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Give agents a durable Markdown knowledge base with persistent linked notes.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "cortex" yet — see the docs or source repo.
Please turn this literature review into a Markdown note, extract five key points, and link it with wikilinks to "Project Background" and "Next Experiments."
A structured Markdown note with key takeaways and links to related notes.
Search the knowledge base for notes related to "vector database selection," rank the results by relevance, and explain how they are connected.
A ranked list of relevant notes plus an explanation of their relationships.
Review the current note graph, identify orphan notes, and suggest wikilinks that could connect each of them.
A list of orphan notes and suggested links to improve the knowledge graph.
Developers building agents can use it to persist important information in Markdown notes and preserve cross-task relationships with wikilinks.
Researchers or writers can organize scattered materials into a searchable, interlinked note base for later retrieval and reuse.
As knowledge grows, users can rely on its search, writing, and management capabilities to maintain an evolving graph of notes.
It provides agents with a durable, Obsidian-compatible knowledge base built on Markdown notes and wikilinks, supporting writing, searching, and managing a graph of notes.
It is suitable for structured knowledge, research records, project context, and interlinked note content that can be represented in Markdown. For more specific support details, see the source repository.
The description emphasizes agent use: persistent storage, retrieval, and managing a knowledge graph through wikilinks, rather than only manual note-taking.
Provide governed memory for Markdown notes with secure AI retrieval and search.
Capture, retrieve, and manage personal thoughts through voice or text.
Search, update, and safely grow a cited, auditable shared knowledge base.
Guide coding agents through structured, evidence-based software engineering with persistent state.
Give AI agents persistent memory, semantic recall, and cross-session knowledge retention.
Persistent memory, code intelligence, and quality enforcement through one MCP endpoint.