Local Context Memory MCP

A production-ready persistent memory system for AI agents, offering searchable memory across sessions with semantic search and support for multiple database backends.

Ever wanted the ChatGPT memory feature butacross all your LLMsand stored onyour own hardware? Ever hate how there's alimit to how many memoriesChatGPT can store, and that youcan't segment your memoriesinto different domains?

Give any AI assistant persistent, unlimited memory that you control completely.

A production-ready persistent memory system for AI agents using theModel Context Protocol (MCP). Works with Claude Desktop, any MCP-compatible client, and gives you the memory features you've been wanting.

- Why This Matters
- Choose Your Implementation
- Tools & Capabilities

- Available Tools
- Available Resources
- Available Prompts

- SQLite + FAISS Implementation
- PostgreSQL + pgvector Implementation

graph LR subgraph "Client" USER[User] CD[Claude Desktop] end subgraph "MCP Server" subgraph "Tools" SM[store_memory] UM[update_memory] SRCH[search_memories] LMD[list_memory_domains] end subgraph "Resources" RES_SQL[memory://query] RES_PG[memory://domain/query] end subgraph "Prompts" SUM[summarize_memories] end end subgraph "Domain Context" DC[PostgreSQL Only] DC2[Multi-domain isolation:<br/>startup, health, personal] end USER --> CD CD -->|MCP Protocol| SM CD -->|MCP Protocol| UM CD -->|MCP Protocol| SRCH CD -->|MCP Protocol| LMD CD -->|MCP Protocol| RES_SQL CD -->|MCP Protocol| RES_PG CD -->|MCP Protocol| SUM LMD -.->|Available in| DC RES_PG -.->|Available in| DC DC --> DC2 classDef client fill:#e3f2fd,stroke:#1976d2,stroke-width:2px,color:#0d47a1 classDef server fill:#f3e5f5,stroke:#7b1fa2,stroke-width:2px,color:#4a148c classDef tool fill:#fff3e0,stroke:#f57c00,stroke-width:2px,color:#e65100 classDef resource fill:#e8f5e8,stroke:#388e3c,stroke-width:2px,color:#1b5e20 classDef prompt fill:#fce4ec,stroke:#c2185b,stroke-width:2px,color:#880e4f classDef domain fill:#f1f8e9,stroke:#689f38,stroke-width:2px,color:#33691e class USER,CD client class SM,UM,SRCH,LMD tool class RES_SQL,RES_PG resource class SUM prompt class DC,DC2 domain

Store new information in persistent memory with automatic semantic indexing.

- SQLite:store_memory(content, source?, importance?)
- PostgreSQL:store_memory(content, domain?, source?, importance?)
- Examples:

- "User prefers TypeScript over JavaScript for new projects"
- "Weekly team meeting every Tuesday at 2 PM PST"

Modify existing memories while preserving search indexing.

- SQLite:update_memory(memory_id, content?, importance?)
- PostgreSQL:update_memory(memory_id, content?, importance?, domain?)
- Use case: Update outdated information or change importance levels

Find relevant memories using semantic or keyword search.

- SQLite:search_memories(query, limit?, use_vector?)
- PostgreSQL:search_memories(query, domain?, limit?)
- Examples:

- "What programming languages does the user prefer?"
- "Recent project decisions about database choices"

Discover available memory domains for organized context switching.

- Returns:["default", "work", "health", "personal"]
- Use case: Switch between different memory contexts

Quick semantic search via URI pattern for simple memory retrieval.

Domain-scoped semantic search for isolated memory contexts.

- Examples:

- memory://work/project deadlines
- memory://health/medication schedule

Generate intelligent summaries of retrieved memory collections.

- Input: List of memory objects
- Output: Structured summary highlighting key patterns and insights
- Use case: Create context summaries for complex topics

SQLite + FAISS Implementation (Original)

graph TB subgraph "Client Layer" CD[Claude Desktop] AI[AI Agent] end subgraph "MCP Server Layer" SMS[SQLite Memory Server] end subgraph "API Layer" SMA[SQLite Memory API] SVA[SQLite Vector API] OE[Ollama Embeddings] SC[Smart Chunker] end subgraph "Storage Layer" SQL[(SQLite Database)] FAISS[(FAISS Index)] end subgraph "External Services" OL[Ollama API] EM[nomic-embed-text] end CD -->|MCP Protocol| SMS AI -->|HTTP/JSON-RPC| SMS SMS --> SMA SMA --> SVA SVA --> SC SVA --> OE SMA -->|Store Metadata| SQL SVA -->|Vector Operations| FAISS OE -->|Generate Embeddings| OL OL --> EM classDef client fill:#e3f2fd,stroke:#1976d2,stroke-width:2px,color:#0d47a1 classDef server fill:#f3e5f5,stroke:#7b1fa2,stroke-width:2px,color:#4a148c classDef api fill:#fff3e0,stroke:#f57c00,stroke-width:2px,color:#e65100 classDef storage fill:#e8f5e8,stroke:#388e3c,stroke-width:2px,color:#1b5e20 classDef external fill:#fce4ec,stroke:#c2185b,stroke-width:2px,color:#880e4f class CD,AI client class SMS server class SMA,SVA,OE,SC api class SQL,FAISS storage class OL,EM external

