LangChain Deep Agents Skill

Expert assistance for building LangChain Deep Agents in Python: stateful agents with virtual filesystems, parallel subagents, tool permissions, human-in-the-loop, and deployment via LangSmith.

Reference corpus: 1473 pages of official docs in references/llms-txt.md (5.4 MB) and references/llms-full.md (10 MB). Use view references/llms-full.md when detailed implementation is needed.

When to Use This Skill

Activate when:

• Building a Deep Agent — creating a stateful agent with virtual filesystem, backends, or subagents
• Configuring subagents — setting up parallel or async subagents with permission inheritance
• Implementing human-in-the-loop — adding approval gates for sensitive tool calls
• Deploying to LangSmith — setting up langgraph.json, Agent Server, or deployment pipelines
• Tracing and evaluating — instrumenting agents with @traceable, running client.evaluate()
• Debugging LangGraph state — working with StateGraph, checkpointers, or thread state
• Using Agent Server API — managing threads, runs, assistants, crons, or streaming
• Integrating retrievers or chains — connecting vector stores, RAG pipelines, or tool middleware

Quick Reference