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Deepagents Typescript Quickstart

LangChainOfficialPublished by LangChain

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  • Software development
  • MIT
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From the author

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.
— 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 Deep Agent using TypeScript. It follows the official quickstart documentation while enforcing specific constraints to keep the setup lightweight and model-agnostic. By replacing external search dependencies like Tavily with provider-native search tools, it simplifies the configuration process and reduces the number of required API keys.

What it does

  • Initializes a new project directory for Deep Agent development.
  • Prompts the user to select a preferred AI provider and model.
  • Configures the agent to use the selected provider's built-in web search capabilities.
  • Installs necessary dependencies while excluding external search tool packages.
  • Implements a research-agent structure based on the official quickstart.
  • Executes a sample research query to verify the agent's functionality.

When to use it

  • When you need to quickly prototype a Deep Agent locally.
  • When you want to experiment with different AI models using a consistent agent framework.
  • When you prefer using provider-native search tools over third-party search APIs.
  • When you are starting a new project and want to avoid polluting existing directories.

Advantages

  • Maintains a clean project structure by isolating the agent in a dedicated directory.
  • Reduces dependency complexity by leveraging built-in provider search tools.
  • Ensures model-agnostic flexibility by allowing user-defined provider selection.
  • Follows official documentation patterns for reliable implementation.

Usage examples

“Help me set up a new Deep Agent in a local folder.”
The agent will create a new directory, ask for your preferred model (e.g., anthropic:claude-sonnet-5), install the required packages, and configure the agent to use the provider's native search tool.
“I want to test a research agent using Google Gemini.”
The agent will scaffold the project, configure the Google provider with its built-in search tool, and run a test query to confirm the agent is working correctly.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/langchain-ai/deepagents-typescript-quickstart/download

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

    mkdir -p .agents/skills && unzip deepagents-typescript-quickstart.zip -d .agents/skills

Documentation by LangChain

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):

    ProviderBuilt-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.

Files

LICENSE

MIT License

Copyright (c) LangChain, Inc.

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

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.

Attribution and licence

Published by
LangChain
Publisher
LangChain (Official)
Source
langchain-ai/langchain-skills · config/skills/deepagents-typescript-quickstart · commit 126c384
Licence
MIT License (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
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Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download deepagents-typescript-quickstart.zip