---
slug: "langchain-skills"
source_type: "readme"
source_url: "https://cdn.jsdelivr.net/gh/langchain-ai/langchain-skills@main/README.md"
repo: "https://github.com/langchain-ai/langchain-skills"
source_file: "README.md"
branch: "main"
---
# LangChain Skills

> **⚠️** — This project is in early development. APIs and skill content may change.

Agent skills for building agents with LangChain, LangGraph, and Deep Agents.

> For LangSmith-specific trace and dataset workflows, use [langsmith-skills](https://github.com/langchain-ai/langsmith-skills).

## Supported Coding Agents

These skills can be installed via [`npx skills`](https://github.com/vercel-labs/skills) for any agent that supports the [Agent Skills specification](https://skills.sh), including Claude Code, Cursor, Windsurf, and more.

## Installation

### Quick Install

Using [`npx skills`](https://github.com/vercel-labs/skills):

**Local** (current project):

```bash
npx skills add langchain-ai/langchain-skills --skill '*' --yes
```
**Global** (all projects):

```bash
npx skills add langchain-ai/langchain-skills --skill '*' --yes --global
```
To link skills to a specific agent (e.g. Claude Code):

```bash
npx skills add langchain-ai/langchain-skills --agent claude-code --skill '*' --yes --global
```

---

### Claude Code Plugin

Install directly as a [Claude Code plugin](https://code.claude.com/docs/en/plugins):

```bash
/plugin marketplace add langchain-ai/langchain-skills
/plugin install langchain-skills@langchain-skills
```

---

### Install Script (Claude Code & Deep Agents CLI only)

Alternatively, clone the repo and use the install script:

```bash
# Install for Claude Code in current directory (default)
./install.sh

# Install for Claude Code in a specific project directory
./install.sh ~/my-project

# Install for Claude Code globally
./install.sh --global

# Install for Deep Agents CLI in a specific project directory
./install.sh --deepagents ~/my-project

# Install for Deep Agents CLI globally (includes agent persona)
./install.sh --deepagents --global
```

| Flag / Argument | Description |
|------|-------------|
| `DIRECTORY` | Target project directory (default: current directory, ignored with `--global`) |
| `--claude` | Install for Claude Code (default) |
| `--deepagents` | Install for Deep Agents CLI |
| `--global`, `-g` | Install globally instead of current directory |
| `--force`, `-f` | Overwrite skills with same names as this package |
| `--yes`, `-y` | Skip confirmation prompts |

## Usage

After installation, set your API keys:

```bash
export OPENAI_API_KEY=<your-key>      # For OpenAI models
export ANTHROPIC_API_KEY=<your-key>   # For Anthropic models
```

Then run your coding agent from the directory where you installed (for local installs) or from anywhere (for global installs).

### Eval Engineering

To install only the eval-engineering skill:

```bash
npx skills add langchain-ai/langchain-skills --skill eval-engineering --yes
```

Or ask Codex: “Install the `eval-engineering` skill from `langchain-ai/langchain-skills`.”

Eval tasks require [Harbor](https://www.harborframework.com/docs). Run Harbor locally with Docker or use a supported cloud environment.

Then ask your coding agent:

```text
Use eval-engineering to inspect this agent and propose two or three Harbor evals. Traces for this agent can be found here [Tracing Project/Location].
```

## Available Skills (21)

### Getting Started
- **ecosystem-primer** - Start-here primer: framework selection (LangChain vs LangGraph vs Deep Agents), env setup, and which skill to load next
- **langchain-dependencies** - Full package version and dependency management reference (Python + TypeScript)

### Quickstarts (local)
Thin wrappers around the official Mintlify quickstarts — ask for provider/model (default `anthropic:claude-sonnet-5`), new directory, provider API key only:
- **langchain-python-quickstart** / **langchain-typescript-quickstart** → [Python](https://docs.langchain.com/oss/python/langchain/quickstart) / [JS](https://docs.langchain.com/oss/javascript/langchain/quickstart) (weather)
- **langgraph-python-quickstart** / **langgraph-typescript-quickstart** → [Python](https://docs.langchain.com/oss/python/langgraph/quickstart) / [JS](https://docs.langchain.com/oss/javascript/langgraph/quickstart) (math)
- **deepagents-python-quickstart** / **deepagents-typescript-quickstart** → [Python](https://docs.langchain.com/oss/python/deepagents/quickstart) / [JS](https://docs.langchain.com/oss/javascript/deepagents/quickstart) (research; provider web search instead of Tavily)

### Deep Agents
- **deep-agents-core** - Agent architecture, harness setup, and SKILL.md format
- **deep-agents-memory** - Memory, persistence, filesystem middleware
- **deep-agents-orchestration** - Subagents, task planning, human-in-the-loop
- **managed-deep-agents** - Managed Deep Agents: deploy with the CLI, use the SDKs, stream runs, connect MCP tools, and build React `useStream` UIs

### LangChain
- **langchain-fundamentals** - Agents with create_agent, tools, structured output, middleware basics
- **langchain-middleware** - Human-in-the-loop approval, custom middleware, Command resume patterns
- **langchain-rag** - RAG pipeline (document loaders, embeddings, vector stores)

### LangGraph
- **langgraph-fundamentals** - StateGraph, nodes, edges, state reducers
- **langgraph-persistence** - Checkpointers, thread_id, cross-thread memory
- **langgraph-cli** - CLI lifecycle: scaffold, dev, build, deploy, langgraph.json config
- **langgraph-human-in-the-loop** - Interrupts, human review, approval workflows

### Evaluation
- **eval-engineering** - Build, run, and audit Harbor evals for an existing agent with user approval

### Utilities
- **swarm** - Dispatch independent work items in parallel and aggregate the results