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Changelog

All notable changes to MemoryGraph will be documented in this file.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

[Unreleased]

Planned (v2.1+)

  • Web visualization dashboard
  • PostgreSQL backend support (pg_graph)
  • Enhanced embedding support
  • Workflow automation templates

[0.8.1] - 2025-12-01

Fixed

  • Fixed FalkorDB integration tests failing in CI/CD when packages not installed
  • Integration tests now properly skip (not fail) when FalkorDB/FalkorDBLite unavailable
  • Fixed mock contamination between unit tests and integration tests

Added

  • Comprehensive documentation for encouraging memory creation
  • New docs/examples/CLAUDE_MD_EXAMPLES.md with ready-to-use templates
  • Memory best practices section in README
  • CLAUDE.md configuration examples for proactive memory storage

[0.8.0] - 2025-12-01

Added - FalkorDB Backend Support

New Graph Database Backends: Added two new high-performance graph database backends powered by FalkorDB.

FalkorDBLite Backend (Embedded)

  • Zero-config embedded graph database: Like SQLite but with native Cypher queries
  • Native graph operations: No NetworkX translation layer, true graph database performance
  • Embedded deployment: Single file, no external server required
  • Installation: pip install memorygraphMCP[falkordblite]
  • Configuration: Set MEMORY_BACKEND=falkordblite, optionally set FALKORDBLITE_PATH
  • Use case: Best for developers who want native graph database features without server setup
  • Performance: Faster than SQLite+NetworkX for complex graph traversals

FalkorDB Backend (Client-Server)

  • High-performance graph database: 500x faster p99 latency than Neo4j (per FalkorDB benchmarks)
  • Redis-based: Built on Redis for exceptional throughput and low latency
  • Client-server architecture: Connect to user-managed FalkorDB instance
  • Installation: pip install memorygraphMCP[falkordb]
  • Configuration: Set MEMORY_BACKEND=falkordb, FALKORDB_HOST, FALKORDB_PORT, FALKORDB_PASSWORD
  • Use case: Production deployments needing high-performance graph operations
  • Deployment: Users must deploy FalkorDB separately (Docker recommended)

Backend Comparison (Updated)

MemoryGraph now supports 5 backend options:

  1. SQLite (default) - Zero-config, 10k memories, embedded
  2. FalkorDBLite (new) - Zero-config, native graph, embedded
  3. FalkorDB (new) - High-performance, client-server, 500x faster than Neo4j
  4. Neo4j - Enterprise graph database, proven at scale
  5. Memgraph - In-memory graph database, fastest analytics

Implementation Details

  • 42 new tests: 21 FalkorDB backend tests + 21 FalkorDBLite backend tests (all passing)
  • Backend abstraction: Both backends implement GraphBackend interface
  • Cypher compatibility: Full Cypher query support with FalkorDB dialect
  • Error handling: Comprehensive exception wrapping and connection management
  • Lazy imports: Optional dependencies loaded only when backend selected
  • Backend factory: Automatic backend selection with MEMORY_BACKEND environment variable

Testing & Quality

  • Total tests: 910 (893 passing, 13 expected integration test skips, 4 skipped)
  • New tests: 42 comprehensive unit tests for both FalkorDB backends
  • Integration tests: Properly skip when FalkorDB not installed (expected behavior)
  • No regressions: All existing backend tests continue to pass
  • Code review: Zero issues found in comprehensive review
  • Benchmark script: Added /scripts/benchmark_backends.py for performance validation

Documentation

  • README.md: Updated with FalkorDB backends, installation examples, performance notes
  • DEPLOYMENT.md: Added FalkorDB and FalkorDBLite sections with configuration examples
  • CONFIGURATION.md: Added environment variable documentation for both backends
  • TROUBLESHOOTING.md: Added FalkorDB-specific troubleshooting (libomp on macOS, connection issues)
  • User responsibility: Clear documentation that FalkorDB client-server deployment is user-managed

Performance

  • FalkorDBLite: Native graph operations without NetworkX overhead
  • FalkorDB: Exceptionally low latency (500x faster p99 than Neo4j per vendor benchmarks)
  • Benchmark script: Included for users to validate performance on their deployment

Breaking Changes

None - all existing backends continue to work unchanged.

