Choosing a Multi-Agent Framework in 2026: CrewAI, AutoGen, MetaGPT, DeerFlow and More

"Multi-agent" covers very different things: a Python library for orchestrating role-based agents, a ready-made runtime that executes hour-long tasks, a research system that simulates a software company. Picking the wrong kind costs more than picking the wrong brand. This guide compares the most-starred open-source options in the AgentIndex Multi-Agent category, based on each project's own documentation as of October 2026, and ends with a decision table.

One fact shapes this comparison more than any feature list: Microsoft AutoGen is now in maintenance mode. If you are starting a new project, that alone changes the shortlist.

First, decide what kind of tool you need

Most options fall into three groups:

1. Frameworks: libraries you program against. You define agents, tools and control flow in code. Examples: CrewAI, AutoGen, Microsoft Agent Framework.
2. Harnesses or runtimes: applications that already include a sandbox, memory, file system and UI, and run agents for you. Examples: DeerFlow 2.0, nanobot.
3. Opinionated systems: a fixed multi-agent workflow for a specific job. Example: MetaGPT's "software company".

If you need to embed agents in your own product, you want a framework. If you want agents to do work for you with minimal code, look at harnesses.

The contenders

CrewAI: role-based Crews plus controllable Flows

CrewAI is a standalone Python framework with two complementary building blocks. Crews are teams of agents with roles, goals, tools and tasks that collaborate and delegate. Flows are event-driven workflows with state, branching and routing for the parts where you need deterministic control. Agents support tools, memory, knowledge sources, checkpointing, async execution, and MCP and A2A integrations.

It is actively developed, MIT licensed and has a large community. Be aware that anonymous telemetry is on by default (it can be disabled) and that managed deployment and observability are part of the commercial CrewAI AMP Suite.

Choose it when you are building production automations in Python and want both autonomous collaboration and explicit control in one framework.

Microsoft AutoGen: influential, now in maintenance mode

AutoGen pioneered many multi-agent conversation patterns. Its layered design offers a Core API for event-driven agents with local and distributed runtimes (Python and .NET), an AgentChat API for common patterns such as group chats, and an Extensions API for model clients and tools. AutoGen Studio provides a no-code prototyping UI.

However, the project states that it will not receive new features and is now community managed. Microsoft recommends that new users start with Microsoft Agent Framework, described as AutoGen's enterprise-ready successor, and provides a migration guide.

Choose it when you already have AutoGen code in production and need time before migrating. For new work, evaluate Agent Framework instead.

Microsoft Agent Framework: the successor

Microsoft Agent Framework is a framework for building, orchestrating and deploying AI agents and multi-agent workflows, with support for Python and .NET. It is MIT licensed and actively developed.

Choose it when you would have picked AutoGen, need .NET support, or work in a Microsoft-centric stack.

MetaGPT: a software company in a box

MetaGPT assigns roles such as product manager, architect, project manager and engineer to LLM agents and runs them through standard operating procedures. From a one-line requirement it produces user stories, competitive analysis, requirements, API designs, documents and code. The team behind it has published notable research, including AFlow on automating agentic workflow generation.

Development has slowed: the repository's last push was in January 2026, and the README targets Python 3.9 to 3.11.

Choose it when you want to study or demonstrate role-based collaboration, or generate a small, well-specified project end to end.

DeerFlow 2.0: a batteries-included agent runtime

DeerFlow started as ByteDance's deep research framework. Version 2.0 is a ground-up rewrite on LangGraph and LangChain that the project calls a "super agent harness": it ships with sub-agents, long-term memory, sandboxes, a file system, skills and scheduled tasks, and can receive tasks from Telegram, Slack, Feishu/Lark, WeChat, WeCom, DingTalk and QQ. It is MIT licensed.

It is heavier to run (plan for at least 4 vCPU and 8 GB RAM to evaluate, more for a shared server) and has powerful capabilities such as command execution, so the project warns against exposing it without proper access control.

Choose it when you want long-running tasks such as research reports, data pipelines or slide generation handled by a ready-made runtime rather than code you write.

nanobot: the lightweight self-hosted option

nanobot describes itself as an ultra-lightweight, self-hosted personal agent framework in Python with a WebUI, tools, memory, MCP, multi-agent workflows, automation and chat app integrations.

Choose it when you want a small, hackable personal agent you can run yourself, rather than an enterprise framework or a heavy runtime.

Ruflo (formerly Claude Flow): swarms around coding agents

Ruflo focuses on orchestrating swarms of agents, with integrations for Claude Code, Codex and other coding agents, adaptive memory and vector RAG. It is MIT licensed and very active.

Choose it when your multi-agent work centres on coordinating coding agents.

Decision table

Practical advice before you commit

• Start with one agent. Many tasks that look like multi-agent problems work fine with a single agent and good tools. Add agents when one clearly cannot hold the whole task.
• Budget for model calls. Every extra agent multiplies requests. Measure cost and latency on a realistic task before scaling up.
• Check project health, not just stars. Look at the last release, open issues and whether the maintainers have announced a successor, as AutoGen did.
• Plan for observability. Multi-agent runs are hard to debug without traces. Check what tracing the framework supports (DeerFlow, for example, integrates LangSmith and Langfuse).

Whichever you choose, the individual tool pages linked above include our notes on strengths, limitations and setup for each project.