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This template is designed to implement an agent capable of interacting with a graph database like Neo4j through a semantic layer using OpenAI function calling. The semantic layer equips the agent with a suite of robust tools, allowing it to interact with the graph databas based on the user's intent. Learn more about the semantic layer template in the corresponding blog post.


The agent utilizes several tools to interact with the Neo4j graph database effectively:

  1. Information tool:
    • Retrieves data about movies or individuals, ensuring the agent has access to the latest and most relevant information.
  2. Recommendation Tool:
    • Provides movie recommendations based upon user preferences and input.
  3. Memory Tool:
    • Stores information about user preferences in the knowledge graph, allowing for a personalized experience over multiple interactions.

Environment Setup​

You need to define the following environment variables


Populating with data​

If you want to populate the DB with an example movie dataset, you can run python The script import information about movies and their rating by users. Additionally, the script creates two fulltext indices, which are used to map information from user input to the database.


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

pip install -U "langchain-cli[serve]"

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

langchain app new my-app --package neo4j-semantic-layer

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

langchain app add neo4j-semantic-layer

And add the following code to your file:

from neo4j_semantic_layer import agent_executor as neo4j_semantic_agent

add_routes(app, neo4j_semantic_agent, path="/neo4j-semantic-layer")

(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-semantic-layer")

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