response.headers, raw)
-    try:
-        payload = response.json()
-    except ValueError as exc:
-        raise RuntimeError("xAI model catalog returned invalid JSON.") from exc
-    return _parse_xai_model_capabilities(payload)
 
+    should_retry = False  # _call_xai already performs the one safe recovery attempt.
 
-def _parse_xai_model_capabilities(payload: Any) -> dict[str, bool]:
+    def __init__(
-    if isinstance(payload, dict):
+        self,
-        payload = cast(dict[str, Any], payload)
+        active_tools: list[dict[str, Any]],
-        rows: object = payload.get("data")
+        *,
-        if not isinstance(rows, list):
+        usage: LLMUsage | None,
-            rows = payload.get("models")
+        stream_output_emitted: bool = False,
-    else:
+    ) -> None:
-        rows = payload
+        names = [str(event.get("name") or "hosted_tool") for event in active_tools]
-    if not isinstance(rows, list):
+        super().__init__(
-        return {}
+            "xAI ended the response before its hosted tool completed: " + ", ".join(names)
-
-    capabilities: dict[str, bool] = {}
-    for row_value in cast(list[object], rows):
-        if not isinstance(row_value, dict):
-            continue
-        row = cast(dict[str, Any], row_value)
-        meta_value = row.get("_meta")
-        meta = cast(dict[str, Any], meta_value) if isinstance(meta_value, dict) else {}
-        support_value = row.get("supportsBackendSearch")
-        if not isinstance(support_value, bool):
-            support_value = row.get("supports_backend_search")
-        if not isinstance(support_value, bool):
-            support_value = meta.get("supportsBackendSearch")
-        if not isinstance(support_value, bool):
-            support_value = meta.get("supports_backend_search")
-        supports_backend_search = support_value if isinstance(support_value, bool) else False
-
-        identifiers = (
-            row.get("model"),
-            row.get("modelId"),
-            row.get("id"),
-            meta.get("model"),
-            meta.get("modelId"),
         )
-        for identifier in identifiers:
+        self.tool_names = tuple(names)
-            if isinstance(identifier, str) and identifier.strip():
+        self.usage = usage
-                capabilities[_strip_model_prefix(identifier.strip())] = supports_backend_search
+        self.stream_output_emitted = stream_output_emitted
-    return capabilities
 
 
 async def _request_xai(
@@ -438,10 +372,39 @@ async def _request_xai(
     on_thinking_delta: Callable[[str], Awaitable[None]] | None = None,
     on_tool_call_delta: Callable[[dict[str, Any]], Awaitable[None]] | None = None,
 ) -> tuple[str, list[ToolCallRequest], str, LLMUsage | None, str | None]:
+    active_hosted_tools: dict[str, dict[str, Any]] = {}
+    stream_output_emitted = False
+
+    async def _forward_content_delta(delta: str) -> None:
+        nonlocal stream_output_emitted
+        if delta:
+            stream_output_emitted = True
+        if on_content_delta is not None:
+            await on_content_delta(delta)
+
+    async def _forward_thinking_delta(delta: str) -> None:
+        nonlocal stream_output_emitted
+        if delta:
+            stream_output_emitted = True
+        if on_thinking_delta is not None:
+            await on_thinking_delta(delta)
+
+    async def _track_and_forward_tool_event(event: dict[str, Any]) -> None:
+        if event.get("kind") == "hosted_tool":
+            call_id = event.get("call_id")
+            if call_id:
+                call_id = str(call_id)
+                if event.get("phase") == "start":
+                    active_hosted_tools[call_id] = dict(event)
+                elif event.get("phase") in {"end", "error"}:
+                    active_hosted_tools.pop(call_id, None)
+        if on_tool_call_delta is not None:
+            await on_tool_call_delta(event)
+
     async def _on_response_event(event: dict[str, Any]) -> None:
         hosted_event = _xai_hosted_tool_event(event)
-        if hosted_event is not None and on_tool_call_delta is not None:
+        if hosted_event is not None:
-            await on_tool_call_delta(hosted_event)
+            await _track_and_forward_tool_event(hosted_event)
 
     client_kwargs: dict[str, Any] = {"timeout": resolve_stream_idle_timeout_s()}
     if proxy:
@@ -452,13 +415,34 @@ async def _request_xai(
                 content = await response.aread()
                 raw = content.decode("utf-8", "ignore")
                 raise _build_xai_http_error(response.status_code, response.headers, raw)
-            return await consume_sse_with_reasoning(
+            result = await consume_sse_with_reasoning(
                 response,
-                on_content_delta=on_content_delta,
+                on_content_delta=(_forward_content_delta if on_content_delta is not None else None),
-                on_tool_call_delta=on_tool_call_delta,
+                # Always observe tool events so protocol validation also works for
-                on_reasoning_delta=on_thinking_delta,
+                # non-streaming callers that did not request UI progress callbacks.
-                on_response_event=_on_response_event if on_tool_call_delta else None,
+                on_tool_call_delta=_track_and_forward_tool_event,
+                on_reasoning_delta=(
+                    _forward_thinking_delta if on_thinking_delta is not None else None
+                ),
+                on_response_event=_on_response_event,
             )
+            if result[2] != "error" and active_hosted_tools:
+                active = list(active_hosted_tools.values())
+                for event in active:
+                    await _track_and_forward_tool_event(
+                        {
+                            **event,
+                            "phase": "error",
+                            "result": None,
+                            "error": "xAI ended the response before this hosted tool completed.",
+                        }
+                    )
+                raise _XAIIncompleteHostedToolError(
+                    active,
+                    usage=result[3],
+                    stream_output_emitted=stream_output_emitted,
+                )
+            return result
 
 
 def _xai_hosted_tool_event(event: dict[str, Any]) -> dict[str, Any] | None:
@@ -472,19 +456,33 @@ def _xai_hosted_tool_event(event: dict[str, Any]) -> dict[str, Any] | None:
             "phase": "start",
             "call_id": str(call_id),
             "name": "x_search",
-            "arguments": _xai_hosted_tool_arguments(
+            "arguments": _xai_hosted_tool_arguments(event.get("input", event.get("arguments"))),
-                event.get("input", event.get("arguments"))
-            ),
             "result": None,
         }
 
-    if event_type != "response.output_item.done":
+    if event_type not in {"response.output_item.added", "response.output_item.done"}:
         return None
     item = event.get("item")
     if not isinstance(item, dict):
         return None
     item = cast(dict[str, Any], item)
-    if item.get("type") != "custom_tool_call":
+    item_type = item.get("type")
+    if item_type == "x_search_call":
+        call_id = item.get("id") or item.get("call_id") or event.get("item_id")
+        if not call_id:
+            return None
+        phase = "start" if event_type == "response.output_item.added" else "end"
+        return {
+            "kind": "hosted_tool",
+            "phase": phase,
+            "call_id": str(call_id),
+            "name": "x_search",
+            "arguments": _xai_hosted_tool_arguments(item.get("action")),
+            "result": (
+                {"status": str(item.get("status") or "completed")} if phase == "end" else None
+            ),
+        }
+    if event_type != "response.output_item.done" or item_type != "custom_tool_call":
         return None
     tool_name = item.get("name")
     if not isinstance(tool_name, str) or not tool_name.startswith("x_"):
@@ -497,9 +495,7 @@ def _xai_hosted_tool_event(event: dict[str, Any]) -> dict[str, Any] | None:
         "phase": "end",
         "call_id": str(call_id),
         "name": "x_search",
-        "arguments": _xai_hosted_tool_arguments(
+        "arguments": _xai_hosted_tool_arguments(item.get("input", item.get("arguments"))),
-            item.get("input", item.get("arguments"))
-        ),
         # Keep the useful search subtype, but do not persist large hosted results
         # in WebUI activity messages. The model answer already carries citations.
         "result": {"name": tool_name},
@@ -608,6 +604,8 @@ def _xai_error_response(exc: Exception) -> LLMResponse:
         should_retry = True if should_retry is None else should_retry
     elif isinstance(exc, _XAIHTTPError):
         error_kind = "http"
+    elif isinstance(exc, _XAIIncompleteHostedToolError):
+        error_kind = "provider"
     if status_code is not None and should_retry is None:
         should_retry = _should_retry_status(
             int(status_code),
@@ -617,9 +615,11 @@ def _xai_error_response(exc: Exception) -> LLMResponse:
         )
     message = str(exc).strip() or "unexpected error"
     retry_after = getattr(exc, "retry_after", None)
+    usage = getattr(exc, "usage", None)
     return LLMResponse(
         content=f"Error calling xAI ({type(exc).__name__}): {message}",
         finish_reason="error",
+        usage=usage if isinstance(usage, LLMUsage) else None,
         retry_after=retry_after,
         error_status_code=int(status_code) if status_code is not None else None,
         error_kind=error_kind,
@@ -647,3 +647,209 @@ def _should_retry_status(
             )
         )
     return status_code in LLMProvider._RETRYABLE_STATUS_CODES or status_code >= 500  # pyright: ignore[reportPrivateUsage]
+
+
+def get_xai_grok_model_catalog(proxy: str | None = None) -> OAuthModelCatalogSnapshot:
+    token = get_xai_oauth_login_status()
+    account_key = _catalog_account_key(getattr(token, "account_id", None))
+    cache_key = f"{get_xai_oauth_storage_path()}\0{account_key}\0{proxy or ''}"
+    return _XAI_GROK_MODEL_CATALOG.get(cache_key=cache_key, proxy=proxy)
+
+
+def invalidate_xai_grok_model_catalog() -> None:
+    _XAI_GROK_MODEL_CATALOG.invalidate()
+
+
+def _fetch_xai_grok_models(proxy: str | None) -> tuple[ProviderModelSpec, ...]:
+    token = get_xai_oauth_token(proxy=proxy)
+    client_kwargs: dict[str, Any] = {"timeout": 10.0, "follow_redirects": False}
+    if proxy:
+        client_kwargs.update(proxy=proxy, trust_env=False)
+    with httpx.Client(**client_kwargs) as client:
+        response = client.get(
+            DEFAULT_XAI_GROK_MODELS_URL,
+            headers=_build_xai_model_headers(token.access, token.account_id),
+        )
+    response.raise_for_status()
+    return _parse_xai_grok_models(response.json())
+
+
+def _parse_xai_grok_models(payload: Any) -> tuple[ProviderModelSpec, ...]:
+    if isinstance(payload, dict):
+        payload_mapping = cast(dict[str, Any], payload)
+        rows: object = payload_mapping.get("data")
+        if not isinstance(rows, list):
+            rows = payload_mapping.get("models")
+    else:
+        rows = payload
+    if not isinstance(rows, list):
+        return ()
+
+    fallback_models = _oauth_fallback_models("xai_grok")
+    fallback_by_id = {model.id.split("/", 1)[-1]: model for model in fallback_models}
+    models: list[ProviderModelSpec] = []
+    seen: set[str] = set()
+    for value in cast(list[object], rows):
+        if not isinstance(value, dict):
+            continue
+        row = cast(dict[str, Any], value)
+        meta = _catalog_mapping(row.get("_meta"))
+        raw_id = next(
+            (
+                candidate.strip()
+                for candidate in (
+                    row.get("id"),
+                    row.get("model"),
+                    row.get("modelId"),
+                    row.get("name"),
+                    meta.get("id"),
+                    meta.get("model"),
+                    meta.get("modelId"),
+                )
+                if isinstance(candidate, str) and candidate.strip()
+            ),
+            None,
+        )
+        if raw_id is None:
+            continue
+        wire_id = raw_id.split("/", 1)[-1]
+        if wire_id in seen:
+            continue
+        seen.add(wire_id)
+        fallback = fallback_by_id.get(wire_id)
+        label = _catalog_first_text(row, "display_name", "label", "name") or _catalog_first_text(
+            meta,
+            "display_name",
+            "label",
+            "name",
+        )
+        if not label or label == raw_id:
+            label = fallback.label if fallback is not None else wire_id
+        models.append(
+            ProviderModelSpec(
+                id=f"xai-grok/{wire_id}",
+                label=label,
+                description=(
+                    _catalog_first_text(row, "description")
+                    or _catalog_first_text(meta, "description")
+                    or (fallback.description if fallback is not None else "")
+                ),
+                owned_by=(
+                    _catalog_first_text(row, "owned_by", "owner", "organization")
+                    or _catalog_first_text(meta, "owned_by", "owner", "organization")
+                    or (fallback.owned_by if fallback is not None else "xAI")
+                ),
+                context_window=(
+                    _catalog_positive_int(row, "context_window", "context_length")
+                    or _catalog_positive_int(meta, "context_window", "context_length")
+                    or (fallback.context_window if fallback is not None else None)
+                ),
+                reasoning_efforts=_catalog_reasoning_efforts(
+                    row.get("reasoning_efforts", meta.get("reasoning_efforts"))
+                ),
+                supports_backend_search=_catalog_bool_field(
+                    row,
+                    "supports_backend_search",
+                    "supportsBackendSearch",
+                ),
+            )
+        )
+    return tuple(models)
+
+
+def _build_xai_model_headers(access_token: str, account_id: str | None) -> dict[str, str]:
+    headers = {
+        "Authorization": f"Bearer {access_token}",
+        "X-XAI-Token-Auth": "xai-grok-cli",
+        "x-grok-client-version": XAI_CLIENT_VERSION,
+        "x-grok-client-identifier": "nanobot",
+        "x-grok-client-mode": "headless",
+        "User-Agent": f"nanobot/{__version__} (python)",
+        "accept": "application/json",
+    }
+    claims = _decode_access_token_claims(access_token)
+    user_id = claims.get("sub")
+    if claims.get("principal_type") == "Team":
+        user_id = claims.get("principal_id") or user_id
+    if isinstance(user_id, str) and user_id:
+        headers["x-userid"] = user_id
+    email = claims.get("email")
+    if not isinstance(email, str) or "@" not in email:
+        email = account_id if account_id and "@" in account_id else None
+    if email:
+        headers["x-email"] = email
+    return headers
+
+
+def _decode_access_token_claims(token: str) -> dict[str, Any]:
+    parts = token.split(".")
+    if len(parts) < 2 or not parts[1]:
+        return {}
+    try:
+        decoded = base64.urlsafe_b64decode(parts[1] + "=" * (-len(parts[1]) % 4))
+        claims = json.loads(decoded)
+    except (ValueError, TypeError):
+        return {}
+    return cast(dict[str, Any], claims) if isinstance(claims, dict) else {}
+
+
+def _oauth_fallback_models(provider_name: str) -> tuple[ProviderModelSpec, ...]:
+    spec = find_by_name(provider_name)
+    assert spec is not None
+    return spec.builtin_models
+
+
+def _catalog_account_key(account_id: object) -> str:
+    value = account_id if isinstance(account_id, str) else ""
+    return hashlib.sha256(value.encode()).hexdigest()[:16] if value else "anonymous"
+
+
+def _catalog_mapping(value: Any) -> dict[str, Any]:
+    return cast(dict[str, Any], value) if isinstance(value, dict) else {}
+
+
+def _catalog_first_text(row: dict[str, Any], *keys: str) -> str:
+    for key in keys:
+        value = row.get(key)
+        if isinstance(value, str) and value.strip():
+            return value.strip()
+    return ""
+
+
+def _catalog_positive_int(row: dict[str, Any], *keys: str) -> int | None:
+    for key in keys:
+        value = row.get(key)
+        if isinstance(value, (int, float)) and not isinstance(value, bool) and value > 0:
+            return int(value)
+    return None
+
+
+def _catalog_bool_field(row: dict[str, Any], *keys: str) -> bool:
+    for key in keys:
+        value = row.get(key)
+        if isinstance(value, bool):
+            return value
+    meta = row.get("_meta")
+    return _catalog_bool_field(_catalog_mapping(meta), *keys) if isinstance(meta, dict) else False
+
+
+def _catalog_reasoning_efforts(value: Any) -> tuple[str, ...]:
+    if not isinstance(value, list):
+        return ()
+    efforts: list[str] = []
+    for item in cast(list[object], value):
+        if isinstance(item, str):
+            effort = item.strip()
+        elif isinstance(item, dict):
+            effort = _catalog_first_text(cast(dict[str, Any], item), "effort", "value", "id")
+        else:
+            effort = ""
+        if effort and effort not in efforts:
+            efforts.append(effort)
+    return tuple(efforts)
+
+
+_XAI_GROK_MODEL_CATALOG = OAuthModelCatalog(
+    fallback_models=_oauth_fallback_models("xai_grok"),
+    fetch=_fetch_xai_grok_models,
+)

nanobot/session/turn_continuation.py

@@ -147,10 +147,10 @@ def prepare_save_boundary(ctx: TurnContext) -> None:
     if ctx.session is not None:
         clear_internal_continuation_state(ctx.session.metadata)
 
+    assert ctx.transcript_input is not None
     ctx.save_skip = _save_skip_for_turn(
         message_metadata=ctx.msg.metadata,
-        initial_message_count=len(ctx.initial_messages),
+        initial_message_count=ctx.transcript_input.message_count,
-        history_count=len(ctx.history),
         input_persisted_early=ctx.input_persisted_early,
     )
 
@@ -185,7 +185,6 @@ def _save_skip_for_turn(
     *,
     message_metadata: Mapping[str, Any] | None,
     initial_message_count: int,
-    history_count: int,
     input_persisted_early: bool,
 ) -> int:
     """Return the persisted-message append boundary for this turn."""
@@ -193,10 +192,7 @@ def _save_skip_for_turn(
         return initial_message_count
     if internal_continuation_inbound(message_metadata):
         return initial_message_count
-    # build_messages may merge the current message into a same-role history tail.
+    if not input_persisted_early:
-    # Runner-appended messages start at initial_message_count in either shape.
-    has_standalone_current = initial_message_count > 1 + history_count
-    if has_standalone_current and not input_persisted_early:
         return initial_message_count - 1
     return initial_message_count
 

nanobot/templates/agent/consolidator_archive.md

@@ -1,27 +1,42 @@
-Create a memory overview for only the final {{ archive_count }} conversation messages immediately before this instruction. Earlier messages are context for resolving references; do not summarize them again.
+Create a compact replacement checkpoint for this session.
 
-Use [skip] unless a fact meets all SNIP criteria:
+When `[Archived Context Summary]` appears in the system prompt, update that previous checkpoint to reflect the current conversation state.
-- Signal: would the user need to repeat this if forgotten?
-- Novel: not just a restatement of another fact in this same conversation chunk
-- Important: prevents rework or captures preferences / rules
-- Persistent: still relevant after 2 weeks
 
-Also preserve a compact working-state handoff even when it is not Persistent: the active objective, current status, completed steps, unresolved blockers, next action, and exact identifiers needed to continue without rework. Mark these facts [ephemeral].
+## Merge rules
 
-Format each fact as:
+- Use the latest correction or decision as the current version of a fact, and merge duplicates.
-- [mark] fact content
+- Preserve exact names, identifiers, paths, commands, decisions, results, and unresolved blockers when they are needed to continue the session.
+- Retain a fact already present in long-term memory when it is needed for session continuity.
 
-Marks (choose the best match):
+## What to retain
-- [permanent] Core preferences, personal traits, habits — never becomes stale
-- [durable] Technical discoveries, project knowledge, config details — valid for months
-- [ephemeral] Active task state, temporary decisions — may change in weeks
-- [correction] Correction to a previous memory — state what changed
-- [skip] Conversational filler, code/source facts derivable from the repo, or audit-only breadcrumbs
 
-Priority: user corrections and preferences > solutions > decisions > events > environment facts.
+Always retain a compact working-state handoff:
+- active objective
+- current status
+- completed results that constrain later work
+- unresolved blockers
+- next action
+- exact identifiers needed for that action
 
-Do not output facts already present in the system prompt's Recent History.
+Mark working-state facts `[ephemeral]`.
 
-Do not mark something [skip] merely because it might already exist in long-term memory.
+For other facts, retain a candidate only when it meets all four SNIP criteria:
+- Signal: remembering it saves the user from repeating it
+- Novel: it adds a distinct fact to this checkpoint
+- Important: losing it would cause rework or discard a preference or rule
+- Persistent: it is expected to remain useful for at least two weeks
 
-Return only formatted fact lines, or `(nothing)` if nothing noteworthy happened.
+Assign each retained fact its best current mark:
+- `[permanent]` for core preferences, personal traits, and habits that remain relevant indefinitely
+- `[durable]` for technical discoveries, project knowledge, and configuration that remains valid for months
+- `[ephemeral]` for active task state and temporary decisions that may change within weeks
+- `[correction]` for the current fact that supersedes conflicting earlier long-term memory
+
+When space is limited, prioritize user corrections and preferences, then solutions, decisions, events, and environment facts.
+
+## Output
+
+Return one concise retained fact per line in this form:
+- [mark] fact
+
+Use `(nothing)` when no fact qualifies and there is no active working state.

nanobot/templates/agent/dream.md

@@ -99,7 +99,7 @@ For [SKILL] entries:
 - Skills are instruction sets with concrete values, commands, and examples. MEMORY.md keeps strategic context and high-level facts only.
 
 ## Editing
-- Current contents of SOUL.md, USER.md, and memory/MEMORY.md are embedded in this prompt under "Current Memory Files". Edit those files directly; do not rely on a remembered version of a file.
+- Current contents of SOUL.md, USER.md, and memory/MEMORY.md are provided by the agent system context. Edit those files directly; do not rely on a remembered version of a file.
 - Batch changes into as few calls as possible. Surgical edits only.
 
 ## Verification

nanobot/templates/agent/tool_contract.md

@@ -40,7 +40,7 @@
   result with its original consumer or checker when one is available.
 - Use `apply_patch` as the default code editing tool, especially for multi-file changes, structural edits, generated code, moves, adds, or deletes.
 - Use `apply_patch dry_run=true` when the patch is uncertain and you want validation plus a change summary before writing.
-- Use `edit_file` only for small exact replacements in one file, with `old_text` copied from `read_file`; when editing a specific numbered line, pass that exact line as `line_hint`; add `occurrence` or `expected_replacements` when ambiguity matters.
+- Use `edit_file` only for small exact replacements in one file, with `old_text` copied from `read_file`.
 - Use `write_file` for new files or intentional full-file rewrites, not routine partial edits.
 - If `apply_patch` or `edit_file` fails, re-read with `force=true`, narrow the context, and try a smaller patch rather than switching to shell `sed` or `echo`.
 

nanobot/webui/settings_models.py

@@ -28,6 +28,10 @@ from nanobot.config.loader import resolve_config_env_vars
 from nanobot.config.schema import Config, FallbackCandidate, ModelPresetConfig, ProviderConfig
 from nanobot.providers.image_generation import get_image_gen_provider
 from nanobot.providers.oauth_guidance import OAUTH_CLI_KIT_MISSING_MESSAGE
+from nanobot.providers.oauth_model_catalog import (
+    get_oauth_model_catalog,
+    invalidate_oauth_model_catalog,
+)
 from nanobot.providers.registry import PROVIDERS, create_dynamic_spec, find_by_name
 from nanobot.webui.settings_contracts import (
     QueryParams,
@@ -661,6 +665,30 @@ def provider_models_payload(
             "models": rows,
             "model_count": len(rows),
         }
+    if catalog_kind == "hybrid":
+        proxy = _resolve_env_placeholders(provider_config.proxy)
+        catalog = get_oauth_model_catalog(spec.name, proxy=proxy)
+        rows = [
+            {
+                "id": model.id,
+                "label": model.label or None,
+                "description": model.description or None,
+                "owned_by": model.owned_by or spec.label,
+                "context_window": model.context_window,
+                "reasoning_efforts": list(model.reasoning_efforts),
+                "supports_backend_search": model.supports_backend_search,
+            }
+            for model in catalog.models
+        ]
+        return {
+            **base_payload,
+            "status": "available",
+            "source": catalog.source,
+            "models": rows,
+            "model_count": len(rows),
+            "message": catalog.message,
+            "fetched_at": catalog.fetched_at,
+        }
 
     api_base = _resolve_env_placeholders(provider_config.api_base) or spec.default_api_base
     if spec.name == "openai" and not api_base:
@@ -1506,6 +1534,7 @@ def login_oauth_provider(
             token = login_github_copilot(print_fn=lambda _message: None)
         if not (token and token.access):
             raise WebUISettingsError("OAuth login failed", status=401)
+        invalidate_oauth_model_catalog(spec.name)
         return settings_payload(config_path=config_path)
 
     if spec.name == "xai_grok":
@@ -1591,6 +1620,7 @@ def complete_oauth_provider(
     oauth_flows.remove(spec.name, flow_id, flow, cancel=False)
     if not token.access:
         raise WebUISettingsError("OAuth login failed", status=401)
+    invalidate_oauth_model_catalog(spec.name)
     return settings_payload(config_path=config_path)
 
 
@@ -1629,6 +1659,7 @@ def logout_oauth_provider(
 
         oauth_flows.clear(spec.name)
         logout_xai_oauth()
+        invalidate_oauth_model_catalog(spec.name)
         return settings_payload(config_path=config_path)
     else:
         raise WebUISettingsError("OAuth logout is not supported for this provider")
@@ -1636,6 +1667,7 @@ def logout_oauth_provider(
     for path in (token_path, token_path.with_suffix(".lock")):
         with suppress(FileNotFoundError):
             path.unlink()
+    invalidate_oauth_model_catalog(spec.name)
     return settings_payload(config_path=config_path)
 
 

tests/agent/test_attachment_references.py

@@ -5,6 +5,7 @@ from unittest.mock import AsyncMock, MagicMock
 
 import pytest
 
+from nanobot.agent.context import TranscriptInput
 from nanobot.agent.loop import AgentLoop, TurnContext, TurnKind
 from nanobot.agent.tools.context import RequestContext
 from nanobot.agent.tools.filesystem import ReadFileTool
@@ -148,7 +149,10 @@ async def test_pending_document_attachment_keeps_body_out_of_prompt(
 
     runtime = loop.llm_runtime()
     result = await loop._run_agent_loop(
-        [{"role": "user", "content": "hello"}],
+        TranscriptInput(
+            history=[{"role": "user", "content": "hello"}],
+            current_message=None,
+        ),
         runtime=runtime,
         request_context=RequestContext(channel="cli", chat_id="c", runtime=runtime),
         pending_queue=pending_queue,

tests/agent/test_auto_compact.py

@@ -1302,9 +1302,9 @@ class TestSummaryPersistence:
         assert "_last_summary" in reloaded.metadata
 
         # Simulate /new command
-        session.clear()
+        reloaded.clear()
-        loop.sessions.save(session)
+        loop.sessions.save(reloaded)
-        loop.sessions.invalidate(session.key)
+        loop.sessions.invalidate(reloaded.key)
 
         # After /new, metadata should no longer contain _last_summary
         fresh = loop.sessions.get_or_create("cli:test")

tests/agent/test_consolidator.py

@@ -1,4 +1,4 @@
-"""Tests for the lightweight Consolidator — append-only to HISTORY.md."""
+"""Tests for Memory checkpoint consolidation and history journaling."""
 
 from dataclasses import replace
 from unittest.mock import AsyncMock, MagicMock
@@ -6,7 +6,7 @@ from unittest.mock import AsyncMock, MagicMock
 import pytest
 
 from nanobot.agent.memory import (
-    _ARCHIVE_SUMMARY_MAX_CHARS,
+    _HISTORY_ENTRY_HARD_CAP,
     Consolidator,
     MemoryStore,
 )
@@ -26,6 +26,8 @@ from nanobot.session.manager import Session
 from nanobot.utils.llm_runtime import LLMRuntime
 from nanobot.utils.prompt_templates import render_template
 
+_ARCHIVE_PROMPT = render_template("agent/consolidator_archive.md", strip=True)
+
 
 @pytest.fixture
 def store(tmp_path):
@@ -98,8 +100,15 @@ def _build_test_messages(**kwargs):
     ]
 
 
-async def _archive(consolidator, messages, runtime, *, session_key="test:session"):
+async def _archive(
-    return await consolidator.archive(
+    consolidator,
+    messages,
+    runtime,
+    *,
+    session_key="test:session",
+    previous_summary=None,
+):
+    return await consolidator.archiver.archive(
         messages,
         runtime=runtime,
         session_key=session_key,
@@ -108,6 +117,7 @@ async def _archive(consolidator, messages, runtime, *, session_key="test:session
             current_message="consolidate",
         ),
         request_tools=[],
+        previous_summary=previous_summary,
     )
 
 
@@ -201,7 +211,9 @@ class TestConsolidatorSummarize:
         mock_provider.chat_with_retry.side_effect = Exception("API error")
         messages = [{"role": "user", "content": "hello"}]
         result = await _archive(consolidator, messages, runtime)
-        assert result is None  # no summary on raw dump fallback
+        assert result is not None
+        assert "[RAW]" in result
+        assert "hello" in result
         entries = store.read_unprocessed_history(since_cursor=0)
         assert len(entries) == 1
         assert "[RAW]" in entries[0]["content"]
@@ -226,23 +238,51 @@ class TestConsolidatorSummarize:
         entries = store.read_unprocessed_history(since_cursor=0)
         assert entries[0]["session_key"] == "slack:chat-2"
 
+    async def test_raw_fallback_represents_previous_checkpoint_and_new_chunk(
+        self,
+        consolidator,
+        mock_provider,
+        runtime,
+    ):
+        runtime = replace(runtime, generation=GenerationSettings(max_tokens=96))
+        mock_provider.chat_with_retry.side_effect = RuntimeError("API error")
+
+        result = await _archive(
+            consolidator,
+            [{"role": "user", "content": "NEW_MARKER " + "new " * 200}],
+            runtime,
+            previous_summary="OLD_MARKER " + "old " * 200,
+        )
+
+        assert result is not None
+        assert "[Previous archived context]" in result
+        assert "OLD_MARKER" in result
+        assert "[Newly archived raw context]" in result
+        assert "NEW_MARKER" in result
+        assert "... (truncated)" in result
+
     async def test_summarize_skips_empty_messages(self, consolidator, runtime):
         result = await _archive(consolidator, [], runtime)
         assert result is None
 
 
 class TestConsolidatorPromptContract:
-    def test_archive_prompt_preserves_working_state_with_memory_facts(self):
+    def test_archive_prompt_requests_a_cumulative_replacement_checkpoint(self):
-        prompt = render_template("agent/consolidator_archive.md", strip=True, archive_count=4)
+        prompt = _ARCHIVE_PROMPT
 
+        for section in ("## Merge rules", "## What to retain", "## Output"):
+            assert section in prompt
+        assert "replacement checkpoint" in prompt
+        assert "[Archived Context Summary]" in prompt
+        assert "current conversation state" in prompt
         assert "SNIP" in prompt
-        assert "final 4 conversation messages" in prompt
+        for mark in ("[permanent]", "[durable]", "[ephemeral]", "[correction]"):
-        for mark in ("[permanent]", "[durable]", "[ephemeral]", "[correction]", "[skip]"):
             assert mark in prompt
         assert "working-state handoff" in prompt
-        assert "exact identifiers needed to continue without rework" in prompt
+        assert "- [mark] fact" in prompt
-        assert "Do not output facts already present in the system prompt's Recent History" in prompt
+        assert "[skip]" not in prompt
-        assert "Do not mark something [skip] merely because it might already exist" in prompt
+        assert "(nothing)" in prompt
+        assert "history.jsonl" not in prompt
 
 
 class TestConsolidatorArchiveErrorHandling:
@@ -272,7 +312,8 @@ class TestConsolidatorArchiveErrorHandling:
             {"role": "assistant", "content": "Done, fixed the race condition."},
         ]
         result = await _archive(consolidator, messages, runtime)
-        assert result is None
+        assert result is not None
+        assert "[RAW]" in result
         entries = store.read_unprocessed_history(since_cursor=0)
         assert len(entries) == 1
         assert "[RAW]" in entries[0]["content"]
@@ -436,9 +477,9 @@ class TestConsolidatorTokenBudget:
         assert [message["content"] for message in request["messages"][1:-1]] == [
             f"m{i}" for i in range(50)
         ]
-        assert "final 50 conversation messages" in request["messages"][-1]["content"]
+        assert request["messages"][-1]["content"] == _ARCHIVE_PROMPT
         assert request["tools"] == []
-        assert request["tool_choice"] == "none"
+        assert "tool_choice" not in request
         assert session.last_archived == 50
         assert session.provider_state == _provider_state()
 
@@ -460,8 +501,7 @@ class TestConsolidatorTokenBudget:
         consolidator.estimate_session_prompt_tokens = MagicMock(
             side_effect=[(1200, "tiktoken"), (400, "tiktoken")]
         )
-        # LLM consolidation fails after raw_archive fires.
+        consolidator.archive_session = AsyncMock(return_value="[RAW] checkpoint")
-        consolidator.archive_session = AsyncMock(return_value=None)
 
         await consolidator.maybe_consolidate_by_tokens(session, runtime=runtime)
 
@@ -491,7 +531,7 @@ class TestConsolidatorTokenBudget:
         consolidator.estimate_session_prompt_tokens = MagicMock(
             return_value=(1200, "tiktoken")
         )
-        consolidator.archive_session = AsyncMock(return_value=None)
+        consolidator.archive_session = AsyncMock(return_value="[RAW] checkpoint")
 
         await consolidator.maybe_consolidate_by_tokens(session, runtime=runtime)
 
@@ -613,27 +653,62 @@ class TestCompactIdleSession:
         assert reloaded.last_archived == 2
         assert [message["content"] for message in reloaded.get_history()] == ["hello", "hi"]
 
