memgraph-toolkit-chatbot

Graph-Aware Agent with LangGraph and Memgraph AI Toolkit

Note

This app was built with the new LangGraph project template and by following the Quickstart instructions from the LangGraph documentation to create a local LangGraph server.

In this directory, you can find code for a simple agent built using the LangGraph framework and the Memgraph AI Toolkit to demonstrate how to integrate graph-based tooling into your LLM stack. LangGraph helps define structured workflows for language agents, while Memgraph provides powerful graph querying capabilities. Together, they make a compelling combination for building intelligent, context-aware applications.

Prerequisite

The agent invokes tools that execute queries against Memgraph database, meaning that you need a running Memgraph instance. In the example, Memgraph should be running on localhost:7687. To start Memgraph MAGE, run the following command in your terminal:

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

Once Memgraph is running, load the data. In this example, Game of Thrones dataset is loaded from Memgraph Lab.

Run the app

To run the app, first install the LangGraph CLI:

# Python >= 3.11 is required.

pip install --upgrade "langgraph-cli[inmem]"

Then, install the dependencies:

pip install -e .

In the end, create .env file. Copy the contents of .env.example provided in the directory, and update it with your API keys. Your .env might look like this:

# To separate your traces from other application
LANGSMITH_PROJECT=new-agent

# Add API keys for connecting to LLM providers, data sources, and other integrations here
OPENAI_API_KEY=""
LANGSMITH_TRACING=""
LANGSMITH_API_KEY=""

LangSmith API key can be generated on their site.

To test your agent, launch the LangGraph development server with:

langgraph dev

This will start a local server and open LangGraph Studio in your browser. To ask the question, add the following JSON to the input:

[
	{
		"role": "user",
		"content": "Can you tell me more about my schema?"
	}
]

And click Submit button. You'll see how the agent invokes show_schema_info() tool to provide the necessary details.