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This template allows you to have conversations with a Neo4j graph database in natural language, using an OpenAI LLM. It transforms a natural language question into a Cypher query (used to fetch data from Neo4j databases), executes the query, and provides a natural language response based on the query results. Additionally, it features a conversational memory module that stores the dialogue history in the Neo4j graph database. The conversation memory is uniquely maintained for each user session, ensuring personalized interactions. To facilitate this, please supply both the user_id and session_id when using the conversation chain.

Environment Setup​

Define the following environment variables:


Neo4j database setup​

There are a number of ways to set up a Neo4j database.

Neo4j Aura​

Neo4j AuraDB is a fully managed cloud graph database service. Create a free instance on Neo4j Aura. When you initiate a free database instance, you'll receive credentials to access the database.

Populating with data​

If you want to populate the DB with some example data, you can run python This script will populate the database with sample movie data.


To use this package, you should first have the LangChain CLI installed:

pip install -U langchain-cli

To create a new LangChain project and install this as the only package, you can do:

langchain app new my-app --package neo4j-cypher-memory

If you want to add this to an existing project, you can just run:

langchain app add neo4j-cypher-memory

And add the following code to your file:

from neo4j_cypher_memory import chain as neo4j_cypher_memory_chain

add_routes(app, neo4j_cypher_memory_chain, path="/neo4j-cypher-memory")

(Optional) Let's now configure LangSmith. LangSmith will help us trace, monitor and debug LangChain applications. You can sign up for LangSmith here. If you don't have access, you can skip this section

export LANGCHAIN_API_KEY=<your-api-key>
export LANGCHAIN_PROJECT=<your-project> # if not specified, defaults to "default"

If you are inside this directory, then you can spin up a LangServe instance directly by:

langchain serve

This will start the FastAPI app with a server is running locally at http://localhost:8000

We can see all templates at We can access the playground at

We can access the template from code with:

from langserve.client import RemoteRunnable

runnable = RemoteRunnable("http://localhost:8000/neo4j-cypher-memory")

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