The Best AI Agent Frameworks (2026)
14 frameworks and official SDKs for building your own AI agent — four official libraries from OpenAI, Anthropic, Google and Microsoft, and ten independent, model-agnostic frameworks — grouped by who built them, not a single forced ranking.
A framework is not a product you sign up for — it's a toolkit you build an agent with, in your own codebase, calling your own model API key. That makes "which one is best" a different question here than it is for the rest of this site: the real split is between four official SDKs, built and maintained by the lab whose models they run best on, and nine independent frameworks that were never designed around one vendor's model.
OpenAI Agents SDK, Claude Agent SDK, Google Agent Development Kit and Microsoft Agent Framework are each a major AI lab's own answer to "how should you build an agent on our models" — free, officially supported, and three of the four reach other vendors' models through a LiteLLM-style integration; Claude Agent SDK is the exception, Claude only, by Anthropic's own design decision. LangChain, CrewAI, LlamaIndex, Pydantic AI, Mastra, Letta, Agno and Microsoft AutoGen are independent and model-agnostic by design — point any of them at whichever provider you choose, at the cost of assembling more of the production plumbing yourself. LangChain remains the default starting point for its integration ecosystem alone; CrewAI is the fastest route to a working multi-agent team; Pydantic AI and Mastra trade breadth for type safety and a native TypeScript stack respectively; and Microsoft AutoGen, the framework that popularized agents talking to agents, is now frozen in favor of its own successor.
How we ranked these
Every entry below is a Published listing that already cleared this site's per-listing quality gate — sourced facts, admitted weaknesses, a "best for" and a "not for," re-verified on a rolling cadence (see /methodology). frameworks is the site's second-largest category by listing count (14, behind only coding-agents' 25) and, unlike every other multi-member category here, had no ranking of its own until it was built — the flagship /best-ai-agents groups these same 14 together in one flat list, which can't give the framework-specific detail a dedicated page can. We split the 14 into the two groups a developer actually chooses between first: an official SDK from the lab whose models you're already committed to, or an independent framework you can point at any provider. Within a group, the order follows rough ecosystem maturity (GitHub stars, funding, production track record), not a score; the live score chip on every card (evidence, honesty, depth, freshness) is computed the same way as that tool's own listing page.
The ranking
All picks at a glance
All 14 compared on one screen. Pick a row to jump to the full entry.
| # | Agent | Best for | From |
|---|---|---|---|
| 1 | OpenAI Agents SDKOfficial SDKs from the major AI labs | Developers who want the fastest, lightest way to ship a straightforward agent (support triage, a tool-using assistant or a sandboxed coding agent) with tracing and guardrails included. | Free |
| 2 | Google Agent Development KitOfficial SDKs from the major AI labs | Teams that want Google's own official, actively-developed agent SDK, especially polyglot teams (Python/Go/Java/TypeScript/Kotlin) or anyone building multi-agent systems that need to interoperate across vendors via the A2A protocol. | Freemium |
| 3 | Microsoft Agent FrameworkOfficial SDKs from the major AI labs | .NET or Python teams already invested in Azure or Microsoft Foundry who want one supported agent SDK, an optional managed hosting path, and a documented migration off AutoGen or Semantic Kernel. | Freemium |
| 4 | Claude Agent SDKOfficial SDKs from the major AI labs | Developers already building on Claude (or Claude Code) who want its exact agent loop, tools and context management as a callable library, and are comfortable being single-vendor on Anthropic in exchange for the tightest possible integration with Claude Code's own capabilities. | Freemium |
| 5 | LangChainIndependent, model-agnostic frameworks | Engineering teams building a bespoke, production-grade agent who want the largest integration ecosystem plus real stateful control. | $39/seat/mo |
| 6 | CrewAIIndependent, model-agnostic frameworks | Developers who want to stand up a role-based, collaborating multi-agent system quickly in Python, with a stable MIT-licensed core and a hosted build and runtime for production. | Free |
