Most coding agents feel like over-engineered black boxes. They rely on cloud APIs, eat RAM like candy, and hide their internals behind layers of abstraction. What if you could build one that fits in a single file, runs on your laptop, and does exactly what you need without the bloat?
Why tiny and native matters
A tiny agent means no unnecessary dependencies. No Docker containers, no Python virtual environments, no gigabytes of model weights. Native means it compiles to a single binary or runs with minimal runtime overhead. This keeps startup times fast and resource usage predictable.
Open source ensures you control the behavior. You can tweak the prompts, adjust the logic, or strip out features you don’t need. No vendor lock-in, no surprise API bills, no waiting for someone else to fix a bug.
The core components
- A small language model that runs locally. Think 7B parameters or fewer.
- A file watcher to monitor your codebase for changes.
- A simple CLI interface for sending commands and receiving output.
- A prompt template that keeps the agent focused on coding tasks.
- Basic error handling and logging to avoid silent failures.
Choosing the right model
You don’t need a 70B parameter model for most coding tasks. Smaller models like CodeGemma 2B or TinyLlama 1.1B are fast, lightweight, and surprisingly capable for code completion, refactoring, and simple explanations. They run on a single GPU or even a decent CPU.
Quantization helps. Converting the model to 4-bit precision reduces memory usage without a huge drop in quality. Tools like GGML or llama.cpp make this straightforward. You’ll trade a little accuracy for a lot of speed and portability.
Building the file watcher
A file watcher triggers the agent when your code changes. In Go, you can use the fsnotify library. In Rust, the notify crate works well. Python has watchdog. The key is to debounce events so you don’t overload the model with rapid-fire changes.
Keep the watcher simple. It should track file saves, not every keystroke. Ignore temporary files, build artifacts, and directories like node_modules or .git. Focus on the files you actually edit.
Designing the prompt
A good prompt turns a general-purpose model into a coding assistant. Start with a clear role. Tell the model it’s an expert developer working on a specific codebase. Include the file context and the task you want help with.
You are an expert Python developer. Here’s a function from my codebase. Suggest a cleaner implementation that handles edge cases better.
Limit the response length. A model that rambles for pages is useless. Set a token limit and ask for concise answers. If the task is complex, break it into smaller steps and handle them one at a time.
Putting it all together
- Load the quantized model into memory at startup. Keep it warm to avoid cold-start delays.
- Start the file watcher in a separate thread or process. Use a queue to manage incoming events.
- When a file changes, read the content and send it to the model with your prompt template.
- Stream the response back to the CLI or insert it directly into your editor via LSP or a plugin.
- Log errors and timeouts. If the model fails, fall back to a simple error message instead of crashing.
Making it useful in real workflows
A coding agent should feel like a pair programmer, not a distraction. Start with small, predictable tasks. Ask it to explain a function, suggest a refactor, or write a docstring. Avoid open-ended requests like 'improve this file' unless you want a mess.
Integrate it with your editor. If you use VS Code, write a simple extension that calls your agent’s CLI. For Neovim, use a Lua script to send the current buffer to the agent and display the response in a split. The less friction, the more you’ll use it.
Keeping it open and maintainable
Open source isn’t just about the license. It’s about making the code easy to understand and modify. Write clear, commented code. Use a simple build system. Document the dependencies and setup steps. If someone can’t run it in 10 minutes, it’s too complicated.
Publish it on GitHub with a permissive license like MIT or Apache 2.0. Include a README that explains what it does, how to build it, and how to contribute. The goal is to let others adapt it to their needs, not just clone and forget.
A tiny native coding agent won’t replace a full IDE or a team of developers. But it can handle the repetitive, low-context tasks that slow you down. Build it small, keep it open, and make it work for you.
This post was originally published on my site. Read the full article and more →
