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deepagents-typescript-quickstart — Claude Code skill: local Deep Agent

Scaffolds a minimal TypeScript Deep Agent from the official quickstart, swapping Tavily for the provider's built-in web search.

Install into Claude Code · Skills CLI (Vercel) All methods and platforms ↓
npx skills add langchain-ai/langchain-skills --skill deepagents-typescript-quickstart -a claude-code
Open on GitHub

What deepagents-typescript-quickstart does

The skill walks the agent through the live LangChain docs instead of recalling an API: it fetches the quickstart page over Docs MCP or HTTP, then builds the research-agent shape around createDeepAgent with a research system prompt and a sample question. Node 22+ is required.

First it asks which model to use and wants a provider:model string such as anthropic:claude-sonnet-5; if you are unsure, it suggests starting there. All work happens in a fresh directory like deep-agent/ so the open project stays untouched. Tavily and @langchain/tavily are dropped — search comes from the provider, so the only secret is that provider's API key in a gitignored .env. LangSmith tracing is skipped unless you ask for it.

Packages come from the quickstart minus Tavily, plus the package for your chosen model. Then the research example runs, output is shown, and the skill stops. Pick a provider outside Anthropic, OpenAI or Google and built-in search may not be there at all, so the model choice is not a formality.

What it helps with

  • scaffold a local TypeScript Deep Agent from scratch
  • swap Tavily for provider-native web search
  • pick a model with a provider:model string
  • build a research agent and run it on a test question
  • keep API keys in a gitignored .env

Example prompts for the agent

  • Scaffold a minimal TypeScript Deep Agent from the LangChain quickstart
  • Which model should this agent use? Suggest a provider:model string
  • Build the research agent in a new deep-agent/ directory and show the output

How to install deepagents-typescript-quickstart

1Skills CLI (Vercel)

npx skills add langchain-ai/langchain-skills --skill deepagents-typescript-quickstart -a claude-code

2Manually: put the files in place

~/.claude/skills/deepagents-typescript-quickstart/SKILL.md

Copy the whole skill folder; for one project use .claude/skills/deepagents-typescript-quickstart/

Step by step, with commands for each platform: How to install skills in Claude Code: five ways

Questions about deepagents-typescript-quickstart

How do I install deepagents-typescript-quickstart in Claude?

Run npx skills add langchain-ai/langchain-skills --skill deepagents-typescript-quickstart -a claude-code. The skill lands at ~/.claude/skills/deepagents-typescript-quickstart/SKILL.md.

What does deepagents-typescript-quickstart do?

It follows the official Deep Agents TypeScript quickstart: builds a research agent with createDeepAgent, runs it, and shows the output. Search uses the provider's built-in tool, so Tavily is not needed.

Which models does it support?

The skill asks for a provider:model string, for example anthropic:claude-sonnet-5. Anthropic, OpenAI and Google expose built-in web search, so those are the ones it suggests.

Source text

SKILL.md · MIT
---
name: deepagents-typescript-quickstart
description: "Scaffold a minimal local Deep Agent in TypeScript by following the official quickstart, using provider-native web search instead of Tavily. Use when the user wants to quickly build or try a Deep Agent locally."
---

# Deep Agents TypeScript quickstart

Follow the live docs — do not invent an alternate API from memory:

**https://docs.langchain.com/oss/javascript/deepagents/quickstart**

Fetch that page (Docs MCP or HTTP) and implement the research-agent shape it shows (`createDeepAgent`, research system prompt, invoke with a research question like “What is LangGraph?”). Requires Node 22+.

## 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 Deep Agents are 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-3.5-flash`. Default if you're unsure: **`anthropic:claude-sonnet-5`**.  
   > We'll use that provider's built-in web search (no separate search API key).

2. Create a **new** directory (e.g. `deep-agent/`) and do all work there — do not pollute the open project.

3. **Do not use Tavily** (or `@langchain/tavily`). Replace the quickstart's search tool with the chosen provider's built-in web search. Look up the current export/tool shape on that provider's LangChain docs (examples as of writing — re-check if needed):

   | Provider | Built-in search tool |
   |----------|----------------------|
   | Anthropic | `@langchain/anthropic` `tools.webSearch_*()` (or equivalent dict) |
   | OpenAI | `{ type: "web_search" }` |
   | Google | `{ google_search: {} }` |

   Prefer Anthropic / OpenAI / Google so provider search is available. Only secret: that provider's API key in `.env` (gitignored). Skip LangSmith tracing unless they ask.

4. Install packages from the quickstart **minus** Tavily; add the provider package for their model.

5. Run the research example, show output, then stop. Point to `deep-agents-core` / customization / Managed Deep Agents for next steps.

© langchain-ai · MIT · langchain-ai/langchain-skills

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