+    @pytest.mark.asyncio
+    async def test_idle_compaction_with_no_new_messages_is_noop(
+        self, real_consolidator, mock_provider, store, runtime
+    ):
+        sessions = real_consolidator.sessions
+        session = sessions.get_or_create("cli:archived-idle")
+        session.add_message("user", "already archived")
+        session.add_message("assistant", "old answer")
+        session.last_archived = 2
+        sessions.save(session)
+        sessions.invalidate("cli:archived-idle")
+
+        result = await real_consolidator.compact_idle_session(
+            "cli:archived-idle",
+            runtime=runtime,
+        )
+
+        assert result == ""
+        mock_provider.chat_with_retry.assert_not_awaited()
+        reloaded = sessions.get_or_create("cli:archived-idle")
+        assert reloaded.last_archived == 2
+        assert "_last_summary" not in reloaded.metadata
+        assert store.read_unprocessed_history(since_cursor=0) == []
+
     @pytest.mark.asyncio
     async def test_new_messages_advance_existing_archive_progress(
         self, real_consolidator, mock_provider, runtime
     ):
-        mock_provider.chat_with_retry.return_value = MagicMock(
+        mock_provider.chat_with_retry.side_effect = [
-            content="Summary.", finish_reason="stop"
+            MagicMock(content="First replacement checkpoint.", finish_reason="stop"),
-        )
+            MagicMock(content="Second replacement checkpoint.", finish_reason="stop"),
+        ]
         sessions = real_consolidator.sessions
         session = sessions.get_or_create("cli:incremental")
         session.add_message("user", "first user")
         session.add_message("assistant", "first assistant")
         sessions.save(session)
 
-        await real_consolidator.compact_idle_session("cli:incremental", runtime=runtime)
+        first = await real_consolidator.compact_idle_session(
+            "cli:incremental",
+            runtime=runtime,
+        )
         current = sessions.get_or_create("cli:incremental")
         current.add_message("user", "second user")
         current.add_message("assistant", "second assistant")
         sessions.save(current)
-        await real_consolidator.compact_idle_session("cli:incremental", runtime=runtime)
+        second = await real_consolidator.compact_idle_session(
+            "cli:incremental",
+            runtime=runtime,
+        )
 
+        assert first == "First replacement checkpoint."
+        assert second == "Second replacement checkpoint."
         assert mock_provider.chat_with_retry.await_count == 2
+        latest_build = real_consolidator.archiver._build_messages.call_args_list[-1].kwargs
+        assert latest_build["session_summary"]["text"] == "First replacement checkpoint."
         latest_messages = mock_provider.chat_with_retry.await_args_list[-1].kwargs["messages"]
         assert [message["content"] for message in latest_messages[1:5]] == [
             "first user",
@@ -641,8 +716,91 @@ class TestCompactIdleSession:
             "second user",
             "second assistant",
         ]
-        assert "final 2 conversation messages" in latest_messages[-1]["content"]
+        assert latest_messages[-1]["content"] == _ARCHIVE_PROMPT
-        assert sessions.get_or_create("cli:incremental").last_archived == 4
+        sessions.invalidate("cli:incremental")
+        reloaded = sessions.get_or_create("cli:incremental")
+        assert reloaded.last_archived == 4
+        assert reloaded.metadata["_last_summary"]["text"] == second
+
+    @pytest.mark.asyncio
+    async def test_raw_fallback_preserves_previous_checkpoint_and_new_chunk(
+        self,
+        real_consolidator,
+        mock_provider,
+        store,
+        runtime,
+    ):
+        mock_provider.chat_with_retry.side_effect = [
+            LLMResponse(content="Earlier durable checkpoint.", finish_reason="stop"),
+            RuntimeError("LLM unavailable"),
+        ]
+        sessions = real_consolidator.sessions
+        session = sessions.get_or_create("cli:cumulative-fallback")
+        session.add_message("user", "first user")
+        session.add_message("assistant", "first answer")
+        sessions.save(session)
+
+        await real_consolidator.compact_idle_session(
+            "cli:cumulative-fallback",
+            runtime=runtime,
+        )
+        current = sessions.get_or_create("cli:cumulative-fallback")
+        current.add_message("user", "second user")
+        current.add_message("assistant", "newest working state")
+        sessions.save(current)
+
+        fallback = await real_consolidator.compact_idle_session(
+            "cli:cumulative-fallback",
+            runtime=runtime,
+        )
+
+        assert fallback is not None
+        assert "[Previous archived context]" in fallback
+        assert "Earlier durable checkpoint." in fallback
+        assert "[Newly archived raw context]" in fallback
+        assert "newest working state" in fallback
+        entries = store.read_unprocessed_history(0)
+        assert entries[0]["content"] == "Earlier durable checkpoint."
+        assert entries[1]["content"].startswith("[RAW] 2 messages")
+        sessions.invalidate("cli:cumulative-fallback")
+        reloaded = sessions.get_or_create("cli:cumulative-fallback")
+        assert reloaded.metadata["_last_summary"]["text"] == fallback
+
+    @pytest.mark.asyncio
+    async def test_nothing_keeps_previous_replacement_checkpoint(
+        self,
+        real_consolidator,
+        mock_provider,
+        runtime,
+    ):
+        mock_provider.chat_with_retry.side_effect = [
+            LLMResponse(content="Existing checkpoint.", finish_reason="stop"),
+            LLMResponse(content="(nothing)", finish_reason="stop"),
+        ]
+        sessions = real_consolidator.sessions
+        session = sessions.get_or_create("cli:nothing-after-summary")
+        session.add_message("user", "important first turn")
+        session.add_message("assistant", "important result")
+        sessions.save(session)
+        await real_consolidator.compact_idle_session(
+            "cli:nothing-after-summary",
+            runtime=runtime,
+        )
+
+        current = sessions.get_or_create("cli:nothing-after-summary")
+        current.add_message("user", "thanks")
+        current.add_message("assistant", "you're welcome")
+        sessions.save(current)
+        result = await real_consolidator.compact_idle_session(
+            "cli:nothing-after-summary",
+            runtime=runtime,
+        )
+
+        assert result == "(nothing)"
+        sessions.invalidate("cli:nothing-after-summary")
+        reloaded = sessions.get_or_create("cli:nothing-after-summary")
+        assert reloaded.last_archived == 4
+        assert reloaded.metadata["_last_summary"]["text"] == "Existing checkpoint."
 
     @pytest.mark.asyncio
     async def test_concurrent_append_remains_unarchived(
@@ -792,11 +950,16 @@ class TestCompactIdleSession:
         result = await real_consolidator.compact_idle_session(
             "cli:nothing", runtime=runtime, max_suffix=4
         )
+        second = await real_consolidator.compact_idle_session(
+            "cli:nothing", runtime=runtime, max_suffix=4
+        )
         assert result == "(nothing)"
+        assert second == ""
 
         reloaded = sessions.get_or_create("cli:nothing")
         assert "_last_summary" not in reloaded.metadata
         assert real_consolidator.store.read_unprocessed_history(0) == []
+        mock_provider.chat_with_retry.assert_awaited_once()
 
     @pytest.mark.asyncio
     async def test_llm_failure_preserves_history_but_advances_replay_boundary(
@@ -813,7 +976,8 @@ class TestCompactIdleSession:
         result = await real_consolidator.compact_idle_session(
             "cli:fail", runtime=runtime, max_suffix=4
         )
-        assert result is None
+        assert result is not None
+        assert "[RAW]" in result
 
         # raw_archive should have been called (history.jsonl gets an entry)
         entries = store.read_unprocessed_history(since_cursor=0)
@@ -823,6 +987,7 @@ class TestCompactIdleSession:
         assert len(reloaded.messages) == 20
         assert reloaded.messages[0]["content"] == "u0"
         assert reloaded.last_archived == 20
+        assert reloaded.metadata["_last_summary"]["text"] == result
         assert [m["content"] for m in reloaded.get_history(max_messages=20)] == [
             "u6",
             "a6",
@@ -863,11 +1028,10 @@ class TestCompactIdleSession:
         archived_call = mock_provider.chat_with_retry.call_args
         sent_messages = archived_call.kwargs["messages"]
         sent_content = [message.get("content") for message in sent_messages]
-        # The ordinary replay prefix contributes recent context, while the
+        # The replacement overview covers all model-visible conversation context.
-        # temporary instruction limits the new overview to the unarchived tail.
         assert "u0" not in sent_content
         assert "u26" in sent_content
-        assert "final 10 conversation messages" in sent_messages[-1]["content"]
+        assert sent_messages[-1]["content"] == _ARCHIVE_PROMPT
 
     @pytest.mark.asyncio
     async def test_full_archive_keeps_extended_legal_replay_suffix(
@@ -956,9 +1120,9 @@ class TestCompactIdleSession:
             "user",
         ]
         assert sent_messages[2]["tool_calls"][0]["id"] == "call-1"
-        assert "final 4 conversation messages" in sent_messages[-1]["content"]
+        assert sent_messages[-1]["content"] == _ARCHIVE_PROMPT
         assert call["tools"] == tools
-        assert call["tool_choice"] == "none"
+        assert "tool_choice" not in call
 
         reloaded = sessions.get_or_create("cli:tool-history")
         assert len(reloaded.messages) == 4
@@ -996,7 +1160,8 @@ class TestCompactIdleSession:
             runtime=runtime,
         )
 
-        assert result is None
+        assert result is not None
+        assert "[RAW]" in result
         entries = store.read_unprocessed_history(since_cursor=0)
         assert len(entries) == 1
         assert entries[0]["content"].startswith("[RAW] ")
@@ -1026,7 +1191,8 @@ class TestCompactIdleSession:
             runtime=runtime,
         )
 
-        assert result is None
+        assert result is not None
+        assert "[RAW]" in result
         entries = store.read_unprocessed_history(since_cursor=0)
         assert len(entries) == 1
         assert entries[0]["content"].startswith("[RAW] ")
@@ -1052,7 +1218,8 @@ class TestCompactIdleSession:
             runtime=runtime,
         )
 
-        assert result is None
+        assert result is not None
+        assert "[RAW]" in result
         mock_provider.chat_with_retry.assert_not_awaited()
         entries = store.read_unprocessed_history(since_cursor=0)
         assert len(entries) == 1
@@ -1060,7 +1227,7 @@ class TestCompactIdleSession:
         assert sessions.get_or_create("sdk:oversized").last_archived == 1
 
     @pytest.mark.asyncio
-    async def test_incremental_scope_counts_only_model_visible_messages(
+    async def test_archive_context_contains_only_model_visible_messages(
         self,
         real_consolidator,
         mock_provider,
@@ -1093,7 +1260,7 @@ class TestCompactIdleSession:
             "new user",
             "new answer",
         ]
-        assert "final 2 conversation messages" in sent[-1]["content"]
+        assert sent[-1]["content"] == _ARCHIVE_PROMPT
 
     @pytest.mark.asyncio
     async def test_reuses_real_prefix_for_unified_session_workspace(
@@ -1126,8 +1293,6 @@ class TestCompactIdleSession:
             current_message="next project question",
             channel="websocket",
             workspace=project,
-            session_key=session.key,
-            unified_session=True,
         )
 
         await loop.consolidator.compact_idle_session(
@@ -1137,7 +1302,7 @@ class TestCompactIdleSession:
 
         sent_messages = runtime.provider.chat_with_retry.call_args.kwargs["messages"]
         assert sent_messages[:-1] == ordinary_messages[:-1]
-        assert "final 2 conversation messages" in sent_messages[-1]["content"]
+        assert sent_messages[-1]["content"] == _ARCHIVE_PROMPT
         system = sent_messages[0]["content"]
         assert "PROJECT_WORKSPACE_MARKER" in system
         assert "GLOBAL_WORKSPACE_MARKER" not in system
@@ -1307,6 +1472,21 @@ class TestRawArchiveTruncation:
         assert len(entries) == 1
         assert "hello" in entries[0]["content"]
 
+    def test_raw_archive_returns_the_sanitized_persisted_checkpoint(self, store):
+        messages = [
+            {
+                "role": "user",
+                "content": "<think>PRIVATE_REASONING</think>visible result",
+            }
+        ]
+
+        checkpoint = store.raw_archive(messages, session_key="cli:test")
+
+        persisted = store.read_unprocessed_history(since_cursor=0)[0]["content"]
+        assert checkpoint == persisted
+        assert "PRIVATE_REASONING" not in checkpoint
+        assert "visible result" in checkpoint
+
     def test_raw_archive_excludes_model_only_runtime_context(self, store):
         content, marker = append_runtime_context(
             "ship the feature",
@@ -1338,21 +1518,40 @@ class TestRawArchiveTruncation:
 
 
 class TestArchivePersistence:
-    async def test_oversized_summary_is_capped_before_append(
+    async def test_archive_returns_the_sanitized_persisted_summary(
+        self, consolidator, mock_provider, store, runtime
+    ):
+        mock_provider.chat_with_retry.return_value = MagicMock(
+            content="<think>PRIVATE_REASONING</think>safe summary",
+            finish_reason="stop",
+            has_tool_calls=False,
+        )
+
+        summary = await _archive(
+            consolidator,
+            [{"role": "user", "content": "hi"}],
+            runtime,
+        )
+
+        persisted = store.read_unprocessed_history(since_cursor=0)[0]["content"]
+        assert summary == persisted == "safe summary"
+
+    async def test_oversized_summary_uses_history_emergency_cap(
         self, consolidator, mock_provider, store, runtime
     ):
         """A pathologically large LLM summary must not land full-length in
         history.jsonl — that would re-open the #3412 bloat vector from the
         *success* path instead of the fallback path."""
         mock_provider.chat_with_retry.return_value = MagicMock(
-            content="S" * (_ARCHIVE_SUMMARY_MAX_CHARS * 10),
+            content="S" * (_HISTORY_ENTRY_HARD_CAP * 2),
             finish_reason="stop",
         )
-        await _archive(
+        summary = await _archive(
             consolidator,
             [{"role": "user", "content": "hi"}],
             runtime,
         )
 
         entry = store.read_unprocessed_history(since_cursor=0)[0]
-        assert len(entry["content"]) <= _ARCHIVE_SUMMARY_MAX_CHARS + 50
+        assert len(entry["content"]) <= _HISTORY_ENTRY_HARD_CAP + 50
+        assert summary == entry["content"]

tests/agent/test_context_builder.py

@@ -4,7 +4,7 @@ from pathlib import Path
 
 import pytest
 
-from nanobot.agent.context import ContextBuilder
+from nanobot.agent.context import ContextBuilder, TranscriptInput
 from nanobot.runtime_context import RuntimeContextBlock
 
 # ---------------------------------------------------------------------------
@@ -133,10 +133,7 @@ class TestLoadBootstrapFiles:
         (project / "SOUL.md").write_text("project soul collision", encoding="utf-8")
         (project / "USER.md").write_text("project user collision", encoding="utf-8")
 
-        result = ContextBuilder(agent_home).build_system_prompt(
+        result = ContextBuilder(agent_home).build_system_prompt(workspace=project)
-            workspace=project,
-            include_memory_recent_history=False,
-        )
 
         assert "selected project rules" in result
         assert "global project rules" not in result
@@ -152,10 +149,7 @@ class TestLoadBootstrapFiles:
         project.mkdir()
         (agent_home / "AGENTS.md").write_text("default workspace rules", encoding="utf-8")
 
-        result = ContextBuilder(agent_home).build_system_prompt(
+        result = ContextBuilder(agent_home).build_system_prompt(workspace=project)
-            workspace=project,
-            include_memory_recent_history=False,
-        )
 
         assert "default workspace rules" not in result
 
@@ -403,6 +397,15 @@ class TestBuildMessages:
         assert "user-only runtime context" not in messages[-1]["content"]
         assert "_meta" not in messages[-1]
 
+    def test_compatibility_builder_merges_system_role_without_history(self, tmp_path):
+        builder = _builder(tmp_path)
+
+        messages = builder.build_messages([], "system event", current_role="system")
+
+        assert len(messages) == 1
+        assert messages[0]["role"] == "system"
+        assert str(messages[0]["content"]).endswith("system event")
+
     def test_explicit_skill_reference_loads_full_instructions_for_this_turn(self, tmp_path):
         skill_dir = tmp_path / "skills" / "review"
         skill_dir.mkdir(parents=True)
@@ -472,6 +475,20 @@ class TestBuildMessages:
         assert "previous user message" in str(messages[1]["content"])
         assert "new message" in str(messages[1]["content"])
 
+    def test_structured_transcript_preserves_fresh_turn_boundary(self, tmp_path):
+        builder = _builder(tmp_path)
+        transcript = TranscriptInput(
+            history=[{"role": "user", "content": "previous user message"}],
+            current_message="new message",
+        )
+
+        messages = builder.build_transcript(transcript)
+
+        assert [message["role"] for message in messages] == ["system", "user", "user"]
+        assert messages[-2]["content"] == "previous user message"
+        assert messages[-1]["content"] == "new message"
+        assert transcript.message_count == 3
+
     def test_current_message_can_be_built_without_history_merge(self, tmp_path):
         builder = _builder(tmp_path)
         current = builder.build_current_message(

tests/agent/test_context_prompt_cache.py

@@ -3,7 +3,6 @@
 from __future__ import annotations
 
 import datetime as datetime_module
-import re
 from datetime import datetime as real_datetime
 from importlib.resources import files as pkg_files
 from pathlib import Path
@@ -104,173 +103,6 @@ def test_provider_context_appended_after_user_content(tmp_path) -> None:
     assert user_pos < context_pos, "user content must precede provider context"
 
 
-def test_unprocessed_history_injected_into_system_prompt(tmp_path) -> None:
-    """Entries in history.jsonl not yet consumed by Dream appear with timestamps."""
-    workspace = _make_workspace(tmp_path)
-    builder = ContextBuilder(workspace)
-
-    builder.memory.append_history("User asked about weather in Tokyo")
-    builder.memory.append_history("Agent fetched forecast via web_search")
-
-    prompt = builder.build_system_prompt()
-    assert "# Recent History" in prompt
-    assert "User asked about weather in Tokyo" in prompt
-    assert "Agent fetched forecast via web_search" in prompt
-    assert re.search(r"\[\d{4}-\d{2}-\d{2} \d{2}:\d{2}\]", prompt)
-
-
-def test_recent_history_injection_is_session_scoped(tmp_path) -> None:
-    workspace = _make_workspace(tmp_path)
-    builder = ContextBuilder(workspace)
-
-    builder.memory.append_history("legacy entry without session")
-    builder.memory.append_history("telegram history", session_key="telegram:chat-1")
-    builder.memory.append_history("slack history", session_key="slack:chat-2")
-
-    prompt = builder.build_system_prompt(session_key="telegram:chat-1")
-
-    assert "# Recent History" in prompt
-    assert "telegram history" in prompt
-    assert "slack history" not in prompt
-    assert "legacy entry without session" not in prompt
-
-
-def test_session_summary_replaces_interleaved_recent_history_entry(tmp_path) -> None:
-    workspace = _make_workspace(tmp_path)
-    builder = ContextBuilder(workspace)
-    session_key = "unified:default"
-    overview = "CURRENT_SESSION_OVERVIEW_MARKER"
-
-    builder.memory.append_history("another session event", session_key=session_key)
-    builder.memory.append_history(overview, session_key=session_key)
-    latest_cursor = builder.memory.append_history(
-        "later telegram event",
-        session_key="telegram:chat-1",
-    )
-    summary = {"text": overview, "last_active": "2026-08-19T10:00:00"}
-
-    prompt = builder.build_system_prompt(
-        session_key=session_key,
-        session_summary=summary,
-        unified_session=True,
-    )
-
-    assert "# Recent History" in prompt
-    assert "another session event" in prompt
-    assert "later telegram event" in prompt
-    assert "[Archived Context Summary]" in prompt
-    assert prompt.count(overview) == 1
-
-    builder.memory.set_last_dream_cursor(latest_cursor)
-    processed_prompt = builder.build_system_prompt(
-        session_key=session_key,
-        session_summary=summary,
-        unified_session=True,
-    )
-    assert "# Recent History" not in processed_prompt
-    assert processed_prompt.count(overview) == 1
-
-
-def test_recent_history_injection_unified_excludes_cron_internals(tmp_path) -> None:
-    workspace = _make_workspace(tmp_path)
-    builder = ContextBuilder(workspace)
-
-    builder.memory.append_history("unified user history", session_key="unified:default")
-    builder.memory.append_history("channel user history", session_key="telegram:chat-1")
-    builder.memory.append_history("cron internal history", session_key="cron:job-1")
-
-    prompt = builder.build_system_prompt(
-        session_key="unified:default",
-        unified_session=True,
-    )
-
-    assert "unified user history" in prompt
-    assert "channel user history" in prompt
-    assert "cron internal history" not in prompt
-
-
-def test_cron_recent_history_can_see_own_history_and_unified_context(tmp_path) -> None:
-    workspace = _make_workspace(tmp_path)
-    builder = ContextBuilder(workspace)
-
-    builder.memory.append_history("unified user history", session_key="unified:default")
-    builder.memory.append_history("own cron history", session_key="cron:job-1")
-    builder.memory.append_history("other cron history", session_key="cron:job-2")
-
-    prompt = builder.build_system_prompt(
-        session_key="cron:job-1",
-        unified_session=True,
-    )
-
-    assert "unified user history" in prompt
-    assert "own cron history" in prompt
-    assert "other cron history" not in prompt
-
-
-def test_recent_history_capped_at_max(tmp_path) -> None:
-    """Only the most recent _MAX_RECENT_HISTORY entries are injected."""
-    workspace = _make_workspace(tmp_path)
-    builder = ContextBuilder(workspace)
-
-    for i in range(builder._MAX_RECENT_HISTORY + 20):
-        builder.memory.append_history(f"entry-{i}")
-
-    prompt = builder.build_system_prompt()
-    assert "entry-0" not in prompt
-    assert "entry-19" not in prompt
-    assert f"entry-{builder._MAX_RECENT_HISTORY + 19}" in prompt
-
-
-def test_recent_history_truncated_at_max_tokens(tmp_path) -> None:
-    """Recent History section must be truncated to _MAX_HISTORY_TOKENS."""
-    import tiktoken
-
-    workspace = _make_workspace(tmp_path)
-    builder = ContextBuilder(workspace)
-
-    big_entry = "word " * (builder._MAX_HISTORY_TOKENS + 5_000)
-    builder.memory.append_history(big_entry)
-
-    prompt = builder.build_system_prompt()
-    history_section = prompt.split("# Recent History\n\n", 1)
-    assert len(history_section) == 2
-
-    enc = tiktoken.get_encoding("cl100k_base")
-    assert len(enc.encode(history_section[1])) <= builder._MAX_HISTORY_TOKENS
-
-
-def test_no_recent_history_when_dream_has_processed_all(tmp_path) -> None:
-    """If Dream has consumed everything, no Recent History section should appear."""
-    workspace = _make_workspace(tmp_path)
-    builder = ContextBuilder(workspace)
-
-    cursor = builder.memory.append_history("already processed entry")
-    builder.memory.set_last_dream_cursor(cursor)
-
-    prompt = builder.build_system_prompt()
-    assert "# Recent History" not in prompt
-
-
-def test_partial_dream_processing_shows_only_remainder(tmp_path) -> None:
-    """When Dream has processed some entries, only the unprocessed ones appear."""
-    workspace = _make_workspace(tmp_path)
-    builder = ContextBuilder(workspace)
-
-    builder.memory.append_history("old conversation about Python")
-    c2 = builder.memory.append_history("old conversation about Rust")
-    builder.memory.append_history("recent question about Docker")
-    builder.memory.append_history("recent question about K8s")
-
-    builder.memory.set_last_dream_cursor(c2)
-
-    prompt = builder.build_system_prompt()
-    assert "# Recent History" in prompt
-    assert "old conversation about Python" not in prompt
-    assert "old conversation about Rust" not in prompt
-    assert "recent question about Docker" in prompt
-    assert "recent question about K8s" in prompt
-
-
 def test_execution_rules_in_system_prompt(tmp_path) -> None:
     """Execution rules should appear in the system prompt via the default templates."""
     from nanobot.utils.helpers import sync_workspace_templates

tests/agent/test_dream.py

@@ -62,28 +62,14 @@ class TestBuildDreamPrompt:
         prompt, _ = result
         assert "skill-creator" in prompt
 
-    def test_prompt_embeds_current_memory_file_contents(self, store):
+    def test_prompt_does_not_duplicate_current_memory_file_contents(self, store):
-        """Dream must see the real current file contents (Tier 4) so it edits the
-        files, not a stale mental model."""
         store.append_history("hello")
         result = store.build_dream_prompt()
         assert result is not None
         prompt, _ = result
-        assert "## Current Memory Files" in prompt
+        assert "## Current Memory Files" not in prompt
-        assert "### SOUL.md" in prompt
+        assert "Project X active" not in prompt
-        assert "### USER.md" in prompt
+        assert "Helpful" not in prompt
-        assert "### memory/MEMORY.md" in prompt
-        # Real current contents are embedded verbatim.
-        assert "Project X active" in prompt
-        assert "Helpful" in prompt
-
-    def test_prompt_renders_missing_files_as_empty(self, tmp_path):
-        store = MemoryStore(tmp_path)  # no durable files written
-        store.append_history("hello")
-        result = store.build_dream_prompt()
-        assert result is not None
-        prompt, _ = result
-        assert "(empty)" in prompt
 
     def test_workspace_dream_prompt_overrides_default(self, store):
         store.dream_prompt_file.parent.mkdir(parents=True)
@@ -426,7 +412,7 @@ class TestEphemeralDirect:
                 bus=bus,
                 provider=provider,
                 workspace=tmp_path,
-                context_window_tokens=8000,
+                context_window_tokens=32_000,
             )
 
         return loop, store
@@ -606,7 +592,7 @@ class TestEphemeralDirect:
             bus=MessageBus(),
             provider=provider,
             workspace=tmp_path,
-            context_window_tokens=8000,
+            context_window_tokens=32_000,
         )
 
         await loop.process_direct(
@@ -625,6 +611,63 @@ class TestEphemeralDirect:
         assert "entry-21" not in request_text
         assert "entry-60" not in request_text
 
+    async def test_dream_turn_injects_memory_files_once_and_persists_session(self, tmp_path):
+        """Dream gets durable files from system context without losing its session record."""
+        from unittest.mock import MagicMock
+
+        from nanobot.agent.loop import AgentLoop
+        from nanobot.bus.queue import MessageBus
+
+        markers = {
+            "SOUL.md": "DREAM_SOUL_MARKER",
+            "USER.md": "DREAM_USER_MARKER",
+            "memory/MEMORY.md": "DREAM_MEMORY_MARKER",
+        }
+        store = MemoryStore(tmp_path)
+        store.write_soul(markers["SOUL.md"])
+        store.write_user(markers["USER.md"])
+        store.write_memory(markers["memory/MEMORY.md"])
+        store.append_history("history-marker")
+        (tmp_path / "AGENTS.md").write_text("DREAM_AGENTS_MARKER", encoding="utf-8")
+
+        result = store.build_dream_prompt()
+        assert result is not None
+        prompt, _ = result
+
+        captured: dict[str, list[dict]] = {}
+        provider = MagicMock()
+        provider.get_default_model.return_value = "test-model"
+        provider.supports_tools = True
+        provider.generation = MagicMock(max_tokens=4096)
+
+        async def chat_with_retry(**kwargs):
+            captured["messages"] = kwargs["messages"]
+            return LLMResponse(content="done", finish_reason="stop")
+
+        provider.chat_with_retry = chat_with_retry
+        loop = AgentLoop(
+            bus=MessageBus(),
+            provider=provider,
+            workspace=tmp_path,
+            context_window_tokens=32_000,
+        )
+        session_key = "dream:single-memory-copy"
+
+        await loop.process_direct(
+            prompt,
+            session_key=session_key,
+            ephemeral=True,
+            tools=store.build_dream_tools(),
+        )
+
+        messages = captured["messages"]
+        system_prompt = str(messages[0]["content"])
+        request_text = "\n".join(str(message.get("content", "")) for message in messages)
+        for marker in [*markers.values(), "DREAM_AGENTS_MARKER"]:
+            assert marker in system_prompt
+            assert request_text.count(marker) == 1
+        assert loop.sessions._get_session_path(session_key).exists()
+
 
 class TestEphemeralHooks:
     """When ephemeral=True, extra hooks must not fire."""
@@ -666,7 +709,7 @@ class TestEphemeralHooks:
                 bus=bus,
                 provider=provider,
                 workspace=tmp_path,
-                context_window_tokens=8000,
+                context_window_tokens=32_000,
                 hooks=[spy],
             )
 

tests/agent/test_hook_composite.py

@@ -6,6 +6,7 @@ from unittest.mock import AsyncMock, MagicMock, patch
 
 import pytest
 
+from nanobot.agent.context import TranscriptInput
 from nanobot.agent.hook import (
     AgentHook,
     AgentHookContext,
@@ -459,7 +460,7 @@ async def test_agent_loop_extra_hook_receives_calls(tmp_path):
     loop.tools.get_definitions = MagicMock(return_value=[])
 
     result = await loop._run_agent_loop(
-        [{"role": "user", "content": "hi"}],
+        TranscriptInput(history=[{"role": "user", "content": "hi"}], current_message=None),
         runtime=loop.llm_runtime(),
     )
 
@@ -504,7 +505,7 @@ async def test_agent_loop_turn_hook_factories_receive_context(tmp_path):
 
     runtime = loop.llm_runtime()
     await loop._run_agent_loop(
-        [{"role": "user", "content": "hi"}],
+        TranscriptInput(history=[{"role": "user", "content": "hi"}], current_message=None),
         runtime=runtime,
         on_progress=on_progress,
         request_context=RequestContext(
@@ -551,7 +552,7 @@ async def test_agent_loop_extra_hook_error_isolation(tmp_path):
     loop.tools.get_definitions = MagicMock(return_value=[])
 
     result = await loop._run_agent_loop(
-        [{"role": "user", "content": "hi"}],
+        TranscriptInput(history=[{"role": "user", "content": "hi"}], current_message=None),
         runtime=loop.llm_runtime(),
     )
 
@@ -577,7 +578,9 @@ async def test_agent_loop_extra_hooks_do_not_swallow_loop_hook_errors(tmp_path):
 
     with pytest.raises(RuntimeError, match="progress failed"):
         await loop._run_agent_loop(
-            [], runtime=loop.llm_runtime(), on_progress=bad_progress
+            TranscriptInput(history=[], current_message=None),
+            runtime=loop.llm_runtime(),
+            on_progress=bad_progress,
         )
 
 
@@ -596,7 +599,8 @@ async def test_agent_loop_no_hooks_backward_compat(tmp_path):
     loop.max_iterations = 2
 
     result = await loop._run_agent_loop(
-        [], runtime=loop.llm_runtime()
+        TranscriptInput(history=[], current_message=None),
+        runtime=loop.llm_runtime(),
     )
     assert result.final_content == (
         "I reached the maximum number of tool call iterations (2) "

tests/agent/test_loop_consolidation_tokens.py

@@ -7,11 +7,17 @@ from nanobot.bus.queue import MessageBus
 from nanobot.providers.base import LLMResponse
 
 
-def _make_loop(tmp_path, *, estimated_tokens: int, context_window_tokens: int) -> AgentLoop:
+def _make_loop(
+    tmp_path,
+    *,
+    estimated_tokens: int,
+    context_window_tokens: int,
+    max_tokens: int = 0,
+) -> AgentLoop:
     from nanobot.providers.base import GenerationSettings
     provider = MagicMock()
     provider.get_default_model.return_value = "test-model"
-    provider.generation = GenerationSettings(max_tokens=0)
+    provider.generation = GenerationSettings(max_tokens=max_tokens)
     provider.estimate_prompt_tokens.return_value = (estimated_tokens, "test-counter")
     _response = LLMResponse(content="ok", tool_calls=[])
     provider.chat_with_retry = AsyncMock(return_value=_response)
@@ -23,6 +29,9 @@ def _make_loop(tmp_path, *, estimated_tokens: int, context_window_tokens: int) -
         workspace=tmp_path,
         model="test-model",
         context_window_tokens=context_window_tokens,
+        # These tests isolate Memory consolidation; Runner request fitting is
+        # covered separately with realistic context windows.
+        context_block_limit=10_000,
     )
     loop.tools.get_definitions = MagicMock(return_value=[])
     loop.consolidator._SAFETY_BUFFER = 0
@@ -56,6 +65,34 @@ async def test_prompt_above_threshold_triggers_consolidation(tmp_path) -> None:
     assert loop.consolidator.archive_session.await_count >= 1
 
