AI agent frameworks compared: LangGraph vs AutoGen vs CrewAI

LangChain dominates with 90k+ GitHub stars, but AutoGen grew 400% in the last year and CrewAI hit 15k stars in six months. Each framework makes different trade-offs on control, complexity, and production readiness. Picking the wrong one costs weeks in refactoring. Here's the breakdown.

Framework overview

LangGraph (part of LangChain ecosystem) models agents as state machines with explicit control flow. You define nodes (LLM calls, tool invocations, human approval gates) and edges (transitions between states). Best for workflows where you need deterministic behavior and audit trails.

AutoGen (Microsoft Research) emphasizes multi-agent collaboration. Agents chat with each other to solve problems. You configure agent roles, conversation patterns, and termination conditions. Best for exploratory tasks where agents negotiate solutions.

CrewAI is task-oriented with a focus on role assignment. You define a crew (team of agents), assign tasks, and let the framework coordinate execution. Simplest API surface, least control over orchestration internals.

Feature comparison: where they differ

• Control flow. LangGraph: explicit state machine. AutoGen: conversation-driven. CrewAI: implicit task delegation.
• Observability. LangGraph has built-in LangSmith integration for traces and logs. AutoGen requires custom instrumentation. CrewAI logs to console by default.
• Human-in-the-loop. LangGraph supports approval nodes natively. AutoGen uses custom agents with `UserProxyAgent`. CrewAI lacks first-class support.
• Error handling. LangGraph lets you define fallback edges. AutoGen relies on agent-level retry logic. CrewAI surfaces exceptions to the orchestrator.
• Tool integration. All three support function calling. LangGraph wraps tools as graph nodes. AutoGen registers tools per agent. CrewAI uses a shared tool registry.

When to use each framework

Use LangGraph if:

• You need deterministic, auditable workflows for regulated industries.
• Your process has clear stages with approval gates or validation checkpoints.
• You want deep observability and integration with LangSmith for monitoring.
• You're comfortable with slightly verbose state machine definitions.

Use AutoGen if:

• Your task benefits from multi-agent collaboration and negotiation.
• You're prototyping research workflows where agents explore solution spaces.
• You want agents to critique each other's outputs before finalizing.
• You don't need strict control flow or deterministic execution order.

Use CrewAI if:

• You want the fastest time-to-first-agent with minimal boilerplate.
• Your use case maps cleanly to role-based task assignment (project manager, researcher, writer).
• You're okay with less control over orchestration internals.
• You prioritize developer ergonomics over advanced customization.

Code pattern comparison summary

LangGraph: Define a graph with typed state, add nodes for LLM calls and tools, specify edges with conditional logic. Compile the graph and invoke with input state. Full control, verbose setup.

AutoGen: Configure agents with system messages and tools. Start a conversation between agents. Monitor chat until termination condition. Flexible, requires careful prompt engineering to keep agents on task.

CrewAI: Instantiate agents with roles. Define tasks with descriptions and expected output. Create a crew and kick off execution. Concise, less visibility into intermediate steps.

Production readiness considerations

LangGraph has the best observability story with LangSmith. You get trace IDs, latency breakdowns, and cost attribution out of the box. Error handling is explicit via fallback edges. Scales well with deterministic workflows.

AutoGen requires more work to instrument. Conversation logs are verbose and need parsing. Error recovery depends on agent prompts, which can be fragile. Best for research and low-volume exploratory tasks.

CrewAI is production-ready for simple workflows but lacks advanced observability hooks. Error handling is basic. Great for MVPs and internal tools, less suited for customer-facing automation.

Selection criteria: pick the right tool

1. Workflow complexity. Multi-stage with gates? LangGraph. Exploratory collaboration? AutoGen. Simple role delegation? CrewAI.
2. Observability needs. Regulated industry or high-stakes automation? LangGraph. Internal tooling? CrewAI works.
3. Team experience. Comfortable with state machines? LangGraph. Prefer conversational agents? AutoGen. Want fast iteration? CrewAI.
4. Integration requirements. Need human approval gates? LangGraph. Want agents to debate solutions? AutoGen. Just need task completion? CrewAI.

All three frameworks benefit from structured context. When agents hand off to downstream systems or humans, portable bundles ensure conversation history travels intact.

Choosing a framework? We've shipped agents on all three—happy to compare notes.