AI Agent Framework Comparison
Data updated · manually curatedQuick take: pick by control vs speed-to-prototype, not by hype.
| Framework | Language | Strengths | Best for |
|---|---|---|---|
| LangGraph LangChain | Python / JS | Graph-based state machines, fine-grained control over agent flow, strong persistence and human-in-the-loop support | Complex multi-step workflows where you need explicit control over state and branching |
| CrewAI CrewAI | Python | Role-based multi-agent teams, fast to prototype, gentle learning curve | Multi-agent collaboration patterns (crew of specialists) without deep framework code |
| OpenAI Agents SDK OpenAI | Python / JS | Lightweight primitives (agents, handoffs, guardrails), first-class OpenAI model integration | Teams standardized on OpenAI models who want a minimal, official abstraction |
| Claude Agent SDK Anthropic | Python / TS | Full coding-agent harness (file tools, bash, subagents, hooks, MCP), batteries included | Agents that operate on code and filesystems; Claude-native workflows |
| AutoGen Microsoft | Python / .NET | Conversation-driven multi-agent patterns, research heritage, event-driven architecture in v0.4 | Research and experimentation with conversational multi-agent topologies |
How to choose
If your agent needs precise branching, checkpoints, or human approval steps, favor control: LangGraph's graph model and Claude Agent SDK's built-in harness both reward teams willing to write more orchestration code. If you want something running by tomorrow, CrewAI and the OpenAI Agents SDK get you a working prototype fastest, at the cost of less visibility into what happens between steps.
Also weigh ecosystem lock-in: frameworks tied to one model provider are convenient until you need to swap models, so if multi-vendor flexibility matters, start with a provider-neutral option like LangGraph or AutoGen.