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LangChain + n8n: Building Multi-Agent Systems

Advanced patterns for coordinating multiple AI agents using LangChain and n8n from production systems.

Why Multi-Agent Systems?

Single AI agents are limited. Multiple specialized agents working together solve complex tasks better. Think: research agent + writing agent + fact-checking agent.

Architecture Pattern

1. Coordinator Agent (n8n workflow)

  • • Receives task from user
  • • Breaks down into subtasks
  • • Routes to specialized agents
  • • Aggregates results

2. Specialized Agents (LangChain)

  • • Research Agent: Web search + data gathering
  • • Analysis Agent: Process and analyze data
  • • Writing Agent: Generate final output

Real Implementation

Project: Market Research Multi-Agent

Built for B2B SaaS client to automate competitive analysis

Agent 1: Web scraper (competitor websites, pricing)

Agent 2: Social media analyzer (sentiment, trends)

Agent 3: Report generator (insights + recommendations)

Result: 40 hours/week manual work → 2 hours automated

Key Learnings

  1. Keep agents focused: One agent = one responsibility
  2. Use message queues: n8n workflows handle coordination
  3. Error handling: Agents can fail, design for retries
  4. Cost control: Monitor LLM usage per agent
  5. Human oversight: Critical decisions need approval

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