How do I install Graph Mem?

Copy the stdio config for Cursor, Claude Desktop, Claude Code, or VS Code on this page. It uses the publisher's install, not a PlugMCP proxy.

Does Graph Mem need an API key?

The publisher lists these environment variables: GRAPHMEM_DB_PATH, GRAPHMEM_EMBEDDING_MODEL, GRAPHMEM_TRAVERSAL_NODE_BUDGET, GRAPHMEM_LOG_LEVEL.

What transport does Graph Mem use?

Graph Mem runs locally over stdio.

When was Graph Mem last checked?

Last checked 2026-11-09. Persistent knowledge-graph memory for AI agents, with local semantic search. No API keys.

Graph Mem MCP server

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


1055 tests	Property-based against a brute-force reference, plus fuzzing on every parser
mypy strict	Clean, enforced in CI — not just configured
Authenticated UI	Host + Origin allow-lists and a session token; a cross-origin write is a `403`, verified against a running server
Bounded	Every traversal, search, and list response has a named, configurable cap and reports truncation
Honest docs	Performance claims come with measurements and a reproducible benchmark; known gaps are written down

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:

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)

Tool	Description
`add_entities`	Batch-create entities with optional observations; auto-merges on name conflict; returns quality screening hints
`add_relationships`	Create typed, directed edges between entities; merges duplicates by max weight
`add_observations`	Attach factual statements to entities with optional source provenance
`update_entity`	Modify entity name, description, properties, or type in-place (rename with collision check)
`update_relationship`	Change weight, type, or properties of an existing edge without delete+re-create
`update_observation`	Edit observation text content in-place with automatic embedding recompute
`delete_entities`	Remove entities with cascade to relationships, observations, and embeddings
`delete_relationships`	Remove specific edges between entities, optionally filtered by type
`delete_observations`	Remove specific observations by ID with ownership validation
`merge_entities`	Combine duplicate entities: moves observations and relationships, deduplicates edges

Read Tools (9)

Tool	Description
`search_nodes`	Hybrid semantic + full-text search with RRF fusion ranking
`search_observations`	Semantic search directly over observation text content
`find_connections`	Multi-hop BFS graph traversal with direction and type filters
`get_entity`	Full entity details with all observations and relationships
`list_entities`	Browse/paginate all entities with optional type filter
`list_relationships`	Browse/paginate relationships with entity, type, or combined filters
`read_graph`	Graph statistics: counts, type distributions, most-connected entities
`get_subgraph`	Extract neighborhood subgraph around seed entities
`find_paths`	Find shortest paths between two entities via BFS

Maintenance Tools (4)

Tool	Description
`graph_health`	Health stats: counts, hotspots, missing descriptions, suggested actions
`compact_observations`	Atomic observation compaction — delete old and add merged summaries in one step
`suggest_connections`	Find semantically similar entities for a node to connect to
`audit_graph`	Full quality screening — disconnected nodes, missing data, weak links. Returns structured findings plus a rendered `report` field

Visualization (1)

Tool	Description
`open_dashboard`	Launch interactive graph visualisation UI and return its URL

Multi-Graph Tools (4)

Tool	Description
`list_graphs`	List all named graphs in the `.graphmem/` directory with entity/relationship/observation counts
`create_graph`	Create a new named graph (`.graphmem/<name>.db`)
`switch_graph`	Switch the active graph — hot-swaps storage, search, and graph engines
`delete_graph`	Delete a named graph (cannot delete the currently active graph)

Multi-graph storage: Each named graph is a separate SQLite database in .graphmem/. The default graph is graph.db. Use --graph <name> 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 tools]
        UI[Dashboard · 1 tool]
    end

    Write --> Engines
    Read --> Engines
    Maint --> Engines

    subgraph Engines
        GE[GraphEngine]
        SE[HybridSearch]
        EE[EmbeddingEngine]
        GT[GraphTraversal]
        EM[EntityMerger]
    end

    Engines --> Storage

    subgraph Storage[SQLite Storage]
        DB[SQLite WAL]
        VEC[sqlite-vec]
        FTS[FTS5]
    end

    UI --> Dashboard[React SPA + aiohttp]
    Dashboard --> DB

Everything lives in a single SQLite database per project. The server communicates over MCP's standard stdio transport (SSE and streamable-http also supported) and stores all data in .graphmem/graph.db at your project root. The database file is portable — copy it between machines, check it into version control, or back it up like any other file.