| 7 | LlamaIndexIndependent, model-agnostic frameworks | Engineers building RAG-heavy or document-heavy agents who want an MIT-licensed OSS core with an optional managed parsing and extraction platform on top. | $50/mo |
| 8 | Pydantic AIIndependent, model-agnostic frameworks | Python teams building type-safe agents who also want realtime voice, image generation and embeddings in one SDK, with optional durable execution via Temporal, DBOS, Prefect or Restate. | Free |
| 9 | MastraIndependent, model-agnostic frameworks | Full-stack JavaScript and TypeScript teams, especially those already building in Next.js or Node, who want agents, durable workflows and memory unified in one framework without adopting a Python-first stack. | $250/mo |
| 10 | LettaIndependent, model-agnostic frameworks | Developers and researchers who specifically need an agent whose memory, skills and identity genuinely persist and evolve across long-running sessions, rather than reset on every call. | $20/mo |
| 11 | AgnoIndependent, model-agnostic frameworks | Python developers who want an Apache 2.0 SDK with memory, knowledge, learning, guardrails and a Control Plane they run inside their own cloud account, and who are comfortable running Docker or a cloud deploy template themselves. | $150/mo |
| 12 | Microsoft AutoGenIndependent, model-agnostic frameworks | Python or .NET developers and researchers exploring multi-agent orchestration who want a mature, free, self-hosted framework. | Free |
| 13 | AgentsKitIndependent, model-agnostic frameworks | JS/TS developers who want an ownable, dependency-light agent toolkit | On quote |
| 14 | LangGraphIndependent, model-agnostic frameworks | Engineering teams building production agents who need cyclical graphs, durable state and replayable runs | $0/seat/mo |
The ranking
Official SDKs from the major AI labs
Editor’s pick
#1
OpenAI Agents SDK
OpenAI's lightweight open-source framework for building agents in Python and TypeScript, with handoffs, guardrails, tracing and a sandboxed workspace mode for long-running work.
Best forDevelopers who want the fastest, lightest way to ship a straightforward agent (support triage, a tool-using assistant or a sandboxed coding agent) with tracing and guardrails included.
The shortest path from idea to a working agent — four primitives (agents, handoffs, guardrails, sessions) plus built-in tracing, MIT-licensed and provider-agnostic through LiteLLM despite the OpenAI branding. Durable state is a manual RunState snapshot rather than automatic checkpointing, so complex branching workflows tend to graduate to LangGraph.
- #2
Google Agent Development Kit
Google's open-source, code-first agent SDK with five language runtimes (Python, TypeScript, Go, Java, Kotlin), native A2A and MCP support, and an optional managed runtime on Google Cloud.
Best forTeams that want Google's own official, actively-developed agent SDK, especially polyglot teams (Python/Go/Java/TypeScript/Kotlin) or anyone building multi-agent systems that need to interoperate across vendors via the A2A protocol.
The broadest reach of any official SDK here — production-grade Python, Go, Java and TypeScript, plus native support for both the Agent2Agent and Model Context Protocol standards. Genuinely model-agnostic beyond Gemini through LiteLLM, but it's a comprehensive framework with a real learning curve, and its docs and tutorials still skew heavily Python.
- #3
Microsoft Agent Framework
Microsoft's unified, MIT-licensed agent SDK for .NET and Python: the supported successor to AutoGen and Semantic Kernel.
Best for.NET or Python teams already invested in Azure or Microsoft Foundry who want one supported agent SDK, an optional managed hosting path, and a documented migration off AutoGen or Semantic Kernel.
Microsoft's unified successor to AutoGen and Semantic Kernel, both now in maintenance mode — reached a production-ready 1.0 in April 2026 with consistent APIs across .NET, Python and Go. Claude and local Ollama models are first-class, not an afterthought, and it ships a production-ops layer (the Agent Harness, Foundry Hosted Agents) most frameworks leave you to build yourself; the honest catch is age, a quarter's worth of production track record against rivals with years.
- #4
Claude Agent SDK
Anthropic's own agent-building library: the same agent loop, tools and context management that power Claude Code, for Python and TypeScript. Claude-only, with no multi-provider escape hatch.
Best forDevelopers already building on Claude (or Claude Code) who want its exact agent loop, tools and context management as a callable library, and are comfortable being single-vendor on Anthropic in exchange for the tightest possible integration with Claude Code's own capabilities.
