Morphik
Multimodal retrieval engine that searches documents visually using ColPali-style page embeddings.
About
Documents full of diagrams, tables, and screenshots are where text-only RAG breaks down, and Morphik attacks the problem by embedding full pages visually. Its ColPali-based retrieval treats each page as an image, so charts and layouts are searched directly instead of being flattened through lossy OCR, and metadata extraction adds bounding boxes and classification on top. The core engine, morphik-core, self-hosts via Docker or direct install with ingestion, deep and shallow search, transformation, and document management APIs, plus MCP support and integrations for Google Workspace, Slack, and Confluence, while a hosted cloud with a free tier is the recommended path since self-hosting receives limited community support. Licensing is Business Source License 1.1: free for personal and indie use and for commercial deployments under 2,000 dollars a month in revenue, with each release converting to Apache-2.0 four years after it ships, so the code is source-available rather than strictly open source. The developer project and its docs live at dev.morphik.ai, and the repository has about 3,600 stars with active 2026 commits.
Should you use Morphik?
Pick it when
Use Morphik when your documents are full of charts, tables, diagrams, or screenshots, such as slide decks or technical reports, and text-only OCR pipelines keep losing the answer; it searches whole pages as images.
Look elsewhere when
Skip it if you need an OSI open-source license or will self-host commercially above 2,000 dollars a month in revenue under BUSL-1.1. Self-hosting also gets only limited support; ColPali or RAGFlow may fit better.
Alternatives to Morphik
- ColPali
MIT code with Apache-2.0 ColQwen weights, no BUSL revenue cap (PaliGemma checkpoints use the Gemma license), but you build ingestion, storage, and APIs on a GPU.
- RAGFlow
Apache-2.0 engine with layout-aware parsing and chunk citations, fully open source, though it extracts text rather than searching page images.
- Docling
MIT parser that recovers tables and layout into Markdown locally, keeping a text pipeline but with less loss than plain OCR.
- R2R
MIT-licensed backend with multimodal ingestion and knowledge graphs, a permissive choice when visual page retrieval is not essential.
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Details
- Category
- RAG & Document Retrieval
- Price
- Freemium
- Platform
- Hybrid
- Difficulty
- Intermediate (3/5)
- License
- BUSL-1.1
- Added
- Jul 29, 2026