The agent framework landscape in 2026 has consolidated around about twelve frameworks that matter, with clear leaders for each kind of workload. The biggest shifts of the last year: Anthropic shipped the Claude Agent SDK (formerly Claude Code SDK) as a first-party agent framework, and Vercel AI SDK 6 added native agent abstractions, pushing its 20M+ monthly downloads into agent territory. Both are now in the top tier alongside LangGraph and OpenAI Agents SDK.
We see all of these in production across Respan's customer base. The pattern: Claude Agent SDK for Anthropic-first agents and coding tasks, LangGraph for complex stateful production agents, Vercel AI SDK for TypeScript stacks, OpenAI Agents SDK for OpenAI-first stacks, CrewAI for fast prototyping, with the rest filling specific niches.
Quick comparison
| Framework | Best for | Language | Production-ready | License |
|---|---|---|---|---|
| Claude Agent SDK | Anthropic-first agents, esp. coding | Python + TS | ✅ | OSS |
| LangGraph | Stateful production agents with complex flow | Python + TS | ✅ | OSS |
| Vercel AI SDK | TypeScript stacks, multi-provider | TypeScript | ✅ | OSS |
| OpenAI Agents SDK | OpenAI-first stacks with handoff patterns | Python + TS | ✅ | OSS |
| CrewAI | Fast prototyping with role-based agents | Python | ✅ | OSS |
| Mastra | TypeScript-first, batteries-included | TypeScript | ✅ | OSS |
| AutoGen / AG2 | Conversational multi-agent | Python | ✅ | OSS (split) |
| Google ADK | Google Cloud + multimodal multi-agent | Python | ✅ | OSS |
| Pydantic AI | Type-safe agents in Python | Python | ✅ | OSS |
| LlamaIndex Agents | RAG-heavy agents | Python | ✅ | OSS |
| Agno | Lightweight Python multi-modal | Python | ⚠️ Growing | OSS |
| SmolAgents | Hugging Face's minimal agent loop | Python | ⚠️ Growing | OSS |
How to choose
Before the list, the criteria that matter:
- State management: Does the framework model state explicitly or is it implicit in the chain?
- Production execution: Retries, timeouts, checkpoints, replay?
- Human-in-the-loop: Can the agent pause for human input mid-run?
- Multi-agent: Can multiple agents coordinate / hand off / run in parallel?
- Observability: Does it integrate with your tracing/eval stack?
- Language support: Python, TypeScript, both?
1. Claude Agent SDK
Best for: Anthropic-first agent workloads, especially coding and computer-use tasks.
The story: Anthropic's first-party agent SDK, formerly known as the Claude Code SDK and renamed to Claude Agent SDK in late 2025. It exposes the same agent loop, tool execution, and context management that powers Claude Code itself. Built-in tools for reading files, running commands, editing code, and computer use are included so you can ship a working agent without implementing tool wiring. Multi-agent sessions and outcomes shipped to public beta in May 2026.
Pros: First-party Anthropic, the best path to use Claude Opus 4.7 and Sonnet 4.6 for agent work. Built-in tools (file ops, bash, edit, search, computer-use) reduce code-to-shipping. The same agent loop battle-tested by Claude Code. Strong sub-agent and multi-agent patterns.
Cons: Anthropic-first. It works with other providers but the design is Claude-centric. Newer than LangGraph as a general-purpose framework (TS and Python SDKs separately maintained). Tighter coupling to Anthropic's product cadence.
License: OSS (MIT). @anthropic-ai/claude-agent-sdk on npm, claude-agent-sdk-python on PyPI / GitHub.
2. LangGraph
Best for: Stateful production agents with complex control flow.
The story: LangGraph from the LangChain team models agents as state graphs: explicit nodes, edges, and persistent state. Surpassed CrewAI in GitHub stars in early 2026, driven by enterprise adoption. The graph model maps cleanly to production requirements (retries, checkpoints, human-in-the-loop, multi-agent orchestration).
Pros: Most mature general-purpose production agent framework. State graph is the right abstraction for complex agents. Time-travel debugging lets you replay any agent run from any checkpoint. Strong observability integration.
Cons: Steeper learning curve than CrewAI. More code than higher-level abstractions. Python and TypeScript supported but Python is primary.
License: OSS (MIT).
Read our deep dive: LangChain vs LangGraph
3. Vercel AI SDK
Best for: TypeScript stacks building production agents across multiple providers.
