- Add ABANDONED value to core Outcome enum in types.py
- Replace duplicate OutcomeType class in mcp/tools.py with alias to Outcome
- Simplify mcp/service.py to use outcome directly (no more silent mapping)
- Add migration 0006 to extend PostgreSQL episode_outcome enum
- Add missing constraints to migration 0005 (ix_facts_unique_triple_global)
This fixes the semantic issue where ABANDONED outcomes were silently
converted to FAILURE, losing information about task abandonment.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Bug Fixes:
- Remove singleton pattern from consolidation/reflection services to
prevent stale database session bugs (session is now passed per-request)
- Add LRU eviction to MemoryToolService._working dict (max 1000 sessions)
to prevent unbounded memory growth
- Replace O(n) list.remove() with O(1) OrderedDict.move_to_end() in
RetrievalCache for better performance under load
- Use deque with maxlen for metrics histograms to prevent unbounded
memory growth (circular buffer with 10k max samples)
- Use full UUID for checkpoint IDs instead of 8-char prefix to avoid
collision risk at scale (birthday paradox at ~50k checkpoints)
Test Updates:
- Update checkpoint test to expect 36-char UUID
- Update reflection singleton tests to expect new factory behavior
- Add reset_memory_reflection() no-op for backwards compatibility
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Auto-fixed linting errors and formatting issues:
- Removed unused imports (F401): pytest, Any, AnalysisType, MemoryType, OutcomeType
- Removed unused variable (F841): hooks variable in test
- Applied consistent formatting across memory service and test files
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Add MCP-compatible tools that expose memory operations to agents:
Tools implemented:
- remember: Store data in working, episodic, semantic, or procedural memory
- recall: Retrieve memories by query across multiple memory types
- forget: Delete specific keys or bulk delete by pattern
- reflect: Analyze patterns in recent episodes (success/failure factors)
- get_memory_stats: Return usage statistics and breakdowns
- search_procedures: Find procedures matching trigger patterns
- record_outcome: Record task outcomes and update procedure success rates
Key components:
- tools.py: Pydantic schemas for tool argument validation with comprehensive
field constraints (importance 0-1, TTL limits, limit ranges)
- service.py: MemoryToolService coordinating memory type operations with
proper scoping via ToolContext (project_id, agent_instance_id, session_id)
- Lazy initialization of memory services (WorkingMemory, EpisodicMemory,
SemanticMemory, ProceduralMemory)
Test coverage:
- 60 tests covering tool definitions, argument validation, and service
execution paths
- Mock-based tests for all memory type interactions
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
