Add 6 new fields to AgentType for better organization and UI display:
- category: enum for grouping (development, design, quality, etc.)
- icon: Lucide icon identifier for UI
- color: hex color code for visual distinction
- sort_order: display ordering within categories
- typical_tasks: list of tasks the agent excels at
- collaboration_hints: agent slugs that work well together
Backend changes:
- Add AgentTypeCategory enum to enums.py
- Update AgentType model with 6 new columns and indexes
- Update schemas with validators for new fields
- Add category filter and /grouped endpoint to routes
- Update CRUD with get_grouped_by_category method
- Update seed data with categories for all 27 agents
- Add migration 0007
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- Update demo_data.json to use "__admin__" as owner_email for all projects
- Add admin user lookup in load_demo_data() with special "__admin__" key
- Remove notification_email from project settings (not a valid field)
This ensures demo projects are visible to the admin user when logged in.
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Add values_callable to all enum columns so SQLAlchemy serializes using
the enum's .value (lowercase) instead of .name (uppercase). PostgreSQL
enum types defined in migrations use lowercase values.
Fixes: invalid input value for enum autonomy_level: "MILESTONE"
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SQLAlchemy's Enum() auto-generates type names from Python class names
(e.g., AutonomyLevel -> autonomylevel), but migrations defined them
with underscores (e.g., autonomy_level). This mismatch caused:
"type 'autonomylevel' does not exist"
Added explicit name parameters to all enum columns to match the
migration-defined type names:
- autonomy_level, project_status, project_complexity, client_mode
- agent_status, sprint_status
- issue_type, issue_status, issue_priority, sync_status
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- Delete `demo_data.json` replaced by structured logic for better modularity.
- Add support for seeding default agent types and new demo data structure.
- Ensure demo mode only executes when explicitly enabled (settings.DEMO_MODE).
- Enhance logging for improved debugging during DB initialization.
- Replace default empty list with `deque` for `memory_retrieval_latency_seconds`
- Prevents unbounded memory growth by leveraging bounded circular buffer behavior
- 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.
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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
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- Connect to MCP servers concurrently instead of sequentially
- Reduce retry settings in test mode (IS_TEST=True):
- 1 attempt instead of 3
- 100ms retry delay instead of 1s
- 2s timeout instead of 30-120s
Reduces MCP E2E test time from ~16s to under 1s.
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- Adjusted TTL durations and sleep intervals across memory and cache tests for consistent expiration behavior.
- Prevented test flakiness caused by timing discrepancies in token expiration and cache cleanup.
- Create shallow copy of VectorIndexEntry when adding similarity score
- Prevents mutation of cached entries that could corrupt shared state
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- Add _escape_like_pattern() helper to escape SQL wildcards (%, _, \)
- Apply escaping in SemanticMemory.search_facts and get_by_entity
- Apply escaping in ProceduralMemory.search and find_best_for_task
Prevents attackers from injecting SQL wildcard patterns through
user-controlled search terms.
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- Add threading.Lock with double-check locking to ScopeManager
- Add asyncio.Lock with double-check locking to MemoryReflection
- Make reset_memory_metrics async with proper locking
- Update test fixtures to handle async reset functions
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- Change source_episode_ids from JSON to JSONB for PostgreSQL consistency
- Add unique constraint for global facts (project_id IS NULL)
- Add CHECK constraint ensuring reinforcement_count >= 1
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- Added `memory_consolidation` to the task list and updated `__all__` in test files.
- Updated `source_episode_ids` in `Fact` model to use JSON for cross-database compatibility.
- Revised related database migrations to use JSONB instead of ARRAY.
- Adjusted test concurrency in Makefile for improved test performance.
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
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Add comprehensive caching layer for the Agent Memory System:
- HotMemoryCache: LRU cache for frequently accessed memories
- Python 3.12 type parameter syntax
- Thread-safe operations with RLock
- TTL-based expiration
- Access count tracking for hot memory identification
- Scoped invalidation by type, scope, or pattern
- EmbeddingCache: Cache embeddings by content hash
- Content-hash based deduplication
- Optional Redis backing for persistence
- LRU eviction with configurable max size
- CachedEmbeddingGenerator wrapper for transparent caching
- CacheManager: Unified cache management
- Coordinates hot cache, embedding cache, and retrieval cache
- Centralized invalidation across all caches
- Aggregated statistics and hit rate tracking
- Automatic cleanup scheduling
- Cache warmup support
Performance targets:
- Cache hit rate > 80% for hot memories
- Cache operations < 1ms (memory), < 5ms (Redis)
83 new tests with comprehensive coverage.
