skillZs
LIVE SKILL TAGS
>>> LIVE SKILLS INDEX <<<
* OPEN SOURCE *
NO LOGIN, NO TRACKING
REAL INSTALL DATA
← back to all skills
langchain-ai/langchain-skills921 installs

langchain-python-quickstart

Scaffold a minimal local LangChain agent in Python by following the official quickstart. Use when the user wants to quickly build or try a LangChain agent locally.

How do I install this agent skill?

npx skills add https://github.com/langchain-ai/langchain-skills --skill langchain-python-quickstart
view source ↗

Is this agent skill safe to install?

  • Gen Agent Trust Hubpass

    This skill provides a helpful way to scaffold a local LangChain agent by following official documentation. It includes security considerations such as external content retrieval and local command execution, which are appropriate for its intended purpose of setting up a development environment.

  • Socketpass

    No alerts

  • Snykwarn

    Risk: MEDIUM · 2 issues

What does this agent skill do?

LangChain Python quickstart

Follow the live docs — do not invent an alternate API from memory:

https://docs.langchain.com/oss/python/langchain/quickstart

Fetch that page (Docs MCP or HTTP) and implement what it shows (weather agent + create_agent).

Local setup constraints

Apply these on top of the quickstart (they keep setup minimal and model-agnostic):

  1. Ask which provider/model to use. Showcase that LangChain is model-agnostic. Suggested prompt:

    Which model should this agent use? Pass a provider:model string — e.g. openai:gpt-5.5, anthropic:claude-sonnet-5, google_genai:gemini-2.5-flash-lite. Default if you're unsure: anthropic:claude-sonnet-5.

    Swap the quickstart's model string for their choice (or the default).

  2. Create a new directory (e.g. langchain-agent/) and do all work there — do not pollute the open project.

  3. Only secret: the provider API key in .env (gitignored). No LangSmith / Tavily unless they ask. Prefer they edit .env themselves — don't paste keys into chat.

  4. Install the provider package needed for their model if the quickstart's base install isn't enough.

  5. Run the example, show output, then stop. Point to langchain-fundamentals for next steps.

Add the canonical catalog link to the repository README so users can inspect current installs and available audits. The publishing guide covers the complete discovery path.

<a href="https://skillzs.dev/skills/langchain-ai/langchain-skills/langchain-python-quickstart">View langchain-python-quickstart on skillZs</a>