MCP-compatible context and memory system: paged code navigation for AI assistants, plus a recorded, verified theory of what is known about the code.
Orven is an MCP-compatible virtual context and memory system. It gives AI assistants a structured, paged view of a project so they can navigate and reason about large codebases without exhausting the context window.
Orven implements the Model Context Protocol and nothing else. Any editor, assistant, or agent that speaks MCP can connect to it, and Orven neither knows nor depends on which client is calling.
A full session: indexing a project with init, reading the dashboard with status, reclaiming working context with reset, and restoring the project with remove.
An assistant working in a real repository has two bad options. It can read whole files, which fills the context window with code it does not need, or it can guess from filenames, which produces wrong answers. Both degrade as the project grows.
Orven applies the memory model an operating system already uses for this problem. The project is parsed once into an index of symbol signatures. The assistant reads that index to orient itself, then requests the specific function or type it needs. Bodies are loaded on demand, tracked while in use, and released when they are not. Verbose material such as logs and diffs is written to disk and replaced in context by a short pointer.
On Orven's own source, a full-source read of roughly 50,000 tokens becomes an index of roughly 4,700, about 90 percent smaller, while every symbol remains one call away. Run npm run bench to measure any project.
From the project README.
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A Model Context Protocol server for persistent project-specific AI agent context.
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