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.
- Skill ID
- langchain-ai/langchain-skills/langchain-python-quickstart
- Publisher
- langchain-ai
- Repository
- langchain-skills
- Installs
- 0
- Files
- 1
- Synced
- Sep 23, 2026
Open any RiverX project, open the Skills panel in the chat, and search for this identifier. The files are fetched from the source repository at install time.
langchain-ai/langchain-skills/langchain-python-quickstartInstalls these files- SKILL.md
What this skill tells the agent
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):
- Ask which provider/model to use. Showcase that LangChain is model-agnostic. Suggested prompt:
Which model should this agent use? Pass aprovider:modelstring — 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).
- Create a new directory (e.g.
langchain-agent/) and do all work there — do not pollute the open project.
- Only secret: the provider API key in
.env(gitignored). No LangSmith / Tavily unless they ask. Prefer they edit.envthemselves — don't paste keys into chat.
- Install the provider package needed for their model if the quickstart's base install isn't enough.
- Run the example, show output, then stop. Point to
langchain-fundamentalsfor next steps.
More from langchain-ai
Use this skill in a project
Open any RiverX project, search the Skills panel for langchain-ai/langchain-skills/langchain-python-quickstart, and install it in one click.