283 karma · joined December 19, 2025
I am on linggen.dev , that is the one make daily agent work easier.
Built in Rust, it is fast to install (seconds) and remains lightweight under heavy workloads, including multiple sessions, extensive tool usage, and sub-agent execution.
Author here. Built this because I wanted one system where I could drop a markdown file and get a new agent
— for coding, but also for scheduled code reviews, architecture checks, or anything else.
The file-based approach is the core idea. An agent is 15 lines of YAML + markdown. A skill is a SKILL.md
directory. A mission is a cron entry pointing at an agent. No SDKs, no plugins, no code changes — just
files.
Happy to go deep on the Rust runtime, multi-agent delegation, or anything else.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 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.
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 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.
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.