Knowhere

🔗 Website | 📄 Docs | 🏠 Self-Host | 🖥️ Dashboard

Overview

Knowhere is a document parsing and retrieval system that turns complex, dirty files into persistent, navigable memory for AI agents—especially across local and offline document collections.

It ingests unstructured documents and produces persistent, navigable memory: parsing, hierarchy reconstruction, multi-modal structuring, and graph construction in a single pipeline. Every result stays connected to its document, section, source pages, and related assets, making the output a natural fit for Agentic RAG, vector-based RAG, or any LLM workflow.

Knowhere 2.0 supports complementary Vision and Text tracks. Text-native documents retain precise extracted structure, while complex PDFs and PowerPoint files can be understood directly as pages by frontier vision models. Both tracks converge into the same memory schema, hierarchy, retrieval engine, and citation model.

[!NOTE] Get started in seconds with Knowhere Cloud. Avoid the complexity of self-deployment. Use our managed API at knowhereto.ai and enjoy $5 in free credits upon registration.

📢 News

• September 8, 2026: 🧭 Introducing agent-native Retrieval 2.0. MapNav has evolved from a fixed navigation workflow into a corpus-native foundation for agents. Knowhere provides a unified schema, hierarchy-aware tools, and resolvable evidence references; the agent decides how to search, traverse, read, and cite. The same foundation powers built-in retrieval and external agents through MCP.
• September 2026: 👁️ Introducing dual-track Document Parsing 2.0. Vision Page and Text Track now converge into one hierarchy-native memory schema for retrieval, understanding, and citation.
• June 1, 2026: 📚 Knowhere now supports ultra-long PDFs and atlas-style documents. The parsing pipeline can process long-form PDFs with hundreds of pages (for example, 300, 500, or more) and route technical atlases or drawing collections through a dedicated layout-aware parser.
• May 7, 2026: 🚀 Knowhere is now Open Source! We have open-sourced our entire stack for document ingestion, parsing, and agentic RAG. You can now self-host the full platform using knowhere-self-hosted. Check out our Contribution Guide to get involved!

Vision + Text: Document Parsing 2.0

Traditional OCR and Document Intelligence pipelines try to extract every element before a model can understand the document. On dirty PDFs and slide decks, mistakes in reading order, layout, tables, or hidden text layers can accumulate into unreliable model context.

Knowhere does not make perfect element-by-element extraction a prerequisite for retrieval. The Text Track preserves precise text and native structure where they are reliable. The Vision Track uses frontier vision models to understand a page or slide as a whole, so visually complex content can still be recalled and understood without first reconstructing every element.