10 Best AI Agent Frameworks of 2026

LangGraph ranks first because it gives developers direct control over how an agent moves between steps, which is what long-running production agents need. LangChain describes it as an agent runtime and low-level orchestration framework: you define state and the paths between nodes, then add human-in-the-loop checks that pause the agent for approval, built-in memory that persists across sessions, and token-by-token streaming of reasoning and actions. The same framework covers single agents, multi-agent teams and hierarchical setups. The library is MIT licensed and works with any model provider. The paid side is LangSmith: the Developer plan is free for one seat with 5k base traces a month, and Plus costs $39 per seat a month with 10k traces and one free small serverless deployment. The trade-off is effort. The graph model asks more of you up front than the OpenAI Agents SDK or CrewAI, and metered usage on LangSmith needs watching.

Google's Agent Development Kit is the most complete option for teams that do not live in a single language. The docs cover Python, TypeScript, Go, Java and Kotlin, and ADK 2.0 adds graph workflows next to the existing sequential, loop and parallel templates. Agents can delegate to sub-agents, call function, MCP and OpenAPI tools, and talk to remote agents through the A2A protocol. ADK also handles live and voice agents, and ships a web interface, a visual builder, a CLI and an API server for running and testing them. Evaluation features include criteria, user simulation and custom metrics. Model support goes beyond Gemini to Claude, OpenAI, Ollama, vLLM and LiteLLM. The framework is Apache 2.0 licensed and free. Its deployment guides point to Google's Agent Runtime, Cloud Run and GKE, which suits Google Cloud teams but means extra work elsewhere, and the sheer number of features makes the first week steeper than with the OpenAI SDK.

The OpenAI Agents SDK is the fastest route from idea to working agent. OpenAI built it around very few primitives: agents with instructions and tools, handoffs or agents-as-tools for delegation, and guardrails that validate inputs and outputs in parallel with the run. On top of that come sessions for persistent memory, human-in-the-loop hooks, MCP tool calling and built-in tracing that connects to OpenAI's evaluation and fine-tuning tools. Newer additions include sandbox agents that work inside isolated, resumable workspaces, and realtime voice agents. The SDK is MIT licensed, available for Python and TypeScript, and replaces OpenAI's earlier Swarm experiment. It uses the Responses API by default, and other providers plug in through LiteLLM or Any-LLM adapters, so it is not locked to OpenAI, but it is clearly tuned for it. There is no hosted deployment layer, so you run agents on your own infrastructure.

CrewAI is the easiest framework to explain to the rest of the business. You describe each agent's role, goal and backstory, assign tasks and group the agents into a crew, in YAML, code or both. Under that simple surface are planning agents that recover from errors, hundreds of open source tools, MCP support, sandboxed code execution through E2B and Daytona, a memory layer that resolves contradictions, and checkpointing that lets you replay or fork a run from any step. Flows add explicit orchestration and state when crews need structure. The framework is MIT licensed. The hosted platform has a free Basic plan with the Studio visual editor, an AI copilot, tracing, OpenTelemetry, guardrails and 50 workflow executions a month. Beyond that, the only option is a custom Enterprise plan with SSO, RBAC, VPC deployment and a 45-day onboarding, so small teams that outgrow Basic have no self-serve step up.

Pydantic AI comes from the team behind the Pydantic validation library, and its focus on types shows. You define an agent with a model string, tools and an output type, and the framework validates what comes back, falling back to another model when a provider fails. The Pydantic AI Harness adds capabilities you snap on as needed, from memory, guardrails and sub-agents to planning and context management, and even ships a complete terminal coding agent built from those parts. Pydantic Evals grades final outputs and tool-call paths, and Pydantic Graph adds typed control flow when one loop is not enough. Instrumentation is plain OpenTelemetry, so traces go to Logfire or any backend you already run. Logfire's Personal plan is free with 10M records a month, and Team costs $49 a month with five seats. The framework is Python only, and the split into several packages means a little more assembly than an all-in-one SDK.

