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# DeepSearch
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DeepSearch is a multi-agent deep web researcher. Given a single research question it plans a set of
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diverse web search queries, crawls and reads the most relevant sources, indexes everything into a
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private vector collection for that run, then runs a chain of agents (report writer, findings
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extractor, linker) grounded on the passages retrieved from that collection to produce a thorough,
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cited markdown report with key findings, a confidence score and source diversity. Progress streams
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live while it works, and afterwards you can chat with the gathered evidence.
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Open it from the **Tools** menu, or go straight to `/tools/deepsearch`. It is public: you do not
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need an account.
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## Running a research job
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1. Type a focused research question.
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2. Set the **depth** (1-4; a depth above 1 also follows the most relevant links found inside
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crawled pages) and the maximum number of **pages** to crawl (up to 30).
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3. Press **Research**. Progress appears immediately: query planning, web search, crawling each
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source, indexing, then the analysis agents.
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4. You can **pause**, **resume** or **cancel** a run at any time.
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5. When it finishes, open the report to read the summary, findings and sources, and to chat
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with the research.
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You can run one job at a time. Targets that resolve to private or local addresses are refused, and
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every fetched URL (including redirects) is checked.
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## How it works
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- **Query planning** expands your question into several complementary searches, and the crawl
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interleaves their results so every angle contributes sources.
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- **Crawling** fetches candidates concurrently, first with a plain HTTP client, falling back to a
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headless browser for JavaScript-heavy pages. A readability extractor isolates the main article
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content of each page (navigation, cookie banners and footers are discarded). For social sites that
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block bots (X, YouTube, Reddit and similar) the readable text supplied by the search engine is
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used directly, so those sources still contribute their real content instead of a login wall. At
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depth above 1 the most relevant links inside crawled pages are followed. Identical content is
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de-duplicated, and a cross-session URL cache tracks pages seen by earlier runs.
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- **Indexing** splits each page into overlapping chunks, embeds them through the AI gateway (with a
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local embedding fallback when the gateway is unavailable), and stores them in a per-session
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ChromaDB collection.
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- **Analysis** retrieves the passages most relevant to your question from that collection and runs
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the report writer, findings extractor and linker agents to write the cited report, extract
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findings, and score overall confidence. The score combines confidence, source diversity and
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coverage. If synthesis fails, the report page marks the run as degraded instead of presenting raw
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source material as a report.
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## Chatting with the research
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Every finished session has a chat pane. Answers are grounded **only** in the sources captured during
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that run, using hybrid retrieval (vector similarity plus keyword/BM25 ranking) over the session
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collection, and every claim is cited back to a source.
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## Exporting
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A finished report can be downloaded as **Markdown**, **JSON** or **PDF** from the report page.
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## Ask Devii
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You can also run research in plain language through the Devii assistant:
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> Run a deep search on the history of the transistor and summarise the findings.
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Devii queues the job, polls it, and reports the score, confidence and a link to the report.
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## Programmatic access
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The same research is available over the API: `POST /tools/deepsearch/run` to queue,
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`GET /tools/deepsearch/{uid}` to poll, and `GET /tools/deepsearch/{uid}/session` for the full report
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(HTML or JSON). See the [Tools](/docs/tools.html) API group for request and response shapes.
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</div>
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<div class="devii-doc-cta">
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<a href="/tools/deepsearch" class="sidebar-link">Open DeepSearch</a>
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</div>
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