Knowhere
Knowhere parses PDFs, spreadsheets, slides, and scans into source-linked structured JSON your agents can cite and verify.
Knowhere's pitch is narrow and defensible: a parser that never loses the page. The 2.0 dual-track parsing and agent-directed retrieval are the reason to look now — your agent can verify a figure against the source instead of trusting a chunk. Pricing is unusually easy to pilot at $0.015 per billable page with no subscription, and we'd run it head-to-head against Unstructured or MinerU on your own filings before committing.
Verified 2d ago · liveness 74/100 · cite: rightaichoice.com/tools/knowhere
- AI engineers building RAG or agent pipelines over contracts, filings, and papers where citations must resolve to a page
- Teams parsing financial or research documents with merged-cell tables, LaTeX formulas, or chemical structures
- Developers who want pay-per-page parsing with no subscription, no minimum spend, and failed jobs free
- Agent builders on Cursor, Claude, or Codex wiring document context in through MCP
- Non-technical users who need a no-code UI or drag-and-drop document console
- Teams parsing clean, simple PDFs where a basic text extractor already works
- Projects needing EPUB, HTML, XML, MP4, MP3, or SKILL.md — listed as coming soon, not shipped
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Skip Knowhere if you need a drag-and-drop document console rather than an API that returns JSON, or if your documents are clean simple PDFs a basic extractor already handles.
Billable pages are counted differently per file type — PDFs by physical page, presentations by slide, images one at a time, Word/text/Markdown by content length, spreadsheets by total row count — so a 6-sheet workbook
Pay-per-page at $0.015 ($1.50 per 100 pages) with no subscription and no minimum spend fits solo AI engineers and small teams piloting document parsing — the free 14-day, no-credit-card trial makes the comparison cheap. Enterprise adds private deployment, custom volume limits, priority processing, and SLAs for regulated or high-volume workloads. At 10,000 pages a month, the calculator on the pricing page puts the run rate at $150/mo.
In short
Knowhere — Knowhere parses PDFs, spreadsheets, slides, and scans into source-linked structured JSON your agents can cite and verify. Best for AI engineers building RAG or agent pipelines over contracts, filings, and papers where citations must resolve to a page, Teams parsing financial or research documents with merged-cell tables, LaTeX formulas, or chemical structures, Developers who want pay-per-page parsing with no subscription, no minimum spend, and failed jobs free. Free to start; paid plans from $0.015.
What's new in Knowhere
Checked todayAcross the latest 5 updates: 2 feature updates, 1 launch, 1 changelog entry and 1 news mention.
PDF to Markdown for RAG: 2026 Comparison of Top Tools
Knowhere published a comparison of Marker v2, MarkItDown, MinerU 4.0 and Knowhere for agentic RAG, covering structure, tables, citations, MCP access, self-hosting, license and price.
VISION-MAP: Knowhere Doesn't Just Read Text. It Sees the Page.
VISION-MAP keeps original page layout intact and annotates it, so agents inspect the actual page instead of a lossy text-only extraction.
How We Built VISION-MAP: Querying Complex Blueprints, Tables, and Scanned Docs
Engineering post on VISION-MAP's approach to complex blueprints, tables and scanned docs without flattening pages into plain text, enabling figure checks against source.
How We Hit 3,000 GitHub Stars in 3 Months — By Doing One Thing MinerU Doesn't
Knowhere positions itself as a memory layer that turns messy documents into structured, updatable memory for agents rather than stopping at text extraction.
Knowhere 2.0: Let agents understand the documents, and find the answers themselves
Knowhere 2.0 runs VISION-MAP and text parsing as dual-track parsing, and shifts retrieval to agent-directed lookups rather than fixed chunk search.
What people actually say about Knowhere — is it worth it?
We scanned public community sources for Knowhere on Jul 14, 2026 and could not establish that the discussion we found is about this tool rather than something else sharing its name. Our own analysis of that scan says the posts were off-subject. Rather than publish a sentiment score built on the wrong subject, we publish nothing here and re-run the scan.