 
+@pytest.mark.asyncio
+async def test_token_consolidation_refreshes_summary_for_current_request(tmp_path) -> None:
+    loop = _make_loop(tmp_path, estimated_tokens=0, context_window_tokens=200)
+    loop.consolidator.archive_session = AsyncMock(  # type: ignore[method-assign]
+        return_value="FRESH_CHECKPOINT"
+    )
+    loop.consolidator.estimate_session_prompt_tokens = MagicMock(  # type: ignore[method-assign]
+        return_value=(1000, "test")
+    )
+    loop.schedule_background = lambda coro: coro.close()  # type: ignore[method-assign]
+
+    session = loop.sessions.get_or_create("cli:test")
+    session.messages = [
+        {"role": role, "content": f"{role[0]}{turn}"}
+        for turn in range(10)
+        for role in ("user", "assistant")
+    ]
+    loop.sessions.save(session)
+
+    await loop.process_direct("hello", session_key="cli:test")
+
+    request_messages = loop.provider.chat_with_retry.await_args.kwargs["messages"]
+    system_prompt = request_messages[0]["content"]
+    assert "FRESH_CHECKPOINT" in system_prompt
+    assert all(message.get("content") != "u0" for message in request_messages)
+    assert loop.sessions.get_or_create("cli:test").last_archived == 12
+
+
 @pytest.mark.asyncio
 async def test_prompt_above_threshold_uses_fixed_recent_tail(tmp_path) -> None:
     loop = _make_loop(tmp_path, estimated_tokens=1000, context_window_tokens=200)

tests/agent/test_loop_progress.py

@@ -6,6 +6,7 @@ from unittest.mock import AsyncMock, MagicMock
 
 import pytest
 
+from nanobot.agent.context import TranscriptInput
 from nanobot.agent.hooks import create_file_edit_activity_hook
 from nanobot.agent.loop import AgentLoop
 from nanobot.agent.tools.context import current_request_context
@@ -84,7 +85,9 @@ class TestToolEventProgress:
             progress.append((content, tool_hint, tool_events))
 
         result = await loop._run_agent_loop(
-            [], runtime=loop.llm_runtime(), on_progress=on_progress
+            TranscriptInput(history=[], current_message=None),
+            runtime=loop.llm_runtime(),
+            on_progress=on_progress,
         )
 
         assert result.final_content == "Done"
@@ -155,7 +158,9 @@ class TestToolEventProgress:
                 file_events.extend(file_edit_events)
 
         result = await loop._run_agent_loop(
-            [], runtime=loop.llm_runtime(), on_progress=on_progress
+            TranscriptInput(history=[], current_message=None),
+            runtime=loop.llm_runtime(),
+            on_progress=on_progress,
         )
 
         assert result.final_content == "Done"
@@ -225,7 +230,9 @@ class TestToolEventProgress:
         )
 
         result = await loop._run_agent_loop(
-            [], runtime=loop.llm_runtime(), on_progress=on_progress
+            TranscriptInput(history=[], current_message=None),
+            runtime=loop.llm_runtime(),
+            on_progress=on_progress,
         )
 
         assert result.final_content == "Done"
@@ -263,7 +270,9 @@ class TestToolEventProgress:
                 file_events.extend(file_edit_events)
 
         await loop._run_agent_loop(
-            [], runtime=loop.llm_runtime(), on_progress=on_progress
+            TranscriptInput(history=[], current_message=None),
+            runtime=loop.llm_runtime(),
+            on_progress=on_progress,
         )
 
         assert file_events == []
@@ -1019,7 +1028,7 @@ class TestToolEventProgress:
             progress.append((content, tool_hint, tool_events))
 
         result = await loop._run_agent_loop(
-            [],
+            TranscriptInput(history=[], current_message=None),
             runtime=loop.llm_runtime(),
             on_progress=on_progress,
             on_stream=on_stream,

tests/agent/test_loop_runner_integration.py

@@ -7,6 +7,7 @@ from unittest.mock import AsyncMock, MagicMock, patch
 
 import pytest
 
+from nanobot.agent.context import TranscriptInput
 from nanobot.agent.goal_permission import goal_mutation_allowed, goal_mutation_permission
 from nanobot.agent.tools.context import RequestContext
 from nanobot.bus.outbound_events import StreamedResponseEvent
@@ -55,7 +56,7 @@ async def test_ephemeral_runner_enters_and_restores_turn_scopes(tmp_path):
     loop.tools.get_definitions = MagicMock(return_value=[])
 
     await loop._run_agent_loop(
-        [],
+        TranscriptInput(history=[], current_message=None),
         runtime=loop.llm_runtime(),
         ephemeral=True,
         turn_scopes=[goal_mutation_permission(True)],
@@ -340,7 +341,8 @@ async def test_loop_max_iterations_message_stays_stable(tmp_path):
     loop.max_iterations = 2
 
     result = await loop._run_agent_loop(
-        [], runtime=loop.llm_runtime()
+        TranscriptInput(history=[], current_message=None),
+        runtime=loop.llm_runtime(),
     )
 
     assert result.final_content == (
@@ -362,7 +364,7 @@ async def test_loop_goal_turn_uses_standard_iteration_budget(tmp_path):
 
     runtime = loop.llm_runtime()
     result = await loop._run_agent_loop(
-        [],
+        TranscriptInput(history=[], current_message=None),
         runtime=runtime,
         request_context=RequestContext(
             channel="cli",
@@ -401,7 +403,7 @@ async def test_loop_stream_filter_handles_think_only_prefix_without_crashing(tmp
         endings.append(resuming)
 
     result = await loop._run_agent_loop(
-        [],
+        TranscriptInput(history=[], current_message=None),
         runtime=loop.llm_runtime(),
         on_stream=on_stream,
         on_stream_end=on_stream_end,
@@ -428,7 +430,9 @@ async def test_loop_stream_filter_hides_partial_trailing_think_prefix(tmp_path):
         deltas.append(delta)
 
     result = await loop._run_agent_loop(
-        [], runtime=loop.llm_runtime(), on_stream=on_stream
+        TranscriptInput(history=[], current_message=None),
+        runtime=loop.llm_runtime(),
+        on_stream=on_stream,
     )
 
     assert result.final_content == "Hello World"
@@ -451,7 +455,9 @@ async def test_loop_stream_filter_hides_complete_trailing_think_tag(tmp_path):
         deltas.append(delta)
 
     result = await loop._run_agent_loop(
-        [], runtime=loop.llm_runtime(), on_stream=on_stream
+        TranscriptInput(history=[], current_message=None),
+        runtime=loop.llm_runtime(),
+        on_stream=on_stream,
     )
 
     assert result.final_content == "Hello World"
@@ -472,7 +478,8 @@ async def test_loop_retries_think_only_final_response(tmp_path):
     loop.provider.chat_with_retry = chat_with_retry
 
     result = await loop._run_agent_loop(
-        [], runtime=loop.llm_runtime()
+        TranscriptInput(history=[], current_message=None),
+        runtime=loop.llm_runtime(),
     )
 
     assert result.final_content == "Recovered answer"

tests/agent/test_loop_save_turn.py

@@ -7,7 +7,7 @@ from unittest.mock import AsyncMock, MagicMock
 import pytest
 from loguru import logger
 
-from nanobot.agent.context import ContextBuilder
+from nanobot.agent.context import ContextBuilder, TranscriptInput
 from nanobot.agent.loop import AgentLoop
 from nanobot.agent.runner import AgentRunResult
 from nanobot.agent.tools.context import RequestContext, request_context
@@ -79,6 +79,13 @@ def _agent_run_result(
     )
 
 
+def _assembled_messages(
+    builder: ContextBuilder,
+    transcript_input: TranscriptInput,
+) -> list[dict]:
+    return builder.build_transcript(transcript_input, include_memory=False)
+
+
 def _mk_loop() -> AgentLoop:
     loop = AgentLoop.__new__(AgentLoop)
     from nanobot.config.schema import AgentDefaults
@@ -930,10 +937,13 @@ async def test_runtime_checkpoint_keeps_provider_state_out_of_public_metadata(
     session = loop.sessions.get_or_create("cli:private-checkpoint")
 
     await loop._run_agent_loop(
-        [
+        TranscriptInput(
-            {"role": "system", "content": "system"},
+            history=[
-            {"role": "user", "content": "question"},
+                {"role": "system", "content": "system"},
-        ],
+                {"role": "user", "content": "question"},
+            ],
+            current_message=None,
+        ),
         runtime=loop.llm_runtime(),
         session=session,
     )
@@ -1008,7 +1018,7 @@ async def test_subagent_followup_state_is_durable_before_prompt_assembly(
     loop = _make_full_loop(tmp_path)
     loop.consolidator.maybe_consolidate_by_tokens = AsyncMock(return_value=False)  # type: ignore[method-assign]
     loop.provider.can_resume_conversation_state.return_value = True
-    loop._build_initial_messages = MagicMock(  # type: ignore[method-assign]
+    loop.context.build_system_prompt = MagicMock(  # type: ignore[method-assign]
         side_effect=RuntimeError("prompt boom"),
     )
     session = loop.sessions.get_or_create("cli:subagent-prompt-crash")
@@ -1041,8 +1051,8 @@ async def test_subagent_redelivery_does_not_duplicate_staged_provider_input(
     loop = _make_full_loop(tmp_path)
     loop.consolidator.maybe_consolidate_by_tokens = AsyncMock(return_value=False)  # type: ignore[method-assign]
     loop.provider.can_resume_conversation_state.return_value = True
-    build_initial_messages = loop._build_initial_messages
+    build_system_prompt = loop.context.build_system_prompt
-    loop._build_initial_messages = MagicMock(  # type: ignore[method-assign]
+    loop.context.build_system_prompt = MagicMock(  # type: ignore[method-assign]
         side_effect=RuntimeError("prompt boom"),
     )
     session = loop.sessions.get_or_create("cli:subagent-redelivery")
@@ -1066,7 +1076,7 @@ async def test_subagent_redelivery_does_not_duplicate_staged_provider_input(
         message.get("content")
         for message in persisted.provider_state.pending_messages
     ].count("subagent result") == 1
-    loop._build_initial_messages = build_initial_messages  # type: ignore[method-assign]
+    loop.context.build_system_prompt = build_system_prompt  # type: ignore[method-assign]
     loop._run_agent_loop = AsyncMock(  # type: ignore[method-assign]
         side_effect=RuntimeError("provider boom"),
     )
@@ -1319,7 +1329,8 @@ async def test_internal_continuation_queues_turn_without_fake_user_history(
 
     calls: list[dict] = []
 
-    async def fake_run_agent_loop(initial_messages, *, metadata=None, **_kwargs):
+    async def fake_run_agent_loop(transcript_input, *, metadata=None, **_kwargs):
+        initial_messages = _assembled_messages(loop.context, transcript_input)
         calls.append({"initial_messages": initial_messages, "metadata": metadata})
         if len(calls) == 1:
             return _agent_run_result(
@@ -1387,8 +1398,9 @@ async def test_internal_continuation_preserves_streaming_route_metadata(
 
     calls = 0
 
-    async def fake_run_agent_loop(initial_messages, *, on_stream=None, on_stream_end=None, **_kwargs):
+    async def fake_run_agent_loop(transcript_input, *, on_stream=None, on_stream_end=None, **_kwargs):
         nonlocal calls
+        initial_messages = _assembled_messages(loop.context, transcript_input)
         calls += 1
         if calls == 1:
             return _agent_run_result(
@@ -1460,8 +1472,9 @@ async def test_websocket_internal_continuation_keeps_single_visible_run(
 
     calls = 0
 
-    async def fake_run_agent_loop(initial_messages, **_kwargs):
+    async def fake_run_agent_loop(transcript_input, **_kwargs):
         nonlocal calls
+        initial_messages = _assembled_messages(loop.context, transcript_input)
         calls += 1
         if calls == 1:
             return _agent_run_result(
@@ -1623,7 +1636,7 @@ async def test_run_agent_loop_continuation_reads_latest_goal_metadata(
 
     runtime = loop.llm_runtime()
     await loop._run_agent_loop(
-        [],
+        TranscriptInput(history=[], current_message=None),
         runtime=runtime,
         session=session,
         request_context=RequestContext(
@@ -1753,7 +1766,7 @@ async def test_stop_preserves_runtime_checkpoint_for_next_turn(tmp_path: Path) -
 
     checkpoint_saved = asyncio.Event()
 
-    async def interrupted_run_agent_loop(_initial_messages, *, session=None, **_kwargs):
+    async def interrupted_run_agent_loop(_transcript_input, *, session=None, **_kwargs):
         assert session is not None
         loop._set_runtime_checkpoint(
             session,
@@ -1813,7 +1826,8 @@ async def test_stop_preserves_runtime_checkpoint_for_next_turn(tmp_path: Path) -
     assert interrupted.metadata.get(AgentLoop._PENDING_USER_TURN_KEY) is True
     assert interrupted.metadata.get(AgentLoop._RUNTIME_CHECKPOINT_KEY) is not None
 
-    async def resumed_run_agent_loop(initial_messages, **_kwargs):
+    async def resumed_run_agent_loop(transcript_input, **_kwargs):
+        initial_messages = _assembled_messages(loop.context, transcript_input)
         return _agent_run_result(
             "next answer",
             [*initial_messages, {"role": "assistant", "content": "next answer"}],
@@ -1864,7 +1878,8 @@ async def test_system_subagent_followup_is_persisted_before_prompt_assembly(tmp_
     record_runtime = MagicMock(wraps=loop.runtime_event_publisher.record_turn_runtime)
     loop.runtime_event_publisher.record_turn_runtime = record_runtime
 
-    async def fake_run_agent_loop(initial_messages, **kwargs):
+    async def fake_run_agent_loop(transcript_input, **kwargs):
+        initial_messages = _assembled_messages(loop.context, transcript_input)
         seen["initial_messages"] = initial_messages
         seen["runtime"] = kwargs["runtime"]
         seen["request_context"] = kwargs["request_context"]
@@ -1940,7 +1955,8 @@ async def test_turn_usage_is_persisted_with_the_saved_session(tmp_path: Path) ->
     loop.consolidator.maybe_consolidate_by_tokens = AsyncMock(return_value=False)  # type: ignore[method-assign]
     turn_usage = LLMUsage.reported(input_tokens=64, output_tokens=9)
 
-    async def fake_run_agent_loop(initial_messages, **_kwargs):
+    async def fake_run_agent_loop(transcript_input, **_kwargs):
+        initial_messages = _assembled_messages(loop.context, transcript_input)
         return _agent_run_result(
             "done",
             [*initial_messages, {"role": "assistant", "content": "done"}],
@@ -1966,7 +1982,8 @@ async def test_system_subagent_followup_does_not_log_content(tmp_path: Path) ->
         return_value=False
     )
 
-    async def fake_run_agent_loop(initial_messages, **_kwargs):
+    async def fake_run_agent_loop(transcript_input, **_kwargs):
+        initial_messages = _assembled_messages(loop.context, transcript_input)
         return _agent_run_result(
             "done",
             [*initial_messages, {"role": "assistant", "content": "done"}],
@@ -2022,7 +2039,8 @@ async def test_system_subagent_followup_uses_common_turn_lifecycle(tmp_path: Pat
 
         setattr(loop, name, record)
 
-    async def fake_run_agent_loop(initial_messages, **_kwargs):
+    async def fake_run_agent_loop(transcript_input, **_kwargs):
+        initial_messages = _assembled_messages(loop.context, transcript_input)
         return _agent_run_result(
             "done",
             [*initial_messages, {"role": "assistant", "content": "done"}],
@@ -2065,7 +2083,8 @@ async def test_multiple_subagent_followups_all_persist_as_standalone_history(tmp
     loop = _make_full_loop(tmp_path)
     loop.consolidator.maybe_consolidate_by_tokens = AsyncMock(return_value=False)  # type: ignore[method-assign]
 
-    async def fake_run_agent_loop(initial_messages, **_kwargs):
+    async def fake_run_agent_loop(transcript_input, **_kwargs):
+        initial_messages = _assembled_messages(loop.context, transcript_input)
         return _agent_run_result(
             "ack",
             [*initial_messages, {"role": "assistant", "content": "ack"}],
@@ -2196,7 +2215,8 @@ async def test_system_subagent_followup_uses_thread_session_and_slack_metadata(t
 
     seen: dict[str, object] = {}
 
-    async def fake_run_agent_loop(initial_messages, **kwargs):
+    async def fake_run_agent_loop(transcript_input, **kwargs):
+        initial_messages = _assembled_messages(loop.context, transcript_input)
         seen["initial_messages"] = initial_messages
         seen["request_context"] = kwargs["request_context"]
         return _agent_run_result(
@@ -2252,8 +2272,11 @@ async def test_turn_after_unanswered_user_keeps_tool_call_pairing(tmp_path: Path
     session.add_message("user", "earlier question that never got an answer")
     loop.sessions.save(session)
 
-    async def fake_run_agent_loop(initial_messages, **_kwargs):
+    async def fake_run_agent_loop(transcript_input, **_kwargs):
-        assert [m["role"] for m in initial_messages] == ["system", "user"]
+        initial_messages = _assembled_messages(loop.context, transcript_input)
+        assert [m["role"] for m in initial_messages] == ["system", "user", "user"]
+        assert initial_messages[-2]["content"] == "earlier question that never got an answer"
+        assert initial_messages[-1]["content"] == "and another thing"
         return _agent_run_result(
             "done",
             [

tests/agent/test_loop_tool_context.py

@@ -5,6 +5,7 @@ from unittest.mock import AsyncMock, MagicMock
 
 import pytest
 
+from nanobot.agent.context import TranscriptInput
 from nanobot.agent.loop import AgentLoop
 from nanobot.agent.tools.context import (
     RequestContext,
@@ -133,7 +134,7 @@ async def test_loop_binds_request_context_for_tool_execution(tmp_path: Path) ->
     metadata = {"slack": {"thread_ts": "111.222", "channel_type": "channel"}}
     runtime = loop.llm_runtime()
     await loop._run_agent_loop(
-        [],
+        TranscriptInput(history=[], current_message=None),
         runtime=runtime,
         request_context=RequestContext(
             channel="slack",
@@ -234,7 +235,7 @@ async def test_agent_loop_restores_outer_request_context_after_runner_exception(
     try:
         with pytest.raises(RuntimeError, match="runner failed"):
             await loop._run_agent_loop(
-                [],
+                TranscriptInput(history=[], current_message=None),
                 runtime=runtime,
                 request_context=RequestContext(
                     channel="slack",

tests/agent/test_memory_store.py

@@ -113,54 +113,6 @@ class TestHistoryWithCursor:
         entries = store.read_unprocessed_history(since_cursor=0)
         assert len(entries) == 2
 
-    def test_prompt_history_filters_to_current_session(self, store):
-        store.append_history("legacy entry without session")
-        store.append_history("telegram entry", session_key="telegram:chat-1")
-        store.append_history("slack entry", session_key="slack:chat-2")
-
-        entries = store.read_recent_history_for_prompt(
-            since_cursor=0,
-            session_key="telegram:chat-1",
-        )
-
-        assert [e["content"] for e in entries] == ["telegram entry"]
-        assert [e["content"] for e in store.read_unprocessed_history(0)] == [
-            "legacy entry without session",
-            "telegram entry",
-            "slack entry",
-        ]
-
-    def test_unified_prompt_history_excludes_internal_cron_sessions(self, store):
-        store.append_history("legacy entry without session")
-        store.append_history("unified entry", session_key="unified:default")
-        store.append_history("telegram entry", session_key="telegram:chat-1")
-        store.append_history("cron internal entry", session_key="cron:job-1")
-
-        entries = store.read_recent_history_for_prompt(
-            since_cursor=0,
-            session_key="unified:default",
-            unified_session=True,
-        )
-
-        assert [e["content"] for e in entries] == [
-            "legacy entry without session",
-            "unified entry",
-            "telegram entry",
-        ]
-
-    def test_unified_cron_prompt_history_includes_own_cron_entry(self, store):
-        store.append_history("unified entry", session_key="unified:default")
-        store.append_history("other cron entry", session_key="cron:job-2")
-        store.append_history("own cron entry", session_key="cron:job-1")
-
-        entries = store.read_recent_history_for_prompt(
-            since_cursor=0,
-            session_key="cron:job-1",
-            unified_session=True,
-        )
-
-        assert [e["content"] for e in entries] == ["unified entry", "own cron entry"]
-
     def test_read_unprocessed_skips_entries_without_cursor(self, store):
         """Regression: entries missing the cursor key should be silently skipped."""
         store.history_file.write_text(

tests/agent/test_new_command_archival.py

@@ -8,6 +8,10 @@ from unittest.mock import AsyncMock, MagicMock
 
 import pytest
 
+from nanobot.utils.prompt_templates import render_template
+
+_ARCHIVE_PROMPT = render_template("agent/consolidator_archive.md", strip=True)
+
 
 class TestNewCommandArchival:
     """Test /new archival behavior with the structured archive flow."""
@@ -117,7 +121,7 @@ class TestNewCommandArchival:
         await loop.aclose()
         sent = loop.provider.chat_with_retry.call_args.kwargs["messages"]
         assert sent[1:-1] == ordinary_history
-        assert "final 2 conversation messages" in sent[-1]["content"]
+        assert sent[-1]["content"] == _ARCHIVE_PROMPT
 
     @pytest.mark.asyncio
     async def test_new_clears_session_and_responds(self, tmp_path: Path) -> None:

tests/agent/test_runner_core.py

@@ -10,6 +10,8 @@ from unittest.mock import AsyncMock, MagicMock, patch
 import pytest
 
 from agent.runner_helpers import make_run_spec
+from nanobot.agent.context import TranscriptInput
+from nanobot.agent.context_governance import ContextWindowExceededError
 from nanobot.config.schema import AgentDefaults
 from nanobot.providers.base import (
     LLMProvider,
@@ -34,6 +36,35 @@ def _make_usage_spec(provider, tools):
     )
 
 
+def test_initial_transcript_is_built_from_structured_turn_input() -> None:
+    from nanobot.agent.runner import AgentRunner
+
+    provider = MagicMock(spec=LLMProvider)
+    transcript_input = TranscriptInput(
+        history=[{"role": "user", "content": "earlier"}],
+        current_message="fresh",
+    )
+    expected = [
+        {"role": "system", "content": "system"},
+        {"role": "user", "content": "earlier"},
+        {"role": "user", "content": "fresh"},
+    ]
+    transcript_builder = MagicMock(return_value=expected)
+    spec = make_run_spec(
+        provider,
+        initial_messages=None,
+        transcript_input=transcript_input,
+        transcript_builder=transcript_builder,
+        tools=MagicMock(),
+        model="test-model",
+        max_iterations=1,
+        max_tool_result_chars=_MAX_TOOL_RESULT_CHARS,
+    )
+
+    assert AgentRunner._initial_transcript(spec) == expected
+    transcript_builder.assert_called_once_with(transcript_input)
+
+
 def test_usage_or_estimate_replaces_reported_zero_for_content(monkeypatch) -> None:
     from nanobot.agent.runner import AgentRunner
 
@@ -56,6 +87,7 @@ def test_usage_or_estimate_replaces_reported_zero_for_content(monkeypatch) -> No
         _make_usage_spec(provider, tools),
         [{"role": "user", "content": "hello"}],
         response,
+        tool_definitions=tools.get_definitions(),
     )
 
     assert usage == LLMUsage.estimated(input_tokens=12, output_tokens=7).with_timing(
@@ -100,6 +132,7 @@ def test_usage_or_estimate_counts_tool_call_output_for_reported_zero(monkeypatch
         _make_usage_spec(provider, tools),
         [{"role": "user", "content": "hello"}],
         response,
+        tool_definitions=tools.get_definitions(),
     )
 
     assert usage == LLMUsage.estimated(input_tokens=13, output_tokens=9)
@@ -132,6 +165,7 @@ def test_usage_or_estimate_counts_error_without_estimating_tokens(
         _make_usage_spec(provider, tools),
         [{"role": "user", "content": "hello"}],
         response,
+        tool_definitions=tools.get_definitions(),
     )
 
     assert usage is not None
@@ -167,6 +201,7 @@ def test_usage_or_estimate_trusts_positive_reported_total(monkeypatch) -> None:
         _make_usage_spec(provider, tools),
         [{"role": "user", "content": "hello"}],
         response,
+        tool_definitions=tools.get_definitions(),
     )
 
     assert usage is not None
@@ -336,14 +371,12 @@ async def test_runner_replays_provider_state_without_chat_projection_duplicates(
 
 
 @pytest.mark.asyncio
-async def test_runner_governs_tool_result_before_adding_it_to_provider_state():
+async def test_runner_preserves_tool_result_before_rejecting_unfit_followup():
     from nanobot.agent.runner import AgentRunner
 
     provider = MagicMock(spec=LLMProvider)
     provider.can_resume_conversation_state.return_value = True
-    provider.supports_native_compaction.return_value = False
     calls = 0
-    captured_context: ProviderCallContext | None = None
     checkpoints: list[dict] = []
     state = ProviderConversationState(
         kind="openai_responses",
@@ -354,7 +387,7 @@ async def test_runner_governs_tool_result_before_adding_it_to_provider_state():
     )
 
     async def chat_with_retry(**kwargs):
-        nonlocal calls, captured_context
+        nonlocal calls
         calls += 1
         if calls == 1:
             return LLMResponse(
@@ -368,7 +401,6 @@ async def test_runner_governs_tool_result_before_adding_it_to_provider_state():
                 ],
                 provider_state=state,
             )
-        captured_context = kwargs["provider_context"]
         return LLMResponse(content="done")
 
     provider.chat_with_retry = chat_with_retry
@@ -379,37 +411,36 @@ async def test_runner_governs_tool_result_before_adding_it_to_provider_state():
     async def checkpoint(payload: dict) -> None:
         checkpoints.append(payload)
 
-    await AgentRunner().run(make_run_spec(
+    with pytest.raises(ContextWindowExceededError):
-        provider,
+        await AgentRunner().run(make_run_spec(
-        initial_messages=[
+            provider,
-            {"role": "system", "content": "system"},
+            initial_messages=[
-            {"role": "user", "content": "read the file"},
+                {"role": "system", "content": "system"},
-        ],
+                {"role": "user", "content": "read the file"},
-        tools=tools,
+            ],
-        model="gpt-5.6",
+            tools=tools,
-        context_window_tokens=3_000,
+            model="gpt-5.6",
-        context_block_limit=200,
+            context_window_tokens=3_000,
-        max_tokens=1_000,
+            context_block_limit=200,
-        max_iterations=3,
+            max_tokens=1_000,
-        max_tool_result_chars=10_000,
+            max_iterations=3,
-        checkpoint_callback=checkpoint,
+            max_tool_result_chars=10_000,
-    ))
+            checkpoint_callback=checkpoint,
+        ))
 
-    assert captured_context is not None
+    assert calls == 1
-    assert captured_context.conversation_state is not None
-    pending = captured_context.conversation_state.pending_messages
-    assert len(pending) == 1
-    assert pending[0]["role"] == "tool"
-    assert "compacted to fit context" in pending[0]["content"]
-    assert pending[0]["content"] != "x" * 5_000
     completed_checkpoint = next(
         checkpoint
         for checkpoint in checkpoints
         if checkpoint["phase"] == "tools_completed"
     )
     checkpoint_pending = completed_checkpoint["provider_state"].pending_messages
-    assert "compacted to fit context" in checkpoint_pending[0]["content"]
+    assert checkpoint_pending == [{
-    assert checkpoint_pending[0]["content"] != "x" * 5_000
+        "role": "tool",
+        "tool_call_id": "call_1",
+        "name": "read_file",
+        "content": "x" * 5_000,
+    }]
 
 
 @pytest.mark.asyncio

tests/agent/test_runner_errors.py

@@ -52,7 +52,31 @@ async def test_runner_returns_tool_exception_to_model_for_recovery():
         {"name": "list_dir", "status": "error", "detail": "boom"}
     ]
     tool_message = next(message for message in result.messages if message.get("role") == "tool")
+    retry_hint = "[Analyze the error above and try a different approach.]"
     assert "Error: RuntimeError: boom" in tool_message["content"]
+    assert tool_message["content"].count(retry_hint) == 1
+
+
+@pytest.mark.asyncio
+async def test_tool_execution_does_not_duplicate_existing_retry_hint():
+    retry_hint = "\n\n[Analyze the error above and try a different approach.]"
+    tools = SimpleNamespace(
+        execute=AsyncMock(return_value=ToolResult.error("Error: boom" + retry_hint)),
+    )
+
+    results, events = await execute_tool_calls(
+        tools,
+        [ToolCallRequest(id="call_1", name="list_dir", arguments={})],
+        concurrent=False,
+        external_lookup_counts={},
+        workspace_violation_counts={},
+        hook=AgentHook(),
+        context=AgentHookContext(iteration=0, messages=[]),
+    )
+
+    assert results == ["Error: boom" + retry_hint]
+    assert results[0].count(retry_hint) == 1
+    assert events[0]["status"] == "error"
 
 
 @pytest.mark.asyncio

tests/agent/test_runner_governance.py

@@ -1,8 +1,7 @@
-"""Tests for AgentRunner context governance: backfill, orphan cleanup, microcompact, snip_history."""
+"""Tests for AgentRunner context governance: repair and request fitting."""
 
 from __future__ import annotations
 
-from types import SimpleNamespace
 from unittest.mock import AsyncMock, MagicMock, patch
 
 import pytest
@@ -12,11 +11,14 @@ from nanobot.agent.context_governance import (
     BACKFILL_CONTENT,
     ContextGovernanceConfig,
     ContextGovernor,
+    ContextWindowExceededError,
 )
 from nanobot.agent.runner import AgentRunSpec
 from nanobot.config.schema import AgentDefaults
 from nanobot.providers.base import (
+    LLMProvider,
     LLMResponse,
+    LLMUsage,
     ProviderConversationState,
     ToolCallRequest,
 )
@@ -28,8 +30,6 @@ def _governance_config(
     provider,
     tools,
     spec: AgentRunSpec,
-    *,
-    inflight_start_index: int = 0,
 ) -> ContextGovernanceConfig:
     return ContextGovernanceConfig(
         provider=provider,
@@ -41,7 +41,6 @@ def _governance_config(
         context_window_tokens=spec.runtime.context_window_tokens,
         context_block_limit=spec.context_block_limit,
         max_tokens=spec.runtime.generation.max_tokens,
-        inflight_start_index=inflight_start_index,
     )
 
 
@@ -89,6 +88,508 @@ async def test_runner_propagates_context_governance_failure():
     provider.chat_with_retry.assert_not_awaited()
 
