# Which Agent Framework in 2026? LangGraph vs CrewAI vs AutoGen vs OpenAI Agents SDK vs Claude Agent SDK

> A decision guide to the major AI agent frameworks — control vs. abstraction, multi-agent models, state and durability, and which fits your project.

Pick an agent framework by how much control you need. LangGraph gives explicit, durable state graphs for production; CrewAI and AutoGen offer fast high-level multi-agent abstractions (roles vs. conversations); the OpenAI Agents SDK is a minimal, standard agent loop; the Claude Agent SDK is the batteries-included path for Claude. Many start high-level and drop to LangGraph when they need control.

There are more agent frameworks than there are good reasons to choose between them on vibes. The useful way to decide is one axis: **how much control do you need over the agent's control flow?** Everything else — multi-agent model, ecosystem, ergonomics — follows from that.

## The control ↔ abstraction spectrum

At one end, you specify the agent's behavior explicitly as a state machine; at the other, you describe roles or a conversation and let the framework coordinate. More abstraction means faster to start and less to maintain; more control means you can guarantee behavior, persist state, and debug production failures.

- **High control / explicit** → LangGraph
- **High-level / multi-agent abstraction** → CrewAI (roles), AutoGen/AG2 (conversation)
- **Minimal, standard loop** → OpenAI Agents SDK
- **Batteries-included on Claude** → Claude Agent SDK

## The frameworks

### [LangGraph](/tools/langgraph) — control and durability

Model the agent as an explicit graph of nodes and edges over shared state. You get persistence (**checkpointing**), **human-in-the-loop** interrupts, branching, and resumable runs — the properties production agents need. The cost is more wiring up front. Best when reliability and debuggability matter more than time-to-first-demo.

### [CrewAI](/tools/crewai) — role-based crews

Describe agents by role and goal, assign tasks, and let the "crew" collaborate. The fastest way to a working multi-agent prototype, with **Flows** available when you need more deterministic, event-driven control. Best for collaborative workflows you want running quickly.

### [AutoGen / AG2](/tools/autogen) — agents as conversation

Agents (and humans) solve tasks by exchanging messages, including group chats and a code-executing agent for generate-run-debug loops. Flexible and great for prototyping and research. (Note the naming: Microsoft's AutoGen and the community AG2 fork.)

### [OpenAI Agents SDK](/tools/openai-agents-sdk) — a minimal standard loop

A small, provider-agnostic framework: the agent loop plus handoffs, guardrails, sessions, and built-in tracing. Few primitives, learned fast. Best when you want a clean standard loop without a heavy abstraction.

### [Claude Agent SDK](/tools/claude-agent-sdk) — batteries included on Claude

Anthropic's first-party toolkit for building agents on Claude, with native tool use, MCP, and subagents. The most complete path if you're committed to Claude.

## How to choose

- **Production agent needing state, checkpoints, HITL** → **LangGraph**.
- **Fast role-based multi-agent prototype** → **CrewAI**.
- **Conversational / research multi-agent or code-exec loops** → **AutoGen/AG2**.
- **A clean, minimal, provider-agnostic loop** → **OpenAI Agents SDK**.
- **Building on Claude, want the smoothest path** → **Claude Agent SDK**.
- **A single model with a few tools** → maybe **no framework** — write the loop directly.

These aren't mutually exclusive. A common trajectory is to prototype high-level (CrewAI/AutoGen), then move the production-critical path to LangGraph once you need control and durability. Whatever you pick, the next two problems are the same everywhere: giving the agent [memory](/guides/concepts/agent-memory-architecture) and making its [tool calling](/guides/concepts/production-tool-calling) robust — and then [making it production-ready](/agents/meta-orchestration/agent-reliability-reviewer).

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_Source: https://agentscamp.com/guides/concepts/agent-frameworks-2026 — Guide on AgentsCamp._