The tightest integration in this group — literally the Claude Code agent harness, tool loop and context management, exposed as a Python or TypeScript library rather than reimplemented. The tradeoff is total: Claude models only, by Anthropic's own design decision, and the compiled CLI binary the wrapper bundles isn't published as source, so it doesn't clear this index's open-source bar the way its three sibling SDKs do.
Independent, model-agnostic frameworks
- #5
LangChain
The most widely-used framework for building LLM apps and agents, with LangGraph for stateful multi-agent control: open-source and free to self-host.
Best forEngineering teams building a bespoke, production-grade agent who want the largest integration ecosystem plus real stateful control.
The default starting point for a custom, production-grade agent — no framework here matches its integration ecosystem, and LangGraph adds genuine stateful, branching control a simple chain library can't. A real learning curve and some API churn across major versions are the price of that breadth; a $125M Series B in October 2025 pushed it past unicorn status, and because the core libraries stay open-source and self-hostable, that funding trajectory doesn't create the vendor lock-in a closed-source tool's ownership change would.
- #6
CrewAI
Open-source Python framework for role-based multi-agent crews, with a hosted build and runtime that adds enterprise governance, native SDKs for the major LLMs, and Ollama via LiteLLM.
Best forDevelopers who want to stand up a role-based, collaborating multi-agent system quickly in Python, with a stable MIT-licensed core and a hosted build and runtime for production.
The fastest way to stand up a working multi-agent "crew" — role-based and unusually readable for how much coordination it buys you, now running at real production scale (2 billion-plus workflows executed, named deployments at PepsiCo, Johnson & Johnson, PwC and the US DoD) since its October 2025 1.0 release. Tight, deterministic control over long-running work still needs the newer Flows layer, and the managed AMP cloud's only self-serve tier is a free 50-execution cap, with nothing priced between that and a custom Enterprise deal.
- #7
LlamaIndex
Open-source data framework for LLM apps, with LlamaParse as the paid agentic OCR for messy documents and LlamaAgents for document workflows.
Best forEngineers building RAG-heavy or document-heavy agents who want an MIT-licensed OSS core with an optional managed parsing and extraction platform on top.
The deepest toolkit when retrieval quality is the actual product — 300-plus connectors and the most sophisticated RAG patterns of any framework in this index, MIT-licensed at the core and free. Its expansion into event-driven Workflows means it's no longer just a RAG library, but agent-first orchestration is still more naturally LangGraph or CrewAI territory, and the strongest document parsing (LlamaParse) sits behind the paid LlamaCloud rather than the free package.
- #8
Pydantic AI
The Python AI SDK for type-safe agents, realtime voice, image generation and embeddings, with every model a string swap away.
Best forPython teams building type-safe agents who also want realtime voice, image generation and embeddings in one SDK, with optional durable execution via Temporal, DBOS, Prefect or Restate.
Brings the same discipline that made Pydantic and FastAPI ubiquitous to agent building — define an agent with Python type hints and every model response is validated against them before your code sees a malformed result. MIT-licensed, genuinely model-agnostic, and its June 2026 2.0 release added real checkpointed durability via Temporal, DBOS, Prefect or Restate — though you still operate that orchestrator yourself, where LangGraph's graph engine is built directly into the framework.
- #9
Mastra
The TypeScript-native agent framework. Agents, durable workflows, memory and observability in one Apache 2.0 package, with an optional managed cloud platform.
Best forFull-stack JavaScript and TypeScript teams, especially those already building in Next.js or Node, who want agents, durable workflows and memory unified in one framework without adopting a Python-first stack.
The one framework here built TypeScript-first instead of ported from Python — a genuine fit for full-stack JS/Next.js teams who don't want to stand up a separate Python service. Backed by a $35M raise (a Spark Capital-led Series A) and named production customers including Replit, Sanity and Brex, but it's TypeScript-only with no Python path at all, and its deepest enterprise controls (RBAC, SSO, audit logs) sit behind a separate source-available license, not the open Apache-2.0 core.
- #10
Letta
Letta is the open-source framework behind Letta Code, a stateful coding agent whose memory, skills, and identity persist across sessions rather than rebuilding context on every call.
Best forDevelopers and researchers who specifically need an agent whose memory, skills and identity genuinely persist and evolve across long-running sessions, rather than reset on every call.
