AI Agent execution using Temporal

Work in progress.

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 a local LLM via Ollama.

Setup

See .env_example for the required environment variables.
Requires an OpenAI key for the gpt-4o model. Set this in the OPENAI_API_KEY environment variable in .env
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.
Install and run Temporal. Follow the instructions in the Temporal documentation to install and run the Temporal server.
Install the dependencies: poetry install

Deprecated:

Install Ollama and the Qwen2.5 14B model (ollama run qwen2.5:14b). (note this model is about 9GB to download).
- Local LLM is disabled as ChatGPT 4o was better for this use case. To use Ollama, examine ./activities/tool_activities.py and rename the functions.

Running the example

Run a Temporal Dev Server

On a Mac

brew install temporal
temporal server start-dev

Run a Temporal Worker

From the /scripts directory:

Run the worker: poetry run python run_worker.py

Then run the API and UI using the instructions below.

API

poetry run uvicorn api.main:app --reload to start the API server.
Access the API at /docs to see the available endpoints.

UI

cd frontend
npm install to install the dependencies.
npm run dev to start the dev server.
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.
UI: Make prettier

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