Document	What is in it
How It Works	Data model, search pipeline, traversal, entity resolution, storage layout, request flow
Architecture	Why the design is what it is — the load-bearing decisions, their costs, and the measured baselines
Security	Threat model, the MCP and browser trust boundaries, and what is deliberately out of scope
Contributing	Local setup and the gates CI enforces

MCP Integration

Graph-Mem MCP is a standard MCP server. It communicates with your agent over the Model Context Protocol (stdio by default, SSE and streamable-http also supported) and exposes 28 tools that the agent calls directly — the same way it calls any other MCP tool. It works with every MCP-compatible agent, IDE, and framework out of the box.

To verify it's working, ask your agent to run read_graph() — it should return the current graph statistics.

Graph Visualisation

graph-mem ui                              # open interactive graph explorer
graph-mem ui --no-open                    # start server without opening browser
graph-mem ui --port 9090                  # use a specific port
graph-mem ui --graph harry-potter         # open a specific named graph

The URL printed by graph-mem ui contains a session token — treat it as a password. The dashboard reads and writes the graph, so the API requires that token in a custom header, and rejects requests whose Origin or Host is not the interface it bound. Without those checks any website you visited while the UI was running could rewrite your knowledge graph. See SECURITY.md for the details.

The open_dashboard MCP tool also starts this UI server and returns the URL to your agent. It always binds localhost: the bind address is deliberately not a tool parameter, so a prompt-injected agent cannot publish your graph to the network.

Dashboard features:

Force-directed graph canvas with real-time physics simulation
Entity type filtering — toggle visibility of entity types via sidebar checkboxes
Click-to-focus — click a node on the graph or sidebar to instantly center and zoom to it
Inline entity editing — click any field (name, type, description) in the detail panel to edit it in-place
Property management — add, edit, and delete individual properties per entity with per-row controls
Observation management — add, edit, and delete observations with confirmation dialogs and inline editing
Relationship navigation — click related entities to navigate the graph
Entity creation and deletion — create new entities from the sidebar, delete with confirmation from the detail panel danger zone
Hybrid search — semantic + keyword search across all entities
Graph picker — switch between named graphs without restarting the server
Physics controls — adjust spring, repulsion, damping, and gravity in real-time
Keyboard shortcuts — Space (reheat), F (fit to view), Escape (deselect)

Data Storage

By default, Graph-Mem stores its database at .graphmem/graph.db relative to the current working directory. You can control this with:

Method	Example	Result
`--project-dir`	`--project-dir /home/user/myproject`	Stores at `/home/user/myproject/.graphmem/graph.db`
`--db`	`--db /custom/path/memory.db`	Stores at exactly that path
`GRAPHMEM_DB_PATH` env	`export GRAPHMEM_DB_PATH=/tmp/test.db`	Stores at that path
Default	(nothing)	`.graphmem/graph.db` relative to CWD

Priority order: --db > --project-dir > GRAPHMEM_DB_PATH env var > default.

The .graphmem/ directory is automatically created if it doesn't exist. Add .graphmem/ to your .gitignore if you don't want to track the database in version control.

CLI Reference

Server

graph-mem server                          # stdio transport (default)
graph-mem server --transport sse          # SSE transport
graph-mem server --db /path/to/graph.db   # custom database path
graph-mem server --project-dir /my/project  # store memory in <dir>/.graphmem/
graph-mem server --project-dir /my/project --graph harry-potter  # use named graph

# Embedding customization
graph-mem server --embedding-model sentence-transformers/all-mpnet-base-v2
graph-mem server --no-onnx --embedding-device cuda
graph-mem server --cache-size 50000

# Tuning
graph-mem server --search-limit 20 --max-hops 6
graph-mem server --log-level DEBUG

All server options:

