The 9 Best AI Agent Frameworks in 2026 (We Tested Every Single One)

Our whole game is building infrastructure for agents, so we work hand in hand with teams shipping agents to production every week. And we keep watching the same movie: solo developers and early-stage startups losing weeks to the wrong framework choice.

We wanted to write the best-frameworks post we wished those teams had found. That meant a test.

What we did: a simple identical agent (one custom tool, one instruction, nothing fancy) built in all nine major frameworks, run live ten times in each on the identical model, every number and every error message kept. Ninety runs, one dataset.

We did this for two reasons.

The first: when we did our own research, most "framework comparison" rankings on Google turned out to be SEO refreshes of a 2024 field. Several still benchmark OpenAI Swarm, which was deprecated in early 2025. Some don't mention Mastra, Pydantic AI, or the Claude Agent SDK, and recommend AutoGen without noting it's been in maintenance mode since late 2025.

The second reason: we wanted to see for ourselves, with every framework on the same terms. Here's what we found.

What is an AI agent framework?

An agent framework is the layer between an LLM API and a production system. The model can decide; the framework handles everything around the decision: tool definitions and calling, the loop that feeds results back to the model, state that survives longer than one request, memory, retries, human approval steps, and some way to see what your agent actually did when it inevitably does something weird.

You can build all of this yourself on raw API calls. Plenty of teams do. The question a framework answers is whether you want to maintain that plumbing or borrow someone else's.

TL;DR: the 2026 rankings

1. Mastra: best full-stack framework for TypeScript teams shipping production agents
2. LangGraph: best for complex stateful workflows in Python
3. OpenAI Agents SDK: best if you're all-in on OpenAI models
4. Vercel AI SDK: best AI toolkit for JS/TS apps that are growing agent features
5. Pydantic AI: best type-safe agents in Python
6. CrewAI: best for fast multi-agent prototypes
7. Claude Agent SDK: best pre-built agent harness (if you're Claude-native)
8. Google ADK: best for teams already on Google Cloud
9. AutoGen / AG2: historically important, hard to recommend for new projects in 2026

How we tested

The toy agent: a billing assistant with a single custom tool, getInvoiceStatus(invoiceId), returning canned JSON. Trivial on purpose. It isolates the framework's ceremony from the problem's difficulty. For all nine frameworks we did clean installs on a Linux box, each at its latest stable release in the first week of July 2026, and measured install time, dependency count, disk footprint, and lines of code.

Then we ran the agent live, ten times per framework, on the identical model (claude-haiku-4-5) with the identical question, capturing latency, token usage, and whether the tool was actually invoked. We instrumented that last one in code rather than taking the model's word for it.

ADK's dependency resolution stalled outright on our first attempt and completed on retry; it also needs an extra LiteLLM package before it will talk to a non-Google model.

Yes, CrewAI's one-tool billing bot installs the Kubernetes client, Apache Arrow, and a vector database. And yes, the Claude Agent SDK's 299 MB includes an entire bundled Node.js CLI. More on both below.

And the live runs, same model, same question, ten times each:

Four readings of that table. First, reliability was a nine-way tie. Every framework called the tool and answered correctly all ninety times; a one-tool agent is a bar everyone clears in 2026, and anyone ranking frameworks on "can it call a tool" is testing 2023's question. Second, latency among the top pack is noise, not signal. Don't buy a framework off half a second. CrewAI was meaningfully slower, with a 28-second cold start on first launch, and ADK paid a visible tax to its LiteLLM translation layer. Latency is also dominated by the model API round trip, not the framework, which is exactly why the tight clustering is unsurprising.

Third, the token column is the durable finding. The same agent answering the same question costs 1,288 input tokens orchestrated by LangGraph and 1,432 by CrewAI. That 11% spread is pure framework-injected prompt overhead, and it multiplies across every call your agent ever makes.

Fourth: the Claude Agent SDK is not like the others, and now we can quantify it. Its runs consumed ~35,000 input tokens each, 25x the field, because you're not getting a thin loop. You're getting Claude Code's entire harness in your context window.

Before the outrage: ~33,000 of those were cache reads, billed at a tenth of the fresh-token rate, so the effective cost is closer to 4-5x the field than 25x.

