best 16 ai agent frameworks

langgraph models an agent as a graph of nodes over a typed shared state, and persists that state at every step via checkpointers — which is what gives it genuine crash-and-deploy resume, first-class human-in-the-loop (interrupt and resume), and retries. it reached 1.0 general availability in late 2025 with a no-breaking-changes-until-2.0 commitment, and langchain cites uber, linkedin and klarna as production users. it's cross-provider, and langchain 1.0 now sits on top of it as the higher-level quick-start layer.

the cost is a steeper learning curve, and the broader langchain ecosystem still carries an abstraction-bloat reputation. but for a production agent that needs to survive real failures and pause for a human, nothing else here is as battle-tested.

the agents sdk gives you an agent (instructions plus tools), handoffs to delegate to another agent, guardrails, sessions, and a built-in tracing dashboard, in python and typescript. it is deliberately minimal and readable, which makes it one of the fastest ways to ship a working agent. despite the name it is cross-provider via litellm, though it's tightest with openai's own responses api. temporal shipped a ga integration in march 2026 for the durability it doesn't do natively.

it explicitly replaces swarm, openai's earlier experimental handoff library, which is now archived-in-spirit and labelled educational — do not start new work there. the agents sdk is the supported path.

pydantic ai brings the pydantic ethos to agents: typed dependencies, structured pydantic-validated outputs, and a graph mode for complex control. it reached v1 general availability in september 2025 with an api-stability commitment, ships native durable execution via temporal (out of beta), first-class opentelemetry and logfire tracing, and first-class mcp support. the team's credibility — pydantic validates data across most of the python ai ecosystem — is a real trust signal.

it's cross-provider across openai, anthropic and google. its multi-agent orchestration is newer and less proven than langgraph or crewai, and its ecosystem is smaller though growing fast, but for a python team that values type safety it's the standout.

google's agent development kit pairs an llmagent with deterministic workflow agents (sequential, parallel, loop) for orchestration, plus built-in evaluation, tracing, and native a2a for agent-to-agent communication. it's ga-grade with a proper deploy path to vertex ai agent engine and cloud run, and hierarchical multi-agent is a genuine strength. it's model-agnostic through litellm (anthropic, meta, mistral, ollama, vllm) even though it's tuned for gemini.

the gravity is real: the best features light up on google cloud and gemini, so the more of your stack lives elsewhere, the less of adk you'll use. within the google ecosystem it's a strong, well-designed choice.

the claude agent sdk exposes claude code's own harness as a library: an autonomous tool-loop with context management and compaction, hooks, mcp servers and subagents with isolated context. unlike the raw messages api it runs the tool-execution loop for you, and its context management across long sessions is the genuine differentiator — it's the piece most frameworks handle worst. it was renamed from 'claude code sdk' in late 2025 to reflect general-agent use beyond coding.

the cost is single-vendor lock-in — it's claude-only, on purpose — so it isn't a multi-provider orchestrator. if you're building file-and-command-using autonomous agents on claude, nothing else is as strong; if you need provider flexibility, look higher on this list.

crewai's headline is the crew: agents with roles and goals that collaborate on tasks, which is the quickest way to reason about a team of agents. for the cases where autonomous crews are too loose, flows add event-driven, deterministic control with state threading and persistence. it's now fully standalone, having dropped its original langchain dependency, and has one of the largest communities in the category.

the honest weakness is the flip side of its strength: autonomy-first crews can be hard to make deterministic and reliable at scale, and the fine-grained control lives in flows, which are newer. it's cross-provider and a strong starting point for role-oriented teams.

microsoft agent framework unifies autogen's multi-agent orchestration with semantic kernel's enterprise foundation (state, filters, telemetry, type safety), across python and .net. it shipped 1.0 ga in april 2026 with stable apis and a long-term-support commitment, plus a2a and mcp interop and azure ai foundry deploy. crucially it's the go-forward successor to both predecessors: autogen is now explicitly in maintenance mode and points new users here, and semantic kernel is supported but de-prioritised for new features.

it's the only credible first-class .net agent framework, which alone justifies its place for microsoft shops. the caveats are that it's young as a merged product and carries heavy azure gravity. (note the licence trap on the old autogen repo: github shows cc-by-4.0 but the code is mit under LICENSE-CODE.)

strands takes a model-driven approach: give the model tools and a prompt and let it drive, with four native multi-agent primitives (swarm, graph, workflow, agents-as-tools). it hit 1.0 in may 2026 and already powers amazon q developer, aws glue and vpc reachability analyzer, with native a2a, a remote session manager and opentelemetry observability. it's model-agnostic across bedrock, anthropic, openai and gemini — you can swap backends without code changes.

