## Customizing the Agent
The agent is set up to allow for multiple goals and to switch back to choosing a new goal at the end of every successful goal. A goal is made up of a list of tools that the agent will guide the user through. 

It may be helpful to review the [architecture](./architecture.md) for a guide and definition of goals, tools, etc.

### Adding a Goal
1.  Open [/tools/goal_registry.py](tools/goal_registry.py) - this file contains descriptions of goals and the tools used to achieve them
2. Pick a name for your goal!
3. Fill out the required elements:
-  `id`: needs to be the same as the name
- `agent_name`: user-facing name for the agent/chatbot
- `agent_friendly_description`: user-facing description of what the agent/chatbot does
- `tools`: the list of tools the goal will walk the user through. 
- Important! The last tool listed must be `list_agents_tool`. This allows the AI to let the user go back to choosing from the list of available goals.
- `description`:
- `starter-prompt`:
- `example_conversation_history`:
4. Add your new goal to the `goal_list` at the bottom using `goal_list.append(your_super_sweet_new_goal)`

### Adding Tools

#### Notes
Tools can be optional - you can indicate this in the tool listing of goal description (see above section re: goal registry) by adding something like, "This step is optional and can be skipped by moving to the next tool." Here is an example from the CalendarConflict tool/step of the [goal_hr_schedule_pto](tools/goal_registry.py#L134) goal:

```
description="Help the user gather args for these tools in order: "
    "1. CurrentPTO: Tell the user how much PTO they currently have "
    "2. FuturePTO: Tell the user how much PTO they will have as of the prospective date "
    "3. CalendarConflict: Tell the user what conflicts if any exist around the prospective date on a list of calendars. This step is optional and can be skipped by moving to the next tool. "
    "4. BookPTO: Book PTO "
```

#### Add to Tool Registry
- `tool_registry.py` contains the mapping of tool names to tool definitions (so the AI understands how to use them)
- `name`:
- `description`:
- `arguments`: These are the _input_ arguments to the tool.

#### Create Each Tool
- The tools themselves are defined in their own files in `/tools` - you can add a subfolder to organize them
- The file name and function name will be the same as each other and should also be the same as the name of the tool, without "tool" - so future_pto_tool would be future_pto.py with a function named future_pto within it.
- The function should have `args: dict` as the input and also return a `dict`
- The return dict should match the output format you specified in the goal's `example_conversation_history`

#### Add to `tools/__init__.py`
- In `tools/__init__.py`, add an import statement for each new tool as well as an applicable return statement in `get_handler`. The tool name here should match the tool name as described in the goal's `description` field.

### Configuring the Starting Goal

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 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.

### Configuring Existing Tools

#### 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.