AI Agent Frameworks

CrewAI47.2k★

CrewAI is the fastest way to get multi-agent AI systems into production. Define agents with roles (Researcher, Writer, Analyst), assign tasks, and let the crew orchestrate. 47k stars, 12 million daily agent executions, and native MCP support. If you're building an AI-powered workflow for your indie SaaS, CrewAI gets you there with the least boilerplate. LangGraph is the enterprise choice for deterministic, auditable agent workflows — graph-based, human-in-the-loop, but steeper learning curve. AutoGen pioneered the conversational pattern but is in maintenance mode. For simple single-agent tasks, you don't need any framework. Use CrewAI if you need multiple agents collaborating on business workflows — content pipelines, research tasks, data processing. The role-based mental model is intuitive. The catch: running three agents sequentially means three LLM calls, higher token costs, and slower execution. Production monitoring is less mature than LangGraph's. Sequential execution by default — parallel is available but less polished. And for latency-sensitive applications (real-time chat, interactive tools), the multi-agent overhead may be prohibitive. Start with one agent and add more only when complexity demands it.

gstack46.9k★

gstack is Y Combinator's president sharing his exact Claude Code setup — and 45K developers took notice. Fifteen slash commands that simulate a virtual engineering team: a CEO who rethinks the product, a designer who catches AI slop, a paranoid reviewer who finds production bugs, a QA lead who opens a real browser and clicks through your app. It's one person's workflow, made universal. If you're a solo founder shipping a product with Claude Code, gstack is the fastest way to go from "me and an AI" to "me and a team of AI specialists." The opinionated structure forces good practices — design review before shipping, QA before deploying, architecture review before building. Alternatives: custom CLAUDE.md files are more flexible but require writing your own. Awesome Codex Subagents has more agents but less curation. Cursor's built-in workflows are the commercial equivalent. The catch: one person's workflow is one person's preferences. Garry Tan builds YC-style products — if your stack, pace, or taste diverges significantly, the opinions become friction, not guidance. At 45K stars it's arguably over-hyped relative to what it actually is: fifteen markdown files. And celebrity open-source projects sometimes get stars for the name, not the substance.

goose33.6k★

Goose is Block's (Square's) open-source AI coding agent — not an autocomplete tool like Copilot, but a full autonomous agent that can plan, build, test, and debug entire features. Think Claude Code but model-agnostic: use Claude for complex work, GPT-4o for routine tasks, or a local Ollama model for private code, all in the same session. If you want AI coding without vendor lock-in or monthly subscriptions, Goose is compelling. Claude Code costs $200/month and locks you to Anthropic. Cursor is $20-200/month with its own model preferences. Goose is free (Apache 2.0) — you just pay for API calls. Block reports 60% of their workforce uses it weekly. The catch: Goose is still young and rough around the edges compared to Claude Code's polish. The extension ecosystem is thin. And "model-agnostic" means you're responsible for picking the right model for each task — there's no single provider optimizing the experience for you. The ceiling is high, but so is the setup cost.

LangGraph27.5k★

LangGraph models AI agents as state machines — nodes process data, edges define transitions, and the graph orchestrates multi-step reasoning with durable execution. If your AI agent needs to branch, loop, retry, or maintain state across steps, LangGraph is the most production-ready framework for it. For teams building complex AI workflows — multi-agent systems, tool-calling chains, human-in-the-loop approvals — LangGraph is the serious option. CrewAI is easier for quick role-based agent prototypes. AutoGen (Microsoft) handles conversational multi-agent patterns but is shifting to maintenance mode. OpenAI's Agents SDK is simpler but less flexible. The catch: The learning curve is steep — you need to think in graphs, nodes, edges, and state schemas. LangGraph inherits LangChain's "abstraction over abstraction" problem. It doesn't natively support MCP or A2A protocols. And the tight coupling to the LangChain ecosystem means if you're not already in that world, you're adopting a lot of opinions. For simple single-agent tasks, this is overkill.

