fix(providers): harden OAuth model discovery

docs/cli-reference.md

@@ -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 provider reads xAI's model catalog and includes the server-hosted `x_search`
-tool only when the selected model advertises `supportsBackendSearch`. 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.
+The default model is `xai-grok/grok-4.6` with a 500,000-token context window.
+The provider reads and caches xAI's online model catalog for both WebUI model
+selection and runtime capabilities. Newly available models appear automatically;
+when discovery fails, the last successful catalog or built-in fallback remains
+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"

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
-exposes the hosted `x_search` tool only when the selected model advertises
-`supportsBackendSearch`; otherwise the model runs without hosted X Search.
+This selects `xai-grok/grok-4.6`. The WebUI model selector reads xAI's online
+model catalog, so newly available subscription models appear without a nanobot
+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

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/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
-            if provider_context is not None
-            else None
+            provider_context.conversation_state 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,
-        model: str | None = None, max_tokens: int = 4096, temperature: float = 0.7,
+        self,
+        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,
-        model: str | None = None, max_tokens: int = 4096, temperature: float = 0.7,
+        self,
+        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({
-            key: value
-            for key, value in item.items()
-            if key != "id"
-        })
+        sanitized_input.append({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 = (
-                    response.status_code in {400, 404, 422}
-                    and any(
-                        marker in raw.lower()
-                        for marker in (
-                            "context_management",
-                            "compact_threshold",
-                            "compaction_trigger",
-                        )
+                compaction_unsupported = response.status_code in {400, 404, 422} and any(
+                    marker in raw.lower()
+                    for marker in (
+                        "context_management",
+                        "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"
-_MODEL_CAPABILITIES_TTL_S = 5 * 60
+_HOSTED_SEARCH_MAX_TURNS = 5
 _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:
-        now = time.monotonic()
-        capabilities = self._model_capabilities
-        if (
-            capabilities is None
-            or now - self._model_capabilities_fetched_at >= _MODEL_CAPABILITIES_TTL_S
-        ):
-            try:
-                capabilities = await _fetch_xai_model_capabilities(
-                    DEFAULT_XAI_GROK_MODELS_URL,
-                    _build_model_headers(token),
-                    proxy=self.proxy,
-                )
-            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 _supports_backend_search(self, model: str) -> bool:
+        catalog = await asyncio.to_thread(
+            get_xai_grok_model_catalog,
+            self.proxy,
+        )
+        if catalog.message:
+            logger.warning(
+                "xAI model catalog unavailable; hosted X Search disabled unless cached: {}",
+                catalog.message,
+            )
+        info = catalog.find(model)
+        return bool(info and info.supports_backend_search)
 
     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 = (
-                isinstance(configured_tools, list)
-                and any(
-                    _is_hosted_x_search_tool(tool)
-                    for tool in cast(list[object], configured_tools)
-                )
+            configured_hosted_search = isinstance(configured_tools, list) 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({
-                    key: value
-                    for key, value in self._extra_body.items()
-                    if key != "tools"
-                })
+                body.update(
+                    {key: value 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:
-                result = await _request_xai(
-                    DEFAULT_XAI_GROK_URL,
-                    headers,
-                    body,
-                    proxy=self.proxy,
-                    on_content_delta=on_content_delta,
-                    on_thinking_delta=on_thinking_delta,
-                    on_tool_call_delta=on_tool_call_delta,
-                )
-            except _XAIHTTPError as exc:
-                if exc.status_code != 401:
-                    raise
-                stage = "oauth_refresh"
-                token = await asyncio.to_thread(
-                    get_xai_oauth_token,
-                    proxy=self.proxy,
-                    force_refresh=True,
-                )
-                self._model_capabilities = None
-                self._model_capabilities_fetched_at = 0.0
-                headers = _build_headers(token.access, wire_model)
-                stage = "xai_request_retry"
-                result = await _request_xai(
-                    DEFAULT_XAI_GROK_URL,
-                    headers,
-                    body,
-                    proxy=self.proxy,
-                    on_content_delta=on_content_delta,
-                    on_thinking_delta=on_thinking_delta,
-                    on_tool_call_delta=on_tool_call_delta,
-                )
+            auth_retried = False
+            hosted_tool_retried = False
+            retry_usage: LLMUsage | None = None
+            while True:
+                try:
+                    result = await _request_xai(
+                        DEFAULT_XAI_GROK_URL,
+                        headers,
+                        body,
+                        proxy=self.proxy,
+                        on_content_delta=on_content_delta,
+                        on_thinking_delta=on_thinking_delta,
+                        on_tool_call_delta=on_tool_call_delta,
+                    )
+                    break
+                except _XAIHTTPError as exc:
+                    if exc.status_code != 401 or auth_retried:
+                        raise
+                    auth_retried = True
+                    stage = "oauth_refresh"
+                    token = await asyncio.to_thread(
+                        get_xai_oauth_token,
+                        proxy=self.proxy,
+                        force_refresh=True,
+                    )
+                    headers = _build_headers(token.access, wire_model)
+                    stage = "xai_request_after_oauth_refresh"
+                except _XAIIncompleteHostedToolError as exc:
+                    retry_usage = _combine_usage(retry_usage, exc.usage)
+                    cannot_recover_stream = exc.stream_output_emitted and on_stream_recover is None
+                    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(
-    url: str,
-    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)
+class _XAIIncompleteHostedToolError(RuntimeError):
+    """A nominally successful xAI stream ended before a hosted tool did."""
 