But the context-window footprint is real, the 8.5s median latency (subprocess plus giant prompt) is real, and the answers three times more verbose than anyone else's are real. That's the "everything on, you subtract" philosophy, measured. Whether it's worth it depends entirely on whether you need what's in the harness.

One more note on method: a toy test measures ceremony, not production readiness. By raw toy-test numbers the OpenAI Agents SDK wins the scaffolding round with 16 flawless lines.

Our ranking weighs the part the toy can't test: what happens in month three, when you need durable state, evals, human approvals, and observability.

1. Mastra: best full-stack framework for TypeScript teams shipping production agents

What does it do? Mastra is a TypeScript framework that bundles the whole production stack (agents, graph-based workflows, memory, RAG, evals, guardrails, tracing, and a local visual studio) into one coherent system, from the team that built Gatsby.

Who is it for? TypeScript teams who want to go from prototype to deployed product without assembling five libraries and an observability vendor.

Most frameworks give you an agent loop and wish you luck with everything else. Mastra's bet is that the everything else is the actual product: suspend-and-resume workflows that can pause indefinitely waiting for a human approval, scorers for evals, per-call cost tracing, and a local studio where you can watch your agent think. It hit 1.0 in January 2026 and has shipped relentlessly since (observational memory, enterprise RBAC, file-based agents), with production users including Replit, PayPal, and Sanity, and a public endorsement from Brex's CTO.

The model routing is the quiet killer feature. You write model: "openai/gpt-4o-mini" as a plain string and get 600+ models across 40+ providers with TypeScript autocomplete and fallbacks. No provider packages, no peer-dependency roulette.

And the type system does real work. In our test, the zod schema you define for a tool's input flows through to the execute function's parameter types; the compiler catches your mistakes before the LLM gets a chance to. The live numbers backed it up, too: fastest median latency of the field, tied with Pydantic AI. Worth sitting with for a second, because Mastra is the heaviest thing in the install table and it ran as fast as the thinnest loop while landing within 6% of the leanest token count. You carry the whole stack and pay almost nothing at runtime for it.

The memory story is ahead of the field as well. Observational Memory compresses tool-heavy conversations 5-40x (plain text lands closer to 3-6x) and scores ~95% on the LongMemEval benchmark. Note that it runs background LLM calls to do it, a cost that won't show up in your agent's own token usage. Budget for it.

Now the limitation: the framework moves faster than its documentation. Two patterns you'll find in tutorials everywhere, the { context } tool signature and agents without an id field, are dead in current versions, and we hit compile errors on both before writing a line of original code. The batteries-included design cuts both ways too. Teams report that when their use case fits Mastra's patterns it's the best DX in the ecosystem, and when it doesn't, escaping the opinions takes work. Enterprise buyers are better covered than the platform's age suggests (Mastra's trust center lists SOC 2 Type II) but some auth packages still carry an experimental_ prefix. And if your stack is Python, none of this matters. Mastra is TypeScript-only and proudly so.

For a TypeScript team building an agent product rather than an agent demo, though, this is the strongest single package in the field right now.

Pricing

The framework is Apache 2.0 and free. Mastra Platform (hosted deployment and observability) has a free starter tier, a $250/team/month Teams tier, and custom enterprise pricing.

2. LangGraph: best for complex stateful workflows in Python

What does it do? LangGraph is LangChain's low-level orchestration runtime: you model your agent as a graph of nodes with durable, checkpointed state that can survive crashes and resume exactly where it stopped.

Who is it for? Python teams building long-running, multi-step agents where "the process died at step 7 of 12" must not mean starting over.

LangGraph is the most production-hardened option on this list. Klarna, Uber, LinkedIn, and Elastic run on it; it went 1.0 in October 2025 and sits at 1.2.x now. Durable execution and first-class human-in-the-loop interrupts set it apart. Most frameworks bolt these on; LangGraph is built around them. It's also lean for the machinery you get (39 packages, 81 megabytes, a 19-second install) and it posted the lowest token overhead of all nine frameworks in our live runs.