Microsoft Agent Framework is the direct successor to AutoGen and Semantic Kernel, built by the same teams. It keeps AutoGen's simple single and multi-agent abstractions and adds Semantic Kernel's enterprise pieces: session-based state, type safety, middleware, filters and telemetry. The framework has four parts. Agents call tools and MCP servers on Microsoft Foundry, Anthropic, Azure OpenAI, OpenAI, Ollama and other providers. The Harness Agent adds planning, todo tracking, context compaction, file access, memory and tool approval for long tasks. Workflows connect agents and functions through functional or graph-based paths, and integrations cover model providers, evaluation services and UI frameworks. It is MIT licensed and supports .NET and Python, with Go in public preview without declarative agents or workflows. Microsoft's own advice is to use a workflow when the steps are known and an agent only when the task is open-ended. Examples assume Azure credentials, so non-Azure teams have more setup.

Mastra is the strongest pick for teams that write TypeScript. The open source framework, licensed under Apache 2.0, covers agents with models, tools, memory and instructions, durable workflows with typed control flow, and observational memory that keeps context across sessions. A model router connects to 40+ providers through one interface, using simple provider/model strings. The hosted Mastra Platform adds observability, deployments and Studio. Its free Starter tier includes 100K observability events, 24 CPU hours and 15 days of retention with unlimited users and projects, which is generous for testing. The step up is steep: Teams costs $250 a month for 1M events, 250 CPU hours, six months of retention, SSO and SOC 2 documents, and extras such as a 24/7 persistent server cost $100 per project. Enterprise features for self-hosting, including RBAC and SSO, sit under a separate commercial license. Python teams should look elsewhere.

Agno takes a different approach from most frameworks: instead of hosting your agents, it gives you a runtime called AgentOS to run in your own cloud, with templates for Render, Railway, Docker, AWS, Fly, GCP, Kubernetes, Azure and Modal. AgentOS handles authentication, role-based access, resume and replay, human approvals, scheduling and durable execution, and serves agents over a REST API, an MCP server for Claude and ChatGPT, and chat apps such as Slack and Discord. You can build with the Agno SDK or plug in the Claude Agent SDK, Codex SDK or LangGraph. The runtime is free and open source under Apache 2.0, and Agno says it never meters events, compute or storage. The paid part is the control plane for live deployments: Pro costs $150 a month with one live connection and three seats, after a 14-day trial. Extra connections at $95 each and SAML SSO at $300 a month add up for larger rollouts.

Letta is built around one idea: agents should keep long-term memory and get better with use. Its open source agent harness, licensed under Apache 2.0, stores memory in repositories the agent reads and edits, supports shared memory between agents, and adds skills, schedules and subagents for recurring work. Developers embed agents through the Letta Agent SDK for TypeScript applications, then choose managed Letta Cloud or run the full runtime themselves with the Letta App Server. Agents can also live in Slack, Telegram, Discord, WhatsApp and Signal through channels. Pricing is the cheapest verified paid entry here: Pro is $20 a month with up to 20 stateful agents and a Letta Auto quota, and the API plan for developers is $20 a month with unlimited agents, $0.10 per active agent and per-second tool execution fees. The free plan caps you at 3 stateful agents. Recent releases lean toward coding and personal assistants, so general app builders should check the SDK docs closely.

LlamaIndex built its name on retrieval-augmented generation, and its framework remains the best option when an agent's main job is working over your documents. The MIT-licensed Python toolkit gives you ingestion pipelines, node parsers, vector store integrations and query engines, and treats a RAG pipeline as one tool an agent can call. Agent Workflows are event-driven, multi-step processes with branching, retries, concurrency, streaming and human-in-the-loop review, and can run as durable services. The company now leads with LlamaParse, its paid parsing and extraction platform for 130+ file formats. Pay-as-you-go costs $0 a month with 10K free credits and then $1.5 per 1,000 credits, while Pro is $500 a month with cheaper credits and a one-time credit bonus for business email domains. For agents that rarely touch documents, the framework's strengths matter less and lighter SDKs are easier to start with.