- Developers building conversational multi-agent systems
- Research teams studying agent collaboration patterns and emergent behavior
- Engineers who need fine-grained control over agent state, messaging, and orchestration
Agents
AutoGen
Multi-agent AI framework from Microsoft Research for building conversational agent systems with AgentChat, Core API, and Extensions.
58K Stars
8.5K Forks
CC-BY-4.0 License
microsoft Maintainer
2026-05-27 Verified
Overview
What is AutoGen?
AutoGen is Microsoft Research's open-source framework for building multi-agent AI systems. It provides three layers — AgentChat for quick prototyping, Core API for fine-grained control, and Extensions for ecosystem integrations — giving developers a flexible foundation for agent collaboration patterns.
Layered architecture
AutoGen provides three layers: AgentChat for rapid prototyping, Core API for fine-grained control, and Extensions for ecosystem integrations.
Developers can start with the simplest layer and add complexity as their needs grow, without rewriting their codebase.Conversation-centric design
Agent conversations are the fundamental primitive. Agents collaborate through structured message passing with built-in conversation patterns.
Conversation-centric design makes it natural to model complex multi-agent interactions like debates, reviews, and iterative refinement.Microsoft ecosystem integration
Backed by Microsoft Research with deep integration into Azure AI services and the broader Microsoft developer ecosystem.
Teams already on Azure get first-class support, and the Microsoft backing means long-term maintenance and enterprise readiness. Use cases
What AutoGen is built for
01
Agent research and experimentation
Use AutoGen to study how agents collaborate, debate, and refine outputs through structured conversations.
02
Complex task decomposition
Break down complex tasks into sub-tasks handled by specialized agents that pass results and context between each other.
03
Automated code generation pipelines
Build agent teams where one agent writes code, another reviews it, and a third tests it, iterating until quality thresholds are met.
Quick start
Get started in seconds
$
pip install autogen-agentchat Comparison
How it stacks up
Choose AutoGen for fine-grained agent control
vs CrewAIAutoGen's Core API offers more control over agent state and messaging. CrewAI's role-based API is more intuitive but less flexible for advanced patterns.
FAQ
Frequently asked questions
What should I check before using AutoGen?
Start with one safe workflow for AutoGen. Inspect official setup instructions, required credentials, execution logs, approval points, and failure recovery before expanding from a sandbox task into production automation.
Is AutoGen open source?
AutoGen is listed with CC-BY-4.0 based on the official source links in this profile. Re-check the repository, model card, or docs before production use.
Who should evaluate AutoGen?
AutoGen is most worth evaluating for developers building conversational multi-agent systems.
Who should use AutoGen?
Developers building conversational multi-agent systems, especially those who need fine-grained control over agent state, messaging, and orchestration patterns.
How does AutoGen compare to other multi-agent frameworks?
AutoGen's main advantage is its layered architecture and conversation-centric design. It offers more low-level control than CrewAI but has a steeper learning curve.
Decision brief
JSON
Should you use AutoGen?
- Developers who only need a single-agent terminal coding tool
- Teams looking for a drag-and-drop workflow builder with a visual UI
- Verified 2026-05-27
- License: CC-BY-4.0
- Repo: microsoft/autogen
- Open-source signal
Deployment
cloud
Permission surface
shell/files, memory, messages, external services
Decision signals
No extra signals recorded
Agent packet
Structured decision data for AutoGen
This packet is the compact machine-readable view agents should use before following source links or taking action.
Capabilities
workflow orchestration
Constraints
open source
Deployment
cloud
Permission surface
shell/files, memory, messages, external services
Recommended workflows
Coding agent workflow, Connector or protocol layer, Memory or RAG workflow
Overview
What AutoGen does
What it is
AutoGen is an open agent resource to evaluate by action surface: what software it can operate, which tools or browser steps it touches, and how much supervision it needs before it can run real work.
Why it matters
Multi-agent systems are becoming the standard approach for complex AI tasks, but building them from scratch is hard. AutoGen provides the primitives — agent registration, message routing, conversation management, state handling — so developers can focus on designing agent behaviors rather than infrastructure.
