Langchain Typescript Quickstart

From the author

Scaffold a minimal local LangChain agent in TypeScript 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 developers to initialize a local LangChain agent using TypeScript. It ensures adherence to the official documentation while maintaining a clean project structure. The process includes selecting a preferred LLM provider, setting up a dedicated directory, managing environment variables securely, and executing a functional weather agent example.

What it does

Initializes a new project directory for LangChain development.
Prompts the user to select a specific LLM provider and model.
Configures the project to use the selected model while remaining model-agnostic.
Sets up a secure .env file for API key management.
Installs necessary provider-specific packages.
Executes a sample weather agent to verify the local setup.

When to use it

When you need to quickly prototype a LangChain agent locally.
When you want to experiment with different LLM providers using a consistent codebase.
When you are starting a new project and want to follow official LangChain best practices.

Advantages

Ensures project isolation by creating a dedicated directory.
Promotes security by keeping API keys in gitignored environment files.
Provides flexibility by allowing users to switch between various LLM providers.
Reduces setup friction by automating the scaffolding process.

Usage examples

“Help me set up a new LangChain agent in TypeScript.”: The agent will create a new directory, ask for your preferred model, configure the environment, and run a basic weather agent example.

Install

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

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

mkdir -p .agents/skills && unzip langchain-typescript-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/javascript/langchain/quickstart

Fetch that page (Docs MCP or HTTP) and implement what it shows (weather agent + createAgent). Requires Node 22+.

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 AutoGineer924 B
LICENSELicence file added by AutoGineer1.0 KiB
SKILL.md1.5 KiB

ATTRIBUTION.md

Attribution

This folder is an unmodified copy of the Agent Skill "langchain-typescript-quickstart", distributed by AutoGineer (https://autogineer.com/agent-skills).

Source: https://github.com/langchain-ai/langchain-skills/tree/126c384d8552dcb476b226c244172186cc3f4328/config/skills/langchain-typescript-quickstart
Commit: 126c384d8552dcb476b226c244172186cc3f4328
Published by: LangChain (https://github.com/langchain-ai)
Publisher: LangChain (https://github.com/langchain-ai)
Licence: MIT License (https://github.com/langchain-ai/langchain-skills/blob/126c384d8552dcb476b226c244172186cc3f4328/LICENSE)
Licence evidence: repository licence LICENSE
Retrieved: 2026-11-07

The skill's own files are unmodified copies. AutoGineer is not affiliated with or endorsed by the author or the publisher.

Files added by AutoGineer

ATTRIBUTION.md: this file, written by AutoGineer
LICENSE: copied from LICENSE

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

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.

Claude API
AnthropicOfficial
Comprehensive reference for integrating Anthropic's Claude API, SDKs, and Managed Agents into your applications.
Data and AIClaude CodeClaude appsOpenAI Codex+14 more
Apache-2.0Medium riskHas scriptsGitHub stars of the source repository188.6k
Transformers
K-Dense· Community
Integrate Hugging Face Transformers for model loading, inference, and fine-tuning across NLP, vision, and audio tasks.
Data and AIClaude CodeClaude appsOpenAI Codex+14 more
MITLow riskGitHub stars of the source repository56.4k
Open Notebook
K-Dense· Community
Integrate with the self-hosted Open Notebook platform to automate research ingestion, context-aware chat, and AI-driven analysis.
Data and AIClaude CodeClaude appsOpenAI Codex+14 more
MITLow riskHas scriptsGitHub stars of the source repository56.4k
Vision Sft
Seth Hobson· Community
A structured workflow for supervised fine-tuning of vision-language models, focusing on adapter configuration and silent failure prevention.
Data and AIClaude CodeClaude appsOpenAI Codex+14 more
MITGitHub stars of the source repository42.3k
LLM Evaluation
Seth Hobson· Community
A comprehensive framework for evaluating LLM application performance using automated metrics, human feedback, and LLM-as-a-judge techniques.
Data and AIClaude CodeClaude appsOpenAI Codex+14 more
MITGitHub stars of the source repository42.3k
Lora Qlora Recipes
Seth Hobson· Community
Provides configuration recipes and best-practice hyperparameters for LoRA and QLoRA supervised fine-tuning.
Data and AIClaude CodeClaude appsOpenAI Codex+14 more
MITGitHub stars of the source repository42.3k