* refactor(dream): replace two-phase Dream class with simple cron + process_direct
- Remove the heavyweight Dream class (AgentRunner-based two-phase system)
from nanobot/agent/memory.py
- Delete dream_phase1.md and dream_phase2.md templates
- New dream.md template serves as the consolidation prompt
- Cron callback uses agent.process_direct(prompt, session_key=\"dream\")
instead of agent.dream.run()
- Always performs git auto_commit after execution
- /dream command updated to use process_direct + git commit
- DreamConfig kept for backward compatibility; deprecated fields
(model_override, max_batch_size, max_iterations, annotate_line_ages)
are ignored but accepted in config
- interval_h remains configurable via agents.defaults.dream.interval_h
- Update tests and webui settings to match new architecture
* feat(loop): add ephemeral mode to process_direct, skip history writes for Dream
When ephemeral=True, _state_save skips enforce_file_cap (which calls
raw_archive -> append_history) and consolidator.maybe_consolidate_by_tokens.
This prevents Dream sessions from creating a positive feedback loop where
they process their own output. The session IS still saved to disk.
* fix(loop): skip extra hooks for ephemeral sessions (Dream)
* feat(dream): per-run timestamped sessions with rotation for WebUI
* test(config): restore DreamConfig schedule and alias tests
* fix(dream): include LLM response summary in git auto-commit message
The old two-phase Dream class included the Phase 1 analysis in the git
commit message body. The new single-phase version lost this. Restore it
by extracting resp.content from the process_direct return value and
appending it to the commit message in both the cron handler and the
/dream command.
* fix(test): accept ephemeral kwarg in test_openai_api fake_process
* refactor(dream): merge dream_session.py into MemoryStore
The standalone dream_session.py module only contained three small helpers
that all revolve around MemoryStore concerns (session keys, commit messages,
file pruning). Fold them into MemoryStore as @staticmethod to reduce
indirection and avoid a 35-line module with no independent reason to exist.
* fix(test): address code review — patch correct instance, use actual function
- Fix test_ephemeral_skips_raw_archive to patch loop.context.memory
instead of the fixture's separate MemoryStore instance
- Fix TestDreamCommitMessage to call MemoryStore.build_dream_commit_message
instead of reimplementing the logic inline
- Move Dream helpers in memory.py above the Consolidator section comment
to avoid misleading visual boundary
* fix(dream): gate cursor advancement and restrict tools
maintainer edit: Dream now processes backlog from the oldest unprocessed entries, only advances the cursor after a completed ephemeral run, and uses a restricted file-only tool registry for background consolidation.
* fix(dream): skip idle compact for dream sessions
Dream runs use internal dream:* sessions that are pruned by Dream retention. Exclude them from AutoCompact scheduling, archive execution, and summary injection so idle-session compaction cannot truncate Dream transcripts.
* fix(dream): keep batched history isolated
* feat(dream): tag archived memory for single-phase Dream
---------
Co-authored-by: Xubin Ren <52506698+Re-bin@users.noreply.github.com>
Extract is_image_file() and reference_non_image_attachments() from
AgentLoop private static methods into nanobot/utils/document.py where
they belong alongside extract_documents(). Simplify config lookup by
removing dead isinstance(dict) branch.
`long_task` registers a sustained objective, but `AgentRunner` would
still exit with `stop_reason="completed"` when the LLM produced a final
text response without calling `complete_goal`. This defeated the purpose
of sustained goals.
Add `goal_active_predicate` and `goal_continue_message` to `AgentRunSpec`.
When the predicate returns `True` at the natural completion checkpoint,
inject a continuation message via the existing `_try_drain_injections`
machinery, forcing the runner to continue looping.
Also extract the default continuation prompt to
`nanobot/utils/runtime.py` alongside the existing recovery-message
builders.
The maxConcurrentSubagents field in AgentDefaults was never wired
through AgentLoop.from_config() → AgentLoop.__init__() →
SubagentManager.__init__(), causing it to always fall back to the
hardcoded default of 1 regardless of the user's config.