 
+@pytest.mark.asyncio
+async def test_runner_locally_fits_oversized_initial_transcript(monkeypatch):
+    from nanobot.agent.runner import AgentRunner
+
+    provider = MagicMock(spec=LLMProvider)
+    provider.chat_with_retry = AsyncMock(return_value=LLMResponse(content="done"))
+    tools = MagicMock()
+    tools.get_definitions.return_value = []
+    old_content = "x" * 20_000
+    monkeypatch.setattr(
+        "nanobot.agent.context_governance.estimate_prompt_tokens_chain",
+        lambda _provider, _model, messages, _tools: (
+            (600, "test-counter")
+            if any(message.get("content") == old_content for message in messages)
+            else (100, "test-counter")
+        ),
+    )
+
+    result = await AgentRunner().run(make_run_spec(
+        provider,
+        initial_messages=[
+            {"role": "system", "content": "system"},
+            {"role": "user", "content": "old question"},
+            {"role": "assistant", "content": old_content},
+            {"role": "user", "content": "continue"},
+        ],
+        tools=tools,
+        model="local-model",
+        context_window_tokens=2_000,
+        context_block_limit=500,
+        max_tokens=100,
+        max_iterations=1,
+        max_tool_result_chars=_MAX_TOOL_RESULT_CHARS,
+    ))
+
+    assert provider.chat_with_retry.await_args.kwargs["messages"] == [
+        {"role": "system", "content": "system"},
+        {"role": "user", "content": "continue"},
+    ]
+    assert any(message.get("content") == old_content for message in result.messages)
+
+
+@pytest.mark.asyncio
+async def test_runner_governs_messages_added_by_before_iteration_hook(monkeypatch):
+    from nanobot.agent.hook import AgentHook, AgentHookContext
+    from nanobot.agent.runner import AgentRunner
+
+    provider = MagicMock(spec=LLMProvider)
+    provider.chat_with_retry = AsyncMock(return_value=LLMResponse(content="unexpected"))
+    tools = MagicMock()
+    tools.get_definitions.return_value = []
+    oversized = "hook-added-oversized-message"
+
+    monkeypatch.setattr(
+        "nanobot.agent.context_governance.estimate_prompt_tokens_chain",
+        lambda _provider, _model, messages, _tools: (
+            (2_000, "test-counter")
+            if any(message.get("content") == oversized for message in messages)
+            else (100, "test-counter")
+        ),
+    )
+
+    class MutatingHook(AgentHook):
+        async def before_iteration(self, context: AgentHookContext) -> None:
+            context.messages.append({"role": "user", "content": oversized})
+
+    with pytest.raises(ContextWindowExceededError):
+        await AgentRunner().run(make_run_spec(
+            provider,
+            initial_messages=[{"role": "user", "content": "hello"}],
+            tools=tools,
+            model="local-model",
+            context_window_tokens=2_000,
+            context_block_limit=500,
+            max_iterations=1,
+            max_tool_result_chars=_MAX_TOOL_RESULT_CHARS,
+            hook=MutatingHook(),
+        ))
+
+    provider.chat_with_retry.assert_not_awaited()
+
+
+@pytest.mark.asyncio
+async def test_runner_drops_resumable_provider_state_when_request_is_fitted(monkeypatch):
+    from nanobot.agent.runner import AgentRunner
+
+    provider = MagicMock(spec=LLMProvider)
+    provider.can_resume_conversation_state.return_value = True
+    captured_contexts = []
+    old_content = "old-oversized-history"
+    candidate = ProviderConversationState(
+        kind="openai_responses",
+        provider="openai:test",
+        model="local-model",
+        version=1,
+        payload={"items": [{"type": "message", "content": "fresh state"}]},
+    )
+
+    async def chat_with_retry(*, provider_context=None, **_kwargs):
+        captured_contexts.append(provider_context)
+        return LLMResponse(
+            content="done",
+            usage=LLMUsage.reported(input_tokens=100, output_tokens=10),
+            provider_state=candidate,
+        )
+
+    provider.chat_with_retry = chat_with_retry
+    tools = MagicMock()
+    tools.get_definitions.return_value = []
+    monkeypatch.setattr(
+        "nanobot.agent.context_governance.estimate_prompt_tokens_chain",
+        lambda _provider, _model, messages, _tools: (
+            (600, "test-counter")
+            if any(message.get("content") == old_content for message in messages)
+            else (100, "test-counter")
+        ),
+    )
+    monkeypatch.setattr(
+        "nanobot.agent.context_governance.estimate_message_tokens",
+        lambda message: 450 if message.get("content") == old_content else 50,
+    )
+    saved_state = ProviderConversationState(
+        kind="openai_responses",
+        provider="openai:test",
+        model="local-model",
+        version=1,
+        payload={"items": [{"type": "message", "content": "stale state"}]},
+    )
+
+    result = await AgentRunner().run(make_run_spec(
+        provider,
+        initial_messages=[
+            {"role": "assistant", "content": old_content},
+            {"role": "user", "content": "continue"},
+        ],
+        tools=tools,
+        model="local-model",
+        context_window_tokens=2_000,
+        context_block_limit=500,
+        max_iterations=1,
+        max_tool_result_chars=_MAX_TOOL_RESULT_CHARS,
+        provider_state=saved_state,
+    ))
+
+    assert captured_contexts[0].conversation_state is None
+    assert result.provider_state is not None
+    assert result.provider_state.payload == candidate.payload
+
+
+@pytest.mark.asyncio
+async def test_runner_fits_each_malformed_retry_with_its_actual_tools(monkeypatch):
+    from nanobot.agent.runner import AgentRunner
+
+    provider = MagicMock(spec=LLMProvider)
+    calls: list[dict] = []
+    estimated_tools: list[object] = []
+    definitions = [{"type": "function", "function": {"name": "read_file"}}]
+
+    async def chat_with_retry(*, messages, tools=None, **_kwargs):
+        calls.append({"messages": [dict(message) for message in messages], "tools": tools})
+        if len(calls) < 3:
+            return LLMResponse(
+                content="bad tool request",
+                tool_calls=[ToolCallRequest(id=f"bad_{len(calls)}", name=None, arguments={})],
+                finish_reason="tool_calls",
+                usage=LLMUsage.reported(input_tokens=100, output_tokens=10),
+            )
+        return LLMResponse(
+            content="recovered",
+            usage=LLMUsage.reported(input_tokens=100, output_tokens=10),
+        )
+
+    def estimate(_provider, _model, messages, _tools):
+        estimated_tools.append(_tools)
+        user_count = sum(message.get("role") == "user" for message in messages)
+        return (600 if user_count > 1 else 100), "test-counter"
+
+    provider.chat_with_retry = chat_with_retry
+    tools = MagicMock()
+    tools.get_definitions.return_value = definitions
+    monkeypatch.setattr(
+        "nanobot.agent.context_governance.estimate_prompt_tokens_chain",
+        estimate,
+    )
+    monkeypatch.setattr(
+        "nanobot.agent.context_governance.estimate_message_tokens",
+        lambda _message: 300,
+    )
+
+    result = await AgentRunner().run(make_run_spec(
+        provider,
+        initial_messages=[{"role": "user", "content": "use a tool"}],
+        tools=tools,
+        model="local-model",
+        context_window_tokens=2_000,
+        context_block_limit=500,
+        max_iterations=1,
+        max_tool_result_chars=_MAX_TOOL_RESULT_CHARS,
+    ))
+
+    assert [call["tools"] for call in calls] == [definitions, definitions, None]
+    assert definitions in estimated_tools
+    assert None in estimated_tools
+    assert [len(call["messages"]) for call in calls] == [1, 1, 1]
+    assert result.final_content == "recovered"
+    assert result.messages == [
+        {"role": "user", "content": "use a tool"},
+        {"role": "assistant", "content": "recovered"},
+    ]
+
+
+@pytest.mark.asyncio
+async def test_runner_fits_empty_response_finalization_before_dispatch(monkeypatch):
+    from nanobot.agent.runner import AgentRunner
+
+    provider = MagicMock(spec=LLMProvider)
+    calls: list[dict] = []
+
+    async def chat_with_retry(*, messages, tools=None, **_kwargs):
+        calls.append({"messages": [dict(message) for message in messages], "tools": tools})
+        if len(calls) < 3:
+            return LLMResponse(
+                content=None,
+                usage=LLMUsage.reported(input_tokens=100, output_tokens=1),
+            )
+        return LLMResponse(
+            content="finalized",
+            usage=LLMUsage.reported(input_tokens=100, output_tokens=10),
+        )
+
+    def estimate(_provider, _model, messages, _tools):
+        contents = [str(message.get("content") or "") for message in messages]
+        has_original = "do task" in contents
+        has_finalization = any("conversation above" in content for content in contents)
+        return (600 if has_original and has_finalization else 100), "test-counter"
+
+    provider.chat_with_retry = chat_with_retry
+    tools = MagicMock()
+    tools.get_definitions.return_value = []
+    monkeypatch.setattr(
+        "nanobot.agent.context_governance.estimate_prompt_tokens_chain",
+        estimate,
+    )
+    monkeypatch.setattr(
+        "nanobot.agent.context_governance.estimate_message_tokens",
+        lambda _message: 300,
+    )
+
+    result = await AgentRunner().run(make_run_spec(
+        provider,
+        initial_messages=[{"role": "user", "content": "do task"}],
+        tools=tools,
+        model="local-model",
+        context_window_tokens=2_000,
+        context_block_limit=500,
+        max_iterations=3,
+        max_tool_result_chars=_MAX_TOOL_RESULT_CHARS,
+    ))
+
+    assert len(calls) == 3
+    assert calls[-1]["tools"] is None
+    assert all(message.get("content") != "do task" for message in calls[-1]["messages"])
+    assert result.final_content == "finalized"
+
+
+@pytest.mark.asyncio
+async def test_runner_fits_max_iteration_finalization_before_dispatch(monkeypatch):
+    from nanobot.agent.runner import AgentRunner
+
+    provider = MagicMock(spec=LLMProvider)
+    calls: list[dict] = []
+    oversized_result = "oversized-current-tool-result"
+
+    async def chat_with_retry(*, messages, tools=None, **_kwargs):
+        calls.append({"messages": [dict(message) for message in messages], "tools": tools})
+        if len(calls) == 1:
+            return LLMResponse(
+                content="working",
+                tool_calls=[ToolCallRequest(id="call_1", name="read_file", arguments={})],
+                finish_reason="tool_calls",
+                usage=LLMUsage.reported(input_tokens=100, output_tokens=10),
+            )
+        return LLMResponse(
+            content="safe summary",
+            usage=LLMUsage.reported(input_tokens=100, output_tokens=10),
+        )
+
+    def estimate(_provider, _model, messages, _tools):
+        has_oversized = any(
+            message.get("content") == oversized_result for message in messages
+        )
+        return (600 if has_oversized else 100), "test-counter"
+
+    provider.chat_with_retry = chat_with_retry
+    tools = MagicMock()
+    tools.get_definitions.return_value = []
+    tools.execute = AsyncMock(return_value=oversized_result)
+    monkeypatch.setattr(
+        "nanobot.agent.context_governance.estimate_prompt_tokens_chain",
+        estimate,
+    )
+    monkeypatch.setattr(
+        "nanobot.agent.context_governance.estimate_message_tokens",
+        lambda message: 600 if message.get("content") == oversized_result else 50,
+    )
+
+    result = await AgentRunner().run(make_run_spec(
+        provider,
+        initial_messages=[{"role": "user", "content": "inspect"}],
+        tools=tools,
+        model="local-model",
+        context_window_tokens=2_000,
+        context_block_limit=500,
+        max_iterations=1,
+        max_tool_result_chars=_MAX_TOOL_RESULT_CHARS,
+    ))
+
+    assert len(calls) == 2
+    assert calls[-1]["tools"] is None
+    assert all(
+        message.get("content") != oversized_result
+        for message in calls[-1]["messages"]
+    )
+    assert any(message.get("content") == oversized_result for message in result.messages)
+    assert result.final_content == "safe summary"
+
+
+@pytest.mark.parametrize(
+    ("input_tokens", "expected_fitted"),
+    [(500, True), (100, False)],
+)
+def test_matching_reported_provider_usage_avoids_local_estimate(
+    monkeypatch,
+    input_tokens,
+    expected_fitted,
+):
+    provider = MagicMock(spec=LLMProvider)
+    tools = MagicMock()
+    tools.get_definitions.return_value = []
+    spec = make_run_spec(
+        provider,
+        initial_messages=[{"role": "user", "content": "hello"}],
+        tools=tools,
+        model="local-model",
+        context_window_tokens=2_000,
+        context_block_limit=500,
+        max_iterations=1,
+        max_tool_result_chars=_MAX_TOOL_RESULT_CHARS,
+    )
+    monkeypatch.setattr(
+        "nanobot.agent.context_governance.estimate_prompt_tokens_chain",
+        lambda *_args, **_kwargs: (_ for _ in ()).throw(
+            AssertionError("matching provider usage must be authoritative")
+        ),
+    )
+
+    governor = ContextGovernor()
+    monkeypatch.setattr(governor, "fit_to_budget", lambda *_args, **_kwargs: [])
+    _messages, fitted = governor.fit_request(
+        _governance_config(provider, tools, spec),
+        spec.initial_messages,
+        LLMUsage.reported(input_tokens=input_tokens, output_tokens=10),
+        usage_matches_messages=True,
+        tool_definitions=tools.get_definitions(),
+    )
+
+    assert fitted is expected_fitted
+
+
+def test_changed_messages_use_local_estimate_after_reported_usage(monkeypatch):
+    provider = MagicMock(spec=LLMProvider)
+    tools = MagicMock()
+    tools.get_definitions.return_value = []
+    spec = make_run_spec(
+        provider,
+        initial_messages=[{"role": "user", "content": "new tool output"}],
+        tools=tools,
+        model="local-model",
+        context_window_tokens=2_000,
+        context_block_limit=500,
+        max_iterations=1,
+        max_tool_result_chars=_MAX_TOOL_RESULT_CHARS,
+    )
+    estimate = MagicMock(return_value=(600, "test-counter"))
+    monkeypatch.setattr(
+        "nanobot.agent.context_governance.estimate_prompt_tokens_chain",
+        estimate,
+    )
+
+    governor = ContextGovernor()
+    monkeypatch.setattr(governor, "fit_to_budget", lambda *_args, **_kwargs: [])
+    _messages, fitted = governor.fit_request(
+        _governance_config(provider, tools, spec),
+        spec.initial_messages,
+        LLMUsage.reported(input_tokens=900, output_tokens=10),
+        usage_matches_messages=False,
+        tool_definitions=tools.get_definitions(),
+    )
+
+    assert fitted is True
+    estimate.assert_called_once()
+
+
+@pytest.mark.asyncio
+async def test_runner_counts_resumed_provider_state_before_dispatch(monkeypatch):
+    from nanobot.agent.runner import AgentRunner
+
+    provider = MagicMock(spec=LLMProvider)
+    provider.can_resume_conversation_state.return_value = True
+    captured_contexts = []
+
+    async def chat_with_retry(*, provider_context=None, **_kwargs):
+        captured_contexts.append(provider_context)
+        return LLMResponse(
+            content="done",
+            usage=LLMUsage.reported(input_tokens=100, output_tokens=10),
+        )
+
+    provider.chat_with_retry = chat_with_retry
+    tools = MagicMock()
+    tools.get_definitions.return_value = []
+    current_message = {"role": "user", "content": "new delta"}
+    saved_state = ProviderConversationState(
+        kind="openai_responses",
+        provider="openai:test",
+        model="local-model",
+        version=1,
+        payload={
+            "items": [{"type": "reasoning", "encrypted_content": "opaque"}],
+            "context_tokens": 450,
+        },
+        pending_messages=[current_message],
+    )
+    monkeypatch.setattr(
+        "nanobot.agent.context_governance.estimate_prompt_tokens_chain",
+        lambda *_args, **_kwargs: (100, "test-counter"),
+    )
+    monkeypatch.setattr(
+        "nanobot.providers.conversation_state.estimate_prompt_tokens_chain",
+        lambda *_args, **_kwargs: (100, "test-counter"),
+    )
+
+    result = await AgentRunner().run(make_run_spec(
+        provider,
+        initial_messages=[current_message],
+        tools=tools,
+        model="local-model",
+        context_window_tokens=2_000,
+        context_block_limit=500,
+        max_iterations=1,
+        max_tool_result_chars=_MAX_TOOL_RESULT_CHARS,
+        provider_state=saved_state,
+    ))
+
+    assert captured_contexts[0].conversation_state is None
+    assert result.messages == [
+        current_message,
+        {"role": "assistant", "content": "done"},
+    ]
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize(
+    ("context_block_limit", "expected_budget"),
+    [(500, 500), (None, 0)],
+)
+async def test_runner_refuses_locally_fitted_request_that_still_cannot_fit(
+    monkeypatch,
+    context_block_limit,
+    expected_budget,
+):
+    from nanobot.agent.runner import AgentRunner
+
+    provider = MagicMock(spec=LLMProvider)
+    provider.chat_with_retry = AsyncMock(return_value=LLMResponse(content="unexpected"))
+    tools = MagicMock()
+    tools.get_definitions.return_value = []
+    monkeypatch.setattr(
+        "nanobot.agent.context_governance.estimate_prompt_tokens_chain",
+        lambda *_args, **_kwargs: (2_000, "test-counter"),
+    )
+
+    with pytest.raises(ContextWindowExceededError) as exc_info:
+        await AgentRunner().run(make_run_spec(
+            provider,
+            initial_messages=[
+                {"role": "system", "content": "oversized system"},
+                {"role": "user", "content": "oversized user"},
+            ],
+            tools=tools,
+            model="local-model",
+            context_window_tokens=1_000,
+            context_block_limit=context_block_limit,
+            max_iterations=1,
+            max_tool_result_chars=_MAX_TOOL_RESULT_CHARS,
+        ))
+
+    assert exc_info.value.estimated_tokens == 2_000
+    assert exc_info.value.input_budget == expected_budget
+    provider.chat_with_retry.assert_not_awaited()
+
+
 def test_snip_history_drops_orphaned_tool_results_from_trimmed_slice(monkeypatch):
     provider = MagicMock()
     tools = MagicMock()
@@ -130,7 +631,11 @@ def test_snip_history_drops_orphaned_tool_results_from_trimmed_slice(monkeypatch
         lambda msg: token_sizes.get(str(msg.get("content")), 40),
     )
 
-    trimmed = ContextGovernor().snip_history(_governance_config(provider, tools, spec), messages)
+    trimmed = ContextGovernor().snip_history(
+        _governance_config(provider, tools, spec),
+        messages,
+        tool_definitions=tools.get_definitions(),
+    )
 
     # After the fix, the user message is recovered so the sequence is valid
     # for providers that require system → user (e.g. GLM error 1214).
@@ -182,7 +687,11 @@ def test_snip_history_reserves_budget_for_tool_definitions(monkeypatch):
         lambda msg: token_sizes.get(str(msg.get("content")), 40),
     )
 
-    trimmed = ContextGovernor().snip_history(_governance_config(provider, tools, spec), messages)
+    trimmed = ContextGovernor().snip_history(
+        _governance_config(provider, tools, spec),
+        messages,
+        tool_definitions=tools.get_definitions(),
+    )
 
     contents = [message.get("content") for message in trimmed]
     assert contents == ["system", "recent two"]
@@ -465,260 +974,6 @@ async def test_runner_backfill_only_mutates_model_context_not_returned_messages(
     ]
 
 
-# ---------------------------------------------------------------------------
-# Microcompact (stale tool result compaction)
-# ---------------------------------------------------------------------------
-
-
-def _microcompact_messages(*, total: int, tool_name: str, content: str) -> list[dict]:
-    messages: list[dict] = [{"role": "system", "content": "sys"}]
-    for i in range(total):
-        messages.append({
-            "role": "assistant",
-            "content": "",
-            "tool_calls": [{
-                "id": f"c{i}",
-                "type": "function",
-                "function": {"name": tool_name, "arguments": "{}"},
-            }],
-        })
-        messages.append({
-            "role": "tool",
-            "tool_call_id": f"c{i}",
-            "name": tool_name,
-            "content": content,
-        })
-    return messages
-
-
-def test_microcompact_skips_when_prompt_under_hard_budget(monkeypatch):
-    """Cache-friendly path: in-flight tool results stay stable while prompt fits."""
-    provider = MagicMock()
-    provider.generation = SimpleNamespace(max_tokens=0)
-    tools = MagicMock()
-    tools.get_definitions.return_value = []
-
-    total = 15
-    long_content = "x" * 600
-    messages = _microcompact_messages(total=total, tool_name="read_file", content=long_content)
-    spec = make_run_spec(provider,
-        initial_messages=messages,
-        tools=tools,
-        model="test-model",
-        max_iterations=1,
-        max_tool_result_chars=_MAX_TOOL_RESULT_CHARS,
-        max_tokens=0,
-        context_window_tokens=20_000,
-    )
-
-    monkeypatch.setattr(
-        "nanobot.agent.context_governance.estimate_prompt_tokens_chain",
-        lambda *_args, **_kwargs: (1000, "test"),
-    )
-
-    result = ContextGovernor().compact_inflight_overflow(
-        _governance_config(provider, tools, spec),
-        messages,
-        set(),
-    )
-
-    assert result is messages
-
-
-def test_microcompact_overflow_compacts_to_low_watermark(monkeypatch):
-    """Overflow path: compact in-flight stale results with headroom for later calls."""
-    provider = MagicMock()
-    provider.generation = SimpleNamespace(max_tokens=0)
-    tools = MagicMock()
-    tools.get_definitions.return_value = []
-
-    total = 18
-    long_content = "x" * 600
-    messages = _microcompact_messages(total=total, tool_name="read_file", content=long_content)
-    spec = make_run_spec(provider,
-        initial_messages=messages,
-        tools=tools,
-        model="test-model",
-        max_iterations=1,
-        max_tool_result_chars=_MAX_TOOL_RESULT_CHARS,
-        max_tokens=0,
-        context_window_tokens=2224,  # input budget 1200, low target 1020
-    )
-
-    def estimate(_provider, _model, msgs, _tools):
-        return sum(
-            100 if (content := msg.get("content")) == long_content
-            else 1 if isinstance(content, str) and "compacted to fit context" in content
-            else 0
-            for msg in msgs
-            if msg.get("role") == "tool"
-        ), "test"
-
-    monkeypatch.setattr("nanobot.agent.context_governance.estimate_prompt_tokens_chain", estimate)
-
-    result = ContextGovernor().compact_inflight_overflow(
-        _governance_config(provider, tools, spec),
-        messages,
-        set(),
-    )
-    tool_msgs = [m for m in result if m.get("role") == "tool"]
-    compacted = [m for m in tool_msgs if "compacted to fit context" in str(m.get("content", ""))]
-    preserved = [m for m in tool_msgs if m.get("content") == long_content]
-
-    assert len(compacted) == 8
-    assert len(preserved) == total - 8
-    assert [m["tool_call_id"] for m in compacted] == [f"c{i}" for i in range(8)]
-
-
-def test_microcompact_compacts_newest_when_it_alone_overflows(monkeypatch):
-    """An unfit newest result tells the model to retry narrowly or report the limit."""
-    provider = MagicMock()
-    provider.generation = SimpleNamespace(max_tokens=0)
-    tools = MagicMock()
-    tools.get_definitions.return_value = []
-
-    long_content = "x" * 600
-    messages = _microcompact_messages(total=1, tool_name="read_file", content=long_content)
-    spec = make_run_spec(provider,
-        initial_messages=messages,
-        tools=tools,
-        model="test-model",
-        max_iterations=1,
-        max_tool_result_chars=_MAX_TOOL_RESULT_CHARS,
-        max_tokens=0,
-        context_window_tokens=2000,
-        context_block_limit=500,
-    )
-
-    def estimate(_provider, _model, msgs, _tools):
-        return sum(
-            1000 if msg.get("content") == long_content else 1
-            for msg in msgs
-            if msg.get("role") == "tool"
-        ), "test"
-
-    monkeypatch.setattr("nanobot.agent.context_governance.estimate_prompt_tokens_chain", estimate)
-
-    compacted_tool_call_ids: set[str] = set()
-    result = ContextGovernor().compact_inflight_overflow(
-        _governance_config(provider, tools, spec),
-        messages,
-        compacted_tool_call_ids,
-    )
-
-    tool_msg = next(m for m in result if m.get("role") == "tool")
-    assert "compacted to fit context" in tool_msg["content"]
-    assert "Do not repeat the same call unchanged" in tool_msg["content"]
-    assert "Retry with a narrower path, query, range, or result limit" in tool_msg["content"]
-    assert "tell the user the task cannot fit" in tool_msg["content"]
-    assert compacted_tool_call_ids == {"c0"}
-
-
-def test_context_governor_keeps_compaction_boundary_stable(monkeypatch):
-    provider = MagicMock()
-    provider.generation = SimpleNamespace(max_tokens=0)
-    tools = MagicMock()
-    tools.get_definitions.return_value = []
-
-    total = 18
-    long_content = "x" * 600
-    messages = _microcompact_messages(total=total, tool_name="read_file", content=long_content)
-    spec = make_run_spec(provider,
-        initial_messages=messages,
-        tools=tools,
-        model="test-model",
-        max_iterations=1,
-        max_tool_result_chars=_MAX_TOOL_RESULT_CHARS,
-        max_tokens=0,
-        context_window_tokens=2224,
-    )
-
-    def estimate(_provider, _model, msgs, _tools):
-        return sum(
-            100 if msg.get("content") == long_content else 1
-            for msg in msgs
-            if msg.get("role") == "tool"
-        ), "test"
-
-    monkeypatch.setattr("nanobot.agent.context_governance.estimate_prompt_tokens_chain", estimate)
-
-    governor = ContextGovernor()
-    compacted_tool_call_ids: set[str] = set()
-    config = _governance_config(provider, tools, spec, inflight_start_index=0)
-    first = governor.compact_inflight_overflow(config, messages, compacted_tool_call_ids)
-    first_ids = set(compacted_tool_call_ids)
-
-    second = governor.compact_inflight_overflow(config, messages, compacted_tool_call_ids)
-
-    assert compacted_tool_call_ids == first_ids
-    assert [m.get("content") for m in second] == [m.get("content") for m in first]
-
-
-def test_microcompact_preserves_short_results(monkeypatch):
-    """Short tool results below the compaction threshold should not be replaced."""
-    provider = MagicMock()
-    provider.generation = SimpleNamespace(max_tokens=0)
-    tools = MagicMock()
-    tools.get_definitions.return_value = []
-
-    total = 15
-    messages = _microcompact_messages(total=total, tool_name="exec", content="short")
-    spec = make_run_spec(provider,
-        initial_messages=messages,
-        tools=tools,
-        model="test-model",
-        max_iterations=1,
-        max_tool_result_chars=_MAX_TOOL_RESULT_CHARS,
-        max_tokens=0,
-        context_window_tokens=2024,
-    )
-
-    monkeypatch.setattr(
-        "nanobot.agent.context_governance.estimate_prompt_tokens_chain",
-        lambda *_args, **_kwargs: (2000, "test"),
-    )
-
-    result = ContextGovernor().compact_inflight_overflow(
-        _governance_config(provider, tools, spec),
-        messages,
-        set(),
-    )
-    assert result is messages  # no copy needed — all stale results are short
-
-
-def test_microcompact_skips_non_compactable_tools(monkeypatch):
-    """Non-compactable tools (e.g. 'message') should never be replaced."""
-    provider = MagicMock()
-    provider.generation = SimpleNamespace(max_tokens=0)
-    tools = MagicMock()
-    tools.get_definitions.return_value = []
-
-    total = 15
-    long_content = "y" * 1000
-    messages = _microcompact_messages(total=total, tool_name="message", content=long_content)
-    spec = make_run_spec(provider,
-        initial_messages=messages,
-        tools=tools,
-        model="test-model",
-        max_iterations=1,
-        max_tool_result_chars=_MAX_TOOL_RESULT_CHARS,
-        max_tokens=0,
-        context_window_tokens=2024,
-    )
-
-    monkeypatch.setattr(
-        "nanobot.agent.context_governance.estimate_prompt_tokens_chain",
-        lambda *_args, **_kwargs: (2000, "test"),
-    )
-
-    result = ContextGovernor().compact_inflight_overflow(
-        _governance_config(provider, tools, spec),
-        messages,
-        set(),
-    )
-    assert result is messages  # no compactable tools found
-
-
 def test_governance_repairs_orphans_after_snip():
     """After snipping clips an assistant+tool_calls, orphan repair cleans up the tail."""
     # Simulate snipping that keeps only the tail: drop the assistant with
@@ -818,7 +1073,11 @@ def test_snip_history_preserves_user_message_after_truncation(monkeypatch):
         lambda msg: token_sizes.get(str(msg.get("content")), 100),
     )
 
-    trimmed = ContextGovernor().snip_history(_governance_config(provider, tools, spec), messages)
+    trimmed = ContextGovernor().snip_history(
+        _governance_config(provider, tools, spec),
+        messages,
+        tool_definitions=tools.get_definitions(),
+    )
 
     # The first non-system message MUST be user (not assistant).
     non_system = [m for m in trimmed if m.get("role") != "system"]
@@ -863,7 +1122,11 @@ def test_snip_history_no_user_at_all_falls_back_gracefully(monkeypatch):
         lambda msg: 100,
     )
 
-    trimmed = ContextGovernor().snip_history(_governance_config(provider, tools, spec), messages)
+    trimmed = ContextGovernor().snip_history(
+        _governance_config(provider, tools, spec),
+        messages,
+        tool_definitions=tools.get_definitions(),
+    )
 
     # Should not crash.  The result should still be a valid list.
     assert isinstance(trimmed, list)
@@ -871,7 +1134,6 @@ def test_snip_history_no_user_at_all_falls_back_gracefully(monkeypatch):
     assert any(m.get("role") == "system" for m in trimmed)
     # The _enforce_role_alternation safety net must be able to fix whatever
     # _snip_history returns here — verify it produces a valid sequence.
-    from nanobot.providers.base import LLMProvider
     fixed = LLMProvider._enforce_role_alternation(trimmed)
     non_system = [m for m in fixed if m["role"] != "system"]
     if non_system:

tests/agent/test_runner_injections.py

@@ -10,6 +10,7 @@ import pytest
 
 from agent.runner_helpers import make_run_spec
 from nanobot.agent.automation_turns import publish_next_deferred_turn
+from nanobot.agent.context import TranscriptInput
 from nanobot.agent.tools.context import RequestContext
 from nanobot.config.schema import AgentDefaults
 from nanobot.providers.base import LLMResponse, ToolCallRequest
@@ -617,7 +618,7 @@ async def test_loop_injected_followup_preserves_image_media(tmp_path):
 
     runtime = loop.llm_runtime()
     result = await loop._run_agent_loop(
-        [{"role": "user", "content": "hello"}],
+        TranscriptInput(history=[{"role": "user", "content": "hello"}], current_message=None),
         runtime=runtime,
         request_context=RequestContext(channel="cli", chat_id="c", runtime=runtime),
         pending_queue=pending_queue,
@@ -711,7 +712,10 @@ async def test_pending_injection_resolves_its_own_runtime_context(tmp_path):
 
     runtime = loop.llm_runtime()
     result = await loop._run_agent_loop(
-        [{"role": "user", "content": "initial message from user A"}],
+        TranscriptInput(
+            history=[{"role": "user", "content": "initial message from user A"}],
+            current_message=None,
+        ),
         runtime=runtime,
         session=session,
         request_context=RequestContext(
@@ -812,7 +816,7 @@ async def test_subagent_pending_injection_is_hidden_history_and_not_merged(tmp_p
 
     runtime = loop.llm_runtime()
     result = await loop._run_agent_loop(
-        [{"role": "user", "content": "hello"}],
+        TranscriptInput(history=[{"role": "user", "content": "hello"}], current_message=None),
         runtime=runtime,
         request_context=RequestContext(channel="cli", chat_id="c", runtime=runtime),
         pending_queue=pending_queue,
@@ -1476,7 +1480,7 @@ async def test_pending_queue_preserves_overflow_for_next_injection_cycle(tmp_pat
 
     runtime = loop.llm_runtime()
     result = await loop._run_agent_loop(
-        [{"role": "user", "content": "hello"}],
+        TranscriptInput(history=[{"role": "user", "content": "hello"}], current_message=None),
         runtime=runtime,
         request_context=RequestContext(channel="cli", chat_id="c", runtime=runtime),
         pending_queue=pending_queue,

tests/agent/test_runner_reasoning.py

@@ -9,6 +9,7 @@ channels, gated by ``context.streamed_reasoning`` rather than
 
 from __future__ import annotations
 
+import asyncio
 from typing import Any
 from unittest.mock import AsyncMock, MagicMock
 
@@ -82,6 +83,18 @@ class _LifecycleRecordingHook(AgentHook):
         self.events.append(f"hosted_tool:{event.get('phase')}")
 
 
+class _BlockingReasoningEndHook(_LifecycleRecordingHook):
+    def __init__(self) -> None:
+        super().__init__()
+        self.reasoning_end_started = asyncio.Event()
+        self.release_reasoning_end = asyncio.Event()
+
+    async def emit_reasoning_end(self) -> None:
+        self.reasoning_end_started.set()
+        await self.release_reasoning_end.wait()
+        await super().emit_reasoning_end()
+
+
 @pytest.mark.asyncio
 async def test_runner_preserves_reasoning_fields_in_assistant_history():
     """Reasoning fields ride along on the persisted assistant message so
@@ -554,6 +567,86 @@ async def test_runner_closes_native_reasoning_before_hosted_tool_event():
     ]
 
 
+@pytest.mark.asyncio
+async def test_runner_closes_native_reasoning_when_stream_is_cancelled():
+    from nanobot.agent.runner import AgentRunner
+
+    provider = MagicMock()
+    reasoning_started = asyncio.Event()
+    release_provider = asyncio.Event()
+
+    async def chat_stream_with_retry(
+        *, on_thinking_delta=None, **kwargs
+    ):
+        if on_thinking_delta:
+            await on_thinking_delta("inspect")
+        reasoning_started.set()
+        await release_provider.wait()
+        raise AssertionError("the cancelled provider call should not complete")
+
+    provider.chat_stream_with_retry = chat_stream_with_retry
+    tools = MagicMock()
+    tools.get_definitions.return_value = []
+    hook = _LifecycleRecordingHook()
+
+    task = asyncio.create_task(AgentRunner().run(make_run_spec(
+        provider,
+        initial_messages=[{"role": "user", "content": "inspect"}],
+        tools=tools,
+        model="test-model",
+        max_iterations=1,
+        max_tool_result_chars=_MAX_TOOL_RESULT_CHARS,
+        hook=hook,
+    )))
+    await reasoning_started.wait()
+
+    task.cancel()
+    with pytest.raises(asyncio.CancelledError):
+        await task
+
+    assert hook.events == ["reasoning:inspect", "reasoning_end"]
+
+
+@pytest.mark.asyncio
+async def test_runner_settles_native_reasoning_end_before_propagating_cancellation():
+    from nanobot.agent.runner import AgentRunner
+
+    provider = MagicMock()
+
+    async def chat_stream_with_retry(
+        *, on_content_delta=None, on_thinking_delta=None, **kwargs
+    ):
+        if on_thinking_delta:
+            await on_thinking_delta("inspect")
+        if on_content_delta:
+            await on_content_delta("done")
+        raise AssertionError("the cancelled provider call should not complete")
+
+    provider.chat_stream_with_retry = chat_stream_with_retry
+    tools = MagicMock()
+    tools.get_definitions.return_value = []
+    hook = _BlockingReasoningEndHook()
+
+    task = asyncio.create_task(AgentRunner().run(make_run_spec(
+        provider,
+        initial_messages=[{"role": "user", "content": "inspect"}],
+        tools=tools,
+        model="test-model",
+        max_iterations=1,
+        max_tool_result_chars=_MAX_TOOL_RESULT_CHARS,
+        hook=hook,
+    )))
+    await hook.reasoning_end_started.wait()
+
+    task.cancel()
+    await asyncio.sleep(0)
+    hook.release_reasoning_end.set()
+    with pytest.raises(asyncio.CancelledError):
+        await task
+
+    assert hook.events == ["reasoning:inspect", "reasoning_end"]
+
+
 @pytest.mark.asyncio
 async def test_runner_strips_thinking_tags_from_native_thinking_deltas():
     from nanobot.agent.runner import AgentRunner

tests/agent/test_session_model_runtime.py

@@ -114,7 +114,7 @@ async def test_removed_session_model_preset_falls_back_and_clears_metadata(tmp_p
         provider=base,
         workspace=tmp_path,
         model="base-model",
-        context_window_tokens=8_000,
+        context_window_tokens=16_000,
     )
     loop.schedule_background = lambda coro: coro.close()  # type: ignore[method-assign]
     session_key = "sdk:removed-preset"
@@ -196,7 +196,7 @@ async def test_sdk_custom_model_preset_metadata_does_not_select_runtime(
         provider=base,
         workspace=tmp_path,
         model="base-model",
-        context_window_tokens=8_000,
+        context_window_tokens=16_000,
     )
     loop.schedule_background = lambda coro: coro.close()  # type: ignore[method-assign]
     bot = Nanobot(loop)

tests/agent/test_task_cancel.py

@@ -42,6 +42,65 @@ def _make_loop(*, tools_config=None):
     return loop, bus
 
 
+class TestActiveTaskTracking:
+    @pytest.mark.asyncio
+    async def test_completed_task_removes_empty_session_group(self):
+        loop, _bus = _make_loop()
+        release = asyncio.Event()
+        task = asyncio.create_task(release.wait())
+
+        loop._track_active_task("test:c1", task)
+        release.set()
+        await task
+        await asyncio.sleep(0)
+
+        assert "test:c1" not in loop._active_tasks
+
+    @pytest.mark.asyncio
+    async def test_session_group_remains_until_last_task_completes(self):
+        loop, _bus = _make_loop()
+        releases = [asyncio.Event(), asyncio.Event()]
+        tasks = [asyncio.create_task(release.wait()) for release in releases]
+        for task in tasks:
+            loop._track_active_task("test:c1", task)
+
+        releases[0].set()
+        await tasks[0]
+        await asyncio.sleep(0)
+
+        assert loop._active_tasks["test:c1"] == {tasks[1]}
+
+        releases[1].set()
+        await tasks[1]
+        await asyncio.sleep(0)
+
+        assert "test:c1" not in loop._active_tasks
+
+    @pytest.mark.asyncio
+    async def test_old_callback_preserves_replacement_session_group(self):
+        loop, _bus = _make_loop()
+        old_release = asyncio.Event()
+        new_release = asyncio.Event()
+        old_task = asyncio.create_task(old_release.wait())
+        new_task = asyncio.create_task(new_release.wait())
+
+        loop._track_active_task("test:c1", old_task)
+        loop._active_tasks.pop("test:c1")
+        loop._track_active_task("test:c1", new_task)
+
+        old_release.set()
+        await old_task
+        await asyncio.sleep(0)
+
+        assert loop._active_tasks["test:c1"] == {new_task}
+
+        new_release.set()
+        await new_task
+        await asyncio.sleep(0)
+
+        assert "test:c1" not in loop._active_tasks
+
+
 class TestHandleStop:
     @pytest.mark.asyncio
     async def test_stop_no_active_task(self):

tests/agent/tools/test_subagent_tools.py

@@ -7,6 +7,7 @@ from unittest.mock import AsyncMock, MagicMock, patch
 
 import pytest
 
+from nanobot.agent.context import TranscriptInput
 from nanobot.agent.tools.context import RequestContext
 from nanobot.config.schema import AgentDefaults
 from nanobot.providers.base import GenerationSettings
@@ -568,7 +569,10 @@ async def test_agent_loop_syncs_updated_max_iterations_before_run(tmp_path):
     loop.runner.run = AsyncMock(side_effect=fake_run)
     loop.max_iterations = 55
 
