This learning guide provides a comprehensive introduction to Agentic Lightening, Microsoft’s innovative open-source framework for training and optimizing AI agents. Whether you’re a complete beginner eager to dive into the world of agentic AI or an experienced developer looking to integrate advanced optimization techniques into your existing agent frameworks (like LangChain or AutoGen), this document will equip you with the knowledge and practical skills you need. We’ll start from the very basics, guiding you through setting up your environment, understanding core concepts, and progressively moving towards advanced topics and real-world projects. Each section includes detailed explanations, hands-on code examples, and challenging exercises to ensure you learn by doing.
Table of Contents
- Introduction to Agentic Lightening
- Discover what Agentic Lightening is, why it’s a game-changer for AI agent development, and its key benefits.
- Get step-by-step instructions to set up your system, install Agentic Lightening, and prepare for your first agent.
- Core Concepts: Agents, Trainers, and the Lightning Server
- Understand the fundamental components of Agentic Lightening: the
LitAgent,Trainer, andAgentLightningServer.
- Understand the fundamental components of Agentic Lightening: the
- Integrating with Existing Agent Frameworks
- Learn how to adapt and optimize agents built with LangChain, OpenAI Agent SDK, AutoGen, and other frameworks using Agentic Lightening.
- Understanding Rollouts and Rewards
- Explore how agents interact with tasks in “rollouts” and how to design effective reward functions for optimization.
- Advanced Optimization Algorithms
- Dive into Reinforcement Learning (RL), Automatic Prompt Optimization (APO), and Supervised Fine-tuning (SFT) within Agentic Lightening.
- Working with Resources and Tracers
- Learn about managing shared resources, collecting interaction data with tracers, and using the
LightningStore.
- Learn about managing shared resources, collecting interaction data with tracers, and using the
- Project 1: Optimizing a Basic QA Agent with Prompt Tuning
- A guided project to build and optimize a simple Question-Answering agent using Agentic Lightening’s prompt optimization capabilities.
- Project 2: Enhancing a LangChain Agent with Reinforcement Learning
- A step-by-step project demonstrating how to integrate Agentic Lightening with a LangChain agent for RL-based performance improvement.
- Bonus Section: Further Learning and Resources
- A curated list of online courses, official documentation, blogs, and communities to continue your Agentic Lightening journey.