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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
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- Add MemoryConsolidationService with Working→Episodic→Semantic/Procedural transfer
- Add Celery tasks for session and nightly consolidation
- Implement memory pruning with importance-based retention
- Add comprehensive test suite (32 tests)
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Add comprehensive indexing and retrieval system for memory search:
- VectorIndex for semantic similarity search using cosine similarity
- TemporalIndex for time-based queries with range and recency support
- EntityIndex for entity-based lookups with multi-entity intersection
- OutcomeIndex for success/failure filtering on episodes
- MemoryIndexer as unified interface for all index types
- RetrievalEngine with hybrid search combining all indices
- RelevanceScorer for multi-signal relevance scoring
- RetrievalCache for LRU caching of search results
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Add scope management system for hierarchical memory access:
- ScopeManager with hierarchy: Global → Project → Agent Type → Agent Instance → Session
- ScopePolicy for access control (read, write, inherit permissions)
- ScopeResolver for resolving queries across scope hierarchies with inheritance
- ScopeFilter for filtering scopes by type, project, or agent
- Access control enforcement with parent scope visibility
- Deduplication support during resolution across scopes
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Implements procedural memory for learned skills and procedures:
Core functionality:
- ProceduralMemory class for procedure storage/retrieval
- record_procedure with duplicate detection and step merging
- find_matching for context-based procedure search
- record_outcome for success/failure tracking
- get_best_procedure for finding highest success rate
- update_steps for procedure refinement
Supporting modules:
- ProcedureMatcher: Keyword-based procedure matching
- MatchResult/MatchContext: Matching result types
- Success rate weighting in match scoring
Test coverage:
- 43 unit tests covering all modules
- matching.py: 97% coverage
- memory.py: 86% coverage
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Implements the episodic memory service for storing and retrieving
agent task execution experiences. This enables learning from past
successes and failures.
Components:
- EpisodicMemory: Main service class combining recording and retrieval
- EpisodeRecorder: Handles episode creation, importance scoring
- EpisodeRetriever: Multiple retrieval strategies (recency, semantic,
outcome, importance, task type)
Key features:
- Records task completions with context, actions, outcomes
- Calculates importance scores based on outcome, duration, lessons
- Semantic search with fallback to recency when embeddings unavailable
- Full CRUD operations with statistics and summarization
- Comprehensive unit tests (50 tests, all passing)
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Implements session-scoped ephemeral memory with:
Storage Backends:
- InMemoryStorage: Thread-safe fallback with TTL support and capacity limits
- RedisStorage: Primary storage with connection pooling and JSON serialization
- Auto-fallback from Redis to in-memory when unavailable
WorkingMemory Class:
- Key-value storage with TTL and reserved key protection
- Task state tracking with progress updates
- Scratchpad for reasoning steps with timestamps
- Checkpoint/snapshot support for recovery
- Factory methods for auto-configured storage
Tests:
- 55 unit tests covering all functionality
- Tests for basic ops, TTL, capacity, concurrency
- Tests for task state, scratchpad, checkpoints
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Implements Sub-Issue #87 of Issue #62 (Agent Memory System).
Core infrastructure:
- memory/types.py: Type definitions for all memory types (Working, Episodic,
Semantic, Procedural) with enums for MemoryType, ScopeLevel, Outcome
- memory/config.py: MemorySettings with MEM_ env prefix, thread-safe singleton
- memory/exceptions.py: Comprehensive exception hierarchy for memory operations
- memory/manager.py: MemoryManager facade with placeholder methods
Directory structure:
- working/: Working memory (Redis/in-memory) - to be implemented in #89
- episodic/: Episodic memory (experiences) - to be implemented in #90
- semantic/: Semantic memory (facts) - to be implemented in #91
- procedural/: Procedural memory (skills) - to be implemented in #92
- scoping/: Scope management - to be implemented in #93
- indexing/: Vector indexing - to be implemented in #94
- consolidation/: Memory consolidation - to be implemented in #95
Tests: 71 unit tests for config, types, and exceptions
Docs: Comprehensive implementation plan at docs/architecture/memory-system-plan.md
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- Introduced a new `context` module and its endpoints for Context Management.
- Added `/context` route to the API router for assembling LLM context, token counting, budget management, and cache invalidation.
- Implemented health checks, context assembly, token counting, and caching operations in the Context Management Engine.
- Included schemas for request/response models and tightened error handling for context-related operations.
- Added stricter budget validation in ContextRanker with explicit error handling for invalid configurations.
