langchain-dependencies
INVOKE THIS SKILL when setting up a new project or when asked about package versions, installation, or dependency management for LangChain,
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About
INVOKE THIS SKILL when setting up a new project or when asked about package versions, installation, or dependency management for LangChain, LangGraph, LangSmith, or Deep Agents. Covers required packages, minimum versions, environment requirements, versioning best practices, and common community tool packages for both Python and TypeScript.
Details
- Kind
- Agent skills
- Topic
- Developer tools
- Publisher
- langchain-ai
- Origin
- skillssh
- Category
- ferramentas
- Stars
- 1,197
- Forks
- 90
- Open pull requests
- 3
- Last push
- 2026-08-29T01:39:54Z
- Repository state
- ativo
- Language
- TypeScript
- Added
- 2026-08-30 15:21:15
- Updated
- 2026-09-10 22:02:34
- Origin id
langchain-ai/langchain-skills/langchain-dependencies
README
# 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 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]. ``` ## 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** - Design and audit Harbor Tasks while building reusable project World knowledge with human review ### Utilities - **swarm** - Dispatch independent work items in parallel and aggregate the results