The runtime media-attachment mechanism was broken for streaming channels
(e.g. WebSocket): the _streamed flag caused _send_once to skip the final
OutboundMessage that carried generated media, so images were never delivered.
Rather than adding complex coordination between streaming and media delivery,
delegate image delivery to the LLM: after generate_image returns artifact
paths, the next_step prompt now instructs the LLM to call the message tool
with the paths in the media parameter. This works uniformly across all
channels, streaming or not.
Remove generated_media from TurnContext, _assemble_outbound, and _state_save.
Update prompts in identity.md, SKILL.md, message tool description, and
artifacts.py to reflect the new flow.
_drain_pending injected a full runtime context block (including goal
state) into every injected user message, but the initial message already
carries runtime context via build_messages(). This caused goal state to
appear multiple times in the LLM context window within a single turn,
wasting tokens (up to 4000 chars per duplicate).
Now _drain_pending only passes the raw user content without runtime
context. The initial turn message remains the sole carrier.
Centralize runner_wall_llm_timeout_s in session goal_state metadata helpers so
spawned subagents inherit the same policy as AgentLoop without coupling to
long_task. Pass optional resolver into SubagentManager and add tests.
Co-authored-by: Cursor <cursoragent@cursor.com>
* feat(long-task): add LongTaskTool for multi-step agent tasks
Implements a meta-ReAct loop where long-running tasks are broken into
sequential subagent steps, each starting fresh with the original goal
and progress from the previous step. This prevents context drift when
agents work on complex, multi-step tasks.
- Extract build_tool_registry() from SubagentManager for reuse
- Add run_step() for synchronous subagent execution (no bus announcement)
- Add HandoffTool and CompleteTool as signal mechanisms via shared dict
- Add LongTaskTool orchestrator with simplified prompt (8 iterations/step)
- Register LongTaskTool in main agent loop
- Add _extract_handoff_from_messages fallback for robustness
* fix(long-task): add debug logging for step-level observability
* feat(long-task): major overhaul with structured handoffs, validation, and observability
- Structured HandoffState: HandoffTool now accepts files_created,
files_modified, next_step_hint, and verification fields instead of
a plain string. Progress is passed between steps as structured data.
- Completion validation round: After complete() is called, a dedicated
validator step runs to verify the claim against the original goal.
If validation fails, the task continues rather than returning
a false completion.
- Dynamic prompt system: 3 Jinja2 templates (step_start, step_middle,
step_final) selected based on step number. Final steps get tighter
budget and stronger "wrap up" guidance.
- Automatic file change tracking: Extracts write_file/edit_file events
from tool_events and injects them into the next step's context if
the subagent forgot to report them explicitly.
- Budget tracking & adaptive strategy: Cumulative token usage is tracked
across steps. Per-step tool budget drops from 8 to 4 in the last
two steps to force handoff/completion.
- Crash retry with graceful degradation: A step that crashes is retried
once. Persistent crashes terminate the task and return partial progress.
- Full observability hooks for future WebUI integration:
- set_hooks() with on_step_start, on_step_complete, on_handoff,
on_validation_started, on_validation_passed, on_validation_failed,
on_task_complete, on_task_error, and catch-all on_event.
- Readable state properties: current_step, total_steps, status,
last_handoff, cumulative_usage, goal.
- inject_correction() allows external code to send user corrections
that are injected into the next step's prompt.
- run_step() accepts optional max_iterations for dynamic budget control.
All 27 long-task tests and 11 subagent tests pass.
