Best AI Agent Frameworks

As of July 2026, the best all-around framework for building agentic LLM applications is LangGraph 1.0, the graph-based orchestration layer that reached its first stable release on October 22, 2025 and runs production agents at Uber, LinkedIn, Klarna, and JP Morgan ^[1]. There is no single winner for every job, so the practical answer is by use case: choose LangGraph for stateful orchestration and control, the OpenAI Agents SDK for OpenAI-native multi-agent handoffs, the Claude Agent SDK for Anthropic-native coding and computer-use agents, CrewAI for fast role-based multi-agent prototypes, Microsoft Agent Framework 1.0 for enterprise .NET and Azure stacks, and LlamaIndex Workflows for retrieval-heavy agents. For type-safe Python pick Pydantic AI V2; for the Google and Gemini ecosystem pick Google ADK 2.0.

Best AI agent frameworks at a glance (by use case)

• Best overall for orchestration and stateful graphs: LangGraph 1.0
• Best OpenAI-native option with the cleanest multi-agent handoffs: OpenAI Agents SDK
• Best for Anthropic-native, coding, and computer-use agents: Claude Agent SDK
• Best for fast role-based multi-agent prototypes: CrewAI 1.14
• Best for enterprise .NET, Azure, and Microsoft stacks: Microsoft Agent Framework 1.0
• Best for RAG-heavy and document agents: LlamaIndex Workflows 1.0
• Best for type-safe Python: Pydantic AI V2
• Best for the Google and Gemini ecosystem: Google ADK 2.0
• Best for research-style conversational multi-agent: the AutoGen lineage (now Microsoft Agent Framework and the AG2 fork)

Summary comparison table

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Last verified: July 2026. Versions and licenses confirmed against official docs and GitHub releases.

1. LangGraph 1.0 (best for orchestration and stateful graphs)

Best for: stateful orchestration, control loops, cyclic and branching workflows, human-in-the-loop. Latest: 1.0, GA October 22, 2025 ^[1]. Languages: Python and JavaScript/TypeScript. Multi-agent: yes, via subgraphs and supervisor or swarm patterns. State and memory: durable checkpointing and built-in persistence, resumable runs, first-class human-in-the-loop ^[1]. Model-agnostic: yes. License: MIT. Notable adopters: Uber, LinkedIn, Klarna, Rippling, JP Morgan, Blackrock, and Cisco ^[1].

LangGraph models an agent as an explicit state graph, so you get precise control over cycles, retries, branching, and approval gates that free-form agent loops tend to hide. Its 1.0 release shipped with no breaking changes and a commitment to backward compatibility until 2.0 ^[1]. The higher-level LangChain 1.0 library (also GA October 22, 2025) sits on top of LangGraph with a create_agent abstraction for teams that want a batteries-included agent without hand-writing a graph ^[1].

2. OpenAI Agents SDK (best for OpenAI-native agents and handoffs)

Best for: OpenAI-model agents, clean multi-agent handoffs, computer-use and sandboxed tool execution. Latest: v0.18.0, July 7, 2026 ^[3]. Languages: Python and TypeScript. Multi-agent: yes, via handoffs. State and memory: sessions plus built-in tracing. Model-agnostic: OpenAI-first, with 100+ providers through an official LiteLLM extension ^[5]. License: MIT ^[5]. Notable adopters: OpenAI first-party agent products and broad startup use.

The SDK is built around four small primitives, Agents, Handoffs, Guardrails, and Sessions, and offers the cleanest handoff model in the ecosystem, where one agent delegates to another through a specialized transfer tool call ^[5]. An April 2026 update, billed as "the next evolution of the Agents SDK," added a model-native harness and native sandbox execution so agents can run code safely, with built-in support for E2B, Modal, Daytona, Cloudflare, Vercel, Runloop, and Blaxel ^[4]. Code mode and subagents are on the roadmap for both languages ^[4].

3. Claude Agent SDK (best for Anthropic-native and coding agents)

Best for: Anthropic-native production agents, coding agents, computer use, and long-horizon tool loops. Latest: Python v0.2.111, July 2026, with a parallel TypeScript SDK ^[6]. Languages: Python and TypeScript. Multi-agent: yes, via subagents; a single dynamic workflow can spawn up to 1,000 agents ^[7]. State and memory: persistent sessions, per-subagent context, hooks, and a permissions system ^[6]. Model-agnostic: Claude-first, running Sonnet 4.6, Opus 4.7, and Opus 4.8 ^[7]. License: MIT ^[6]. Notable adopters: it underpins Claude Code and Anthropic Managed Agents ^[7].

Renamed from the Claude Code SDK in September 2025, the Claude Agent SDK ships the same harness that powers Claude Code: a tool-use loop with file editing, bash, web search and fetch, in-process Model Context Protocol servers, hooks, and fine-grained permissions ^[6]. In May 2026 Anthropic added dynamic workflows that fan many subagents out in parallel, each with its own context window, capped at 1,000 agents per run ^[7].

