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 or a local LLM of your choice using Ollama.

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

Agent Goal Configuration

The agent can be configured to pursue different goals using the AGENT_GOAL environment variable in your .env file.

Goal: Find an event in Australia / New Zealand, book flights to it and invoice the user for the cost

• AGENT_GOAL=goal_event_flight_invoice (default) - Helps users find events, book flights, and arrange train travel with invoice generation
  • This is the scenario in the video above

Goal: Find a Premier League match, book train tickets to it and invoice the user for the cost

• AGENT_GOAL=goal_match_train_invoice - Focuses on Premier League match attendance with train booking and invoice generation
  • This goal was part of Temporal's Replay 2025 conference keynote demo
  • Note, there is failure built in to this demo (the train booking step) to show how the agent can handle failures and retry. See Tool Configuration below for details.

If not specified, the agent defaults to goal_event_flight_invoice. Each goal comes with its own set of tools and conversation flows designed for specific use cases. You can examine tools/goal_registry.py to see the detailed configuration of each goal.

See the next section for tool configuration for each goal.

Tool Configuration

Agent Goal: goal_event_flight_invoice (default)

• The agent uses a mock function to search for events. This has zero configuration.
• By default the agent uses a mock function to search for flights.
  • If you want to use the real flights API, go to tools/search_flights.py and replace the search_flights function with search_flights_real_api that exists in the same file.
  • It's free to sign up at RapidAPI
  • This api might be slow to respond, so you may want to increase the start to close timeout, TOOL_ACTIVITY_START_TO_CLOSE_TIMEOUT in workflows/workflow_helpers.py
• 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
    • Set permissions for read-write on: Credit Notes, Invoices, Customers and Customer Sessions
  • 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.

Agent Goal: goal_match_train_invoice

NOTE: This goal was developed for an on-stage demo and has failure (and its resolution) built in to show how the agent can handle failures and retry.

• Finding a match requires a key from Football Data. Sign up for a free account, then see the 'My Account' page to get your API token. Set FOOTBALL_DATA_API_KEY to this value.
  • If you're lazy go to tools/search_fixtures.py and replace the search_fixtures function with the mock search_fixtures_example that exists in the same file.
• We use a mock function to search for trains. Start the train API server to use the real API: python thirdparty/train_api.py
• • The train activity is 'enterprise' so it's written in C# and requires a .NET runtime. See the .NET backend section for details on running it.
• 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.

LLM Provider Configuration

The agent can use OpenAI's GPT-4o, Google Gemini, Anthropic Claude, 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=anthropic for Anthropic Claude
• LLM_PROVIDER=deepseek for DeepSeek-V3
• LLM_PROVIDER=ollama for running LLMs via Ollama (not recommended for this use case)

Option 1: OpenAI

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: Anthropic Claude (recommended)

I find that Claude Sonnet 3.5 performs better than the other hosted LLMs for this use case.

To use Anthropic:

1. Obtain an Anthropic API key and set it in the ANTHROPIC_API_KEY environment variable in .env.
2. Set LLM_PROVIDER=anthropic in your .env file.

Option 4: Deepseek-V3

To use Deepseek-V3:

1. Obtain a Deepseek API key and set it in the DEEPSEEK_API_KEY environment variable in .env.
2. Set LLM_PROVIDER=deepseek in your .env file.

Option 5: 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: I found the other (hosted) LLMs to be MUCH more reliable for this use case. However, you can switch to Ollama if desired, and choose a suitably large model if your computer has the resources.

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

Python Search Trains API

Agent Goal: goal_match_train_invoice only

Required to search and book trains!

poetry run python thirdparty/train_api.py

# example url
# http://localhost:8080/api/search?from=london&to=liverpool&outbound_time=2025-04-18T09:00:00&inbound_time=2025-04-20T09:00:00

Python Train Legacy Worker

Agent Goal: goal_match_train_invoice only

These are Python activities that fail (raise NotImplemented) to show how Temporal handles a failure. You can run these activities with.

poetry run python scripts/run_legacy_worker.py 

The activity will fail and be retried infinitely. To rescue the activity (and its corresponding workflows), kill the worker and run the .NET one in the section below.

.NET (enterprise) Worker ;)

Agent Goal: goal_match_train_invoice only

We have activities written in C# to call the train APIs.

cd enterprise
dotnet build # ensure you brew install dotnet@8 first!
dotnet run

If you're running your train API above on a different host/port then change the API URL in Program.cs. Otherwise, be sure to run it using python thirdparty/train_api.py.

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

TODO

• 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.
• 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.
• 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!