temporal-ai-agent/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

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. We recommend starting with fin.

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 (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.

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

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.
• 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.

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.

• 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.

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.

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.

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!