How do I install the Deepagents Python Quickstart skill?

Run npx skillmds@latest add langchain-ai-langchain-skills/deepagents-python-quickstart in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Deepagents Python Quickstart safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: makes network calls, reads secrets. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Deepagents Python Quickstart?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Deepagents Python Quickstart free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Deepagents Python Quickstart?

langchain-ai (@langchain-ai-langchain-skills) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Deepagents Python Quickstart by langchain-ai-langchain-skills

Name: Deepagents Python Quickstart
Rating: 4.2 (5 reviews)
Author: langchain-ai

Deepagents Python Quickstart

Scaffold a minimal local Deep Agent in Python 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.

langchain-ai Updated Sep 10, 2026

File contents

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": {}}`

---name: deepagents-python-quickstartdescription: Scaffold a minimal local Deep Agent in Python 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 Python quickstartFollow 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 constraintsApply 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 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.4. Install `deepagents` (+ `python-dotenv`) and the provider package for their model — not `tavily-python`.5. Run the research example, show output, then stop. Point to `deep-agents-core` / customization / Managed Deep Agents for next steps.

Frequently asked questions