Why Your AI Coding Agent Should Run Locally

Most AI coding tools route your source code through a cloud service. Your files get uploaded, processed on someone else's server, and the results come back. That works — until you think about what you're actually sending: proprietary business logic, API keys in config files, internal documentation, and half-finished features your competitors would love to see.

A local AI agent takes a different approach. It runs entirely on your machine. Your code never leaves your filesystem. The only external call is to the LLM API itself — and you pick which provider that is.

What "Local-First" Actually Means

A local-first architecture means the agent process, its memory, and all file operations happen on your hardware. There's no intermediary service between you and your model provider.

With muxd, that looks like this:

• The daemon runs as a local process on your machine
• Sessions are stored in a local SQLite database — not a cloud backend
• File reads, writes, and edits happen through direct filesystem access
• API calls go straight to your chosen provider (Claude, GPT, or any OpenAI-compatible endpoint)

No telemetry. No data collection. No account required beyond your API key.

Why Privacy Matters for Coding Agents

A coding agent isn't just reading your code — it's reading all your code. It greps through your repo, reads config files, inspects your git history, and executes shell commands. That level of access demands trust.

When you use a private coding agent that runs locally, you get a clear trust boundary: your code goes to the LLM provider you chose, and nowhere else. You can audit every API call. You can run the agent on an air-gapped network if you want.

For teams working on proprietary software, regulated industries, or anything involving customer data, a no-cloud AI setup isn't just nice to have — it's a compliance requirement.

The Performance Advantage

Local agents also tend to be faster. There's no round-trip to a cloud orchestration layer. The agent reads your files directly from disk, runs tools in your local shell, and streams responses as they arrive. The only latency is the LLM inference itself.

With muxd, the daemon architecture means the agent stays warm between sessions. You connect to a running process instead of cold-starting a new one every time.

Getting Started with a Local Agent

Setting up muxd takes one command:

curl -fsSL https://raw.githubusercontent.com/batalabs/muxd/main/install.sh | bash

Add your API key, and you're running a fully local AI coding agent. No sign-up, no subscription, no data leaving your machine.

Check the getting started guide for the full setup, or explore the 18 built-in tools the agent has access to.

The Bottom Line

Cloud-based coding agents trade your privacy for convenience. A local AI agent gives you the same capabilities — tool use, file editing, shell access, multi-model support — without the trade-off. Your code stays on your machine, your sessions stay in local storage, and you keep full control over what gets sent where.

If you care about where your source code goes, run your agent locally. It's that simple.