AutoGen
by Various
A programming framework for agentic AI
Apps
AutoGen
Added 1 June 2026
Overview
AutoGen is a Python framework for building multi-agent systems where LLM-powered agents collaborate to solve tasks through conversation and code execution. Agents can be customized with different roles, tools, and conversation patterns to orchestrate complex workflows.
Best for
Best for
Developers building multi-agent systems in Python who need structured agent orchestration and code execution capabilities.
Use cases
- Building conversational AI systems where multiple agents debate or collaborate on solutions
- Automating multi-step workflows that require reasoning, code generation, and execution
- Prototyping agentic applications without managing low-level orchestration logic
Notes
AutoGen is a Python framework for building multi-agent systems where LLM-powered agents collaborate to solve tasks through conversation and code execution. Agents can be customized with different roles, tools, and conversation patterns to orchestrate complex workflows.
58,610 stars on GitHub. Last updated 2026-04-15. Licensed CC-BY-4.0.
Use cases
- Building conversational AI systems where multiple agents debate or collaborate on solutions
- Automating multi-step workflows that require reasoning, code generation, and execution
- Prototyping agentic applications without managing low-level orchestration logic
Pros
- Handles agent communication and state management, reducing boilerplate for multi-agent setups
- Supports code execution within agent loops for grounding outputs in real computation
- Active open-source project with substantial community adoption (58k+ stars)
Cons
- Requires Python and familiarity with agent design patterns, steeper learning curve than single-agent frameworks
- Performance and cost scale with number of agents and conversation turns, can become expensive with large models
- Limited built-in observability and debugging tools for complex multi-agent interactions
Indexed from awesome-generative-ai and enriched against its public facts.
Pros
- Handles agent communication and state management, reducing boilerplate for multi-agent setups
- Supports code execution within agent loops for grounding outputs in real computation
- Active open-source project with substantial community adoption (58k+ stars)
Cons
- Requires Python and familiarity with agent design patterns, steeper learning curve than single-agent frameworks
- Performance and cost scale with number of agents and conversation turns, can become expensive with large models
- Limited built-in observability and debugging tools for complex multi-agent interactions
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