Migration Guide

No migration needed for existing users. New users can choose FalkorDB backends:

  • MEMORY_BACKEND=falkordblite - Embedded graph database (zero-config)
  • MEMORY_BACKEND=falkordb - Client-server (requires FalkorDB instance)

Dependencies

  • Added falkordblite>=1.0.0 to optional dependencies
  • Added falkordb>=1.0.0 to optional dependencies
  • Both included in all extras: pip install memorygraphMCP[all]

Enhanced

  • Backend ecosystem expanded from 3 to 5 options
  • Badge updated from "3 Backends" to "5 options"
  • Backend comparison table now includes all 5 backends
  • Performance characteristics documented for each backend

Documentation

  • Consolidated workplan documents into single unified WORKPLAN.md
  • Archived completed Phase 8 tasks to docs/archive/completed-tasks-2025-01.md
  • Removed duplicate marketing documentation (marketing-plan.md, MARKETING_EXECUTIVE_SUMMARY.md)
  • Centralized all active tasks in WORKPLAN.md for easier tracking

[2.0.0] - 2025-11-28

BREAKING CHANGES - Project Renamed to MemoryGraph

Project renamed from claude-code-memory to memorygraph to better reflect universal MCP compatibility. Originally built for Claude Code, MemoryGraph now emphasizes its support for any MCP-enabled coding agent (Cursor, Continue, etc.).

What Changed

Package & CLI:

  • Package name: claude-code-memory → memorygraph
  • CLI command: claude-memory → memorygraph
  • Python module: claude_memory → memorygraph
  • Default database path: ~/.claude-memory/ → ~/.memorygraph/

Branding:

  • Project name: "Claude Code Memory Server" → "MemoryGraph"
  • Subtitle: "MCP Memory Server for AI Coding Agents"
  • Positioning: Generic MCP server, compatible with any MCP-enabled coding agent
  • Documentation: Updated to emphasize MCP standard and universal compatibility

Repository (to be updated):

  • GitHub: gregorydickson/claude-code-memory → gregorydickson/memorygraph
  • PyPI: claude-code-memory → memorygraph

Why This Change?

  • Better Branding: More descriptive, not Claude-specific
  • Universal MCP Compatibility: Works with any MCP-enabled coding agent
  • Professional Naming: "MemoryGraph" describes functionality (graph-based memory)
  • Broader Audience: Appeals to all MCP client users, not just Claude Code

Migration Required

For Existing Users: See MIGRATION.md for complete upgrade guide.

Quick Migration:

  1. pip uninstall claude-code-memory && pip install memorygraphMCP
  2. Update MCP config: "claude-memory" → "memorygraph"
  3. Move database: mv ~/.claude-memory ~/.memorygraph (or use MEMORY_SQLITE_PATH env var)
  4. Restart your coding agent

What Stayed the Same?

  • All features and functionality
  • Environment variables (still use MEMORY_* prefix)
  • Database schema and compatibility
  • All 44 MCP tools
  • Backend support (SQLite, Neo4j, Memgraph)
  • Still optimized for Claude Code, but now explicitly supports all MCP clients

[1.0.0] - 2025-11-28

Production Release - Phase 8: Deployment & Production Readiness

Major Features

  • Zero-Config Default: SQLite backend with no setup required
  • Three-Tier Complexity Model: Lite (8 tools) → Standard (15 tools) → Full (44 tools)
  • Multi-Backend Support: SQLite (default), Neo4j, and Memgraph
  • Tool Profiling System: Choose complexity level via MEMORY_TOOL_PROFILE env var
  • CLI Command: memorygraph with flags for backend, profile, and logging
  • Docker Support: Complete Docker Compose configurations for all backends
  • PyPI Publication: Install via pip install memorygraphMCP

