AutoGen
AutoGen strives to revolutionize the use of Large Language Models (LLMs) by providing an innovative Multi-Agent Conversation Framework. This framework is ...
What is AutoGen?
AutoGen is an open-source framework from Microsoft that enables you to build applications using multiple AI agents that communicate with each other. Instead of working with a single LLM, you create specialise agents that can take on different roles, ask questions of each other, and work together to solve problems. This is particularly useful for complex tasks that benefit from different perspectives or expertise. The framework handles the conversation flow between agents, manages LLM calls, and lets you define when agents should hand off work to each other. It's designed for developers who want to move beyond simple prompt-response interactions and build systems where AI agents actively collaborate.
Key Features
Multi-agent conversation framework
Define multiple AI agents with different roles and capabilities that interact with each other
Configurable agent behaviour
Set instructions, tools, and stopping conditions for each agent
Tool integration
Connect agents to external APIs, code execution environments, and data sources
Conversation management
Automatic handling of message flow, context, and turn-taking between agents
Support for multiple LLM providers
Compatible with OpenAI, Azure OpenAI, and other model providers
Code execution
Agents can write and execute code to solve problems or validate solutions
Pros & Cons
Advantages
- Open source and free to use, with active development and community support
- Flexible design lets you create complex workflows without building conversation logic from scratch
- Works with multiple LLM providers, so you're not locked into one service
- Good documentation and examples for common use cases like data analysis and code generation
Limitations
- Requires development experience to set up and configure agents effectively
- Can become expensive at scale if using paid LLM APIs, as multi-agent systems make more API calls
- Debugging agent interactions can be challenging when things don't work as expected
Use Cases
Data analysis workflows where one agent gathers data and another analyses it
Software development tasks where agents collaborate on code generation and testing
Customer service systems that route inquiries between specialist agents
Research and information gathering where agents explore sources and synthesise findings
Complex problem-solving requiring multiple perspectives or expertise areas