The one entry built on a genuinely different premise — agents as stateful, persistent objects with git-versioned memory that survive across sessions, instead of a stateless call graph reassembled on every request. It grew directly out of the Berkeley MemGPT research its founders co-authored, and the open core is real and active (23.9k-plus GitHub stars, Apache 2.0, a $10M Felicis-led seed). The ecosystem is a fraction of LangChain's size, so it's worth the switch only when persistent, evolving memory is the actual requirement, not a bounded stateless task.
- #11
Agno
An Apache 2.0 Python agent SDK and AgentOS runtime, with a Control Plane you run in your own cloud.
Best forPython developers who want an Apache 2.0 SDK with memory, knowledge, learning, guardrails and a Control Plane they run inside their own cloud account, and who are comfortable running Docker or a cloud deploy template themselves.
Fast, opinionated and model-agnostic, with memory, knowledge and multi-agent Teams built into the free Apache-2.0 core; its paid AgentOS layer is genuinely BYOC, deployed into your own cloud rather than a shared multi-tenant one. Its widely-repeated "10,000x faster than LangGraph" claim has no independent benchmark behind it this research could find — read it as a vendor claim, not a verified result.
- #12
Microsoft AutoGen
Microsoft's open-source framework for orchestrating conversations between multiple AI agents. Now in maintenance mode.
Best forPython or .NET developers and researchers exploring multi-agent orchestration who want a mature, free, self-hosted framework.
The framework that popularized agents conversing with agents, and still the most-starred name in the category at 59.8k stars — but confirmed frozen, not just announced: no tagged release since September 2025, and Microsoft explicitly points new projects at its own successor, Microsoft Agent Framework, while the original authors' community fork AG2 stays genuinely active in parallel. Worth learning the patterns from or extending an existing AutoGen codebase; evaluate the Agent Framework or AG2 first for anything new.
- #13
AgentsKit
Open-source, MIT-licensed JS/TS toolkit for building AI agents with a 10KB zero-dependency core and 50 built-in integrations.
Best forJS/TS developers who want an ownable, dependency-light agent toolkit
The newest and least proven framework here, included because this page covers the whole category. A sub-10 KB, zero-dependency TypeScript core with the broadest model-adapter coverage in this list — 25 adapters spanning a catalogue of 140 providers — plus a 17-backend memory layer and a registry that installs runnable agent source. MIT, with 22 packages published to npm and active commits. But it is four months old with 18 GitHub stars and no independent usage reports yet, so treat the breadth as claimed-and-published rather than community-proven, and weigh it against Mastra if you want TypeScript-native with a track record.
- #14
LangGraph
Open-source, MIT-licensed framework for building stateful, multi-agent AI workflows on cyclical graphs.
Best forEngineering teams building production agents who need cyclical graphs, durable state and replayable runs
LangChain's own lower-level graph engine, not a rival to it — explicit nodes and edges, checkpointed state, replay and human-in-the-loop interrupts, running in production at LinkedIn, Uber and Klarna. The added structure is real overhead if a role-based framework like CrewAI would already do the job.