+    should_retry = False  # _call_xai already performs the one safe recovery attempt.
 
-def _parse_xai_model_capabilities(payload: Any) -> dict[str, bool]:
-    if isinstance(payload, dict):
-        payload = cast(dict[str, Any], payload)
-        rows: object = payload.get("data")
-        if not isinstance(rows, list):
-            rows = payload.get("models")
-    else:
-        rows = payload
-    if not isinstance(rows, list):
-        return {}
-
-    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"),
+    def __init__(
+        self,
+        active_tools: list[dict[str, Any]],
+        *,
+        usage: LLMUsage | None,
+        stream_output_emitted: bool = False,
+    ) -> None:
+        names = [str(event.get("name") or "hosted_tool") for event in active_tools]
+        super().__init__(
+            "xAI ended the response before its hosted tool completed: " + ", ".join(names)
         )
-        for identifier in identifiers:
-            if isinstance(identifier, str) and identifier.strip():
-                capabilities[_strip_model_prefix(identifier.strip())] = supports_backend_search
-    return capabilities
+        self.tool_names = tuple(names)
+        self.usage = usage
+        self.stream_output_emitted = stream_output_emitted
 
 
 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:
-            await on_tool_call_delta(hosted_event)
+        if hosted_event is not None:
+            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_tool_call_delta=on_tool_call_delta,
-                on_reasoning_delta=on_thinking_delta,
-                on_response_event=_on_response_event if on_tool_call_delta else None,
+                on_content_delta=(_forward_content_delta if on_content_delta is not None else None),
+                # Always observe tool events so protocol validation also works for
+                # non-streaming callers that did not request UI progress callbacks.
+                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(
-                event.get("input", event.get("arguments"))
-            ),
+            "arguments": _xai_hosted_tool_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(
-            item.get("input", item.get("arguments"))
-        ),
+        "arguments": _xai_hosted_tool_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/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/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"

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):
-        return {"grok-4.5": supports_backend_search}
+    def fake_catalog(*_args, **_kwargs):
+        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",
-        fake_fetch,
+        "nanobot.providers.xai_grok_provider.get_xai_grok_model_catalog",
+        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={
-        "parallel_tool_calls": False,
-        "tools": [hosted_tool, {"type": "code_interpreter", "container": "auto"}],
-    })
+    provider = XAIGrokProvider(
+        extra_body={
+            "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=[{
-            "type": "function",
-            "function": {
-                "name": "read_file",
-                "description": "Read a file",
-                "parameters": {"type": "object"},
-            },
-        }],
+        tools=[
+            {
+                "type": "function",
+                "function": {
+                    "name": "read_file",
+                    "description": "Read a file",
+                    "parameters": {"type": "object"},
+                },
+            }
+        ],
     )
 
     assert response.content == "ok"
-    assert bodies[0]["tools"] == [{
-        "type": "function",
-        "name": "read_file",
-        "description": "Read a file",
-        "parameters": {"type": "object"},
-    }]
+    assert bodies[0]["tools"] == [
+        {
+            "type": "function",
+            "name": "read_file",
+            "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"},
         }
     ]
-
-
-@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)
+    assert "max_turns" not in bodies[0]
+    assert bodies[0]["instructions"] == ""
 