the community is smaller than the leaders and the deepest integration is on bedrock and agentcore, so it's most compelling if aws is your home. that it's used in aws's own shipping services is a real credibility point.

llamaindex workflows model an agent as steps that consume and emit typed events, with the runtime routing events to subscribing steps — an async-first, clean event model that reached its first stable 1.0 release. its real advantage is what it sits on: llamaindex's data and rag stack, so when your agent is fundamentally retrieval-over-your-data, this is the most natural home. agentworkflow orchestrates multiple agents over the same event bus.

the trade-off is package sprawl — the broader llamaindex surface is large — and it reads more as a data framework with agents than a pure agent runtime. cross-provider, and strongest for data-centric agents.

agno (formerly phidata) is a fast, full-stack agent library plus agentos, a production api-server runtime for hosting agents. its standout move: as of v2.6 agentos became framework-agnostic, able to run agents from the claude agent sdk, langgraph and dspy behind a unified protocol — a real differentiator if you want one runtime over many frameworks. it's cross-provider with teams and workflows for multi-agent.

the caveat is churn: v2 was a breaking rewrite that made the core classes stateless and forced migrations, and coming on top of the phidata rename it dented the project's stability perception. the tech is strong; weigh the recent instability.

mastra, from the gatsby team, is a typescript-native framework bundling agents, deterministic workflows (with suspend/resume for human-in-the-loop), memory, rag, evals, tracing and mcp, with a local dev playground and serverless/edge deploy. among typescript-first options it's the most complete, and its dx is a genuine strength. it's cross-provider, built on the vercel ai sdk's provider layer.

it's younger and has a smaller ecosystem than the python leaders, and its durable execution is workflow-based rather than temporal-grade. the licence is apache-2.0 for the core with a separately-licensed commercial ee/ directory — github mislabels it, but the file is clear — which matters to some buyers.

the vercel ai sdk is the provider-abstraction and streaming/ui layer that a large share of typescript llm apps are built on, with an agent loop on top (generatetext/streamtext with tools and stopwhen, plus an agent abstraction). its model abstraction and streaming dx are excellent and its adoption is enormous. cross-provider is the whole point.

but as a full agent-orchestration framework it's thinner than mastra — multi-agent is lighter, and it's not a durable-execution engine — so for heavy orchestration you pair it with a workflow engine. ranked on the agent-framework axis specifically, that's why it's below mastra despite far larger overall usage. (licence note: github mislabels it, but the file is apache-2.0.)

smolagents from hugging face centres on code-agents: the react loop where the model writes and runs python each step, rather than emitting json tool calls. it's deliberately barebones and genuinely elegant, with sandboxed execution via e2b, modal, docker or blaxel, and managed sub-agents for multi-agent. it's cross-provider across hugging face, inference and litellm models.

by design it's thin on production reliability primitives — no durable execution, limited human-in-the-loop — so it shines for lightweight and research work more than for hardened production agents. within that scope, the code-first paradigm is powerful.

letta (the productised memgpt) runs agents as a long-lived server with tiered memory — a core context plus archival vector memory persisted in postgres — so agents accumulate and recall memory across sessions. you interact via a rest api and sdks, and it runs managed or self-hosted. for memory-centric use cases it's best-in-class and genuinely distinctive.

the trade-off is that it's an opinionated, server-centric architecture rather than a general orchestration framework, so it's narrower than most of this list. it's cross-provider. reach for it when persistent memory is the actual problem you're solving.

dspy from stanford nlp is a framework for programming and optimizing llm pipelines — signatures and modules plus automatic prompt/weight optimizers like mipro and gepa, a reflective evolutionary optimizer that reportedly beats rl methods with far fewer rollouts. it's production-used and can build react agents, but reliability and durability aren't its focus; its optimized prompts often feed the other frameworks on this list.

that's why it ranks low here specifically — it's the wrong tool if you want an orchestration runtime, and the right tool if you want to squeeze quality out of a pipeline. cross-provider, and genuinely valuable at what it actually does.

ag2 is the community fork of autogen, founded by autogen's original creators after they left microsoft, carrying the autogen 0.2 lineage (conversableagent, groupchat) forward and branding itself 'the open-source agentos'. it's active — a beta async/event-driven rewrite launched in 2026 — with retries, tool use, human-in-the-loop via conversation patterns, and observability integrations, all cross-provider.

the honest risk is momentum: at under 5,000 stars it's directly overshadowed by microsoft agent framework, which now owns the 'autogen successor' narrative. if you specifically want the familiar autogen conversation model with backward compatibility it's a reasonable pick; underwrite its longevity carefully.