temporal-ai-agent/docs/setup.md

Setup Guide

Initial Configuration

This application uses .env files for configuration. Copy the .env.example file to .env and update the values:

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

SHOW_CONFIRM=True

We recommend setting this to False in most cases, as it can clutter the conversation with confirmation messages.

Quick Start with Makefile

We've provided a Makefile to simplify the setup and running of the application. Here are the main commands:

# Initial setup
make setup              # Creates virtual environment and installs dependencies
make setup-venv         # Creates virtual environment only
make install            # Installs all dependencies

# Running the application
make run-worker         # Starts the Temporal worker
make run-api            # Starts the API server
make run-frontend       # Starts the frontend development server

# Additional services
make run-train-api      # Starts the train API server
make run-legacy-worker  # Starts the legacy worker
make run-enterprise     # Builds and runs the enterprise .NET worker

# Development environment setup
make setup-temporal-mac # Installs and starts Temporal server on Mac

# View all available commands
make help

Manual Setup (Alternative to Makefile)

If you prefer to run commands manually, see the sections below for detailed instructions on setting up the backend, frontend, and other components.

Agent Goal Configuration

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

Single Agent Mode (Default) By default, the agent operates in single-agent mode using a specific goal. If unset, the default is goal_event_flight_invoice.

To set a specific single goal:

AGENT_GOAL=goal_event_flight_invoice

Multi-Agent Mode (Experimental) The agent also supports an experimental multi-agent mode where users can choose between different agent types during the conversation. To enable this mode:

AGENT_GOAL=goal_choose_agent_type

When using multi-agent mode, you can control which agent categories are available using GOAL_CATEGORIES in your .env file. If unset, all categories are shown. Available categories include hr, travel-flights, travel-trains, fin, ecommerce, mcp-integrations, and food. We recommend starting with fin:

GOAL_CATEGORIES=hr,travel-flights,travel-trains,fin

Note: Multi-agent mode is experimental and allows switching between different agents mid-conversation, but single-agent mode provides a more focused experience.

MCP (Model Context Protocol) tools are available for enhanced integration with external services. See the MCP Tools Configuration section for setup details.

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

LLM Configuration

Note: We recommend using OpenAI's GPT-4o or Claude 3.5 Sonnet for the best results. There can be significant differences in performance and capabilities between models, especially for complex tasks.

The agent uses LiteLLM to interact with various LLM providers. Configure the following environment variables in your .env file:

• LLM_MODEL: The model to use (e.g., "openai/gpt-4o", "anthropic/claude-3-sonnet", "google/gemini-pro", etc.)
• LLM_KEY: Your API key for the selected provider
• LLM_BASE_URL: (Optional) Custom base URL for the LLM provider. Useful for:
  • Using Ollama with a custom endpoint
  • Using a proxy or custom API gateway
  • Testing with different API versions

LiteLLM will automatically detect the provider based on the model name. For example:

• For OpenAI models: openai/gpt-4o or openai/gpt-3.5-turbo
• For Anthropic models: anthropic/claude-3-sonnet
• For Google models: google/gemini-pro
• For Ollama models: ollama/mistral (requires LLM_BASE_URL set to your Ollama server)

Example configurations:

# For OpenAI
LLM_MODEL=openai/gpt-4o
LLM_KEY=your-api-key-here

# For Anthropic
LLM_MODEL=anthropic/claude-3-sonnet
LLM_KEY=your-api-key-here

# For Ollama with custom URL
LLM_MODEL=ollama/mistral
LLM_BASE_URL=http://localhost:11434

For a complete list of supported models and providers, visit the LiteLLM documentation.

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.

You can also run a local Temporal server using Docker Compose. See the Development with Docker section below.

Running the Application

Docker

• All services are defined in docker-compose.yml (includes a Temporal server).
• Dev overrides (mounted code, live‑reload commands) live in docker-compose.override.yml and are auto‑merged on docker compose up.
• To start development mode (with hot‑reload):

  docker compose up -d
  # quick rebuild without infra:
  docker compose up -d --no-deps --build api train-api worker frontend
  

• To run production mode (ignore dev overrides):

  docker compose -f docker-compose.yml up -d
  

Default urls:

• Temporal UI: http://localhost:8080
• API: http://localhost:8000
• Frontend: http://localhost:5173

Local Machine (no docker)

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

MCP Tools Configuration

MCP (Model Context Protocol) tools enable integration with external services without custom implementation. The system automatically handles MCP server lifecycle and tool discovery.