4. CrewAI 1.14 (best for role-based multi-agent prototypes)

Best for: fast role-based multi-agent prototypes and business-process automation. Latest: v1.14.5, March 2026 ^[8]. Languages: Python. Multi-agent: yes, via role-based Crews plus event-driven Flows ^[8]. State and memory: pluggable memory, knowledge, and RAG backends, with typed Pydantic state that survives between Flow steps ^[8]. Model-agnostic: yes, through LiteLLM. License: MIT for the open-source core, with CrewAI AMP and CrewAI Factory as enterprise deployment options ^[8]. Notable adopters: the company reports that roughly 60% of the Fortune 500 use CrewAI, up from about half during its 2024 enterprise beta ^[9].

CrewAI lets you describe agents as roles with goals and backstories and assemble them into a Crew in very little code, which makes it the quickest way to stand up a team-of-agents prototype. Flows layer on deterministic, event-driven control using the @start(), @listen(), and @router() decorators to define explicit execution paths for production ^[8].

5. Microsoft Agent Framework 1.0 (best for enterprise .NET and Azure)

Best for: enterprise .NET and Azure stacks and governed multi-agent workflows. Latest: 1.0, GA April 3, 2026 ^[11]. Languages: .NET (C#) and Python, with the same APIs across both ^[10]. Multi-agent: yes, via graph-based workflows plus native Agent-to-Agent (A2A) protocol ^[10][11]. State and memory: session-based state, context providers, checkpointing, and middleware ^[10]. Model-agnostic: yes, supporting Microsoft Foundry, Azure OpenAI, OpenAI, Anthropic, Ollama, and more ^[10]. License: MIT ^[10]. Notable adopters: Microsoft, Azure, and .NET enterprises.

Microsoft Agent Framework is the direct successor to and convergence of AutoGen and Semantic Kernel, built by the same teams ^[10]. It pairs AutoGen's simple agent abstractions with Semantic Kernel's enterprise features, such as type safety, telemetry, and middleware, and adds graph-based workflows for explicit multi-agent orchestration ^[10]. Both predecessors were placed in maintenance mode in 2026, so new Microsoft-stack projects should start here; MCP and A2A ship native at 1.0 ^[11].

6. LlamaIndex Workflows 1.0 (best for RAG-heavy agents)

Best for: RAG-heavy and document-centric agents. Latest: Workflows 1.0, June 22, 2026 ^[12]. Languages: Python and TypeScript. Multi-agent: yes, via multi-agent workflows. State and memory: typed workflow state, resource injection, and OpenTelemetry-compatible observability ^[12]. Model-agnostic: yes. License: MIT. Notable adopters: enterprise document and retrieval teams.

LlamaIndex pairs the deepest data and retrieval tooling in the ecosystem with an event-driven Workflows engine as its composition primitive and llama-deploy as the production runtime ^[12]. Workflows 1.0 is the first standalone release of the engine, with its own package and typed state in both Python and TypeScript ^[12]. If your agent's hard problem is grounding on large private corpora, this is the natural pick.

7. Pydantic AI V2 (best for type-safe Python)

Best for: type-safe Python agents and durable long-running workflows. Latest: v2.0.0, June 23, 2026, stable after seven betas ^[13]. Languages: Python. Multi-agent: yes, via agent delegation and a graph API. State and memory: durable execution through Temporal, DBOS, and Restate integrations ^[13]. Model-agnostic: yes. License: MIT. Notable adopters: built by the Pydantic team, whose validation library sits under most of the Python AI stack.

Pydantic AI brings the validation discipline of Pydantic to agents. V2 centers on a capabilities primitive that bundles an agent's tools, hooks, instructions, and model settings into one composable unit reaching every layer of the agent ^[13]. Structured, type-checked outputs plus first-class durable execution make it a strong production choice for Python-only teams.

8. Google ADK 2.0 (best for the Google and Gemini ecosystem)

Best for: Google Cloud and Gemini ecosystems and A2A multi-agent delegation. Latest: Python 2.0, GA May 19, 2026, plus ADK for Java 1.0 ^[14]. Languages: Python, Java, Go, and TypeScript ^[14]. Multi-agent: yes, via a Task API for structured agent-to-agent delegation and a graph-based Workflow Runtime ^[14]. State and memory: the Workflow Runtime handles routing, fan-out and fan-in, loops, retries, state, and nested workflows ^[14]. Model-agnostic: Gemini-first, with other models available through LiteLLM. License: Apache-2.0. Notable adopters: it powers Google's Gemini Enterprise agent platform.

ADK is Google's code-first agent toolkit. Version 2.0 added the graph-based Workflow Runtime and the Task API, and the project now spans four languages ^[14]. It is the most natural fit when you are already on Vertex AI and Gemini and want native A2A interoperability.

9. AutoGen and AG2 (legacy conversational multi-agent)

Best for: research-style conversational multi-agent experiments. Status: maintenance mode, superseded by Microsoft Agent Framework ^[11]. Languages: Python (AutoGen also shipped a .NET variant). Multi-agent: yes, via conversational GroupChat patterns. License: MIT.

