Graph-Mem

Graph-Mem MCP

Persistent knowledge graph memory for AI agents and IDEs

Graph-Mem MCP is a universal MCP server that gives any agent or IDE persistent, structured memory through a knowledge graph. It combines graph storage, semantic vector search, and multi-hop traversal in a single package — install it, add it to your MCP config, and your agent gains memory that survives across sessions. It works everywhere MCP does.

Built To Be Trusted With Your Data

Works With

graph-mem is a standard MCP server, so it works with any MCP-compatible agent, IDE, or framework. graph-mem install additionally writes the skill file straight into the right place for these 13, each at a path cited against the vendor's own documentation:

Claude Code OpenCode Cursor Windsurf Codex CLI

Gemini CLI GitHub Copilot Amp Kiro Roo Code

Continue Antigravity Droid (Factory) add yours →

Using something else? The MCP config below is all you need; the skill file is a convenience, not a requirement. Adding your agent to the installer takes a documented path and about ten lines — see Adding an Agent.

What is this?

AI agents forget everything between sessions. They re-read files, re-discover architecture, and repeat mistakes. Graph-Mem MCP solves this by providing persistent, per-project knowledge graphs that any MCP-compatible agent can read and write to. The graph builds organically as the agent works — extracting entities, decisions, and relationships from every conversation. It runs as a standard MCP server with 28 tools that plug into any agent, IDE, or framework that supports the Model Context Protocol.

Why a graph, not just a vector store?

Vector search finds _similar_ things. Graphs find _connected_ things. When an agent asks "what depends on the auth service?", a vector store returns text that mentions auth. A knowledge graph traverses the actual dependency edges and returns every upstream consumer — even ones that never mention "auth" in their description. Graph-Mem gives you both: vector similarity for fuzzy discovery, graph traversal for structural queries.

Use Cases

• Agent memory — Give any AI coding agent persistent context across sessions
• IDE integration — Add knowledge graph tools to Cursor, Windsurf, Copilot, or any MCP-enabled IDE
• Agent building — Use as the memory layer when building custom AI agents and workflows
• Research & knowledge management — Build structured knowledge bases with semantic search
• Multi-project context — Maintain separate knowledge graphs per project with multi-graph support

Quick Start

1. Install:

pip install graphmem-mcp

Or run it without installing — uvx fetches and isolates it the way npx does for Node:

uvx --from graphmem-mcp graph-mem server

Listed in the official MCP Registry as io.github.Sathvik-1007/graphmem-mcp, so MCP-aware clients can discover and install it directly.

2. Install the skill for your agent:

graph-mem install claude       # Claude Code

graph-mem install opencode # OpenCode graph-mem install codex # Codex CLI graph-mem install gemini # Gemini CLI graph-mem install cursor # Cursor graph-mem install windsurf # Windsurf graph-mem install amp # Amp graph-mem install antigravity # Antigravity graph-mem install copilot # GitHub Copilot graph-mem install kiro # Kiro graph-mem install roocode # Roo Code graph-mem install continue # Continue graph-mem install droid # Droid (Factory)

This writes a skill file that teaches your agent how to use all 28 MCP tools — when to search, when to add entities, naming conventions, and common workflows.

3. Configure MCP by adding this to your agent's MCP config:

{

"mcpServers": { "graph-mem": { "command": "graph-mem", "args": ["server"] } } }

With full customization:

{

"mcpServers": { "graph-mem": { "command": "graph-mem", "args": [ "server", "--project-dir", "/path/to/my/project", "--embedding-model", "sentence-transformers/all-mpnet-base-v2", "--use-onnx", "--cache-size", "20000", "--log-level", "INFO" ] } } }

That's it. Your agent now has persistent memory. Verify by asking it to run read_graph().

One-Prompt Setup

Paste this into your agent's chat to get started immediately:

I want you to give yourself persistent memory using graph-mem. Run the following:

pip install graphmem-mcp graph-mem install claude # or: opencode, codex, gemini, cursor, windsurf, amp,

antigravity, copilot, kiro, roocode, continue, droid

This installs a skill file that teaches you how to use all 28 MCP tools. The server should already be configured in your MCP config. If not, add it:

{ "mcpServers": { "graph-mem": { "command": "graph-mem", "args": ["server", "--project-dir", "/path/to/your/project"] } } }

Now start using the knowledge graph:

1. read_graph() to see current state
2. search_nodes("relevant topic") to find existing knowledge
3. add_entities, add_relationships, add_observations as you learn things
4. update_observation / update_relationship to fix mistakes in-place
5. open_dashboard() to explore the graph visually in your browser
6. At session end, capture anything important you discovered

Your goal: build a rich knowledge graph of this project so future sessions start with full context instead of from zero. Search before adding to avoid duplicates. Be specific with entity names and types.

Installation

Option 1: pip (Recommended)

pip install graphmem-mcp

graph-mem server

Option 2: uvx (zero pre-install)

uvx --from graphmem-mcp graph-mem server

uvx downloads the package into an isolated environment and runs it in one command. Nothing to pre-install beyond uv.

Option 3: From source

git clone https://github.com/Sathvik-1007/GraphMem-MCP

cd graph-mem pip install -e ".[full,dev]" graph-mem server

Optional extras

pip install "graphmem-mcp[embeddings]"   # sentence-transformers for local embeddings

pip install "graphmem-mcp[onnx]" # ONNX runtime for embedding inference pip install "graphmem-mcp[ui]" # aiohttp for interactive graph visualisation pip install "graphmem-mcp[full]" # all of the above

Tools

Graph-Mem exposes 28 MCP tools — ten for writing, nine for reading, four for maintenance, four for multi-graph management, and one utility. Full CRUD on every primitive: entities, relationships, and observations can all be created, read, updated, and deleted.

Write Tools (10)

Read Tools (9)

Maintenance Tools (4)

Visualization (1)

Multi-Graph Tools (4)

Multi-graph storage: Each named graph is a separate SQLite database in .graphmem/. The default graph is graph.db. Use --graph on CLI commands to target a specific graph.

Architecture

graph TD

Agent[Any MCP-Compatible Agent or IDE] --> Transport

subgraph Transport STDIO[stdio] SSE[SSE] HTTP[streamable-http] end

Transport --> Tools

subgraph Tools[Graph-Mem MCP Server — 28 MCP Tools] Write[Write · 10 tools] Read[Read · 9 tools] Maint[Maintenance · 4 tools] Graph[Multi-Graph · 4 to