Adding MCP Tools to Goals

Configure MCP servers in your goal definitions using either:

1. Predefined configurations from shared/mcp_config.py
2. Custom MCPServerDefinition objects

Example using Stripe MCP Server:

from shared.mcp_config import get_stripe_mcp_server_definition

mcp_server_definition=get_stripe_mcp_server_definition(
    included_tools=["list_products", "create_customer", "create_invoice"]
)

See the file goals/stripe_mcp.py for an example of how to use MCP tools in a an AgentGoal.

MCP Environment Variables

Set required API keys and configuration in your .env file:

# For Stripe MCP Server
STRIPE_API_KEY=sk_test_your_stripe_key_here

goal_event_flight_invoice does not require a Stripe key. If STRIPE_API_KEY is unset, that scenario falls back to a mock invoice.

Accessing Your Test API Keys

It's free to sign up for a Stripe account and generate test keys (no real money is involved). Use the Developers Dashboard to create, reveal, delete, and rotate API keys. Navigate to the API Keys tab in your dashboard or visit https://dashboard.stripe.com/test/apikeys directly.

For detailed guidance on adding MCP tools, see adding-goals-and-tools.md.

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

Configuring Agent Goal: goal_event_flight_invoice

• The agent uses a mock function to search for events. This has zero configuration.
• Flight Search: The agent intelligently handles flight searches:
  • Default behavior: If no RAPIDAPI_KEY is set, the agent generates realistic flight data with smart pricing based on route type (domestic, international, trans-Pacific)
  • Real API (optional): To use live flight data, set RAPIDAPI_KEY in your .env 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
  • The smart generation creates realistic pricing (e.g., US-Australia routes $1200-1800, domestic flights $200-800) with appropriate airlines for each region
• 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 (test mode only, no real money) * Set permissions for read-write on: Credit Notes, Invoices, Customers and Customer Sessions
• If you don't have a Stripe key, comment out the STRIPE_API_KEY in the .env file, and a dummy invoice will be created rather than a Stripe invoice. The function can be found in tools/create_invoice.py – this is the default behavior for goal_event_flight_invoice.

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
  • 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.

• Omit FOOTBALL_DATA_API_KEY from .env for the SearchFixtures tool to automatically return mock Premier League fixtures. Finding a real match requires a key from Football Data. Sign up for a free account, then see the 'My Account' page to get your API token.
• 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 (test mode only)
  • If the key is missing this goal won't generate a real invoice – only goal_event_flight_invoice falls back to a mock invoice
  • 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!

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 ;)

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.

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.

• AGENT_GOAL=goal_fin_move_money - This scenario can initiate a secondary workflow to move money. Check out this repo - 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:

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 - 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:

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.

Goals: Ecommerce

Make sure you have the mock orders you want in (such as those with real tracking numbers) in the mock orders file.

Goal: Food Ordering with MCP Integration (Stripe Payment Processing)

• AGENT_GOAL=goal_food_ordering - Demonstrates food ordering with Stripe payment processing via MCP
  • Uses Stripe's MCP Server (Agent Toolkit) for payment operations
  • Requires STRIPE_API_KEY in your .env file
  • Requires products in Stripe with metadata key use_case=food_ordering_demo. Run tools/food/setup/create_stripe_products.py to set up pizza menu items
  • Example of MCP tool integration without custom implementation
  • This is an excellent demonstration of MCP (Model Context Protocol) capabilities

Customizing the Agent Further

• tool_registry.py contains the mapping of tool names to tool definitions (so the AI understands how to use them)
• goals/ contains descriptions of goals and the tools used to achieve them
• The tools themselves are defined in their own files in /tools

For more details, check out adding goals and tools guide.

Setup Checklist

[ ] copy .env.example to .env
[ ] Select an LLM and add your API key to .env
[ ] (Optional) set your starting goal and goal category in .env
[ ] (Optional) configure your Temporal Cloud settings in .env
[ ] poetry run python scripts/run_worker.py
[ ] poetry run uvicorn api.main:app --reload
[ ] cd frontend, npm install, npx vite
[ ] Access the UI at http://localhost:5173

And that's it! Happy AI Agent Exploring!