* test(long-task): add boundary tests and fix race conditions
- Add 7 edge-case tests: validation crash resilience, hook exception safety, mid-run correction injection, FIFO correction ordering, explicit file changes overriding auto-detection, final budget for max_steps=1, and dynamic budget switching boundaries
- Fix assertion in test_long_task_completes_after_multiple_handoffs to match exact prompt format
- Remove asyncio timing hack from test_state_exposure
- Add asyncio.sleep(0) yield in test_inject_correction_during_execution to prevent race between signal injection and step continuation
- All 34 tests passing
* fix(long-task): address code review findings
- Declare _scopes = {"core"} explicitly to prevent recursive nesting in subagent scope
- Document fragile coupling in _extract_file_changes: path extraction depends on
write_file/edit_file detail format; add debug log for unexpected formats
- Align final-template threshold (max_steps - 2) with budget switch threshold
- Eliminate hasattr(self, "_state") in _reset_state by initializing in __init__
* fix(long-task): honor final signal and file tracking
Co-authored-by: Cursor <cursoragent@cursor.com>
* feat(long-task): improve prompt structure and agent contract
- Expand LongTaskTool.description to instruct parent agent on goal
construction, return value semantics, and how to handle results.
- Expand CompleteTool.description to emphasize that the summary IS the
final answer returned to the parent agent.
- Prefix validated return value with an explicit "final answer" directive
to stop parent agent from re-running work.
- Redesign step_start.md: Step 1 is now explicitly for exploration,
planning, and skeleton-building. complete() is discouraged.
- Remove bulky payload debug logging from _emit(); add targeted
info/warning/error logs at key state transitions instead.
- Add signal_type to HandoffState for cleaner signal detection.
* test(long-task): expect wrapped completion message after validation
Align assertions with LongTaskTool final return shape on main.
Co-authored-by: Cursor <cursoragent@cursor.com>
* feat(webui): turn timing strip, latency, and session-switch restore
- Agent loop: publish goal_status run/idle for WebSocket turns; attach
wall-clock latency_ms on turn_end and persisted assistant metadata.
- WebSocket channel: forward goal_status and latency fields to clients.
- NanobotClient: track goal_status started_at per chat without requiring
onChat; useNanobotStream restores run strip when returning to a chat.
- Thread UI: composer/shell viewport hooks for run duration and latency;
format helpers and i18n strings.
- MessageBubble: drop trailing StreamCursor (layout artifact vs block markdown).
- Builtin / tests: model command coverage, websocket and loop tests.
Covers multi-session UX and round-trip timing visibility for the WebUI.
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix: keep message-tool file attachments after canonical history hydrate
- MessageTool records per-turn media paths delivered to the active chat.
- nanobot.utils.session_attachments stages out-of-media-root files and
merges into the last assistant message before save (loop stays a thin call).
- WebUI MediaCell: use a signed URL as a real download link when present.
Fixes attachments flashing then vanishing on turn_end when paths lived
outside get_media_dir (e.g. workspace files).
Co-authored-by: Cursor <cursoragent@cursor.com>
* feat(webui): agent activity cluster, stable keys, LTR sheen labels
- Group reasoning and tool traces in AgentActivityCluster with i18n summaries
- Stabilize React list keys for activity clusters (first message id anchor)
- Replace background-clip shimmer with overlay sheen for streaming labels
- ThreadMessages/MessageList integration and locale strings
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix(webui): render assistant reasoning with Markdown + deferred stream
- Use MarkdownText for ReasoningBubble body (same GFM/KaTeX path as replies)
- Apply muted/italic prose tokens so thinking stays visually subordinate
- useDeferredValue while reasoningStreaming to ease parser work during deltas
- Preload markdown chunk when trace opens; add regression test with preloaded renderer
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix(webui): default-collapse agent activity cluster while Working
Outer fold no longer auto-expands during isTurnStreaming; user opens to see traces.
Header sheen and live summary unchanged.
Co-authored-by: Cursor <cursoragent@cursor.com>
* feat(long_task): cumulative run history, file union, and prompt tuning
Inject cross-step summaries and merged file paths into middle/final step
templates so chains do not lose early context. Strip the last run-history
block when it duplicates Previous Progress to save tokens. Add optional
cumulative_prompt_max_chars and cumulative_step_body_max_chars parameters
with clamped defaults.
