LAST UPDATED: FEBRUARY 8, 2026

CrewAI vs LangGraph vs AutoGen: Which Should You Use?

Three frameworks, three philosophies. CrewAI gives you role-based teams that ship fast. LangGraph gives you stateful graphs with precise control. AutoGen gives you conversational agents that negotiate solutions. Here's how to choose.

What Each Framework Actually Does

CrewAI: Role-Based Agent Teams

CrewAI organizes agents as a "crew" where each agent has a defined role, goal, and backstory. You describe what each agent should do in natural language, assign them tasks, and the framework handles coordination. A typical setup might include a Researcher agent that gathers information, a Writer that drafts content, and an Editor that refines it. The framework manages task sequencing, memory sharing, and output formatting.

The mental model is a team of specialists collaborating on a project. You define who they are and what they should accomplish, and CrewAI orchestrates the work. This makes prototyping fast — a working multi-agent system in under an hour. CrewAI also offers a "Flows" feature for more structured orchestration with explicit sequences and conditional routing. The tradeoff is that when the framework's orchestration doesn't match your needs, you're working against the abstraction.

LangGraph: Stateful Agent Graphs

LangGraph models agent workflows as directed graphs. Nodes are actions — calling an LLM, executing a tool, making a decision — and edges define how control flows between them. You build the graph explicitly: every branch, every loop, every conditional handoff is something you designed. State is a first-class concept where you define exactly what information persists between steps and how it transforms.

The mental model is a flowchart that can loop. Each node can invoke AI, and transitions follow rules you specify. This makes LangGraph more verbose — a workflow that takes 20 lines in CrewAI might take 100 in LangGraph — but those extra lines give you explicit control over error handling, retry logic, human approval gates, and complex branching. LangGraph also supports both Python and JavaScript/TypeScript, giving it flexibility for full-stack teams.

AutoGen: Conversational Agent Groups

AutoGen, developed by Microsoft, takes a fundamentally different approach. Instead of assigning tasks or defining graphs, you create agents that converse with each other. A UserProxy agent represents human input, an AssistantAgent provides AI reasoning, and you can add specialized agents that contribute domain expertise. Agents take turns speaking in a group chat, responding to each other's messages, critiquing each other's work, and iterating until they converge on a solution.

The mental model is a structured conversation. You define participants, give them personas and capabilities, and let the discussion produce results. This is particularly powerful for problems where the right answer emerges from debate — code review where one agent writes and another critiques, research where one agent proposes hypotheses and another stress-tests them, or planning where multiple perspectives need to be reconciled. AutoGen 0.4 (AG2) introduced a significant architecture refresh with an event-driven runtime, improved type safety, and better support for custom agent types.

Where Each Framework Shines

CrewAI: Speed to Working Prototype

CrewAI's strength is how quickly you go from idea to running system. Defining an agent as "a senior research analyst who specializes in market analysis" and handing it a task is genuinely intuitive. For teams that need something working by end of week — a content pipeline, a research workflow, an internal automation — CrewAI is the practical choice. The CrewAI Enterprise platform adds monitoring, deployment, and collaboration features for teams that outgrow the open-source version.

LangGraph: Production Reliability

When you need to guarantee that a human reviews every agent decision above a certain risk threshold, or that a failed API call triggers a specific fallback rather than a generic retry, or that state persists across sessions for multi-day workflows — that's where LangGraph pays off. Every edge in the graph is a decision you made and can debug. The LangGraph Platform provides deployment infrastructure with persistence, streaming, and visual debugging through LangSmith.

AutoGen: Iterative Problem Solving

AutoGen excels when the problem benefits from multiple perspectives iterating toward a solution. Code generation is a prime example: one agent writes code, another reviews it, a third tests it, and they loop until the code passes. Research synthesis works similarly — one agent gathers sources, another evaluates methodology, a third identifies contradictions. The conversational pattern produces higher-quality outputs for complex problems because each agent's contribution is refined by the others.

AutoGen's native code execution sandbox (Docker-based) is also a standout feature. Agents can write and run code safely during conversations, evaluate the results, and iterate — making it particularly strong for data science workflows, automated testing, and any task where running code is part of the reasoning process.

Practical Considerations

Team Experience

CrewAI is the most accessible — its natural-language agent definitions read like job descriptions, making it approachable for product managers and domain experts. LangGraph requires comfort with graph theory and state machines, fitting teams with strong backend engineering experience. AutoGen sits in between: the conversation metaphor is intuitive, but configuring agent behaviors, managing turn-taking, and setting up code execution environments requires solid Python skills.

Ecosystem and Community

LangGraph benefits from the broader LangChain ecosystem — extensive documentation, wide integrations, and LangSmith for production monitoring. AutoGen benefits from Microsoft's backing and a large community that shares notebooks, templates, and research applications. CrewAI has a smaller but fast-growing community with active template sharing and an enterprise platform for production deployment. All three are actively maintained with regular releases.

Migration Paths

Starting with CrewAI for prototyping and moving to LangGraph for production is a well-worn path — the core logic transfers even though orchestration code changes. Moving to or from AutoGen requires more rework since the conversational architecture is fundamentally different from task-based or graph-based approaches. If you anticipate needing AutoGen's debate-style reasoning, it's better to start there than to migrate later.

Cost Structure

All three frameworks are open-source and free for self-hosted use. The cost differences appear in commercial add-ons: CrewAI Enterprise for hosted deployment, LangSmith for tracing and monitoring, and AutoGen Studio for visual agent building. In every case, the LLM API costs dwarf the framework costs — optimize your prompts and agent interaction patterns before worrying about platform pricing.

Can You Combine Them?

Yes. Some teams use CrewAI for structured workflows, LangGraph for mission-critical processes, and AutoGen for research and code generation tasks — all within the same system. Since all three are Python-based, they can coexist in a codebase and share tool configurations. The hybrid approach works best when there's a clear boundary between workflow types. If that boundary is blurry, pick one framework and go deep rather than managing three orchestration layers.

EXPLORE MORE

Related Resources