“LangChain”

Framework for building LLM-powered applications with agents, chains, and RAG.

langchain knowledge

用于 LangChain 相关问题，获取官方文档和代码示例

AGENTS.md from langchain-ai/langchain: 391 lines covering Corridor security analysis, Project architecture and context, Core development principles, Model…

AGENTS.md from langchain-ai/langchainjs: 449 lines covering Corridor security analysis, Project Overview, Repository Structure, Development Setup and more.

LangChain framework for building LLM apps with agents, chains, and RAG across providers.

AGENTS.md from langchain-ai/deepagents: 164 lines covering Corridor security analysis, Development workflow, PR conventions, Core development principles and…

LangChain / LangGraph engineering pitfalls and verified fixes.

Advanced guide for Arabic Langchain

INVOKE THIS SKILL when building ANY retrieval-augmented generation (RAG) system.

AGENTS.md from langchain-ai/open-swe: 68 lines covering Constant gardening, Conventions, Testing, Pull Requests and more.

Expert guidance for LangChain and LangGraph development with Python, covering chain composition, agents, memory, and RAG implementations.

Master the LangChain framework for building sophisticated LLM applications with agents, chains, memory, and tool integration.

Run LangChain Open Deep Research or LangGraph-based iterative research workflows with configurable retrieval, reflection, and server-backed execution.

Plan, implement, or review langchain architecture work in an existing codebase with compatibility, security, and verification controls.

Design LLM applications using LangChain 1.x and LangGraph for agents, memory, and tool integration.

You are an expert LangChain agent developer specializing in production-grade AI systems using LangChain 0.1+ and LangGraph.

INVOKE THIS SKILL when setting up a new project or when asked about package versions, installation, or dependency management for LangChain, LangGraph…

Create LangChain agents with create_agent, define tools, and use middleware for human-in-the-loop and error handling.

Use this skill for public-safe reasoning about RAG and LangChain pipelines, document loading, splitting, embedding, vector stores, retrievers, prompts…

Translate a validated AGER/OKF AgentGraph into a LangChain Deep Agents project (skills from AgentNodes, FilesystemBackend).

INVOKE THIS SKILL when you need human-in-the-loop approval, custom middleware, or structured output.

Bind a Cursor agent to langchain-deep-agents-ager.

first end-to-end chain after chunks exist.