Best AI Agent Frameworks

LangGraph is the framework we would pick for a serious production agent. You model your agent as a graph: each step is a node, and you decide exactly how control moves between them. That takes more code than a one-line agent, but it pays off when the agent runs for hours, needs a person to approve a step, or crashes halfway. Durable execution saves state after each step, so the agent resumes where it stopped.

The ecosystem is the largest here: 42,182 stars on the Python repo, a JavaScript version, and 44 million PyPI downloads last month. It works with almost any model through LangChain's integrations. The paid side is LangSmith for tracing, evaluation and deployment. It is optional, but it is where LangChain pushes you, and usage-based compute billing can be hard to predict.

Pick it if you need fine control, human approval steps and crash recovery. Skip it if you want a simple agent in 20 lines; the graph model and LangChain's layers are more than a small project needs.

Pydantic AI feels like writing normal Python. It comes from the team behind Pydantic, the validation library most Python AI tools already use, and it brings the same idea to agents: define your inputs, outputs and tools as typed objects, and the framework checks them for you. Switching models is a one-string change.

Version 2 (June 2026) added a capabilities system. You attach MCP servers, web search or durability to an agent as plug-ins. The durable execution story is strong: Temporal, DBOS and Prefect integrations are first-party, so an agent can survive restarts without you writing that plumbing. Release pace is very fast (2.48.0 by 23 September).

The community is smaller than LangGraph's or CrewAI's (20,132 stars), and it is Python only.

Pick it if you write Python, care about type safety and tests, and want freedom to change model providers. Skip it if your team works in TypeScript, or you want a visual builder or a managed platform bundled in.

Google's Agent Development Kit (ADK) is the most complete multi-language option. It ships in Python, TypeScript, Go, Java and Kotlin, and it has the best built-in support for A2A, the open protocol that lets agents from different vendors hand work to each other. Google created A2A, and ADK treats it as a first-class feature.

ADK 2.0 (May 2026) added graph-based workflows, so you can mix fixed code paths with free-form model reasoning, similar to LangGraph. ADK also manages context actively: it filters and summarises old messages instead of just stacking them up. It works with Gemini, Claude, OpenAI models, Ollama and vLLM, and deploys to Google's Agent Runtime, Cloud Run or GKE, or any container host.

The docs and defaults lean toward Gemini and Google Cloud, and the Python version gets features first.

Pick it if you need Go, Java or Kotlin, or you are building a system of agents that must talk to each other. Skip it if you want to stay far from Google Cloud or need the biggest pool of community examples.

The OpenAI Agents SDK is the easiest serious framework to learn. It has three core ideas: agents (a model plus instructions and tools), handoffs (one agent passing the task to another) and guardrails (checks on inputs and outputs). You can read the docs in an afternoon.

It has grown well beyond that core. It now includes sessions with SQLite, Redis or MongoDB storage, built-in tracing, human-in-the-loop approval, sandbox agents that work inside isolated file systems, and realtime voice agents. MCP tools are built in. Non-OpenAI models work through LiteLLM or Any-LLM adapters, though OpenAI's own models and hosted tools get the best support.

The version number is still below 1.0 (0.22.3), so expect some breaking changes, and there is no native A2A.

Pick it if you want to ship a working multi-agent app quickly, or you already build on GPT-6 Astra or other OpenAI models. Skip it if you need deep workflow control, guaranteed crash recovery, or you mainly use other model providers.

The Vercel AI SDK is the most used TypeScript AI library by a wide margin: 17.9 million npm downloads in one week of September 2026. It began as a way to stream model answers into a React app, and it is still the best tool for that. One API covers OpenAI, Anthropic, Google and dozens of other providers.

AI SDK 7 (June 2026) made it a real agent framework. It added ToolLoopAgent for standard agent loops, WorkflowAgent for durable runs that survive restarts, tool approval for human-in-the-loop, sandbox sessions for running commands, and OpenTelemetry tracing. It also supports MCP Apps, which let a tool show its own small interface.

It is still a toolkit more than an agent platform. Memory, evaluation and multi-agent patterns take more of your own code than in Mastra.

Pick it if you build web apps in TypeScript and want agents that stream into your UI. Skip it if you work in Python, or you want memory, evals and a dev dashboard included out of the box.

Mastra is the most complete agent framework for TypeScript. Where the Vercel AI SDK gives you building blocks, Mastra gives you the whole backend: agents, graph workflows, memory, RAG, evals, tracing and a local dev studio to test it all. It connects to 40+ model providers through one interface (a vendor figure) and can both use MCP servers and publish your agents as MCP servers.

It reached 1.0 in January 2026 and ships very often (1.69.0 by 23 September). Mastra Cloud adds hosting and observability, with a free Starter tier and a $250/month Teams plan.

One detail matters for companies: the repo is mostly Apache 2.0, but code in ee/ folders (such as some auth and agent-builder features) needs a paid enterprise licence for production use.

Pick it if you are a TypeScript team that wants memory, workflows, evals and a dev UI in one package. Skip it if you work in Python, or you need a fully permissive licence for every feature.

