Product Launch
anonymous
1 points
10 comments
Posted14 days agoActive14 days ago
Linggen
github.commvpdeveloper_tooldevelopers
Key Features
local-first memory layerindexing of documentsvisual map of file dependencies
Tech Stack
Rust
Key Features
Tech Stack
Linggen is a local-first memory layer that gives AI persistent context across repos, docs, and time. It integrates with Cursor / Zed via MCP and keeps everything on-device.
I built this because I kept re-explaining the same context to AI across multiple projects. Happy to answer any questions.
When using Claude Desktop, it connects to Linggen via a local MCP server (localhost), so indexing and memory stay on-device. The LLM can query that local context, but Linggen doesn’t push your data to the cloud.
Claude’s web UI doesn’t support local MCP today — if it ever does, it would just be a localhost URL.
The distinction I’m trying to make is that Linggen itself doesn’t sync or store project data in the cloud; retrieval and indexing stay local, and exposure to the LLM is scoped and intentional.
In particular, I don't know which parts of my data might get sent to Claude, so even if I hope it's only a small fraction, anything could in principle be transmitted.
The tradeoff is simply model quality vs locality, which is why Linggen focuses on controlling retrieval rather than claiming zero data ever leaves the device. Using a local LLM is straightforward if that’s the requirement.
What it can control is the retrieval boundary: what gets selected locally and exposed to the model. If nothing is returned, nothing is sent.
If a strict zero-exfiltration setup is required, then a fully local model would indeed be the right option.
The key difference is that it works across projects. While working on project A, I can ask: “How does project B send messages?” and have that context retrieved and applied, without manually opening or loading docs.
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