The cost is the learning curve. State schemas, nodes, conditional edges: a simple tool-calling agent takes noticeably more code and a different mental model than any other framework here. And LangChain's habit of moving abstractions hasn't fully died. The create_react_agent helper that half the internet's tutorials use now emits a deprecation warning pointing you to create_agent, which also silently renamed a core parameter. The new API is clean. The path to it is littered with stale tutorials. Watch two production details as well: large state objects bloat checkpoints, and the managed deployment platform doesn't run on Vercel or Cloudflare Workers by design, a hard constraint if your stack is serverless.

If your agents are conversational and short-lived, this is more machinery than you need. If they run for hours and touch money, it's the machinery you want.

Pricing

MIT and free. The commercial layer is LangSmith observability (free dev tier, $39/seat/month Plus) and LangSmith Deployment, priced per node executed. Budget for the deployment layer to become its own line item as traffic grows.

3. OpenAI Agents SDK: best if you're all-in on OpenAI models

What does it do? A deliberately minimal Python framework (agents, handoffs, guardrails, sessions, tracing) that's tuned to how OpenAI trains its models, now with sandboxed computer use.

Who is it for? Teams who've already decided their models are OpenAI's and want the shortest path from that decision to a working agent.

Our test told the story in one number: 16 lines. The @function_tool decorator reads your type hints and docstring, and the docstring is the tool schema. Nothing broke, nothing needed configuring, nothing demanded a mental model. It's the least framework-y framework here, and for a lot of teams that's exactly right. The April 2026 update added controlled sandboxes and a model-native harness, and the pitch (that agents behave best on the harness their model was trained against) is plausible.

The trade is the obvious one. It supports other models through LiteLLM, which is how we ran claude-haiku-4-5 on it for this test; if anything our numbers understate it, since we ran it off its home platform. But the tracing UI, hosted tools, and sandbox harness all live on OpenAI's side of the fence, and the SDK is still pre-1.0 with the API churn that implies. This is less a framework choice than a vendor choice wearing a framework costume. Make it with your eyes open.

Pricing

MIT and free; you pay standard OpenAI API usage.

4. Vercel AI SDK: best AI toolkit for JS/TS apps growing agent features

What does it do? The dominant TypeScript AI library (unified provider API, streaming chat UI hooks, structured output) which has grown real agent primitives: a tool-loop agent class and, as of v7 in June 2026, durable workflow agents that survive deploys.

Who is it for? Product teams with an existing Next.js/React app who want to add agentic features to it, rather than build an agent-first system.

By raw adoption this is the biggest thing on the list. Its weekly npm downloads run several multiples of LangChain's JS package, and teams ship real products on it fast (Vercel reports Thomson Reuters built CoCounsel's interface with three developers in two months). It also turned in the cleanest sheet of our entire test: the lightest install of all nine frameworks at 15 packages and 53 MB, and the only framework where neither the install nor the code pushed back. ToolLoopAgent worked exactly as documented on the first try. If your agent is a feature inside a web product (a support panel, an AI form-filler, a copilot sidebar), the AI SDK's UI hooks plus its agent loop is the path of least resistance, and v7's WorkflowAgent closes a long-standing gap with durable state.

The caveats. Major versions arrive fast and break things; v5's migration was a full architectural overhaul, and teams budget real hours for each major. The defaults gently route you through Vercel's paid AI Gateway, so while the SDK is open source, the gravity is commercial. And if you deploy on Vercel itself, function timeouts cap long-running agents (minutes, not hours, depending on tier), which is exactly the workload agents grow into. For deep multi-agent orchestration you'll also outgrow it. That's partly why Mastra, which began as a layer on top of it, exists.

Pricing

Free and open source. Vercel monetizes the surrounding platform (Gateway, hosting, sandboxes) on usage.

5. Pydantic AI: best type-safe agents in Python

What does it do? An agent framework from the Pydantic team that brings the FastAPI feeling to agents: typed agents, dependency injection, and validated structured output with automatic retry when the model returns garbage.

Who is it for? Python teams who believe schemas are how you make LLMs behave, and who want the framework to enforce that belief.

