AutoGen
AIA Microsoft Research framework for building multi-agent LLM applications that converse, coordinate, and include human participation.
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Free Available
Starting from Free
AutoGen
AI
A Microsoft Research framework for building multi-agent LLM applications that converse, coordinate, and include human participation.
-(0 Reviews)
Free Available
Starting from Free
About AutoGen
AutoGen is an open-source framework and ecosystem for creating multi-agent, agentic LLM applications that can operate autonomously or alongside humans. It provides layered, extensible APIs and components so developers can work at high- or low-level abstractions, orchestrate conversations between multiple agents, integrate tools and human inputs, and benchmark agent performance. The ecosystem includes developer tools such as AutoGen Studio (a no-code GUI for prototyping multi-agent workflows) and AutoGen Bench (a benchmarking suite), and supports extensions for multiple model backends and tool integrations to accelerate prototyping and research.
Key Features
Multi-Agent Framework: Provides primitives and APIs to create interoperating agents that can converse, delegate, and coordinate to accomplish complex tasks across workflows.
Layered Extensible Design: Uses a layered architecture with clear separation of responsibilities, enabling usage from high-level AgentChat APIs to low-level components for custom orchestration and control.
Human-in-the-Loop & Tool Integration: Supports mixed modes combining LLMs, human inputs, and external tools, allowing agents to request human guidance or call external services during workflows.
AutoGen Studio (No-Code GUI): A graphical builder to prototype, configure, and run multi-agent workflows without writing code, speeding iteration for designers and non-developers.
AutoGen Bench (Benchmarking Suite): Provides tooling to evaluate and compare agent performance, enabling reproducible experiments and measurement of agent behaviors and outcomes.
Multi-Backend Model Support: Extensions and integrations (e.g., OpenAI, HuggingFace) let developers plug different model providers and swap models for experimentation and prototyping.
Python SDK & Requirements: Delivered primarily as a Python framework (requires Python 3.10+), with installable components and extensions to run AgentChat and connect to model clients.
Automatic Code Execution & Prototyping (via integrations like HFAutogen): Enables rapid prototyping that can automate execution of generated code and streamline the transition from idea to working prototype.
Multi-agent conversation orchestration allowing agents to converse and coordinate on tasks
Layered and extensible architecture supporting high-level APIs and low-level components
AutoGen Studio: no-code GUI for building and prototyping multi-agent workflows
AutoGen Bench: benchmarking suite for evaluating agent performance
AgentChat component for advanced multi-agent orchestrations
Extensions/plugins model (e.g., autogen-ext[openai]) for integrating external model providers
Automatic code execution support to accelerate prototyping and testing
Compatible with OpenAI and community integrations for HuggingFace (e.g., HFAutogen)
Use Cases
Prototyping multi-agent workflows: Rapidly design and test coordinated agent conversations to solve tasks such as research assistants, planning pipelines, or complex request decomposition.
Human-AI collaborative applications: Build systems where agents escalate to humans for verification or combined decision-making in customer support, review, or approval workflows.
Benchmarking agent behaviors: Use AutoGen Bench to compare agent strategies, conversation flows, and model choices for research experiments and performance tuning.
No-code multi-agent apps: Empower product teams and designers to assemble and run agentic workflows without writing code using AutoGen Studio for quick demos and proofs-of-concept.
Integrating multiple model providers: Swap and test different LLM backends (OpenAI, HuggingFace, etc.) for cost, latency, or capability tradeoffs in agent orchestration.
Research and ideation for agentic AI: Explore agentic patterns, messaging protocols, and orchestration approaches in academic or internal R&D before moving to production frameworks like Semantic Kernel.
Prototyping multi-agent workflows and agent interactions without heavy infra setup
Research and experimentation on agentic AI patterns and coordination strategies
Building autonomous or semi-autonomous agent systems that collaborate with humans
Benchmarking agent designs and comparing agent performance using AutoGen Bench
Integrating external language model providers (OpenAI, HuggingFace) into multi-agent apps
Screenshots
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