PostgreSQL + pgvector Implementation (New)

graph TB subgraph "Client Layer" CD[Claude Desktop] AI[AI Agent] end subgraph "MCP Server Layer" PMS[PostgreSQL Memory Server] end subgraph "API Layer" PMA[PostgreSQL Memory API] OE[Ollama Embeddings] end subgraph "PostgreSQL Database" subgraph "Domain Tables" DT1[default_memories] DT2[startup_memories] DT3[health_memories] end PGV[pgvector Extension] end subgraph "External Services" OL[Ollama API] EM[nomic-embed-text] end CD -->|MCP Protocol| PMS AI -->|HTTP/JSON-RPC| PMS PMS --> PMA PMA --> OE PMA -->|SQL + Vector Ops| PGV PGV --> DT1 PGV --> DT2 PGV --> DT3 OE -->|Generate Embeddings| OL OL --> EM classDef client fill:#e3f2fd,stroke:#1976d2,stroke-width:2px,color:#0d47a1 classDef server fill:#f3e5f5,stroke:#7b1fa2,stroke-width:2px,color:#4a148c classDef api fill:#fff3e0,stroke:#f57c00,stroke-width:2px,color:#e65100 classDef storage fill:#e8f5e8,stroke:#388e3c,stroke-width:2px,color:#1b5e20 classDef external fill:#fce4ec,stroke:#c2185b,stroke-width:2px,color:#880e4f class CD,AI client class PMS server class PMA,OE api class DT1,DT2,DT3,PGV storage class OL,EM external

- Semantic Search: Uses Ollama embeddings for intelligent memory retrieval
- Smart Chunking: Automatically breaks down long text for better search results
- MCP Standard: Full MCP protocol compliance for Claude Desktop integration
- Docker Ready: Simple containerized deployment with self-contained images
- Fallback Search: Automatic fallback to text search when vector search unavailable

- Local Storage: Everything runs locally with SQLite + FAISS files
- Zero Setup: No database server required
- Portable: Single directory contains all data

- Domain Segmentation: Separate memory contexts (startup, health, personal, etc.)
- Production Ready: ACID compliance, concurrent access, replication support
- Native Vector Ops: Efficient similarity search without separate index files
- Scalable: Handles large datasets with proper indexing

Recommended: Use Docker for the easiest setup!Skip all dependency management and get running in seconds.

# Run immediately - no setup required! docker run --rm -i -v ./memory-data:/app/data cunicopia/local-memory-mcp:sqlite

PostgreSQL Version(with domain segmentation):

# Run immediately - includes full PostgreSQL database! docker run --rm -i -v ./postgres-data:/var/lib/postgresql/data cunicopia/local-memory-mcp:postgres

That's it! The containers are self-contained and handle all dependencies automatically.

- Docker (for containerized deployment) or Python 3.12+ (for local installation)
- Ollama withnomic-embed-textmodel (optional but recommended for enhanced semantic search)

Ollama enables enhanced semantic search with vector embeddings. Without it, the system falls back to text-based search.

- macOS/Windows: Download installer fromollama.com/download
- Linux:curl -fsSL https://ollama.com/install.sh | sh

# Install the embedding model ollama pull nomic-embed-text:v1.5 # Verify it's running (should show localhost:11434) curl http://localhost:11434/api/tags

{ "mcpServers": { "localMemoryMCP-SQLite": { "command": "docker", "args": ["run", "--rm", "-i", "-v", "/path/to/your/memory-data:/app/data", "cunicopia/local-memory-mcp:sqlite"] }, "localMemoryMCP-PostgreSQL": { "command": "docker", "args": ["run", "--rm", "-i", "-v", "/path/to/your/postgres-data:/var/lib/postgresql/data", "cunicopia/local-memory-mcp:postgres"] } } }

Volume Paths:Replace/path/to/your/memory-dataand/path/to/your/postgres-datawith any directory where you want to store your memories (e.g.,~/Documents/memory-data,/Users/yourname/my-memories, etc.)

Local Installation Configuration (Alternative)

Note:Only use if you cannot use Docker. Requires manual setup first (seeManual Installation).

{ "mcpServers": { "localMemoryMCP": { "command": "bash", "args": ["/path/to/local-memory-mcp/run_sqlite.sh"] } } }

// Store a memory store_memory( "User prefers Python for backend development", "conversation", 0.8 ) // Search memories search_memories("programming preferences", 5, true) // Get memories via resource // Access: memory://programming

// List available domains list_memory_domains() // Returns: ["default", "startup", "health"] // Store memories in different domains store_memory( "Series A funding closed at $10M", "startup", // domain "meeting", // source 0.9 // importance ) store_memory( "User has peanut allergy", "health", "medical_record", 1.0 ) // Search within specific domain search_memories("funding", "startup", 5) // Get memories via resource // Access: memory://startup/funding%20strategy

