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Show HN: Linggen – A local-first memory layer for your AI (Cursor, Zed, Claude)

https://github.com/linggen/linggen
9•linggen•2h ago
Hi HN,

Working with multiple projects, I got tired of re-explaining our complex multi-node system to LLMs. Documentation helped, but plain text is hard to search without indexing and doesn't work across projects. I built Linggen to solve this.

My Workflow: I use the Linggen VS Code extension to "init my day." It calls the Linggen MCP to load memory instantly. Linggen indexes all my docs like it’s remembering them—it is awesome. One click loads the full architectural context, removing the "cold start" problem.

The Tech:

Local-First: Rust + LanceDB. Code and embeddings stay on your machine. No accounts required.

Team Memory: Index knowledge so teammates' LLMs get context automatically.

Visual Map: See file dependencies and refactor "blast radius."

MCP-Native: Supports Cursor, Zed, and Claude Desktop.

Linggen saves me hours. I’d love to hear how you manage complex system context!

Repo: https://github.com/linggen/linggen Website: https://linggen.dev

Comments

linggen•1h ago
Hi HN, I’m the author.

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.

Y_Y•44m ago
How can it stay on your device if you use Claude?
linggen•1m ago
Good question. Linggen itself always runs locally.

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.

gostsamo•19m ago
How is it better than keeping project documentation and telling the agent to load the necessary parts? does it compress the info somehow or helps with context management?
linggen•4m ago
Compared to plain docs, Linggen indexes project knowledge into a vector store that the LLM can query directly.

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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