Added

  • SQLite Backend as Default:

    • Zero configuration required
    • NetworkX for graph operations
    • Optimized for <10k memories
    • WAL mode, connection pooling, indexes
    • Full feature parity with graph backends

  • Tool Profiling:

    • lite profile: 8 core tools (default)
    • standard profile: 15 tools (core + intelligence)
    • full profile: All 44 tools (graph analytics, workflows, proactive AI)
    • Environment variable: MEMORY_TOOL_PROFILE
    • CLI flag: --profile lite|standard|full

  • CLI Implementation:

    • Entry point: memorygraph command
    • Flags: --backend, --profile, --log-level
    • Commands: --version, --show-config, --health
    • Helpful error messages and validation

  • Docker Deployment:

    • Base Dockerfile (Python 3.11-slim)
    • docker-compose.yml - SQLite mode (default)
    • docker-compose.neo4j.yml - Neo4j with Browser
    • docker-compose.full.yml - Memgraph with Lab
    • Health checks and optimized settings

  • Documentation Overhaul:

    • README.md: Completely rewritten, beginner-friendly
    • Quick Start section (30-second setup)
    • "Choose Your Mode" comparison table
    • Feature badges (Zero Config, 3 Backends, etc.)
    • MCP specification introduction explaining open standard architecture
    • Multiple example MCP configurations (8 examples covering all modes)
    • FULL_MODE.md: Advanced features guide
    • DEPLOYMENT.md: Complete deployment guide
    • CLAUDE_CODE_SETUP.md: Step-by-step integration
    • TOOL_PROFILES.md: Complete tool reference

  • uvx Support (Documentation):

    • Package works with uvx memorygraph out of the box
    • Option 3 in installation methods for quick testing
    • Installation method comparison table
    • CI/CD integration examples (GitHub Actions, GitLab CI)
    • Clear guidance: uvx for testing, pip for production
    • Warning against using uvx for persistent MCP servers

  • Package Configuration:

    • Version: 1.0.0
    • Updated repository URLs (gregorydickson)
    • Optional dependencies: neo4j, memgraph, intelligence, dev, all
    • Python 3.10+ support
    • MIT License
    • Production/Stable status

Changed

  • Default Backend: Neo4j → SQLite (for zero-config experience)
  • Default Profile: Full → Lite (8 tools, simpler onboarding)
  • Repository Owner: ViralV00d00 → gregorydickson
  • Architecture: Modular backend factory pattern
  • Tool Registration: All 44 tools registered with profile filtering

Backend Compatibility

  • SQLite: All 44 tools supported (default)
  • Neo4j: All 44 tools supported (optimal for production)
  • Memgraph: All 44 tools supported (fastest analytics)

Test Status

  • Total tests: 409
  • Passing: 401/409 (98%)
  • Coverage: 93%
  • Backends tested: SQLite, Neo4j, Memgraph

Breaking Changes

  • Default backend changed from Neo4j to SQLite
  • Environment variable NEO4J_URI no longer required by default
  • Tool profile filtering may hide tools in lite/standard modes

Migration Guide

  • Existing users: Set MEMORY_BACKEND=neo4j to keep current setup
  • New users: Zero config required, just pip install memorygraphMCP
  • Upgrading profiles: No data migration needed, just change MEMORY_TOOL_PROFILE

Deployment Options

  1. pip install (recommended): pip install memorygraphMCP
  2. Docker: docker compose up -d
  3. From source: git clone && pip install -e .
  4. uvx (testing/CI): uvx memorygraph

Performance

  • SQLite: <100ms queries for 10k memories
  • Neo4j: 10x faster graph operations at scale
  • Memgraph: 100x faster complex analytics

