deepagents-typescript-quickstart
Skill1 starsChanged 48 days agoMITView on GitHub
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.
- Reads credentials
3 other projects use a version of this, 1 of them edited.
Adopted by
Most recently taken up by
- langchain-ai/langchain-skillsword for word, first seen Oct 13, 2026
- gerald-donkor/poli-intelword for word, first seen Oct 11, 2026
- zoujindouGithub/agent-skillsedited, first seen Sep 2, 2026
Other versions3 projects use a version of this.
Word for word · 2
- langchain-ai/langchain-skills★ 1.3k100% same
- gerald-donkor/poli-intel★ 1100% same
Edited · 1
- zoujindouGithub/agent-skills★ 111% same
Wrote their own version · 0
None found.
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository10 other files it gives its agents.
- AGENTS.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- claude-apiSkill
- deep-agents-coreSkill
- deep-agents-memorySkill
- deep-agents-orchestrationSkill
- langchain-dependenciesSkill
- langchain-typescript-quickstartSkill
- langgraph-typescript-quickstartSkill
- swarmSkill
The file
We split this skill into the rules it gives an agent
4 rules. About 0 are common, 1 is distinctive, and 3 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈1
Distinctive · estimate
This author's own choices. Worth a look.
3
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 8
Follow the live docs — do not invent an alternate API from memory:
- Very specific: line 23
Create a new directory (e.g.
- Very specific: line 33
Prefer Anthropic / OpenAI / Google so provider search is available.
- Very specific: line 37
Run the research example, show output, then stop.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
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):
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
@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/anthropictools.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.- Install packages from the quickstart minus Tavily; add the provider package for their model.
- Run the research example, show output, then stop. Point to
deep-agents-core/ customization / Managed Deep Agents for next steps.
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Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.