Which integrations are available for this server?

Integrates with OpenAI's embedding models to generate high-quality semantic vector representations for stored decisions and learnings. Supports PostgreSQL with the pgvector extension as a scalable backend for storing and performing similarity searches on agent memories and project context. Provides a local SQLite-based storage system for persistent vector memory, allowing AI agents to save and retrieve semantic context across sessions without external dependencies.

How do I use MCP Vector Memory?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@MCP Vector Memory search for the architectural decisions we made about the database" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

MCP Vector Memory

Persistent vector memory for AI coding agents. Give your AI agents long-term memory that survives across conversations.

Works with Antigravity · VS Code / Copilot · Claude Code · Codex

The Problem

AI coding agents forget everything between conversations. Every new session starts from zero — they repeat questions, lose architectural decisions, and forget how your project is set up.

The Solution

MCP Vector Memory gives your agents persistent, semantic memory. Agents can save and search memories using natural language. Memories are stored locally in SQLite with vector similarity search — no API keys, no cloud, no cost.

Agent: "I need to set up the database" → search_memory("database setup") → Returns: "PostgreSQL 18 with pgvector, HNSW index, user=app_user..." → Agent works WITH full context from past sessions

Quick Start

1. Install

pip install mcp-vector-memory

2. Configure Your IDE

Add to ~/.gemini/antigravity/mcp_config.json:

{ "mcpServers": { "memory": { "command": "mcp-vector-memory", "args": [], "env": {} } } }

Create .vscode/mcp.json in your workspace:

{ "mcp": { "servers": { "memory": { "command": "mcp-vector-memory", "args": [], "env": {} } } } }

Add to ~/.claude/claude_desktop_config.json:

{ "mcpServers": { "memory": { "command": "mcp-vector-memory", "args": [], "env": {} } } }

Add to your Codex MCP configuration:

{ "mcpServers": { "memory": { "command": "mcp-vector-memory", "args": [], "env": {} } } }

3. Restart your IDE

That's it. Your agents now have persistent memory.

Tools Available

Tool	Description
`search_memory`	Semantic search over past memories
`save_memory`	Save a decision, context, or learning
`list_projects`	List all projects with memory counts
`get_stats`	Memory system statistics

How It Works

┌─────────────────┐ ┌──────────────────────┐ ┌─────────────┐ │ AI Agent │────▶│ MCP Vector Memory │────▶│ SQLite │ │ (any IDE) │◀────│ (local process) │◀────│ + vectors │ └─────────────────┘ └──────────────────────┘ └─────────────┘ │ Embeddings (all-MiniLM-L6-v2) runs locally

Agent calls save_memory → text is embedded locally → stored in SQLite with vector index
Agent calls search_memory → query is embedded → SQLite finds most similar memories
Everything runs locally. No API calls, no cloud, no cost.

Configuration

All configuration is via environment variables:

Variable	Default	Description
`MCP_MEMORY_BACKEND`	`sqlite`	Database backend (`sqlite` or `postgres`)
`MCP_MEMORY_DATA_DIR`	`~/.mcp-vector-memory`	SQLite data directory
`MCP_MEMORY_EMBEDDING_PROVIDER`	`local`	Embedding provider (`local` or `openai`)
`MCP_MEMORY_EMBEDDING_MODEL`	`all-MiniLM-L6-v2`	Embedding model name

PostgreSQL Backend (Optional)

For production deployments with PostgreSQL + pgvector:

pip install mcp-vector-memory[postgres]

MCP_MEMORY_BACKEND=postgres PGHOST=localhost PGPORT=5432 PGUSER=mcp_memory PGPASSWORD=your_password PGDATABASE=mcp_memory

Docker (PostgreSQL)

docker compose up -d

See docker-compose.yml for the full setup.

OpenAI Embeddings (Optional)

For higher quality embeddings via OpenAI API:

pip install mcp-vector-memory[openai] export OPENAI_API_KEY=sk-... export MCP_MEMORY_EMBEDDING_PROVIDER=openai

Contributing

Contributions are welcome! Please see CONTRIBUTING.md for guidelines.

License

MIT — see LICENSE

This server cannot be installed

-

security - not tested

A

license - permissive license

-

quality - not tested

How are these scores calculated?

Resources

GitHub Repository

Need Help?

Report Issue

Related Servers

MCP Vector Memory

MCP Vector Memory

The Problem

The Solution

Quick Start

1. Install

2. Configure Your IDE

3. Restart your IDE

Tools Available

How It Works

Configuration

PostgreSQL Backend (Optional)

Docker (PostgreSQL)

OpenAI Embeddings (Optional)

Contributing

License

Resources

New MCP Servers

Latest Blog Posts

MCP directory API