Extend the existing on_progress callback to carry structured tool-event
payloads alongside the plain-text hint, so channels can render rich
tool execution state (start/finish/error, arguments, results, file
attachments) rather than only the pre-formatted hint string.
Changes
-------
- AgentLoop._tool_event_start_payload() — builds a version-1 start
payload from a ToolCallRequest
- AgentLoop._tool_event_result_extras() — extracts files/embeds from a
tool result dict
- AgentLoop._tool_event_finish_payloads() — maps tool_calls +
tool_results + tool_events from AgentHookContext into finish payloads
- _LoopHook.before_execute_tools() — passes tool_events=[...] to
on_progress together with the existing tool_hint flag
- _LoopHook.after_iteration() — emits a second on_progress call with
the finish payloads once tool results are available
- _bus_progress() — forwards tool_events as _tool_events in OutboundMessage
metadata so channel implementations can read them
- on_progress type widened to Callable[..., Awaitable[None]] on all
public entry points; _cli_progress updated to accept and ignore
tool_events
The contract is additive: callers that only accept (content, *, tool_hint)
continue to work unchanged. Callers that also accept tool_events receive
the structured data.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Discord threads use their own channel IDs, so allowChannels was blocking
thread replies unless each thread ID was listed explicitly.
- Include the thread parent channel ID as an allowlist candidate
- Enforce allow_channels on slash commands (previously bypassed)
- Show parent channel ID in runtime context, reply to the thread
- Fix subagent cancel key via effective_key propagation
- Detect bot mentions via raw_mentions and reply-to-bot references
- Cache seen thread channels for outbound delivery
- Ignore system messages that become empty prompts
When the main agent spawns multiple sub-agents, each completion
independently triggered a new _dispatch, causing 3-4 user-visible
responses instead of a single comprehensive report.
- Extend _drain_pending to block-wait on pending_queue when sub-agents
are still running, keeping the runner loop alive for in-order injection
- Pass pending_queue in the system message path so subsequent sub-agent
results can still be injected mid-turn via a new dispatch
A new configuration block has been added for the web fetch tool, which
allows forcing the tool to use the local readability-lxml mode.
Combined with the previous option to modify the user agent, allows
bypassing most Cloudflare captchas and JS proof-of-work.
Assisted-by: Jo'Zahir:Qwen3.6-35B-A3B
Non-priority slash commands (e.g. /new, /help, /dream-log) arriving
while a session has an active LLM turn were silently queued into the
pending injection buffer and later injected as raw user messages into
the LLM conversation. This caused the model to respond to "/new" as
plain text instead of executing the command.
Root cause: the run() loop only checked priority commands (/stop,
/restart, /status) before routing messages to the pending queue. All
other command tiers (exact, prefix) bypassed command dispatch entirely.
Changes:
- Add CommandRouter.is_dispatchable_command() to match exact/prefix
tiers, mirroring the existing is_priority() pattern.
- In run(), intercept dispatchable commands before pending queue
insertion and dispatch them directly via _dispatch_command_inline().
- Extract _cancel_active_tasks() from cmd_stop for reuse; cmd_new now
cancels active tasks before clearing the session to prevent shared
mutable state corruption from concurrent asyncio coroutines.
- Update /new semantics: stops active task first, then clears session.
- Update documentation in help text, docs, and Discord command list.
When a user sends /stop to interrupt an active agent turn, the task is
cancelled via CancelledError. Previously, the cancellation handler just
logged and re-raised, discarding any tool results and assistant messages
accumulated during the interrupted turn.
The runtime checkpoint mechanism already persists partial turn state
(assistant messages, completed tool results, pending tool calls) into
session metadata via _emit_checkpoint. However, this checkpoint was only
materialized into session history on the NEXT incoming message via
_restore_runtime_checkpoint — not at cancellation time.
Now the CancelledError handler in _dispatch calls
_restore_runtime_checkpoint immediately, so the partial context is
preserved in session history. This means the next message the user sends
will see all the work that was done before /stop, rather than starting
from scratch.
Fixes#2966
Includes 3 tests verifying checkpoint restoration on cancellation.
