crewai-multi-agent

What problem does it solve? Coordinating multiple LLM agents with distinct roles, shared memory, and ordered task execution requires significant boilerplate; this Skill provides ready patterns for building multi-agent systems with CrewAI without LangChain dependencies. ## Core Features & Use Cases - Role-Based Agent Teams: Define agents with roles, goals, and backstories, then assign tasks with context passing between them. - Sequential and Hierarchical Processes: Run tasks in order or let an auto-created manager agent delegate work across the team. - YAML Configuration and Flows: Organize larger projects with agents.yaml/tasks.yaml files, or use event-driven Flows with routers for conditional logic. - Use Case: Build a content pipeline where a researcher agent gathers trends on a topic and a writer agent turns the findings into a markdown blog post, executed via crew.kickoff(). ## Quick Start Create a CrewAI crew with a researcher agent and a writer agent that sequentially research a topic and produce a markdown report.