Haystack24.6k★

Haystack is the AI orchestration framework for teams that want production RAG without LangChain's sprawl. Built by deepset, it gives you modular pipelines with explicit control over retrieval, routing, memory, and generation. Less magic, more engineering. If you're building a retrieval-augmented generation system — chatbots over your docs, semantic search, knowledge bases — Haystack is the structured choice. LangChain has a broader scope and more integrations but its abstraction layers frustrate production teams. LlamaIndex focuses specifically on data indexing and retrieval. Commercially, cloud AI platforms (Azure AI, AWS Bedrock) offer managed RAG with less control. Benchmarks show lower framework overhead (~5.9ms) and less token waste than LangChain. The documentation is "drastically better" according to teams that evaluated both. The pipeline model makes debugging transparent. The catch: Haystack is narrower than LangChain. If you need complex agentic workflows, multi-step reasoning chains, or broad tool integration, LangChain's flexibility wins. The community and tutorial ecosystem is smaller. And if you're prototyping quickly, LangChain's "throw it together" approach ships faster — Haystack's structure is an investment that pays off in production, not in hackathons.

langchainjs17.3k★

LangChain.js is the JavaScript counterpart to the agent engineering platform that 57% of organizations now use in production. Standard interfaces for agents, models, embeddings, vector stores, and tool calling — plus LangGraph for building controllable agent workflows with branching and state management. After the "LangChain is bloated" discourse of 2024, the team refocused. The core is leaner, LangGraph handles orchestration separately, and LangSmith provides observability. 89% of teams using it have implemented some form of tracing. Compared to Vercel AI SDK (lighter, React-focused), LangChain.js is more comprehensive. Compared to writing raw API calls, it saves weeks of boilerplate. Use this when you're building a TypeScript/JavaScript AI application that needs tool calling, retrieval, or multi-step agent workflows. Skip this for simple chatbot wrappers — you don't need the abstraction. The catch: the abstraction tax is real. Debugging through LangChain's layers is harder than debugging raw API calls. And the ecosystem moves fast — breaking changes between versions still catch teams off guard. MIT license.

OpenMAIC12.3k★

OpenMAIC replaces passive video lectures with AI-powered interactive classrooms where you learn alongside AI teachers, TAs, and classmates who actually argue with each other. Drop in a topic or document, and it generates slides, quizzes, simulations, and project-based activities — all delivered by agents with distinct teaching styles. Validated with 700+ students at Tsinghua over two years. If you're building an edtech product or creating internal training, OpenMAIC is the most complete open-source classroom simulation available. The multi-agent approach (teacher + TA + classmates) creates genuine discussion dynamics that solo-chatbot tutoring can't match. Khan Academy's Khanmigo is the commercial equivalent but closed. Duolingo-style bots are narrower in scope. Custom GPT tutors lack the classroom social dynamics. The catch: AGPL license is a hard constraint for commercial use — you'll need to open-source your modifications or negotiate a license. It's research-grade software from an academic lab, not a production SaaS. Running the full multi-agent classroom locally needs serious compute. And 12K stars doesn't mean production-ready — it means the demo is impressive.

skills4.8k★

MiniMax Skills is a framework of production-quality AI agent skills for frontend, fullstack, Android, and iOS development. Built by MiniMax (the team behind the M2.5 and M2.7 models), these skills integrate with their Agent platform and auto-load based on file type — Word formatting, PowerPoint editing, Excel calculations. The standout is the Office Skills integration: upload a research framework, and the agent automatically fetches data, organizes analysis, and outputs formatted reports. The frontend skill combines UI design, animations, AI-generated assets, and copywriting with React/Next.js. Compared to gstack (prompt-based, general), MiniMax Skills are more structured. Compared to VoltAgent's collections (breadth), MiniMax goes deeper per skill. Use this when you're in the MiniMax ecosystem and want turnkey agent capabilities for specific development domains. Skip this if you're not using MiniMax — these skills are optimized for their models and Agent platform. The catch: ecosystem lock-in. The skills work best with MiniMax models, and the C# listing on GitHub is misleading — the actual skills span Python, TypeScript, and Kotlin. Community is smaller than Claude Code or Codex ecosystems.

ClawTeam3.5k★

ClawTeam is swarm intelligence for AI agents — one command spawns a coordinated team that decomposes goals, assigns tasks, and communicates in real-time. A leader agent breaks work into sub-tasks, specialized workers execute autonomously, and a shared task board handles dependency resolution. Think of it as a multi-agent Kanban board that runs itself. If you're building complex workflows that need parallel execution — research teams, analysis pipelines, multi-perspective decision-making — ClawTeam handles the orchestration you'd otherwise hand-code. Works with Claude Code, Codex, OpenClaw, and most CLI agents. CrewAI is the closest alternative but more opinionated about agent roles. AutoGen requires more boilerplate. LangGraph gives you more control but less automation. The catch: swarm coordination sounds magical until you debug it. When Agent 3 blocks on Agent 5's output and Agent 5 misunderstood the task, tracing the failure is painful. The framework is young (March 2026), documentation is thin, and the "one command" simplicity hides real complexity. Best for structured, well-decomposed problems — not open-ended creative work.