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This package uses open source models hosted on FireworksAI to do retrieval using an agent architecture. By default, this does retrieval over Arxiv.

We will use Mixtral8x7b-instruct-v0.1, which is shown in this blog to yield reasonable results with function calling even though it is not fine tuned for this task:

Environment Setup​

There are various great ways to run OSS models. We will use FireworksAI as an easy way to run the models. See here for more information.

Set the FIREWORKS_API_KEY environment variable to access Fireworks.


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 retrieval-agent-fireworks

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

langchain app add retrieval-agent-fireworks

And add the following code to your file:

from retrieval_agent_fireworks import chain as retrieval_agent_fireworks_chain

add_routes(app, retrieval_agent_fireworks_chain, path="/retrieval-agent-fireworks")

(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/retrieval-agent-fireworks")

Help us out by providing feedback on this documentation page: