AutoGen vs CrewAI

Both are catalogued under Multi-Agent Orchestration. The figures come from the GitHub API; the assessments are ours.

AutoGenAgents that solve problems by conversing with one anotherOfficialSource-available

CrewAIRole-based multi-agent teams with delegation built inOfficialMIT

At a glance

At a glance	AutoGen	CrewAI
License	Source-available	MIT
Languages	Python, C#	Python
Deployment	Self-hosted / Runs locally	Self-hosted / Managed cloud
Maturity	Established	Established
Stars	60.4k	57.1k
Star growth over the last 7 days	+2 ★	+19 ★
Forks	9.1k	8.2k
Open issues	991	790
Last commit	15 Apr 2026	15 Aug 2026
Activity	Active	Active

What each one does

AutoGen

Microsoft Research's take on multi-agent systems, where progress emerges from a structured conversation between specialised agents. Strong for exploration and research-flavoured problems. Two things to check before adopting: the API was substantially reworked between major versions, and GitHub reports the repository licence as CC-BY-4.0 — a content licence, not an OSI-approved software one. Confirm the terms with the maintainers before commercial use.

Full entry →

CrewAI

Models a task as a crew of agents with named roles and goals that hand work to each other. The metaphor makes a multi-agent design readable at a glance, which is why it demos so well. Whether several role-playing agents beat one well-prompted agent on your task is worth measuring before committing — the answer is often no.

Full entry →

What you can do

AutoGen

Wrap an agent as a tool — AgentTool turns an AssistantAgent into a callable tool, so a general assistant can delegate to a math or chemistry specialist inside one run.
Attach MCP servers to an agent — McpWorkbench with StdioServerParams launches a server such as @playwright/mcp and exposes its tools to the agent, with max_tool_iterations bounding the loop.
Prototype without writing code — autogenstudio ui --port 8080 --appdir ./my-app opens a GUI for assembling multi-agent workflows, which the README states is not meant to be production-ready.
Drop to the runtime layer — The Core API underneath AgentChat exposes event-driven message passing with local and distributed runtimes, and is reachable from .NET as well as Python.
Plan a migration path — AutoGen is in maintenance mode and the README routes new projects to Microsoft Agent Framework, limiting accepted contributions to bug fixes, security patches and documentation improvements.

CrewAI

Scaffold a project from the CLI — crewai create crew <project_name> puts crew.py and main.py directly under src/<project>/, with agents.yaml and tasks.yaml one level down in src/<project>/config/; crewai run from the project root executes the result.
Define agents and tasks in YAML — Each agent gets role, goal and backstory in agents.yaml, while each task gets description, expected_output, an assigned agent and an optional output_file in tasks.yaml.
Switch to hierarchical delegation — Process.sequential runs tasks in declared order, while the hierarchical process automatically assigns a manager to the crew to coordinate planning, delegation and validation of results.
Wrap crews in event-driven Flows — Flow[StateModel] with @start, @listen and @router decorators, plus or_ and and_ conditions, gives Pydantic-typed state and conditional branching around crew.kickoff() calls.
Teach a coding agent the framework — /plugin marketplace add crewAIInc/skills then /plugin install crewai-skills@crewai-plugins loads four skills into Claude Code — getting-started, design-agent, design-task and ask-docs, which queries the live CrewAI docs MCP server — and npx skills add crewaiinc/skills installs the same set for Cursor, Codex and Windsurf.