Multi-Agent AI Systems with LangGraph
Explore how LangGraph and LangSmith enable developers to build production-ready multi-agent workflows, coordinating multiple intelligent agents to solve complex tasks.
Built with LangGraph and LangSmith, this guide breaks down how to architect multi-agent systems:
✅ Understand multi-agent frameworks (e.g., supervisor, network, hierarchical)
✅ Learn handoff patterns where agents pass control and share state
✅ Step-by-step implementation of an AI system using LangGraph’s multi-agent features
✅ Integrate LangSmith for observability, evaluation, and debugging
✅ Real-world use cases: travel planning, research pipelines, and AWS deployments with Mistral
📚 Sources & Highlights
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FutureSmart AI – Multi-Agent System Tutorial with LangGraph: step-by-step build a scalable modular multi-agent system using LangGraph https://medium.com/%40cthecm/building-a-multi-agent-ai-platform-with-langgraph-and-langsmith-6d3e03c14b11
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Building a Multi-Agent AI Platform with LangGraph and LangSmith: rolling multiple sub-agents with LangGraph orchestration and LangSmith monitoring https://medium.com/%40cthecm/building-a-multi-agent-ai-platform-with-langgraph-and-langsmith-6d3e03c14b11
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LangChain official docs – Build multi-agent systems: includes architectural patterns (supervisor, network, hierarchical) and handoff primitives in LangGraph https://langchain-ai.github.io/langgraph/how-tos/multi_agent/?utm_source=chatgpt.com
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AWS blog – Build a Multi-Agent System with LangGraph and Mistral on AWS: real-world deployment integrating Bedrock models https://aws.amazon.com/blogs/machine-learning/build-a-multi-agent-system-with-langgraph-and-mistral-on-aws/?utm_source=chatgpt.com