[0.6.0] - 2025-11-28

Added - Phase 7: Proactive Features & Advanced Analytics

  • Session Start Intelligence: Automatic briefings when Claude Code starts

    • Project detection and context loading
    • Recent activity summary (last 7 days)
    • Unresolved problems identification
    • Relevant pattern suggestions with effectiveness scores
    • Deprecation warnings for outdated approaches
    • Configurable verbosity (minimal/standard/detailed)
    • MCP tool: get_session_briefing
    • MCP resource: memory://session/briefing/{project_name}

  • Predictive Suggestions Engine: Proactive suggestions based on current context

    • Entity-based suggestion matching
    • Pattern relevance scoring with transparency
    • Related context discovery ("You might also want to know...")
    • Evidence-based recommendations with source attribution
    • MCP tools: get_suggestions, suggest_related_memories

  • Issue Warning System: Proactive issue detection

    • Deprecated approach detection using DEPRECATED_BY relationships
    • Known problem pattern matching
    • Severity-based filtering (low/medium/high)
    • Mitigation suggestions for identified issues
    • MCP tool: check_for_issues

  • Outcome Learning System: Track and learn from solution effectiveness

    • Outcome recording (success/failure with context)
    • Effectiveness score updates using Bayesian updating
    • Pattern effectiveness propagation with dampening
    • Confidence scoring based on usage history
    • Designed decay mechanism (documented, not yet implemented)
    • MCP tool: record_outcome

  • Advanced Analytics Queries: Insights into knowledge graph structure

    • Graph visualization data export (D3/vis.js compatible)
    • Solution similarity analysis using Jaccard similarity
    • Solution effectiveness prediction
    • Learning path recommendations
    • Knowledge gap identification (unsolved problems, sparse entities)
    • Memory ROI tracking (value scoring, usage statistics)
    • MCP tools: get_graph_visualization, find_similar_solutions, predict_solution_effectiveness, recommend_learning_paths, identify_knowledge_gaps, track_memory_roi

  • Architecture & Documentation:

    • ADR 008: Proactive Intelligence Architecture
    • 63 new tests (55 passing, 87% quality)
    • Comprehensive module documentation
    • Backend-agnostic implementation
    • Total test count: 409 (346 + 63 new)

Changed

  • Architecture health score: A+ (98/100)
  • Total MCP tools: 30 (19 core + 11 proactive)
  • Overall test coverage: 90%+ maintained

[0.5.0] - 2025-11-28

Added - Phase 6: Claude Code Integration

  • Context Capture Module: Automatic development context tracking

    • Task context capture (description, goals, files involved)
    • Command execution tracking (command, output, error, success)
    • Error pattern analysis with frequency tracking
    • Solution effectiveness tracking
    • Privacy-first: automatic sanitization of API keys, passwords, tokens, emails
    • 99% test coverage

  • Project Analysis Module: Project-aware memory intelligence

    • Project detection from config files (8 language types supported)
    • Codebase analysis (file counts, languages, frameworks)
    • Git integration for file change tracking
    • Code pattern identification (API endpoints, classes, async patterns)
    • Framework detection (React, Vue, FastAPI, Django, etc.)
    • 88% test coverage

  • Workflow Tracking Module: Development workflow optimization

    • Workflow action tracking with session management
    • Workflow suggestions based on successful patterns
    • Optimization recommendations (slow actions, repeated failures)
    • Session state management for continuity
    • Next-step suggestions based on context
    • 96% test coverage

  • 11 New MCP Tools for Integration:

    • capture_task: Capture task context
    • capture_command: Capture command execution
    • track_error_solution: Track solution effectiveness
    • detect_project: Detect project information
    • analyze_project: Analyze codebase structure
    • track_file_changes: Track git changes
    • identify_patterns: Identify code patterns
    • track_workflow: Track workflow actions
    • suggest_workflow: Get workflow suggestions
    • optimize_workflow: Get optimization recommendations
    • get_session_state: Get current session state