Side by side
All 14 compared. Scroll the table sideways, the fact column stays pinned →
| Feature | OpenAI Agents SDK | Google Agent Development Kit | Microsoft Agent Framework | Claude Agent SDK | LangChain | CrewAI | LlamaIndex | Pydantic AI | Mastra | Letta | Agno | Microsoft AutoGen | AgentsKit | LangGraph |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Description | OpenAI's lightweight open-source framework for building agents in Python and TypeScript, with handoffs, guardrails, tracing and a sandboxed workspace mode for long-running work. | Google's open-source, code-first agent SDK with five language runtimes (Python, TypeScript, Go, Java, Kotlin), native A2A and MCP support, and an optional managed runtime on Google Cloud. | Microsoft's unified, MIT-licensed agent SDK for .NET and Python: the supported successor to AutoGen and Semantic Kernel. | Anthropic's own agent-building library: the same agent loop, tools and context management that power Claude Code, for Python and TypeScript. Claude-only, with no multi-provider escape hatch. | The most widely-used framework for building LLM apps and agents, with LangGraph for stateful multi-agent control: open-source and free to self-host. | Open-source Python framework for role-based multi-agent crews, with a hosted build and runtime that adds enterprise governance, native SDKs for the major LLMs, and Ollama via LiteLLM. | Open-source data framework for LLM apps, with LlamaParse as the paid agentic OCR for messy documents and LlamaAgents for document workflows. | The Python AI SDK for type-safe agents, realtime voice, image generation and embeddings, with every model a string swap away. | The TypeScript-native agent framework. Agents, durable workflows, memory and observability in one Apache 2.0 package, with an optional managed cloud platform. | Letta is the open-source framework behind Letta Code, a stateful coding agent whose memory, skills, and identity persist across sessions rather than rebuilding context on every call. | An Apache 2.0 Python agent SDK and AgentOS runtime, with a Control Plane you run in your own cloud. | Microsoft's open-source framework for orchestrating conversations between multiple AI agents. Now in maintenance mode. | Open-source, MIT-licensed JS/TS toolkit for building AI agents with a 10KB zero-dependency core and 50 built-in integrations. | Open-source, MIT-licensed framework for building stateful, multi-agent AI workflows on cyclical graphs. |
| Pricing | Free | Free | Free | Free | Free | Free (month (50 workflow executions)) | $0 / month | Free | Free | $0 / month | Free | Free | Paid | $0/seat/month |
| API | Yes | Yes | Yes | Yes | Yes | Yes | Yes | - | Yes | Yes | Yes | - | Yes | Yes |
| Tags |
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Frequently asked questions
- What is the best AI agent framework?
- It depends on what you're optimizing for. LangChain remains the default for the widest integration ecosystem and LangGraph's stateful control; CrewAI is the fastest route to a working multi-agent "crew"; Pydantic AI trades breadth for type-safe, validated outputs; Mastra is the one built TypeScript-first. If you want the exact harness a model lab ships for its own models, OpenAI Agents SDK, Google ADK, Microsoft Agent Framework and Claude Agent SDK are each a major lab's official answer.
- What's the difference between an official SDK and an independent framework?
- An official SDK — OpenAI Agents SDK, Claude Agent SDK, Google ADK, Microsoft Agent Framework — is built and maintained by the lab whose models it's optimized for, and ships the same production patterns that lab uses internally. An independent framework — LangChain, CrewAI, LlamaIndex, Pydantic AI, Mastra, Letta, Agno, Microsoft AutoGen — was designed to be model-agnostic from day one, so switching providers is a config change rather than a framework migration. Three of the four official SDKs (OpenAI's, Google's and Microsoft's) also reach other vendors' models through a LiteLLM-style integration; Claude Agent SDK is the exception, Claude models only, by Anthropic's own design decision.
- Are these AI agent frameworks free?
- The framework itself is free in all 12 cases — 11 are open-source (Apache-2.0 or MIT) and fully self-hostable; the Claude Agent SDK's wrapper packages are MIT-licensed, but the CLI binary they bundle isn't published as source, so use is governed by Anthropic's Commercial Terms of Service instead. What costs money is what you build on top: your model provider's API usage, and any optional managed layer (LangSmith, CrewAI AMP, Mastra Cloud, Letta Cloud, Agno's AgentOS, Google's Agent Runtime, Microsoft's Foundry Hosted Agents) for observability or hosted production infrastructure.
- Is Microsoft AutoGen still worth using?
- To learn the multi-agent patterns it popularized, or to extend an existing AutoGen codebase, yes. For a new production build, no — AutoGen hasn't tagged a release since September 2025, and Microsoft has moved both AutoGen and Semantic Kernel into maintenance mode specifically to push development toward Microsoft Agent Framework, its unified successor. The original AutoGen authors also maintain a separately active community fork, AG2, that still controls the old PyPI packages.
- Which framework is best for multi-agent orchestration?
- CrewAI's role-based "crew" model is the fastest way to get multiple agents collaborating, and LangGraph (LangChain's graph layer) offers the deepest stateful, branching control for complex handoffs. Microsoft Agent Framework and Google ADK both ship graph-based orchestration with sequential, parallel and conditional routing built in, and Letta's stateful, memory-persistent design is the one built specifically around agents whose context needs to survive across sessions rather than reset on each run.