/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.
$ npx -y skills add langchain-ai/langchain-skills --skill langchain-python-quickstart --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/langchain-python-quickstart
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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.
SKILL.md
langchain-python-quickstart.SKILL.mdname: langchain-python-quickstart
description: "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."
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.
Read more
name: langchain-python-quickstart description: "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."
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.
⚠️ — 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.
Repo: langchain-ai/langchain-skills
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