Flag	Description	Default
`--transport`	`stdio`, `sse`, or `streamable-http`	`stdio`
`--db`	Path to SQLite database file	`.graphmem/graph.db`
`--project-dir`	Project root; DB at `<dir>/.graphmem/graph.db`	CWD
`--graph`	Named graph (resolves to `<dir>/.graphmem/<name>.db`)	`graph`
`--host`	Bind address (SSE/HTTP only)	`127.0.0.1`
`--port`	Port (SSE/HTTP only)	`8080`
`--embedding-model`	HuggingFace model ID for embeddings	`all-MiniLM-L6-v2`
`--use-onnx / --no-onnx`	Use the ONNX embedding backend (needs `optimum[onnxruntime]`)	off
`--embedding-device`	`cpu` or `cuda`	`cpu`
`--cache-size`	Embedding LRU cache max entries	`10000`
`--search-limit`	Default max results for `search_nodes`	`10`
`--max-hops`	Default max depth for `find_connections`	`4`
`--log-level`	`DEBUG` / `INFO` / `WARNING` / `ERROR` / `CRITICAL`	`WARNING`

Skill Installation

graph-mem install <agent>                 # project-level install
graph-mem install <agent> --global        # global/user-level install
graph-mem install <agent> --domain code   # use domain overlay (code, research, general)

Where the skill is installed

Every path below is cited against the vendor's current documentation. An agent whose install location cannot be cited is not listed here — a guessed path reports success, writes a file, and the agent never reads it, which is worse than no support. Six agents were removed on exactly those grounds.

Agent	Project path	User-level path	Written as	Source
`claude`	`.claude/skills/graph-mem/SKILL.md`	`~/.claude/skills/graph-mem/SKILL.md`	own file	docs
`opencode`	`.opencode/skills/graph-mem/SKILL.md`	`~/.config/opencode/skills/graph-mem/SKILL.md`	own file	docs
`codex`	`AGENTS.md`	`~/.codex/AGENTS.md`	section	docs
`gemini`	`GEMINI.md`	`~/.gemini/GEMINI.md`	section	docs
`cursor`	`.cursor/rules/graph-mem.mdc`	—	own file	docs
`windsurf`	`.windsurf/rules/graph-mem.md`	`~/.codeium/windsurf/memories/global_rules.md`	own file	docs
`amp`	`.agents/skills/graph-mem/SKILL.md`	`~/.config/agents/skills/graph-mem/SKILL.md`	own file	docs
`antigravity`	`AGENTS.md`	`~/.gemini/AGENTS.md`	section	docs
`copilot`	`.github/copilot-instructions.md`

Agents whose target file is shared — AGENTS.md, GEMINI.md, .github/copilot-instructions.md, Windsurf's global rules — get a delimited section written into it. Anything you already have in the file survives, and re-installing replaces the section instead of appending a second copy.

Want another agent supported? See Adding an Agent — it takes a documented path and about ten lines.

Graph Management

graph-mem init                            # create .graphmem/ directory
graph-mem init --project-dir /my/project  # create in specific directory
graph-mem init --graph research           # create a named graph
graph-mem status                          # print graph statistics
graph-mem status --json                   # graph statistics as JSON
graph-mem status --graph research         # status of a named graph
graph-mem export --format json            # export entire graph
graph-mem export --output backup.json     # export to file
graph-mem import graph.json               # import graph from file
graph-mem validate                        # run integrity checks

All management commands accept --db, --project-dir, and --graph for targeting a specific database.

Multi-graph support: Use --graph <name> to work with named graphs stored as .graphmem/<name>.db. Without --graph, commands target the default graph.db. The MCP tools list_graphs, create_graph, switch_graph, and delete_graph provide runtime graph management for agents.

Configuration

All settings are optional. Defaults work out of the box. Every setting can be controlled via CLI flags (see graph-mem server --help), environment variables, or both. CLI flags take precedence over environment variables, which take precedence over defaults.

