2.3 KiB
AI Agent execution using Temporal
Work in progress (very early!).
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 search for flights, see send_message.py). The AI will respond with clarifications and ask for any missing information (e.g., origin city, destination, travel dates). It uses a local LLM via Ollama.
Setup
- Install Ollama and the Mistral model (
ollama run qwen2.5:14b). (note this model is more than 10GB to download). - Install and run Temporal. Follow the instructions in the Temporal documentation to install and run the Temporal server.
- Install the dependencies:
poetry install
Running the example
From the /scripts directory:
-
Run the worker:
poetry run python run_worker.py -
In another terminal run the client with a prompt.
Example:
poetry run python send_message.py 'I want to book a flight' -
View the worker's output for the response.
-
Give followup prompts by signaling the workflow.
Example:
poetry run python send_message.py 'From San Francisco' -
Get the conversation history summary by querying the workflow.
Example:
poetry run python get_history.py -
To end the chat session, run
poetry run python end_chat.py
The chat session will end if it has collected enough information to complete the task or if the user explicitly ends the chat session.
Run query get_tool_data to see the data the tool has collected so far.
TODO
- This is currently a good single tool workflow. It could be a child as part of a planning workflow (multiple tools).
- I should integrate another tool. Perhaps something that consumes web sites hunting for destinations to go to in the first place.
- I should make this workflow execute a Search for flights as right now it will finish without doing anything.
- I need to add a human in the loop confirmation step before it executes tools.
- I need to build a chat interface so it's not cli-controlled. Also want to show some 'behind the scenes' of the agents being used as they run.