  • 75 New Integration Tests: Comprehensive test suite

    • 27 context capture tests
    • 18 project analysis tests
    • 30 workflow tracking tests
    • All tests passing (100% pass rate)
    • Total test count: 346 (271 + 75 new)

  • Security Features:

    • Automatic redaction of sensitive data patterns
    • Safe subprocess handling with timeouts
    • Graceful handling of missing git repositories
    • No credential leaking into memories

Enhanced

  • Memory type system: Added task, observation, file_change, error_pattern, workflow_action, code_pattern
  • Entity types: Added session, file, project entities
  • Relationship usage: Leverages existing types (PART_OF, INVOLVES, EXECUTED_IN, EXHIBITS, SOLVES, IN_SESSION, FOLLOWS)

Documentation

  • ADR 007: Claude Code Integration Architecture
  • Updated enhancement plan with Phase 6 completion
  • Integration module documentation
  • Security sanitization patterns documented

[0.4.0] - 2025-11-28

Added - Phase 5: Intelligence Layer

  • Entity Extraction Module: Automatic entity identification from memory content

    • Support for 12 entity types (FILE, FUNCTION, CLASS, ERROR, TECHNOLOGY, CONCEPT, etc.)
    • Regex-based pattern matching with 82% coverage
    • Optional NLP support via spaCy
    • Confidence scoring and deduplication
    • Automatic entity linking with MENTIONS relationships

  • Pattern Recognition Module: Identify reusable patterns from accumulated memories

    • Find similar problems using keyword/entity matching
    • Extract common patterns from entity co-occurrences
    • Pattern confidence scoring based on frequency
    • Context-aware pattern suggestions
    • 95% test coverage

  • Temporal Memory Module: Track how information changes over time

    • Version history traversal via PREVIOUS relationships
    • Point-in-time state queries
    • Entity timeline tracking
    • Version diff comparison
    • 97% test coverage

  • Context-Aware Retrieval Module: Intelligent context assembly

    • Multi-factor relevance ranking (entities + keywords + recency)
    • Token-limited context formatting
    • Project-scoped context retrieval
    • Session briefings (last 24 hours)
    • Relationship traversal for related context

  • 94 New Intelligence Tests: Comprehensive test coverage

    • 28 entity extraction tests
    • 23 pattern recognition tests
    • 21 temporal memory tests
    • 22 context retrieval tests
    • All passing with high coverage (82-97%)

  • 7 New MCP Tools:

    • find_similar_solutions: Find similar problems and their solutions
    • suggest_patterns_for_context: Get relevant pattern suggestions
    • get_memory_history: View version history for a memory
    • track_entity_timeline: Track entity usage over time
    • get_intelligent_context: Smart context retrieval with ranking
    • get_project_summary: Comprehensive project overview
    • get_session_briefing: Recent activity summary

Features

  • Smart Entity Recognition: Automatically identify technologies, errors, files, concepts
  • Pattern Learning: Extract reusable patterns from successful solutions
  • Version Tracking: Complete audit trail of how memories evolve
  • Intelligent Ranking: Relevance-based context with entity + keyword + recency scoring
  • Backend Agnostic: All intelligence features work across Neo4j, Memgraph, SQLite

Architecture

  • Modular intelligence layer with 4 specialized modules
  • Clean separation of concerns (extraction, recognition, temporal, retrieval)
  • Extensible design for future ML/NLP enhancements
  • Zero-dependency core with optional advanced features

Documentation

  • docs/adr/006-intelligence-layer-architecture.md: ADR for intelligence design
  • Comprehensive module docstrings with examples
  • Type hints on all public APIs
  • Usage examples for all MCP tools

Performance

  • Entity extraction: <100ms for typical memory
  • Pattern recognition: <200ms with 1000+ memories
  • Context retrieval: <500ms for complex queries
  • Temporal queries: <100ms for version chains

Test Metrics

  • Total Tests: 271 (up from 177)
  • Pass Rate: 100%
  • Intelligence Coverage: 82-97% across modules
  • Overall Coverage: 66%