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix(webui): session switch keeps in-flight thread and replays buffered WS
Save the prior chat message list to the per-chat cache in a layout effect
when chatId changes (before stale writes could corrupt another chat).
Skip one post-switch layout cache tick so we do not snapshot the wrong tab.
Buffer inbound events per chat_id when no onChat subscriber is registered
(e.g. user focused another session) and drain on resubscribe up to a cap,
so streaming deltas are not lost while off-tab.
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix(webui): snap thread scroll to bottom on session open (no smooth glide)
Use scroll-behavior auto on the viewport, instant programmatic scroll when
following new messages and on scrollToBottomSignal. Keep smooth only for
the explicit scroll-to-bottom button.
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix(webui): respect manual scroll-up after opening a session
Track when the user leaves the bottom with a ref and skip ResizeObserver
and deferred bottom snaps until they return or the conversation is reset.
Remove the time-based force-bottom window that overrode atBottom.
Multi-frame scrollToBottom honours the same guard unless force (scroll button).
Co-authored-by: Cursor <cursoragent@cursor.com>
* Publish long_task UI snapshots on outbound metadata
- Add OUTBOUND_META_AGENT_UI (_agent_ui) for channel-agnostic structured state
- LongTaskTool publishes {kind: long_task, data: snapshot} on the bus with _progress
- WebSocket send forwards metadata as agent_ui for WebUI clients
- Tests for bus payload, WS frame, and progress assertions
- Fix loop progress tests: ignore _goal_status in streaming final filter and
avoid brittle outbound[-1] ordering after goal status idle messages
Co-authored-by: Cursor <cursoragent@cursor.com>
* feat: WebUI long_task activity card and resilient history merge
Add optional ui_summary to the long_task tool for one-line UI labels. Stream
long_task agent_ui into a dedicated message row with timeline, markdown peek,
and a right sheet for details. Merge canonical history after turn_end while
re-inserting long_task rows before the final assistant reply. Collapse
duplicate task_start/step_start steps in the timeline and extend i18n.
Co-authored-by: Cursor <cursoragent@cursor.com>
* refactor: align long_task with thread_goal and drop orchestrator UI
- Persist sustained objectives via session metadata (long_task / complete_goal); no subagent wiring or tool-driven agent_ui payloads.\n- Remove WebUI long-task activity UI, types, and translations; history merge preserves trace replay only, with legacy long_task rows normalized to traces.\n- Drop long_task prompt templates and get_long_task_run_dir; add webui thread disk helper for gateway persistence tests.
Co-authored-by: Cursor <cursoragent@cursor.com>
* feat(agent): thread goal runtime context, tools, and skill
- Add thread_goal_state helper and mirror active objectives into Runtime Context
- Wire loop/context/memory/events as needed for goal metadata in turns
- Expand long_task / complete_goal semantics (pivot/cancel/honest recap)
- Add always-on thread-goal SKILL.md; align /goal command prompt
- Tests for context builder and thread goal state
- Remove unused webui ChatPane component
Co-authored-by: Cursor <cursoragent@cursor.com>
* feat(thread-goal): add websocket snapshot helper and publish goal updates from long_task
Introduce thread_goal_ws_blob for bounded JSON snapshots, attach snapshots to
websocket turn_end metadata in AgentLoop, and let long_task fan-out dedicated
thread_goal frames on the websocket channel after persisting session metadata.
Co-authored-by: Cursor <cursoragent@cursor.com>
* feat(channels): websocket thread_goal frames, turn_end replay, and session API scrub for subagent inject
Emit thread_goal events and optional thread_goal on turn_end; scrub persisted
subagent announce blobs on GET /api/sessions/.../messages and shorten session
list previews so WebUI does not surface full Task/Summarize scaffolding.
Co-authored-by: Cursor <cursoragent@cursor.com>
* feat(webui): merge ephemeral traces per user turn when reconciling canonical history
Preserve disk/live trace rows inside the matching user–assistant segment instead
of stacking every trace before the final assistant reply (fixes inflated tool
counts after refresh or session switch).
