temporal-ai-agent/setup.md

# Setup Guide
## Initial 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
```

Then add API keys, configuration, as desired.

If you want to show confirmations/enable the debugging UI that shows tool args, set
```bash
SHOW_CONFIRM=True
```

### Agent Goal Configuration

The agent can be configured to pursue different goals using the `AGENT_GOAL` environment variable in your `.env` file. If unset, default is `goal_choose_agent_type`. 

If the first goal is `goal_choose_agent_type` the agent will support multiple goals using goal categories defined by `GOAL_CATEGORIES` in your .env file. If unset, default is all.
```bash
GOAL_CATEGORIES=hr,travel-flights,travel-trains,fin
```

See the section Goal-Specific Tool Configuration below for tool configuration for specific goals.

### 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`



## Goal-Specific Tool Configuration
Here is configuration guidance for specific goals. Travel and financial goals have configuration & setup as below.
### 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` - Helps users find events, book flights, and arrange train travel with invoice generation
    - This is the scenario in the [original video](https://www.youtube.com/watch?v=GEXllEH2XiQ)

#### Configuring Agent Goal: goal_event_flight_invoice 
* 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/)
        * 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.

### Goal: Find a Premier League match, book train tickets to it and invoice the user for the cost (Replay 2025 Keynote)
- `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](https://www.youtube.com/watch?v=YDxAWrIBQNE)
    - 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.
#### Configuring 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](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.

##### 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
```

 ##### 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.
 
 ```bash
 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 ;)
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`.

#### Goals: FIN - Money Movement and Loan Application
Make sure you have the mock users you want (such as yourself) in [the account mock data file](./tools/data/customer_account_data.json).

- `AGENT_GOAL=goal_fin_move_money` - This scenario _can_ initiate a secondary workflow to move money. Check out [this repo](https://github.com/temporal-sa/temporal-money-transfer-java) - you'll need to get the worker running and connected to the same account as the agentic worker. 
By default it will _not_ make a real workflow, it'll just fake it. If you get the worker running and want to start a workflow, in your [.env](./.env):
```bash
FIN_START_REAL_WORKFLOW=FALSE #set this to true to start a real workflow
```
- `AGENT_GOAL=goal_fin_loan_application` - This scenario _can_ initiate a secondary workflow to apply for a loan. Check out [this repo](https://github.com/temporal-sa/temporal-latency-optimization-scenarios) - you'll need to get the worker running and connected to the same account as the agentic worker. 
By default it will _not_ make a real workflow, it'll just fake it. If you get the worker running and want to start a workflow, in your [.env](./.env):
```bash
FIN_START_REAL_WORKFLOW=FALSE #set this to true to start a real workflow


#### Goals: HR/PTO
Make sure you have the mock users you want in (such as yourself) in [the PTO mock data file](./tools/data/employee_pto_data.json).


## Customizing the Agent Further
- `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

For more details, check out [adding goals and tools guide](./adding-goals-and-tools.md).