Quickstart

3: Set up a virtual environment

Skip this step if you already have a virtual environment.

python -m venv .venv && source .venv/bin/activate

4: Install Jay

pip install jay_ai

5: Install OpenAI

Skip this step if it’s already installed.

pip install "openai>=1.0,<2.0"

6: Initialize Your Project

jay init

7: Run Locally

This command will launch a local playground where you can interact with your agent as you develop it:

jay run --agent agent/main.py --connect

You can close the process when you’re done interacting with your agent locally.

8: Deploy to Production

First, generate a requirements.txt that contains your latest dependencies:

pip freeze > requirements.txt

Then, deploy to production:

jay deploy --requirement requirements.txt --agent agent/main.py

This step will take a few minutes to complete.

9: Connect to the Deployed Agent

jay connect --agent agent/main.py

10 (optional): Modify Your Agent

You can change your LLM’s responses by opening agent/main.py, then navigating to the llm_response_handler function.

You can update your LLM to respond with a joke about the moon by making the following modification:

async def llm_response_handler(input: LLMResponseHandlerInput):
    client = AsyncOpenAI(api_key=os.environ["OPENAI_API_KEY"])
    messages = input["messages"] + [{"role": "user", "content": "Tell me a joke about the moon."}]
    completion = await client.chat.completions.create(
        model="gpt-4o",
        messages=messages,
        stream=True,
    )
    return completion

Test this change locally by running:

jay run --agent agent/main.py --connect

Then, you can redeploy in production by running:

jay deploy --requirement requirements.txt --agent agent/main.py

And finally, you can connect to the deployed agent by running:

jay connect --agent agent/main.py