development

Best AI Agent Frameworks

Compare the best AI agent frameworks in 2026 — LangGraph, CrewAI, OpenAI Agents SDK, Claude Agent SDK, Google ADK, Microsoft Agent Framework and more — with languages, model support, licences, pricing and a decisive pick for every kind of build.

Updated July 2026

Quick answer: There is no single best AI agent framework in 2026 — the right pick depends on your language, your model strategy and how complex your orchestration is. For flexible, production-grade orchestration of complex stateful agents, LangGraph is the most battle-tested (38.1K GitHub stars, 1.0 shipped in late 2025, already running agents at Uber, LinkedIn and Klarna). For a model-flexible, code-first default, OpenAI’s Agents SDK (Python and TypeScript, billed only at standard API rates) is the broadest-reach choice. For the fastest route to a role-based multi-agent prototype, CrewAI (54,000+ stars, MIT) has the lowest barrier to entry. If you are standardising on one model or cloud, use its first-party SDK — Claude Agent SDK for Anthropic, Google ADK 2.0 for Gemini, or the Microsoft Agent Framework for .NET and Azure. For type-safe Python pick Pydantic AI, for TypeScript teams Mastra, and for low-code pipelines n8n.

This page is about frameworks for building your own agents. If you want an agent you can use off the shelf — ChatGPT Agent, Claude Code, Manus, Devin — see our best AI agents guide instead; this is its developer twin. Below we cover eleven frameworks with current versions, licences, model support, the interoperability protocols that connect them, and a decisive pick for every kind of build. The one thing every framework shares: it is only ever as good as the model reasoning underneath it, so pair your choice with a strong model from our best AI models ranking.

The current state of AI agent frameworks: 2026

If you are still deciding whether you need an agent at all, what is agentic AI? covers the concepts — the agent loop, MCP and A2A, and how much autonomy is realistic — before you pick a library.

Building an agent used to mean wiring an LLM to a while-loop by hand. In 2026 that job has a mature toolchain, and the market has split cleanly into three layers: orchestration frameworks (LangGraph, CrewAI), first-party model SDKs (Claude Agent SDK, OpenAI Agents SDK, Google ADK, Microsoft Agent Framework) and low-code builders (n8n and its rivals). Demand has followed: search volume for “claude agent sdk” alone rose from roughly 50 to 14,800 monthly queries in a year (Morph).

Five shifts define where the category sits now.

1. Every major lab now ships an agent SDK. Anthropic’s Claude Agent SDK, OpenAI’s Agents SDK and AgentKit, Google’s Agent Development Kit and Microsoft’s Agent Framework all reached general availability or a stable release in the first half of 2026. The build-your-own-agent decision is no longer “which framework” but increasingly “first-party SDK or model-agnostic framework”.

2. The frameworks hit 1.0. LangGraph reached its first stable 1.0 release in late 2025 and sits at v1.2.9 as of July 2026; LlamaIndex Workflows shipped 1.0; Microsoft Agent Framework hit 1.0 GA on 3 April 2026; Google’s ADK reached 2.0 in May–June 2026. The era of “expect breaking changes every week” is largely over for the leaders.

3. Microsoft merged its two frameworks into one. On 3 April 2026 Microsoft unified AutoGen and Semantic Kernel into a single Microsoft Agent Framework for .NET and Python, and moved both predecessors into maintenance mode (European AI & Cloud Summit). If you are on AutoGen or Semantic Kernel today, the migration path now points to one SDK.

4. Interoperability protocols became the connective tissue. The Model Context Protocol (MCP) crossed 97 million monthly SDK downloads by February 2026 and is supported by every major provider, while Google’s Agent2Agent (A2A) protocol passed 150 organisations in its first year (Linux Foundation). Both now sit under the Linux Foundation’s Agentic AI Foundation. Framework choice matters less when your tools and agents are portable across them.

5. The reliability question outweighs the framework question. Gartner expects over 40% of agentic-AI projects to be cancelled by the end of 2027 on cost, unclear value and weak controls, and researchers showed in April 2026 that the major agent benchmarks can be gamed to near-perfect scores. The framework is rarely why an agent project fails — scope, evaluation and human oversight are. Choose a framework you can ship and observe, then spend your effort on the task, not the tool.

