Multi-Agent Patterns & LangGraph for Complex Task Resolution

What Are Multi-Agent Patterns?

Multi-Agent Patterns are architectural approaches that enable multiple AI agents to work in coordination. Each agent has a specific expertise area and solves complex tasks by breaking them down.

Core Patterns

LangGraph — Node-Based State Machine

LangGraph models agents as nodes and defines transitions between them using graphs. Supports conditional edges, loops, and human-in-the-checkpoint.

CrewAI — Team-Based Coordination

CrewAI defines agents as team members. Each agent has a role, goal, and backstory. Proactively collaborates.

AutoGen — Conversational Communication

Microsoft's multi-agent framework. Provides coordination through messaging between agents. Supports code execution and function calling.

AutoGPT — Chain-of-Thought Execution

Self-goal-setting agent architecture. Works with plan-do-check-adapt cycle. Built on LangChain.

Practical Applications

• Research & Summarization: Different agents scan different sources, one agent synthesizes
• Code Review: Security, performance, style checked by separate agents
• Content Generation: Research, writing, and editor agents work in coordination
• Customer Support: Classification and response in separate agents

Error Management

• Each agent should have independent fault tolerance
• Timeout and retry mechanisms should be established
• Communication logs should be kept for debugging
• Graceful degradation should be ensured

Conclusion

Multi-agent systems are ideal for problems that a single agent cannot solve. LangGraph and CrewAI are leading frameworks in this space.