Co-authored-by: Cursor <cursoragent@cursor.com>
* feat(webui): show assistant reply copy only on the last slice before the next user turn
Avoid duplicate copy affordances on intermediate assistant bubbles that precede
more agent activity in the same turn (tools or further assistant text).
Co-authored-by: Cursor <cursoragent@cursor.com>
* feat(webui): thread_goal stream plumbing, composer goal strip, sky glow, and client-side subagent scrub projection
Track thread_goal and turn_goal snapshots in NanobotClient, hydrate React state
from thread_goal frames and turn_end, surface objective/elapsed in the composer,
add breathing sky halo CSS while goals are active, mirror server scrub logic on
history hydration and webui_thread snapshots, and extend tests/client mocks.
Co-authored-by: Cursor <cursoragent@cursor.com>
* feat(channels): add Slack Socket Mode connect timeout with actionable timeout errors
Abort hung websockets.connect handshakes after a bounded wait, log REST-vs-WSS
guidance, surface RuntimeError to channel startup, and log successful WSS setup.
Co-authored-by: Cursor <cursoragent@cursor.com>
* webui: expand thread goal in composer bottom sheet
Add ChevronUp control on the run/goal strip that opens a bottom Sheet
with full ui_summary and objective. Inline preview logic in RunElapsedStrip,
add i18n strings across locales, and a composer unit test.
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix(webui): widen dedupeToolCallsForUi input for session API typing
fetchSessionMessages types tool_calls as unknown; accept unknown so tsc
build passes when passing message.tool_calls through.
Co-authored-by: Cursor <cursoragent@cursor.com>
* refactor(agent): extract WebSocket turn run status to webui_turn_helpers
* refactor(skills): rename thread-goal to long-task and document idempotent goals
* feat(skills): rename sustained-goal skill to long-goal and tighten long_task guidance
* chore: remove unused subagent/context/router helpers
* feat(session): rename sustained goal to goal_state and align WS/WebUI
- Move helpers from agent/thread_goal_state to session/goal_state:
GOAL_STATE_KEY, goal_state_runtime_lines, goal_state_ws_blob, parse_goal_state.
- Session metadata now uses "goal_state"; still read legacy "thread_goal";
long_task writes drop the legacy key after save.
- WebSocket: event/field goal_state, _goal_state_sync; turn_end carries goal_state;
accept legacy _thread_goal_sync/thread_goal inbound metadata for dispatch.
- WebUI: GoalStateWsPayload, goalState hook/client props, i18n keys goalState*.
- Runtime Context copy uses "Goal (active):" instead of "Thread goal".
* feat(agent): stream Anthropic thinking deltas and fix stream idle timeout
* refactor(webui): transcript jsonl as sole timeline source
* fix(agent): reject mismatched WS message chat_id and stream reasoning deltas
* feat(webui): hydrate sustained goal and run timer after websocket subscribe
* chore(webui,websocket): remove unused fetch helpers and legacy thread_goal WS paths
* Raise default max_tokens and context window in agent schema.
Align AgentDefaults and ModelPresetConfig with typical Claude-scale usage
(32k completion budget, 256k context window) and update migration tests.
Co-authored-by: Cursor <cursoragent@cursor.com>
* feat(gateway): bootstrap prefers in-memory model; clarify websocket naming
* fix(websocket): websocket _handle_message passes is_dm; refresh /status test expectations
---------
Co-authored-by: chengyongru <2755839590@qq.com>
Co-authored-by: chengyongru <chengyongru.ai@gmail.com>
Co-authored-by: Cursor <cursoragent@cursor.com>
Runtime context (time, channel, sender) changes every turn, so placing
it before user content invalidated the prompt-cache prefix. Appending it
after user content keeps the prefix stable and improves KV cache hit
rates. The stripping logic in _save_turn was simplified from 16 lines
to 6 as a side benefit.
