deepagents-python-quickstart

Builds your first Deep Agent in Python: a small research assistant that searches the web to answer a question, following LangChain's official quickstart.

WHEN TO USE

SCOPE

USE FOR

Pick the AI model the agent runs on, such as Claude, GPT or Gemini, with Claude Sonnet as the default.
Use the chosen provider's own web search instead of signing up for a separate search service.
Keep the example in its own new folder so your open project stays untouched.
Run the research example once, see the answer, and get pointers for customizing it next.

This is a saved copy of the skill's SKILL.md instructions. It may differ from the upstream file or the version you install.

In this document (2)

Deep Agents Python quickstart

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

https://docs.langchain.com/oss/python/deepagents/quickstart

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

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 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).
Create a new directory (e.g. deep-agent/) and do all work there — do not pollute the open project.
Do not use Tavily (or any second search vendor). Replace the quickstart's internet_search / Tavily tool with the chosen provider's built-in web search. Look up the current tool shape on that provider's LangChain chat docs (examples as of writing — re-check if needed):

Provider Built-in search tool
Anthropic {"type": "web_search_20260209", "name": "web_search", "max_uses": 5}
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.
Install deepagents (+ python-dotenv) and the provider package for their model — not tavily-python.
Run the research example, show output, then stop. Point to deep-agents-core / customization / Managed Deep Agents for next steps.

Provider	Built-in search tool
Anthropic	`{"type": "web_search_20260209", "name": "web_search", "max_uses": 5}`
OpenAI	`{"type": "web_search"}`
Google	`{"google_search": {}}`

This skill ships inside the LangChain Agent-Building Skills plugin.

Install just this skill

Run this outside your AI tool, using Node.js 22.20.0 or newer. Skills CLI 1.7.2 skips skill selection; prompts cancel installation without an interactive terminal. If choosing Project when asked about installation scope, open your project's folder before running this.

Install checklist

Check selected tools if a picker appears; Claude Code may be unchecked under Additional agents. ↑/↓ moves, Space toggles selections, Enter confirms.
Without a tool picker, check Installing to: for your intended tool. Stop if it names a different tool.
If asked, choose Symlink (Recommended) for shared files or Copy to all agents for separate copies; review the summary before confirming installation.
Install the find-skills skill? defaults to Yes, adding another skill for all projects. Choose No if you don't want that extra installation.

Skills CLI 1.7.2 sends install data to skills.sh by default, including the source, skill names, target tools and file details; GitHub sources are reported only when GitHub confirms the repository is public, while other remote sources can also be reported. Confirmed-public GitHub sources also trigger a security-audit request. Before installing, set the environment variable DO_NOT_TRACK=1 or DISABLE_TELEMETRY=1 to disable both.

npx skills add langchain-ai/langchain-skills --skill "deepagents-python-quickstart" --full-depth

After installing for Claude Code, open it in the installation project if you chose Project; Global makes the skill available across projects on this computer. Once Claude Code loads the skill, its description helps Claude decide when to use it.

If installation created a top-level skills folder that did not exist when your Claude Code session started, run /reload-skills in that session. Run it again after later changes in that folder.

Install the whole plugin

This route installs the LangChain Agent-Building Skills plugin package selected for your AI tool.

Claude Code

Add LangChain's marketplace, then install the plugin

Type both lines inside a Claude Code session, not at your shell prompt. The first adds LangChain's marketplace, which is this repository; the second opens the details of the langchain-skills plugin, and Claude Code installs it once you pick one of the install options shown. Afterwards, LangChain's README asks you to set OPENAI_API_KEY or ANTHROPIC_API_KEY, for whichever of those model providers you build with.

/plugin marketplace add langchain-ai/langchain-skills

/plugin install langchain-skills@langchain-skills

Open plugin page ↗

View on GitHub

Deep Agents Python quickstart

Local setup constraints

Account or credentials required

Install just this skill

Install the whole plugin

Add LangChain's marketplace, then install the plugin

More skills

MCP keyword matches