Best AI agent frameworks in 2026, compared

The field settled around eleven frameworks in 2026 (JetBrains, LangChain). We have ordered them by current developer standing for building production agents, but the “Best for” column matters more than the rank — these tools solve different problems.

How to read the table: “Any” model support means the framework is model-agnostic — you can point it at Claude Opus 5, GPT-5.6, Gemini 3.5 Pro or an open-weight model like MiniMax M3. “First” (e.g. “OpenAI-first”) means it is model-agnostic but tuned for, and best-documented on, that vendor’s models. Licence matters for commercial use: MIT and Apache 2.0 are permissive, while n8n’s Sustainable Use License is fair-code — free for internal business use but restricted for reselling the software itself (n8n).

The frameworks in depth

1. LangGraph — most production-proven orchestration

Language: Python, JavaScript/TypeScript Licence: MIT (open source); LangGraph Platform is a paid managed layer Latest: v1.2.9 (July 2026); 1.0 GA late 2025 Backer: LangChain Model support: Any

LangGraph is the most battle-tested framework for complex agents, with 38.1K GitHub stars and a 1.0 release that landed in late 2025 after more than a year powering production agents at Uber, LinkedIn and Klarna. It models an agent as a directed graph with conditional edges, giving you explicit control over cycles, branching, retries and human-approval steps — the things ad-hoc loops handle badly.

Why it wins: durable execution is first-class. Agent state persists automatically, so a server restart or a multi-day approval process resumes exactly where it left off without custom database code. That reliability, plus the deepest control over multi-agent graphs, makes it the default for teams building sophisticated, long-running workflows.

Limitations: the graph model has the steepest learning curve on this list, and the surrounding LangChain ecosystem can feel heavy if all you need is a simple tool-calling loop. Both LangChain and LangGraph committed to no breaking changes before 2.0, which reduces the historical churn complaint.

Best for: production teams building complex, stateful agents that need branching, retries and human-in-the-loop control.

2. OpenAI Agents SDK / AgentKit — model-flexible, code-first default

Language: Python, TypeScript Licence: MIT (open source); usage billed at standard OpenAI API rates Latest: enterprise update April 2026; Agent Builder winding down 30 Nov 2026 Backer: OpenAI Model support: Any, OpenAI-first

OpenAI’s Agents SDK is the code-first successor to Swarm, and the broader AgentKit toolkit adds build, deploy and optimisation tooling around it — sandboxing, subagents, a “code mode”, and built-in tracing to visualise, debug and evaluate agent flows. An April 2026 update added safety and enterprise features. There are no platform fees — you pay only for the API calls your agents make.

Why it matters: it is the shortest path to a production agent for the huge base of teams already on OpenAI, and although it is OpenAI-first it can call other providers’ models. The tracing and evaluation tooling is among the best built-in observability of any first-party SDK.

Limitations: OpenAI announced on 3 June 2026 that it is winding down the visual Agent Builder and Evals products, with availability ending 30 November 2026 — build in the code-first SDK, not the drag-and-drop builder, for longevity.

Best for: teams on OpenAI who want a model-flexible, code-first SDK with strong built-in tracing.

3. CrewAI — fastest role-based multi-agent framework

Language: Python Licence: MIT (open source); CrewAI Enterprise (AMP) is a paid managed product Latest: 54,000+ GitHub stars (June 2026) Backer: CrewAI, Inc. ($18M funding, Series A led by Insight Partners) Model support: Any

CrewAI is the most popular standalone multi-agent framework, with 54,000+ GitHub stars and a role-based mental model: each agent gets a persona, a set of tools and a task, and you assemble them into a “crew” that collaborates. That abstraction gives it the lowest barrier to entry when work splits naturally into specialist roles (Turing). The company reports the framework powering 1.4 billion agentic automations a month across customers including PwC, IBM, NVIDIA and 60% of the Fortune 500.

Why it matters: it is the quickest way to stand up a team-of-agents prototype, and the open-core model means you can start MIT-licensed and add the commercial CrewAI Enterprise (AMP) layer — SOC 2, SSO, audit trails, monitoring dashboards — when you need governance.

Limitations: the role-based abstraction that makes prototyping fast gives you less fine-grained control than LangGraph over complex state and branching; independent reviews put it around 8.7/10, strong on ergonomics, weaker on deep customisation.

