Top AI Agent SDKs in 2025: A Ranked List

"Top AI agent SDK" is a broad search, and the honest answer is that there isn't one winner — there's a short list of tools that solve different parts of the same problem: giving an LLM the ability to call functions, hold state across turns, and coordinate with other agents. Below is a ranked rundown of the SDKs developers actually reach for in production, what each one is built for, and where the trade-offs show up.

This isn't a popularity contest based on GitHub stars. It's ranked by how often each SDK shows up in real production stacks, how mature its tool-calling and multi-agent support is, and how much lock-in it creates.

1. LangGraph (LangChain)

LangGraph is the graph-based successor to plain LangChain chains, and it's become the default choice for anyone building agents with cycles, branching, or human-in-the-loop checkpoints. You define nodes (LLM calls, tool calls, custom logic) and edges (conditional routing), and LangGraph handles state persistence between steps.

Strengths: mature ecosystem, built-in checkpointing, works with almost any model provider, strong community docs. Trade-offs: the abstraction layer is heavy — debugging a LangGraph agent means reading through several layers of wrapper classes before you hit the actual API call.

2. OpenAI Agents SDK

OpenAI's own SDK (successor to the Assistants API in spirit) gives you agents, handoffs between agents, guardrails, and tracing out of the box, with a much thinner abstraction than LangChain. It's designed to be readable — an agent is a Python object with instructions, tools, and a model, and handoffs are just function calls that return another agent.

Strengths: minimal boilerplate, built-in tracing dashboard, first-party support. Trade-offs: tightly coupled to OpenAI's models and infrastructure; using it with other providers requires workarounds.

3. CrewAI

CrewAI is built specifically for multi-agent collaboration — you define a "crew" of agents with roles (researcher, writer, reviewer), assign them tasks, and CrewAI manages the handoff sequence. It's popular for workflows that map naturally onto a team structure: one agent drafts, another critiques, another finalizes.

Strengths: fast to prototype role-based workflows, good for sequential or hierarchical task pipelines. Trade-offs: less flexible than a graph-based approach when your workflow doesn't fit the "crew of roles" mental model.

4. AutoGen / AG2

Microsoft's AutoGen (now continued as AG2 under a community fork) pioneered the conversational multi-agent pattern — agents that talk to each other in a shared chat loop until a task is resolved. It's strong for research-style workflows where agents need to critique and revise each other's output.

Strengths: flexible conversation patterns, good for code-generation and debugging agents. Trade-offs: conversational loops can be harder to make deterministic or production-safe compared to explicit graphs.

5. Vercel AI SDK

If you're building a web app with a chat interface, the Vercel AI SDK is the most practical choice — it's not a full agent framework, but it has first-class support for streaming, tool calling, and multi-step agent loops directly from React/Next.js, with minimal server-side plumbing.

Strengths: best-in-class streaming UX, tight framework integration, TypeScript-first. Trade-offs: less suited for complex multi-agent orchestration outside a web app context.

6. LlamaIndex Agents

LlamaIndex started as a retrieval framework and has grown solid agent support, particularly for agents that need to reason over large document sets — RAG-heavy workflows where tool calls are mostly about querying indexes rather than hitting external APIs.

Strengths: excellent for document-heavy and RAG-driven agents. Trade-offs: less general-purpose than LangGraph or the OpenAI Agents SDK for non-retrieval tasks.

Where the underlying model access fits in

Every SDK on this list needs a model provider underneath it, and that's a separate decision from picking the framework. If your agent runs on Claude and you're calling it through a ChatGPT-style subscription rather than the native Anthropic API, most of these SDKs won't work out of the box — they expect standard HTTPS API keys, streaming responses, and structured tool-call output.

That's the gap SubToAPI fills: it turns your existing Claude access into a standard API you can drop into any of the SDKs above. You get a sub_live_... key, /v1/messages-style endpoints, streaming, and tool use support — so LangGraph, the OpenAI Agents SDK (pointed at a compatible endpoint), or your own custom agent loop can all call it the same way they'd call a native provider key. See /docs/tools for tool-calling specifics and /docs/streaming for how streamed responses are formatted.

const res = await fetch("https://api.subtoapi.app/v1/messages", {
  method: "POST",
  headers: {
    "Authorization": `Bearer ${process.env.SUBTOAPI_KEY}`,
    "Content-Type": "application/json"
  },
  body: JSON.stringify({
    model: "claude-sonnet-4",
    max_tokens: 1024,
    tools: [/* your tool schema */],
    messages: [{ role: "user", content: "Check the weather in Lisbon and summarize it." }]
  })
});

Whichever agent framework calls that endpoint doesn't need to know it's not talking to a native provider key — it's a standard messages API with tool use and streaming.

How to actually pick one

Instead of "best," ask these questions:

• Do you need multi-agent coordination, or just tool calling? If it's just tool calling with occasional multi-step reasoning, a lightweight SDK like the OpenAI Agents SDK or the Vercel AI SDK is faster to ship than LangGraph.
• Is your workflow role-based or graph-based? Role-based (researcher/writer/reviewer) fits CrewAI. Conditional branching and loops fit LangGraph.
• Are you building a web UI or a backend service? Web UI with streaming chat → Vercel AI SDK. Backend automation → LangGraph or the OpenAI Agents SDK.
• How much lock-in can you tolerate? Provider-native SDKs (OpenAI Agents SDK) are the leanest but the least portable. LangGraph and CrewAI are more provider-agnostic.

Most teams end up combining two: a thin agent SDK for orchestration and a separate API layer for the actual model calls. That separation is what keeps you from having to rewrite your agent logic if you switch providers later — check /pricing if you're evaluating a Claude-backed API layer alongside your SDK choice.

questions

Is LangGraph better than the OpenAI Agents SDK? Neither is universally better — LangGraph is more flexible for complex, branching, multi-provider workflows, while the OpenAI Agents SDK is simpler and faster to ship for straightforward agent-with-tools use cases.

Can I use these SDKs with Claude instead of GPT models? Yes, most of them support Anthropic models natively or through a compatible endpoint. Tools like SubToAPI give you a standard Messages API for Claude that plugs into the same integration points these SDKs already expect — see /docs/messages.

Do I need a full agent SDK for simple tool-calling tasks? No. If your agent just needs to call one or two tools per turn, a direct API integration with streaming and tool support (see /docs/quickstart) is often simpler and easier to debug than a full framework.