There's a strong argument that Pydantic AI is the rightful owner of this category: Pydantic validation is already the schema layer inside the OpenAI SDK, Google ADK, LangChain, and CrewAI. V2 shipped in June 2026 with "capabilities" (composable bundles of tools, hooks, and settings) plus YAML agent specs and durable execution. The structured-output story is the best in Python. Define a Pydantic model as your output_type and the framework validates, retries, and hands you a real object. Outside evidence is favorable, too: Nextbuild's self-published 90-day five-framework benchmark scored its developer experience 8/10 against LangChain's 5/10 and credited its type system with catching 23 bugs before production. A vendor's own numbers, but the most detailed head-to-head we found. And it's one of the only frameworks with native token budget controls: hard caps on requests, tokens, and tool calls, built in rather than bolted on.

The knock is velocity as instability. V1 burned through 104 point releases in nine months, and the team openly shortened its breaking-change window from six months to three. We felt it in our own test: result.usage() from every V1 tutorial is now a property, not a method, and the change ate our first ten runs. It's also lighter on batteries than Mastra or LangGraph (no built-in toolbox, no visual studio) and its enterprise track record is thinner. But the core abstractions are the most tasteful in Python right now, and its live numbers were flawless: fastest median latency of the field, tied with Mastra, and the leanest output tokens of all nine.

Pricing

MIT and free. The team monetizes Logfire, its observability platform, which the framework integrates with deeply (and is openly a funnel for).

6. CrewAI: best for fast multi-agent prototypes

What does it do? Role-based multi-agent orchestration: you define agents as personas (role, goal, backstory), group them into crews, and let them collaborate. There's also a deterministic "Flows" mode for auditable pipelines.

Who is it for? Teams who want a working multi-agent demo this afternoon, and researchers exploring agent collaboration patterns.

Credit first: CrewAI is the fastest route from idea to "look, three agents wrote a market report." The community is huge (one of the largest in the field), the template ecosystem is deep, and in our test the code worked first try with zero errors.

The install is another matter: 856 megabytes and 143 packages for a one-tool billing bot, roughly ten times LangGraph's footprint, including the Kubernetes client, a vector database, ONNX runtime, and a PDF parser we didn't ask for. The framework also makes you write persona fiction ("backstory:") for tasks that are one sentence everywhere else. The community's long-standing advice is telling: crews for prototypes, Flows for anything real, because autonomous crews burn tokens on agent chatter (published estimates put multi-agent runs at 3-5x a single-agent equivalent) and occasionally delegate in circles. Our single-agent runs already showed the pattern in miniature, with the highest token overhead and slowest steady-state latency of the eight conventional frameworks.

The production record backs the caution with receipts: a stop-sequence bug inflated per-call costs tenfold (GitHub issue #3836), one practitioner's five-agent experiment racked up $414 in API costs over its run, print and log don't work inside Task callbacks, and unit testing individual agents is a documented gap. AWS's own framework comparison rates CrewAI "weak" on multimodal and merely "adequate" on workflow complexity. It's a great sketchpad. Treat the path to production with suspicion.

Pricing

MIT and free. The cloud platform runs from a free tier (~50 executions/month) through a ~$25/month professional tier to custom enterprise contracts.

7. Claude Agent SDK: best pre-built agent harness

What does it do? Exposes the production harness behind Claude Code as an SDK: you inherit a complete agent loop with file tools, bash, permissions, subagents, and hooks, then restrict it down to your use case.

Who is it for? Teams building agents that do real computer work (editing files, running commands, browsing) who'd rather start from a proven harness than assemble one.

The philosophy is the inverse of everything above. Additive frameworks make you register every capability; the Claude Agent SDK starts with everything on and you subtract. For coding-agent-shaped problems the results speak for themselves: Mozilla's Firefox team used it to find and fix hundreds of security bugs. The @tool decorator creating in-process MCP servers is a nice piece of design.

Fair warning: several 2026 rankings put this framework top-three, calling it the fastest-growing choice for Anthropic-native agents, and if your problem looks like Claude Code's problem they're right. We rank it lower because the general case carries costs, and our benchmark put numbers on them. ~35,000 input tokens per query against the field's ~1,350 (mostly discounted cache reads, so effective cost lands nearer 4-5x than 25x, but all 35k occupy your context window either way). 8.5s median latency against the field's 2.5s. A 299 MB install, because the Python package drives a bundled Node CLI as a subprocess. Answers three times more verbose than anyone else's. Add that it's still 0.x and locks you to Claude models on Anthropic's terms, terms that wobbled in mid-2026 when a billing restructure for SDK usage was announced and then paused. Capable harness, short leash. At least now you know what the leash weighs.

