Langchain Python Quickstart

From the author

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.

— the description by LangChain

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a structured workflow for initializing a basic LangChain agent locally. It guides the user through the official quickstart process while ensuring a clean project structure, proper environment configuration, and model flexibility. By prioritizing local setup and secure credential management, it helps developers quickly prototype agentic behaviors without unnecessary overhead.

What it does

Creates a dedicated project directory for the agent implementation.
Prompts the user to select a preferred LLM provider and model.
Implements the official LangChain quickstart weather agent pattern.
Configures environment variables securely using a gitignored .env file.
Installs necessary provider-specific dependencies.
Executes the agent and verifies the output.

When to use it

When you need to rapidly prototype a LangChain agent locally.
When you want to test how different LLM providers integrate with LangChain.
When you are starting a new LangChain project and want a clean, standard foundation.

Advantages

Ensures a clean, isolated development environment.
Promotes model-agnostic development practices.
Maintains security by keeping API keys out of chat history.
Follows official documentation standards for reliability.

Usage examples

“Help me set up a basic LangChain agent.”: The agent will create a new directory, ask for your preferred model provider, set up a .env file for your API key, and run the weather agent example from the official documentation.

Install

Download the skill as a ZIP:
https://autogineer.com/agent-skills/langchain-ai/langchain-python-quickstart/download

Unzip it into the cross-client skills folder of your project:

mkdir -p .agents/skills && unzip langchain-python-quickstart.zip -d .agents/skills

Documentation by LangChain

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 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).
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 .env themselves — 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-fundamentals for next steps.

Files

ATTRIBUTION.mdAdded by AutoGineer916 B
LICENSELicence file added by AutoGineer1.0 KiB
SKILL.md1.4 KiB

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs an API key, Needs Python

Security

No issues found by our static scan· No issues found

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

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