agent-context-graph-claude

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_index

async def main():
    memgraph = Memgraph()
    create_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
get_schema	Fetch graph schema
get_page_rank	Compute PageRank scores
get_node_neighborhood	Find nodes within distance
search_node_vectors	Vector similarity search

👉 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