Agent Lightning: a flexible framework for optimizing AI intelligences

Agent Lightning is an open source framework developed by a team of Microsoft researchers to help developers optimize the performance of AI intelligences. It supports a variety of intelligence development frameworks, such as OpenAI Agents SDK, AutoGen, and LangChain, and enhances the performance of intelligences through techniques such as reinforcement learning (RL).Agent Lightning provides flexible tools for complex scenarios such as multi-round interactions, dynamic contexts, and multi-intelligence collaboration. It decouples the intelligence framework from the reinforcement learning training system through two core modules, Lightning Server and Lightning Client, allowing developers to optimize without modifying the intelligence code. This framework is suitable for developers who want to improve the efficiency of intelligences, especially in the areas of search, code generation and interactive task completion. The project is hosted on GitHub, and community contributions of code and suggestions are welcome.

Function List

• Supports multiple intelligence frameworks: Compatible with OpenAI Agents SDK, AutoGen, LangChain and other mainstream frameworks.
• Reinforcement Learning Optimization: using reinforcement learning techniques to optimize the performance of intelligences and improve long-term interaction.
• Error monitoring: Track the execution status of intelligences through Lightning Server to detect and handle errors.
• Multi-round interaction support: Handle complex multi-round interaction scenarios and maintain context consistency.
• Multi-intelligent body coordination: supports multiple intelligent bodies to work together, suitable for complex task scenarios.
• Flexible Extension: Provides an open interface that allows developers to customize optimization algorithms and functions.

Using Help

Agent Lightning is an open source framework for developers to optimize AI intelligences. Here is a detailed guide to help users get started quickly.

Installation process

1. Preparing the development environment
   • Ensure that Python 3.10 or higher is installed, as Agent Lightning relies on the Python environment for its core functionality.
   • Install Git in order to clone the project code from GitHub.
   • Optional: Install Visual Studio Code or other Python-enabled IDE for easy debugging.
2. Cloning Project Code
   Run the following command in the terminal to clone the Agent Lightning code locally:

   git clone https://github.com/microsoft/agent-lightning.git
   cd agent-lightning
   

3. Installation of dependencies
   The project relies on several Python libraries (e.g. verl for reinforcement learning support). Run it in the project directory:

   pip install -r requirements.txt
   

   If you need to use a specific smartbody framework (such as AutoGen or LangChain), install additional dependencies according to their documentation. For example, install AutoGen:

   pip install autogen-agentchat autogen-ext[openai]
   

4. Configuration environment
   • If using the OpenAI Agents SDK, you need to configure the OpenAI API key. Add the key to the environment variable:

     export OPENAI_API_KEY="your-api-key"
     

   • For other frameworks (e.g. AutoGen), please refer to their official documentation to configure the appropriate environment.
5. Running Lightning Server and Client
   Agent Lightning consists of Lightning Server and Lightning Client. Start the server:

   python lightning_server.py
   

   Start the client in another terminal:

   python lightning_client.py
   

   The server pulls tasks from the task pool and the client is responsible for executing the intelligent body workflow.

Main Functions

• Optimizing the Intelligent Body
  The core function of Agent Lightning is to optimize intelligences through reinforcement learning. The user can define a pool of tasks that contain tasks that need to be completed by the intelligences. For example, creating a JSON file tasks.jsonThe content is as follows:

  [
  {"task_id": 1, "description": "Generate a Python script for sorting a list"},
  {"task_id": 2, "description": "Answer a user query about weather"}
  ]
  

  Configure the task pooling path into Lightning Server's configuration file, and the server automatically assigns tasks to intelligences and optimizes their performance through reinforcement learning.

• error monitoring
  Lightning Server provides an error monitoring feature that detects the failure state of an intelligent body while performing a task. For example, if an intelligent body gets stuck in a multi-round interaction, the server records an error log, which is stored in the logs/error_log.txt in. Users can analyze the problem by viewing the logs:

  cat logs/error_log.txt
  

• multi-intelligence coordination
  For scenarios that require the collaboration of multiple intelligences, the user can config.yaml Define multiple intelligent body roles in the Example:

  agents:
  - id: agent1
  framework: autogen
  role: code_generator
  - id: agent2
  framework: langchain
  role: query_responder
  

  At runtime, Lightning Server coordinates these intelligences to ensure smooth task assignment and execution.

Featured Function Operation

• Enhanced Learning Optimization
  Agent Lightning uses the verl library for reinforcement learning optimization. The user can customize the reinforcement learning strategy, for example by adjusting the reward function. Example reward function configuration:

  def reward_function(task_result):
  if task_result.success:
  return 1.0
  return -1.0
  

  Integrate this function into the lightning_server.py in order to optimize the long-term performance of the intelligentsia.

• Multi-framework support
  Users can choose from different frameworks for intelligences. For example, use AutoGen to create a dialog intelligent body:

  from autogen import AssistantAgent
  agent = AssistantAgent(name="example_agent", llm_config={"model": "gpt-4o"})
  

  Register this intelligence with Agent Lightning's client to run the optimization process.

caveat

• Ensure that the network connection is stable, as Lightning Server needs to communicate with the APIs of the Intelligent Body Framework (e.g. OpenAI).
• The project is currently in development and it is recommended to check the GitHub repository regularly for the latest updates:

  git pull origin main
  

• If you run into problems, submit feedback on the GitHub Issues page or refer to the community Q&A discussion.

application scenario

1. Code generation optimization
   Developers use Agent Lightning to optimize code-generating intelligences. Through reinforcement learning, intelligences generate more efficient code with fewer errors for automated programming tasks.
2. Search Assistant Development
   In the search scenario, Agent Lightning can optimize the query processing capability of the intelligent body, improve the accuracy and response speed of search results, and is suitable for building intelligent customer service systems.
3. Multi-Intelligent Body Collaboration Tasks
   In scenarios where multiple intelligences are required to collaborate (e.g., team assignments), Agent Lightning coordinates multiple intelligences to ensure tasks are completed efficiently, making it suitable for enterprise automation processes.

QA

1. What intelligent body frames are supported by Agent Lightning?
   OpenAI Agents SDK, AutoGen and LangChain are currently supported, and more frameworks may be extended in the future.
2. How do you handle intelligent body execution errors?
   Lightning Server keeps an error log, which is stored in the logs/error_log.txt in. The user can adjust the smartbody configuration or task definition based on the logs.
3. Do I need to have knowledge of intensive learning to use it?
   No. Agent Lightning provides a simplified interface that allows developers to use reinforcement learning optimization by simply configuring tasks and reward functions.