LLM Agent Frameworks Compared (2026): LangGraph, CrewAI, Claude SDK & More
A 2026 comparison of LLM agent frameworks — Claude Agent SDK, OpenAI Agents SDK, Google ADK, LangGraph, CrewAI, and more — by language, multi-agent model, and protocol support.
If you read an agent framework comparison written before 2026, most of its version numbers and recommendations are already out of date. The collected sources for this article — Morph LLM, LangChain, Chatbase, dasroot, and Towards Data Science, all from 2026 — agree on one thing: the agent framework landscape consolidated fast this year, and the question is no longer whether to use one but which one you will still be happy with in six months.
This article compares the frameworks that recurred across those sources, on the criteria they actually evaluated. For picking the underlying assistant tools that pair with these frameworks, see our AI coding tools comparison.
What changed in 2026
The collected sources document several releases that reset earlier comparisons:
- Microsoft Agent Framework 1.0 went GA on April 3, 2026, merging AutoGen and Semantic Kernel into one .NET and Python SDK.
- Google ADK shipped 1.0 for Java and Go, making it a four-language SDK (Python, TypeScript, Java, Go).
- CrewAI passed 52,000 GitHub stars (1.14.6, late May 2026), with the project reporting roughly 2 billion agent executions over the prior year.
- The protocol layer consolidated: ACP merged into A2A under the Linux Foundation, and MCP crossed 200 server implementations.
One operational note from the sources for Claude-based stacks: as of June 15, 2026, Claude Agent SDK and non-interactive runs draw from a separate monthly Agent SDK credit on subscription plans. If you run agents in CI or scheduled jobs, budget for that separately.
The criteria that matter
The sources did not rank on hype. They compared frameworks on the dimensions that affect production:
- Provider-native vs independent — is it optimized for one model family, or model-agnostic?
- Multi-agent model — subagents, handoffs, role-based crews, or graph nodes?
- Protocol support — MCP for tools, A2A for agent-to-agent.
- Language coverage — Python only, or also TypeScript, Java, Go?
Comparison table
The table below follows the categorization in the collected 2026 sources: provider-native SDKs (Claude, OpenAI, Google) versus independent frameworks.
| Framework | Languages | Multi-agent | MCP | A2A | Best for |
|---|---|---|---|---|---|
| Claude Agent SDK | Python, TS | Subagents | Native (deepest) | No | Coding agents, OS access |
| OpenAI Agents SDK | Python, TS | Handoffs | Adopted | No | Lightweight handoff chains |
| Google ADK | Python, TS, Java, Go | Hierarchical | Via adapters | Native | Enterprise, multi-language |
| LangGraph | Python, TS | Graph nodes | Via adapters | No | Stateful workflows |
| CrewAI | Python | Role-based crews | Native | Native | Rapid prototyping |
| Smolagents | Python | Multi-agent | Supported | No | Code-generating agents |
| Pydantic AI | Python | No | No | No | Type-safe structured output |
Per-criterion verdict
Depth vs flexibility. Provider-native SDKs (Claude Agent SDK, OpenAI Agents SDK, Google ADK) trade portability for tighter integration. The independent frameworks (LangGraph, CrewAI, Smolagents, Pydantic AI) keep you free to swap models. Neither is universally better; the sources are explicit that the right choice depends on whether you prioritize integration depth or model independence.
Multi-agent style. LangGraph models work as explicit graph nodes, which suits stateful, branching workflows where you need to see and control transitions. CrewAI’s role-based crews are faster to stand up for prototypes. Claude’s subagents and OpenAI’s handoffs are lighter-weight delegation patterns within one provider’s ecosystem.
Protocol support. MCP is now broadly supported (200+ servers), so tool access is less of a differentiator than it was. A2A (agent-to-agent) is where frameworks still diverge: Google ADK and CrewAI support it natively, while most others do not yet.
Recommendation by use case
- Coding agents or OS-level access: Claude Agent SDK, for its deepest MCP integration — budget for the separate Agent SDK credit.
- You already build on OpenAI: OpenAI Agents SDK for simple handoff chains.
- Enterprise with Java/Go services: Google ADK, the only four-language option with native A2A.
- Complex, stateful workflows: LangGraph, where explicit graph state pays off.
- Fast prototypes: CrewAI’s role-based crews.
- Type-safe structured output: Pydantic AI.
The honest takeaway from the 2026 sources: protocol support has largely converged, so base your choice on language coverage, multi-agent model, and whether you want to stay model-agnostic. Re-check version facts before committing — this space moved faster in 2026 than any single article can keep up with.
Note: Release dates, versions, and pricing above are from sources published in 2026 and change frequently. Verify each framework’s current docs before adopting.