The story: Vercel AI SDK has 20M+ monthly downloads and is the dominant TypeScript toolkit for AI applications. AI SDK 6 added native agent abstractions: the Agent class for reusable definitions and ToolLoopAgent for production-ready tool execution loops. Unified API across 25+ AI providers including OpenAI, Anthropic, Google, AWS Bedrock, Azure OpenAI, xAI Grok, and Mistral. Used by Thomson Reuters, Notion, and many others.
Pros: Largest TypeScript install base of any agent framework. Multi-provider out of the box. Tight integration with Next.js / Vercel infra. Strong streaming and React hooks for chat UIs. MCP integration built in.
Cons: TypeScript / JavaScript only. Less feature-rich than LangGraph on complex graph-state patterns. Most natural inside the Vercel ecosystem.
License: OSS (Apache 2.0).
4. OpenAI Agents SDK
Best for: OpenAI-first stacks that benefit from handoff patterns.
The story: OpenAI's Agents SDK shipped March 2025, replacing the experimental Swarm framework with a production-grade toolkit. The core abstraction is handoffs: agents transfer control to each other explicitly, carrying conversation context through the transition.
Pros: Production-grade quality. Tight integration with OpenAI models. Clean handoff semantics. Good docs.
Cons: OpenAI-first design. It works with other providers but the cleanest path is OpenAI. Less rich state model than LangGraph.
License: OSS.
5. CrewAI
Best for: Fast prototyping with role-based agents.
The story: CrewAI's role-based DSL is the lowest learning curve in the agent space. You define agents by roles ("researcher", "writer", "reviewer") and they collaborate. ~20 lines to a working multi-agent demo.
Pros: Fastest framework to prototype with. Role-based abstraction is intuitive. Active community.
Cons: Less control over execution than LangGraph. The role-based model can hide complexity until production scale where you need finer control. Some teams migrate from CrewAI to LangGraph as production complexity grows.
License: OSS.
6. Mastra
Best for: TypeScript-first developers wanting a batteries-included framework.
The story: Mastra is the TypeScript framework from the team behind Gatsby. Launched October 2024, hit 1.0 in January 2026, reached 22k+ GitHub stars in 15 months and 300k+ weekly npm downloads at 1.0. Provider-agnostic via the Mastra Model Router with 3,300+ models from 94 providers indexed. Primitives for tool use, memory, multi-step reasoning, MCP tool sharing, workflow state persistence with time-travel debugging, built-in guardrails / scorers / evals / tracing, and Mastra Studio for inspection.
Pros: Strongest TypeScript-first agent framework with batteries-included approach. Time-travel workflow debugging. Tight MCP integration. Deploys anywhere.
Cons: Smaller production footprint than LangGraph or Claude Agent SDK. Overlaps with Vercel AI SDK on TypeScript turf; choose based on whether you want Mastra's opinionated workflow primitives or Vercel's lighter approach.
License: OSS.
7. AutoGen / AG2
Best for: Conversational multi-agent patterns.
The story: Microsoft's AutoGen pioneered conversational agent teams. The big story in 2025-2026 is the AutoGen / AG2 split: Microsoft pushed AutoGen v0.4+ as a rewrite, and the community continued the proven v0.2 lineage as AG2. Both ship today; production users mostly went with AG2.
Pros: Mature conversational multi-agent patterns. Strong research backing. AG2 has stable APIs.
Cons: Conversational model is less suited to non-chat workflows. Confusing branding (AutoGen vs AG2). Microsoft's v0.4 rewrite has been disruptive.
License: OSS.
8. Google ADK (Agent Development Kit)
Best for: Google Cloud users + multimodal multi-agent.
The story: Google's ADK is the multi-agent framework backed by Google's infrastructure. Distinctive features: A2A (Agent-to-Agent) protocol for inter-agent communication and strong multimodal capabilities (text + audio + video + image in the same agent loop).
Pros: Native multimodal agent support. A2A protocol is novel and well-designed. Deep Vertex AI / Google Cloud integration.
Cons: Newer / smaller community. Most natural on Google Cloud; less polished outside that environment.
License: OSS.
9. Pydantic AI
Best for: Type-safe agents in Python.
The story: From the Pydantic team, an agent framework with first-class typed inputs/outputs. If you're a Pydantic-shop and care about type safety, this is the natural choice.