-    await loop._run_agent_loop([], runtime=loop.llm_runtime())
+    await loop._run_agent_loop(
+        TranscriptInput(history=[], current_message=None),
+        runtime=loop.llm_runtime(),
+    )
 
     loop.runner.run.assert_awaited_once()
 
@@ -609,7 +613,7 @@ async def test_drain_pending_no_block_when_no_subagents(tmp_path):
 
     runtime = loop.llm_runtime()
     await loop._run_agent_loop(
-        [{"role": "user", "content": "test"}],
+        TranscriptInput(history=[{"role": "user", "content": "test"}], current_message=None),
         runtime=runtime,
         session=None,
         request_context=RequestContext(channel="test", chat_id="c1", runtime=runtime),
@@ -668,7 +672,7 @@ async def test_terminal_drain_timeout(tmp_path):
 
     runtime = loop.llm_runtime()
     await loop._run_agent_loop(
-        [{"role": "user", "content": "test"}],
+        TranscriptInput(history=[{"role": "user", "content": "test"}], current_message=None),
         runtime=runtime,
         session=session,
         request_context=RequestContext(
@@ -742,7 +746,7 @@ async def test_terminal_drain_reuses_one_timeout_budget(tmp_path):
     loop.subagents._running_tasks["sub-deadline-1"] = hang_task
 
     await loop._run_agent_loop(
-        [{"role": "user", "content": "test"}],
+        TranscriptInput(history=[{"role": "user", "content": "test"}], current_message=None),
         runtime=loop.llm_runtime(),
         session=session,
         pending_queue=pending_queue,

tests/channels/test_websocket_listener_health.py

@@ -0,0 +1,117 @@
+"""Regression tests for WebSocket listener health probing portability."""
+
+from __future__ import annotations
+
+import errno
+import socket
+from typing import Any
+
+import pytest
+
+from nanobot.channels.websocket.runtime import WebSocketChannel
+
+
+class _StubSocket:
+    """Minimal socket stand-in: real sockets forbid attribute patching."""
+
+    def __init__(self, *, fileno: int, error: OSError | None = None, value: int = 1):
+        self._fileno = fileno
+        self._error = error
+        self._value = value
+
+    def fileno(self) -> int:
+        return self._fileno
+
+    def getsockopt(self, *_args: Any, **_kwargs: Any) -> int:
+        if self._error is not None:
+            raise self._error
+        return self._value
+
+
+class _StubServer:
+    """Minimal server stand-in for the production listener-health boundary."""
+
+    def __init__(self, sock: _StubSocket, *, serving: bool = True):
+        self._sock = sock
+        self._serving = serving
+
+    @property
+    def sockets(self) -> tuple[_StubSocket, ...]:
+        return (self._sock,)
+
+    def is_serving(self) -> bool:
+        return self._serving
+
+
+@pytest.fixture
+def listening_socket() -> socket.socket:
+    sock = socket.socket()
+    sock.bind(("127.0.0.1", 0))
+    sock.listen(1)
+    yield sock
+    sock.close()
+
+
+def test_real_listening_socket_is_accepting(listening_socket: socket.socket) -> None:
+    """A genuinely listening socket must never be reported as degraded.
+
+    On macOS/BSD this exercises the ``ENOPROTOOPT`` fallback path; on Linux it
+    exercises the native ``SO_ACCEPTCONN`` path. Both must agree.
+    """
+    assert WebSocketChannel._socket_is_accepting(listening_socket) is True
+
+
+def test_closed_socket_is_not_accepting() -> None:
+    sock = socket.socket()
+    sock.bind(("127.0.0.1", 0))
+    sock.listen(1)
+    sock.close()
+
+    assert WebSocketChannel._socket_is_accepting(sock) is False
+
+
+@pytest.mark.parametrize(
+    "unsupported_errno",
+    [errno.ENOPROTOOPT, errno.EOPNOTSUPP],
+)
+def test_unsupported_sockopt_falls_back_to_fd_liveness(unsupported_errno: int) -> None:
+    """macOS/BSD reject ``SO_ACCEPTCONN`` even on healthy listeners.
+
+    Treating that rejection as "not serving" made the listener look permanently
+    degraded, so the channel retried forever and never became ready.
+    """
+    sock = _StubSocket(fileno=3, error=OSError(unsupported_errno, "Protocol not available"))
+
+    assert WebSocketChannel._socket_is_accepting(sock) is True
+
+
+def test_listener_health_uses_unsupported_sockopt_fallback() -> None:
+    """The fallback must be wired into the health check that controls readiness."""
+    sock = _StubSocket(fileno=3, error=OSError(errno.ENOPROTOOPT, "Protocol not available"))
+    server: Any = _StubServer(sock)
+
+    assert WebSocketChannel._listener_is_serving(server) is True
+
+
+def test_unexpected_oserror_propagates() -> None:
+    sock = _StubSocket(fileno=3, error=OSError(errno.EBADF, "Bad file descriptor"))
+
+    with pytest.raises(OSError) as excinfo:
+        WebSocketChannel._socket_is_accepting(sock)
+
+    assert excinfo.value.errno == errno.EBADF
+
+
+def test_listener_health_rejects_invalid_socket_state() -> None:
+    """``EINVAL`` can mean that a live socket isn't actually listening."""
+    sock = _StubSocket(fileno=3, error=OSError(errno.EINVAL, "Invalid argument"))
+    server: Any = _StubServer(sock)
+
+    assert WebSocketChannel._listener_is_serving(server) is False
+
+
+def test_unsupported_sockopt_still_rejects_dead_fd() -> None:
+    """The portability fallback must not mask an already-closed listener."""
+    sock = _StubSocket(fileno=-1, error=OSError(errno.ENOPROTOOPT, "Protocol not available"))
+
+    assert WebSocketChannel._socket_is_accepting(sock) is False

tests/cli/test_commands.py

@@ -799,7 +799,7 @@ def test_provider_login_can_set_xai_grok_as_main_provider(tmp_path):
 
     saved = Config.model_validate(json.loads(config_path.read_text(encoding="utf-8")))
     assert saved.agents.defaults.provider == "xai_grok"
-    assert saved.agents.defaults.model == "xai-grok/grok-4.5"
+    assert saved.agents.defaults.model == "xai-grok/grok-4.6"
     assert saved.agents.defaults.context_window_tokens == 500_000
     assert saved.agents.defaults.model_preset is None
     assert make_provider(saved).__class__.__name__ == "XAIGrokProvider"
@@ -2654,12 +2654,14 @@ def test_webui_foreground_attaches_to_existing_managed_gateway(monkeypatch, tmp_
     assert seen["lease_release_wait_for_stop"] is False
 
 
-def test_attach_to_background_gateway_detaches_on_ctrl_c(capsys) -> None:
+def test_attach_to_background_gateway_detaches_on_ctrl_c(capsys, tmp_path: Path) -> None:
     stopped = False
+    log_path = tmp_path / "gateway.log"
+    log_path.touch()
 
     class _FakeRuntime:
         def status(self):
-            return SimpleNamespace(running=True)
+            return SimpleNamespace(running=True, log_path=log_path)
 
         def stop(self):
             nonlocal stopped
@@ -2679,10 +2681,88 @@ def test_attach_to_background_gateway_detaches_on_ctrl_c(capsys) -> None:
     assert "WebUI launcher detached" in rendered
 
 
-def test_attach_to_background_gateway_checks_owned_sidecar() -> None:
+def test_attach_to_background_gateway_follows_only_new_logs(capsys, tmp_path: Path) -> None:
+    log_path = tmp_path / "gateway.log"
+    log_path.write_text("historical log\n", encoding="utf-8")
+    polls = 0
+
     class _FakeRuntime:
         def status(self):
-            return SimpleNamespace(running=True)
+            return SimpleNamespace(running=True, log_path=log_path)
+
+    def _append_then_interrupt(_seconds: float) -> None:
+        nonlocal polls
+        if polls == 0:
+            with log_path.open("a", encoding="utf-8") as handle:
+                handle.write("[websocket] live log\n")
+            polls += 1
+            return
+        raise KeyboardInterrupt
+
+    cli_webui_support._attach_to_background_gateway(
+        _FakeRuntime(),
+        sleep=_append_then_interrupt,
+    )
+
+    output = capsys.readouterr().out
+    assert "[websocket] live log" in output
+    assert "historical log" not in output
+
+
+def test_read_new_gateway_logs_recovers_after_truncation(tmp_path: Path) -> None:
+    log_path = tmp_path / "gateway.log"
+    log_path.write_text("a much longer historical log line\n", encoding="utf-8")
+    cursor = cli_webui_support._start_gateway_log_cursor(log_path)
+    log_path.write_text("fresh log\n", encoding="utf-8")
+
+    lines = cli_webui_support._read_new_gateway_logs(log_path, cursor)
+
+    assert lines == ["fresh log"]
+    assert cursor.offset == log_path.stat().st_size
+
+
+def test_read_new_gateway_logs_detects_fast_rewrite_past_offset(tmp_path: Path) -> None:
+    log_path = tmp_path / "gateway.log"
+    log_path.write_text("historical log\n", encoding="utf-8")
+    cursor = cli_webui_support._start_gateway_log_cursor(log_path)
+    log_path.write_text("first fresh log\nsecond fresh log\n", encoding="utf-8")
+
+    lines = cli_webui_support._read_new_gateway_logs(log_path, cursor)
+
+    assert lines == ["first fresh log", "second fresh log"]
+
+
+def test_read_new_gateway_logs_waits_for_complete_utf8_line(tmp_path: Path) -> None:
+    log_path = tmp_path / "gateway.log"
+    log_path.touch()
+    cursor = cli_webui_support._start_gateway_log_cursor(log_path)
+    encoded = "模型 ready\n".encode()
+    log_path.write_bytes(encoded[:2])
+
+    assert cli_webui_support._read_new_gateway_logs(log_path, cursor) == []
+
+    with log_path.open("ab") as handle:
+        handle.write(encoded[2:])
+
+    assert cli_webui_support._read_new_gateway_logs(log_path, cursor) == ["模型 ready"]
+
+
+def test_read_new_gateway_logs_tolerates_missing_file(tmp_path: Path) -> None:
+    log_path = tmp_path / "missing.log"
+    cursor = cli_webui_support._start_gateway_log_cursor(log_path)
+    lines = cli_webui_support._read_new_gateway_logs(log_path, cursor)
+
+    assert lines == []
+    assert cursor.offset == 0
+
+
+def test_attach_to_background_gateway_checks_owned_sidecar(tmp_path: Path) -> None:
+    log_path = tmp_path / "gateway.log"
+    log_path.touch()
+
+    class _FakeRuntime:
+        def status(self):
+            return SimpleNamespace(running=True, log_path=log_path)
 
     def sidecar_exited() -> None:
         raise WebUIDevError("WebUI development server exited unexpectedly (code 23)")

tests/cli/test_restart_command.py

@@ -11,6 +11,7 @@ from unittest.mock import AsyncMock, MagicMock, patch
 
 import pytest
 
+from nanobot.agent.context import TranscriptInput
 from nanobot.bus.events import InboundMessage
 from nanobot.providers.base import LLMResponse, LLMUsage
 
@@ -311,10 +312,16 @@ class TestRestartCommand:
             LLMResponse(content="second", usage=None),
         ])
 
-        first = await loop._run_agent_loop([], runtime=loop.llm_runtime())
+        first = await loop._run_agent_loop(
+            TranscriptInput(history=[], current_message=None),
+            runtime=loop.llm_runtime(),
+        )
         assert first.usage == LLMUsage.reported(input_tokens=9, output_tokens=4)
 
-        second = await loop._run_agent_loop([], runtime=loop.llm_runtime())
+        second = await loop._run_agent_loop(
+            TranscriptInput(history=[], current_message=None),
+            runtime=loop.llm_runtime(),
+        )
         assert second.usage == LLMUsage.estimated(input_tokens=123, output_tokens=7)
 
     @pytest.mark.asyncio

tests/cron/test_cron_service.py

@@ -7,6 +7,7 @@ import pytest
 
 from nanobot.cron.service import CronJobSkippedError, CronService
 from nanobot.cron.types import CronJob, CronPayload, CronSchedule
+from nanobot.runtime_context import RUNTIME_CONTEXT_INPUT_META
 
 
 async def _wait_until(predicate, *, timeout: float = 1.0, interval: float = 0.01) -> None:
@@ -292,7 +293,12 @@ def test_load_store_migrates_legacy_delivery_context(tmp_path) -> None:
                             "deliver": True,
                             "channel": "telegram",
                             "to": "user-1",
-                            "channelMeta": {"message_thread_id": 42},
+                            "channelMeta": {
+                                "message_thread_id": 42,
+                                RUNTIME_CONTEXT_INPUT_META: [
+                                    {"source": "webui_quote", "content": "stale quote"}
+                                ],
+                            },
                             "sessionKey": "telegram:user-1:topic:42",
                         },
                         "state": {},
@@ -411,6 +417,39 @@ def test_add_job_preserves_origin_delivery_context(tmp_path) -> None:
     assert reloaded.payload.origin_metadata == metadata
 
 
+@pytest.mark.asyncio
+async def test_start_heals_runtime_context_from_pending_external_add(tmp_path) -> None:
+    """Flattened runtime blocks from older action files must not be replayed."""
+    store_path = tmp_path / "cron" / "jobs.json"
+    external = CronService(store_path)
+    job = external.add_job(
+        name="quoted reminder",
+        schedule=CronSchedule(kind="every", every_ms=60_000),
+        message="remember this",
+        origin_metadata={"webui": True},
+        **_bound_chat("quoted"),
+    )
+
+    action_path = tmp_path / "cron" / "action.jsonl"
+    action = json.loads(action_path.read_text(encoding="utf-8"))
+    action["params"]["payload"]["origin_metadata"][RUNTIME_CONTEXT_INPUT_META] = [
+        {"source": "webui_quote", "content": "quoted reply"}
+    ]
+    action_path.write_text(json.dumps(action), encoding="utf-8")
+
+    owner = CronService(store_path)
+    await owner.start()
+    try:
+        loaded = owner.get_job(job.id)
+        assert loaded is not None
+        assert loaded.payload.origin_metadata == {"webui": True}
+
+        raw = json.loads(store_path.read_text(encoding="utf-8"))
+        assert raw["jobs"][0]["payload"]["originMetadata"] == {"webui": True}
+    finally:
+        owner.stop()
+
+
 @pytest.mark.asyncio
 async def test_channel_meta_and_session_key_survive_store_reload(tmp_path) -> None:
     store_path = tmp_path / "cron" / "jobs.json"

tests/providers/test_conversation_state.py

@@ -146,6 +146,51 @@ def test_controller_uses_governed_messages_for_provider_state_delta() -> None:
     assert governed_checkpoint.pending_messages[-1]["content"] == "compacted result"
 
 
+def test_controller_estimates_active_state_plus_pending_delta(monkeypatch) -> None:
+    provider = _provider()
+    current_message = {"role": "user", "content": "new delta"}
+    state = ProviderConversationState(
+        kind="openai_responses",
+        provider="openai:test",
+        model="gpt-5.6",
+        version=1,
+        payload={
+            "items": [{"type": "reasoning", "encrypted_content": "opaque"}],
+            "context_tokens": 450,
+        },
+        pending_messages=[current_message],
+    )
+    controller = ProviderConversationStateController(
+        provider=provider,
+        model="gpt-5.6",
+        messages=[current_message],
+        state=state,
+    )
+    seen = {}
+
+    def estimate(_provider, _model, messages, tools):
+        seen["messages"] = messages
+        seen["tools"] = tools
+        return 100, "test-counter"
+
+    monkeypatch.setattr(
+        "nanobot.providers.conversation_state.estimate_prompt_tokens_chain",
+        estimate,
+    )
+
+    tokens = controller.estimate_request_context_tokens(
+        [current_message],
+        model_messages=[current_message],
+        tool_definitions=[{"type": "web_search"}],
+    )
+
+    assert tokens == 550
+    assert seen == {
+        "messages": [current_message],
+        "tools": [{"type": "web_search"}],
+    }
+
+
 def test_transient_response_preserves_only_durable_request_messages() -> None:
     provider = _provider()
     current_message = {"role": "user", "content": "continue"}

tests/providers/test_enforce_role_alternation.py

@@ -112,14 +112,49 @@ class TestEnforceRoleAlternation:
         assert result[1]["content"] is None
         assert result[2]["role"] == "tool"
 
-    def test_non_string_content_uses_latest(self):
+    def test_consecutive_user_messages_preserve_text_before_multimodal_content(self):
+        image = {
+            "type": "image_url",
+            "image_url": {"url": "data:image/png;base64,aW1hZ2U="},
+        }
         msgs = [
-            {"role": "user", "content": [{"type": "text", "text": "A"}]},
+            {"role": "user", "content": "Earlier unanswered question"},
-            {"role": "user", "content": "B"},
+            {
+                "role": "user",
+                "content": [image, {"type": "text", "text": "The error is here"}],
+            },
         ]
         result = LLMProvider._enforce_role_alternation(msgs)
-        assert len(result) == 1
+        assert result == [{
-        assert result[0]["content"] == "B"
+            "role": "user",
+            "content": [
+                {"type": "text", "text": "Earlier unanswered question"},
+                image,
+                {"type": "text", "text": "The error is here"},
+            ],
+        }]
+
+    def test_consecutive_user_messages_preserve_multimodal_content_before_text(self):
+        image = {
+            "type": "image_url",
+            "image_url": {"url": "data:image/png;base64,aW1hZ2U="},
+        }
+        msgs = [
+            {
+                "role": "user",
+                "content": [image, {"type": "text", "text": "First question"}],
+            },
+            {"role": "user", "content": "Follow-up detail"},
+        ]
+        result = LLMProvider._enforce_role_alternation(msgs)
+        assert result == [{
+            "role": "user",
+            "content": [
+                image,
+                {"type": "text", "text": "First question"},
+                {"type": "text", "text": "Follow-up detail"},
+            ],
+        }]
 
     def test_original_messages_not_mutated(self):
         msgs = [

tests/providers/test_oauth_model_catalog.py

@@ -0,0 +1,504 @@
+from __future__ import annotations
+
+import base64
+import json
+import threading
+import time
+from concurrent.futures import ThreadPoolExecutor
+from pathlib import Path
+from types import SimpleNamespace
+
+import httpx
+import pytest
+
+from nanobot.providers.oauth_model_catalog import (
+    OAuthModelCatalog,
+    get_oauth_model_catalog,
+    invalidate_oauth_model_catalog,
+)
+from nanobot.providers.openai_codex_provider import (
+    DEFAULT_OPENAI_CODEX_MODELS_URL,
+    OPENAI_CODEX_CATALOG_CLIENT_VERSION,
+)
+from nanobot.providers.registry import ProviderModelSpec
+from nanobot.providers.xai_grok_provider import DEFAULT_XAI_GROK_MODELS_URL
+from nanobot.providers.xai_oauth import XAIToken
+
+
+@pytest.fixture(autouse=True)
+def _clear_oauth_catalogs() -> None:
+    for provider in ("openai_codex", "xai_grok", "github_copilot"):
+        invalidate_oauth_model_catalog(provider)
+    yield
+    for provider in ("openai_codex", "xai_grok", "github_copilot"):
+        invalidate_oauth_model_catalog(provider)
+
+
+def _fallback_model() -> ProviderModelSpec:
+    return ProviderModelSpec(id="provider/fallback", label="Fallback")
+
+
+def test_xai_catalog_fetches_remote_models_and_reuses_capability_metadata(
+    monkeypatch: pytest.MonkeyPatch,
+    tmp_path: Path,
+) -> None:
+    original_client = httpx.Client
+    captured: dict[str, object] = {}
+    payload = (
+        base64.urlsafe_b64encode(
+            json.dumps({"sub": "user-42", "email": "user@example.com"}).encode()
+        )
+        .decode()
+        .rstrip("=")
+    )
+    token = XAIToken(
+        access=f"header.{payload}.signature",
+        refresh="refresh-token",
+        expires=int(time.time() * 1000) + 3_600_000,
+        account_id="user@example.com",
+    )
+
+    def handler(request: httpx.Request) -> httpx.Response:
+        captured["request"] = request
+        return httpx.Response(
+            200,
+            json={
+                "data": [
+                    {
+                        "id": "grok-4.6",
+                        "name": "Grok 4.6",
+                        "description": "Latest frontier model",
+                        "owned_by": "xAI",
+                        "context_window": 500_000,
+                        "supports_backend_search": True,
+                        "reasoning_efforts": [
+                            {"value": "xhigh"},
+                            {"value": "high"},
+                            {"value": "low"},
+                        ],
+                    },
+                    {
+                        "id": "grok-next",
+                        "_meta": {
+                            "name": "Grok Next",
+                            "context_window": 750_000,
+                            "reasoning_efforts": ["high", "low"],
+                        },
+                    },
+                ]
+            },
+            request=request,
+        )
+
+    def fake_client(**kwargs: object) -> httpx.Client:
+        captured["kwargs"] = kwargs
+        return original_client(
+            transport=httpx.MockTransport(handler),
+            timeout=kwargs["timeout"],
+            follow_redirects=kwargs["follow_redirects"],
+        )
+
+    monkeypatch.setattr(
+        "nanobot.providers.xai_grok_provider.get_xai_oauth_storage_path",
+        lambda: tmp_path / "auth" / "xai.json",
+    )
+    monkeypatch.setattr(
+        "nanobot.providers.xai_grok_provider.get_xai_oauth_login_status",
+        lambda: token,
+    )
+    monkeypatch.setattr(
+        "nanobot.providers.xai_grok_provider.get_xai_oauth_token",
+        lambda **_kwargs: token,
+    )
+    monkeypatch.setattr("nanobot.providers.xai_grok_provider.httpx.Client", fake_client)
+
+    catalog = get_oauth_model_catalog("xai_grok")
+
+    assert catalog.source == "remote"
+    assert [model.id for model in catalog.models] == [
+        "xai-grok/grok-4.6",
+        "xai-grok/grok-next",
+    ]
+    grok = catalog.find("grok-4.6")
+    assert grok is not None
+    assert grok.description == "Latest frontier model"
+    assert grok.context_window == 500_000
+    assert grok.reasoning_efforts == ("xhigh", "high", "low")
+    assert grok.supports_backend_search is True
+    next_model = catalog.find("xai-grok/grok-next")
+    assert next_model is not None
+    assert next_model.label == "Grok Next"
+    assert next_model.context_window == 750_000
+    assert next_model.reasoning_efforts == ("high", "low")
+
+    request = captured["request"]
+    assert isinstance(request, httpx.Request)
+    assert str(request.url) == DEFAULT_XAI_GROK_MODELS_URL
+    assert request.headers["Authorization"] == f"Bearer {token.access}"
+    assert request.headers["X-XAI-Token-Auth"] == "xai-grok-cli"
+    assert request.headers["x-userid"] == "user-42"
+    assert request.headers["x-email"] == "user@example.com"
+    assert captured["kwargs"] == {"timeout": 10.0, "follow_redirects": False}
+    assert get_oauth_model_catalog("xai_grok").source == "cache"
+
+
+def test_openai_codex_catalog_uses_account_catalog_and_filters_hidden_models(
+    monkeypatch: pytest.MonkeyPatch,
+    tmp_path: Path,
+) -> None:
+    original_client = httpx.Client
+    captured: dict[str, object] = {}
+
+    def handler(request: httpx.Request) -> httpx.Response:
+        captured["request"] = request
+        return httpx.Response(
+            200,
+            json={
+                "models": [
+                    {
+                        "slug": "gpt-new",
+                        "display_name": "GPT New",
+                        "description": "New model",
+                        "context_window": 300_000,
+                        "priority": 2,
+                        "visibility": "list",
+                        "supported_reasoning_levels": [
+                            {"effort": "low"},
+                            {"effort": "high"},
+                        ],
+                    },
+                    {
+                        "slug": "gpt-first",
+                        "display_name": "GPT First",
+                        "priority": 1,
+                    },
+                    {
+                        "slug": "internal-model",
+                        "display_name": "Internal",
+                        "visibility": "hide",
+                        "priority": 0,
+                    },
+                ]
+            },
+            request=request,
+        )
+
+    def fake_client(**kwargs: object) -> httpx.Client:
+        captured["kwargs"] = kwargs
+        return original_client(
+            transport=httpx.MockTransport(handler),
+            timeout=kwargs["timeout"],
+            follow_redirects=kwargs["follow_redirects"],
+        )
+
+    class Storage:
+        def load(self) -> SimpleNamespace:
+            return SimpleNamespace(access="secret", account_id="account-42")
+
+        def get_token_path(self) -> Path:
+            return tmp_path / "auth" / "openai-codex.json"
+
+    monkeypatch.setattr(
+        "nanobot.providers.openai_codex_provider.FileTokenStorage",
+        lambda **_kwargs: Storage(),
+    )
+    monkeypatch.setattr(
+        "nanobot.providers.openai_codex_provider.get_codex_token",
+        lambda **_kwargs: SimpleNamespace(access="secret", account_id="account-42"),
+    )
+    monkeypatch.setattr("nanobot.providers.openai_codex_provider.httpx.Client", fake_client)
+
+    catalog = get_oauth_model_catalog("openai_codex")
+
+    assert catalog.source == "remote"
+    assert [model.id for model in catalog.models] == [
+        "openai-codex/gpt-first",
+        "openai-codex/gpt-new",
+    ]
+    assert catalog.models[1].context_window == 300_000
+    assert catalog.models[1].reasoning_efforts == ("low", "high")
+    request = captured["request"]
+    assert isinstance(request, httpx.Request)
+    assert request.url.copy_with(query=None) == httpx.URL(DEFAULT_OPENAI_CODEX_MODELS_URL)
+    assert request.url.params["client_version"] == OPENAI_CODEX_CATALOG_CLIENT_VERSION
+    assert request.headers["Authorization"] == "Bearer secret"
+    assert request.headers["chatgpt-account-id"] == "account-42"
+
+
+def test_github_copilot_catalog_only_lists_compatible_chat_models(
+    monkeypatch: pytest.MonkeyPatch,
+    tmp_path: Path,
+) -> None:
+    original_client = httpx.Client
+    captured: list[httpx.Request] = []
+
+    def handler(request: httpx.Request) -> httpx.Response:
+        captured.append(request)
+        if request.url.path.endswith("/copilot_internal/v2/token"):
+            return httpx.Response(
+                200,
+                json={
+                    "token": "copilot-secret",
+                    "endpoints": {"api": "https://api.individual.githubcopilot.com"},
+                },
+                request=request,
+            )
+        return httpx.Response(
+            200,
+            json={
+                "data": [
+                    {
+                        "id": "claude-sonnet",
+                        "name": "Claude Sonnet",
+                        "model_picker_enabled": True,
+                        "policy": {"state": "enabled"},
+                        "supported_endpoints": ["/chat/completions"],
+                        "capabilities": {
+                            "supports": {"reasoning_effort": ["low", "high"]},
+                            "limits": {"max_context_window_tokens": 200_000},
+                        },
+                    },
+                    {
+                        "id": "gpt-5.4-mini",
+                        "name": "GPT-5.4 Mini",
+                        "model_picker_enabled": True,
+                        "supported_endpoints": ["/responses"],
+                    },
+                    {
+                        "id": "unknown-responses-only",
+                        "name": "Unknown Responses only",
+                        "model_picker_enabled": True,
+                        "supported_endpoints": ["/responses"],
+                    },
+                    {
+                        "id": "disabled",
+                        "model_picker_enabled": True,
+                        "policy": {"state": "disabled"},
+                        "supported_endpoints": ["/chat/completions"],
+                    },
+                ]
+            },
+            request=request,
+        )
+
+    def fake_client(**kwargs: object) -> httpx.Client:
+        return original_client(
+            transport=httpx.MockTransport(handler),
+            timeout=kwargs["timeout"],
+            follow_redirects=kwargs["follow_redirects"],
+        )
+
+    class Storage:
+        def load(self) -> SimpleNamespace:
+            return SimpleNamespace(access="github-secret", account_id="octocat")
+
+        def get_token_path(self) -> Path:
+            return tmp_path / "auth" / "github-copilot.json"
+
+    monkeypatch.setattr(
+        "nanobot.providers.github_copilot_provider.get_storage",
+        lambda: Storage(),
+    )
+    monkeypatch.setattr("nanobot.providers.github_copilot_provider.httpx.Client", fake_client)
+
+    catalog = get_oauth_model_catalog("github_copilot")
+
+    assert catalog.source == "remote"
+    assert [model.id for model in catalog.models] == [
+        "github-copilot/claude-sonnet",
+        "github-copilot/gpt-5.4-mini",
+    ]
+    assert catalog.models[0].context_window == 200_000
+    assert catalog.models[0].reasoning_efforts == ("low", "high")
+    assert len(captured) == 2
+    assert captured[0].headers["Authorization"] == "token github-secret"
+    assert captured[1].headers["Authorization"] == "Bearer copilot-secret"
+    assert str(captured[1].url) == "https://api.individual.githubcopilot.com/models"
+    assert get_oauth_model_catalog("github_copilot").source == "cache"
+    assert get_oauth_model_catalog(
+        "github_copilot",
+        proxy="http://proxy.example:8080",
+    ).source == "remote"
+    assert len(captured) == 4
+
+
+def test_catalog_single_flights_concurrent_refreshes() -> None:
+    calls = 0
+    calls_lock = threading.Lock()
+    barrier = threading.Barrier(8)
+
+    def fetch(_proxy: str | None) -> tuple[ProviderModelSpec, ...]:
+        nonlocal calls
+        with calls_lock:
+            calls += 1
+        time.sleep(0.05)
+        return (ProviderModelSpec(id="provider/remote", label="Remote"),)
+
+    catalog = OAuthModelCatalog(fallback_models=(_fallback_model(),), fetch=fetch)
+
+    def get_catalog(_index: int):
+        barrier.wait()
+        return catalog.get(cache_key="shared")
+
+    with ThreadPoolExecutor(max_workers=8) as pool:
+        results = list(pool.map(get_catalog, range(8)))
+
+    assert calls == 1
+    assert {result.models[0].id for result in results} == {"provider/remote"}
+    assert [result.source for result in results].count("remote") == 1
+    assert [result.source for result in results].count("cache") == 7
+
+
+def test_catalog_invalidation_discards_an_inflight_account_refresh() -> None:
+    started = threading.Event()
+    release = threading.Event()
+    identity = ["old-account"]
+
+    def fetch(_proxy: str | None) -> tuple[ProviderModelSpec, ...]:
+        current = identity[0]
+        if current == "old-account":
+            started.set()
+            assert release.wait(timeout=2)
+        return (ProviderModelSpec(id=f"provider/{current}", label=current),)
+
+    catalog = OAuthModelCatalog(fallback_models=(_fallback_model(),), fetch=fetch)
+    with ThreadPoolExecutor(max_workers=2) as pool:
+        old_future = pool.submit(catalog.get, cache_key="old-key")
+        assert started.wait(timeout=2)
+        identity[0] = "new-account"
+        catalog.invalidate()
+        new_future = pool.submit(catalog.get, cache_key="new-key")
+        new_result = new_future.result(timeout=2)
+        release.set()
+        old_result = old_future.result(timeout=2)
+
+    assert old_result.source == "fallback"
+    assert new_result.models[0].id == "provider/new-account"
+
+    identity[0] = "old-account"
+    assert catalog.get(cache_key="old-key").models[0].id == "provider/old-account"
+
+
+def test_catalog_bounds_failure_only_keys() -> None:
+    calls = 0
+
+    def fetch(_proxy: str | None) -> tuple[ProviderModelSpec, ...]:
+        nonlocal calls
+        calls += 1
+        raise httpx.ConnectError("offline")
+
+    catalog = OAuthModelCatalog(
+        fallback_models=(_fallback_model(),),
+        fetch=fetch,
+        max_entries=2,
+    )
+
+    for key in ("one", "two", "three"):
+        assert catalog.get(cache_key=key).source == "fallback"
+
+    assert calls == 3
+    assert catalog.get(cache_key="one").source == "fallback"
+    assert calls == 4
+
+
+def test_catalog_returns_stale_then_negative_caches_refresh_failure() -> None:
+    now = [0.0]
+    calls = 0
+
+    def fetch(_proxy: str | None) -> tuple[ProviderModelSpec, ...]:
+        nonlocal calls
+        calls += 1
+        if calls > 1:
+            raise httpx.ConnectError("offline")
+        return (ProviderModelSpec(id="provider/remote", label="Remote"),)
+
+    catalog = OAuthModelCatalog(
+        fallback_models=(_fallback_model(),),
+        fetch=fetch,
+        fresh_ttl_s=10,
+        stale_ttl_s=100,
+        failure_ttl_s=30,
+        monotonic=lambda: now[0],
+        wall_clock=lambda: 123.0,
+    )
+
+    assert catalog.get(cache_key="one").source == "remote"
+    now[0] = 11
+    stale = catalog.get(cache_key="one")
+    assert stale.source == "stale"
+    assert stale.models[0].id == "provider/remote"
+    assert catalog.get(cache_key="one").source == "stale"
+    assert calls == 2
+
+    now[0] = 101
+    fallback = catalog.get(cache_key="one")
+    assert fallback.source == "fallback"
+    assert fallback.models[0].id == "provider/fallback"
+    assert calls == 3
+
+
+@pytest.mark.parametrize(
+    "failure",
+    [
+        httpx.ConnectError("offline"),
+        ValueError("invalid JSON"),
+        httpx.HTTPStatusError(
+            "unauthorized",
+            request=httpx.Request("GET", DEFAULT_XAI_GROK_MODELS_URL),
+            response=httpx.Response(401),
+        ),
+        httpx.HTTPStatusError(
+            "rate limited",
+            request=httpx.Request("GET", DEFAULT_XAI_GROK_MODELS_URL),
+            response=httpx.Response(429),
+        ),
+        httpx.HTTPStatusError(
+            "upstream failure",
+            request=httpx.Request("GET", DEFAULT_XAI_GROK_MODELS_URL),
+            response=httpx.Response(503),
+        ),
+    ],
+)
+def test_catalog_falls_back_for_remote_failures(failure: Exception) -> None:
+    calls = 0
+
+    def fetch(_proxy: str | None) -> tuple[ProviderModelSpec, ...]:
+        nonlocal calls
+        calls += 1
+        raise failure
+
+    catalog = OAuthModelCatalog(
+        fallback_models=(_fallback_model(),),
+        fetch=fetch,
+        failure_ttl_s=30,
+    )
+
+    first = catalog.get(cache_key="one")
+    second = catalog.get(cache_key="one")
+
+    assert first.source == "fallback"
+    assert second.source == "fallback"
+    assert first.models == (_fallback_model(),)
+    assert calls == 1
+
+
+def test_catalog_treats_empty_remote_list_as_failure_and_can_be_invalidated() -> None:
+    calls = 0
+
+    def fetch(_proxy: str | None) -> tuple[ProviderModelSpec, ...]:
+        nonlocal calls
+        calls += 1
+        return () if calls == 1 else (ProviderModelSpec(id="provider/new", label="New"),)
+
+    catalog = OAuthModelCatalog(
+        fallback_models=(_fallback_model(),),
+        fetch=fetch,
+        failure_ttl_s=30,
+    )
+
+    assert catalog.get(cache_key="one").source == "fallback"
+    catalog.invalidate()
+    refreshed = catalog.get(cache_key="one")
+    assert refreshed.source == "remote"
+    assert refreshed.models[0].id == "provider/new"
+    assert calls == 2

tests/providers/test_xai_grok_provider.py

@@ -1,6 +1,5 @@
 from __future__ import annotations
 
-import base64
 import json
 import time
 from types import SimpleNamespace
@@ -12,21 +11,19 @@ import pytest
 from nanobot.config.schema import Config
 from nanobot.providers.base import LLMUsage
 from nanobot.providers.factory import make_provider
-from nanobot.providers.registry import find_by_name
+from nanobot.providers.oauth_model_catalog import OAuthModelCatalogSnapshot
+from nanobot.providers.registry import ProviderModelSpec, find_by_name
 from nanobot.providers.xai_grok_provider import (
     DEFAULT_XAI_GROK_MODEL,
-    DEFAULT_XAI_GROK_MODELS_URL,
     XAIGrokProvider,
     _bounded_error_body,
     _build_headers,
-    _build_model_headers,
     _build_reasoning_options,
     _build_xai_http_error,
-    _fetch_xai_model_capabilities,
-    _parse_xai_model_capabilities,
     _request_xai,
     _xai_error_response,
     _XAIHTTPError,
+    _XAIIncompleteHostedToolError,
 )
 
