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langchain-dev-guide

ob-labs/agentseek

LangChain / LangGraph engineering pitfalls and verified fixes. Covers DeepAgents, structured output, OpenAI-compatible model integration (including Chinese provider adapters: DeepSeek, Qwen, GLM, etc.), middleware, streaming, multi-agent orchestration, and other common development issues. Use when hitting unexpected behavior, making architecture decisions, or integrating Chinese LLM providers during LangChain development.

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安装命令

npx skills add https://github.com/ob-labs/agentseek --skill langchain-dev-guide

SKILL.md

LangChain Dev Guide

A systematic summary of typical issues, non-obvious behaviors, and verified solutions encountered in real engineering with the LangChain / LangGraph ecosystem. Every entry comes from a real development scenario and is organized by category.

[!IMPORTANT] This skill is an engineering practice reference, not an introductory tutorial. Each entry assumes the developer is already familiar with basic LangChain concepts (agent, tool, message, graph).

How to Use

  1. First use the "Scenario Index" below to locate the category file your problem belongs to.
  2. When unsure which category applies, search keywords directly in the "Common Issues Quick Reference".
  3. For ContextSeek / semantic memory: start with contextseek-middleware.md to identify your scenario, then go to contextseek-params.md for specific parameter configuration issues.
  4. Once you find the relevant section, read it in depth — every entry follows the structure Symptom → Cause → Solution → Lessons learned.

Scenario Index