Best AI Agent Frameworks in 2026: LangGraph, CrewAI, OpenAI Agents SDK and More

An agent framework gives you the loop around a language model: calling tools, keeping state between steps, handing work between agents and recovering when something fails. AgentGid tracks 37 of them; all are open source and free to install, so the real costs are the model you call and the time it takes your team to learn the framework.

The short version

As of Oct 10, 2026:

• Most downloaded: LangChain (41.1M/week), AI SDK (29.8M/week), Claude Agent SDK (19.7M/week), LangGraph (15.9M/week).
• Most GitHub stars: LangChain (148K), MetaGPT (70.8K), AutoGen (61.3K), CrewAI (59.5K).

Downloads include CI pipelines and dependencies pulled in by other packages, so a framework that many libraries depend on will look bigger than its direct user base. Stars measure attention rather than use. Read both as a shortlist, not a verdict.

The live ranking

#	Agent	Gid Score	Downloads 7d	7d	30d	Stars	Latest release
4	LangChainLangChain	78	41.1M	▼down 7.8%	▼down 16.9%	148K+46/day	1.4.42d ago
14	LangGraphLangChain	70	15.9M	▲up 1.3%	▼down 9.4%	43K+52/day	1.2.144d ago
16	AI SDKVercel	68	29.8M	▼down 11.6%	▲up 44.6%	27.2K+20/day	6.0.303yesterday
21	Strands AgentsAWS	63	8.7M	▼down 0.4%	▲up 6.3%	8,751+20/day	0.2.02d ago
22	Claude Agent SDKAnthropic	63	19.7M	▼down 5.1%	▲up 11.4%	8,237+5/day	0.2.1652d ago
31	MastraMastra	59	2M	▼down 8.1%	▲up 43.8%	28.7K+25/day	1.75.03d ago
33	Deep AgentsLangChain	58	2M	▲up 2.5%	▲up 21.3%	30.1K+23/day	0.1.833d ago
38	Cloudflare Agents SDKCloudflare	57	1.9M	▼down 13.7%	▲up 47.2%	5,800+13/day	0.27.03d ago
40	CopilotKit NewCopilotKit	57	592K	▼down 13.2%	▲up 23.9%	37.9K+31/day	1.78.0yesterday
43	DSPyStanford NLP	55	1.3M	▼down 1.4%	▼down 1.6%	38.6K+16/day	3.4.015d ago
44	OpenAI Agents SDKOpenAI	55	5.2M	▼down 3.6%	▼down 35.2%	29.9K+21/day	0.23.18d ago
50	AgnoAgno	54	513K	▲up 3.1%	▼down 8.7%	42.6K+25/day	3.1.22d ago
52	CrewAICrewAI	52	640K	▲up 9.0%	—	59.5K+34/day	1.15.27yesterday
63	eve NewVercel	50	1.2M	▼down 10.2%	▲up 73.4%	5,511+10/day	0.75.12d ago
72	Agent Development Kit (ADK)Google	48	2.6M	▼down 6.6%	▼down 33.2%	21.8K+10/day	2.11.08d ago

All agent frameworks

Which kind of framework do you need?

Frameworks differ less in what they can do than in how much they decide for you.

Low-level orchestration: you design the flow

LangGraph models an agent as a graph of steps with persistent state, so you control exactly what happens when, can pause for human approval and resume after a crash. It suits production systems where predictability matters more than speed of prototyping. LangChain sits on top with model integrations and prebuilt agents; Deep Agents adds planning, a virtual file system and sub-agents for long tasks.

Role-based multi-agent teams

CrewAI organises work as a crew of agents with roles and tasks, plus event-driven "flows". It is quick to get a multi-agent prototype running and readable for non-specialists. AutoGen and its community continuation AG2 take a conversation-between-agents approach; Microsoft's newer Microsoft Agent Framework is the successor to both AutoGen and Semantic Kernel, so it is the safer pick for a new Microsoft-stack project.

Vendor SDKs: thin and close to one model family

OpenAI Agents SDK, Claude Agent SDK, Google's ADK and AWS Strands Agents are small, well documented and tuned for their maker's models and cloud. The Claude Agent SDK exposes the same loop that powers Claude Code. They are the fastest path if you have already chosen a model provider; most can call other models too, with less polish.

Typed and data-first Python

Pydantic AI builds agents around type-checked structured outputs, which makes them easier to test. LlamaIndex and Haystack start from retrieval over your own documents and add agents on top. DSPy replaces hand-written prompts with modules that can be optimised automatically.

TypeScript

Mastra is one of the most complete TypeScript agent frameworks, with memory, evals and observability built in. Vercel's AI SDK is a lighter toolkit with one API across providers and streaming UI helpers. VoltAgent and Inngest's AgentKit are other options; LangGraph and the OpenAI Agents SDK also ship TypeScript versions.

Head-to-head comparisons

• LangGraph vs CrewAI
• LangGraph vs Google ADK
• CrewAI vs AutoGen
• LangGraph vs LangChain

How to choose

1. Language first. Python has the widest choice; in TypeScript, start with Mastra or the AI SDK.
2. Model provider second. If you are committed to one provider, try its SDK before anything heavier.
3. Decide how much control you need. A support agent that must follow policy step by step benefits from an explicit graph; a research assistant can live with a looser loop.
4. Check the boring parts. Persistence, retries, tracing and human-in-the-loop are what break in production. Prefer a framework where they are built in or have a clear integration.
5. Look at release activity. Frameworks change fast. The agent pages show the latest release and how many shipped in the last 90 days; a quiet repository is a risk.

Frameworks or no framework?

A common path is to start with a plain loop around a model API and add a framework only when they need durable state, multi-agent handoffs or observability. That is a reasonable path for a first agent. If you want to see what others built and with which tools, browse the use cases, and for the basics of building one, read how to build an AI agent.