Pros: Type safety as a first-class citizen. Clean Python ergonomics. Strong validation.
Cons: Less feature-rich than LangGraph or CrewAI. Smaller community.
License: OSS.
10. LlamaIndex Agents
Best for: RAG-heavy agents that already use LlamaIndex.
The story: LlamaIndex expanded into agents in 2025-2026. If your stack is built around LlamaIndex's RAG primitives, the agent layer is a natural extension.
Pros: Tight integration with LlamaIndex retrieval. RAG-first agent patterns built in.
Cons: Less general-purpose than LangGraph. Most useful when RAG is the primary agent activity.
License: OSS.
Read our deep dive: LlamaIndex vs LangChain
11. Agno
Best for: Lightweight Python multi-modal agents.
The story: Agno (formerly Phidata) is a Python framework focused on lightweight agents with multimodal capabilities and minimal abstraction overhead.
Pros: Minimal API surface. Good multimodal support. Easy to start.
Cons: Less production tooling than LangGraph or Crew. Smaller community.
License: OSS.
12. SmolAgents
Best for: Hugging Face users wanting a minimal agent loop.
The story: From Hugging Face, a minimal agent loop (a few hundred lines of code total). Useful as both a working framework and a learning artifact ("here's the smallest possible agent that works").
Pros: Minimal and readable. Good for understanding what agents really are. Integrates with HF ecosystem.
Cons: Minimal by design, not a replacement for LangGraph or CrewAI for complex production agents.
License: OSS (Apache 2.0).
How to choose
Quick decision framework:
- Anthropic-first stack, especially for coding agents? → Claude Agent SDK
- Building a complex production agent with state, retries, multi-agent? → LangGraph
- TypeScript stack with multi-provider needs? → Vercel AI SDK (or Mastra if you want more batteries-included)
- OpenAI-first stack with handoff patterns? → OpenAI Agents SDK
- Prototyping fast with role-based agents? → CrewAI
- Conversational multi-agent (chat-style)? → AG2 (or AutoGen v0.4 if you trust Microsoft's rewrite)
- Google Cloud + multimodal? → Google ADK
- Type safety matters? → Pydantic AI
- RAG is primary activity? → LlamaIndex Agents
- Want minimal abstraction? → Agno or SmolAgents
Common pitfalls
- Picking based on hype, not fit. "Everyone's using LangGraph" is true and not a reason. Pick based on your specific workload shape.
- Skipping observability. Every framework on this list integrates with LLM tracing via OpenTelemetry or vendor-specific SDKs. Wire this on day one, not month six.
- Multi-agent for the sake of it. Many "multi-agent" patterns can be solved with a single well-designed agent. Don't add coordination complexity unless the workload demands it.
- Not running evals. Agent quality drifts. Quality scores tied to prompt versions and tool versions catch regressions before users do.
FAQ
Which agent framework is most popular? By install base: Vercel AI SDK (20M+ monthly npm downloads, though much of that is non-agent usage). By production adoption for complex agents: LangGraph. For Anthropic-first work and coding specifically: Claude Agent SDK. For prototyping volume: CrewAI. OpenAI Agents SDK is growing fast as the OpenAI-first answer.
Should I use CrewAI or LangGraph? CrewAI for fast prototyping. LangGraph for production agents that need explicit state and control flow. Many teams start with CrewAI and migrate to LangGraph as complexity grows.
Is AutoGen or AG2 better? AG2 (the community-continued v0.2 lineage) is more battle-tested in production. AutoGen v0.4+ is Microsoft's rewrite, promising but newer. Production users mostly went with AG2.
Can I switch frameworks later? Yes, but it's real work. The agent business logic is reusable; the framework-specific orchestration code has to be rewritten. Picking right the first time matters.
Do these frameworks work with non-OpenAI / non-Anthropic models? Yes. All of them are model-agnostic in principle. Some have tighter integration with specific providers (OpenAI Agents SDK with OpenAI; Google ADK with Vertex AI), but you can wire any model into any framework.
Which has the best documentation? LangGraph and CrewAI have the broadest documentation. OpenAI Agents SDK has the cleanest. AG2's docs improved meaningfully through 2026.
Do I need a framework at all? For simple linear flows, no. For multi-tool agents with branching, retries, or human-in-the-loop, yes. The framework adds value when complexity warrants it.