How AutoGen Works
Most LLM frameworks chain calls sequentially. AutoGen takes a different approach: it creates multiple agents that talk to each other, negotiate, and iteratively refine their outputs. The architecture is built around conversation as the unit of computation.
What autogen Does
Multi-agent AI framework for building autonomous agent teams that collaborate
AutoGen is a framework for creating multi-agent AI applications that can act autonomously or work alongside humans. It provides high-level abstractions for creating conversational agents, team orchestration, and includes a visual studio interface for building agent workflows without code. The framework supports multiple LLM providers and includes specialized agents for code execution, tool usage, and human interaction.
Architecture Overview
autogen is organized into 4 layers, with 10 components and 8 connections between them.
How Data Flows Through autogen
Messages flow between agents through the runtime's message passing system, with LLM calls, tool executions, and human interactions forming conversation chains that continue until termination conditions are met.
1Message Creation
User input or agent response creates TextMessage or ToolCallMessage objects
2Agent Processing
AssistantAgent receives message, processes through model client to generate response
3Tool Execution
If response contains tool calls, CodeExecutorAgent or other specialized agents execute them
4Response Generation
Tool results are formatted into ToolCallResultMessage and sent back to conversation
5Team Orchestration
GroupChat manages multi-agent conversations using speaker selection policies
6Termination Check
TerminationCondition evaluates if conversation should continue or stop
System Dynamics
Beyond the pipeline, autogen has runtime behaviors that shape how it responds to load, failures, and configuration changes.
Data Pools
Conversation History
Message sequences accumulate during agent conversations
Type: in-memory
Studio Database
Persisted agent configurations, conversation logs, and user sessions
Type: database
Model Context Cache
Cached conversation contexts to manage token limits
Type: cache
Feedback Loops
Multi-turn Conversation
Trigger: Agent response generates new message → Next agent in team processes and responds (exits when: Termination condition met)
Type: recursive
Code Execution Retry
Trigger: Code execution fails with error → Assistant agent modifies code based on error feedback (exits when: Successful execution or max retries)
Type: retry
Human Input Loop
Trigger: HumanInputMode requires user feedback → System prompts user and waits for input (exits when: User provides input or types exit keyword)
Type: polling
Control Points
Temperature Setting
Max Messages
Human Input Mode
Code Execution Timeout
Delays
LLM API Calls
Duration: 1-10 seconds
Code Execution
Duration: 0.1-30 seconds
Message Queue Processing
Duration: milliseconds
Technology Choices
autogen is built with 10 key technologies. Each serves a specific role in the system.
Key Components
- AssistantAgent (class): Primary conversational agent that uses LLM to generate responses and can use tools
- CodeExecutorAgent (class): Specialized agent that executes code in sandboxed environments and returns results
- GroupChat (class): Orchestrates multi-agent conversations with speaker selection and termination logic
- OpenAIChatCompletionClient (class): Wrapper for OpenAI API providing standardized LLM interface with tool calling support
- AgentRuntime (class): Low-level runtime for message passing between agents with RPC and pub/sub capabilities
- FunctionWithRequirements (class): Wraps Python functions with their package dependencies for safe code execution
- McpManager (component): React component managing Model Context Protocol server integrations in the UI
- ConversableAgent (class): .NET base class for agents with conversation capabilities and human input modes
- AnthropicClientAgent (class): .NET agent implementation using Anthropic's Claude models with streaming support
- MessageFilterAgent (class): Agent wrapper that filters conversation messages based on source and count criteria
Who Should Read This
Developers exploring multi-agent systems, or teams building AI workflows that require collaboration between specialized agents.
This analysis was generated by CodeSea from the microsoft/autogen source code. For the full interactive visualization — including pipeline graph, architecture diagram, and system behavior map — see the complete analysis.
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Frequently Asked Questions
What is autogen?
Multi-agent AI framework for building autonomous agent teams that collaborate
How does autogen's pipeline work?
autogen processes data through 6 stages: Message Creation, Agent Processing, Tool Execution, Response Generation, Team Orchestration, and more. Messages flow between agents through the runtime's message passing system, with LLM calls, tool executions, and human interactions forming conversation chains that continue until termination conditions are met.
What tech stack does autogen use?
autogen is built with Pydantic (Data validation and serialization for agent configurations and messages), FastAPI (Web API framework for AutoGen Studio backend services), React (Frontend UI framework for the visual studio interface), OpenAI SDK (Integration with OpenAI models for chat completion and tool calling), Docker (Container runtime for sandboxed code execution in CodeExecutorAgent), and 5 more technologies.
How does autogen handle errors and scaling?
autogen uses 3 feedback loops, 4 control points, 3 data pools to manage its runtime behavior. These mechanisms handle error recovery, load distribution, and configuration changes.
How does autogen compare to langchain?
CodeSea has detailed side-by-side architecture comparisons of autogen with langchain. These cover tech stack differences, pipeline design, and system behavior.