LangGraph is the pick when you want the control flow written down explicitly and executions that survive a restart. The cost is verbosity: a simple tool loop takes noticeably more code than in a higher-level framework. If your agent is one model calling a handful of tools, several lighter options below will do it in a fraction of the lines.
8 tools reviewedLast reviewed Ranked by us, not by votes
What counts as AI agent frameworks
Libraries for building applications in which a language model plans and calls tools in a loop until a task is done. They supply the loop itself plus the parts around it: tool and schema definition, conversation and state persistence, multi-agent delegation, human approval steps, and tracing. They are developer libraries rather than finished products, so you bring your own model API keys and hosting.
How we judged them
Entries were judged on three things: debuggability, meaning how readily you can see which step produced a bad result; how much behavior the framework hides behind its abstractions, since hidden prompt assembly and control flow is what makes production incidents hard to diagnose; and production readiness, covering durable execution, state persistence, human approval, telemetry, and licensing clarity. Breadth of language support and how tightly a framework is tied to one model vendor were treated as secondary. Every entry was checked against the vendor's own documentation or source repository during research.
All 8 AI agent frameworks in this guide, in ranked order.
langchain.com · Library is free and open source. The associated LangSmith platform is tiered: a free single seat tier, a per seat team tier, and custom enterprise, with consumption metered in compute and storage units above included trace allowances.
Best for Teams that want to define agent control flow explicitly and need long running executions that survive failures and pause for human approval.
An MIT licensed orchestration library that models a stateful agent as an explicit graph of nodes and edges, paired with an optional commercial platform for tracing and deployment.
Strengths
Control flow is a graph you declare, so the path a run took is inspectable rather than inferred from prompt behavior.
Durable execution with checkpointing: runs persist through failures and resume from the point where they stopped.
Human in the loop is a first class feature; agent state can be inspected and modified mid run.
Available for Python and JavaScript/TypeScript, and usable without the LangChain core library as a dependency.
Widely deployed, so common failure modes and patterns are well documented outside the vendor's own material.
Where it falls short
The graph API is more verbose than higher level frameworks; a simple tool loop takes noticeably more code.
The richest debugging and evaluation experience runs through LangSmith, a separate commercial product.
Self-hosted and hybrid deployment of that commercial platform is limited to the Enterprise tier; the free and per seat tiers are cloud only.
The surrounding LangChain ecosystem has gone through several major version migrations, each with its own documented upgrade guide.
ai.pydantic.dev · Framework is free and open source under MIT. The optional Logfire observability service is a separate commercial product with its own free tier and usage based metering.
Best for Python teams that want static typing, schema validated tool calls, and vendor neutral tracing without much framework abstraction in the way.
An MIT licensed Python agent framework from the Pydantic team that types the agent loop end to end and validates tool arguments and outputs with Pydantic models.
Strengths
Agents, dependencies, and outputs are type checked, so wiring mistakes surface before runtime rather than as a bad model response.
OpenTelemetry native: it emits standard spans for model and tool calls that any OTLP backend can consume, not only the vendor's own.
Models are swappable by string across OpenAI, Anthropic, Google, Bedrock, Azure AI Foundry, Groq, Mistral, xAI, and Ollama among others.
First party durable execution integrations with Temporal, DBOS, and Prefect for long running work and human approval steps.
A built in test model runs the loop offline with no API keys, which makes deterministic unit tests practical.
Where it falls short
Python only; there is no first party JavaScript or TypeScript SDK.
Newer and smaller than LangChain or LlamaIndex, with fewer prebuilt integrations and third party examples to copy.
The lowest friction observability path is Pydantic's own Logfire service, though the OpenTelemetry output means you are not locked into it.
Under active development with frequent releases, so pinning versions matters more than with older projects.
Best for Teams that want a minimal agent loop they can read end to end in an afternoon, with tracing included and OpenAI models as the primary target.
An MIT licensed, deliberately small multi-agent framework from OpenAI built on a handful of primitives: agents, handoffs, guardrails, sessions, and tracing.
Strengths
The primitive set is small enough to read in full, so there is little hidden behavior to reverse engineer during an incident.
Tracing is built in and spans model calls, tools, agents, guardrails, and handoffs rather than being added afterwards.
Official Python and TypeScript SDKs, both MIT licensed.
Guardrails and human in the loop approvals are first class, including resumable approval flows.
Covers voice and realtime agent patterns in the same programming model as text agents.
Where it falls short
Traces upload to OpenAI servers and require an OpenAI API key even when you run another provider; the alternative is turning tracing off.
Non-OpenAI providers are supported but degrade: the documentation notes missing structured output support, no multimodal input, and Responses-only tools being unavailable when falling back to Chat Completions.
Third party adapters such as LiteLLM insert a compatibility layer where feature support and request semantics vary by provider.
Governance and roadmap sit with a model vendor, which is a consideration if you intend to stay provider neutral long term.
Best for File and command oriented agents, especially coding and repository automation, where you want a proven harness instead of assembling a tool loop yourself.
