temporal-ai-agent/README.md

Temporal AI Agent

This demo shows a multi-turn conversation with an AI agent running inside a Temporal workflow. The goal is to collect information towards a goal. There's a simple DSL input for collecting information (currently set up to use mock functions to search for events, book flights around those events then create an invoice for those flights). The AI will respond with clarifications and ask for any missing information to that goal. It uses ChatGPT 4o but can be made to use a local LLM via Ollama (see the section about this at the bottom of this README).

Watch the demo (5 minute YouTube video)

Configuration

This application uses .env files for configuration. Copy the .env.example file to .env and update the values:

cp .env.example .env

LLM Provider Configuration

The agent can use either OpenAI's GPT-4o, Google Gemini, or a local LLM via Ollama. Set the LLM_PROVIDER environment variable in your .env file to choose the desired provider:

• LLM_PROVIDER=openai for OpenAI's GPT-4o
• LLM_PROVIDER=google for Google Gemini
• LLM_PROVIDER=ollama for the local LLM via Ollama (not recommended for this use case)

Option 1: OpenAI Configuration

If using OpenAI, ensure you have an OpenAI key for the GPT-4o model. Set this in the OPENAI_API_KEY environment variable in .env.

Option 2: Google Gemini

To use Google Gemini:

1. Obtain a Google API key and set it in the GOOGLE_API_KEY environment variable in .env.
2. Set LLM_PROVIDER=google in your .env file.

Option 3: Local LLM via Ollama (not recommended)

To use a local LLM with Ollama:

1. Install Ollama and the Qwen2.5 14B model.

   • Run ollama run <OLLAMA_MODEL_NAME> to start the model. Note that this model is about 9GB to download.
   • Example: ollama run qwen2.5:14b

2. Set LLM_PROVIDER=ollama in your .env file and OLLAMA_MODEL_NAME to the name of the model you installed.

Note: The local LLM is disabled by default as ChatGPT 4o was found to be MUCH more reliable for this use case. However, you can switch to Ollama if desired.

Agent Tools

• Requires a Rapidapi key for sky-scrapper (how we find flights). Set this in the RAPIDAPI_KEY environment variable in .env
  • It's free to sign up and get a key at RapidAPI
  • If you're lazy go to tools/search_flights.py and replace the get_flights function with the mock search_flights_example that exists in the same file.
• Requires a Stripe key for the create_invoice tool. Set this in the STRIPE_API_KEY environment variable in .env
  • It's free to sign up and get a key at Stripe
  • If you're lazy go to tools/create_invoice.py and replace the create_invoice function with the mock create_invoice_example that exists in the same file.

Configuring Temporal Connection

By default, this application will connect to a local Temporal server (localhost:7233) in the default namespace, using the agent-task-queue task queue. You can override these settings in your .env file.

Use Temporal Cloud

See .env.example for details on connecting to Temporal Cloud using mTLS or API key authentication.

Sign up for Temporal Cloud

Use a local Temporal Dev Server

On a Mac

brew install temporal
temporal server start-dev

See the Temporal documentation for other platforms.

Running the Application

Python Backend

Requires Poetry to manage dependencies.

1. python -m venv venv

2. source venv/bin/activate

3. poetry install

Run the following commands in separate terminal windows:

1. Start the Temporal worker:

poetry run python scripts/run_worker.py

2. Start the API server:

poetry run uvicorn api.main:app --reload

Access the API at /docs to see the available endpoints.

React UI

Start the frontend:

cd frontend
npm install
npx vite

Access the UI at http://localhost:5173

Customizing the Agent

• tool_registry.py contains the mapping of tool names to tool definitions (so the AI understands how to use them)
• goal_registry.py contains descriptions of goals and the tools used to achieve them
• The tools themselves are defined in their own files in /tools
• Note the mapping in tools/__init__.py to each tool
• See main.py where some tool-specific logic is defined (todo, move this to the tool definition)

TODO

• I should prove this out with other tool definitions outside of the event/flight search case (take advantage of my nice DSL).
• Currently hardcoded to the Temporal dev server at localhost:7233. Need to support options incl Temporal Cloud.
• Continue-as-new shouldn't be a big consideration for this use case (as it would take many conversational turns to trigger). Regardless, I should ensure that it's able to carry the agent state over to the new workflow execution.
• Tests would be nice!