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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.
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