Best for: teams that want role-based multi-agent workflows running quickly, with an enterprise upgrade path.

4. Claude Agent SDK — best for building on the Claude Code harness

Language: Python, TypeScript Licence: MIT (open source); programmatic use billed at Anthropic API rates Latest: Dynamic Workflows and Performance Outcomes added June 2026 Backer: Anthropic Model support: Claude only (Opus 5, Sonnet 5, Haiku 4.5)

The Claude Agent SDK exposes the same primitives that power Claude Code — the developer-favourite agentic coding tool — so you can build production agents on a harness that is already proven at repo scale. It ships a full runtime: an agent loop, custom tools, hooks, MCP integration, subagents with their own context windows and models, session persistence, a permission system and a memory stack. In June 2026 Anthropic added Dynamic Workflows, where a lead agent fans out tens to hundreds of parallel subagents in a single session, and Performance Outcomes, where a separate grader sends each subagent back to revise until its result meets a rubric.

Why it matters: if your agents live on Claude, this is the most capable and best-integrated option — the same scaffolding that makes Claude Code strong, exposed as an SDK. It now sits under Anthropic’s Managed Agents, and programmatic use moved onto a separate credit pool on 15 June 2026.

Limitations: model choice is Claude-only, so it is not the pick if you need multi-provider flexibility. Heavy Opus 5 usage adds up — route routine subagent steps to Haiku 4.5 to control cost.

Best for: developers standardising on Claude who want the Claude Code harness as a programmable SDK.

5. Microsoft Agent Framework — enterprise .NET and Azure default

Language: .NET, Python Licence: MIT (open source) Latest: 1.0 GA on 3 April 2026 Backer: Microsoft Model support: Any, Azure-first

The Microsoft Agent Framework unifies the two frameworks Microsoft used to ship — AutoGen and Semantic Kernel — into one SDK for .NET and Python, generally available since 3 April 2026. It combines AutoGen’s simple agent abstractions with Semantic Kernel’s enterprise features — session-based state, type safety, middleware and telemetry — and adds graph-based workflows for explicit multi-agent orchestration, with native MCP and A2A support.

Why it matters: it is the natural first-party path for the large base of enterprises already on .NET and Azure, and the only leading framework with .NET as a first-class language. AutoGen and Semantic Kernel are now in maintenance mode, so new Microsoft-stack projects should start here.

Limitations: value concentrates inside the Azure ecosystem, and teams on the older frameworks face a migration. It is younger as a unified product than LangGraph or CrewAI, though it inherits both predecessors’ maturity.

Best for: enterprise teams on .NET or Azure who want one first-party, MCP-native agent SDK.

6. Google ADK 2.0 — Gemini and Google Cloud first-party toolkit

Language: Python, TypeScript, Go, Java, Kotlin Licence: Apache 2.0 (open source) Latest: Python 2.0 GA 19 May 2026; Go 2.0 GA 30 June 2026 Backer: Google Model support: Any, Gemini-first

Google’s Agent Development Kit is a code-first, open-source toolkit for building, evaluating and deploying agents, and ADK 2.0 — Python GA at Google I/O on 19 May 2026, Go GA on 30 June — added a graph-based Workflow Runtime with routing, fan-out/fan-in, loops, retries, state management and human-in-the-loop, plus a Task API for structured agent-to-agent delegation. It supports the widest range of languages on this list: Python, TypeScript, Go, Java and Kotlin.

Why it matters: it is the shortest path to production for teams on Gemini and Google Cloud, integrates directly with the Gemini Enterprise agent platform, and its multi-language reach makes it a fit for JVM and Go shops that most Python-first frameworks ignore.

Limitations: best-documented and best-integrated on Gemini and Google Cloud, so some of its value is ecosystem-bound, and the Go and TypeScript ports trail the Python SDK in maturity.

Best for: teams building on Gemini or Google Cloud, or anyone needing Go, Java or Kotlin support.

