Root cause: The demo data's model_params was missing `top_p`, but the
Zod schema required all three fields (temperature, max_tokens, top_p).
This caused silent validation failures when editing agent types.
Fixes:
1. Add getInitialValues() that ensures all required fields have defaults
2. Handle nested validation errors in handleFormError (e.g., model_params.top_p)
3. Add useEffect to reset form when agentType changes
4. Add console.error logging for debugging validation failures
5. Update demo data to include top_p in all agent types
The form now properly initializes with safe defaults for any missing
fields from the API response, preventing silent validation failures.
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Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
When form validation fails (e.g., personality_prompt is empty), the form
would silently not submit. Now it shows a toast with the first error
and navigates to the tab containing the error field.
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Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
When running in Docker, the frontend needs to use 'http://backend:8000'
as the backend URL for Next.js rewrites. This env var is set to use
the Docker service name for proper container-to-container communication.
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Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
The rewrite was using 'http://backend:8000' which only resolves inside
Docker network. When running Next.js locally (npm run dev), the hostname
'backend' doesn't exist, causing ENOTFOUND errors.
Now uses NEXT_PUBLIC_API_BASE_URL env var with fallback to localhost:8000
for local development. In Docker, set NEXT_PUBLIC_API_BASE_URL=http://backend:8000.
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Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Dashboard changes:
- Update useDashboard hook to fetch real projects from API
- Calculate stats (active projects, agents, issues) from real data
- Keep pending approvals as mock (no backend endpoint yet)
Demo data additions:
- API Gateway Modernization project (active, complex)
- Customer Analytics Dashboard project (completed)
- DevOps Pipeline Automation project (active, complex)
- Added sprints, agent instances, and issues for each new project
Total demo data: 6 projects, 14 agents, 22 issues
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Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- 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.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
register_vector() requires the vector type to exist in PostgreSQL before
it can register the type codec. Move CREATE EXTENSION to a separate
_ensure_pgvector_extension() method that runs before pool creation.
This fixes the "unknown type: public.vector" error on fresh databases.
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Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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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Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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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Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- 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
- Skip SSE connection in demo mode (MSW doesn't support SSE).
- Remove unused `useProjectEvents` and related real-time hooks from `Dashboard`.
- Temporarily disable activity feed SSE until a global endpoint is available.
- 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>
- 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.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- 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
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- 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.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- 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
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- 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
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Moved tests/unit/models/memory/ to tests/models/memory/ to avoid
Python import path conflicts when pytest collects all tests.
The conflict was caused by tests/models/ and tests/unit/models/ both
having __init__.py files, causing Python to confuse app.models.memory
imports.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- 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
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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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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>
- 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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Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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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Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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)
Closes#90🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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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Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Added detailed unit tests for `/context` endpoints, covering health checks, context assembly, token counting, budget retrieval, and cache invalidation.
- Included edge cases, error handling, and input validation for context-related operations.
- Improved test coverage for the Context Management module with mocked dependencies and integration scenarios.
- Introduced end-to-end tests for MCP workflows, including server discovery, authentication, context engine operations, error handling, and input validation.
- Added full lifecycle tests for agent workflows, covering type management, instance spawning, status transitions, and admin-only operations.
- Enhanced test coverage for real-world MCP and Agent scenarios across PostgreSQL and async environments.
- 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.
- Introduced integration tests covering backend, LLM Gateway, Knowledge Base, and Context Engine.
- Includes health checks, tool listing, token counting, and end-to-end MCP flows.
- Added `RUN_INTEGRATION_TESTS` environment flag to enable selective test execution.
- Includes a quick health check script to verify service availability before running tests.
- Added new targets for testing (`test`, `test-backend`, `test-mcp`, `test-frontend`, etc.) and validation (`validate`, `validate-all`).
- Enhanced help section to reflect updates, including detailed descriptions for testing, validation, and new MCP-specific commands.
- Improved developer workflow by centralizing testing and linting processes in the Makefile.
- Refactored database batch operations to ensure transaction atomicity and simplify nested structure.
- Added `Makefile` for `knowledge-base` and `llm-gateway` modules to streamline development workflows.
- Simplified `Dockerfile` for `llm-gateway` by removing multi-stage builds and optimizing dependencies.
- Improved code readability in `collection_manager` and `failover` modules with refined logic.
- Minor fixes in `test_server` and Redis health check handling for better diagnostics.
- Added `mcp-llm-gateway` and `mcp-knowledge-base` services to `docker-compose.dev.yml`, `docker-compose.deploy.yml`, and `docker-compose.yml` for AI agent capabilities.
- Configured health checks, environment variables, and dependencies for MCP services.
- Included updated resource limits and deployment settings for production environments.
- Connected backend and agent services to the MCP servers.
Document the pre-commit hook setup, behavior, and rationale for
protecting only main/dev branches while allowing flexibility on
feature branches.
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Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