Viability Score
How well maintained and how widely used is Knowhere? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- Parse 10+ formats: DOCX, PDF, JPG, PPTX, XLSX, CSV, PNG, MD, JSON, TXT
- VISION-MAP rendering that preserves the original page layout for agent inspection
- Dual-track parsing (vision map plus text) introduced in Knowhere 2.0
- Agent-directed retrieval: the agent picks which page or region to read next
- Structured JSON output with page-level citations and source regions
- Extract tables with complex merged cells and boundary detection
- Recognize mathematical formulas and chemical structures in consumable formats
- Generate navigable document maps of headings, tables, formulas, and page relationships
- Trace every extracted element back to its page and bounding box
- Progressive context so agents scan first and expand on demand
- REST API with webhook or polling delivery
- Python and Node.js SDKs plus curl examples
- MCP support so Cursor, Claude, and Codex agents search a shared cloud document Brain
- Parse a public document by URL or upload a file to the processing queue
About Knowhere
Knowhere is an API-first document parsing API for AI engineers who need more than a flat text dump. Send it a PDF, DOCX, PPTX, XLSX, CSV, Markdown, JSON, TXT, or a JPG/PNG scan, and it returns structured JSON where every extracted element traces back to a page and a bounding box — so a downstream agent can reopen page 12 and check a figure rather than trusting a flattened chunk. The September 2026 Knowhere 2.0 release split parsing into two tracks: VISION-MAP, which keeps the original page layout intact so agents inspect real visuals, and a parallel text track. Retrieval turned agent-directed in the same release: the agent chooses which page or region to read next instead of receiving one pre-chunked blob. That progressive-context behavior is the part flat extractors can't match. The pipeline is documented in four stages — ingest the file, capture every page's text and visual regions, map headings, tables, formulas, and page relationships into a navigable document map, then return traceable context. Tables with complex merged cells and boundary detection are handled as a first-class case rather than an afterthought. Math formulas and chemical structures come back in formats downstream systems can consume. Integration is programmer-shaped: REST API with webhook or polling delivery, Python and Node.js SDKs with curl examples, and MCP support so agents in Cursor, Claude, and Codex can search a shared cloud document Brain via Knowhere Notebook. Billing is metered at $0.015 per billable page ($1.50 per 100 pages) with no subscription and no minimum spend; failed jobs aren't charged.
Behind the Verdict
The question Knowhere answers is simple: what happens when the text isn't enough? Diagrams, merged-cell tables, formulas, and scanned blueprints all break naive extractors, and those are exactly the documents where an agent's answer needs to be checkable. We'd reach for Knowhere when the downstream workflow has to cite a page. If your agent has to say "revenue was $81.6B, per page 1 of the CFO commentary," a parser that keeps page provenance saves you from building a verification layer yourself. The agent-directed retrieval in 2.0 is the real differentiator. Instead of one pre-chunked blob handed to the model, the agent scans a document map and expands on demand — which cuts token spend and, in Knowhere's own comparison charts, iterations. If you're cost-sensitive on context, that's worth measuring on your corpus. Where it bites: this is an API product. There's no no-code console, so a team without engineering capacity will struggle. The ten supported formats cover most business documents, but EPUB, HTML, XML, MP4, MP3, and SKILL.md are still listed as coming soon. And the billable-page rule differs by file type — PDFs by physical page, presentations by slide, images one apiece, Word and Markdown by content length, spreadsheets by total row count — so estimate from your actual mix, not from file counts. On a clean, text-only PDF, a basic extractor may be cheaper and fast enough. Knowhere earns its price on the messy stuff. The closest alternative is Unstructured, which is broader and more established for straightforward pipelines. MinerU competes on table and formula extraction. Where Knowhere pulls ahead is source tracing plus progressive context: the agent gets a map, not a dump, and can verify. Pilot it on your own contracts or filings before you commit.
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Real-world workflow fit
Concrete scenarios for the personas Knowhere actually fits — and what changes day-one when you adopt it.
Install the Python SDK, loop `client.parse(file=file)` over a directory of contract PDFs, and index the returned JSON with page-level citations.
Outcome: Retrieval results point back to a named page and bounding box, so a quoted clause can be checked against the source document.
Send a folder of 500-page annual reports through the REST API, pull the tables out of the document map, and join the extracted rows to an internal model.
Outcome: Merged-cell revenue tables arrive as structured JSON instead of a re-typed spreadsheet, with each figure traceable to its page and region.
Connect Knowhere through MCP and search a shared cloud document set via Knowhere Notebook so the agent chooses which page to read next.
Outcome: The agent scans structure first and expands on demand, rather than receiving one pre-chunked text blob.
Use Cases
- Parse 500-page financial reports into structured JSON tables for automated analysis
- Extract LaTeX formulas from research papers and feed them into a knowledge graph
- Convert scanned PDF invoices into clean data for ERP ingestion
- Build RAG on legal contracts with source traceability per clause
- Process mixed document collections (PDF, DOCX, images) through one API endpoint
- Let a Cursor or Claude agent query a shared annotated document set via MCP
- Verify a figure quoted by an agent against the original page it came from
- Query complex blueprints or scanned technical documents for specific figures
Limitations
- Billing is metered per billable page at $0.015 ($1.50 per 100 pages) with no subscription or minimum spend; credits are deducted only when a job completes successfully (failed jobs are not charged).
- Billable-page counting varies by file type — PDFs by physical page, presentations by slide, images one at a time, Word/text/Markdown by content length, and spreadsheets by total row count — so cost is not predictable from file count alone.
- Standard file limits are PDF 100M, PPTX 100M, DOCX 50M, and XLSX 50M; higher volume, private deployment, or custom SLAs require contacting the team.
