LangChain Skills is a collection of agent skills for building software agents with LangChain, LangGraph, and Deep Agents. Coding agents that support the Agent Skills specification, including Claude Code, Cursor, and Windsurf, can install and use these skills. The catalogue lists the project's skills and its Claude Code plugin.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add langchain-ai/langchain-skills --skill deepagents-python-quickstartgit clone --depth 1 https://github.com/langchain-ai/langchain-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/langchain-ai/langchain-skills/deepagents-python-quickstart)<a href="https://agentmods.dev/skills/langchain-ai/langchain-skills/deepagents-python-quickstart"><img src="https://agentmods.dev/badge/skills/langchain-ai/langchain-skills/deepagents-python-quickstart/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/langchain-ai/langchain-skills/deepagents-python-quickstart"><img src="https://agentmods.dev/badge/skills/langchain-ai/langchain-skills/deepagents-python-quickstart.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00048 | $0.00544 |
| Opus 5.5 | $0.00019 | $0.00218 |
| Sonnet 5.5 | $0.00010 | $0.00109 |
| Haiku 4.5 | $0.00005 | $0.00054 |
Grade A, and why
deepagents-python-quickstart scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
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:modelstring — 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 — nottavily-python. -
Run the research example, show output, then stop. Point to
deep-agents-core/ customization / Managed Deep Agents for next steps.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 38 lines · 48 tokens per session scan A a7a7c80cc332
deepagents-python-quickstart is a skill published in the GitHub repository langchain-ai/langchain-skills (1,313 stars, last pushed 4d ago), licensed MIT. It adds 48 tokens to every session and 544 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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