CrewAI gives you multi-agent orchestration without the LangChain baggage. You define agents with roles and goals, chain tasks together, and let them collaborate autonomously on complex workflows. It's built for production use with memory, 50+ built-in tools, and decent observability. The sequential and hierarchical process modes cover most patterns, from simple pipelines to manager-delegated workflows. YAML config keeps things clean when your crews get bigger. If you need specialized AI agents working together rather than a single bot doing everything, this is the straightforward option. Lighter than LangGraph, more structured than rolling your own agent loops.

skills.sh installs
1K
GitHub stars
14.4K
last updated
Aug 21, 2026

Install

$ npx -y skills add orchestra-research/ai-research-skills --skill crewai-multi-agent --agent claude-code

Installs into .claude/skills of the current project.

Files

CrewAI - Multi-Agent Orchestration Framework

Build teams of autonomous AI agents that collaborate to solve complex tasks.

When to use CrewAI

Use CrewAI when:

  • Building multi-agent systems with specialized roles
  • Need autonomous collaboration between agents
  • Want role-based task delegation (researcher, writer, analyst)
  • Require sequential or hierarchical process execution
  • Building production workflows with memory and observability
  • Need simpler setup than LangChain/LangGraph

Key features:

  • Standalone: No LangChain dependencies, lean footprint
  • Role-based: Agents have roles, goals, and backstories
  • Dual paradigm: Crews (autonomous) + Flows (event-driven)
  • 50+ tools: Web scraping, search, databases, AI services
  • Memory: Short-term, long-term, and entity memory
  • Production-ready: Tracing, enterprise features

Use alternatives instead:

  • LangChain: General-purpose LLM apps, RAG pipelines
  • LangGraph: Complex stateful workflows with cycles
  • AutoGen: Microsoft ecosystem, multi-agent conversations
  • LlamaIndex: Document Q&A, knowledge retrieval

Quick start

Installation

Sponsors

Details

Repository
github.com/orchestra-research/ai-research-skills
Categories
AI & Agent Building, Data Science & ML, Automation & Workflows
First seen
Jun 3, 2026

More from orchestra-research/ai-research-skills

All 95
1qodex-ai avatarmulti-agent-orchestrationqodex-ai/ai-agent-skillsDesign and coordinate multi-agent systems where specialized agents work together to solve complex problems. Covers agent communication, task delegation, workflo2.1Kskills.sh installs
2langchain-ai avatarlangchain-raglangchain-ai/langchain-skillsINVOKE THIS SKILL when building ANY retrieval-augmented generation (RAG) system. Covers document loaders, RecursiveCharacterTextSplitter, embeddings (OpenAI), a18.4Kskills.sh installs
3yaklang avatarllm-prompt-injectionyaklang/hack-skillsLLM prompt injection playbook. Use when testing AI/LLM applications for direct injection, indirect injection via RAG/browsing, tool abuse, data exfiltration, MC3.9Kskills.sh installs
4inference-sh avatarai-rag-pipelineinference-sh/skillsBuild RAG (Retrieval Augmented Generation) pipelines with web search and LLMs. Tools: Tavily Search, Exa Search, Exa Answer, Claude, GPT-4, Gemini via OpenRoute1Kskills.sh installs
5davila7 avataragent-memory-systemsdavila7/claude-code-templatesMemory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term 728skills.sh installs
6moizibnyousaf avatarllm-application-devmoizibnyousaf/ai-agent-skillsBuilding applications with Large Language Models - prompt engineering, RAG patterns, and LLM integration. Use for AI-powered features, chatbots, or LLM-based au466skills.sh installs