What are the best agentic AI frameworks in 2026?

LangGraph is where many production teams land when an agent has to survive a crash. It describes itself as a low-level orchestration framework for long-running, stateful agents, and that's the honest pitch: you draw a graph of nodes and edges, every step writes its state to a checkpointer, and a run can stop for a human approval and pick up where it left off. Durable execution, human-in-the-loop and short and long-term memory are documented features, not plugins.

It doesn't force LangChain on you. The docs say it plainly: LangGraph does not require LangChain. You can call any model SDK from a node, which answers a frequent complaint about the LangChain family (layers of abstraction you didn't ask for). LangGraph is MIT licensed and has a JavaScript and TypeScript version, LangGraph.js.

Where it costs money is observability and hosting. LangSmith, the tracing and evaluation product, has a free Developer plan with 1 seat and 5k base traces a month. Plus is $39 per seat per month with 10k traces included, billed monthly. You don't need either to run LangGraph, but tracing is what you'll want the first time an agent misbehaves.

The catch: the learning curve. State schemas, reducers, checkpointers and threads are a lot of vocabulary for a two-step agent. In a 2026 developer thread on production frameworks, one practitioner summed it up as "powerful but complex". If your agent is one prompt and three tools, it's more framework than you need.

Tip: never ship with the in-memory saver. MemorySaver and InMemorySaver keep checkpoints in RAM, so a restart loses every run. Use PostgresSaver, give each conversation a stable thread ID so a retry resumes instead of starting over, and keep that ID under 255 characters: a longer one overflows the Postgres column and throws a database error. Our full LangChain and LangGraph review covers the rest of the ecosystem, and our LangChain alternatives page covers teams moving away from it.

The OpenAI Agents SDK keeps its core small: agents (a model with instructions and tools), handoffs (one agent passes the conversation to another), guardrails (checks on input and output), sessions, human-in-the-loop and tracing. Many multi-agent patterns fit in those few ideas, and that's why it gets you from zero to a working demo quickly.

Despite the name, it isn't locked to OpenAI. The README calls it provider-agnostic, supporting the OpenAI Responses and Chat Completions APIs as well as 100+ other LLMs. There's a JavaScript and TypeScript version too, plus realtime voice agents and sandbox agents for long jobs in a container. The SDK is MIT licensed and free; your bill is the model tokens.

Know one default before you ship: tracing is enabled by default, and traces go to OpenAI's dashboard. You can switch it off globally with OPENAI_AGENTS_DISABLE_TRACING=1, and the docs note that tracing is unavailable for organizations under a Zero Data Retention policy. If your security team reads data flows, tell them about this one up front.

The catch: durability. Sessions keep conversation history, but a run that must survive a crash goes through an outside engine: the docs list durable execution integrations with Dapr, Temporal, Restate and DBOS. That's one more system to run next to your agent.

Tip: start with handoffs between two specialist agents rather than one agent with twenty tools. Smaller tool lists per agent are easier to debug, and the trace view shows you exactly which agent made which choice.

Pydantic AI comes from the team behind Pydantic, the validation library inside the OpenAI SDK, the Anthropic SDK, the Google ADK and LangChain. Its promise is the FastAPI feeling for agents: you declare the output type, the framework validates it, and a malformed model answer becomes a retry instead of a crash at 3 a.m.

Model freedom is its other strength. The docs say no flagship feature is locked to one vendor, and switching models is a string change, with model fallback built in. For long jobs, Pydantic AI attaches to durable engines you may already run: Temporal, DBOS, Prefect and Restate are among the durable execution integrations in its docs, so agents survive restarts. Pydantic Graph and Pydantic Evals cover workflows and grading.

The framework is MIT licensed and free. The paid part is Logfire, Pydantic's observability platform: Personal is free up to 10 million records a month with ingestion paused past the cap, Team is $49 a month with 5 seats included, Growth is $249 a month. Past the 10 million records included on Team and Growth, each extra million costs $2, and each Team seat beyond the five is $25; the plan lets you set a hard price cap. You can also send traces to any OpenTelemetry backend instead.

The catch: for multi-agent orchestration you'll write more of the coordination yourself. In a 2026 developer thread on production frameworks, one practitioner who had run CrewAI and LangGraph before said Pydantic AI is now how their team runs its platforms and agents, for its type validation and its observability through Logfire.

Tip: put your business rules in the output model (ranges, enums, required fields). Validation errors are sent back to the model to fix, which saves you writing your own retry loop.

CrewAI is approachable by design. You describe agents by role, goal and backstory, give them tasks, and a crew runs them in sequence or with a manager. For event-driven control you switch to Flows, which add precise steps, single LLM calls and crews as building blocks. It's a standalone Python framework with its own primitives, MIT licensed.

That role metaphor is why non-specialists get results fast, and why CrewAI heads a section in most of the comparison guides in the top 10. A content pipeline or a research crew is a natural first project, and CrewAI says over 100,000 developers have been certified through its community courses.

The paid side is the CrewAI platform (AMP): managed deployment, tracing, governance and support. The Basic plan is free with 50 workflow executions a month; Enterprise is custom pricing. The open-source framework stays free either way.

