# langchain-development

Gives your agent a rulebook for LangChain and LangGraph work in Python: chain composition, agents, memory, RAG. Useful once a Python LLM project outgrows one file.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 681 installs · ★ 286
- Author: Mindrally
- License: Apache-2.0 · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/Mindrally/skills/tree/HEAD/langchain-development/SKILL.md
- Updated: 2026-10-08
- HTML: https://affpapa.org/en/nevibecode/skills/langchain-development

## What it does

The skill turns the assistant into a LangChain, LangGraph and Python LLM expert. It asks for short technical answers with runnable Python examples, a functional and declarative style, and no classes where they are not needed. Variable names should carry auxiliary verbs like `is_active` or `has_context`, and PEP 8 is enforced strictly.

Most of the content is project structure plus rules for each part of the stack. Code goes into `chains/`, `agents/`, `tools/`, `memory/`, `prompts/`, `retrievers/`, `callbacks/`, `utils/`, `tests/` and `config/`. For chain composition it pushes LCEL with the `|` operator, `invoke()` for single inputs, `batch()` for many, `stream()` for token streaming. Separate sections cover tools built with the `@tool` decorator and a Pydantic schema, conversation memory, LangGraph state as a `TypedDict` with reducers and checkpointing, and RAG: text splitters, chunk size with overlap, vector stores, multi-query retrieval and re-ranking.

LangSmith, error handling and testing get their own blocks. Tracing turns on with `LANGCHAIN_TRACING_V2=true`, fallback chains are suggested for critical paths, and async `ainvoke` and `abatch` for I/O-bound work. The rules assume `langchain`, `langchain-core`, `langchain-community`, `langgraph`, `langsmith`, `python-dotenv` and `pydantic` are installed — the examples will not run without them.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add Mindrally/skills --skill langchain-development -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/langchain-development/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/langchain-development/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add Mindrally/skills --skill langchain-development -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/langchain-development/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add Mindrally/skills --skill langchain-development -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/langchain-development/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install Mindrally/skills langchain-development
```

2. **Manually: put the files in place**
- File: `.github/skills/langchain-development/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add Mindrally/skills --skill langchain-development -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/Mindrally/skills --path langchain-development
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/langchain-development/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add Mindrally/skills --skill langchain-development -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/Mindrally/skills/tree/HEAD/langchain-development/SKILL.md

## When to use it
- compose a retriever-prompt-model chain with LCEL
- define an agent tool with a Pydantic argument schema
- model LangGraph state as a TypedDict with reducers
- split documents into chunks with overlap for RAG
- turn on LangSmith tracing and lay out modules

## Example prompts
- Build a retrieval chain with LCEL: retriever, prompt, model, output parser
- Define a database search tool with @tool and a Pydantic schema
- Set up LangGraph agent state with TypedDict and add checkpointing

## Source text · SKILL.md · Apache-2.0

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 4f47fbeda6be1774.

BEGIN THIRD-PARTY FILE 4f47fbeda6be1774
````text
---
name: langchain-development
description: Expert guidance for LangChain and LangGraph development with Python, covering chain composition, agents, memory, and RAG implementations.
metadata:
  maintainer: Mindrally
  source: https://github.com/Mindrally/skills
---

# LangChain Development

You are an expert in LangChain, LangGraph, and building LLM-powered applications with Python.

## Key Principles

- Write concise, technical responses with accurate Python examples
- Use functional, declarative programming; avoid classes where possible
- Prefer iteration and modularization over code duplication
- Use descriptive variable names with auxiliary verbs (e.g., is_active, has_context)
- Follow PEP 8 style guidelines strictly

## Code Organization

### Directory Structure

Organize code into logical modules based on functionality:

```
project/
├── chains/           # LangChain chain definitions
├── agents/           # Agent configurations and tools
├── tools/            # Custom tool implementations
├── memory/           # Memory and state management
├── prompts/          # Prompt templates and management
├── retrievers/       # RAG and retrieval components
├── callbacks/        # Custom callback handlers
├── utils/            # Utility functions
├── tests/            # Test files
└── config/           # Configuration files
```

### Naming Conventions

- Use snake_case for files, functions, and variables
- Use PascalCase for classes
- Prefix private functions with underscore
- Use descriptive names that indicate purpose (e.g., `create_retrieval_chain`, `build_agent_executor`)

## LangChain Expression Language (LCEL)

### Chain Composition

- Use LCEL for composing chains with the pipe operator (`|`)
- Prefer `RunnableSequence` and `RunnableParallel` for complex workflows
- Implement proper error handling with `RunnableLambda`

```python
from langchain_core.runnables import RunnableParallel, RunnablePassthrough

chain = (
    RunnableParallel(
        context=retriever,
        question=RunnablePassthrough()
    )
    | prompt
    | llm
    | output_parser
)
```

### Best Practices

- Always use `invoke()` for single inputs, `batch()` for multiple inputs
- Use `stream()` for real-time token streaming
````
END THIRD-PARTY FILE 4f47fbeda6be1774

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