Why Multi-Agent Teams?
Multi-agent systems are the next evolution of AI applications — instead of one model doing everything, specialized agents collaborate like a team. The awesome-llm-apps repository contains 13 multi-agent team implementations.
Coordinator-Worker Pattern
A coordinator agent decomposes tasks and dispatches to specialists. Used in: AI Finance Team, AI Legal Team, AI Competitor Intelligence Team. The coordinator maintains overall context and synthesizes results from each specialist.
Pipeline Pattern
Agents process work sequentially, each adding value. Used in: AI Recruitment Team (job description → screen resumes → outreach → schedule interviews). Each agent hands off its output to the next in the chain.
Ensemble Pattern
Multiple agents tackle the same problem from different angles, and a synthesizer combines their outputs. Used in: Multimodal Coding Agent Team, Multimodal Design Agent Team.
Key Implementations
• Finance Agent Team — data analyst + market researcher + report writer
• Legal Agent Team — contract review + compliance + case law research (supports local LLMs for sensitive documents)
• Recruitment Team — job descriptions + resume screening + outreach + scheduling
• Competitor Intelligence — web scraping + pricing analysis + sentiment tracking
• Coding Agent Team — PM + architect + frontend + backend + QA
• Services Agency (CrewAI) — client intake + solution design + implementation planning
Common Patterns Across Teams
Each team demonstrates agent specialization, tool use, inter-agent communication, and output synthesis. The key insight: keep agents focused on narrow tasks with clear interfaces, and use the coordinator to manage complexity.
Explore all 13 teams in the awesome-llm-apps repository.