7. Pydantic AI — best type-safe Python framework

Language: Python Licence: MIT (open source) Latest: v1.85.1 (April 2026); 18.8K GitHub stars Backer: Pydantic Model support: Any

Pydantic AI applies Pydantic’s validation engine — the library underneath most modern Python data tooling — directly to LLM interactions, giving you type-safe agents with automatic validation and self-correction of malformed model output. Released in November 2024, it has grown to 18.8K stars and reached a stable v1 line, making it one of the fastest-adopted frameworks in the Python ecosystem.

Why it matters: for Python teams that already use Pydantic, it offers the cleanest developer experience on this list — structured, validated outputs with minimal ceremony, and none of the ecosystem weight of LangChain.

Limitations: it is a lighter-weight framework than LangGraph, so very complex multi-agent graphs may outgrow it; it is Python-only.

Best for: Python developers who want type safety, validated outputs and a clean, minimal API.

8. LlamaIndex Workflows — best for data- and RAG-heavy agents

Language: Python, TypeScript Licence: MIT (open source) Latest: Workflows 1.0 Backer: LlamaIndex Model support: Any

LlamaIndex Workflows is an event-driven orchestration layer for multi-agent systems in plain Python or TypeScript, where steps communicate through events rather than explicit graph edges. It sits on top of LlamaIndex’s long-standing strength in retrieval and indexing, which makes it the natural choice when your agent’s core job is reasoning over documents, knowledge bases or internal corpora.

Why it matters: if your agent is fundamentally a retrieval-augmented (RAG) system — search over your own data — LlamaIndex gives you the best-integrated indexing and query tooling of any framework here, with the event-driven model handling dynamic multi-step flows.

Limitations: for tool-heavy operational agents (CRM, ops, multi-API workflows) that are not data-centric, a general orchestrator like LangGraph or a TypeScript-native option like Mastra is often a better fit.

Best for: agents whose primary job is search and reasoning over documents and knowledge bases.

9. Mastra — best for TypeScript teams

Language: TypeScript Licence: Open source Latest: actively developed through 2026 Backer: Mastra Model support: Any (built-in model router)

Mastra is a TypeScript-native agent framework that bundles agents, workflows, memory, evaluations and observability into one package, with a built-in model router and a Studio development environment. It exists to close the gap between the Python-dominated framework world and the large number of TypeScript teams that need production-grade agent infrastructure without dropping to Python.

Why it matters: most agent frameworks are Python-first; Mastra gives TypeScript and Node teams a first-class option, and a common 2026 stack pairs the Vercel AI SDK as the substrate with Mastra on top for workflows, memory and evals.

Limitations: it is younger and smaller than the Python leaders, and TypeScript-only, so it is not the pick for Python-centric teams or JVM stacks.

Best for: TypeScript and Node teams building production agents who want one integrated framework.

10. AWS Strands Agents — best for deploying on AWS

Language: Python, TypeScript Licence: Apache 2.0 (open source) Latest: 1.0 (multi-agent orchestration); 1M+ downloads Backer: AWS, with community contributions from Anthropic, Meta, PwC and others Model support: Any, any cloud

Strands Agents is AWS’s open-source SDK that takes a model-driven approach — describe the model, tools and prompt, and the agent loop handles the rest in a few lines of code. It is model-agnostic and cloud-agnostic, but ships deployment patterns for AWS Lambda, Fargate and Bedrock AgentCore, and an experimental bidirectional streaming mode for real-time voice agents. Community backers include Anthropic, Meta and PwC.

Why it matters: for teams deploying on AWS it is the shortest path from prototype to a production agent running on Bedrock AgentCore, with the surrounding AWS deployment tooling built in — while remaining permissively licensed and portable if you move clouds.

Limitations: its deepest integrations assume the AWS stack, and it is newer than LangGraph or CrewAI, with a smaller community despite fast download growth.

Best for: teams building and deploying agents on AWS and Amazon Bedrock.

11. n8n — best low-code option

Language: Low-code (visual, Node-based) Licence: Sustainable Use License (fair-code) Latest: 198.5K GitHub stars Backer: n8n GmbH Model support: Any (OpenAI, Anthropic, Google, Hugging Face)

n8n is the leading low-code option, sitting between no-code tools like Zapier and full-code SDKs. It has a native AI Agent node, multi-agent orchestration, LLM-chain and vector-store nodes, memory for conversational agents, and native connections to OpenAI, Anthropic, Google and Hugging Face — all wired together visually. With 198.5K GitHub stars it is one of the most popular projects on GitHub, and it lets you build, for example, an agent that reads incoming email, decides an action, checks a database and replies, with no code.

