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crewai-multi-agent

Coordinate multiple autonomous agents with memory and sequential or hierarchical execution.

1.0k|117|Updated Feb 26, 2026
Search Tags:#automation#workflow#multi-agent#orchestration#memory#crewai
One-click install
npx skills add https://github.com/OpenLAIR/dr-claw --skill crewai-multi-agent-openlair
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: crewai-multi-agent
Source: https://github.com/OpenLAIR/dr-claw/tree/main/skills/agents/crewai
Command: npx skills add https://github.com/OpenLAIR/dr-claw --skill crewai-multi-agent-openlair

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Coordinates teams of autonomous agents to collaborate on complex tasks without LangChain dependencies, enabling scalable, memory-enabled workflows.

Core Features & Use Cases

  • Standalone orchestration with role-based agents (no LangChain dependency)
  • Memory support (short-term, long-term, entity) and both sequential and hierarchical execution
  • Production-ready tooling with observability, plus references for deeper guidance

Quick Start

Run a minimal crew with two agents and a simple task to observe autonomous collaboration.

Frequently Asked Questions about crewai-multi-agent

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I coordinate multiple autonomous agents for complex tasks without LangChain?▼

Multi-agent orchestration allows autonomous agents to collaborate on complex tasks without LangChain by using a standalone framework. It supports sequential and hierarchical execution with memory-enabled workflows for production.

Does CrewAI support long-term memory for multi-agent workflows?▼

Yes, multi-agent workflows support short-term, long-term, and entity memory. This memory-enabled collaboration allows autonomous agents to maintain context across sequential and hierarchical production tasks.

What is the difference between sequential and hierarchical execution in multi-agent orchestration?▼

Sequential execution processes agents in a linear chain, while hierarchical execution delegates tasks through a management structure. Both orchestration patterns coordinate autonomous agents within memory-enabled production workflows.

Can I use multi-agent orchestration for product development and research workflows?▼

Yes, multi-agent orchestration supports research, product development, and production workflows. Autonomous agents collaborate on complex tasks using memory and role-based assignments to scale your automation pipelines.

Do I need LangChain to run memory-enabled autonomous agents?▼

No, you do not need LangChain to run memory-enabled autonomous agents. This multi-agent orchestration approach operates as a standalone workflow with built-in short-term, long-term, and entity memory support.

What are the limitations of using standalone multi-agent orchestration for automation?▼

Standalone multi-agent orchestration requires careful configuration of role-based agents and memory settings for complex automation. While it provides observability and production-ready tooling, scaling hierarchical workflows demands significant resource management.

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