[0.3.0] - 2025-11-27

Added - Phase 3: Multi-Backend Support

  • Abstract Backend Layer: Introduced GraphBackend interface for database abstraction
  • Neo4j Backend: Refactored existing Neo4j code into neo4j_backend.py implementing GraphBackend
  • Memgraph Backend: Added full Memgraph support using Bolt protocol and Cypher compatibility
  • SQLite Fallback: Implemented zero-dependency SQLite + NetworkX fallback backend
  • Backend Factory: Automatic backend selection with priority-based fallback (Neo4j → Memgraph → SQLite)
  • Configuration System: Centralized configuration with multi-backend environment variables
  • 36 New Backend Tests: Comprehensive test suite for all backend implementations
    • 19 Neo4j backend tests
    • 17 Backend factory tests

Features

  • Automatic Backend Selection: MEMORY_BACKEND=auto tries backends in order until one connects
  • Explicit Backend Selection: Support for neo4j, memgraph, sqlite via env var
  • Zero Setup Option: SQLite fallback requires no external database
  • Cypher Dialect Adaptation: Automatic query translation for Memgraph compatibility
  • Health Checks: Backend health monitoring with connection status and statistics

Configuration

  • New environment variables for multi-backend support:
    • MEMORY_BACKEND: Backend selection (neo4j|memgraph|sqlite|auto)
    • MEMORY_NEO4J_URI, MEMORY_NEO4J_USER, MEMORY_NEO4J_PASSWORD
    • MEMORY_MEMGRAPH_URI, MEMORY_MEMGRAPH_USER, MEMORY_MEMGRAPH_PASSWORD
    • MEMORY_SQLITE_PATH: SQLite database file location
    • Backward compatibility with NEO4J_* environment variables

Documentation

  • docs/CYPHER_COMPATIBILITY.md: Comprehensive guide to Cypher dialect differences
  • Backend comparison matrix with feature support
  • Migration guide between backends
  • Performance considerations for each backend

Technical Details

  • All backends implement identical GraphBackend interface
  • Neo4j backend supports full-text search with FULLTEXT INDEX
  • Memgraph backend with Cypher dialect adaptations
  • SQLite backend uses FTS5 for full-text search, NetworkX for graph operations
  • Async/await throughout all backend implementations
  • Connection pooling and retry logic in Neo4j/Memgraph backends

Testing

  • Test coverage maintained at 76% (now covering 98 tests)
  • All 98 tests passing (100% pass rate)
  • Backend-specific test suites with mocking
  • Integration tests verify cross-backend compatibility

Breaking Changes

  • None - existing Neo4j deployments continue to work without changes
  • Default behavior unchanged when using existing NEO4J_* environment variables

[0.2.0] - 2025-11-27

Added - Phase 2.5: Technical Debt Resolution

  • Custom exception hierarchy with detailed error information
  • Comprehensive error handling across all modules
  • AsyncIO refactoring for all database operations
  • Full MCP protocol compliance with isError flags
  • 62 comprehensive unit tests across all modules:
    • 28 database tests (Neo4j connection, CRUD, relationships, statistics)
    • 19 server tests (MCP handlers, validation, error handling)
    • 8 exception tests (custom exceptions, error hierarchy)
    • 7 model tests (Pydantic validation, serialization)

Fixed

  • All async database operations now properly use AsyncIO
  • Server handlers correctly set isError=True for error responses
  • Comprehensive exception handling in all handler methods
  • Mock return values aligned with actual implementation
  • API parameter names standardized (depth → max_depth, memory_type → type)

Improved

  • Test coverage increased from 67% to 76%:
    • database.py: 65% → 71%
    • server.py: 38% → 63%
    • models.py: 97%
  • All 62 tests passing (100% pass rate)
  • Better error messages with context and validation details
  • Consistent exception handling patterns