Why it matters: it is the right tool when the agent is one step in a broader automation pipeline and you want to wire it into hundreds of existing integrations without writing a framework yourself. Rivals Dify, Langflow and Flowise have all passed 100K stars, so the low-code agent space is crowded and competitive.

Limitations: n8n uses the Sustainable Use License (fair-code), which is free for internal business use but restricts reselling the software itself — read the licence before embedding it in a commercial product. Complex, highly custom agent logic still lands better in a code-first framework.

Best for: teams that want to add agents to automation pipelines with minimal code.

Feature comparison: the full matrix

Every framework in this table supports MCP for tools, which is why we treat interoperability, not lock-in, as the default in 2026 — see the protocols section below.

Use-case specific recommendations

For complex, stateful production agents

Winner: LangGraph

When one workflow needs cycles, branching, retries or a human-approval step, LangGraph’s directed-graph model and automatic durable state are the most production-proven combination — already running agents at Uber, LinkedIn and Klarna. Alternative: Microsoft Agent Framework if you are on .NET/Azure and want graph workflows first-party.

For a fast multi-agent prototype

Winner: CrewAI

CrewAI’s role-based crews are the lowest-friction way to stand up cooperating agents when the work splits into specialist roles. Alternative: OpenAI Agents SDK if you want code-first handoffs with built-in tracing.

For building on a single model provider

Winner: the first-party SDK for that provider

Use the Claude Agent SDK on Anthropic, the OpenAI Agents SDK on OpenAI, Google ADK 2.0 on Gemini, or the Microsoft Agent Framework on Azure — each gives the shortest path to production and the deepest native tooling when you have committed to one model family. Pair with the right model from our best AI models ranking.

For agentic coding specifically

Winner: Claude Agent SDK

For agents that write and edit code, the Claude Agent SDK exposes the same harness as Claude Code, the developer-favourite coding tool — see our best AI for coding and best AI code review guides for the tools built on this pattern.

For type-safe Python

Winner: Pydantic AI

Pydantic AI gives Python teams validated, structured outputs and self-correction with a clean, minimal API and none of LangChain’s ecosystem weight. Alternative: LlamaIndex Workflows if the agent is fundamentally retrieval over your own data.

For TypeScript teams

Winner: Mastra

Mastra is the strongest TypeScript-native option, bundling agents, workflows, memory and evals with a built-in model router. Alternative: LangGraph’s JS/TS build if you want the graph model in TypeScript.

For deploying on AWS

Winner: AWS Strands Agents

Strands ships model-agnostic agents with native deployment patterns for Lambda, Fargate and Bedrock AgentCore, so AWS teams get the shortest route to production without lock-in.

For low-code and automation pipelines

Winner: n8n

n8n adds a native AI Agent node to hundreds of existing integrations, ideal when the agent is one step in a broader automation rather than a standalone application.

For value and self-hosting

Winner: any MIT/Apache framework + an open-weight model

Every leading framework except n8n is permissively licensed, so the cost is the model, not the framework. Run LangGraph or the Claude Agent SDK’s open tooling against an open-weight model such as GLM-5.2 (MIT) or MiniMax M3 for data-sovereign agents at roughly a tenth of frontier-model cost.

The interoperability layer: MCP, A2A and ACP

In 2026 the protocols connecting agents matter as much as the framework you build in, because they let your tools and agents move across frameworks and models rather than lock to one.

Model Context Protocol (MCP) is the standard for connecting an agent to tools and data — “USB-C for tool connectivity”, a vertical link from agent to tool. It crossed 97 million monthly SDK downloads by February 2026 and is supported by Anthropic, OpenAI, Google, Microsoft and Amazon, and by every framework in the table above (DEV).

Agent2Agent (A2A) is the standard for agents built by different vendors or frameworks to discover each other and coordinate — “HTTP for agent collaboration”, a horizontal link from agent to agent. Introduced by Google in April 2025 and now governed by the Linux Foundation, it passed 150 organisations in its first year with production deployments across supply chain, financial services and IT operations.