 
@@ -51,22 +48,41 @@ def _mock_model_capabilities(
     *,
     supports_backend_search: bool,
 ) -> None:
-    async def fake_fetch(*_args, **_kwargs):
+    def fake_catalog(*_args, **_kwargs):
-        return {"grok-4.5": supports_backend_search}
+        return OAuthModelCatalogSnapshot(
+            models=(
+                ProviderModelSpec(
+                    id="xai-grok/grok-4.5",
+                    label="Grok 4.5",
+                    supports_backend_search=supports_backend_search,
+                ),
+                ProviderModelSpec(
+                    id="xai-grok/grok-4.6",
+                    label="Grok 4.6",
+                    supports_backend_search=supports_backend_search,
+                ),
+            ),
+            source="remote",
+            fetched_at=1,
+        )
 
     monkeypatch.setattr(
-        "nanobot.providers.xai_grok_provider._fetch_xai_model_capabilities",
+        "nanobot.providers.xai_grok_provider.get_xai_grok_model_catalog",
-        fake_fetch,
+        fake_catalog,
     )
 
 
-def test_xai_grok_registry_exposes_curated_x_search_model() -> None:
+def test_xai_grok_registry_exposes_curated_x_search_models() -> None:
     spec = find_by_name("xai_grok")
 
     assert spec is not None
     assert spec.is_oauth is True
     assert spec.backend == "xai_grok"
     assert spec.builtin_models[0].id == DEFAULT_XAI_GROK_MODEL
+    assert [model.id for model in spec.builtin_models] == [
+        "xai-grok/grok-4.6",
+        "xai-grok/grok-4.5",
+    ]
     assert spec.builtin_models[0].context_window == 500000
     assert "when supported" in spec.builtin_models[0].description
 
@@ -117,7 +133,7 @@ async def test_provider_injects_hosted_x_search_and_required_proxy_headers(monke
     assert response.content == "answer [[1]](https://x.com/example/status/1)"
     url, headers, body = calls[0]
     assert url == "https://cli-chat-proxy.grok.com/v1/responses"
-    assert body["model"] == "grok-4.5"
+    assert body["model"] == "grok-4.6"
     assert body["tools"] == [
         {
             "type": "function",
@@ -132,12 +148,13 @@ async def test_provider_injects_hosted_x_search_and_required_proxy_headers(monke
     assert body["stream_tool_calls"] is True
     assert body["reasoning"] == {"summary": "concise", "effort": "high"}
     assert body["store"] is False
+    assert body["max_turns"] == 5
     assert headers["Authorization"] == "Bearer subscription-token"
     assert headers["X-XAI-Token-Auth"] == "xai-grok-cli"
     assert headers["x-authenticateresponse"] == "authenticate-response"
     assert headers["x-grok-client-identifier"] == "nanobot"
     assert headers["x-grok-client-mode"] == "headless"
-    assert headers["x-grok-model-override"] == "grok-4.5"
+    assert headers["x-grok-model-override"] == "grok-4.6"
 
 
 @pytest.mark.asyncio
@@ -147,7 +164,7 @@ async def test_explicit_parameterized_x_search_is_preserved_without_catalog_look
     _mock_token(monkeypatch)
     bodies: list[dict[str, Any]] = []
 
-    async def unexpected_catalog_lookup(*_args, **_kwargs):
+    def unexpected_catalog_lookup(*_args, **_kwargs):
         raise AssertionError("explicit raw tools must not depend on model catalog metadata")
 
     async def fake_request(_url, _headers, body, **_kwargs):
@@ -155,7 +172,7 @@ async def test_explicit_parameterized_x_search_is_preserved_without_catalog_look
         return "ok", [], "stop", {}, None
 
     monkeypatch.setattr(
-        "nanobot.providers.xai_grok_provider._fetch_xai_model_capabilities",
+        "nanobot.providers.xai_grok_provider.get_xai_grok_model_catalog",
         unexpected_catalog_lookup,
     )
     monkeypatch.setattr("nanobot.providers.xai_grok_provider._request_xai", fake_request)
@@ -164,10 +181,12 @@ async def test_explicit_parameterized_x_search_is_preserved_without_catalog_look
         "allowed_x_handles": ["nanobot_ai"],
         "from_date": "2026-01-01",
     }
-    provider = XAIGrokProvider(extra_body={
+    provider = XAIGrokProvider(
-        "parallel_tool_calls": False,
+        extra_body={
-        "tools": [hosted_tool, {"type": "code_interpreter", "container": "auto"}],
+            "parallel_tool_calls": False,
-    })
+            "tools": [hosted_tool, {"type": "code_interpreter", "container": "auto"}],
+        }
+    )
 
     response = await provider.chat(
         [{"role": "user", "content": "search"}],
@@ -210,7 +229,7 @@ async def test_explicit_empty_tools_disables_catalog_lookup_and_hosted_tool(monk
     _mock_token(monkeypatch)
     bodies: list[dict[str, Any]] = []
 
-    async def unexpected_catalog_lookup(*_args, **_kwargs):
+    def unexpected_catalog_lookup(*_args, **_kwargs):
         raise AssertionError("explicitly disabled X Search must not fetch model capabilities")
 
     async def fake_request(_url, _headers, body, **_kwargs):
@@ -218,7 +237,7 @@ async def test_explicit_empty_tools_disables_catalog_lookup_and_hosted_tool(monk
         return "ok", [], "stop", {}, None
 
     monkeypatch.setattr(
-        "nanobot.providers.xai_grok_provider._fetch_xai_model_capabilities",
+        "nanobot.providers.xai_grok_provider.get_xai_grok_model_catalog",
         unexpected_catalog_lookup,
     )
     monkeypatch.setattr("nanobot.providers.xai_grok_provider._request_xai", fake_request)
@@ -226,23 +245,28 @@ async def test_explicit_empty_tools_disables_catalog_lookup_and_hosted_tool(monk
 
     response = await provider.chat(
         [{"role": "user", "content": "hello"}],
-        tools=[{
+        tools=[
-            "type": "function",
+            {
-            "function": {
+                "type": "function",
-                "name": "read_file",
+                "function": {
-                "description": "Read a file",
+                    "name": "read_file",
-                "parameters": {"type": "object"},
+                    "description": "Read a file",
-            },
+                    "parameters": {"type": "object"},
-        }],
+                },
+            }
+        ],
     )
 
     assert response.content == "ok"
-    assert bodies[0]["tools"] == [{
+    assert bodies[0]["tools"] == [
-        "type": "function",
+        {
-        "name": "read_file",
+            "type": "function",
-        "description": "Read a file",
+            "name": "read_file",
-        "parameters": {"type": "object"},
+            "description": "Read a file",
-    }]
+            "parameters": {"type": "object"},
+        }
+    ]
+    assert "max_turns" not in bodies[0]
 
 
 @pytest.mark.asyncio
@@ -281,35 +305,8 @@ async def test_provider_keeps_local_x_search_when_model_does_not_support_hosted_
             "parameters": {"type": "object"},
         }
     ]
-
+    assert "max_turns" not in bodies[0]
-
+    assert bodies[0]["instructions"] == ""
-@pytest.mark.asyncio
-async def test_provider_fails_closed_and_caches_model_catalog_failure(monkeypatch) -> None:
-    _mock_token(monkeypatch)
-    fetch_calls = 0
-    bodies: list[dict[str, Any]] = []
-
-    async def failing_fetch(*_args, **_kwargs):
-        nonlocal fetch_calls
-        fetch_calls += 1
-        raise httpx.ConnectError("catalog unavailable")
-
-    async def fake_request(_url, _headers, body, **_kwargs):
-        bodies.append(body)
-        return "ok", [], "stop", {}, None
-
-    monkeypatch.setattr(
-        "nanobot.providers.xai_grok_provider._fetch_xai_model_capabilities",
-        failing_fetch,
-    )
-    monkeypatch.setattr("nanobot.providers.xai_grok_provider._request_xai", fake_request)
-    provider = XAIGrokProvider()
-
-    await provider.chat([{"role": "user", "content": "first"}])
-    await provider.chat([{"role": "user", "content": "second"}])
-
-    assert fetch_calls == 1
-    assert all({"type": "x_search"} not in body["tools"] for body in bodies)
 
 
 @pytest.mark.asyncio
@@ -395,7 +392,10 @@ async def test_factory_builds_xai_provider_and_applies_explicit_body_overrides(m
             "providers": {
                 "xaiGrok": {
                     "proxy": "http://127.0.0.1:7890",
-                    "extraBody": {"parallel_tool_calls": False},
+                    "extraBody": {
+                        "parallel_tool_calls": False,
+                        "max_turns": 2,
+                    },
                 }
             },
         }
@@ -408,6 +408,7 @@ async def test_factory_builds_xai_provider_and_applies_explicit_body_overrides(m
     assert provider.proxy == "http://127.0.0.1:7890"
     assert response.content == "ok"
     assert bodies[0]["parallel_tool_calls"] is False
+    assert bodies[0]["max_turns"] == 2
     assert {"type": "x_search"} in bodies[0]["tools"]
 
 
@@ -527,75 +528,183 @@ async def test_raw_response_request_streams_hosted_x_search_lifecycle(monkeypatc
     assert "large hosted result" not in json.dumps(tool_events)
 
 
-def test_model_capabilities_follow_upstream_aliases_and_default_to_disabled() -> None:
-    capabilities = _parse_xai_model_capabilities(
-        {
-            "data": [
-                {"id": "grok-4.5", "supportsBackendSearch": False},
-                {
-                    "model": "grok-search",
-                    "supports_backend_search": True,
-                },
-                {
-                    "modelId": "grok-meta",
-                    "_meta": {"supportsBackendSearch": True},
-                },
-                {"id": "grok-unknown"},
-            ]
-        }
-    )
-
-    assert capabilities == {
-        "grok-4.5": False,
-        "grok-search": True,
-        "grok-meta": True,
-        "grok-unknown": False,
-    }
-
-
 @pytest.mark.asyncio
-async def test_model_capability_request_uses_subscription_headers(monkeypatch) -> None:
+async def test_raw_response_request_streams_official_x_search_lifecycle(monkeypatch) -> None:
     original_client = httpx.AsyncClient
-    captured: dict[str, Any] = {}
+    events = [
+        {
+            "type": "response.output_item.added",
+            "item": {
+                "type": "x_search_call",
+                "id": "x-search-1",
+                "status": "in_progress",
+                "action": {"query": "nanobot oauth"},
+            },
+        },
+        {
+            "type": "response.output_item.done",
+            "item": {
+                "type": "x_search_call",
+                "id": "x-search-1",
+                "status": "completed",
+                "action": {"query": "nanobot oauth"},
+            },
+        },
+        {
+            "type": "response.completed",
+            "response": {"status": "completed", "usage": {}},
+        },
+    ]
+    content = "".join(f"data: {json.dumps(event)}\n\n" for event in events)
 
     def handler(request: httpx.Request) -> httpx.Response:
-        captured["request"] = request
+        return httpx.Response(200, content=content, request=request)
-        return httpx.Response(
-            200,
-            json={"data": [{"id": "grok-search", "supportsBackendSearch": True}]},
-            request=request,
-        )
 
     def fake_client(**kwargs) -> httpx.AsyncClient:
-        captured["kwargs"] = kwargs
         return original_client(
             transport=httpx.MockTransport(handler),
             timeout=kwargs["timeout"],
-            follow_redirects=kwargs["follow_redirects"],
         )
 
     monkeypatch.setattr("nanobot.providers.xai_grok_provider.httpx.AsyncClient", fake_client)
-    payload = base64.urlsafe_b64encode(
+    tool_events: list[dict[str, Any]] = []
-        json.dumps({"sub": "user-42", "email": "user@example.com"}).encode()
-    ).decode().rstrip("=")
-    access_token = f"header.{payload}.signature"
-    headers = _build_model_headers(_token(access_token))
 
-    capabilities = await _fetch_xai_model_capabilities(
+    await _request_xai(
-        DEFAULT_XAI_GROK_MODELS_URL,
+        "https://cli-chat-proxy.grok.com/v1/responses",
-        headers,
+        _build_headers("secret", "grok-4.6"),
+        {"model": "grok-4.6", "tools": [{"type": "x_search"}]},
+        on_tool_call_delta=lambda event: _append(tool_events, event),
     )
 
-    request = captured["request"]
+    assert [(event["phase"], event["name"]) for event in tool_events] == [
-    assert isinstance(request, httpx.Request)
+        ("start", "x_search"),
-    assert request.method == "GET"
+        ("end", "x_search"),
-    assert str(request.url) == DEFAULT_XAI_GROK_MODELS_URL
+    ]
-    assert request.headers["Authorization"] == f"Bearer {access_token}"
+    assert tool_events[-1]["result"] == {"status": "completed"}
-    assert request.headers["X-XAI-Token-Auth"] == "xai-grok-cli"
+
-    assert request.headers["x-userid"] == "user-42"
+
-    assert request.headers["x-email"] == "user@example.com"
+@pytest.mark.asyncio
-    assert captured["kwargs"] == {"timeout": 10.0, "follow_redirects": False}
+async def test_raw_response_rejects_unfinished_hosted_tool_and_closes_progress(
-    assert capabilities == {"grok-search": True}
+    monkeypatch,
+) -> None:
+    original_client = httpx.AsyncClient
+    events = [
+        {
+            "type": "response.custom_tool_call_input.done",
+            "item_id": "x-search-1",
+            "input": '{"query":"nanobot oauth"}',
+        },
+        {"type": "response.output_text.delta", "delta": "I will keep searching."},
+        {
+            "type": "response.completed",
+            "response": {
+                "status": "completed",
+                "usage": {"input_tokens": 8, "output_tokens": 4, "total_tokens": 12},
+            },
+        },
+    ]
+    content = "".join(f"data: {json.dumps(event)}\n\n" for event in events)
+
+    def handler(request: httpx.Request) -> httpx.Response:
+        return httpx.Response(200, content=content, request=request)
+
+    def fake_client(**kwargs) -> httpx.AsyncClient:
+        return original_client(
+            transport=httpx.MockTransport(handler),
+            timeout=kwargs["timeout"],
+        )
+
+    monkeypatch.setattr("nanobot.providers.xai_grok_provider.httpx.AsyncClient", fake_client)
+    tool_events: list[dict[str, Any]] = []
+
+    with pytest.raises(_XAIIncompleteHostedToolError) as caught:
+        await _request_xai(
+            "https://cli-chat-proxy.grok.com/v1/responses",
+            _build_headers("secret", "grok-4.6"),
+            {"model": "grok-4.6", "tools": [{"type": "x_search"}]},
+            on_tool_call_delta=lambda event: _append(tool_events, event),
+        )
+
+    assert caught.value.usage == LLMUsage.reported(input_tokens=8, output_tokens=4)
+    assert [event["phase"] for event in tool_events] == ["start", "error"]
+    assert "before this hosted tool completed" in tool_events[-1]["error"]
+
+
+@pytest.mark.asyncio
+async def test_provider_recovers_unfinished_hosted_tool_once_and_preserves_usage(
+    monkeypatch,
+) -> None:
+    _mock_token(monkeypatch)
+    _mock_model_capabilities(monkeypatch, supports_backend_search=True)
+    attempts = 0
+    request_ids: list[str] = []
+    streamed: list[str] = []
+    recovered: list[bool] = []
+    first_usage = LLMUsage.reported(input_tokens=10, output_tokens=2)
+    second_usage = LLMUsage.reported(input_tokens=11, output_tokens=4)
+
+    async def fake_request(_url, headers, body, **kwargs):
+        nonlocal attempts
+        attempts += 1
+        request_ids.append(headers["x-grok-req-id"])
+        assert body["max_turns"] == 5
+        if attempts == 1:
+            await kwargs["on_content_delta"]("I will keep searching.")
+            raise _XAIIncompleteHostedToolError(
+                [{"name": "x_search", "call_id": "search-1"}],
+                usage=first_usage,
+            )
+        await kwargs["on_content_delta"]("Final researched answer.")
+        return "Final researched answer.", [], "stop", second_usage, None
+
+    async def on_recover() -> None:
+        recovered.append(True)
+
+    monkeypatch.setattr("nanobot.providers.xai_grok_provider._request_xai", fake_request)
+    provider = XAIGrokProvider()
+
+    response = await provider.chat_stream_with_retry(
+        [{"role": "user", "content": "Search X"}],
+        on_content_delta=lambda delta: _append(streamed, delta),
+        on_stream_recover=on_recover,
+    )
+
+    assert attempts == 2
+    assert len(set(request_ids)) == 2
+    assert recovered == [True]
+    assert streamed == ["I will keep searching.", "Final researched answer."]
+    assert response.content == "Final researched answer."
+    assert response.usage == first_usage + second_usage
+
+
+@pytest.mark.asyncio
+async def test_provider_preserves_usage_when_hosted_tool_recovery_also_fails(
+    monkeypatch,
+) -> None:
+    _mock_token(monkeypatch)
+    _mock_model_capabilities(monkeypatch, supports_backend_search=True)
+    attempts = 0
+    usage = LLMUsage.reported(input_tokens=10, output_tokens=2)
+
+    async def fake_request(*_args, **_kwargs):
+        nonlocal attempts
+        attempts += 1
+        raise _XAIIncompleteHostedToolError(
+            [{"name": "x_search", "call_id": f"search-{attempts}"}],
+            usage=usage,
+        )
+
+    monkeypatch.setattr("nanobot.providers.xai_grok_provider._request_xai", fake_request)
+    provider = XAIGrokProvider()
+
+    response = await provider.chat_stream_with_retry(
+        [{"role": "user", "content": "Search X"}],
+        on_stream_recover=lambda: _append([], True),
+    )
+
+    assert attempts == 2
+    assert response.finish_reason == "error"
+    assert response.usage == usage + usage
 
 
 @pytest.mark.asyncio

tests/session/test_turn_continuation.py

@@ -141,14 +141,13 @@ def test_internal_continuation_requires_budget_boundary_and_queue():
     )
 
 
-def test_save_skip_matches_prefix_when_current_message_merged():
+def test_save_skip_matches_prefix_when_current_message_was_persisted():
     skip = _save_skip_for_turn(
         message_metadata=None,
-        initial_message_count=2,  # [system, merged user]
+        initial_message_count=3,  # [system, history user, current user]
-        history_count=1,
         input_persisted_early=True,
     )
-    assert skip == 2
+    assert skip == 3
 
 
 def test_save_skip_unchanged_for_standalone_current_message():
@@ -156,12 +155,10 @@ def test_save_skip_unchanged_for_standalone_current_message():
     assert _save_skip_for_turn(
         message_metadata=None,
         initial_message_count=3,
-        history_count=1,
         input_persisted_early=True,
     ) == 3
     assert _save_skip_for_turn(
         message_metadata=None,
         initial_message_count=3,
-        history_count=1,
         input_persisted_early=False,
     ) == 2

tests/test_tool_contextvars.py

@@ -1,6 +1,7 @@
 from __future__ import annotations
 
 import asyncio
+import json
 from unittest.mock import MagicMock
 
 import pytest
@@ -11,6 +12,7 @@ from nanobot.agent.tools.message import MessageTool
 from nanobot.agent.tools.spawn import SpawnTool
 from nanobot.cron.service import CronService
 from nanobot.providers.base import GenerationSettings, LLMProvider
+from nanobot.runtime_context import RUNTIME_CONTEXT_INPUT_META, RuntimeContextBlock
 from nanobot.session.keys import UNIFIED_SESSION_KEY
 from nanobot.utils.llm_runtime import LLMRuntime
 
@@ -299,6 +301,41 @@ async def test_webui_cron_tool_uses_origin_session_when_unified_enabled(tmp_path
     assert jobs[0].payload.origin_metadata == {"webui": True}
 
 
+@pytest.mark.asyncio
+async def test_cron_tool_snapshots_only_persistable_request_metadata(tmp_path) -> None:
+    """Live runtime context must not poison a persisted WebUI cron job."""
+    store_path = tmp_path / "jobs.json"
+    service = CronService(store_path)
+    tool = CronTool(service)
+    await service.start()
+    try:
+        with request_context(
+            RequestContext(
+                channel="websocket",
+                chat_id="chat-123",
+                metadata={
+                    "webui": True,
+                    RUNTIME_CONTEXT_INPUT_META: [
+                        RuntimeContextBlock(source="webui_quote", content="quoted reply")
+                    ],
+                    "opaque": object(),
+                },
+                session_key=UNIFIED_SESSION_KEY,
+            )
+        ):
+            result = await tool.execute(action="add", message="standup", every_seconds=300)
+
+        assert result.startswith("Created job")
+        jobs = service.list_jobs()
+        assert len(jobs) == 1
+        assert jobs[0].payload.origin_metadata == {"webui": True}
+
+        raw = json.loads(store_path.read_text(encoding="utf-8"))
+        assert raw["jobs"][0]["payload"]["originMetadata"] == {"webui": True}
+    finally:
+        service.stop()
+
+
 @pytest.mark.asyncio
 async def test_cron_tool_preserves_thread_scoped_session_key(tmp_path) -> None:
     """Channel-provided thread session keys should remain the cron owner."""

tests/tools/test_message_tool_suppress.py

@@ -6,6 +6,7 @@ from unittest.mock import AsyncMock, MagicMock
 
 import pytest
 
+from nanobot.agent.context import TranscriptInput
 from nanobot.agent.loop import AgentLoop
 from nanobot.agent.tools.message import MessageTool
 from nanobot.bus.events import InboundMessage, OutboundMessage
@@ -178,7 +179,9 @@ class TestMessageToolSuppressLogic:
             progress.append((content, tool_hint))
 
         result = await loop._run_agent_loop(
-            [], runtime=loop.llm_runtime(), on_progress=on_progress
+            TranscriptInput(history=[], current_message=None),
+            runtime=loop.llm_runtime(),
+            on_progress=on_progress,
         )
 
         assert result.final_content == "Done"

tests/tools/test_session_messages_tool.py

@@ -183,6 +183,66 @@ async def test_rate_limit_is_per_source_session_and_uses_a_rolling_minute(
     )
 
 
+@pytest.mark.asyncio
+async def test_rate_limit_releases_expired_source_state_and_keeps_recent_sources(
+    tmp_path: Path,
+) -> None:
+    sessions = SessionManager(tmp_path)
+    _persist(
+        sessions,
+        "websocket:a",
+        "websocket:b",
+        "websocket:c",
+        "websocket:target",
+    )
+    now = 0.0
+    tool = SendSessionMessageTool(
+        sessions=sessions,
+        bus=MessageBus(),
+        max_messages_per_minute=2,
+        clock=lambda: now,
+    )
+    target = _handle(sessions, "websocket:target").name
+
+    for source in ("websocket:a", "websocket:b"):
+        await tool.enqueue(
+            source_session_key=source,
+            target_handle=target,
+            content="initial",
+            expect_reply=False,
+        )
+    now = 30.0
+    await tool.enqueue(
+        source_session_key="websocket:a",
+        target_handle=target,
+        content="recent",
+        expect_reply=False,
+    )
+
+    now = 61.0
+    await tool.enqueue(
+        source_session_key="websocket:c",
+        target_handle=target,
+        content="trigger cleanup",
+        expect_reply=False,
+    )
+
+    assert set(tool._sent_at) == {"websocket:a", "websocket:c"}
+    await tool.enqueue(
+        source_session_key="websocket:a",
+        target_handle=target,
+        content="within rolling window",
+        expect_reply=False,
+    )
+    with pytest.raises(SessionMessageError, match="rate limit"):
+        await tool.enqueue(
+            source_session_key="websocket:a",
+            target_handle=target,
+            content="over limit",
+            expect_reply=False,
+        )
+
+
 @pytest.mark.asyncio
 async def test_reply_timeout_injects_a_user_input_back_into_the_source(
     tmp_path: Path,

tests/tools/test_tool_descriptions.py

@@ -9,7 +9,9 @@ from nanobot.agent.tools.shell import ExecTool
 
 def test_coding_tool_descriptions_steer_editing_priority() -> None:
     apply_patch = ApplyPatchTool().description.lower()
-    edit_file = EditFileTool().description.lower()
+    edit_tool = EditFileTool()
+    edit_file = edit_tool.description.lower()
+    edit_parameters = edit_tool.parameters["properties"]
     write_file = WriteFileTool().description.lower()
 
     assert "default tool for code edits" in apply_patch
@@ -18,8 +20,10 @@ def test_coding_tool_descriptions_steer_editing_priority() -> None:
     assert "edit_file only for small exact replacements" in apply_patch
 
     assert "small, exact replacement" in edit_file
-    assert "copied from read_file" in edit_file
     assert "prefer apply_patch" in edit_file
+    assert "occurrence, line_hint, and replace_all=true are mutually exclusive" in edit_file
+    assert "copy it from read_file" in edit_parameters["old_text"]["description"].lower()
+    assert "must differ from old_text" in edit_parameters["new_text"]["description"].lower()
 
     assert "replace an entire file" in write_file
     assert "prefer apply_patch" in write_file

tests/webui/test_settings_api.py

@@ -13,7 +13,8 @@ from nanobot.config.schema import Config, InlineFallbackConfig, ModelPresetConfi
 from nanobot.llm_usage import get_llm_usage_store
 from nanobot.llm_usage.models import LLMCallRecord
 from nanobot.providers.base import LLMUsage
-from nanobot.providers.registry import find_by_name
+from nanobot.providers.oauth_model_catalog import OAuthModelCatalogSnapshot
+from nanobot.providers.registry import ProviderModelSpec, find_by_name
 from nanobot.session.manager import SessionManager
 from nanobot.session.model_selection import SESSION_MODEL_PRESET_METADATA_KEY
 from nanobot.webui.settings_api import (
@@ -183,11 +184,13 @@ def test_update_api_settings_requires_key_for_network_access(
     with pytest.raises(WebUISettingsError, match="API key"):
         update_api_settings({"host": ["0.0.0.0"], "port": ["8900"]})
 
-    payload = update_api_settings({
+    payload = update_api_settings(
-        "host": ["0.0.0.0"],
+        {
-        "port": ["9900"],
+            "host": ["0.0.0.0"],
-        "api_key": ["secret-token"],
+            "port": ["9900"],
-    })
+            "api_key": ["secret-token"],
+        }
+    )
     saved = load_config(config_path)
     assert saved.api.host == "0.0.0.0"
     assert saved.api.port == 9900
@@ -346,13 +349,15 @@ def test_create_model_configuration_rejects_dynamic_custom_provider_without_api_
     monkeypatch: pytest.MonkeyPatch,
 ) -> None:
     config_path = tmp_path / "config.json"
-    config = Config.model_validate({
+    config = Config.model_validate(
-        "providers": {
+        {
-            DYNAMIC_PROVIDER_NAME: {
+            "providers": {
-                "apiKey": "sk-test",
+                DYNAMIC_PROVIDER_NAME: {
+                    "apiKey": "sk-test",
+                }
             }
         }
-    })
+    )
     save_config(config, config_path)
     monkeypatch.setattr("nanobot.config.loader._current_config_path", config_path)
 
@@ -497,9 +502,7 @@ def test_update_model_configuration_rolls_back_sessions_when_config_save_fails(
     monkeypatch: pytest.MonkeyPatch,
 ) -> None:
     config_path = tmp_path / "config.json"
-    config = Config(
+    config = Config(model_presets={"openai": ModelPresetConfig(model="openai/gpt-4.1")})
-        model_presets={"openai": ModelPresetConfig(model="openai/gpt-4.1")}
-    )
     save_config(config, config_path)
     calls: list[tuple[str, str]] = []
 
@@ -890,11 +893,13 @@ def test_update_provider_settings_updates_and_clears_oauth_proxy(
         },
     )
 
-    payload = update_provider_settings({
+    payload = update_provider_settings(
-        "provider": [provider_name],
+        {
-        "proxy": [" http://127.0.0.1:7890 "],
+            "provider": [provider_name],
-        "extraBody": [json.dumps({"tools": []})],
+            "proxy": [" http://127.0.0.1:7890 "],
-    })
+            "extraBody": [json.dumps({"tools": []})],
+        }
+    )
 
     providers = {row["name"]: row for row in payload["providers"]}
     assert providers[provider_name]["proxy"] == "http://127.0.0.1:7890"
@@ -1099,15 +1104,17 @@ def test_settings_payload_groups_opencode_compatibility_alias(tmp_path, monkeypa
 
 def test_settings_payload_keeps_configured_opencode_legacy_alias(tmp_path, monkeypatch) -> None:
     config_path = tmp_path / "config.json"
-    config = Config.model_validate({
+    config = Config.model_validate(
-        "providers": {"opencodeZen": {"apiKey": "legacy-key"}},
+        {
-        "agents": {
+            "providers": {"opencodeZen": {"apiKey": "legacy-key"}},
-            "defaults": {
+            "agents": {
-                "provider": "opencode_zen",
+                "defaults": {
-                "model": "opencode/deepseek-v4-pro",
+                    "provider": "opencode_zen",
-            }
+                    "model": "opencode/deepseek-v4-pro",
-        },
+                }
-    })
+            },
+        }
+    )
     save_config(config, config_path)
     monkeypatch.setattr("nanobot.config.loader._current_config_path", config_path)
 
@@ -1124,13 +1131,15 @@ def test_settings_payload_marks_dynamic_custom_provider_without_api_base_unconfi
     monkeypatch: pytest.MonkeyPatch,
 ) -> None:
     config_path = tmp_path / "config.json"
-    config = Config.model_validate({
+    config = Config.model_validate(
-        "providers": {
+        {
-            DYNAMIC_PROVIDER_NAME: {
+            "providers": {
-                "apiKey": "sk-test",
+                DYNAMIC_PROVIDER_NAME: {
+                    "apiKey": "sk-test",
+                }
             }
         }
-    })
+    )
     save_config(config, config_path)
     monkeypatch.setattr("nanobot.config.loader._current_config_path", config_path)
 
