# 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](https://openai.com/index/hello-gpt-4o/), [Anthropic Claude](https://www.anthropic.com/claude), [Google Gemini](https://gemini.google.com), [Deepseek-V3](https://www.deepseek.com/) or a local LLM of your choice using [Ollama](https://ollama.com).

[Watch the demo (5 minute YouTube video)](https://www.youtube.com/watch?v=GEXllEH2XiQ)

[![Watch the demo](./agent-youtube-screenshot.jpeg)](https://www.youtube.com/watch?v=GEXllEH2XiQ)

## Configuration

This application uses `.env` files for configuration. Copy the [.env.example](.env.example) file to `.env` and update the values:

```bash
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 APAC, 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 is a new goal that is part of an upcoming conference talk

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](https://rapidapi.com/apiheya/api/sky-scrapper)
    * 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](https://stripe.com/)
    * 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

* Finding a match requires a key from [Football Data](https://www.football-data.org). 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](#net-(enterprise)-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](https://stripe.com/)
    * 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](https://ollama.ai) (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](https://ollama.com) and the [Qwen2.5 14B](https://ollama.com/library/qwen2.5) 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](.env.example) for details on connecting to Temporal Cloud using mTLS or API key authentication.

[Sign up for Temporal Cloud](https://temporal.io/get-cloud)

### Use a local Temporal Dev Server

On a Mac
```bash
brew install temporal
temporal server start-dev
```
See the [Temporal documentation](https://learn.temporal.io/getting_started/python/dev_environment/) for other platforms.


## Running the Application

### Python Backend

Requires [Poetry](https://python-poetry.org/) 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:
```bash
poetry run python scripts/run_worker.py
```

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

### React UI
Start the frontend:
```bash
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!
```bash
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
```

### .NET (enterprise) Backend ;)
> Agent Goal: goal_match_train_invoice only

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