deepagents-typescript-quickstart

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.