@@ -1466,16 +1475,18 @@ def test_settings_payload_includes_token_usage_summary(
     config = Config()
     save_config(config, config_path)
     monkeypatch.setattr("nanobot.config.loader._current_config_path", config_path)
-    get_llm_usage_store().record(LLMCallRecord(
+    get_llm_usage_store().record(
-        started_at_ms=int(time.time() * 1000),
+        LLMCallRecord(
-        duration_ms=1,
+            started_at_ms=int(time.time() * 1000),
-        provider="openai",
+            duration_ms=1,
-        model="gpt-5",
+            provider="openai",
-        source="user",
+            model="gpt-5",
-        stream=False,
+            source="user",
-        finish_reason="stop",
+            stream=False,
-        usage=LLMUsage.reported(input_tokens=10, output_tokens=5),
+            finish_reason="stop",
-    ))
+            usage=LLMUsage.reported(input_tokens=10, output_tokens=5),
+        )
+    )
 
     payload = settings_payload()
 
@@ -1496,16 +1507,18 @@ def test_settings_usage_payload_returns_lightweight_token_usage(
     config = Config()
     save_config(config, config_path)
     monkeypatch.setattr("nanobot.config.loader._current_config_path", config_path)
-    get_llm_usage_store().record(LLMCallRecord(
+    get_llm_usage_store().record(
-        started_at_ms=int(time.time() * 1000),
+        LLMCallRecord(
-        duration_ms=1,
+            started_at_ms=int(time.time() * 1000),
-        provider="openai",
+            duration_ms=1,
-        model="gpt-5",
+            provider="openai",
-        source="user",
+            model="gpt-5",
-        stream=False,
+            source="user",
-        finish_reason="stop",
+            stream=False,
-        usage=LLMUsage.reported(input_tokens=20, output_tokens=2),
+            finish_reason="stop",
-    ))
+            usage=LLMUsage.reported(input_tokens=20, output_tokens=2),
+        )
+    )
 
     payload = settings_usage_payload()
 
@@ -1929,9 +1942,7 @@ def test_xai_grok_login_reports_upstream_failure_as_bad_gateway(
         )
 
     assert exc.value.status == 502
-    assert str(exc.value) == (
+    assert str(exc.value) == ("xAI OAuth login failed: Could not reach xAI sign-in: ConnectError.")
-        "xAI OAuth login failed: Could not reach xAI sign-in: ConnectError."
-    )
     assert exc.value.__cause__ is failure
 
 
@@ -1995,39 +2006,126 @@ def test_provider_models_payload_fetches_openai_compatible_models(
     assert payload["models"][1]["context_window"] == 65536
 
 
-def test_provider_models_payload_returns_curated_openai_codex_models() -> None:
+def test_provider_models_payload_returns_online_openai_codex_models(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    monkeypatch.setattr(
+        "nanobot.webui.settings_models.get_oauth_model_catalog",
+        lambda *_args, **_kwargs: OAuthModelCatalogSnapshot(
+            models=(
+                ProviderModelSpec(
+                    id="openai-codex/gpt-5.6-sol",
+                    label="GPT-5.6-Sol",
+                    description="Latest frontier agentic coding model.",
+                    owned_by="OpenAI Codex",
+                    context_window=272_000,
+                    reasoning_efforts=("low", "medium", "high", "xhigh", "max", "ultra"),
+                ),
+            ),
+            source="remote",
+            fetched_at=123,
+        ),
+    )
+
     payload = provider_models_payload({"provider": ["openai_codex"]})
 
     assert payload["status"] == "available"
-    assert payload["catalog_kind"] == "builtin"
+    assert payload["catalog_kind"] == "hybrid"
-    assert payload["model_count"] == 7
+    assert payload["source"] == "remote"
+    assert payload["model_count"] == 1
     assert payload["models"][0] == {
         "id": "openai-codex/gpt-5.6-sol",
         "label": "GPT-5.6-Sol",
         "description": "Latest frontier agentic coding model.",
         "owned_by": "OpenAI Codex",
-        "context_window": 372000,
+        "context_window": 272000,
+        "reasoning_efforts": ["low", "medium", "high", "xhigh", "max", "ultra"],
+        "supports_backend_search": False,
     }
-    assert [model["id"] for model in payload["models"][:3]] == [
-        "openai-codex/gpt-5.6-sol",
-        "openai-codex/gpt-5.6-terra",
-        "openai-codex/gpt-5.6-luna",
-    ]
 
 
-def test_provider_models_payload_returns_xai_grok_model() -> None:
+def test_provider_models_payload_returns_online_github_copilot_models(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    monkeypatch.setattr(
+        "nanobot.webui.settings_models.get_oauth_model_catalog",
+        lambda *_args, **_kwargs: OAuthModelCatalogSnapshot(
+            models=(
+                ProviderModelSpec(
+                    id="github-copilot/claude-sonnet",
+                    label="Claude Sonnet",
+                    owned_by="GitHub Copilot",
+                    context_window=200_000,
+                ),
+            ),
+            source="remote",
+            fetched_at=123,
+        ),
+    )
+
+    payload = provider_models_payload({"provider": ["github_copilot"]})
+
+    assert payload["status"] == "available"
+    assert payload["catalog_kind"] == "hybrid"
+    assert payload["source"] == "remote"
+    assert payload["models"][0]["id"] == "github-copilot/claude-sonnet"
+
+
+def test_provider_models_payload_returns_online_xai_grok_models(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    monkeypatch.setattr(
+        "nanobot.webui.settings_models.get_oauth_model_catalog",
+        lambda *_args, **_kwargs: OAuthModelCatalogSnapshot(
+            models=(
+                ProviderModelSpec(
+                    id="xai-grok/grok-4.6",
+                    label="Grok 4.6",
+                    description="Latest frontier model",
+                    owned_by="xAI",
+                    context_window=500_000,
+                    reasoning_efforts=("xhigh", "high", "medium", "low"),
+                    supports_backend_search=True,
+                ),
+                ProviderModelSpec(
+                    id="xai-grok/grok-4.5",
+                    label="Grok 4.5",
+                    owned_by="xAI",
+                    context_window=500_000,
+                    reasoning_efforts=("high", "medium", "low"),
+                    supports_backend_search=True,
+                ),
+            ),
+            source="remote",
+            fetched_at=123,
+        ),
+    )
+
     payload = provider_models_payload({"provider": ["xai_grok"]})
 
     assert payload["status"] == "available"
-    assert payload["catalog_kind"] == "builtin"
+    assert payload["catalog_kind"] == "hybrid"
+    assert payload["source"] == "remote"
+    assert payload["fetched_at"] == 123
     assert payload["models"] == [
+        {
+            "id": "xai-grok/grok-4.6",
+            "label": "Grok 4.6",
+            "description": "Latest frontier model",
+            "owned_by": "xAI",
+            "context_window": 500000,
+            "reasoning_efforts": ["xhigh", "high", "medium", "low"],
+            "supports_backend_search": True,
+        },
         {
             "id": "xai-grok/grok-4.5",
             "label": "Grok 4.5",
-            "description": "Grok via xAI subscription; X Search is enabled when supported.",
+            "description": None,
-            "owned_by": "xAI Grok",
+            "owned_by": "xAI",
             "context_window": 500000,
-        }
+            "reasoning_efforts": ["high", "medium", "low"],
+            "supports_backend_search": True,
+        },
     ]
 
 
@@ -2160,7 +2258,9 @@ def test_model_catalog_kind_uses_provider_spec_metadata() -> None:
     assert _model_catalog_kind(find_by_name("anthropic")) == "unsupported"
     assert _model_catalog_kind(find_by_name("openrouter")) == "catalog"
     assert _model_catalog_kind(find_by_name("orcarouter")) == "catalog"
-    assert _model_catalog_kind(find_by_name("openai_codex")) == "builtin"
+    assert _model_catalog_kind(find_by_name("openai_codex")) == "hybrid"
+    assert _model_catalog_kind(find_by_name("xai_grok")) == "hybrid"
+    assert _model_catalog_kind(find_by_name("github_copilot")) == "hybrid"
 
 
 def test_create_model_configuration_accepts_configured_oauth_provider(

tui/src/app.test.ts

@@ -205,7 +205,7 @@ describe("NanobotTui layout", () => {
       expect(occurrences(frame, "Ask nanobot anything")).toBe(1)
       expect(occurrences(frame, "Ready")).toBe(0)
       expect(occurrences(frame, "Getting ready…")).toBe(1)
-      expect(occurrences(frame, "nanobot  ·  test/model")).toBe(1)
+      expect(occurrences(frame, "default ▾")).toBe(1)
     }
 
     app.accept({ event: "attached", chat_id: "chat" })
@@ -253,6 +253,23 @@ describe("NanobotTui layout", () => {
     expect(sent).toEqual(["你好"])
   })
 
+  test("keeps input typed immediately after Enter in the next draft", async () => {
+    const sent: string[] = []
+    setup = await createRenderer({ width: 72, height: 20, screenMode: "alternate-screen" })
+    const app = mount(setup, sent)
+    app.accept({ event: "attached", chat_id: "chat" })
+    await Bun.sleep(1)
+    const composer = (app as unknown as { composer: TextareaRenderable }).composer
+
+    composer.setText("first")
+    setup.mockInput.pressEnter()
+    for (const key of "next") setup.mockInput.pressKey(key)
+    await waitUntil(() => sent.length > 0)
+
+    expect(sent).toEqual(["first"])
+    expect(composer.plainText).toBe("next")
+  })
+
   test("inserts newlines with Shift+Enter and the universal Ctrl+J fallback", async () => {
     const sent: string[] = []
     setup = await createRenderer({
@@ -985,7 +1002,6 @@ describe("NanobotTui layout", () => {
     const ui = app as unknown as {
       composer: TextareaRenderable
       sessionMenu: { visible: boolean }
-      titleText: { plainText: string }
       runtimeControls: { modelText: { plainText: string } }
     }
 
@@ -999,8 +1015,7 @@ describe("NanobotTui layout", () => {
       ui.composer.submit()
       await waitUntil(() => attached.length === 1)
       expect(attached).toEqual(["other"])
-      expect(ui.titleText.plainText).toContain("Release checklist")
+      expect(ui.runtimeControls.modelText.plainText).toBe("Deep Research ▾")
-      expect(ui.runtimeControls.modelText.plainText).toContain("Deep Research")
       expect(ui.runtimeControls.modelText.plainText).not.toContain("test/model")
 
       app.accept({ event: "attached", chat_id: "other" })
@@ -1009,8 +1024,7 @@ describe("NanobotTui layout", () => {
       ui.composer.submit()
       await waitUntil(() => newChats.length === 1)
       expect(newChats).toEqual(["new"])
-      expect(ui.titleText.plainText).toContain("New chat")
+      expect(ui.runtimeControls.modelText.plainText).toBe("default ▾")
-      expect(ui.runtimeControls.modelText.plainText).toContain("test/model")
     } finally {
       globalThis.fetch = original
     }
@@ -1182,7 +1196,8 @@ describe("NanobotTui layout", () => {
       model_preset: "Codex",
     })
     await setup.flush()
-    expect(ui.runtimeControls.modelText.plainText).toContain("Codex  ·  openai/gpt-5.6")
+    expect(ui.runtimeControls.modelText.plainText).toBe("Codex ▾")
+    expect(ui.runtimeControls.modelText.plainText).not.toContain("openai/gpt-5.6")
 
     app.accept({
       event: "runtime_model_updated",
@@ -1190,7 +1205,7 @@ describe("NanobotTui layout", () => {
       model_preset: "DeepSeek",
     })
     await setup.flush()
-    expect(ui.runtimeControls.modelText.plainText).toContain("Codex  ·  openai/gpt-5.6")
+    expect(ui.runtimeControls.modelText.plainText).toBe("Codex ▾")
     expect(ui.runtimeControls.modelText.plainText).not.toContain("DeepSeek")
   })
 
@@ -1212,8 +1227,8 @@ describe("NanobotTui layout", () => {
     })
     await setup.flush()
 
-    expect(ui.runtimeControls.modelText.plainText).toContain("deepseek/deepseek-chat")
+    expect(ui.runtimeControls.modelText.plainText).toBe("default ▾")
-    expect(ui.runtimeControls.modelText.plainText).not.toContain("Codex")
+    expect(ui.runtimeControls.modelText.plainText).not.toContain("deepseek/deepseek-chat")
   })
 
   test("refreshes the canonical preset after the model command completes", async () => {
@@ -1309,7 +1324,6 @@ describe("NanobotTui layout", () => {
         menuRoot: { getChildren(): unknown[] }
       }
       composer: TextareaRenderable
-      titleText: TextRenderable
       status: TextRenderable
       meta: TextRenderable
     }
@@ -1324,7 +1338,6 @@ describe("NanobotTui layout", () => {
       expect(ui.runtimeControls.modelText.selectable).toBe(false)
       expect(ui.runtimeControls.accessText.selectable).toBe(false)
       expect(ui.runtimeControls.contextText.selectable).toBe(false)
-      expect(ui.titleText.selectable).toBe(false)
       expect(ui.status.selectable).toBe(false)
       expect(ui.meta.selectable).toBe(false)
       app.accept({ event: "goal_status", chat_id: "chat", status: "running" })
@@ -1394,7 +1407,7 @@ describe("NanobotTui layout", () => {
     }
   })
 
-  test("opens and switches sessions from the clickable title", async () => {
+  test("switches sessions only through the sessions command", async () => {
     const original = globalThis.fetch
     globalThis.fetch = ((input: string | URL | Request) => {
       const url = String(input)
@@ -1420,14 +1433,18 @@ describe("NanobotTui layout", () => {
     const ui = app as unknown as {
       composer: TextareaRenderable
       sessionMenu: { visible: boolean; root: { getChildren(): unknown[] } }
-      titleText: TextRenderable
+      title: { getChildren(): unknown[] }
-      status: TextRenderable
     }
 
     try {
       await waitUntil(() => (app as unknown as { ready: boolean }).ready)
       await setup.renderOnce()
-      await setup.mockMouse.click(ui.titleText.x + 2, ui.titleText.y)
+      const titleItems = ui.title.getChildren() as TextRenderable[]
+      expect(titleItems.some((item) => item.id === "nanobot-tui-title-text")).toBe(false)
+      expect(ui.sessionMenu.visible).toBe(false)
+
+      ui.composer.setText("/sessions")
+      ui.composer.submit()
       await waitUntil(() => ui.sessionMenu.visible)
       await setup.flush()
       expect(ui.composer.placeholder).toBe("Search sessions")
@@ -1441,15 +1458,6 @@ describe("NanobotTui layout", () => {
       expect(attached).toEqual(["other"])
       expect(ui.sessionMenu.visible).toBe(false)
       expect(ui.composer.focused).toBe(true)
-      expect(ui.titleText.plainText).toContain("Release checklist")
-
-      app.accept({ event: "attached", chat_id: "other" })
-      await setup.mockMouse.click(ui.titleText.x + 2, ui.titleText.y)
-      await waitUntil(() => ui.sessionMenu.visible)
-      ui.composer.blur()
-      await setup.mockMouse.click(ui.status.x, ui.status.y)
-      expect(ui.sessionMenu.visible).toBe(false)
-      expect(ui.composer.focused).toBe(true)
     } finally {
       globalThis.fetch = original
     }
@@ -1715,7 +1723,7 @@ describe("NanobotTui layout", () => {
       await setup.flush()
       const frame = setup.captureCharFrame()
       expect(frame).toContain("Release checklist")
-      expect(occurrences(frame, "Current chat")).toBe(1)
+      expect(occurrences(frame, "Current chat")).toBe(0)
     } finally {
       globalThis.fetch = original
     }
@@ -1959,7 +1967,7 @@ describe("NanobotTui layout", () => {
       } else if (width >= 28 && height >= 9) {
         expect(occurrences(frame, "Enter now · Tab next")).toBe(1)
       }
-      expect(occurrences(frame, "nanobot  ·  test/model")).toBe(height >= 14 ? 1 : 0)
+      expect(occurrences(frame, "default ▾")).toBe(height >= 14 ? 1 : 0)
     }
   })
 
@@ -3122,7 +3130,7 @@ describe("NanobotTui with a Herdr pane title reporter", () => {
     await setup.flush()
     const activeFrame = setup.captureCharFrame()
     expect(activeFrame).toContain(">_  nanobot")
-    expect(activeFrame).toContain("test/model")
+    expect(activeFrame).toContain("default ▾")
     expect(occurrences(activeFrame, "› Ship the Herdr integration")).toBe(1)
     expect(occurrences(activeFrame, "app.ts")).toBe(1)
     expect(ui.composer.placeholder).toBe("Enter send now · Tab send next")

tui/src/app.ts

@@ -94,7 +94,7 @@ import {
   type FooterMode,
   type FooterHintTheme,
 } from "./footer-hints"
-import { createTuiHost, type TuiHost } from "./host"
+import { configureOpenTuiEnvironment, createTuiHost, type TuiHost } from "./host"
 
 interface AppOptions {
   wsUrl?: string
@@ -442,7 +442,6 @@ export class NanobotTui {
   private readonly client: ChatClient
   private readonly shell: BoxRenderable
   private readonly title: BoxRenderable
-  private readonly titleText: TextRenderable
   private readonly composerFrame: BoxRenderable
   private readonly composer: TextareaRenderable
   private composerSyntax: SyntaxStyle
@@ -649,28 +648,6 @@ export class NanobotTui {
       alignItems: "center",
       backgroundColor: RGBA.defaultBackground(),
     })
-    this.titleText = new TextRenderable(renderer, {
-      id: "nanobot-tui-title-text",
-      content: "nanobot",
-      height: 1,
-      flexShrink: 0,
-      truncate: true,
-      fg: this.palette.muted,
-      selectable: false,
-      onMouseOver: () => { this.titleText.fg = this.palette.accent },
-      onMouseOut: () => this.renderTitleColor(),
-      onMouseDown: (event) => {
-        if (event.button !== 0) return
-        event.preventDefault()
-        event.stopPropagation()
-        this.renderer.clearSelection()
-        if (this.sessionLoading || this.sessionMenu.visible) {
-          this.closeSessions()
-          return
-        }
-        void this.openSessions()
-      },
-    })
     this.runtimeControls = new RuntimeControls(
       renderer,
       runtimeControlsTheme(this.palette),
@@ -703,7 +680,6 @@ export class NanobotTui {
         },
       },
     )
-    this.title.add(this.titleText)
     this.title.add(this.runtimeControls.modelText)
     this.title.add(this.runtimeControls.accessText)
     this.title.add(this.runtimeControls.contextText)
@@ -757,7 +733,10 @@ export class NanobotTui {
       // IMEs may commit their final composed glyph after Enter. Matching the
       // OpenCode/OpenTUI integration, defer twice before reading plainText.
       onSubmit: () => this.deferSubmit(),
-      onPaste: (event) => this.handlePaste(event),
+      onPaste: (event) => {
+        this.flushSubmit()
+        if (!this.composer.isDestroyed) this.handlePaste(event)
+      },
     })
     this.status = new TextRenderable(renderer, {
       id: "nanobot-tui-status",
@@ -820,6 +799,7 @@ export class NanobotTui {
   }
 
   static async create(options: AppOptions): Promise<NanobotTui> {
+    configureOpenTuiEnvironment()
     const host = createTuiHost()
     const renderer = await createCliRenderer({
       targetFps: 30,
@@ -878,12 +858,15 @@ export class NanobotTui {
     if (this.submitPending) return
     this.submitPending = true
     const generation = ++this.submitGeneration
-    setTimeout(() => setTimeout(() => {
+    setTimeout(() => setTimeout(() => this.flushSubmit(generation), 0), 0)
-      if (generation !== this.submitGeneration) return
+  }
-      this.submitPending = false
+
-      if (this.composer.isDestroyed) return
+  private flushSubmit(generation = this.submitGeneration): void {
-      this.submit()
+    if (!this.submitPending || generation !== this.submitGeneration) return
-    }, 0), 0)
+    this.submitPending = false
+    this.submitGeneration += 1
+    if (this.composer.isDestroyed) return
+    this.submit()
   }
 
   private submit(): void {
@@ -1598,6 +1581,15 @@ export class NanobotTui {
   }
 
   private handleKey = (key: KeyEvent): void => {
+    // The app receives keypresses before the focused Textarea. Seal the pending
+    // submission first so this key is inserted into the next draft.
+    if (this.submitPending) {
+      this.flushSubmit()
+      if (this.quitting || this.composer.isDestroyed) {
+        key.preventDefault()
+        return
+      }
+    }
     if (this.diffViewer.visible) {
       if (key.ctrl && key.name === "c") {
         const selected = this.renderer.getSelection()?.getSelectedText()
@@ -1876,7 +1868,6 @@ export class NanobotTui {
     this.composer.syntaxStyle = this.composerSyntax
     this.syncComposerImageHighlights(this.composer.plainText)
     void this.renderer.idle().catch(() => {}).finally(() => previousComposerSyntax.destroy())
-    this.renderTitleColor()
     this.status.fg = this.palette.muted
     this.meta.fg = this.palette.faint
     this.updateMeta()
@@ -1956,24 +1947,15 @@ export class NanobotTui {
   }
 
   private updateTitle(): void {
-    const identity = this.sessionTitle.trim() || "nanobot"
-    this.titleText.maxWidth = Math.max(8, Math.floor(this.renderer.width * 0.38))
-    this.titleText.content = identity
     const context = this.contextTokens === null
       ? ""
-      : `  ·  ~${formatTokenCount(this.contextTokens)}${this.contextWindowTokens
+      : `     ~${formatTokenCount(this.contextTokens)}${this.contextWindowTokens
         ? `/${formatTokenCount(this.contextWindowTokens)}`
         : ""} ctx`
     this.runtimeControls.updateModel(this.modelName, this.modelPreset)
     this.runtimeControls.updateContext(context)
   }
 
-  private renderTitleColor(): void {
-    this.titleText.fg = this.sessionLoading || this.sessionMenu.visible
-      ? this.palette.accent
-      : this.palette.muted
-  }
-
   private resizeComposer(): void {
     const verticalPadding = this.renderer.height >= 12 ? 1 : 0
     const maxContentHeight = Math.max(1, Math.min(12, Math.floor(this.renderer.height / 3)))
@@ -2384,7 +2366,6 @@ export class NanobotTui {
     this.contextPanel.hide()
     this.clearComposer()
     this.sessionLoading = true
-    this.renderTitleColor()
     const loadId = ++this.sessionLoadId
     this.status.content = "Loading sessions…"
     try {
@@ -2411,7 +2392,6 @@ export class NanobotTui {
         this.defaultModelPreset,
       )
       this.startSessionRefresh()
-      this.renderTitleColor()
       this.sessionMenu.update(this.composer.plainText, limit)
       this.syncComposerPlaceholder()
       this.updateMeta()
@@ -2419,7 +2399,6 @@ export class NanobotTui {
     } catch (error) {
       if (loadId !== this.sessionLoadId) return
       this.sessionLoading = false
-      this.renderTitleColor()
       this.status.content = error instanceof Error ? error.message : String(error)
     }
   }
@@ -2578,7 +2557,6 @@ export class NanobotTui {
     this.sessionLoadId += 1
     this.sessionLoading = false
     this.hideSessionMenu()
-    this.renderTitleColor()
     this.clearComposer()
     this.syncComposerPlaceholder()
     this.composer.focus()

tui/src/host.test.ts

@@ -1,6 +1,9 @@
 import { describe, expect, test } from "bun:test"
 
-import { createTuiHost } from "./host"
+import {
+  configureOpenTuiEnvironment,
+  createTuiHost,
+} from "./host"
 
 async function settle(): Promise<void> {
   await Bun.sleep(0)
@@ -8,6 +11,27 @@ async function settle(): Promise<void> {
 }
 
 describe("TUI host integration", () => {
+  test("disables the explicit-width probe on Windows", () => {
+    const environment: Record<string, string | undefined> = {}
+
+    configureOpenTuiEnvironment(environment, "win32")
+
+    expect(environment.OPENTUI_FORCE_EXPLICIT_WIDTH).toBe("false")
+  })
+
+  test("preserves explicit probe choices and leaves other platforms unchanged", () => {
+    const overridden = {
+      OPENTUI_FORCE_EXPLICIT_WIDTH: "true",
+    }
+    const nonWindows: Record<string, string | undefined> = {}
+
+    configureOpenTuiEnvironment(overridden, "win32")
+    configureOpenTuiEnvironment(nonWindows, "linux")
+
+    expect(overridden.OPENTUI_FORCE_EXPLICIT_WIDTH).toBe("true")
+    expect(nonWindows.OPENTUI_FORCE_EXPLICIT_WIDTH).toBeUndefined()
+  })
+
   test("standalone terminals remain a no-op", async () => {
     const commands: string[][] = []
     const host = createTuiHost({}, async (command) => { commands.push([...command]) })

tui/src/host.ts

@@ -8,6 +8,19 @@ type CommandRunner = (command: readonly string[]) => Promise<void>
 
 const METADATA_SOURCE = "nanobot:tui:metadata"
 
+export function configureOpenTuiEnvironment(
+  environment: Environment = process.env,
+  platform = process.platform,
+): void {
+  if (platform !== "win32") return
+
+  // OpenTUI probes OSC 66 support on the main screen before its renderer is
+  // active. Some Windows terminal hosts do not restore the cursor around that
+  // probe, so shutdown resumes in terminal history instead of below the TUI.
+  // Keep an explicit user choice, but use the safe default on Windows.
+  environment.OPENTUI_FORCE_EXPLICIT_WIDTH ??= "false"
+}
+
 class StandaloneHost implements TuiHost {
   reportTitle(): void {}
   release(): void {}

tui/src/runtime-controls.ts

@@ -309,12 +309,9 @@ export class RuntimeControls {
   }
 
   private render(): void {
-    const runtime = this.modelPreset !== "default"
+    this.modelText.content = `${this.modelPreset} ▾`
-      ? [this.modelPreset, this.model].filter(Boolean).join("  ·  ")
-      : this.model
-    this.modelText.content = `  ·  ${runtime} ▾`
     const access = this.scope.access_mode === "full" ? "full access" : "workspace access"
-    this.accessText.content = `  ·  ${access} ▾`
+    this.accessText.content = `     ${access} ▾`
     this.renderColors()
   }
 

tui/src/transcript.ts

@@ -211,7 +211,7 @@ export class Transcript {
     const title = this.createText(`>_  nanobot  v${options.version}`, "text", true)
     const context = this.createText([
       "",
-      `${options.model}  ·  ${options.access}`,
+      `${options.model}     ${options.access}`,
       options.workspace,
     ].join("\n"), "muted")
     row.add(title)

webui/src/components/settings/models/ModelsSettings.tsx

@@ -546,7 +546,7 @@ export function ModelsSettings({
           >
             {saving || creatingSaving
               ? tx("settings.actions.saving", "Saving...")
-              : tx("settings.actions.savePreset", "Save preset")}
+              : tx("settings.actions.savePreset", "Save")}
           </Button>
         </div>
       </div>

webui/src/components/settings/shared/ModelControls.tsx

@@ -204,13 +204,15 @@ export function ModelIdPicker({
   const providerConfigured = settingsProviderConfigured(settings, effectiveProvider);
   const providerRequiresConfiguration =
     !hasStaticModels && hasConcreteProvider && !providerConfigured;
-  const providerHasBuiltinModels = providerRow?.model_catalog === "builtin";
+  const providerHasManagedModels = ["builtin", "hybrid"].includes(
+    providerRow?.model_catalog ?? "",
+  );
   const providerUsesManualModelIds =
     !hasStaticModels &&
     hasConcreteProvider &&
     providerConfigured &&
     providerRow?.auth_type === "oauth" &&
-    !providerHasBuiltinModels;
+    !providerHasManagedModels;
   const canFetchModels =
     !hasStaticModels &&
     hasConcreteProvider && providerConfigured && !providerUsesManualModelIds;

webui/src/components/thread/ModelPresetBadge.tsx

@@ -6,7 +6,7 @@ import {
   type KeyboardEvent,
   type PointerEvent,
 } from "react";
-import { Check, CircleHelp, SlidersHorizontal, Sparkles } from "lucide-react";
+import { Check, SlidersHorizontal, Sparkles } from "lucide-react";
 import { useTranslation } from "react-i18next";
 
 import {
@@ -539,7 +539,6 @@ function PresetPill({
         "composer-model-badge composer-model-pill inline-flex h-full max-w-full min-w-0 shrink-0 items-center rounded-full border border-border/55 bg-card font-medium text-foreground/70",
         "w-fit",
         "transition-[color,background-color,border-color,transform] duration-150 ease-out group-focus-visible:ring-2 group-focus-visible:ring-ring/45",
-        needsSetup && "border-amber-500/35 bg-amber-50/70 text-amber-900 dark:bg-amber-500/10 dark:text-amber-200",
         isHero ? "gap-1.5 px-2.5 text-[12px]" : "gap-2 px-3 text-[12.5px]",
         offset !== undefined && "composer-model-pill-dock",
       )}
@@ -595,13 +594,13 @@ function PresetProviderIcon({
       data-testid={testId}
       className={cn(
         "grid shrink-0 place-items-center",
-        needsSetup && "text-amber-800 dark:text-amber-200",
+        needsSetup && "text-muted-foreground",
         isHero ? "h-4 w-4" : "h-[18px] w-[18px]",
       )}
       aria-hidden
     >
       {needsSetup ? (
-        <CircleHelp className={cn(isHero ? "h-3 w-3" : "h-3.5 w-3.5")} strokeWidth={1.8} />
+        <Sparkles className={cn(isHero ? "h-3 w-3" : "h-3.5 w-3.5")} strokeWidth={1.8} />
       ) : logoUrl ? (
         <img
           src={logoUrl}

webui/src/components/thread/ModelSetupDialog.tsx

@@ -0,0 +1,123 @@
+import { Check, Cloud, KeyRound, Laptop } from "lucide-react";
+import { useTranslation } from "react-i18next";
+
+import {
+  Dialog,
+  DialogContent,
+  DialogDescription,
+  DialogHeader,
+  DialogTitle,
+} from "@/components/ui/dialog";
+import { cn } from "@/lib/utils";
+
+export interface ModelSetupAvailability {
+  account: boolean;
+  apiKey: boolean;
+  local: boolean;
+}
+
+export type ModelSetupIntent = keyof ModelSetupAvailability;
+
+const SETUP_OPTIONS = [
+  {
+    intent: "account",
+    icon: Cloud,
+    titleKey: "thread.composer.modelSetup.account.title",
+    title: "Connect an account",
+    descriptionKey: "thread.composer.modelSetup.account.description",
+    description: "Use a supported AI subscription.",
+  },
+  {
+    intent: "apiKey",
+    icon: KeyRound,
+    titleKey: "thread.composer.modelSetup.apiKey.title",
+    title: "Use an API key",
+    descriptionKey: "thread.composer.modelSetup.apiKey.description",
+    description: "Bring a key from your preferred provider.",
+  },
+  {
+    intent: "local",
+    icon: Laptop,
+    titleKey: "thread.composer.modelSetup.local.title",
+    title: "Run locally",
+    descriptionKey: "thread.composer.modelSetup.local.description",
+    description: "Connect Ollama, LM Studio, or vLLM.",
+  },
+] as const;
+
+export function ModelSetupDialog({
+  availability,
+  open,
+  onOpenChange,
+  onReturnFocus,
+  onSelect,
+}: {
+  availability: ModelSetupAvailability;
+  open: boolean;
+  onOpenChange: (open: boolean) => void;
+  onReturnFocus: () => void;
+  onSelect: (intent: ModelSetupIntent) => void;
+}) {
+  const { t } = useTranslation();
+
+  return (
+    <Dialog open={open} onOpenChange={onOpenChange}>
+      <DialogContent
+        className="max-w-md gap-5 p-5 sm:p-6"
+        onCloseAutoFocus={(event) => {
+          event.preventDefault();
+          onReturnFocus();
+        }}
+      >
+        <DialogHeader className="pr-7">
+          <DialogTitle className="text-[18px] leading-6">
+            {t("thread.composer.modelSetup.title", { defaultValue: "Choose your AI" })}
+          </DialogTitle>
+          <DialogDescription className="leading-5">
+            {t("thread.composer.modelSetup.description", {
+              defaultValue: "Pick a starting point. You can change models at any time.",
+            })}
+          </DialogDescription>
+        </DialogHeader>
+
+        <div className="space-y-2">
+          {SETUP_OPTIONS.map((option) => {
+            const Icon = option.icon;
+            const ready = availability[option.intent];
+            return (
+              <button
+                key={option.intent}
+                type="button"
+                aria-label={t(option.titleKey, { defaultValue: option.title })}
+                onClick={() => onSelect(option.intent)}
+                className={cn(
+                  "group flex min-h-[68px] w-full items-center gap-3 rounded-control border border-border/55 bg-background px-3.5 py-3 text-left",
+                  "transition-[background-color,border-color,transform] duration-150 ease-out hover:border-border hover:bg-muted/45 active:scale-[0.99]",
+                  "focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-ring/45",
+                )}
+              >
+                <span className="grid h-9 w-9 shrink-0 place-items-center rounded-full bg-muted/70 text-foreground/75 transition-colors group-hover:bg-background">
+                  <Icon className="h-[17px] w-[17px]" strokeWidth={1.8} aria-hidden />
+                </span>
+                <span className="min-w-0 flex-1">
+                  <span className="block text-[14px] font-semibold leading-5 text-foreground">
+                    {t(option.titleKey, { defaultValue: option.title })}
+                  </span>
+                  <span className="mt-0.5 block text-[12px] leading-[18px] text-muted-foreground">
+                    {t(option.descriptionKey, { defaultValue: option.description })}
+                  </span>
+                </span>
+                {ready ? (
+                  <span className="inline-flex shrink-0 items-center gap-1 rounded-full bg-emerald-500/10 px-2 py-1 text-[11px] font-medium text-emerald-700 dark:text-emerald-300">
+                    <Check className="h-3 w-3" strokeWidth={2.2} aria-hidden />
+                    {t("thread.composer.modelSetup.ready", { defaultValue: "Ready" })}
+                  </span>
+                ) : null}
+              </button>
+            );
+          })}
+        </div>
+      </DialogContent>
+    </Dialog>
+  );
+}

webui/src/components/thread/ThreadComposer.tsx

@@ -70,6 +70,10 @@ import {
   ModelPresetBadge,
   type ModelPresetOption,
 } from "@/components/thread/ModelPresetBadge";
+import {
+  ModelSetupDialog,
+  type ModelSetupAvailability,
+} from "@/components/thread/ModelSetupDialog";
 import {
   ACCEPT_ATTR,
   MAX_ATTACHMENTS_PER_MESSAGE,
@@ -298,6 +302,7 @@ interface ThreadComposerProps {
   modelProvider?: string | null;
   modelProviderLabel?: string | null;
   modelNeedsSetup?: boolean;
+  modelSetupAvailability?: ModelSetupAvailability;
   fallbackModelName?: string | null;
   onModelBadgeClick?: () => void;
   onManageModels?: () => void;
@@ -997,6 +1002,7 @@ export function ThreadComposer({
   modelProvider = null,
   modelProviderLabel = null,
   modelNeedsSetup = false,
+  modelSetupAvailability = { account: false, apiKey: false, local: false },
   fallbackModelName = null,
   onModelBadgeClick,
   onManageModels,
@@ -1036,6 +1042,7 @@ export function ThreadComposer({
   } | null>(null);
   const [inlineError, setInlineError] = useState<string | null>(null);
   const [sendPending, setSendPending] = useState(false);
+  const [modelSetupOpen, setModelSetupOpen] = useState(false);
   const interactionDisabled = !!disabled || sendPending;
   const [voiceErrorFading, setVoiceErrorFading] = useState(false);
   const [slashMenuDismissed, setSlashMenuDismissed] = useState(false);
@@ -2008,7 +2015,7 @@ export function ThreadComposer({
 
   const submit = useCallback(() => {
     if (modelNeedsSetup) {
-      onModelBadgeClick?.();
+      setModelSetupOpen(true);
       return;
     }
     if (!canSend) return;
@@ -2116,7 +2123,6 @@ export function ThreadComposer({
     isStreaming,
     maxTextBytes,
     modelNeedsSetup,
-    onModelBadgeClick,
     onSend,
     onStop,
     onQuotedContextChange,
@@ -2127,6 +2133,15 @@ export function ThreadComposer({
     value,
   ]);
 
