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.
Point Dream skill creation at a readable builtin skill-creator template, keep skill writes rooted at the workspace, and document the new skill discovery behavior in README.
Made-with: Cursor
* 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
Prefer the more user-friendly idleCompactAfterMinutes name for auto compact while keeping sessionTtlMinutes as a backward-compatible alias. Update tests and README to document the retained recent-context behavior and the new preferred key.
Keep a legal recent suffix in idle auto-compacted sessions so resumed chats preserve their freshest live context while older messages are summarized. Recover persisted summaries even when retained messages remain, and document the new behavior.
Make Consolidator.archive() return the summary string directly instead
of writing to history.jsonl then reading back via get_last_history_entry().
This eliminates a race condition where concurrent _archive calls for
different sessions could read each other's summaries from the shared
history file (cross-user context leak in multi-user deployments).
Also removes Consolidator.get_last_history_entry() — no longer needed.
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.
Keep tool-call assistant messages valid across provider sanitization and avoid trailing user-only history after model errors. This prevents follow-up requests from sending broken tool chains back to the gateway.
- Adjusted message handling in AgentRunner to ensure that historical messages remain unchanged during context governance.
- Introduced tests to verify that backfill operations do not alter the saved message boundary, maintaining the integrity of the conversation history.
- Merged latest main (no conflicts)
- Added test_llm_error_not_appended_to_session_messages: verifies error
content stays out of session messages
- Added test_streamed_flag_not_set_on_llm_error: verifies _streamed is
not set when LLM returns an error, so ChannelManager delivers it
Made-with: Cursor
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.
Resolved conflict in azure_openai_provider.py by keeping main's
Responses API implementation (role alternation not needed for the
Responses API input format).
Made-with: Cursor
Preserve path folding for quoted exec command paths with spaces so hint previews do not fall back to mid-path truncation. Add regression coverage for quoted Unix and Windows path cases.
Made-with: Cursor
1. exec tool hints previously used val[:40] blind character truncation,
cutting paths mid-segment. Now detects file paths via regex and
abbreviates them with abbreviate_path. Supports Windows, Unix
absolute, and ~/ home paths.
2. Deduplication now compares fully formatted hint strings instead of
tool names alone. Fixes ls /Desktop and ls /Downloads being
incorrectly merged as "ls /Desktop × 2".
Co-authored-by: xzq.xu <zhiqiang.xu@nodeskai.com>
- Move _tool_hint implementation from loop.py to nanobot/utils/tool_hints.py
- Keep thin delegation in AgentLoop._tool_hint for backward compat
- Update test imports to test format_tool_hints directly
Made-with: Cursor
- Introduced a helper method `_for_each_hook_safe` to reduce code duplication in hook method implementations.
- Updated error logging to include the method name for better traceability.
- Improved the `SkillsLoader` class by adding a new method `_skill_entries_from_dir` to simplify skill listing logic.
- Enhanced skill loading and filtering logic, ensuring workspace skills take precedence over built-in ones.
- Added comprehensive tests for `SkillsLoader` to validate functionality and edge cases.
Dream Phase 2 uses fail_on_tool_error=True, which terminates the entire
run on the first tool error (e.g. old_text not found in edit_file).
Normal agent runs default to False so the LLM can self-correct and retry.
Dream should behave the same way.
- Add GitStore class wrapping dulwich for memory file versioning
- Auto-commit memory changes during Dream consolidation
- Add /dream-log and /dream-restore commands for history browsing
- Pass tracked_files as constructor param, generate .gitignore dynamically
Replace single-stage MemoryConsolidator with a two-stage architecture:
- Consolidator: lightweight token-budget triggered summarization,
appends to HISTORY.md with cursor-based tracking
- Dream: cron-scheduled two-phase processor that analyzes HISTORY.md
and updates SOUL.md, USER.md, MEMORY.md via AgentRunner with
edit_file tools for surgical, fault-tolerant updates
New files: MemoryStore (pure file I/O), Dream class, DreamConfig,
/dream and /dream-log commands. 89 tests covering all components.
