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| title: Web Research Agent | |
| emoji: 🔎 | |
| colorFrom: gray | |
| colorTo: red | |
| sdk: docker | |
| app_port: 7860 | |
| pinned: false | |
| # Web Research Agent 🔎 | |
| Ask a question → get a **cited briefing** built from live web sources you can verify — then **dig deeper** | |
| into any claim, section, or source, in your own words, and it appends an expandable research branch. | |
| > Project **#13** of the *"30 AI Projects in 15 Days"* build-in-public challenge. | |
| > Focus: **multi-step agent orchestration** (plan → search → read → reflect → cite). | |
| ## What makes it different | |
| Most research tools hand you a static answer. This one is **explorable**: click *dig deeper* on any | |
| citation, highlighted sentence, or source, type what you want investigated, and it spawns a scoped | |
| research pass rendered inline as a branch — which you can dig into again. A recursive research tree. | |
| ## How it works (honest by construction) | |
| - **Plan** — the question is broken into targeted search queries. | |
| - **Search** — keyless, multi-source: general web (DuckDuckGo / SearXNG / Bing) + Google News, and for | |
| the *technical* area, arXiv + GitHub. No API keys required. | |
| - **Read** — top results are fetched and extracted to clean text (redirects resolved, SSRF-guarded). | |
| - **Reflect** — deep mode critiques its own draft for gaps and runs another round. | |
| - **Cite** — the writer uses ONLY the sources it actually read, cites every claim inline `[n]`, and | |
| says what's missing rather than inventing. Citations render as **domain chips**; click one for the | |
| supporting passage. | |
| ## Modes & areas | |
| - **Modes:** `quick` (search → read → cited answer) · `deep` (plan → gather → reflect → report). | |
| - **Areas:** `web` · `opportunity` · `technical` (adds arXiv + GitHub) · `competitive` · `client` — | |
| each blends the right sources. | |
| ## Run it | |
| ```bash | |
| pip install -r requirements.txt | |
| python app.py # http://127.0.0.1:7860 (web UI) | |
| python agent.py "your question" --mode deep --area technical # CLI | |
| # Backend: local Ollama by default (free/private); set RESEARCH_LLM_BACKEND=claude + ANTHROPIC_API_KEY for cloud. | |
| # Optional: RESEARCH_SAVE=1 banks each brief to a memory store so research compounds (local use). | |
| ``` | |
| ## Two tiers | |
| - **This hosted demo** narrates with a hosted model, keyless search. | |
| - **GritAI Studio** runs the whole thing on your own local GPU fleet — free per-run, fully private — | |
| and can bank every brief to your own knowledge base so research compounds over time. | |
| --- | |
| Built by **Robert Lucyk** · [GritAI Solutions](https://gritai.solutions) · part of the 30-in-15 challenge. | |