mcp-memgraph

Memgraph AI Toolkit

    

Build powerful AI applications with graph-powered RAG using Memgraph. This toolkit provides everything you need to integrate knowledge graphs into your GenAI workflows.

🚀 Quick Setup

Start Memgraph

docker run -p 7687:7687 \
  --name memgraph \
  memgraph/memgraph-mage:latest \
  --schema-info-enabled=true

Install Packages

# Core toolbox
pip install memgraph-toolbox

# LangChain integration
pip install langchain-memgraph

# MCP server
pip install mcp-memgraph

# Unstructured to Graph
pip install unstructured2graph

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📚 Usage Examples

unstructured2graph - Build Knowledge Graphs from Documents

Transform PDFs, URLs, and documents into queryable knowledge graphs:

import asyncio
from memgraph_toolbox.api.memgraph import Memgraph
from lightrag_memgraph import MemgraphLightRAGWrapper
from unstructured2graph import from_unstructured, create_property_index


async def main():
    memgraph = Memgraph()
    create_property_index(memgraph, "Chunk", "hash")

    lightrag = MemgraphLightRAGWrapper()
    await lightrag.initialize(working_dir="./lightrag_storage")

    # Ingest documents from URLs or local files
    await from_unstructured(
        sources=["https://example.com/doc.pdf", "./local_file.md"],
        memgraph=memgraph,
        lightrag_wrapper=lightrag,
        link_chunks=True,
    )
    await lightrag.afinalize()


asyncio.run(main())

👉 Full Documentation | Examples

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langchain-memgraph - LangChain Integration

Natural Language Queries with MemgraphQAChain

from langchain_memgraph.graphs.memgraph import MemgraphLangChain
from langchain_memgraph.chains.graph_qa import MemgraphQAChain
from langchain_openai import ChatOpenAI

graph = MemgraphLangChain(url="bolt://localhost:7687")

chain = MemgraphQAChain.from_llm(
    ChatOpenAI(temperature=0),
    graph=graph,
    model_name="gpt-4-turbo",
    allow_dangerous_requests=True,
)

response = chain.invoke("Who are the main characters in the dataset?")
print(response["result"])

Build Agents with MemgraphToolkit

from langchain.chat_models import init_chat_model
from langchain_memgraph import MemgraphToolkit
from langchain_memgraph.graphs.memgraph import MemgraphLangChain
from langgraph.prebuilt import create_react_agent

llm = init_chat_model("gpt-4o-mini", model_provider="openai")
db = MemgraphLangChain(url="bolt://localhost:7687")
toolkit = MemgraphToolkit(db=db, llm=llm)

agent = create_react_agent(llm, toolkit.get_tools())
events = agent.stream({"messages": [("user", "Find all Person nodes")]})

👉 Full Documentation

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mcp-memgraph - Model Context Protocol Server

Expose Memgraph to LLMs via MCP. Run with Docker:

# HTTP mode (recommended)
docker run --rm -p 8000:8000 memgraph/mcp-memgraph:latest

# Stdio mode for MCP clients
docker run --rm -i -e MCP_TRANSPORT=stdio memgraph/mcp-memgraph:latest

Available Tools:

| Tool | Description | | ------------------------ | ----------------------------------------------------- | | run_query | Execute Cypher queries | | search_schema | Search the graph schema by regex pattern | | get_node_schema | Get full schema definition of a node by its labels | | get_relationship_schema| Get full schema definition of a relationship | | get_enum_schema | Get schema definition of an enum by its name |

👉 Full Documentation

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sql2graph Agent - Automated Database Migration

Migrate from MySQL/PostgreSQL to Memgraph with AI assistance:

cd agents/sql2graph
uv run main.py

👉 Full Documentation

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🛠️ Packages Overview

| Package | Description | Install | | ------------------------------------------------------- | ---------------------------- | -------------------------------- | | memgraph-toolbox | Core utilities for Memgraph | pip install memgraph-toolbox | | langchain-memgraph | LangChain tools and chains | pip install langchain-memgraph | | mcp-memgraph | MCP server for LLMs | pip install mcp-memgraph | | unstructured2graph | Document to graph conversion | pip install unstructured2graph | | sql2graph | Database migration agent | See docs |

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❓ FAQ

Which databases are supported? Memgraph is the primary target. The sql2graph agent supports MySQL and PostgreSQL as source databases.

Do I need an LLM API key? Yes, for features like entity extraction (unstructured2graph) and natural language queries (langchain-memgraph).

Can I use local LLMs? Yes! LangChain integration supports any LangChain-compatible model, including Ollama.

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🤝 Community

• GitHub Issues
• Discord
• Documentation

⭐ If you find this toolkit helpful, please star the repository!

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🧪 Developing Locally

You can build and test each package directly from your repo.

Core tests

uv pip install -e memgraph-toolbox[test]
pytest -s memgraph-toolbox/src/memgraph_toolbox/tests

LangChain integration tests

Create a .env file with your OPENAI_API_KEY, as the tests depend on LLM calls:

uv pip install -e integrations/langchain-memgraph[test]
pytest -s integrations/langchain-memgraph/tests

MCP integration tests

uv pip install -e integrations/mcp-memgraph[test]
pytest -s integrations/mcp-memgraph/tests

Agent integration tests

uv pip install -e integrations/agents[test]
pytest -s integrations/agents/tests

To run a complete migration workflow with the agent:

cd integrations/agents
uv run main.py

Note: The agent requires both MySQL and Memgraph connections. Set up your environment variables in .env based on .env.example.

If you are running any test on macOS in zsh, add "" to the command:

uv pip install -e memgraph-toolbox"[test]"