How to evaluate it
Start with one safe workflow for AutoGen. Inspect official setup instructions, required credentials, execution logs, approval points, and failure recovery before expanding from a sandbox task into production automation.
Facts
Known metadata and operating surface
These fields are separated from editorial interpretation so agents can reason over facts and missing checks.
Resource type agent
Category Agents
Maturity active
Difficulty Unknown
License CC-BY-4.0
Pricing open source
Verified 2026-05-27
Source confidence high
Risk level elevated
Fit matrix
Where AutoGen fits in an agent stack
strong
Coding agent workflow
AutoGen has multiple signals for coding agent workflow, including matching tags, capabilities, category, or positioning.
- Run a small repository change and inspect the diff, tests, and rollback path.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
strong
Connector or protocol layer
AutoGen has multiple signals for connector or protocol layer, including matching tags, capabilities, category, or positioning.
- Connect one low-risk service, then inspect schemas, auth scope, errors, and logs.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
strong
Memory or RAG workflow
AutoGen has multiple signals for memory or rag workflow, including matching tags, capabilities, category, or positioning.
- Create, update, retrieve, correct, and delete memory or retrieval objects with real data.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
partial
Reusable skill workflow
AutoGen has at least one signal for reusable skill workflow, but should be checked against a real task before adoption.
- Run one skill end to end and check whether it produces evidence or structured output.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
weak
Browser automation
AutoGen is not primarily positioned for browser automation in the current metadata.
- Run one non-sensitive website task and inspect clicks, waits, retries, and changed URLs.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
weak
Evaluation and observability
AutoGen is not primarily positioned for evaluation and observability in the current metadata.
- Add one repeatable test case and confirm results can run again in review or CI.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
Inputs and outputs
What an agent should inspect
Likely inputs
- Repositories, files, issues, terminal output, and test results
- Documents, user facts, entities, context, or retrieval queries
- Tool schemas, API requests, service resources, and auth scopes
- Official setup instructions and a small real workflow
Likely outputs
- Diffs, commits, explanations, test results, or review notes
- Retrieved context, memory updates, graph relations, or citations
- A decision on whether this resource fits the target workflow
Evidence
Sources, claims, and missing checks
Claims are marked separately from source links so future crawlers and reviewers can update them without rewriting the page.
GitHub github
Repository source for code, license, issues, releases, and implementation details.
Homepage homepageOfficial or project-controlled source for this resource profile.
Docs docsDocumentation source for setup, API shape, and operational behavior.
AutoGen is listed as open source.
License metadata: CC-BY-4.0AutoGen has a recorded GitHub repository: microsoft/autogen.
Resource facts and GitHub source link.AutoGen supports these recorded deployment modes: cloud.
OpenAgent decision signal metadata.AutoGen is tagged with workflow orchestration capabilities.
OpenAgent capability taxonomy.- Repository freshness has not been recorded.
Next action
How to start evaluating AutoGen
Inspect repository
Check license, recent activity, issues, examples, and security-sensitive code paths.
Open sourceOpen Homepage
Start from the official source before adopting third-party instructions.
Open sourceRead setup docs
Use docs as the source of truth for installation and supported interfaces.
Open sourceInstall AutoGen
Install the AgentChat package for quick prototyping, or install autogen-core for the full Core API.
pip install autogen-agentchat Compare
Alternatives and nearby resources
Use related resources to compare category fit, license, deployment model, and first-workflow behavior.
FAQ
Common questions about AutoGen
What should I check before using AutoGen?
Start with one safe workflow for AutoGen. Inspect official setup instructions, required credentials, execution logs, approval points, and failure recovery before expanding from a sandbox task into production automation.
Is AutoGen open source?
AutoGen is listed with CC-BY-4.0 based on the official source links in this profile. Re-check the repository, model card, or docs before production use.
Who should evaluate AutoGen?
AutoGen is most worth evaluating for developers building conversational multi-agent systems.
Who should use AutoGen?
Developers building conversational multi-agent systems, especially those who need fine-grained control over agent state, messaging, and orchestration patterns.
How does AutoGen compare to other multi-agent frameworks?
AutoGen's main advantage is its layered architecture and conversation-centric design. It offers more low-level control than CrewAI but has a steeper learning curve.