AI Agent Frameworks in 2026: LangGraph vs Mastra vs Vercel AI SDK vs OpenAI Agents SDK vs Pydantic AI

Every time someone asks me which agent framework to use, I have to ask three questions back before I can answer. What language are you in? How long-running is the agent? How much do you care about being able to swap out the model later? Without those answers, any recommendation is a coin flip, and that is exactly why most of the comparison posts you find online are not very useful. They list features. They do not tell you which framework will quietly destroy your week six months from now.

I have shipped production code on five of the major agent frameworks in the last year and built throwaway prototypes on three more. The differences between them are real. The marketing pages do a bad job of surfacing those differences because every framework’s pitch is "we let you build agents" and the actual job of an agent framework is to manage the long tail of state, retries, tool execution, observability, and model interactions in a way that does not eat your codebase alive when the requirements change.

This is the comparison I wish I had read a year ago. It covers Vercel AI SDK, LangGraph, Mastra, OpenAI Agents SDK, Pydantic AI, and the Claude Agent SDK. It is opinionated and based on what actually broke in production, not on what the docs claim.

What An Agent Framework Is Actually For

Before the comparison, the framing matters. An agent is not a model. An agent is a model plus a loop plus a set of tools plus a memory of what just happened. The framework’s job is to manage the loop and the surrounding plumbing.

That plumbing has more parts than people give it credit for. The framework is responsible for, at minimum:

  • Calling the model with the right messages, system prompt, tools, and configuration.
  • Parsing tool calls out of the model’s response, validating the arguments, and dispatching them to the right handler.
  • Holding state between turns.
  • Handling failure. Model timeouts, tool failures, rate limits, and context window overflows.
  • Streaming. Tokens, tool calls, intermediate steps ideally arriving at the UI as soon as they are available.
  • Observability. Knowing what the agent did and why.

Frameworks differ on which of those they handle for you, how opinionated their solutions are, and how easy it is to opt out of any one piece. That is the lens for the comparison.

Vercel AI SDK

The default if you are in JavaScript or TypeScript and you want to ship something that works without thinking too hard about the framework choice. The v6 release earlier this year leveled up the agent primitives substantially.

What it does well

  • Streaming is best in class.
  • The generateText and streamText APIs feel minimal, predictable, and easy to reason about.
  • Tool calls are typed end to end with Zod schemas.
  • The provider abstraction lets you swap between providers by changing one string.
  • The agent loop is the right size: opinionated enough to save boilerplate, open enough that you can step out of it when you need to.

What it does not do

  • Long-running durable execution.
  • Does not impose a graph or state machine model.

Where it bites

  • The flexibility means two engineers may end up writing two different patterns for the same problem.
  • Very TypeScript-shaped.

When to pick it

  • You are in a TypeScript or Next.js stack.
  • You want to be able to swap models.

LangGraph

The framework people pick when they have outgrown chains and want to model their agent as an explicit state machine.

What it does well

  • Explicit state.
  • Persistence is first class with checkpointing built in.
  • Multi-agent patterns are well supported.

What it does not do

  • There is a learning curve and a vocabulary to absorb.

Where it bites

  • The control flow is explicit, which means refactoring an agent often means redrawing the graph.

When to pick it

  • Your agent has real branching logic.
  • You are in Python and want the most mature option.

Mastra

The TypeScript-first option that bundles the things you need without forcing the LangChain mental model on you.

What it does well

  • Sensible defaults across the whole agent stack.
  • Memory is a first-class concept.

What it does not do

  • It is younger than the alternatives.

Where it bites

  • The bundled approach means you are taking a position on a lot of decisions at once.

When to pick it

  • You want the integrated experience: workflows, memory, evals, agents in one toolkit.

OpenAI Agents SDK

OpenAI’s official entry into the agent framework space.

What it does well

  • Tight integration with OpenAI’s hosted tools.
  • Small and easy to read SDK.

What it does not do

  • Multi-provider abstraction.

Where it bites

  • Lock-in to OpenAI.

When to pick it

  • You are committed to OpenAI’s platform.

Pydantic AI

The Python option for teams that care about type safety.

What it does well

  • Type safety is real.
  • Structured outputs from the model are validated automatically.

What it does not do

  • It is not trying to be everything.

Where it bites

  • The minimalism is a feature until it is not.

When to pick it

  • You are in Python. You want type safety to do real work for you.

Claude Agent SDK

Anthropic’s official SDK for building agents on top of Claude.

What it does well

  • The Claude-specific features are wired in idiomatically.

What it does not do

  • Multi-provider story is intentionally weak.

Where it bites

  • Same lock-in tradeoff as the OpenAI Agents SDK.

When to pick it

  • You are committed to Claude as your model.

What About LlamaIndex, CrewAI, AutoGen, etc.

The frameworks above are the ones I would actually pick for a production agent in 2026.

How to Actually Decide

Three questions in this order:

  1. What language is your stack in?
  2. How important is provider portability?
  3. How complex is the agent’s control flow?

What You Will Wish You Had Built In Anyway

  • Observability is on you.
  • Cost tracking is on you.
  • Eval discipline is on you.
  • Security on tool execution is on you.
  • Memory beyond the conversation window is partially on you.

What I Would Actually Do Today

If I were starting a new agent project this week, the decision would look like this:

  • For a SaaS product with a chat interface, in TypeScript: Vercel AI SDK with a tight component registry.
  • For a multi-step automation that has to survive restarts: LangGraph in Python, with checkpointing turned on.
  • For a content or research workflow with multiple agents: Mastra if TypeScript, LangGraph if Python.