Content
# Memgraph AI Toolkit
[](https://pypi.org/project/memgraph-toolbox/)
[](https://pypi.org/project/langchain-memgraph/)
[](https://pypi.org/project/mcp-memgraph/)
[](https://pypi.org/project/unstructured2graph/)
[](https://discord.gg/memgraph)
Build powerful AI applications with graph-powered RAG using [Memgraph](https://memgraph.com/). This toolkit provides everything you need to integrate knowledge graphs into your GenAI workflows.
## 🚀 Quick Setup
### Start Memgraph
```bash
docker run -p 7687:7687 \
--name memgraph \
memgraph/memgraph-mage:latest \
--schema-info-enabled=true
```
### Install Packages
```bash
# Core toolbox
pip install memgraph-toolbox
# LangChain integration
pip install langchain-memgraph
# MCP server
pip install mcp-memgraph
# Unstructured to Graph
pip install unstructured2graph
```
---
## 📚 Usage Examples
### unstructured2graph - Build Knowledge Graphs from Documents
Transform PDFs, URLs, and documents into queryable knowledge graphs:
```python
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](https://memgraph.com/docs/ai-ecosystem/unstructured2graph) | [Examples](/unstructured2graph/examples/)
---
### langchain-memgraph - LangChain Integration
#### Natural Language Queries with MemgraphQAChain
```python
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
```python
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](https://memgraph.com/docs/ai-ecosystem/integrations#langchain)
---
### mcp-memgraph - Model Context Protocol Server
Expose Memgraph to LLMs via MCP. Run with Docker:
```bash
# 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](https://memgraph.com/docs/ai-ecosystem/integrations#model-context-protocol-mcp)
---
### sql2graph Agent - Automated Database Migration
Migrate from MySQL/PostgreSQL to Memgraph with AI assistance:
```bash
cd agents/sql2graph
uv run main.py
```
👉 [Full Documentation](https://memgraph.com/docs/ai-ecosystem/agents#sql2graph-agent)
---
## 🛠️ Packages Overview
| Package | Description | Install |
| ------------------------------------------------------- | ---------------------------- | -------------------------------- |
| [memgraph-toolbox](/memgraph-toolbox/) | Core utilities for Memgraph | `pip install memgraph-toolbox` |
| [langchain-memgraph](/integrations/langchain-memgraph/) | LangChain tools and chains | `pip install langchain-memgraph` |
| [mcp-memgraph](/integrations/mcp-memgraph/) | MCP server for LLMs | `pip install mcp-memgraph` |
| [unstructured2graph](/unstructured2graph/) | Document to graph conversion | `pip install unstructured2graph` |
| [sql2graph](/agents/sql2graph/) | Database migration agent | See docs |
---
## ❓ 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.
---
## 🤝 Community
- [GitHub Issues](https://github.com/memgraph/ai-toolkit/issues)
- [Discord](https://discord.gg/memgraph)
- [Documentation](https://memgraph.com/docs/ai-ecosystem)
⭐ If you find this toolkit helpful, please star the repository!
---
## 🧪 Developing Locally
You can build and test each package directly from your repo.
### Core tests
```bash
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:
```bash
uv pip install -e integrations/langchain-memgraph[test]
pytest -s integrations/langchain-memgraph/tests
```
### MCP integration tests
```bash
uv pip install -e integrations/mcp-memgraph[test]
pytest -s integrations/mcp-memgraph/tests
```
### Agent integration tests
```bash
uv pip install -e integrations/agents[test]
pytest -s integrations/agents/tests
```
To run a complete migration workflow with the agent:
```bash
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:
```bash
uv pip install -e memgraph-toolbox"[test]"
```