Strands Agents is AWS's open-source agent SDK. Its idea is model-driven: instead of drawing a workflow, you give a capable model a prompt and tools and let it plan. That keeps code short. In September 2026 the project moved into a single harness-sdk repository that holds the Python and TypeScript SDKs plus a new Strands harness, a pre-assembled agent with tuned defaults you create in one call.

It is model-agnostic, with first-class support for Amazon Bedrock, Anthropic, OpenAI and Gemini. MCP, streaming, structured output and multi-agent patterns are built in, and it runs in your own process with no hosted control plane.

Star counts understate its use (7,735), because much of the adoption is inside AWS customers. The repo rename also means older tutorials point to archived repositories.

Pick it if you build on AWS and want short, model-led agent code. Skip it if you want explicit step-by-step workflow control or a large public community to learn from.

Microsoft Agent Framework (MAF) is the direct successor to both AutoGen and Semantic Kernel, built by the same teams. Microsoft says it combines AutoGen's simple multi-agent ideas with Semantic Kernel's enterprise features: session state, type safety, middleware and telemetry. AutoGen's own repo now says it is in maintenance mode and tells new users to start here.

It is the best choice for .NET developers, with full C# and Python support and a Go version in public preview. It has agents, graph-based workflows, a new batteries-included harness agent for long tasks, and both MCP and A2A. Agents can be hosted on Microsoft Foundry with a couple of extra lines. It supports Foundry, Azure OpenAI, OpenAI, Anthropic and Ollama models.

The docs and samples assume Azure, and the community is still rebuilding after the switch from AutoGen.

Pick it if you work in .NET or Azure, or you are migrating an AutoGen or Semantic Kernel project. Skip it if you have no Microsoft stack and want the most community examples.

CrewAI is the most starred agent framework on GitHub (58,949) and the easiest way to think about multi-agent systems. You define a crew of agents, each with a role, a goal and tools, then give them tasks. For more control, Flows let you script the steps with events and state. It is built from scratch, not on top of LangChain.

It now covers the production basics: memory, knowledge sources, checkpointing, async runs, and both MCP and A2A. It works with most model providers, including local models through Ollama.

The role-play style is great for demos and research-style tasks, but it can make runs less predictable and harder to debug than a graph. The hosted platform has a free tier with only 50 executions a month, and the Enterprise plan has no public price.

Pick it if you want to prototype a multi-agent team quickly in Python, or your use case maps well to roles. Skip it if you need tight, step-by-step control over every action, or you need clear public pricing for hosting.

Agno (formerly Phidata) is more than a framework: it is a framework plus a runtime called AgentOS. You build agents with the Python SDK, then run them as a service with a REST API, a Postgres database for sessions and traces, an MCP server and a web control plane. Everything runs in your own cloud, so your data stays with you.

Version 3.0 (August 2026) doubles down on this "agent platform" idea. Agents can be exposed through Slack, Telegram, WhatsApp, Discord, AG-UI and A2A, and they can pull live context from Slack, Drive and MCP sources. With 42,320 stars, it has a large following.

The trade-off is scope. If you only want a small agent loop inside an existing app, AgentOS is more than you need, and the paid control plane starts at $150/month once you connect a live deployment.

Pick it if you want to self-host a full agent platform with a UI, tracing and memory. Skip it if you just need a lightweight library, or you work outside Python.

LlamaIndex made its name as the go-to library for RAG (retrieval-augmented generation): loading documents, splitting them, storing them in a vector database and feeding the right parts to a model. It has one of the largest integration catalogues for data loaders, embedding models and vector stores, and 52,300 GitHub stars.

It also has agent tools and an event-driven Workflows system for multi-step agents. But the company is open about its focus: its README says the main goal is now document parsing and extraction through LlamaParse, its paid platform. The open-source framework is still maintained and updated often (0.14.25 on 21 September), yet it is no longer where the most agent innovation happens.

Pick it if your agent's main job is to read, search or extract data from lots of documents, such as PDFs, contracts or reports. Skip it if you are building general-purpose agents; LangGraph, Pydantic AI or ADK are better focused on that.

The Claude Agent SDK gives you the same engine that runs Claude Code, as a library. Out of the box your agent can read, write and edit files, run shell commands and search the web. It also includes Claude Code's permission system, hooks, subagents, sessions you can resume or fork, skills, plugins and MCP. No other SDK here ships this much working agent behaviour on day one. Its 7.8 million weekly npm downloads show how widely it is used.

It ranks last here for one reason: it only runs Claude models. That is a real lock-in for a framework, even though Claude Opus 5.5 is one of the strongest agent models available. Use is governed by Anthropic's commercial terms rather than a plain open-source licence. If you want Anthropic to host the agent loop for you, it offers a separate product, Managed Agents.

Pick it if you want a powerful coding or file-handling agent quickly and you are happy to build on Claude. Skip it if you need to switch model providers, run local models, or require an OSI open-source licence.