* refactor(agent): ground runtime decisions in structured evidence
Use producer status and structured errors for tool recovery, preserve memory context without lexical confirmation, and remove predetermined receipt and skill conclusions.
Require explicit MCP discovery and authenticated schedule trial actions. Preserve authorization, ownership, isolation, and bounded execution; update documentation and regression coverage.
* test(sandbox): expect read_file initialization error callback
* fix(agent): address evidence review follow-ups
* fix(agent): preserve retry budgets for OpenAI transport errors
* fix(agent): expose tool execution status in model requests
Project validated execution metadata and exact tool text into request-only JSON so status survives provider serialization. Preserve stored results and receipt hashes, cover SDK and graph boundaries, and migrate replay input hashes without changing recorded responses.
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Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
extract_message_text falls back to str(content), so a message whose content is None becomes the truthy literal 'None'. That string then reaches signal detection and the memory-update prompt as if the user had written it. The canonical helper (utils/messages.py:message_content_to_text) and the mem0 copy both return '' for this shape and the canonical one documents exactly this failure mode.
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
* feat(memory): signals-based update pipeline + always-on watermark/trivial filter
Refactor the DeerMem memory update pipeline (message_processing -> queue ->
updater) around a signals frozenset seam, replacing the
(filtered, correction_detected, reinforcement_detected) 3-tuple with
(filtered, signals: frozenset[str]) end to end.
message_processing:
- Externalize signal-detection patterns to YAML (message_patterns/*.yaml).
- Extend signals from correction/reinforcement to a 6-class set
(correction/reinforcement/preference/identity/goal/decision); detect_signals
returns a frozenset aligned with the fact category enum.
- Pure-acknowledgment turns ("ok"/"好的"/...) are always filtered out before
enqueue (whole-message fullmatch), saving an extraction LLM call.
queue (core/queue.py):
- In-memory list + debounce timer, with flush_sync (graceful-shutdown drain
that joins an in-flight worker under a hard timeout) and queue_max_depth
backpressure (signal-bearing updates always admitted; QueueFull otherwise).
- Same-key updates coalesce with a signal union; per-batch success/fail summary.
updater (core/updater.py):
- head500+tail500 message truncation (replaces the 1000-char head chop).
- Always-on per-thread watermark: feed only messages added since the last
extraction. The watermark is in-memory and is not advanced on failure, so a
failed/lost update is re-fed on the next conversation turn.
- [MANUAL] prompt marker for user-authored facts (source.type="manual").
- Post-invoke extraction_callback (host-injected) emitting facts_extracted /
facts_accepted / rejected_low_confidence; the host default logs metrics and
flags >60% rejection.
Confidence filtering remains in _apply_updates (the existing
fact_confidence_threshold check); there is no separate write gate.
Consolidation stays opt-in (lossy). The ABC add/add_nowait signature is
unchanged, so the summarization flush hook and host are unaffected.
Tests: add test_message_processing_signals, test_updater_truncation,
test_updater_watermark; update queue/updater/consolidation/staleness/pluggable
tests for the signals seam.
Co-Authored-By: Claude <noreply@anthropic.com>
* fix(memory): harden update pipeline per PR review
- Catch QueueFull in DeerMem.add/add_nowait so backpressure degrades to
'update skipped' instead of propagating into after_agent /
summarization_hook and breaking the agent run (peer middlewares
self-guard; MemoryMiddleware was the lone exception). Emergency
(add_nowait) always admits under backpressure -- its data cannot be
re-fed next turn.
- Rewrite the watermark from index-based to content/identity-based
(_message_identity + _feed_after_watermark) so it stays correct when
summarization removes the conversation front -- an index watermark
pointed at the wrong message and silently skipped un-extracted tail
turns. The emergency flush bypasses the watermark (bypass_watermark on
ConversationContext, threaded through update_memory) and coexists with
(does not replace) a pending normal update, so a flush cannot drop a
pending update's un-extracted tail.
- Populate facts_accepted / rejected_low_confidence inside _apply_updates
at the real confidence-filter site (passed_threshold) instead of
re-deriving the threshold in _finalize_update -- eliminates metric drift.
- Emit extraction metrics in a finally with an 'attempted' flag so
exception failures (parse error, apply_changes raise after retry) are
observable, not only the happy path.
- Re-detect signals on the post-watermark feed for the extraction hint so
it no longer references turns the LLM cannot see; admission-time signals
still drive backpressure.
- Move the post-batch reschedule inside the queue lock to close a
non-atomic self._timer race with a concurrent add.
Co-Authored-By: Claude <noreply@anthropic.com>
* fix(memory): address follow-up review nits (LRU, metric name, docstring)
- Bound the in-memory watermark cache with a configurable LRU
(watermark_max_keys, default 4096, 0=unbounded). A dropped key re-extracts
one batch on that thread's next turn (the documented restart behavior), so
eviction is safe and preserves the content-identity watermark's
front-removal guarantee. Adds _watermark_get/_watermark_set helpers and a
bounded-LRU regression test.
- Rename the extraction metric facts_accepted -> facts_passed_confidence so
the name matches what the >60% rejection-rate warning assumes (a
confidence-gate signal, not a persisted-fact count); drop the stale
"historical semantics" justification. Brand-new callback, one consumer.
- Fix the stale test_message_processing_signals module docstring: the signals
seam is already swapped to frozenset, and a stale stage-numbering prefix is
removed.
Co-Authored-By: Claude <noreply@anthropic.com>
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Co-authored-by: Claude <noreply@anthropic.com>
