- Require both history_entry and memory_update, reject null/empty values
- Fallback to tool_choice=auto when provider rejects forced function call
- After 3 consecutive consolidation failures, raw-archive messages to
HISTORY.md without LLM summarization to prevent context window overflow
Some providers (e.g. Dashscope in thinking mode) reject object-style
tool_choice with "does not support being set to required or object".
Retry once with tool_choice="auto" instead of failing silently.
Made-with: Cursor
On Windows, sys.argv[0] may be just "nanobot" without full path when
running from PATH. os.execv() doesn't search PATH, causing restart to
fail with "No such file or directory".
Fix by using `python -m nanobot` instead of relying on sys.argv[0].
Fixes#1937
Fix issue #1823: Memory consolidation does not inherit agent temperature
and maxTokens configuration.
The agent's configured generation parameters were not being passed through
to the memory consolidation call, causing it to fall back to default values.
This resulted in the consolidation response being truncated before the
save_memory tool call was emitted.
- Pass temperature, max_tokens, reasoning_effort from AgentLoop to
MemoryConsolidator and then to MemoryStore.consolidate()
- Forward these parameters to the provider.chat_with_retry() call
Fixes#1823
Share assistant message construction between the main agent and subagents, and add a regression test to keep reasoning_content and thinking_blocks in follow-up tool rounds.
Move consolidation policy into MemoryConsolidator, keep backward compatibility for legacy config, and compress history by token budget instead of message count.
Subagent's _run_subagent() was dropping reasoning_content and
thinking_blocks when building assistant messages for the conversation
history. Providers like Deepseek Reasoner require reasoning_content on
every assistant message when thinking mode is active, causing a 400
BadRequestError on the second LLM round-trip.
Align with the main AgentLoop which already preserves these fields via
ContextBuilder.add_assistant_message().
Closes#1834
MCP SDK's anyio cancel scopes can leak CancelledError on timeout or
failure paths. Since CancelledError is a BaseException (not Exception),
it escapes both MCPToolWrapper.execute() and ToolRegistry.execute(),
crashing the agent loop.
Now catches CancelledError and returns a graceful error to the LLM,
while still re-raising genuine task cancellations from /stop.
Also catches general Exception for other MCP failures (connection
drops, invalid responses, etc.).
Related: #1055
Major changes:
- Replace message-count-based memory window with token-budget-based compression
- Add max_tokens_input, compression_start_ratio, compression_target_ratio config
- Implement _maybe_compress_history() that triggers based on prompt token usage
- Use _build_compressed_history_view() to provide compressed history to LLM
- Refactor MemoryStore.consolidate() -> consolidate_chunk() for chunk-based compression
- Remove last_consolidated from Session, use _compressed_until metadata instead
- Add background compression scheduling to avoid blocking message processing
Key improvements:
- Compression now based on actual token usage, not arbitrary message counts
- Better handling of long conversations with large context windows
- Non-destructive compression: old messages remain in session, but excluded from prompt
- Automatic compression when history exceeds configured token thresholds
Ensure all modules using PEP 604 union syntax (X | Y) include
the future annotations import for Python <3.10 compatibility.
While the project requires >=3.11, this avoids import-time
TypeErrors when running tests on older interpreters.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
These two files from upstream use PEP 604 union syntax (str | None)
without the future annotations import. While the project requires
Python >=3.11, this makes local testing possible on 3.9/3.10.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