The previous fix hardcoded session_key_override as channel:chat_id which
broke unified session mode where pending queues use "unified:default".
Propagate the effective key from _set_tool_context through SpawnTool
into the origin dict so _announce_result routes to the correct pending
queue in both normal and unified session modes.
- Add inline rationale for persisting before ContextBuilder and for
passing current_message="" on subagent follow-ups (avoids
double-projection after merge).
- Skip persistence for empty subagent content (no-op messages should
not pollute history).
- Add regression test covering the empty-content guard.
Made-with: Cursor
Add a built-in tool that lets the agent inspect and modify its own
runtime state (model, iterations, context window, etc.).
Key features:
- inspect: view current config, usage stats, and subagent status
- modify: adjust parameters at runtime (protected by type/range validation)
- Subagent observability: inspect running subagent tasks (phase,
iteration, tool events, errors) — subagents are no longer a black box
- Watchdog corrects out-of-bounds values on each iteration
- Enabled by default in read-only mode (self_modify: false)
- All changes are in-memory only; restart restores defaults
- Comprehensive test suite (90 tests)
Includes a self-awareness skill (always-on) with progressive disclosure:
SKILL.md for core rules, references/examples.md for detailed scenarios.
- Pass resolved self.context_window_tokens to Consolidator instead of
raw parameter that could be None, preventing consolidation failures
- Calculate percentage against input budget (ctx - max_completion - 1024)
instead of raw context window, consistent with Consolidator/snip formulas
- Pass actual max_completion_tokens from provider to build_status_content
- Cap percentage display at 999 to prevent runaway values
- Add tests for budget-based percentage and cap behavior
Bug 1: _drain_pending did not call extract_documents on follow-up
messages arriving mid-turn. Documents attached to queued messages were
silently dropped because _build_user_content only handles images.
Fix: call extract_documents before _build_user_content in _drain_pending.
Bug 2: extract_documents read the entire file into memory (up to 50 MB)
just to check 16 bytes of magic header for MIME detection.
Fix: read only the first 16 bytes via open()+read(16) instead of
Path.read_bytes().
Added regression tests for both bugs.
Made-with: Cursor
Move extract_documents() to nanobot.utils.document as a reusable helper
and call it once in AgentLoop._process_message, the single entry point
for all message processing (API + all channels).
This replaces the previous API-only _extract_documents() in server.py,
ensuring Telegram, Feishu, Slack, WeChat, and all other channels also
benefit from automatic document text extraction.
Adds a configurable max_file_size guard (default 50 MB) to skip
oversized files gracefully, preventing unbounded memory/CPU usage
from channel-downloaded attachments.
- server.py: removed _extract_documents and related imports
- document.py: added extract_documents() with size limit
- loop.py: calls extract_documents() at the top of _process_message
- Tests updated: 70 related tests pass
Made-with: Cursor
Keep the API file upload branch current with main, enforce the documented JSON base64 per-file limit, and avoid leaking document extraction error strings into user prompts.
Made-with: Cursor
Prevent proactive compaction from archiving sessions that have an
in-flight agent task, avoiding mid-turn context truncation when a
task runs longer than the idle TTL.
Track text-only user messages that were flushed before the turn loop completes, then materialize an interrupted assistant placeholder on the next request so session history stays legal and later turns do not skip their own assistant reply.
Made-with: Cursor
Use session.add_message for the pre-turn user-message flush and add focused regression tests for crash-time persistence and duplicate-free successful saves.
Made-with: Cursor
The existing runtime_checkpoint mechanism preserves the in-flight
assistant/tool state if the process dies mid-turn, but the triggering
user message is only written to session history at the end of the turn
via _save_turn(). If the worker is killed (OOM, SIGKILL, a self-
triggered systemctl restart, container eviction, etc.) before the turn
completes, the user's message is silently lost: on restart, the session
log only shows the interrupted assistant turn without any record of
what the user asked. Any recovery tooling built on top of session logs
cannot reply because it has no prompt to reply to.