Environment Variable	CLI Flag	Default	Description
`GRAPHMEM_DB_PATH`	`--db`	`.graphmem/graph.db`	Database file path
`GRAPHMEM_BACKEND_TYPE`	--	`sqlite`	Storage backend
`GRAPHMEM_EMBEDDING_MODEL`	`--embedding-model`	`all-MiniLM-L6-v2`	HuggingFace model ID
`GRAPHMEM_USE_ONNX`	`--use-onnx / --no-onnx`	`false`	Use ONNX runtime if available
`GRAPHMEM_EMBEDDING_DEVICE`	`--embedding-device`	`cpu`	Inference device (`cpu` or `cuda`)
`GRAPHMEM_CACHE_SIZE`	`--cache-size`	`10000`	Embedding cache max entries
`GRAPHMEM_SEARCH_LIMIT`	`--search-limit`	`10`	Default search result limit
`GRAPHMEM_MAX_HOPS`	`--max-hops`	`4`	Default max traversal depth
`GRAPHMEM_LOG_LEVEL`	`--log-level`	`WARNING`	Logging verbosity
`GRAPHMEM_TRANSPORT`	`--transport`	`stdio`	MCP transport protocol

Performance

Measured numbers, not adjectives. Reproduce the traversal figures with python benchmarks/bench_traversal.py; the full table is in docs/ARCHITECTURE.md.

Traversal — breadth-first with a global visited set, one indexed adjacency query per hop. A dense 60-node graph traverses to depth 6 in 5 ms. The obvious recursive-CTE formulation enumerates every simple path instead: on a 14-node graph it materialised 1,409,006 intermediate rows in 6.4 seconds to return the same 13 entities that BFS returns in 1 ms.

Graph canvas — Barnes-Hut force simulation (θ = 0.9). 2000 nodes run at 215 fps; 5000 nodes, the API's own cap, at 80 fps. The previous all-pairs implementation managed 30 fps and 3.7 fps respectively. Approximation error against the exact sum is 1.30% RMS, and settled layouts are equivalent.

Storage

WAL journal with PRAGMA tuning — concurrent reads, memory-mapped I/O, so a large database does not consume proportional RAM
One connection with a write lock held for the outermost transaction, and BEGIN IMMEDIATE so a read-to-write upgrade cannot fail unretryably
Batched bulk operations, and every IN (...) chunked below SQLite's bound-variable limit

Embeddings

Content-hash cache keyed by (hash, model), so two models coexist instead of clobbering each other; LRU eviction, batched reads and writes
Model loads lazily in a background thread at startup, and inference runs in a worker thread — neither blocks the event loop
Optional ONNX backend when optimum[onnxruntime] is installed and --use-onnx is set, falling back to PyTorch. Off by default; this project publishes no benchmark for it, so no speedup is claimed here.

Bounds — every traversal, search, and list response is capped by a named, configurable limit, and a capped response says so rather than returning a silent subset.

Development

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

# Using uv (recommended)
uv venv
uv pip install -e ".[full,dev]"

# Or using pip
python -m venv .venv
source .venv/bin/activate
pip install -e ".[full,dev]"

Running Tests

pytest                            # all tests
pytest tests/test_graph/          # graph engine tests
pytest tests/test_server/         # MCP server tool tests (all 28 tools)
pytest tests/test_cli/            # CLI command tests
pytest tests/test_models/         # data model tests
pytest tests/test_semantic/       # search + vector tests
pytest tests/test_storage/        # storage backend tests
pytest tests/test_db/             # database + migration tests
pytest tests/test_utils/          # config, logging, ID generation tests
pytest -x -q                      # stop on first failure, quiet output

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License

MIT

Add this server

Host a copy with our keys and logs.

Server facts

Tools

Questions

How do I install Graph Mem?

Does Graph Mem need an API key?

What transport does Graph Mem use?

When was Graph Mem last checked?

Documentation

Graph-Mem MCP

Built To Be Trusted With Your Data

Works With

What is this?

Why a graph, not just a vector store?

Use Cases

Quick Start

One-Prompt Setup

Installation

Option 1: pip (Recommended)

Option 2: uvx (zero pre-install)

Option 3: From source

Optional extras

Tools

Write Tools (10)

Read Tools (9)

Maintenance Tools (4)

Visualization (1)

Multi-Graph Tools (4)

Architecture

Further reading

MCP Integration

Graph Visualisation

Data Storage

CLI Reference

Server

Skill Installation

Where the skill is installed

Graph Management

Configuration

Performance

Development

Running Tests

Star History

License