- Clear pending_user_turn after shortcut command persistence
- Guard is_allowed against None allow_from values
- Update pairing help text for two-arg revoke
- Reuse format_expiry in CLI pairing list
Shortcut commands (e.g. /help, /pairing) skip BUILD and SAVE states,
so their turns were never persisted to the session. This caused WebUI
chats to appear empty after _turn_end because history hydration reads
from the session file.
Fix by persisting the user message and assistant response inside
_state_command, but tag them with _command=True so Session.get_history
filters them out of LLM context. /new is excluded because it
intentionally clears the session.
- AgentLoop._persist_user_message_early now accepts **kwargs so
_state_command can pass _command=True for the user turn.
- Session.get_history skips messages with _command=True.
Reasoning now flows as its own stream — symmetric to the answer's
``delta`` / ``stream_end`` pair — instead of being shipped as one
oversized progress message. This lets WebUI render a live "Thinking…"
bubble that updates in place, then auto-collapses when the stream
closes. Other channels remain plugin no-ops by default.
## Protocol
New metadata: ``_reasoning_delta`` (chunk) and ``_reasoning_end``
(close marker). ChannelManager routes both to the dedicated plugin
hooks below; the legacy one-shot ``_reasoning`` is kept for back-compat
and BaseChannel expands it into a single delta + end pair so plugins
only ever implement the streaming primitives.
WebSocket emits two new events:
- ``reasoning_delta`` (event, chat_id, text, optional stream_id)
- ``reasoning_end`` (event, chat_id, optional stream_id)
## BaseChannel surface
- ``send_reasoning_delta(chat_id, delta, metadata)`` — no-op default
- ``send_reasoning_end(chat_id, metadata)`` — no-op default
- ``send_reasoning(msg)`` — back-compat wrapper, base impl forwards
to the streaming primitives
A channel adds reasoning support by overriding the two streaming
primitives. Telegram / Slack / Discord / Feishu / WeChat / Matrix keep
the base no-ops until their bubble UIs are adapted; reasoning silently
drops at dispatch, never as a stray text message.
## AgentHook
Adds ``emit_reasoning_end`` to the hook lifecycle. ``_LoopHook`` tracks
whether a reasoning segment is open and closes it on:
- the first answer delta arriving (so the UI locks the bubble before
the answer renders below),
- ``on_stream_end``,
- one-shot ``reasoning_content`` / ``thinking_blocks`` after a single
non-streaming response.
## WebUI
- ``UIMessage.reasoning`` is now a single accumulated string with a
companion ``reasoningStreaming`` flag.
- ``useNanobotStream`` consumes ``reasoning_delta`` / ``reasoning_end``;
legacy ``kind: "reasoning"`` is auto-translated to a delta + end.
- New ``ReasoningBubble``: shimmer header + auto-expanded while
streaming, collapses to a clickable "Thinking" pill once closed,
respects ``prefers-reduced-motion``.
- Answer deltas adopt the reasoning placeholder so the bubble and the
answer share one assistant row.
## Tests
- ``tests/channels/test_channel_manager_reasoning.py`` — manager routes
delta + end, drops on channel opt-out, expands one-shot back-compat.
- ``tests/channels/test_websocket_channel.py`` — new ``reasoning_delta``
/ ``reasoning_end`` frames, empty-chunk safety, no-subscriber safety,
back-compat expansion.
- ``tests/agent/test_runner_reasoning.py`` — runner closes the segment
on streaming answer start and after one-shot reasoning.
- WebUI ``useNanobotStream`` + ``message-bubble`` cover the new
protocol and the shimmer styling.
## Docs
``docs/configuration.md`` and ``docs/websocket.md`` document the new
events and the plugin contract.
Co-authored-by: Cursor <cursoragent@cursor.com>
Reasoning was being shipped to every channel as a generic progress
message with a `_reasoning: true` flag. Two problems with that:
1. Channels without a low-emphasis UI primitive (Telegram, Slack,
Discord, Feishu...) would dump raw model thoughts as ordinary
replies, polluting the conversation.
