What problem does it solve?
Setting up a first LangChain agent often involves guessing at APIs, picking a model provider, and figuring out project structure. This Skill walks through the official LangChain Python quickstart so you get a working local agent without inventing outdated API calls from memory.
Core Features & Use Cases
- Live-docs-driven setup: Fetches the official LangChain quickstart page and implements the weather agent example with
create_agent exactly as documented.
- Model-agnostic configuration: Prompts you to choose any provider via a
provider:model string (e.g. openai:gpt-5.5, anthropic:claude-sonnet-5, google_genai:gemini-2.5-flash-lite) and installs the matching provider package.
- Clean local environment: Creates a dedicated directory with only the provider API key in a gitignored
.env file, then runs the example and shows the output.
- Use Case: You want to try LangChain for the first time. The Skill asks which model to use, scaffolds a
langchain-agent/ directory, installs dependencies, and runs a working weather agent in minutes.
Quick Start
Ask your coding agent to build a minimal local LangChain agent in Python using the official quickstart with your preferred model provider.
