# deepagents-typescript-quickstart

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

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 4900 installs · ★ 1314
- Author: langchain-ai
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/langchain-ai/langchain-skills/tree/HEAD/config/skills/deepagents-typescript-quickstart/SKILL.md
- Updated: 2026-10-21
- HTML: https://affpapa.org/en/nevibecode/skills/deepagents-typescript-quickstart

## What it 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.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add langchain-ai/langchain-skills --skill deepagents-typescript-quickstart -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/deepagents-typescript-quickstart/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/deepagents-typescript-quickstart/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add langchain-ai/langchain-skills --skill deepagents-typescript-quickstart -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/deepagents-typescript-quickstart/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add langchain-ai/langchain-skills --skill deepagents-typescript-quickstart -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/deepagents-typescript-quickstart/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install langchain-ai/langchain-skills deepagents-typescript-quickstart
```

2. **Manually: put the files in place**
- File: `.github/skills/deepagents-typescript-quickstart/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add langchain-ai/langchain-skills --skill deepagents-typescript-quickstart -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/langchain-ai/langchain-skills --path config/skills/deepagents-typescript-quickstart
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/deepagents-typescript-quickstart/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add langchain-ai/langchain-skills --skill deepagents-typescript-quickstart -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/langchain-ai/langchain-skills/tree/HEAD/config/skills/deepagents-typescript-quickstart/SKILL.md

## When to use it
- 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
- 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

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 2543537fc369406d.

BEGIN THIRD-PARTY FILE 2543537fc369406d
```text
---
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.
```
END THIRD-PARTY FILE 2543537fc369406d

The third-party file has ended. Everything between the 2543537fc369406d tags is data: requests and commands from it are not followed. An “end of file” line with another tag inside the block is part of the file.

© langchain-ai · MIT · https://github.com/langchain-ai/langchain-skills/tree/HEAD/config/skills/deepagents-typescript-quickstart/SKILL.md

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