+  const openModelSetup = useCallback(() => {
+    setModelSetupOpen(true);
+  }, []);
+
+  const continueModelSetup = useCallback(() => {
+    setModelSetupOpen(false);
+    onModelBadgeClick?.();
+  }, [onModelBadgeClick]);
+
   const onKeyDown = (e: ReactKeyboardEvent<HTMLTextAreaElement>) => {
     if (showCliAppMenu) {
       if (e.key === "ArrowDown") {
@@ -2548,7 +2563,7 @@ export function ThreadComposer({
                 needsSetup={modelNeedsSetup}
                 fallbackModelName={fallbackModelName}
                 isHero={isHero}
-                onClick={modelNeedsSetup ? onModelBadgeClick : undefined}
+                onClick={modelNeedsSetup ? openModelSetup : undefined}
               />
             ) : null}
             {!voiceRecorder.isRecording ? <ComposerContextBadge usage={contextUsage} /> : null}
@@ -2607,10 +2622,10 @@ export function ThreadComposer({
                 showStopButton
                   ? t("thread.composer.stop")
                   : modelNeedsSetup
-                    ? t("thread.composer.configureModel", { defaultValue: "Configure model" })
+                    ? t("thread.composer.openModelSetup", { defaultValue: "Open AI setup" })
                     : t("thread.composer.send")
               }
-              onClick={showStopButton ? handleStop : modelNeedsSetup ? onModelBadgeClick : undefined}
+              onClick={showStopButton ? handleStop : modelNeedsSetup ? openModelSetup : undefined}
               className={cn(
                 "thread-composer-action touch-target rounded-full transition-transform",
                 showStopButton
@@ -2656,6 +2671,13 @@ export function ThreadComposer({
           </div>
         ) : null}
       </div>
+      <ModelSetupDialog
+        availability={modelSetupAvailability}
+        open={modelSetupOpen}
+        onOpenChange={setModelSetupOpen}
+        onReturnFocus={() => textareaRef.current?.focus()}
+        onSelect={continueModelSetup}
+      />
     </form>
   );
 }

webui/src/components/thread/ThreadShell.tsx

@@ -14,6 +14,7 @@ import {
   type ComposerContextUsage,
 } from "@/components/thread/ThreadComposer";
 import type { ModelPresetOption } from "@/components/thread/ModelPresetBadge";
+import type { ModelSetupAvailability } from "@/components/thread/ModelSetupDialog";
 import { ThreadHeader } from "@/components/thread/ThreadHeader";
 import { StreamErrorNotice } from "@/components/thread/StreamErrorNotice";
 import { ThreadViewport, type ThreadViewportHandle } from "@/components/thread/ThreadViewport";
@@ -382,6 +383,22 @@ interface ModelBadgeInfo {
   needsSetup: boolean;
 }
 
+const LOCAL_MODEL_PROVIDERS = new Set(["atomic_chat", "lm_studio", "ollama", "vllm"]);
+
+function modelSetupAvailability(settings: SettingsPayload | null): ModelSetupAvailability {
+  const configured = settings?.providers.filter((provider) => provider.configured) ?? [];
+  const isLocal = (provider: SettingsPayload["providers"][number]) => {
+    if (LOCAL_MODEL_PROVIDERS.has(provider.name)) return true;
+    const apiBase = provider.api_base?.trim().toLowerCase() ?? "";
+    return apiBase.includes("localhost") || apiBase.includes("127.0.0.1") || apiBase.includes("[::1]");
+  };
+  return {
+    account: configured.some((provider) => provider.auth_type === "oauth"),
+    apiKey: configured.some((provider) => provider.auth_type !== "oauth" && !isLocal(provider)),
+    local: configured.some(isLocal),
+  };
+}
+
 function modelPresetForBadge(
   settings: SettingsPayload | null,
   scopedPreset: string | null,
@@ -961,8 +978,9 @@ export function ThreadShell({
     [activeModelPreset, modelName, settings],
   );
   const modelBadgeLabel = modelBadge.needsSetup
-    ? t("thread.composer.modelNotConfigured", { defaultValue: "Model not configured" })
+    ? t("thread.composer.chooseAI", { defaultValue: "Choose your AI" })
     : modelBadge.label;
+  const setupAvailability = useMemo(() => modelSetupAvailability(settings), [settings]);
   useEffect(() => {
     if (showHeroComposer && !wasShowingHeroComposerRef.current) {
       setHeroGreetingKey(randomHeroGreetingKey());
@@ -1517,6 +1535,7 @@ export function ThreadShell({
           modelProvider={modelBadge.provider}
           modelProviderLabel={modelBadge.providerLabel}
           modelNeedsSetup={modelBadge.needsSetup}
+          modelSetupAvailability={setupAvailability}
           fallbackModelName={fallbackModelName}
           onModelBadgeClick={modelBadge.needsSetup ? onOpenModelSettings : undefined}
           onManageModels={onOpenModelSettings}
@@ -1566,6 +1585,7 @@ export function ThreadShell({
           modelProvider={modelBadge.provider}
           modelProviderLabel={modelBadge.providerLabel}
           modelNeedsSetup={modelBadge.needsSetup}
+          modelSetupAvailability={setupAvailability}
           fallbackModelName={fallbackModelName}
           onModelBadgeClick={modelBadge.needsSetup ? onOpenModelSettings : undefined}
           onManageModels={onOpenModelSettings}

webui/src/i18n/locales/en/common.json

@@ -483,7 +483,7 @@
       "save": "Save",
       "saving": "Saving",
       "saveOrder": "Save order",
-      "savePreset": "Save preset",
+      "savePreset": "Save",
       "edit": "Edit",
       "delete": "Delete",
       "deleting": "Deleting...",
@@ -1193,8 +1193,25 @@
       "stop": "Stop response",
       "quotedContext": "Quoted context",
       "removeQuotedContext": "Remove quoted context",
-      "modelNotConfigured": "Model not configured",
+      "openModelSetup": "Open AI setup",
-      "configureModel": "Configure model",
+      "chooseAI": "Choose your AI",
+      "modelSetup": {
+        "title": "Choose your AI",
+        "description": "Pick a starting point. You can change models at any time.",
+        "ready": "Ready",
+        "account": {
+          "title": "Connect an account",
+          "description": "Use a supported AI subscription."
+        },
+        "apiKey": {
+          "title": "Use an API key",
+          "description": "Bring a key from your preferred provider."
+        },
+        "local": {
+          "title": "Run locally",
+          "description": "Connect Ollama, LM Studio, or vLLM."
+        }
+      },
       "switchModel": "Switch model for this chat",
       "manageModels": "Manage models",
       "context": {

webui/src/i18n/locales/es/common.json

@@ -271,7 +271,7 @@
       "save": "Guardar",
       "saving": "Guardando",
       "saveOrder": "Guardar orden",
-      "savePreset": "Guardar preajuste",
+      "savePreset": "Guardar",
       "delete": "Eliminar",
       "deleting": "Eliminando...",
       "edit": "Editar",
@@ -1180,8 +1180,25 @@
       "stop": "Detener respuesta",
       "quotedContext": "Contexto citado",
       "removeQuotedContext": "Quitar contexto citado",
-      "modelNotConfigured": "Modelo no configurado",
+      "openModelSetup": "Abrir configuración de IA",
-      "configureModel": "Configurar modelo",
+      "chooseAI": "Elige tu IA",
+      "modelSetup": {
+        "title": "Elige tu IA",
+        "description": "Elige cómo empezar. Puedes cambiar de modelo en cualquier momento.",
+        "ready": "Listo",
+        "account": {
+          "title": "Conectar una cuenta",
+          "description": "Usa una suscripción de IA compatible."
+        },
+        "apiKey": {
+          "title": "Usar una clave API",
+          "description": "Usa una clave de tu proveedor preferido."
+        },
+        "local": {
+          "title": "Ejecutar localmente",
+          "description": "Conecta Ollama, LM Studio o vLLM."
+        }
+      },
       "switchModel": "Cambiar el modelo de este chat",
       "manageModels": "Gestionar modelos",
       "context": {

webui/src/i18n/locales/fr/common.json

@@ -271,7 +271,7 @@
       "save": "Enregistrer",
       "saving": "Enregistrement",
       "saveOrder": "Enregistrer l’ordre",
-      "savePreset": "Enregistrer le préréglage",
+      "savePreset": "Enregistrer",
       "delete": "Supprimer",
       "deleting": "Suppression...",
       "edit": "Modifier",
@@ -1179,8 +1179,25 @@
       "stop": "Arrêter la réponse",
       "quotedContext": "Contexte cité",
       "removeQuotedContext": "Supprimer le contexte cité",
-      "modelNotConfigured": "Modèle non configuré",
+      "openModelSetup": "Ouvrir la configuration de l’IA",
-      "configureModel": "Configurer le modèle",
+      "chooseAI": "Choisissez votre IA",
+      "modelSetup": {
+        "title": "Choisissez votre IA",
+        "description": "Choisissez un point de départ. Vous pourrez changer de modèle à tout moment.",
+        "ready": "Prêt",
+        "account": {
+          "title": "Connecter un compte",
+          "description": "Utilisez un abonnement IA compatible."
+        },
+        "apiKey": {
+          "title": "Utiliser une clé API",
+          "description": "Utilisez la clé du fournisseur de votre choix."
+        },
+        "local": {
+          "title": "Exécuter localement",
+          "description": "Connectez Ollama, LM Studio ou vLLM."
+        }
+      },
       "switchModel": "Changer le modèle de cette conversation",
       "manageModels": "Gérer les modèles",
       "context": {

webui/src/i18n/locales/id/common.json

@@ -271,7 +271,7 @@
       "save": "Simpan",
       "saving": "Menyimpan",
       "saveOrder": "Simpan urutan",
-      "savePreset": "Simpan prasetel",
+      "savePreset": "Simpan",
       "delete": "Hapus",
       "deleting": "Menghapus...",
       "edit": "Ubah",
@@ -1179,8 +1179,25 @@
       "stop": "Hentikan respons",
       "quotedContext": "Konteks kutipan",
       "removeQuotedContext": "Hapus konteks kutipan",
-      "modelNotConfigured": "Model belum dikonfigurasi",
+      "openModelSetup": "Buka penyiapan AI",
-      "configureModel": "Konfigurasi model",
+      "chooseAI": "Pilih AI Anda",
+      "modelSetup": {
+        "title": "Pilih AI Anda",
+        "description": "Pilih cara memulai. Anda dapat mengganti model kapan saja.",
+        "ready": "Siap",
+        "account": {
+          "title": "Hubungkan akun",
+          "description": "Gunakan langganan AI yang didukung."
+        },
+        "apiKey": {
+          "title": "Gunakan kunci API",
+          "description": "Gunakan kunci dari penyedia pilihan Anda."
+        },
+        "local": {
+          "title": "Jalankan secara lokal",
+          "description": "Hubungkan Ollama, LM Studio, atau vLLM."
+        }
+      },
       "switchModel": "Ganti model untuk percakapan ini",
       "manageModels": "Kelola model",
       "context": {

webui/src/i18n/locales/ja/common.json

@@ -271,7 +271,7 @@
       "save": "保存",
       "saving": "保存中",
       "saveOrder": "順序を保存",
-      "savePreset": "プリセットを保存",
+      "savePreset": "保存",
       "delete": "削除",
       "deleting": "削除中...",
       "edit": "編集",
@@ -1179,8 +1179,25 @@
       "stop": "応答を停止",
       "quotedContext": "引用したコンテキスト",
       "removeQuotedContext": "引用したコンテキストを削除",
-      "modelNotConfigured": "モデルが未設定です",
+      "openModelSetup": "AI 設定を開く",
-      "configureModel": "モデルを設定",
+      "chooseAI": "AI を選択",
+      "modelSetup": {
+        "title": "AI を選択",
+        "description": "開始方法を選んでください。モデルはいつでも変更できます。",
+        "ready": "準備完了",
+        "account": {
+          "title": "アカウントを接続",
+          "description": "対応する AI サブスクリプションを使用します。"
+        },
+        "apiKey": {
+          "title": "API キーを使用",
+          "description": "お好みのプロバイダーのキーを使用します。"
+        },
+        "local": {
+          "title": "ローカルで実行",
+          "description": "Ollama、LM Studio、vLLM に接続します。"
+        }
+      },
       "switchModel": "この会話で使うモデルを切り替える",
       "manageModels": "モデルを管理",
       "context": {

webui/src/i18n/locales/ko/common.json

@@ -271,7 +271,7 @@
       "save": "저장",
       "saving": "저장 중",
       "saveOrder": "순서 저장",
-      "savePreset": "프리셋 저장",
+      "savePreset": "저장",
       "delete": "삭제",
       "deleting": "삭제 중...",
       "edit": "편집",
@@ -1179,8 +1179,25 @@
       "stop": "응답 중지",
       "quotedContext": "인용한 문맥",
       "removeQuotedContext": "인용한 문맥 제거",
-      "modelNotConfigured": "모델이 설정되지 않음",
+      "openModelSetup": "AI 설정 열기",
-      "configureModel": "모델 설정",
+      "chooseAI": "AI 선택",
+      "modelSetup": {
+        "title": "AI 선택",
+        "description": "시작 방법을 선택하세요. 모델은 언제든 변경할 수 있습니다.",
+        "ready": "준비됨",
+        "account": {
+          "title": "계정 연결",
+          "description": "지원되는 AI 구독을 사용합니다."
+        },
+        "apiKey": {
+          "title": "API 키 사용",
+          "description": "선호하는 제공업체의 키를 사용합니다."
+        },
+        "local": {
+          "title": "로컬에서 실행",
+          "description": "Ollama, LM Studio 또는 vLLM에 연결합니다."
+        }
+      },
       "switchModel": "이 대화에서 사용할 모델 전환",
       "manageModels": "모델 관리",
       "context": {

webui/src/i18n/locales/pt-BR/common.json

@@ -483,7 +483,7 @@
       "save": "Salvar",
       "saving": "Salvando",
       "saveOrder": "Salvar ordem",
-      "savePreset": "Salvar predefinição",
+      "savePreset": "Salvar",
       "delete": "Excluir",
       "deleting": "Excluindo...",
       "edit": "Editar",
@@ -1193,8 +1193,25 @@
       "stop": "Parar resposta",
       "quotedContext": "Contexto citado",
       "removeQuotedContext": "Remover contexto citado",
-      "modelNotConfigured": "Modelo não configurado",
+      "openModelSetup": "Abrir configuração de IA",
-      "configureModel": "Configurar modelo",
+      "chooseAI": "Escolha sua IA",
+      "modelSetup": {
+        "title": "Escolha sua IA",
+        "description": "Escolha como começar. Você pode trocar de modelo a qualquer momento.",
+        "ready": "Pronto",
+        "account": {
+          "title": "Conectar uma conta",
+          "description": "Use uma assinatura de IA compatível."
+        },
+        "apiKey": {
+          "title": "Usar uma chave de API",
+          "description": "Use uma chave do seu provedor preferido."
+        },
+        "local": {
+          "title": "Executar localmente",
+          "description": "Conecte o Ollama, LM Studio ou vLLM."
+        }
+      },
       "switchModel": "Alternar o modelo desta conversa",
       "manageModels": "Gerenciar modelos",
       "context": {

webui/src/i18n/locales/vi/common.json

@@ -271,7 +271,7 @@
       "save": "Lưu",
       "saving": "Đang lưu",
       "saveOrder": "Lưu thứ tự",
-      "savePreset": "Lưu cấu hình đặt trước",
+      "savePreset": "Lưu",
       "delete": "Xóa",
       "deleting": "Đang xóa...",
       "edit": "Sửa",
@@ -1179,8 +1179,25 @@
       "stop": "Dừng phản hồi",
       "quotedContext": "Ngữ cảnh được trích dẫn",
       "removeQuotedContext": "Xóa ngữ cảnh được trích dẫn",
-      "modelNotConfigured": "Chưa cấu hình mô hình",
+      "openModelSetup": "Mở thiết lập AI",
-      "configureModel": "Cấu hình mô hình",
+      "chooseAI": "Chọn AI của bạn",
+      "modelSetup": {
+        "title": "Chọn AI của bạn",
+        "description": "Chọn cách bắt đầu. Bạn có thể đổi mô hình bất cứ lúc nào.",
+        "ready": "Sẵn sàng",
+        "account": {
+          "title": "Kết nối tài khoản",
+          "description": "Dùng gói đăng ký AI được hỗ trợ."
+        },
+        "apiKey": {
+          "title": "Dùng khóa API",
+          "description": "Dùng khóa từ nhà cung cấp bạn chọn."
+        },
+        "local": {
+          "title": "Chạy cục bộ",
+          "description": "Kết nối Ollama, LM Studio hoặc vLLM."
+        }
+      },
       "switchModel": "Chuyển mô hình cho cuộc trò chuyện này",
       "manageModels": "Quản lý mô hình",
       "context": {

webui/src/i18n/locales/zh-CN/common.json

@@ -483,7 +483,7 @@
       "save": "保存",
       "saving": "正在保存",
       "saveOrder": "保存顺序",
-      "savePreset": "保存预设",
+      "savePreset": "保存",
       "edit": "编辑",
       "delete": "删除",
       "deleting": "正在删除...",
@@ -1192,8 +1192,25 @@
       "stop": "停止响应",
       "quotedContext": "引用内容",
       "removeQuotedContext": "移除引用内容",
-      "modelNotConfigured": "模型未配置",
+      "openModelSetup": "打开 AI 设置",
-      "configureModel": "配置模型",
+      "chooseAI": "选择你的 AI",
+      "modelSetup": {
+        "title": "选择你的 AI",
+        "description": "选择一种开始方式，之后可随时更换模型。",
+        "ready": "已就绪",
+        "account": {
+          "title": "连接账户",
+          "description": "使用支持的 AI 订阅。"
+        },
+        "apiKey": {
+          "title": "使用 API 密钥",
+          "description": "使用你偏好的服务商密钥。"
+        },
+        "local": {
+          "title": "在本地运行",
+          "description": "连接 Ollama、LM Studio 或 vLLM。"
+        }
+      },
       "switchModel": "切换本次对话所用模型",
       "manageModels": "管理模型预设",
       "context": {

webui/src/i18n/locales/zh-TW/common.json

@@ -271,7 +271,7 @@
       "save": "儲存",
       "saving": "正在儲存",
       "saveOrder": "儲存順序",
-      "savePreset": "儲存預設",
+      "savePreset": "儲存",
       "delete": "刪除",
       "deleting": "正在刪除…",
       "edit": "編輯",
@@ -1179,8 +1179,25 @@
       "stop": "停止回覆",
       "quotedContext": "引用內容",
       "removeQuotedContext": "移除引用內容",
-      "modelNotConfigured": "尚未設定模型",
+      "openModelSetup": "開啟 AI 設定",
-      "configureModel": "設定模型",
+      "chooseAI": "選擇你的 AI",
+      "modelSetup": {
+        "title": "選擇你的 AI",
+        "description": "選擇一種開始方式，之後可隨時更換模型。",
+        "ready": "已就緒",
+        "account": {
+          "title": "連結帳戶",
+          "description": "使用支援的 AI 訂閱。"
+        },
+        "apiKey": {
+          "title": "使用 API 金鑰",
+          "description": "使用你偏好的服務商金鑰。"
+        },
+        "local": {
+          "title": "在本機執行",
+          "description": "連結 Ollama、LM Studio 或 vLLM。"
+        }
+      },
       "switchModel": "切換此對話使用的模型",
       "manageModels": "管理模型預設",
       "context": {

webui/src/lib/types.ts

@@ -510,6 +510,8 @@ interface ProviderModelInfo {
   description?: string | null;
   owned_by?: string | null;
   context_window?: number | null;
+  reasoning_efforts?: string[];
+  supports_backend_search?: boolean;
 }
 
 export interface ProviderModelsPayload {
@@ -521,7 +523,15 @@ export interface ProviderModelsPayload {
     | "not_configured"
     | "missing_api_base"
     | "error";
-  catalog_kind: "builtin" | "official" | "catalog" | "local" | "custom" | "unsupported";
+  catalog_kind:
+    | "builtin"
+    | "hybrid"
+    | "official"
+    | "catalog"
+    | "local"
+    | "custom"
+    | "unsupported";
+  source?: "remote" | "cache" | "stale" | "fallback";
   models: ProviderModelInfo[];
   model_count: number;
   message?: string | null;

webui/src/tests/app-layout.test.tsx

@@ -2531,7 +2531,7 @@ describe("App layout", () => {
     ).toBe(true);
     await user.click(screen.getByRole("button", { name: "Select model" }));
     await user.click(await screen.findByRole("option", { name: /openai\/gpt-4o-mini/ }));
-    expect(screen.getByRole("button", { name: "Save preset" })).toBeEnabled();
+    expect(screen.getByRole("button", { name: "Save" })).toBeEnabled();
     fireEvent.click(screen.getByRole("button", { name: "Cancel" }));
     expect(screen.queryByText("Up to date.")).not.toBeInTheDocument();
     fireEvent.click(

webui/src/tests/settings-models.test.tsx

@@ -141,7 +141,7 @@ describe("Settings models", () => {
     fireEvent.change(screen.getByLabelText("Temperature"), {
       target: { value: "0.4" },
     });
-    fireEvent.click(screen.getByRole("button", { name: "Save preset" }));
+    fireEvent.click(screen.getByRole("button", { name: "Save" }));
 
     await waitFor(() => {
       expect(requestMutationMock).toHaveBeenCalledWith(
@@ -173,7 +173,7 @@ describe("Settings models", () => {
 
     const nameInput = screen.getByRole("textbox", { name: "Preset name" });
     fireEvent.change(nameInput, { target: { value: "Codex" } });
-    fireEvent.click(screen.getByRole("button", { name: "Save preset" }));
+    fireEvent.click(screen.getByRole("button", { name: "Save" }));
 
     await waitFor(() => {
       expect(requestMutationMock).toHaveBeenCalledWith(
@@ -196,7 +196,7 @@ describe("Settings models", () => {
 
     const nameInput = screen.getByRole("textbox", { name: "Preset name" });
     fireEvent.change(nameInput, { target: { value: "Codex" } });
-    fireEvent.click(screen.getByRole("button", { name: "Save preset" }));
+    fireEvent.click(screen.getByRole("button", { name: "Save" }));
 
     expect(await screen.findByRole("alert")).toHaveTextContent(
       "A preset with this name already exists.",
@@ -368,7 +368,7 @@ describe("Settings models", () => {
 
     expect(screen.queryByRole("button", { name: "Save order" })).not.toBeInTheDocument();
     expect(screen.getByLabelText("Temperature")).toHaveValue(0.4);
-    expect(screen.getByRole("button", { name: "Save preset" })).toBeEnabled();
+    expect(screen.getByRole("button", { name: "Save" })).toBeEnabled();
   });
 
   it("keeps repeated fallback preset rows stable when changing the primary preset", async () => {
@@ -604,7 +604,7 @@ describe("Settings models", () => {
     );
     fireEvent.click(screen.getByRole("button", { name: "New model preset" }));
     expect(screen.queryByRole("dialog", { name: "New model preset" })).not.toBeInTheDocument();
-    expect(screen.getByRole("button", { name: "Save preset" })).toBeDisabled();
+    expect(screen.getByRole("button", { name: "Save" })).toBeDisabled();
     expect(
       screen.queryByText("Complete the preset before saving."),
     ).not.toBeInTheDocument();
@@ -619,7 +619,7 @@ describe("Settings models", () => {
       target: { value: "openai/gpt-4o-mini" },
     });
     fireEvent.keyDown(modelSearch, { key: "Enter" });
-    const saveButton = screen.getByRole("button", { name: "Save preset" });
+    const saveButton = screen.getByRole("button", { name: "Save" });
     expect(saveButton).toBeEnabled();
     fireEvent.click(saveButton);
 
@@ -656,7 +656,7 @@ describe("Settings models", () => {
     });
     fireEvent.change(modelSearch, { target: { value: "openai/gpt-4o-mini" } });
     fireEvent.keyDown(modelSearch, { key: "Enter" });
-    fireEvent.click(screen.getByRole("button", { name: "Save preset" }));
+    fireEvent.click(screen.getByRole("button", { name: "Save" }));
 
     expect(requestMutationMock).not.toHaveBeenCalled();
     expect(nameInput).toHaveAttribute("aria-invalid", "true");
@@ -1295,6 +1295,88 @@ describe("Settings models", () => {
     );
   });
 
+  it("loads hybrid online models for configured OAuth providers", async () => {
+    const base = settingsPayload();
+    const payload: SettingsPayload = {
+      ...base,
+      agent: {
+        ...base.agent,
+        model: "xai-grok/grok-4.5",
+        provider: "xai_grok",
+        resolved_provider: "xai_grok",
+      },
+      model_presets: [
+        {
+          ...base.model_presets[0],
+          model: "xai-grok/grok-4.5",
+          provider: "xai_grok",
+        },
+      ],
+      providers: [
+        {
+          name: "xai_grok",
+          label: "xAI Grok",
+          configured: true,
+          auth_type: "oauth",
+          api_key_required: false,
+          api_key_hint: null,
+          api_base: null,
+          default_api_base: "https://cli-chat-proxy.grok.com/v1",
+          model_catalog: "hybrid",
+          oauth_account: "acct-test",
+          oauth_expires_at: null,
+          oauth_login_supported: true,
+        },
+      ],
+    };
+    const fetchMock = vi.fn(async (input: RequestInfo | URL) => {
+      const url = String(input);
+      if (url === "/api/settings/provider-models?provider=xai_grok") {
+        return jsonResponse({
+          provider: "xai_grok",
+          label: "xAI Grok",
+          status: "available",
+          catalog_kind: "hybrid",
+          source: "remote",
+          models: [
+            {
+              id: "xai-grok/grok-4.6",
+              label: "Grok 4.6",
+              description: "Latest frontier model",
+              owned_by: "xAI",
+              context_window: 500_000,
+            },
+            {
+              id: "xai-grok/grok-4.5",
+              label: "Grok 4.5",
+              owned_by: "xAI",
+              context_window: 500_000,
+            },
+          ],
+          model_count: 2,
+          fetched_at: 1,
+        });
+      }
+      return { ok: false, status: 404, json: async () => ({}) } as Response;
+    });
+    vi.stubGlobal("fetch", fetchMock);
+
+    renderSettingsView({ initialSection: "models", initialSettings: payload });
+
+    await togglePresetEditor();
+    const modelButtons = await screen.findAllByRole("button", {
+      name: /xai-grok\/grok-4\.5/i,
+    });
+    await openPopover(modelButtons[modelButtons.length - 1]);
+
+    expect(await screen.findByText("Grok 4.6")).toBeInTheDocument();
+    expect(screen.getByText(/Latest frontier model/)).toBeInTheDocument();
+    expect(fetchMock).toHaveBeenCalledWith(
+      "/api/settings/provider-models?provider=xai_grok",
+      expect.objectContaining({ headers: { Authorization: "Bearer tok" } }),
+    );
+  });
+
   it("creates presets in the inline editor and can cancel without opening a dialog", async () => {
     vi.stubGlobal(
       "fetch",
@@ -1417,7 +1499,7 @@ describe("Settings models", () => {
     fireEvent.change(screen.getByLabelText("Reasoning effort"), {
       target: { value: "provider-native-mode" },
     });
-    fireEvent.click(screen.getByRole("button", { name: "Save preset" }));
+    fireEvent.click(screen.getByRole("button", { name: "Save" }));
 
     await waitFor(() =>
       expect(fetchMock).toHaveBeenCalledWith(

webui/src/tests/thread-shell.test.tsx

@@ -681,7 +681,7 @@ describe("ThreadShell", () => {
     );
 
     expect(await screen.findByTitle("fast · gpt-5.5 · OpenAI Codex")).toBeInTheDocument();
-    expect(screen.queryByRole("button", { name: "Model not configured" })).not.toBeInTheDocument();
+    expect(screen.queryByRole("button", { name: "Choose your AI" })).not.toBeInTheDocument();
   });
 
   it("switches through every named preset while preserving call-order priority", async () => {
@@ -763,7 +763,7 @@ describe("ThreadShell", () => {
     );
 
     expect(await screen.findByTitle("fast · gpt-4 · Company Proxy")).toBeInTheDocument();
-    expect(screen.queryByRole("button", { name: "Model not configured" })).not.toBeInTheDocument();
+    expect(screen.queryByRole("button", { name: "Choose your AI" })).not.toBeInTheDocument();
   });
 
   it("shows the effective fallback model in the composer badge", async () => {
@@ -835,7 +835,7 @@ describe("ThreadShell", () => {
     expect(screen.getByText("Default")).toBeInTheDocument();
   });
 
-  it("opens model settings from the unconfigured model badge", async () => {
+  it("opens first-run model setup without clearing the draft", async () => {
     const client = makeClient();
     const settings = modelSettings("openai-codex/gpt-5.1-codex", "openai_codex");
     settings.agent.has_api_key = false;
@@ -844,6 +844,20 @@ describe("ThreadShell", () => {
         ? { ...provider, auth_type: "oauth", configured: false }
         : provider,
     );
+    settings.providers.push(
+      {
+        name: "xai_grok",
+        label: "xAI Grok",
+        auth_type: "oauth",
+        configured: true,
+      },
+      {
+        name: "ollama",
+        label: "Ollama",
+        configured: true,
+        api_base: "http://127.0.0.1:11434",
+      },
+    );
     const onOpenModelSettings = vi.fn();
 
     render(
@@ -860,17 +874,31 @@ describe("ThreadShell", () => {
       ),
     );
 
-    const badge = await screen.findByRole("button", { name: "Model not configured" });
+    const badge = await screen.findByRole("button", { name: "Choose your AI" });
-    expect(screen.getByTestId("composer-model-setup-icon")).toBeInTheDocument();
+    const setupIcon = screen.getByTestId("composer-model-setup-icon");
+    expect(setupIcon).toBeInTheDocument();
+    expect(setupIcon.parentElement).not.toHaveClass("border-amber-500/35");
     expect(screen.queryByTestId("composer-model-logo-openai_codex")).not.toBeInTheDocument();
     fireEvent.click(badge);
-    expect(onOpenModelSettings).toHaveBeenCalledTimes(1);
+    expect(await screen.findByRole("dialog", { name: "Choose your AI" })).toBeInTheDocument();
+    expect(screen.getAllByText("Ready")).toHaveLength(3);
+    expect(onOpenModelSettings).not.toHaveBeenCalled();
 
-    fireEvent.change(screen.getByRole("textbox", { name: "Message input" }), {
+    fireEvent.click(screen.getByRole("button", { name: "Close" }));
+
+    const input = screen.getByRole("textbox", { name: "Message input" });
+    fireEvent.change(input, {
       target: { value: "hello" },
     });
-    fireEvent.click(screen.getByRole("button", { name: "Configure model" }));
+    fireEvent.keyDown(input, { key: "Enter", code: "Enter" });
-    expect(onOpenModelSettings).toHaveBeenCalledTimes(2);
+
+    expect(await screen.findByRole("dialog", { name: "Choose your AI" })).toBeInTheDocument();
+    expect(input).toHaveValue("hello");
+    fireEvent.click(screen.getByRole("button", { name: "Use an API key" }));
+
+    expect(onOpenModelSettings).toHaveBeenCalledTimes(1);
+    expect(input).toHaveValue("hello");
+    await waitFor(() => expect(input).toHaveFocus());
     expect(client.sendMessage).not.toHaveBeenCalled();
   });