I’m Federico, a visual designer and full-stack developer.
About six months ago, I had an idea I kept coming back to: could I turn documents and datasets into connected knowledge, explore the relationships visually, and ask questions without sending my material to a cloud inference service?
Why I built Knemora?
After a lot of research, architecture decisions, late nights, and interface iterations, that idea became Knemora.
How it works?
It’s an open-source, local-first AI (SI) Second Brain. You bring your documents and datasets; Knemora compiles knowledge into a portable Markdown wiki, connects pages, and makes that knowledge available through search, a native graph, and chat with source citations.
Architecture:
The diagrams below show the system architecture and the knowledge/inference workflow.
A few choices behind the architecture:
- Portable knowledge: Markdown pages with frontmatter and explicit links. The graph is a rebuildable view of those connections.
- Local inference: llama.cpp with Qwen3-8B Q3_K_M as the default model.
- Retrieval: BM25 search and bounded graph navigation, without a vector database.
- Traceability: original documents are preserved, and generated claims carry source references.
- Dataset analysis: Polars performs structured calculations, keeping numerical results grounded in computation.
The stack includes Vue 3, FastAPI, Google ADK, SQLite, and Sigma.js/Graphology.
The initial alpha targets macOS Apple Silicon. Node.js 24 with npm is required; the launcher manages the remaining runtimes and model downloads. Initial setup needs internet access, while the core knowledge and inference workflow runs locally afterward. There’s also an isolated demo with interconnected sample knowledge.
As a designer, I care about making the interface clear and the graph useful. As a developer, I care about keeping the underlying knowledge portable and understandable.
Open-source software has helped me throughout my career, and I’m proud to contribute something of my own. This is an early alpha, with plenty left to improve.
Code, documentation, and setup:
github.com/federicolaterra/knemora
Website:
knemora.space
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