- FastMCP: Python MCP server framework
- SQLite: Structured metadata and text search fallback
- FAISS: Vector similarity search
- Ollama: Local embedding generation (optional)
- Smart Chunker: Text processing for optimal retrieval

- FastMCP: Python MCP server framework
- PostgreSQL: Full database with metadata and vector storage
- pgvector: Native PostgreSQL vector similarity search
- Ollama: Local embedding generation (optional)
- Domain Tables: Isolated memory contexts for better organization

- OLLAMA_API_URL: Ollama endpoint (default:http://localhost:11434)
- OLLAMA_EMBEDDING_MODEL: Model name (default:nomic-embed-text)
- MCP_SERVER_NAME: Server name for MCP (default:Local Context Memory)

- MCP_DATA_DIR: Data storage path (default:./data)

- POSTGRES_HOST: Database host (default:localhost)
- POSTGRES_PORT: Database port (default:5432)
- POSTGRES_DB: Database name (default:postgres)
- POSTGRES_USER: Database user (default:postgres)
- POSTGRES_PASSWORD: Database password (required)
- DEFAULT_MEMORY_DOMAIN: Default domain for memories (default:default)

pip install -r requirements.sqlite.txt python src/sqlite_memory_server.py

pip install -r requirements.pgvector.txt python src/postgres_memory_server.py

Docker support is fully functional with self-contained containers! Both SQLite and PostgreSQL versions run completely independently.

# Run pre-built images directly (recommended) # SQLite version - replace './data' with your preferred data directory docker run --rm -i -v ./data:/app/data cunicopia/local-memory-mcp:sqlite # PostgreSQL version - replace './postgres_data' with your preferred data directory docker run --rm -i -v ./postgres_data:/var/lib/postgresql/data cunicopia/local-memory-mcp:postgres

# Only needed if you want to build yourself docker build -f Dockerfile.sqlite_version -t local-memory-mcp:sqlite_version . docker build -f Dockerfile.postgres_version -t local-memory-mcp:postgres_version .

⚠️We strongly recommend using Docker instead- it handles all dependencies automatically. Use this section only if Docker is not available or you have specific requirements.

git clone https://github.com/cunicopia-dev/local-memory-mcp cd local-memory-mcp pip install -r requirements.sqlite.txt python src/sqlite_memory_server.py

PostgreSQL + pgvector (Production, Scalable)

# Install PostgreSQL + pgvector sudo apt install postgresql postgresql-contrib sudo apt install postgresql-17-pgvector # Adjust version as needed # Change directory to where you downloaded the repo cd /path/to/local-memory-mcp # Set up database # PLEASE NOTE: We create a user and basic password here, please change this if you want to host it locally psql < sql/create_user.sql psql -U postgres < sql/setup_database.sql # Install Python dependencies pip install -r requirements.pgvector.txt # Configure connection (edit .env file) cp .env.example .env # Run server python src/postgres_memory_server.py

# Install PostgreSQL and pgvector (Homebrew-based) brew install postgresql@17 brew services start postgresql@17 # Link psql and other tools if needed brew link --force postgresql@17 # Install pgvector extension (PostgreSQL must be running) # This installs the extension into your local PostgreSQL environment brew install pgvector # OPTIONAL: If pgvector doesn't register properly, you can manually build it # git clone --branch v0.8.0 https://github.com/pgvector/pgvector.git # cd pgvector # make && make install # PLEASE NOTE: We create a user and basic password here, please change this if you want to host it locally psql < sql/create_user.sql # Set up database psql -U postgres -f sql/setup_database.sql # Install Python dependencies pip install -r requirements.pgvector.txt # Configure connection (edit .env file) cp .env.example .env # Run server python src/postgres_memory_server.py

⚠️ SECURITY CAUTION:Please go to sql/create_user.sql and create a more secure user and password, the ones listed are for example purposes only! Please protect your data and take your data security seriously.

Embeddings, vector search, document storage, and full-text search with the open-source AI application database

Brain-inspired persistent memory MCP server with hybrid BM25+vector search, ACT-R activation scoring, FadeMem decay, and knowledge graphs — 17 tools, fully local via Ollama, zero API keys.

Semantic search through Dickens' classic tale. Find passages by meaning, theme, or concept - not just keywords.

An MCP server providing semantic search capabilities for APLCart data.

MCP server for Christian scholarship and research — scripture, Greek/Hebrew word data, cross-references, patristic texts, and semantic search,

Local code analysis MCP server with 25+ tools: semantic search, call graph tracing, dependency analysis, and symbol navigation. Built with Tree-sitter and CozoDB. Supports Go, Python, JS, TS.

A local-first code indexer that enhances LLMs with deep code understanding. It integrates with AI assistants via the Model Context Protocol (MCP) and supports AI-powered semantic search.

A server for managing structured project context using SQLite, with support for vector embeddings for semantic search and Retrieval Augmented Generation (RAG).

Access and search EPUB ebook collections using semantic vector search.

Search 286 episodes of product management wisdom from Lenny Rachitsky. Semantic search across 300+ hours of transcripts.