What should developers look for when choosing an AI agent framework?

Seven things, in this order: how much control you need over the workflow (graph vs autonomous loop), durable state and resume after failure, human-in-the-loop approval, model and vendor lock-in, built-in tracing and evaluation, your language (Python or TypeScript), and licence plus release cadence. As of October 2026 that usually leads to LangGraph for controlled stateful workflows, a vendor SDK (OpenAI Agents SDK, Claude Agent SDK, Google ADK) when you are committed to one model provider, CrewAI for role-based multi-agent work, Pydantic AI for typed Python services, and Mastra or the Vercel AI SDK in TypeScript.

Do I need an agent framework at all?

Not always. A single model call with a tool loop, written against the provider's SDK, is enough for many assistants and is easier to debug. Adopt a framework when you need persistent state across steps or sessions, retries and resume after a crash, human approval steps, several cooperating agents, or tracing you would otherwise build yourself.

Are vendor agent SDKs a lock-in risk?

Some. The OpenAI Agents SDK, Claude Agent SDK and Google ADK are open source (MIT or Apache-2.0) and most can call other models, but each is designed around its owner's model, tools and hosting. If you expect to switch or mix models, prefer a model-neutral framework such as LangGraph, Pydantic AI, CrewAI or Mastra and keep the model behind an interface.

Which agent frameworks are actively maintained in October 2026?

All the major ones pushed code within the last day as of October 20, 2026: LangGraph (about 43,600 GitHub stars), CrewAI (59,900; v1.15.28, October 14), OpenAI Agents SDK (30,200; v0.24.0, October 15), Mastra (28,900; @mastra/core 1.78.0, October 16), Vercel AI SDK (27,400), Google ADK (21,900; v2.12.0, October 13), Pydantic AI (20,700; v2.58.0, October 20), Microsoft Agent Framework (14,200; python-1.22.0, October 15) and the Claude Agent SDK for Python (8,300; v0.2.168, October 15).

Quick Answer

How to Choose an AI Agent Framework in 2026: 7 Criteria

Published: October 20, 2026

The short answer

Choose an agent framework by how much control you need over the workflow, then by state, lock-in and observability — not by GitHub stars. As of October 20, 2026:

You need a predictable, auditable workflow with branches, retries and approvals → LangGraph.
You are committed to one model provider → its SDK: OpenAI Agents SDK, Claude Agent SDK or Google ADK.
The work splits into roles (researcher, writer, reviewer) → CrewAI.
A typed Python service that happens to call an LLM → Pydantic AI.
Your stack is TypeScript → Mastra for full agents and workflows, the Vercel AI SDK for a lighter tool loop in a web app.
A Microsoft and Azure estate → Microsoft Agent Framework.

For download-based popularity see most popular AI agent frameworks; for head-to-heads see LangGraph vs CrewAI vs Mastra.

The seven criteria

1. Control: graph or loop? Agent frameworks sit on a line from explicit graphs (you define nodes and edges; the model decides inside nodes) to autonomous loops (the model decides the next step and which tool to call). Graphs (LangGraph, Mastra workflows, CrewAI Flows) are easier to test, audit and put in front of regulators. Loops (vendor SDKs, Pydantic AI agents) are faster to build and better for open-ended tasks. Most production systems use a graph with loops inside some nodes.

2. Durable state. Ask: if the process dies on step 7 of 12, does the run resume from step 7? Does conversation and task state survive between sessions? Checkpointing to a database is built into LangGraph; elsewhere you may need a durable execution engine underneath — see best durable execution platforms for agents.

3. Human in the loop. Approval before a payment, an email or a production deploy should be a first-class pause-and-resume, not a polling hack. Test it in the prototype.

4. Model and vendor lock-in. Vendor SDKs get the newest features of their own models first (tools, computer use, caching) but are tuned to them. If you expect to route between current models for cost, pick a model-neutral framework.

5. Observability and evaluation. You will debug agents from traces. Check for built-in tracing (OpenAI Agents SDK, LangGraph with LangSmith, Pydantic AI with Logfire), OpenTelemetry export, and a way to replay a failed run. See best LLM observability tools.

6. Language and deployment. Python has the most choice. In TypeScript, Mastra and the Vercel AI SDK lead. Check where it runs: serverless time limits break long agent runs.

7. Licence and cadence. All frameworks below are open source; Mastra is Apache-2.0 except its ee/ directories, which carry a separate enterprise licence. Weekly releases mean fast fixes and frequent breaking changes — pin versions.

The shortlist at a glance

GitHub data read October 20, 2026.

Framework	Language	Style	Licence	Stars	Latest release
LangGraph	Python, JS	Explicit graph, checkpointed state	MIT	~43,600	Active daily (Oct 19)
CrewAI	Python	Role-based crews + Flows	MIT	~59,900	1.15.28 (Oct 14)
OpenAI Agents SDK	Python, JS	Agent loop, handoffs, guardrails, tracing	MIT	~30,200	v0.24.0 (Oct 15)
Claude Agent SDK	Python, TS	Claude Code’s agent harness as a library	MIT	~8,300 (Python)	v0.2.168 (Oct 15)
Google ADK	Python, others	Multi-agent, deploys to Vertex AI	Apache-2.0	~21,900	v2.12.0 (Oct 13)
Pydantic AI	Python	Typed agents, dependency injection	MIT	~20,700	v2.58.0 (Oct 20)
Microsoft Agent Framework	Python, .NET	Agents + workflows, Azure integration	MIT	~14,200	python-1.22.0 (Oct 15)
Mastra	TypeScript	Agents, workflows, memory, evals	Apache-2.0 (+ `ee/`)	~28,900	@mastra/core 1.78.0 (Oct 16)
Vercel AI SDK	TypeScript	Model-neutral tool loop for apps	Open source	~27,400	Active daily

A one-day bake-off

Build the same small agent — one tool call, one approval step, one deliberate crash — in your two finalists. Score: lines of code, whether the crash resumes, how clearly the trace shows what happened, and how hard it is to swap the model. The winner is usually obvious by the afternoon.

Last verified: October 20, 2026.

The short answer

The seven criteria

The shortlist at a glance

A one-day bake-off

Sources