 
 @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,
-            json={"data": [{"id": "grok-search", "supportsBackendSearch": True}]},
-            request=request,
-        )
+        return httpx.Response(200, content=content, 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(
-        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))
+    tool_events: list[dict[str, Any]] = []
 
-    capabilities = await _fetch_xai_model_capabilities(
-        DEFAULT_XAI_GROK_MODELS_URL,
-        headers,
+    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),
     )
 
-    request = captured["request"]
-    assert isinstance(request, httpx.Request)
-    assert request.method == "GET"
-    assert str(request.url) == DEFAULT_XAI_GROK_MODELS_URL
-    assert request.headers["Authorization"] == f"Bearer {access_token}"
-    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 capabilities == {"grok-search": True}
+    assert [(event["phase"], event["name"]) for event in tool_events] == [
+        ("start", "x_search"),
+        ("end", "x_search"),
+    ]
+    assert tool_events[-1]["result"] == {"status": "completed"}
+
+
+@pytest.mark.asyncio
+async def test_raw_response_rejects_unfinished_hosted_tool_and_closes_progress(
+    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/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({
-        "host": ["0.0.0.0"],
-        "port": ["9900"],
-        "api_key": ["secret-token"],
-    })
+    payload = update_api_settings(
+        {
+            "host": ["0.0.0.0"],
+            "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({
-        "providers": {
-            DYNAMIC_PROVIDER_NAME: {
-                "apiKey": "sk-test",
+    config = Config.model_validate(
+        {
+            "providers": {
+                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(
-        model_presets={"openai": ModelPresetConfig(model="openai/gpt-4.1")}
-    )
+    config = Config(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({
-        "provider": [provider_name],
-        "proxy": [" http://127.0.0.1:7890 "],
-        "extraBody": [json.dumps({"tools": []})],
-    })
+    payload = update_provider_settings(
+        {
+            "provider": [provider_name],
+            "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({
-        "providers": {"opencodeZen": {"apiKey": "legacy-key"}},
-        "agents": {
-            "defaults": {
-                "provider": "opencode_zen",
-                "model": "opencode/deepseek-v4-pro",
-            }
-        },
-    })
+    config = Config.model_validate(
+        {
+            "providers": {"opencodeZen": {"apiKey": "legacy-key"}},
+            "agents": {
+                "defaults": {
+                    "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({
-        "providers": {
-            DYNAMIC_PROVIDER_NAME: {
-                "apiKey": "sk-test",
+    config = Config.model_validate(
+        {
+            "providers": {
+                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(
-        started_at_ms=int(time.time() * 1000),
-        duration_ms=1,
-        provider="openai",
-        model="gpt-5",
-        source="user",
-        stream=False,
-        finish_reason="stop",
-        usage=LLMUsage.reported(input_tokens=10, output_tokens=5),
-    ))
+    get_llm_usage_store().record(
+        LLMCallRecord(
+            started_at_ms=int(time.time() * 1000),
+            duration_ms=1,
+            provider="openai",
+            model="gpt-5",
+            source="user",
+            stream=False,
+            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(
-        started_at_ms=int(time.time() * 1000),
-        duration_ms=1,
-        provider="openai",
-        model="gpt-5",
-        source="user",
-        stream=False,
-        finish_reason="stop",
-        usage=LLMUsage.reported(input_tokens=20, output_tokens=2),
-    ))
+    get_llm_usage_store().record(
+        LLMCallRecord(
+            started_at_ms=int(time.time() * 1000),
+            duration_ms=1,
+            provider="openai",
+            model="gpt-5",
+            source="user",
+            stream=False,
+            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) == (
-        "xAI OAuth login failed: Could not reach xAI sign-in: ConnectError."
-    )
+    assert str(exc.value) == ("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["model_count"] == 7
+    assert payload["catalog_kind"] == "hybrid"
+    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.",
-            "owned_by": "xAI Grok",
+            "description": None,
+            "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(

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/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...",

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",

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",

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",

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",

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",

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",

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",

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...",

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",

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(