apo

APO Example

This example folder contains three complementary tutorials that demonstrate different aspects of Agent-Lightning. It's compatible with Agent-lightning v0.2 or later.

Overview

The folder showcases three distinct use cases: using the built-in APO algorithm to train a room selection agent, creating custom training algorithms from scratch, and debugging agents effectively. Each tutorial is self-contained and demonstrates a specific workflow.

Requirements

Follow the installation guide to install Agent-Lightning and APO-extra dependencies. All examples also require an OpenAI-compatible API service.

Included Files

File/Directory	Description
room_selector.py	Room booking agent implementation using function calling
room_selector_apo.py	Training script using the built-in APO algorithm to optimize prompts
room_tasks.jsonl	Dataset with room booking scenarios and expected selections
apo_custom_algorithm.py	Tutorial on creating custom algorithms (runnable as algo or runner)
apo_custom_algorithm_trainer.py	Shows how to integrate custom algorithms into the Trainer
apo_debug.py	Tutorial demonstrating various agent debugging techniques
legacy_apo_client.py	Deprecated APO client implementation compatible with Agent-lightning v0.1.x
legacy_apo_server.py	Deprecated APO server implementation compatible with Agent-lightning v0.1.x

Sample 1: Using Built-in APO Algorithm

The room_selector_apo.py script demonstrates how to use Agent-Lightning's built-in APO (Asynchronous Prompt Optimization) algorithm to train a room booking agent. The agent learns to select meeting rooms based on duration, attendee count, equipment needs, accessibility requirements, and availability.

Run the training with:

python room_selector_apo.py

This script initializes the APO algorithm with beam search parameters, loads the room booking dataset, and optimizes the agent's prompt template through iterative training. The algorithm automatically manages the training loop, gradient computation, and prompt updates. Read more about this example in Train the First Agent with APO.

Sample 2: Creating Custom Algorithms

The apo_custom_algorithm.py and apo_custom_algorithm_trainer.py files teach you how to implement custom training algorithms from scratch. This is useful when the built-in algorithms don't fit your specific needs. See Custom Algorithm tutorial for more details.

Option A: Run algorithm and runner separately

Start the store, algorithm, and runner in three separate terminals:

# Terminal 1: Start the store
agl store

# Terminal 2: Run the algorithm
python apo_custom_algorithm.py algo

# Terminal 3: Run the rollout runner
python apo_custom_algorithm.py runner

Option B: Run integrated version

Use the integrated trainer that handles all components:

python apo_custom_algorithm_trainer.py

Sample 3: Debugging Agents

The apo_debug.py script demonstrates multiple approaches to debugging agents in Agent-Lightning:

python apo_debug.py

Read more about this example in Debugging Agents.

Appendix: Dataset Format

The room_tasks.jsonl file contains meeting scenarios with the following structure:

{
  "id": "s01",
  "task_input": {
    "date": "2025-10-13",
    "time": "16:30",
    "duration_min": 30,
    "attendees": 12,
    "needs": ["projector", "confphone"],
    "accessible_required": true
  },
  "expected_choice": "Nova"
}