langchain-multiagent

You can coordinate multiple agents in LangChain using reusable patterns like subagents, handoffs, skills, and custom workflows. These patterns provide structured prompts, state management, and deterministic routing to orchestrate modular agent architectures.

Agent handoffs are best implemented using reusable LangChain patterns that manage state and context during complex dialogues. This approach allows specialized agents to transfer control seamlessly while maintaining consistent context management.

A router pattern in LangChain works by classifying user queries and directing them to the appropriate specialized agent or skill. This deterministic routing ensures scalable coordination and targeted responses across multi-agent workflows.

Yes, you can dynamically load skills in LangChain to handle specialized tasks within a multi-agent workflow. This modular architecture allows agents to acquire specific capabilities as needed for research, summarization, and response generation.

Using a supervisor subagent pattern is an effective way to scale multi-agent workflows in LangChain. It provides structured orchestration for coordinating specialized agents, managing state, and ensuring consistent context across complex tasks.

You should use custom workflow patterns in LangChain when standard subagents cannot cover your specific orchestration scenarios. Custom workflows allow you to design end-to-end processes like RAG pipelines with tailored state management and routing.