- Introduced `_get_valid_token_count()` helper to validate and safeguard token counts.
- Enhanced XML escaping in Claude adapter to prevent injection risks from scores and unhandled content.
- Added timeout enforcement for token counting, scoring, and compression with detailed error handling.
- Introduced tenant isolation in context caching using project and agent identifiers.
- Enhanced budget management with stricter checks for critical context overspending and buffer limitations.
- Optimized per-context locking with cleanup to prevent memory leaks in concurrent environments.
- Updated default assembly timeout settings for improved performance and reliability.
- Improved XML escaping in Claude adapter for safety against injection attacks.
- Standardized token estimation using model-specific ratios.
- Cleaned up unnecessary comments in `__all__` definitions for better readability.
- Adjusted indentation and formatting across modules for improved clarity (e.g., long lines, logical grouping).
- Simplified conditional expressions and inline comments for context scoring and ranking.
- Replaced some hard-coded values with type-safe annotations (e.g., `ClassVar`).
- Removed unused imports and ensured consistent usage across test files.
- Updated `test_score_not_cached_on_context` to clarify caching behavior.
- Improved truncation strategy logic and marker handling.
- Replace hard-coded limits with configurable settings (e.g., cache memory size, truncation strategy, relevance settings).
- Optimize parallel execution in token counting, scoring, and reranking for source diversity.
- Improve caching logic:
- Add per-context locks for safe parallel scoring.
- Reuse precomputed fingerprints for cache efficiency.
- Make truncation, scoring, and ranker behaviors fully configurable via settings.
- Add support for middle truncation, context hash-based hashing, and dynamic token limiting.
- Refactor methods for scalability and better error handling.
Tests: Updated all affected components with additional test cases.
Phase 7 of Context Management Engine - Main Engine:
- Add ContextEngine as main orchestration class
- Integrate all components: calculator, scorer, ranker, compressor, cache
- Add high-level assemble_context() API with:
- System prompt support
- Task description support
- Knowledge Base integration via MCP
- Conversation history conversion
- Tool results conversion
- Custom contexts support
- Add helper methods:
- get_budget_for_model()
- count_tokens() with caching
- invalidate_cache()
- get_stats()
- Add create_context_engine() factory function
Tests: 26 new tests, 311 total context tests passing
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Phase 5 of Context Management Engine - Model Adapters:
- Add ModelAdapter abstract base class with model matching
- Add DefaultAdapter for unknown models (plain text)
- Add ClaudeAdapter with XML-based formatting:
- <system_instructions> for system context
- <reference_documents>/<document> for knowledge
- <conversation_history>/<message> for chat
- <tool_results>/<tool_result> for tool outputs
- XML escaping for special characters
- Add OpenAIAdapter with markdown formatting:
- ## headers for sections
- ### Source headers for documents
- **ROLE** bold labels for conversation
- Code blocks for tool outputs
- Add get_adapter() factory function for model selection
Tests: 33 new tests, 256 total context tests passing
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Add TokenCalculator with LLM Gateway integration for accurate token
counting with in-memory caching and fallback character-based estimation.
Implement TokenBudget for tracking allocations per context type with
budget enforcement, and BudgetAllocator for creating budgets based on
model context window sizes.
- TokenCalculator: MCP integration, caching, model-specific ratios
- TokenBudget: allocation tracking, can_fit/allocate/deallocate/reset
- BudgetAllocator: model context sizes, budget creation and adjustment
- 35 comprehensive tests covering all budget functionality
Part of #61 - Context Management Engine
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- Added `record_action` in `RateLimiter` for precise tracking of slot consumption post-validation.
- Introduced deduplication mechanism for warning alerts in `CostController` to prevent spamming.
- Refactored `CostController`'s session and daily budget alert handling for improved clarity.
- Implemented test suites for `CostController` and `SafetyGuardian` to validate changes.
- Expanded integration testing to cover deduplication, validation, and loop detection edge cases.
Improved code readability and uniformity by standardizing line breaks, indentation, and inline conditions across safety-related services, models, and tests, including content filters, validation rules, and emergency controls.
The ContentFilter was appending references to DEFAULT_PATTERNS objects,
so when tests modified patterns (e.g., disabling them), those changes
persisted across test runs. Use dataclass replace() to create copies.
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- Add MCPSafetyWrapper for safe MCP tool execution
- Add MCPToolCall/MCPToolResult models for MCP interactions
- Add SafeToolExecutor context manager
- Add SafetyMetrics collector with Prometheus export support
- Track validations, approvals, rate limits, budgets, and more
- Support for counters, gauges, and histograms
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