Pricing

The SDK is MIT and free; you pay Anthropic token costs via API or subscription.

8. Google ADK: best for teams already on Google Cloud

What does it do? Google's code-first agent framework: multi-agent hierarchies, a deterministic workflow runtime (new in 2.0), evaluation tooling, and a managed deployment path onto Vertex/Gemini Enterprise.

Who is it for? GCP shops, especially ones standardizing on Gemini, and the rare team that needs agents in Java, Go, or Kotlin.

ADK has arguably the broadest language coverage in the field (Python, Java, Go, TypeScript, plus beta Kotlin/Android) and 2.0's workflow runtime, rolled out May-June 2026, addressed the biggest earlier gap by adding deterministic execution with fan-out, retries, and human-in-the-loop. It powers agents inside Google's own products, which counts for something.

Off GCP, the case thins fast, and our test measured the friction directly. ADK was the only framework whose install stalled outright (completing on retry), running a non-Google model requires an extra LiteLLM wrapper package the base install doesn't include, and its steady-state median of 3.4s stayed measurably above the pack: the visible tax of routing Anthropic through Gemini-shaped plumbing. The framework runs anywhere, but the managed path is Google Cloud-only; several SDKs are still beta; reviewers commonly describe a steep learning curve; and Google's mid-cycle rebrand of the surrounding platform (Vertex AI into the Gemini Enterprise Agent Platform) is a reminder of how Google platform bets sometimes age. Fine choice inside the ecosystem, hard sell outside it.

Pricing

Apache 2.0 and free. Costs come through Gemini API usage and the managed Agent Engine, whose multi-API cost structure reviewers find hard to predict.

9. AutoGen / AG2: hard to recommend for new projects

What does it do? The pioneering conversation-driven multi-agent framework from Microsoft Research (group chats, nested conversations, human proxies), now split three ways.

Who is it for? In 2026: researchers, and teams maintaining existing AutoGen systems.

AutoGen deserves the historical credit; half the multi-agent patterns in every other framework trace back to it. But the name now points at three different things: the original microsoft/autogen (officially in maintenance mode, no new features), Microsoft Agent Framework (the true successor, merged with Semantic Kernel, aimed at Azure/.NET enterprises), and AG2 (the community fork run by AutoGen's original creators, active but with a small maintainer base and ~4.3k stars against the original's frozen ~50k). Answering "should I use AutoGen?" now requires a paragraph, which is itself the answer.

To be fair to AG2, we tested it, and it ran respectably: 10/10 tool calls, second-leanest token overhead in the field, competitive latency. But getting there meant discovering that version 0.14, released weeks ago, replaced the configuration API the current, official docs described. LLMConfig stopped accepting the documented keyword arguments and dropped its context-manager pattern in the same release. That's not a knock on the maintainers; it's what a framework mid-transition with a small team looks like, and you should price it in.

Notably, most of the "best frameworks 2026" posts you'll find still recommend AutoGen with none of this mentioned, a decent litmus test for whether a comparison you're reading is current. If you're a Microsoft-stack enterprise, evaluate Microsoft Agent Framework instead (1.0 in April 2026, and Semantic Kernel's lineage powers Microsoft's own Copilot). Otherwise, the ideas live on in better-maintained packages.

Pricing

All open source and free; Microsoft monetizes the successor via Azure consumption.

At a glance

How to actually choose

Start from your language, not from feature lists. The TypeScript question is Mastra vs. Vercel AI SDK, and it resolves on whether you're building an agent product (Mastra) or adding agent features to a web product (AI SDK). The Python question is LangGraph vs. Pydantic AI vs. the OpenAI Agents SDK, and it resolves on how much durable orchestration you need versus how locked to one model vendor you're willing to be.