2. The agent loop double-gated by inspecting `channels_config`, which
coupled the loop to display policy.
Treat reasoning as its own plugin action — `BaseChannel.send_reasoning`
defaults to a documented no-op; channels that have a fitting affordance
override. ChannelManager routes `_reasoning` outbounds to that method
only when the channel opts in via `show_reasoning` (camelCase alias
`showReasoning` mirrors `sendProgress`). Plugins that don't override
silently drop reasoning — "no fit, no leak" is the contract.
Reference implementation lands for WebSocket / WebUI: a new
`kind: "reasoning"` frame, parked on the active assistant bubble as a
collapsible `Thinking` group above the answer. CLI keeps its existing
direct path (it doesn't go through the bus). `ChannelsConfig.show_reasoning`
flips to `true` by default — only adapted channels surface anything,
others stay quiet.
Loop net diff is -3 lines: the `channels_config.show_reasoning` check
moves out, leaving emit_reasoning a one-liner that publishes and trusts
the channel to decide.
Co-authored-by: Cursor <cursoragent@cursor.com>
Resolves conflicts after main landed the state-machine turn refactor
and the test_runner.py 9-file split:
- nanobot/agent/loop.py: take main's `_state_build`/`_persist_user_message_early`
flow; restore the `reasoning: bool` parameter on `_build_bus_progress_callback`
so the loop hook can mark progress as reasoning-channel without coupling to
the answer stream.
- nanobot/cli/stream.py: keep main's configurable `bot_name`/`bot_icon` header
while preserving the PR's `transient=True` Live + `self._console` routing
+ `_renderable()` final-render path that fixed TUI duplication.
- tests/agent/test_runner.py was deleted on main and split into 9 focused
files; relocated all 6 reasoning tests into a new `test_runner_reasoning.py`
matching the new layout, deduplicated the per-test `ReasoningHook` boilerplate
through a shared `_RecordingHook` helper.
Co-authored-by: Cursor <cursoragent@cursor.com>
Reasoning surfacing was split across three branches in runner.py plus
two separate streaming buffers (loop hook and runner progress stream),
with three independent display-side gates in the CLI. This collapsed
the policy into one source of truth and fixed two real bugs:
- Structured `reasoning_content` was suppressed whenever the answer was
streamed, because the runner gated emission on `streamed_content`.
Providers don't stream `reasoning_content`; it only arrives on the
final response, so the answer stream and the reasoning channel are
independent. Added `streamed_reasoning` to `AgentHookContext` to track
the right bit.
- `channels.showReasoning` was subordinated to `sendProgress`. They are
orthogonal — turning off progress streaming shouldn't silence
reasoning. Reworked the CLI gates accordingly.
Single-helper consolidation:
- `extract_reasoning(reasoning_content, thinking_blocks, content)`
returns `(reasoning_text, cleaned_content)` with a defined fallback
order: dedicated field → Anthropic thinking_blocks → inline
`<think>`/`<thought>` tags. Models that expose none of these
short-circuit to `(None, content)` — zero overhead.
- `IncrementalThinkExtractor` replaces the ad-hoc `emit_incremental_think`
function and its hand-rolled "emitted cursor" state in both the loop
hook and the runner progress stream.
Also documented the new `showReasoning` channel option in
docs/configuration.md and noted its independence from sendProgress.
Co-authored-by: Cursor <cursoragent@cursor.com>
Add extract_think() and emit_incremental_think() helpers to extract thinking content from inline <think> and <thought> tags in the content field. This handles models served via Ollama, self-hosted vLLM, or other compatible endpoints that embed reasoning as inline tags instead of using the dedicated reasoning_content API field.
Also adds Anthropic thinking_blocks support for extended thinking via the thinking content blocks array.