Agent Client Protocol (ACP) lets one vendor’s agent run inside another’s tool, and is used by IDEs and coding tools to stay model-agnostic. Both MCP and A2A now sit under the Linux Foundation’s Agentic AI Foundation, launched in December 2025 with OpenAI, Anthropic, Google, Microsoft, AWS and Block as co-founders — a rare cross-industry standardisation. The practical takeaway: build on MCP for tools and, where relevant, A2A for cross-agent coordination, and your framework choice becomes a reversible decision rather than a lock-in.

Pricing: what you’ll actually pay

The frameworks themselves are almost all free — the real cost is model tokens and, optionally, a managed platform.

Cost strategy: agents multiply token use — every step, tool call and retry bills — so the framework is rarely the expensive part. Pin routine subagent steps to a cheap or open model, reserve a flagship like Opus 5 or GPT-5.6 for the hard reasoning, and cap agent runs with step limits and human checkpoints. For the underlying per-token model prices, see our best LLM APIs comparison.

What developers actually think

The framework is rarely why a project fails

Gartner expects 40%+ of agentic-AI projects to be cancelled by 2027 — but the cited reasons are cost, unclear value and weak controls, not framework choice. The teams that ship pick a framework they can observe and evaluate, then aim it at a narrow, measurable process.

Convergence has reduced the stakes of the choice

With every framework supporting MCP and the leaders all reaching 1.0, switching cost has fallen. Developers increasingly treat the framework as a reversible decision and put their effort into tools, evaluation and prompt design — the parts that actually determine reliability.

First-party SDKs versus model-agnostic frameworks is the real debate

The 2026 split is less LangGraph-versus-CrewAI and more “commit to one model’s SDK for the shortest path, or stay model-agnostic for flexibility”. Teams that expect to swap models — for cost, for capability, or as a hedge against access changes — lean toward LangGraph, CrewAI or Pydantic AI; teams standardising on one provider take its SDK.

Benchmarks deserve scepticism

After researchers showed in April 2026 that the major agent benchmarks can be gamed, developers increasingly run small private evaluations on their own tasks and weight real behaviour — does the agent recover from errors, does it know when to stop — over any leaderboard.

Recent developments reshaping the market (2026)

Microsoft Agent Framework hits 1.0 GA (3 Apr). Microsoft unified AutoGen and Semantic Kernel into a single .NET + Python SDK with native MCP and A2A, and moved both predecessors into maintenance mode (Digital Applied).

OpenAI winds down Agent Builder (announced 3 Jun). OpenAI is discontinuing the visual Agent Builder and Evals products from 30 November 2026, steering developers to the code-first Agents SDK.

Google ADK reaches 2.0 (May–Jun). ADK Python 2.0 went GA at Google I/O on 19 May 2026 and ADK Go 2.0 on 30 June, adding a graph-based Workflow Runtime and an agent-to-agent Task API across five languages (adk.dev).

Claude Agent SDK adds Dynamic Workflows (Jun). Anthropic upgraded its subagent pattern to fan out hundreds of parallel subagents per session with rubric-graded Performance Outcomes, and moved programmatic use onto a separate credit pool on 15 June 2026.

A2A passes 150 organisations (Apr). Google’s agent-to-agent protocol marked its first year under the Linux Foundation with 150+ supporting organisations and production deployments across multiple industries (Linux Foundation).

LangGraph and LlamaIndex Workflows hit 1.0. The two most-used open orchestration layers reached their first stable releases, committing to no breaking changes before 2.0 (LangChain).

Frequently asked questions

What is an AI agent framework?

An AI agent framework is a software library that gives you the building blocks to create an AI agent — a model equipped with tools, memory and a goal that takes multiple steps on its own to finish a task. Rather than wiring an LLM to a loop by hand, a framework provides the agent loop, tool-calling, state and memory management, multi-agent orchestration and observability. Leading examples in 2026 include LangGraph, CrewAI, the OpenAI Agents SDK and the Claude Agent SDK.

What is the best AI agent framework in 2026?

There is no single best framework — it depends on your needs. For complex, stateful production agents, LangGraph is the most battle-tested. For a fast role-based multi-agent prototype, CrewAI has the lowest barrier to entry. For a model-flexible, code-first default, the OpenAI Agents SDK has the broadest reach. If you have committed to one model provider, use its first-party SDK. Match the framework to your language, model strategy and orchestration complexity.

