Ontos-AI / knowhere
Knowhere extracts, parses, and outputs structured chunks ready for AI Agents and RAG.
Why this job: works in the areas we cover, but does not say which job it does



What it does
- Dual-track Parsing. Vision and Text tracks handle different document conditions while producing the same downstream schema.
- Vision Page Understanding. Frontier vision models make complex PDF and PowerPoint content recallable without requiring perfect element-by-element OCR or layout reconstruction.
- Hierarchy-native Memory. Section nodes preserve document paths, page ranges, summaries, entities, and linked assets instead of returning disconnected chunks.
- Cross-document Memory Graph. Page-derived typed entities and keywords connect related documents across a namespace.
- Agent-native Retrieval. Built-in and MCP-connected agents explore the same corpus schema and hierarchy-aware tools; classic retrieval remains available for deterministic top-K search.
Get started
uv sync --all-packagesYou need: Python 3.11+; Docker with docker compose
- Maintenance
- Active
- Last commit today
- Stars
- 3,942
- +63 this week
- Licence
- Apache-2.0
- Allows business use
- Downloads for
- None listed
- No release files
- Latest version
- v1.2.49
- Released today
- Built with
- Python
- 95% of the code
Is it looked after?
- Contributors
- 2
- Last 90 days
- Issues
- 69% closed
- 25 still open
- Pull requests
- 384 merged
- 24 waiting
- Releases
- 105
- 30+ in the last year
More: languages, topics and project record
Languages
- Python 95%
- HTML 5%
- Shell <1%
- Makefile <1%
Topics
- ai-agents
- claude-code
- cursor
- langchain
- rag-pipeline
- skills
- agent-harness
- codex
- document-intelligence
- mutli-agent
- vector-database
Project record
- JobOther AI tools
- StatusListed
- Commits2,230
- Forks339
- Watching84
- Stars in 28 days+337
- CreatedApr 30, 2026
- First seen by usNov 7, 2026
- CheckedNov 9, 2026