Anthropic's library that exposes the same agent loop, built in tools, and context management that run Claude Code, programmable from Python or TypeScript.
Strengths
The loop, context compaction, and tool set come from a widely used production agent rather than a reference implementation.
Built in file, shell, and web search tools remove a large amount of scaffolding for agents that operate on a workspace.
Hooks, subagents, permissions, sessions, and MCP support are all first class, giving fine grained control over what runs without approval.
Loads skills, commands, and memory from project and user configuration directories, so setup is portable between the CLI and the SDK.
Where it falls short
Use is governed by Anthropic's Commercial Terms of Service rather than a permissive open source license.
Available as a library for Python and TypeScript only; other languages must run the CLI as a subprocess.
Built around Claude models, so it is not a provider neutral abstraction the way Pydantic AI or the OpenAI SDK adapters are.
Anthropic does not permit third party developers to offer claude.ai login or rate limits for products built on it without prior approval; API key authentication is required.
Branding rules restrict product naming: 'Claude Code' and Claude Code visual elements are not permitted for products built on the SDK.
mastra.ai · Open core. Framework free under Apache 2.0; hosted platform has a free starter tier, a flat rate team tier, and custom enterprise, with consumption metering on top of included allowances. Self-hosted enterprise is quoted individually.
Best for JavaScript and TypeScript teams who want agents to live in the same repository and deployment target as their web application.
A TypeScript agent framework covering agents, workflows, tools, memory, and evals, with an Apache 2.0 core and an optional hosted platform.
Strengths
Written for TypeScript rather than ported to it, so it fits Node, React, and Next.js codebases without a separate Python service.
Typed workflow steps with explicit retries and branching, which keeps multi step control flow readable and reviewable.
Ships deployers for Vercel, Netlify, and Cloudflare, or runs as a standalone Hono server on any Node host.
Built in evals score agent runs against repeatable checks before changes ship.
Broad model provider coverage through a gateway rather than a single vendor binding.
Where it falls short
TypeScript only; teams standardized on Python have no first party SDK here.
Only the core framework is Apache 2.0; enterprise features are source available under Mastra's own license, which is not OSI approved.
The hosted platform meters several dimensions at once (observability events, CPU hours, data egress, gateway tokens, database rows, and storage), which makes cost forecasting harder than a single metric would.
Self-hosting the framework is free, but advanced controls for self-hosted deployments sit behind a custom priced enterprise tier.
Best for Enterprises already standardized on Azure or .NET that need multi-agent workflows with session state, middleware, and telemetry.
Microsoft's MIT licensed agent framework for .NET, Python, and Go, presented in its own documentation as the direct successor to both AutoGen and Semantic Kernel.
Strengths
The only option here with a first party .NET SDK, alongside Python and a preview Go SDK.
Separates autonomous agents from graph based workflows, so you can force explicit execution paths where determinism matters.
Session based state management, middleware for intercepting agent actions, and telemetry carried over from the Semantic Kernel lineage.
Includes an opinionated Harness Agent with planning, todo tracking, context compaction, tool approval, and observability already wired up.
Supports Microsoft Foundry, Azure OpenAI, OpenAI, Anthropic, and Ollama among other model providers.
Where it falls short
It consolidates two predecessor projects, so existing AutoGen or Semantic Kernel code requires a documented migration rather than an upgrade.
The Go SDK is in public preview and is missing declarative agents, RAG, CodeAct, and functional workflows.
Documentation and quickstarts default to Azure Foundry endpoints and Azure credentials, so non-Azure setups require extra configuration.
Microsoft's own notice states that connecting to third party servers, agents, or non-Azure Direct models is at your own risk and falls outside Microsoft Product Terms.
developers.llamaindex.ai · Framework free and open source under MIT. The optional LlamaCloud managed service is priced separately and offers a free tier.
crewai.com · Framework free and open source under MIT. Managed platform has a free tier capped by monthly workflow executions with purchasable overage, plus a custom priced enterprise tier that includes self-hosting.
Best for Getting a multi-agent prototype running quickly when the problem maps cleanly onto named roles and a sequence of tasks.
An MIT licensed standalone Python framework that models agent systems as role playing teams called Crews, combined with event driven Flows for explicit control.
Strengths
The role, goal, and backstory model is quick to learn and produces compact, readable multi-agent code.
Standalone with its own primitives for agents, tasks, crews, flows, and tools; it is not built on LangChain.
Flows add event driven control and conditional branching when autonomous delegation is too loose for the task.
MIT licensed, with an enterprise tier that can be deployed on your own infrastructure.
Where it falls short
The role playing abstraction assembles more of the prompt and control flow on your behalf than graph or typed approaches, which means a bad result is harder to attribute to a specific step.
Python only, and pinned to Python 3.10 or newer and below 3.14; there is no first party JavaScript or TypeScript SDK.
The managed platform's free tier is capped by monthly workflow executions, with paid overage beyond that.
Enterprise pricing is not published and requires contacting sales.