Ranked · Agent frameworks
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.
The ranking
16 agents, compared. Start with fit and price, then open the full assessment. Payment does not alter this ranking. Selection and method ↓
All picks at a glance
Compare fit and price, then choose a name to read the full assessment.
| # | Agent | Best for | Starts at |
|---|---|---|---|
| 1 | OpenAI Agents SDK (Python)Official SDKs from the major AI labsvs LangChain | 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 Kit (ADK)Official SDKs from the major AI labsvs Microsoft Agent Framework | 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. | Free |
| 3 | Microsoft Agent FrameworkOfficial SDKs from the major AI labsvs Google Agent Development Kit (ADK) | .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. | Free |
| 4 | Claude Agent SDKOfficial SDKs from the major AI labsvs Google Agent Development Kit (ADK) | 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. | From $1/million input tokens |
| 5 | LangChainIndependent, model-agnostic frameworksvs OpenAI Agents SDK (Python) | Engineering teams building a bespoke, production-grade agent who want the largest integration ecosystem plus real stateful control. | Free, then $39/seat/month |
| 6 | CrewAIIndependent, model-agnostic frameworksvs LangChain | 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 frameworksvs LangChain | 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. | Free, then $50/month |
| 8 | Pydantic AIIndependent, model-agnostic frameworksvs LangChain | 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 frameworksvs LangChain | 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. | Free, then $250/month |
| 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. | Free, then $20/month |
| 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. | Free, then $150/month |
| 12 | AutoGenIndependent, model-agnostic frameworksvs Microsoft Agent Framework | Python or .NET developers and researchers exploring multi-agent orchestration who want a mature, free, self-hosted framework. | Free |
| 13 | AgentsKitVerifiedIndependent, model-agnostic frameworks | JS/TS developers who want an ownable, dependency-light agent toolkit | Free |
| 14 | LangGraphIndependent, model-agnostic frameworksvs LangChain | Engineering teams building production agents who need cyclical graphs, durable state and replayable runs | Free, then $39/month |
| 15 | LiveKit AgentsIn this category, not yet ranked | Engineering teams that want to own their voice-agent stack end to end, self-hosted or on LiveKit Cloud, without being locked into one vendor's API. | Free, then $50/month |
| 16 | LangflowIn this category, not yet ranked | Free |
Selection, context, and ranking method
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, so you can 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.
Every entry below is a Published listing that already cleared this site's per-listing quality gate. That gate wants 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
Official SDKs from the major AI labs
Editor’s pick
#1
OpenAI Agents SDK (Python)
Open-source Python framework for building agents with tools, handoffs, guardrails and tracing.
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 (ADK)
Open-source framework for building, evaluating and deploying AI agents in Python, TypeScript, Go, Java or Kotlin.
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
Open-source .NET, Python and Go SDK for building AI agents and graph-based multi-agent workflows.
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
Python and TypeScript library that runs Claude Code's agent loop and tools inside your own application.
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
Open source agent frameworks plus LangSmith, a hosted platform to trace, evaluate, deploy and govern agents.
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 multi-agent framework with a managed platform for deploying and governing agent workflows.
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 agent framework plus a credit-metered document parsing, extraction and indexing platform.
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
Open-source Python agent framework from the Pydantic team, with a typed agent loop and swappable models.
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
Open-source TypeScript framework for building AI agents, with a hosted platform for running and observing them.
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.
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
Python SDK, self-hosted AgentOS runtime and hosted control plane for building and operating agent platforms.
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
AutoGen
Open-source Microsoft framework for building single and multi-agent AI applications in Python.
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
AgentsKitVerified
Composable TypeScript packages for agent runtime, tools, memory, RAG and headless chat UI.
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 orchestration framework and runtime for stateful, long-running LLM agents, from LangChain.
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.
In this category, not yet ranked
- #15
LiveKit Agents
Open-source Python and Node.js framework for voice AI agents, with LiveKit Cloud hosting, telephony and observability.
Best forEngineering teams that want to own their voice-agent stack end to end, self-hosted or on LiveKit Cloud, without being locked into one vendor's API.
- #16
Langflow
Open-source visual builder for AI agents, RAG apps and MCP servers, with an API server included.
Side by side
Scroll across to compare every product. Feature names stay in view.One card per question, with every product answering it in turn.
| Feature | OpenAI Agents SDK (Python) | Google Agent Development Kit (ADK) | Microsoft Agent Framework | Claude Agent SDK | LangChain | CrewAI | LlamaIndex | Pydantic AI | Mastra | Letta | Agno | AutoGen | AgentsKitVerified | LangGraph | LiveKit Agents | Langflow |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Description | Open-source Python framework for building agents with tools, handoffs, guardrails and tracing. | Open-source framework for building, evaluating and deploying AI agents in Python, TypeScript, Go, Java or Kotlin. | Open-source .NET, Python and Go SDK for building AI agents and graph-based multi-agent workflows. | Python and TypeScript library that runs Claude Code's agent loop and tools inside your own application. | Open source agent frameworks plus LangSmith, a hosted platform to trace, evaluate, deploy and govern agents. | Open-source multi-agent framework with a managed platform for deploying and governing agent workflows. | Open-source agent framework plus a credit-metered document parsing, extraction and indexing platform. | Open-source Python agent framework from the Pydantic team, with a typed agent loop and swappable models. | Open-source TypeScript framework for building AI agents, with a hosted platform for running and observing them. | Stateful agent harness, cloud and SDK for agents with persistent, git-versioned memory. | Python SDK, self-hosted AgentOS runtime and hosted control plane for building and operating agent platforms. | Open-source Microsoft framework for building single and multi-agent AI applications in Python. | Composable TypeScript packages for agent runtime, tools, memory, RAG and headless chat UI. | Open-source orchestration framework and runtime for stateful, long-running LLM agents, from LangChain. | Open-source Python and Node.js framework for voice AI agents, with LiveKit Cloud hosting, telephony and observability. | Open-source visual builder for AI agents, RAG apps and MCP servers, with an API server included. |
| Category | Agent frameworks | Agent frameworks | Agent frameworks | Agent frameworks | Agent frameworks, Agent platforms | Agent frameworks | Agent frameworks | Agent frameworks | Agent frameworks | Agent frameworks, Agent platforms, Coding agents | Agent frameworks, Agent platforms | Agent frameworks | Agent frameworks | Agent frameworks | Voice agents, Agent frameworks | Agent frameworks |
| Pricing | From Free | From Free | From Free | From $1/million input tokens | From Free | From Free | From Free | From Free | From Free | From Free | From Free | From Free | From Free | From Free | From Free | From Free |
| 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.
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