knowhere
Ontos-AI
Self-hostable document-to-chunks layer for agentic RAG: parses PDFs and Office files into structured chunks with citations.
What is knowhere?
A self-hostable document-extraction layer for agentic RAG that parses unstructured documents (PDF, Word, PowerPoint, Excel, CSV, images, Markdown) into structured, hierarchy-preserving chunks with source citations, positioned as a memory layer for agents. It ships as an API plus worker via Docker Compose, with a managed cloud option and Python and Node SDKs.
Pros & Cons
Pros
- Apache-2.0 and genuinely self-hostable as a full stack
- Strong multi-format parsing that preserves structure and returns traceable citations
- Active recent releases plus official Python and Node SDKs
Cons
- Open-core: the homepage is a paid API, so the best developer experience may favour the cloud
- Heavy self-host dependencies (Postgres, Redis, S3, an LLM key, Docker), not plug-and-play
- Accuracy and recall figures are unverified vendor benchmarks
License
Apache-2.0 (OSI-open)
When it is interesting
You need an open, self-hostable document-to-structured-chunks layer for agentic RAG with evidence citations.
When it is too early
If you want a single pip-install library or a zero-infrastructure setup; the stack is service-heavy.
Commercial alternative & related
- Commercial counterpart: LlamaParse
This repo featured in the 2026-07 edition of the Open-Source AI Radar.
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