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Mobile app automation and verification built for AI coding agents — CLI, MCP server, and Node API
★ 4,9 Tsd. SterneMIT
Anleitung ansehen →deep-research ist ein Open-Source-Projekt aus der Kategorie KI-Agenten von u14app (u14app/deep-research auf GitHub). Es hat 4.694 Sterne auf GitHub und 1.059 Forks, wird unter der Lizenz MIT veröffentlicht und wurde zuletzt am 18. Juni 2026 auf GitHub aktualisiert.
Deep Research with any LLM: in-depth reports in minutes, with an SSE API and MCP server built in
The recommended path is a free Gemini API key; alternatively configure any supported LLM and search provider.
Use the one-click Vercel/Cloudflare deploy, the Docker image, or a local dev install.
Set your LLM (and optional search) API keys in the environment file or settings.
Enter a topic, answer the clarifying questions, review the plan, and generate the report.
Topic → clarifying questions → research plan → SERP-driven collection → report, with the option to re-research from any stage.
Thinking/Task model split across many providers, and search via SearXNG, Tavily, Firecrawl, Exa, Bocha, Brave, or the model itself.
Upload text, Office, and PDF files to ground research in your own documents.
Edit reports in WYSIWYM or Markdown modes, adjust reading level and length, translate the full text, and generate a knowledge graph with one click.
Expose research as a service: a streaming SSE API and an MCP server (Streamable HTTP and SSE transports) for other AI tools.
Research data stays in local browser storage; deploy to Vercel, Cloudflare, Docker, or as a static export, and install it as a PWA.
Clone the project and enter its directory.
git clone https://github.com/u14app/deep-research.git
cd deep-researchInstall dependencies with pnpm and copy the environment template (Node.js 18.18+ recommended).
pnpm install
cp env.tpl .env.localStart the app and open it in your browser; add your LLM API key in .env.local or the UI first.
pnpm dev
# then open http://localhost:3000The u14app team; a hosted instance runs at research.u14.app.
The software is MIT-licensed and free. You bring your own model access — a free Gemini API key is the recommended path, and local models via Ollama avoid per-call costs.
It needs an LLM; Gemini's free tier works per the README, and search providers are optional since some models and SearXNG can cover search.
It offers a similar staged research workflow but is open source, model-agnostic, self-hostable, and keeps data in your browser — with API and MCP access the hosted products don't offer in the same way.
Get a Gemini API key, use the one-click Vercel deploy or run `pnpm install && pnpm dev` locally, then start a research topic at http://localhost:3000.
Mobile app automation and verification built for AI coding agents — CLI, MCP server, and Node API
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