AutoGen pioneered conversational multi-agent orchestration, but Microsoft folded it into Microsoft Agent Framework and placed the original library in maintenance mode in 2026 ^[11]. The original authors maintain a community fork, AG2, for teams that want to stay on the classic API ^[15]. New projects should prefer Agent Framework or one of the options above.

Honorable mentions

• Semantic Kernel: Microsoft's enterprise SDK, now converged into Agent Framework and in maintenance mode ^[11].
• smolagents: Hugging Face's minimal, code-first agent library under Apache-2.0, strong for code-writing agents.
• DSPy: programs, not prompts, with optimizers that compile and tune agent pipelines instead of hand-crafted instructions.
• Agno, Mastra (TypeScript-native), n8n, and Dify: fast-growing options for TypeScript teams and for low-code or visual builders.

Which framework is best for production?

For a language-agnostic, control-first production system, LangGraph 1.0 is the most battle-tested, with durable state and human-in-the-loop as first-class primitives and named deployments at Uber, LinkedIn, and JP Morgan ^[1]. On the Microsoft stack, Microsoft Agent Framework 1.0 is the production answer, with stable APIs, long-term support, and native MCP and A2A ^[11]. For teams standardized on OpenAI models, the OpenAI Agents SDK plus its native sandbox is the safest managed path ^[4].

LangChain vs CrewAI vs AutoGen: which should you choose?

Choose LangChain, with LangGraph underneath, when you need explicit, durable control over a stateful workflow with cycles, retries, and approval steps ^[1]. Choose CrewAI when the work splits cleanly into specialist roles and you want a multi-agent crew running in a few dozen lines ^[8]. Choose the AutoGen lineage only for research-style conversational agents, and note that active development has moved to Microsoft Agent Framework, with AG2 as the community fork ^[11][15]. For most new 2026 projects the shortlist is LangGraph or CrewAI in Python, the OpenAI or Claude SDK when you are committed to one model provider, and Microsoft Agent Framework on .NET.

References

1. LangChain, "LangChain and LangGraph Agent Frameworks Reach v1.0 Milestones," October 22, 2025. https://www.langchain.com/blog/langchain-langgraph-1dot0 ↩
2. LangChain Changelog, "LangGraph 1.0 is now generally available." https://changelog.langchain.com/announcements/langgraph-1-0-is-now-generally-available
3. OpenAI Agents SDK (Python), GitHub Releases (v0.18.0, July 7, 2026). https://github.com/openai/openai-agents-python/releases ↩
4. OpenAI, "The next evolution of the Agents SDK," April 2026. https://openai.com/index/the-next-evolution-of-the-agents-sdk/ ↩
5. OpenAI Agents SDK documentation (Agents, Handoffs, Guardrails, LiteLLM model support). https://openai.github.io/openai-agents-python/ ↩
6. Anthropic, Claude Agent SDK for Python, GitHub (v0.2.111, MIT license). https://github.com/anthropics/claude-agent-sdk-python ↩
7. MarkTechPost, "Anthropic Ships Claude Opus 4.8 Alongside Dynamic Workflows and Cheaper Fast Mode, With Workflows Capped at 1,000 Subagents," May 28, 2026. https://www.marktechpost.com/2026/05/28/anthropic-ships-claude-opus-4-8-alongside-dynamic-workflows-and-cheaper-fast-mode-with-workflows-capped-at-1000-subagents/ ↩
8. CrewAI, documentation and changelog (v1.14.5, Flows, AMP and Factory). https://docs.crewai.com/en/changelog ↩
9. CrewAI, enterprise adoption disclosure (company-reported Fortune 500 usage), May 2026. https://www.crewai.com/ ↩
10. Microsoft Learn, "Microsoft Agent Framework Overview." https://learn.microsoft.com/en-us/agent-framework/overview/ ↩
11. Microsoft, "Microsoft Agent Framework Version 1.0" (GA April 3, 2026; AutoGen and Semantic Kernel in maintenance mode), Microsoft Agent Framework devblog. https://devblogs.microsoft.com/agent-framework/microsoft-agent-framework-version-1-0/ ↩
12. LlamaIndex, "Announcing Workflows 1.0: A Lightweight Framework for Agentic Systems," June 22, 2026. https://www.llamaindex.ai/blog/announcing-workflows-1-0-a-lightweight-framework-for-agentic-systems ↩
13. Pydantic AI, PyPI and GitHub (v2.0.0, June 23, 2026; durable execution). https://pypi.org/project/pydantic-ai/ ↩
14. Google, "Welcome to ADK 2.0," Agent Development Kit documentation, plus "Announcing ADK for Java 1.0.0," Google Developers Blog. https://adk.dev/2.0/ ↩
15. AG2, community fork of AutoGen, GitHub. https://github.com/ag2ai/ag2 ↩