⚠️ — 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.
These skills can be installed via npx skills for any agent that supports the Agent Skills specification, including Claude Code, Cursor, Windsurf, and more.
Using npx skills:
Local (current project):
npx skills add langchain-ai/langchain-skills --skill '*' --yesGlobal (all projects):
npx skills add langchain-ai/langchain-skills --skill '*' --yes --globalTo link skills to a specific agent (e.g. Claude Code):
npx skills add langchain-ai/langchain-skills --agent claude-code --skill '*' --yes --globalInstall directly as a Claude Code plugin:
/plugin marketplace add langchain-ai/langchain-skills
/plugin install langchain-skills@langchain-skillsAlternatively, clone the repo and use the install script:
# 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 |
After installation, set your API keys:
export OPENAI_API_KEY=<your-key> # For OpenAI models
export ANTHROPIC_API_KEY=<your-key> # For Anthropic modelsThen run your coding agent from the directory where you installed (for local installs) or from anywhere (for global installs).
To install only the eval-engineering skill:
npx skills add langchain-ai/langchain-skills --skill eval-engineering --yesOr ask Codex: “Install the eval-engineering skill from langchain-ai/langchain-skills.”
Eval tasks require Harbor. Run Harbor locally with Docker or use a supported cloud environment.
Then ask your coding agent:
Use the eval-engineering skill to create a new eval Task for this project. Review the current repository and existing evals. Traces for this agent can be found here [optional Tracing Project/Location].
- 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)
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 / JS (weather)
- langgraph-python-quickstart / langgraph-typescript-quickstart → Python / JS (math)
- deepagents-python-quickstart / deepagents-typescript-quickstart → Python / JS (research; provider web search instead of Tavily)
- 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
useStreamUIs
- 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-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
- eval-engineering - Design and audit Harbor Tasks while building reusable project World knowledge with human review
- swarm - Dispatch independent work items in parallel and aggregate the results
