2026.09.16
Knowledge Base: A Desktop Knowledge Base for Local Notes and AI
Knowledge Base combines Markdown, full-text search, backlinks, synchronization, imports, and optional AI features for users who want more control over their data. Review sync, provider, and AGPL licensing considerations before adoption.
What it is for
Knowledge Base is a local-first desktop knowledge-base application designed to bring notes, source material, and relationships between ideas into one workspace. The project is built with Tauri 2.x, React 19, Rust, and SQLite. Its supplied project information lists support for Windows, macOS, and Linux, and describes the application as having no cloud dependency. However, the available sources do not specify minimum hardware, exact operating-system requirements, or performance limits, so they do not establish suitability for low-powered devices or very large libraries.
Who it may suit
The application may suit researchers, developers, writers, and small teams that want to manage personal or internal material in Markdown while retaining more control over where notes are stored. Backlinks and the knowledge graph are relevant to users who organize concepts and references as connected material. SQLite FTS5 full-text search is aimed at finding content across an accumulated note collection rather than relying only on folders or titles.
Users who already operate WebDAV, S3, or a sync drive can use those services as part of a multi-device workflow. The project describes note-level incremental synchronization, automatic bidirectional scheduling, and full-library ZIP backups. The supplied sources do not explain conflict resolution, recovery after failed synchronization, or a compatibility matrix for storage providers. Test the workflow with copies before entrusting it with the primary library.
Documented features
The editor supports Markdown, backlinks, and a knowledge graph. Import formats include .md, .txt, .pdf, and .docx, with automatic encoding detection. Other listed features include an encrypted Vault for notes, hidden notes protected by a PIN, a custom data directory, and in-app updates. AI capabilities support OpenAI-compatible endpoints, Ollama, and custom providers, together with a tool-calling framework. This gives users several deployment patterns, but the sources do not provide a security audit or a guarantee about any particular provider.
Release v1.62.0 added a desktop web-clipping entry point. The release notes say clipped content can be inserted into the current note, and also mention fixes for code-block line-number drift and terminal-log paste formatting. The release lists Windows, macOS Apple Silicon, macOS Intel, Linux deb, and AppImage builds. Installation requirements beyond those package labels are not confirmed by the supplied material.
Limits and deployment considerations
Licensing should be reviewed first. The README states that the project uses GNU AGPL-3.0 together with a commercial license. It identifies closed-source commercial distribution, packaged sales, some SaaS deployments without source availability, and redistribution inside proprietary software as cases requiring further commercial licensing consideration. Organizations should have legal counsel review the applicable terms before modifying, integrating, or distributing the application; “open source” alone does not answer every commercial-use question.
Local-first also does not mean that data always remains on one machine. When WebDAV, S3, a sync drive, or an external AI provider is enabled, information may be sent to those services or model endpoints. The provided sources do not establish claims about retention, transport encryption, key management, or security auditing. For sensitive material, evaluate providers, credential storage, backup permissions, and access boundaries separately. Hardware, capacity, and performance guarantees are likewise not documented in the supplied sources.
Overall, Knowledge Base focuses on a controllable local note workflow that combines search, relationships, synchronization, imports, and optional AI. A sensible adoption path is to start with a small, non-sensitive library, test imports and synchronization conflicts, confirm model and storage costs, and resolve licensing questions before expanding deployment.
Sources
Sources checked: 2026-09-16