- Output is source-linked structured JSON intended for agent/code workflows rather than a no-code document console.
as of 2026-10-09
Verification history
We have re-verified Knowhere 10 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 10 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Knowhere tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Free trial
$0
Pay-as-you-go
$0.015 per billable page ($1.50 per 100 pages)
Ideal for
Solo developers and teams with variable or spiky document volumes who do not want a recurring subscription.
What this tier adds
Adds metered processing at $0.015 per billable page ($1.50 per 100 pages), no subscription and no minimum spend, with failed jobs free and credits expiring 3 months after purchase.
Enterprise
Custom
Ideal for
Regulated or high-volume organizations that need private or dedicated infrastructure and contractual guarantees.
What this tier adds
Adds private or dedicated deployment, custom volume limits, priority processing and support, and custom SLAs and terms.
Where the pricing makes sense
The company stage and team size where Knowhere's pricing actually pencils out — and where peers do it cheaper.
Pay-per-page at $0.015 ($1.50 per 100 pages) with no subscription and no minimum spend fits solo AI engineers and small teams piloting document parsing — the free 14-day, no-credit-card trial makes the comparison cheap. Enterprise adds private deployment, custom volume limits, priority processing, and SLAs for regulated or high-volume workloads. At 10,000 pages a month, the calculator on the pricing page puts the run rate at $150/mo.
Setup time & first value
How long it actually takes to get something useful out of Knowhere — broken out by persona, not the marketing-page minute.
Developers: minutes to first parsed document — generate an API key from the dashboard, `pip install knowhere-python-sdk`, and call `client.parse(file=file)`. Teams standardizing on the API: an afternoon to wire webhook or polling delivery into a pipeline plus error handling. Enterprise: onboarding time depends on the private deployment and SLA terms, which are scoped with the Knowhere team.
Switching to or from Knowhere
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From a flat text extractor: swap the extract call for a Knowhere parse call and keep the returned page-level citations instead of discarding document position.
- →From MinerU or Unstructured: run the same document set through Knowhere's pay-per-page API on the 14-day trial and compare table and formula output before switching pipelines.
- →From a custom PDF parser: keep the ingest and mapping stages and replace only the extraction step, since Knowhere returns structured JSON with source regions rather than a proprietary format.
- ↗To a plain text extractor: the JSON output carries full text per region, so a basic extractor can ingest it if you no longer need citations or bounding boxes.
- ↗To a self-hosted parser: export your parsed JSON and document maps, then reproduce the pipeline stages internally if API-only processing no longer fits your constraints.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Knowhere”, and we withheld 6: 6 could not be judged, because “Knowhere” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Knowhere.
Official links
Tools that pair well with Knowhere
Common stack mates teams adopt alongside Knowhere, with the specific reason each pairing earns its keep.
Unstructured.io
Unstructured turns messy PDFs, scans, and forms into structured, AI-ready data for RAG and agents.
Mistral OCR
Mistral OCR 3 turns PDFs, scans, forms, and handwriting into structured markdown or JSON via API at $2 per 1,000 pages.
PDF.ai
Chat with any PDF, or turn PDFs into structured JSON through a REST API
Featured Head-to-Head Comparisons
Knowhere vs Spider Cloud
Choose Knowhere if your core need is parsing messy, complex documents (PDFs with formulas, tables, chemical structures) into structured JSON for AI agents and RAG—especially if you require pixel-perfect accuracy and source traceability. Choose Spider Cloud if your priority is crawling and scraping live web pages at scale, with features like AI-powered extraction and stealth anti-detection, and you want a freemium pay-as-you-go model. They are complementary: Knowhere for static documents, Spider Cloud for dynamic web data.
Knowhere vs Screenplayiq
ScreenplayIQ and Knowhere serve completely different needs: one is for script analysis and box office forecasting, the other for parsing complex documents into structured data. Choose ScreenplayIQ if you're a screenwriter or producer seeking data-driven feedback on a feature film script. Choose Knowhere if you're a developer building AI agents that need reliable extraction from tables, formulas, or chemical structures — but be ready for an API-first, pay-per-page model.
Knowhere vs Temporal Ai
If your primary need is building AI agents or microservices that must survive crashes and maintain state across long-running steps, Temporal AI is the clear choice—it's battle-tested by OpenAI and offers automatic retries, human-in-the-loop, and multiple SDKs. But if you're focused on extracting structured data from complex documents (PDFs with tables, formulas, chemical structures) to feed into a RAG pipeline, Knowhere's API-first precision and hierarchical output are unmatched. They solve different problems; pick based on your bottleneck: reliability via orchestration or quality of parsed data.
Alternatives to Knowhere
View all
Unstructured.io
Unstructured turns messy PDFs, scans, and forms into structured, AI-ready data for RAG and agents.
Mistral OCR
Mistral OCR 3 turns PDFs, scans, forms, and handwriting into structured markdown or JSON via API at $2 per 1,000 pages.
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