Welcome to a new series where we explore the practicalities of running a fully local, AI-augmented development environment.
The goal isn't just to have a chat box next to your code. We want to build a workflow where AI agents are integrated directly into your source control and IDE, capable of doing real work without sending your data to a third-party cloud.
The Core Stack
In this series, we'll be focusing on a few key components:
- Gitea: A lightweight, self-hosted Git service that acts as our central hub.
- Lemonade: A specialized tool for managing LLM contexts and agent interactions.
- CLI Agents: Custom-built agents that can be triggered by system events or manual commands.
What We're Building
Over the next few posts, we will implement several key patterns:
- Issue-Triggered Agents: Imagine creating an issue in Gitea ("Refactor the authentication middleware") and having an agent automatically pick it up, analyze the code, and propose a solution.
- Streaming CLI Interactions: Agents that live in your terminal, capable of running tests, fixing errors, and streaming their reasoning and results back to you in real-time.
- Autonomous Code Review: A local bot that reviews your Pull Requests for security vulnerabilities, style consistency, and architectural alignment.
Why Local?
Running your LLM workflow locally offers three major advantages:
- Privacy: Your proprietary code never leaves your infrastructure.
- Latency: No round-trips to an API; the model is right there on your NVMe or GPU.
- Customization: You can fine-tune models or adjust system prompts without worrying about provider-level filtering or rate limits.
Stay tuned as we dive into the setup of Gitea and our first Lemonade-powered agent in the next post.
This is part 1 of the Local LLM Workflow series.