“langchain”

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

用于 LangChain 相关问题，获取官方文档和代码示例

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

LangChain / LangGraph engineering pitfalls and verified fixes.

Advanced guide for Arabic Langchain

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

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.

before chunking, whenever context comes from files or URLs.

Fetches and references LangGraph Python documentation to build stateful agents, create multi-agent workflows, and implement human-in-the-loop patterns.

Debug LangChain and LangGraph agents by fetching execution traces from LangSmith Studio.

Break down a coding task into a structured implementation plan with clear steps, file identification, and risk assessment.

Use for GPU-accelerated machine learning on tabular data using NVIDIA cuML.