Technical Debt Paid

  • ✅ Async refactoring complete
  • ✅ Custom exceptions implemented
  • ✅ Test infrastructure created
  • ✅ Error handling standardized
  • ✅ MCP protocol compliance verified

[0.1.0] - 2025-06-28

Added

  • Initial project setup with Python packaging (pyproject.toml)
  • Comprehensive Neo4j-based MCP memory server implementation
  • Complete data models with Pydantic validation
  • 8 core MCP tools for memory management:
    • store_memory - Store memories with context and metadata
    • get_memory - Retrieve specific memories by ID
    • search_memories - Advanced search with filtering
    • update_memory - Modify existing memories
    • delete_memory - Remove memories and relationships
    • create_relationship - Link memories with typed relationships
    • get_related_memories - Find connected memories
    • get_memory_statistics - Database analytics
  • Advanced relationship system with 7 categories and 35 relationship types:
    • Causal: CAUSES, TRIGGERS, LEADS_TO, PREVENTS, BREAKS
    • Solution: SOLVES, ADDRESSES, ALTERNATIVE_TO, IMPROVES, REPLACES
    • Context: OCCURS_IN, APPLIES_TO, WORKS_WITH, REQUIRES, USED_IN
    • Learning: BUILDS_ON, CONTRADICTS, CONFIRMS, GENERALIZES, SPECIALIZES
    • Similarity: SIMILAR_TO, VARIANT_OF, RELATED_TO, ANALOGY_TO, OPPOSITE_OF
    • Workflow: FOLLOWS, DEPENDS_ON, ENABLES, BLOCKS, PARALLEL_TO
    • Quality: EFFECTIVE_FOR, INEFFECTIVE_FOR, PREFERRED_OVER, DEPRECATED_BY, VALIDATED_BY
  • Neo4j database connection and management with connection pooling
  • Comprehensive schema documentation and API reference
  • GitHub project management with issues, labels, and milestones
  • Complete implementation plan with 7-phase roadmap
  • Test suite foundation with model validation tests
  • Docker-ready configuration with environment variable support

Documentation

  • README.md with complete project overview and usage instructions
  • Schema documentation with all node types and relationships
  • Implementation plan with detailed phase breakdown
  • API documentation for all MCP tools
  • Development setup and configuration guide

Infrastructure

  • GitHub repository with proper issue tracking
  • Git workflow with semantic commit messages
  • Automated schema initialization
  • Performance optimization with indexes and constraints
  • Error handling and logging throughout

Dependencies

  • mcp>=1.0.0 - Model Context Protocol SDK
  • neo4j>=5.0.0 - Neo4j Python driver
  • pydantic>=2.0.0 - Data validation and parsing
  • python-dotenv>=1.0.0 - Environment variable management

Project Milestones

Phase 1: Foundation Setup ✅ COMPLETED (2025-06-28)

  • Issues Closed: #1, #2, #3
  • Commits: 2 major commits with 12 files added
  • Status: All core infrastructure and basic MCP server operational

Phase 2: Core Memory Operations 🔄 IN PROGRESS

  • Target: January 2025
  • Focus: CRUD operations, entity management, basic relationships
  • Issues: #12-25 (to be created)

Phase 3: Advanced Relationship System 📋 PLANNED

  • Target: February 2025
  • Focus: All 35 relationship types, weighted properties, intelligence

Phase 4: Claude Code Integration 📋 PLANNED

  • Target: February-March 2025
  • Focus: Development context capture, workflow integration

Phase 5: Advanced Intelligence 📋 PLANNED

  • Target: March-April 2025
  • Focus: Pattern recognition, automatic relationship detection

Phase 6: Advanced Query & Analytics 📋 PLANNED

  • Target: April-May 2025
  • Focus: Complex queries, effectiveness tracking, visualization

Phase 7: Integration & Optimization 📋 PLANNED

  • Target: May 2025
  • Focus: Production readiness, performance optimization, deep integration

License

This project is licensed under the MIT License - see the LICENSE file for details.