Then ask three questions. What happens when the process dies mid-run: do you need checkpointed resume (LangGraph, Mastra workflows) or is stateless retry fine? Who approves the dangerous steps: is human-in-the-loop a core flow or an afterthought? And who's watching: is observability built in, or are you buying it separately from the vendor whose framework you chose (a pattern you'll notice every one of these companies shares)? Pick the observability tool with the framework, not after. Retrofitting tracing is rework.

Four quieter criteria bite later, so check them now. Token budget controls: multi-agent chatter can multiply costs 3-5x, and only some frameworks (Pydantic AI notably) let you set hard caps natively. Serverless compatibility: LangGraph's managed platform won't run on Vercel or Cloudflare Workers, while Vercel's own timeouts cap long-running agents, so know your runtime before you commit. Compliance: if you sell to enterprises, check SOC 2 status per vendor rather than assuming; coverage varies more than the vendors' ages suggest. And whatever you pick, build your tool integrations as MCP servers. It's the one investment that survives a framework switch, because orchestration code doesn't port and prompts and tools do.

One more thing worth pricing in: every framework on this list is free, and every company behind one makes money when you deploy through them. That's not sinister; it's the business model of the entire category. But it means "free and open source" is the beginning of the cost conversation, not the end.

FAQ

What's the best AI agent framework overall in 2026?

There isn't one, and anyone who names a single winner without asking about your stack is selling something. For TypeScript product teams we'd start with Mastra; for Python teams with heavy orchestration needs, LangGraph; for OpenAI-committed teams, the Agents SDK.

Mastra vs. LangGraph: which one?

Usually decided by language before features. If both languages are truly on the table: LangGraph has deeper production history and finer-grained control over state, while Mastra gives you more of the stack out of the box (studio, evals, tracing) and a better developer experience. Teams with complex non-LLM branching logic sometimes find Mastra's workflow API constraining where LangGraph's raw graphs flex.

Mastra vs. Vercel AI SDK?

Mastra started as a layer on top of the AI SDK, and the lineage shows. The AI SDK is the better pick when the agent is a feature inside your app; Mastra when the agent is the app. They interoperate well.

Is CrewAI production-ready?

Its Flows mode, with deterministic pipelines, has a reasonable production story. Autonomous crews are best treated as a prototyping tool. That's the consistent community verdict, not just ours.

Do I need a framework at all?

For a single agent with a couple of tools and no persistence needs, maybe not. The OpenAI and Anthropic APIs plus 100 lines of your own loop is a legitimate architecture, and you'll understand every line. Frameworks earn their keep when you need durable state, human approvals, evals, and observability. The production stuff, in other words.

What about LlamaIndex, Haystack, Smolagents?

All real, all narrower. LlamaIndex and Haystack are strongest when your agent problem is mostly a retrieval problem. Smolagents is a lovely minimal option from Hugging Face for code-first experimentation; its core agent loop is about a thousand lines, readable in a sitting. We focused this list on general-purpose production frameworks.

What about n8n, Flowise, and the visual builders?

A different category, deliberately excluded: this list is code-first frameworks. Visual builders are a legitimate answer when the team isn't engineers or the workflow is mostly integration glue with an LLM step in the middle. If your agent's core logic is complex enough to argue about frameworks, you'll usually hit the visual tools' ceiling.

Why does this ranking look different from other 2026 lists?

Mostly freshness and language coverage. The rankings that put LangGraph alone at the top and skip Mastra, Pydantic AI, and the vendor SDKs are largely describing the 2024 field; several still review OpenAI Swarm, deprecated over a year ago. And nearly every comparison in the top search results is published by a company selling an adjacent product, which shapes what gets included. Ours has a bias too: we build agents ourselves, in TypeScript more often than not, and the ranking reflects what we'd actually pick up for production work.

Are these frameworks safe to depend on?

The category is young and moving fast, so expect breaking changes everywhere. In our test, five of the nine frameworks hit us with API drift, a deprecation, or a stalled install before we'd written any original code, and several of those were post-1.0 releases. Pin your versions, read changelogs, and audit your dependency tree; this year's supply-chain incidents across the npm ecosystem apply to agent frameworks as much as anything else.

How much do these actually cost at scale?

The frameworks are free; the bills are model tokens and observability/deployment platforms. Watch multi-agent frameworks especially. Autonomous agent chatter multiplies token spend in ways single-agent designs don't.