Orchestration
CrewAI
Multi-agent orchestration framework for building collaborative AI agent teams
Best use cases
Use cases not added yet.
Alternatives
Alternatives not added yet.
CrewAI
CrewAI is a cutting-edge framework for orchestrating autonomous AI agents that work together collaboratively to complete complex tasks.
Key Features
Role-Based Architecture
Each agent in CrewAI has a specific role and expertise:
from crewai import Agent
researcher = Agent(
role='Senior Research Analyst',
goal='Uncover cutting-edge developments in AI',
backstory='Expert analyst with deep AI knowledge',
verbose=True
)
Task Delegation
Agents can delegate tasks based on expertise:
from crewai import Task
research_task = Task(
description='Identify emerging AI trends',
agent=researcher,
expected_output='A comprehensive report'
)
Crew Orchestration
from crewai import Crew
my_crew = Crew(
agents=[researcher, writer, reviewer],
tasks=[research_task, writing_task, review_task],
process=Process.sequential
)
result = my_crew.kickoff()
Use Cases
- Research Teams: Automated information gathering and analysis
- Content Creation: Collaborative writing and editing
- Code Development: Multi-agent code review and generation
- Business Intelligence: Market analysis and reporting
Getting Started
pip install crewai
Visit crewai.com for complete documentation and tutorials.
Related docs
AI Agent Architectures
Designing and building agent systems — ReAct, Plan-and-Execute, tool-augmented agents, multi-agent systems, memory architectures, and production patterns
Prompt Chaining and Workflow Patterns
Building complex LLM applications with multi-step workflows — chaining, routing, aggregation, human-in-the-loop, and production workflow design
Data Platform Orchestrator Agent Implementation Guide
Architecture, workflow design, metrics, and rollout guidance for a data platform orchestrator agent in production.
Alternatives and adjacent tools
Data Platform Orchestrator Agent
Data Platform agent blueprint focused on coordinate multiple specialists, route shared state, and decide when a workflow should continue, pause, or escalate for analysts and engineers need better query generation, pipeline debugging, and dataset explanation across changing schemas.
Data Platform Router Agent
Data Platform agent blueprint focused on classify incoming work and send it to the right queue, specialist, toolchain, or escalation path with minimal latency for analysts and engineers need better query generation, pipeline debugging, and dataset explanation across changing schemas.
Developer Productivity Orchestrator Agent
Developer Productivity agent blueprint focused on coordinate multiple specialists, route shared state, and decide when a workflow should continue, pause, or escalate for engineering teams want reliable help with issue triage, runbook guidance, and change review without obscuring system ownership.