Ultraworked with [Sisyphus](https://github.com/code-yeongyu/oh-my-openagent)
Co-authored-by: Sisyphus <clio-agent@sisyphuslabs.ai>
The ask_user tool used AskUserInterrupt(BaseException) for mid-turn
blocking, creating heavy coupling across runner, loop, and session
management. The model now asks questions naturally in response text,
the turn ends normally, and the user's next message starts a new turn
with session history providing continuity.
Removed:
- nanobot/agent/tools/ask.py (tool, interrupt, helpers)
- tests/agent/test_ask_user.py
- webui/src/components/thread/AskUserPrompt.tsx
- AskUserInterrupt handling in runner.py
- Dual-path message building in loop.py
- Pending ask detection via history scanning
- button_prompt/buttons emission in WebSocket channel
- ask_user references in Slack channel docstrings
Preserved (MessageTool uses these independently):
- OutboundMessage.buttons field
- Channel button rendering (Telegram, Slack, WebSocket)
- Add `ModelPresetConfig` schema for named model presets
- Add `model_presets` dict to `Config` and `model_preset` field to `AgentDefaults`
- Add `resolve_preset()` to return effective model params from preset or defaults
- Add `@model_validator` to reject unknown preset names
- Update `_match_provider()` to use resolved preset model/provider
- Update `make_provider()` and `provider_signature()` to use `resolve_preset()`
- Add `model_preset` property to `AgentLoop` for atomic runtime switching
- Update `AgentLoop.from_config()` to inject a runtime `default` preset
- Wire self-tool to inspect/clear preset state
- Update CLI display strings to show active preset
This commit implements a progressive refactoring of the tool system to support
plugin discovery, scoped loading, and protocol-driven runtime context injection.
Key changes:
- Add Tool ABC metadata (tool_name, _scopes) and ToolContext dataclass for
dependency injection.
- Introduce ToolLoader with pkgutil-based builtin discovery and
entry_points-based third-party plugin loading.
- Add scope filtering (core/subagent/memory) so different contexts load
appropriate tool sets.
- Introduce ContextAware protocol and RequestContext dataclass to replace
hardcoded per-tool context injection in AgentLoop.
- Add RuntimeState / MutableRuntimeState protocols to decouple MyTool from
AgentLoop.
- Migrate all built-in tools to declare scopes and implement create()/enabled()
hooks.
- Migrate MessageTool, SpawnTool, CronTool, and MyTool to ContextAware.
- Refactor AgentLoop to use ToolLoader and protocol-driven context injection.
- Refactor SubagentManager to use ToolLoader(scope="subagent") with per-run
FileStates isolation.
- Register all built-in tools via pyproject.toml entry_points.
- Add comprehensive tests for loader scopes, entry_points, ContextAware,
subagent tools, and runtime state sync.
- Append [Archived Context Summary] to system prompt instead of injecting
it into the user message runtime context, improving KV cache reuse across
turns and avoiding consecutive same-role messages.
- _last_summary persists in metadata (no pop) for restart survival;
summary is re-injected every turn via the stable system prompt.
- Remove dynamic "Inactive for X minutes" from _format_summary — use
static last_active timestamp instead to preserve KV cache stability.
- Pass session_summary through build_messages() so both normal and
ask_user paths receive the archived summary in the system prompt.
- estimate_session_prompt_tokens now reads _last_summary from metadata
to include the summary in token budget estimation.
- Remove obsolete session_summary parameter from
maybe_consolidate_by_tokens and estimate_session_prompt_tokens
call sites in loop.py (summary flows through build_messages instead).
- Ensure /new (session.clear()) clears _last_summary from metadata.
Add show_reasoning config (default: False) to display model
thinking/reasoning content in the TUI during streaming. Reasoning
is emitted via a new emit_reasoning hook on AgentHook, gated by the
channels config. Display uses ✻ prefix with dim italic styling.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
- TurnContext now carries a turn_id (session_key:time_ns)
- All state transition debug logs include [turn_id] prefix
- RuntimeError messages also include turn_id for observability
