Temporal AI Agent
This demo shows a multi-turn conversation with an AI agent running inside a Temporal workflow. The purpose of the agent is to collect information towards a goal, running tools along the way. There's a simple DSL input for collecting information (currently set up to use mock functions to search for public events, search for flights around those events, then create a test Stripe invoice for the trip).
The AI will respond with clarifications and ask for any missing information to that goal. You can configure it to use ChatGPT 4o, Anthropic Claude, Google Gemini, Deepseek-V3, Grok or a local LLM of your choice using Ollama.
It's really helpful to watch the demo (5 minute YouTube video) to understand how interaction works.
Setup and Configuration
See the Setup guide.
Customizing Interaction & Tools
See the guide to adding goals and tools.
Architecture
Productionalization & Adding Features
- In a prod setting, I would need to ensure that payload data is stored separately (e.g. in S3 or a noSQL db - the claim-check pattern), or otherwise 'garbage collected'. Without these techniques, long conversations will fill up the workflow's conversation history, and start to breach Temporal event history payload limits.
- A single worker can easily support many workflows - setting workflow ID differently would enable this.
- Continue-as-new shouldn't be a big consideration for this use case (as it would take many conversational turns to trigger). Regardless, we should verify that it's able to carry the agent state over to the new workflow execution.
- Perhaps the UI should show when the LLM response is being retried (i.e. activity retry attempt because the LLM provided bad output)
- Tests would be nice! See tests.
See the todo for more details.
See the guide to adding goals and tools for more ways you can add features.
For Temporal SAs
Check out the slides here and the enablement guide here (TODO).