/agents-crewai

name: crewai-multi-agent
description: Multi-agent orchestration framework for autonomous AI collaboration. Use when building teams of specialized agents working together on complex tasks, when you need role-based agent collaboration with memory, or for production workflows requiring sequential/hierarchical execution. Built without LangChain dependencies for lean, fast execution.
version: 1.0.0
author: Orchestra Research
license: MIT
tags: [Agents, CrewAI, Multi-Agent, Orchestration, Collaboration, Role-Based, Autonomous, Workflows, Memory, Production]
dependencies: [crewai>=1.2.0, crewai-tools>=1.2.0]

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

# Core framework
pip install crewai

# With 50+ built-in tools
pip install 'crewai[tools]'

Create project with CLI

# Create new crew project
crewai create crew my_project
cd my_project

# Install dependencies
crewai install

# Run the crew
crewai run

Simple crew (code-only)

from crewai import Agent, Task, Crew, Process

# 1. Define agents
researcher = Agent(
    role="Senior Research Analyst",
    goal="Discover cutting-edge developments in AI",
    backstory="You are an expert analyst with a keen eye for emerging trends.",
    verbose=True
)

writer = Agent(
    role="Technical Writer",
    goal="Create clear, engaging content about technical topics",
    backstory="You excel at explaining complex concepts to general audiences.",
    verbose=True
)

# 2. Define tasks
research_task = Task(
    description="Research the latest developments in {topic}. Find 5 key trends.",
    expected_output="A detailed report with 5 bullet points on key trends.",
    agent=researcher
)

write_task = Task(
    description="Write a blog post based on the research findings.",
    expected_output="A 500-word blog post in markdown format.",
    agent=writer,
    context=[research_task]  # Uses research output
)

# 3. Create and run crew
crew = Crew(
    agents=[researcher, writer],
    tasks=[research_task, write_task],
    process=Process.sequential,  # Tasks run in order
    verbose=True
)

# 4. Execute
result = crew.kickoff(inputs={"topic": "AI Agents"})
print(result.raw)

Core concepts

Agents - Autonomous workers

from crewai import Agent

agent = Agent(
    role="Data Scientist",                    # Job title/role
    goal="Analyze data to find insights",     # What they aim to achieve
    backstory="PhD in statistics...",         # Background context
    llm="gpt-4o",                             # LLM to use
    tools=[],                                 # Tools available
    memory=True,                              # Enable memory
    verbose=True,                             # Show reasoning
    allow_delegation=True,                    # Can delegate to others
    max_iter=15,                              # Max reasoning iterations
    max_rpm=10                                # Rate limit
)

Tasks - Units of work

from crewai import Task

task = Task(
    description="Analyze the sales data for Q4 2024. {context}",
    expected_output="A summary report with key metrics and trends.",
    agent=analyst,                            # Assigned agent
    context=[previous_task],                  # Input from other tasks
    output_file="report.md",                  # Save to file
    async_execution=False,                    # Run synchronously
    human_input=False                         # No human approval needed
)

Crews - Teams of agents

from crewai import Crew, Process

crew = Crew(
    agents=[researcher, writer, editor],      # Team members
    tasks=[research, write, edit],            # Tasks to complete
    process=Process.sequential,               # Or Process.hierarchical
    verbose=True,
    memory=True,                              # Enable crew memory
    cache=True,                               # Cache tool results
    max_rpm=10,                               # Rate limit
    share_crew=False                          # Opt-in telemetry
)

# Execute with inputs
result = crew.kickoff(inputs={"topic": "AI trends"})

# Access results
print(result.raw)                             # Final output
print(result.tasks_output)                    # All task outputs
print(result.token_usage)                     # Token consumption

Process types

Sequential (default)

Tasks execute in order, each agent completing their task before the next:

crew = Crew(
    agents=[researcher, writer],
    tasks=[research_task, write_task],
    process=Process.sequential  # Task 1 → Task 2 → Task 3
)

Hierarchical

Auto-creates a manager agent that delegates and coordinates:

crew = Crew(
    agents=[researcher, writer, analyst],
    tasks=[research_task, write_task, analyze_task],
    process=Process.hierarchical,  # Manager