This patch appends the incoming user message to the session and flushes
it to disk immediately after the session is loaded and before the agent
loop runs, then adjusts the _save_turn skip offset so the final
persistence step does not duplicate it.
Limited to textual content (isinstance(msg.content, str)); list-shaped
content (media blocks) still flows through _save_turn's sanitization at
end of turn, preserving existing behavior for those cases.
* feat(agent): add mid-turn message injection for responsive follow-ups
Allow user messages sent during an active agent turn to be injected
into the running LLM context instead of being queued behind a
per-session lock. Inspired by Claude Code's mid-turn queue drain
mechanism (query.ts:1547-1643).
Key design decisions:
- Messages are injected as natural user messages between iterations,
no tool cancellation or special system prompt needed
- Two drain checkpoints: after tool execution and after final LLM
response ("last-mile" to prevent dropping late arrivals)
- Bounded by MAX_INJECTION_CYCLES (5) to prevent consuming the
iteration budget on rapid follow-ups
- had_injections flag bypasses _sent_in_turn suppression so follow-up
responses are always delivered
Closes#1609
* fix(agent): harden mid-turn injection with streaming fix, bounded queue, and message safety
- Fix streaming protocol violation: Checkpoint 2 now checks for injections
BEFORE calling on_stream_end, passing resuming=True when injections found
so streaming channels (Feishu) don't prematurely finalize the card
- Bound pending queue to maxsize=20 with QueueFull handling
- Add warning log when injection batch exceeds _MAX_INJECTIONS_PER_TURN
- Re-publish leftover queue messages to bus in _dispatch finally block to
prevent silent message loss on early exit (max_iterations, tool_error, cancel)
- Fix PEP 8 blank line before dataclass and logger.info indentation
- Add 12 new tests covering drain, checkpoints, cycle cap, queue routing,
cleanup, and leftover re-publish
Rename the auto compact module to autocompact.py for a cleaner path while keeping the AutoCompact type and behavior unchanged. Update the agent loop import to match.
When a user is idle for longer than a configured TTL, nanobot **proactively** compresses the session context into a summary. This reduces token cost and first-token latency when the user returns — instead of re-processing a long stale context with an expired KV cache, the model receives a compact summary and fresh input.
Each MCP server now connects in its own asyncio.Task to isolate anyio
cancel scopes and prevent 'exit cancel scope in different task' errors
when multiple servers (especially mixed transport types) are configured.
Changes:
- connect_mcp_servers() returns dict[str, AsyncExitStack] instead of None
- Each server runs in separate task via asyncio.gather()
- AgentLoop uses _mcp_stacks dict to track per-server stacks
- Tests updated to handle new API
Rename the boolean flag in _sanitize_persisted_blocks and alias the imported helper so session persistence cannot crash with TypeError when truncation is enabled.
Add allowed_env_keys config field to selectively forward host environment variables (e.g. GOPATH, JAVA_HOME) into the sandboxed subprocess environment, while keeping the default allow-list unchanged.
When the LLM returns an error (e.g. 429 quota exceeded, stream timeout),
streaming channels silently drop the error message because `_streamed=True`
is set in metadata even though no content was actually streamed.
This change:
- Skips setting `_streamed` when stop_reason is "error", so error messages
go through the normal channel.send() path and reach the user
- Stops appending error content to session history, preventing error
messages from polluting subsequent conversation context
- Exposes stop_reason from _run_agent_loop to enable the above check
Introduce a disabled_skills option in the config schema that allows
users to specify a list of skill names to be excluded. The setting is
threaded from config through Nanobot -> AgentLoop -> ContextBuilder ->
SkillsLoader. Disabled skills are filtered out from list_skills,
get_always_skills, and build_skills_summary. Four new test cases cover
the filtering behavior.
Add reraise parameter to AgentHook so hooks can opt out of exception
swallowing in CompositeHook._for_each_hook_safe. _LoopHook sets
reraise=True to let its exceptions propagate. _LoopHookChain is removed
and replaced with CompositeHook([loop_hook] + extra_hooks).
Signed-off-by: Lingao Meng <menglingao@xiaomi.com>