The catch: control. In a 2026 developer thread on production frameworks, one practitioner called CrewAI approachable but hard to debug at scale, and free-running crews are where that bites. And CrewAI collects anonymous telemetry by default, separately from the tracing of its paid platform.

Tip: there are two switches, not one. CREWAI_DISABLE_TELEMETRY=true stops the anonymous telemetry, and OTEL_SDK_DISABLED does too, but neither turns off the platform's tracing: for that, set CREWAI_TRACING_ENABLED=false or pass tracing=False on the Crew or Flow. Any value other than true, 1, yes or on is ignored and leaves telemetry on. Then use Flows, not free-running crews, for anything a customer sees.

Google's Agent Development Kit treats agents like regular software: code-first, versioned, testable. ADK 2.0 adds a graph-based workflow runtime (routing, fan-out and fan-in, loops, retry, human-in-the-loop) and a Task API for structured delegation between agents. It ships in Python, Java, Kotlin, Go and TypeScript.

It's optimized for Gemini but not tied to it: the README says ADK is model-agnostic, deployment-agnostic and compatible with other frameworks. Tools come from pre-built functions, OpenAPI specs or MCP servers. The framework is Apache 2.0 and free; you pay when you host it on Google Cloud (Cloud Run, or Agent Runtime, formerly Vertex AI Agent Engine), billed by usage like any other cloud service.

The catch: outside Google Cloud, you bring your own observability and deployment story. In our comparison, ADK makes the most sense when your data and models already live in Google Cloud.

Tip: use ADK's built-in evaluation from day one. Write a handful of expected conversations as test cases and run them on every change; it catches prompt regressions before your users do.

Microsoft Agent Framework is where AutoGen and Semantic Kernel went. Microsoft's docs call it the direct successor, created by the same teams: AutoGen's simple agent abstractions plus Semantic Kernel's enterprise features (session state, type safety, middleware, telemetry), with graph-based workflows on top. It runs on .NET and Python, with Go in public preview.

For multi-agent work you get sequential, concurrent, handoff and group collaboration patterns, with checkpointing, streaming, human-in-the-loop and time-travel. Model support covers Microsoft Foundry, Azure OpenAI, OpenAI, Anthropic, Ollama and more. The framework is MIT licensed and free; Azure and Foundry usage is billed by Microsoft.

The catch: it's young under this name (version 1.0 is the first production-ready release, with stable APIs and a long-term support commitment), and the docs lean hard on Azure. Microsoft also states that when you connect third-party models or servers, you do so at your own risk and own the data-flow questions. Python teams with no Microsoft stack may prefer a lighter start.

Tip: if you run AutoGen today, plan the move now. The AutoGen repository is archived, and Microsoft says it no longer ships bug fixes or security fixes for it. Official migration guides exist for both AutoGen and Semantic Kernel.

Mastra is built for TypeScript from the start, not ported from Python. You get agents, a graph-based workflow engine with .then(), .branch() and .parallel(), suspend and resume for human approval, memory, RAG, evals and observability, all in one package. It routes to 40+ model providers through one interface and can expose your agents as MCP servers.

For a product team on Next.js, that means the agent lives in the same repo, the same types and the same deploy as the app. Start with npm create mastra@latest.

Pricing has two layers. The framework core is Apache 2.0 and free, but code in ee/ folders sits under the Mastra Enterprise License and needs a license for production use. The hosted Mastra platform is priced per workspace each month: Free at $0 with 100K observability events, Starter at $25 with one small dedicated project, Team at $250 with two medium projects and the SOC 2 report, and custom Enterprise pricing for SSO, RBAC and audit logs. Usage past the allowances is billed on top, for example $10 per extra 100K observability events.

The catch: it's aimed at TypeScript teams, and its multi-agent features are recent. Check which features you rely on live in ee/ before your legal team asks.

Tip: use workflow suspend and resume with persistent storage for anything that waits on a person. Mastra keeps the execution state, so the run can sit for days and resume exactly where it stopped.

LlamaIndex started as the RAG framework and still has deep data tooling: over 300 integration packages for LLMs, embeddings and vector stores, plus Workflows to chain agents, connectors and tools. If your agent's job is to answer from a pile of documents, it's a natural fit.

Be clear about where the company is going. The README now says its focus is document parsing and extraction through LlamaParse, and that while the open-source framework stays available, its primary focus has shifted toward LlamaParse. The framework still works; just expect most new investment to go to LlamaParse.

The framework is MIT and free. LlamaParse is billed in credits: pay-as-you-go is $0 a month with 10K free credits, then $1.50 per 1,000 credits; Pro is $500 a month with 400K included credits and a lower rate of $1.25 per 1,000 credits.

The catch: general multi-agent orchestration isn't the company's focus; the README says the current focus is document parsing and extraction.

Tip: you can use LlamaParse for ingestion and run your agent logic in another framework. Many teams do exactly that, feeding parsed output into a LangGraph or Pydantic AI agent as a tool.