LangGraph vs CrewAI — which should I use?

Pick CrewAI when the work splits naturally into specialist roles and you want a multi-agent prototype running quickly with an intuitive mental model. Pick LangGraph when one workflow needs cycles, branching, retries or a human-approval step, and you need durable state and fine-grained control for production. CrewAI optimises for speed of setup; LangGraph optimises for production control.

Are AI agent frameworks free?

Almost all of them are free and open-source. LangGraph, CrewAI, the OpenAI Agents SDK, the Claude Agent SDK, the Microsoft Agent Framework, Pydantic AI and LlamaIndex are MIT-licensed; Google ADK and AWS Strands are Apache 2.0. The exception is n8n, which uses the fair-code Sustainable Use License — free for internal business use but restricted for reselling. In every case, the real cost is model tokens and any optional managed platform, not the framework itself.

Which AI agent framework is best for beginners?

For a first code-based agent, CrewAI (role-based, intuitive) and Pydantic AI (clean, type-safe) have the gentlest learning curves. If you would rather not write much code, n8n lets you build agents visually. LangGraph is more powerful but has the steepest learning curve, so it is better as a second step once you understand the fundamentals.

Do I need a framework to build an AI agent?

No — you can build a simple tool-calling agent with just a model provider’s API and a loop. A framework earns its place once you need durable state, multi-agent orchestration, memory, human-in-the-loop checkpoints or observability. For a quick prototype, start minimal; for anything you intend to run in production and maintain, a framework saves you from rebuilding that infrastructure yourself.

What is MCP and why does it matter for agent frameworks?

The Model Context Protocol (MCP) is an open standard for connecting an agent to external tools and data sources — think of it as a universal adapter between agents and tools. It matters because every leading framework now supports it, so tools you build against MCP work across LangGraph, CrewAI, the OpenAI and Claude SDKs and the rest. That portability turns framework choice into a reversible decision rather than a lock-in. By February 2026 MCP had crossed 97 million monthly SDK downloads.

Can I use any model with these frameworks?

Most are model-agnostic — LangGraph, CrewAI, Pydantic AI, LlamaIndex, Mastra, AWS Strands and n8n all let you point at Claude, GPT-5.6, Gemini or an open-weight model. The first-party SDKs are the exception in degree: the Claude Agent SDK is Claude-only, while the OpenAI, Google and Microsoft SDKs are model-flexible but tuned for their own models. If multi-model flexibility matters, choose a model-agnostic framework and build on MCP.

What’s the difference between an AI agent framework and an AI agent?

A framework is what you build an agent with; an agent is what you use. Frameworks like LangGraph and the Claude Agent SDK are developer libraries for creating custom agents. Ready-made agents like ChatGPT Agent, Claude Code or Manus are finished products you use off the shelf — covered in our best AI agents guide. This page is about the frameworks; that page is about the agents.

Conclusion: how to choose in 2026

The agent-framework market matured fast in 2026 — the leaders hit 1.0, every major lab shipped an SDK, and MCP made the choice more reversible than it has ever been. Choose by language, model strategy and complexity, then spend your effort on the task and its evaluation, not the tool.

• Most production-proven: LangGraph — complex, stateful agents with durable state.
• Model-flexible default: OpenAI Agents SDK — code-first, strong built-in tracing.
• Fastest multi-agent prototype: CrewAI — role-based crews, MIT.
• Building on one provider: Claude Agent SDK, Google ADK 2.0 or Microsoft Agent Framework.
• Type-safe Python: Pydantic AI. TypeScript: Mastra. AWS: Strands. Low-code: n8n.
• Data/RAG agents: LlamaIndex Workflows.

Whatever you build with, the framework is rarely the variable that decides success — scope, evaluation and human oversight are. Point your agent at a measurable job, build on MCP so your tools stay portable, and keep a human in the loop on anything consequential. For the agents these frameworks produce, see our best AI agents guide; for the models underneath them, our best AI models ranking.

This guide is updated as frameworks, SDKs and protocols evolve. Versions, star counts, licences and pricing are current as of 31 July 2026 and change frequently — verify with the project before committing. Frameworks are open-source unless noted; n8n uses the fair-code Sustainable Use License.