Local Agent
A lightweight orchestration layer for running AI agents locally against models that run on your own hardware. No API keys, no usage limits, no data leaving the machine.
Most AI tooling assumes the model lives in the cloud. You send text to an API, the API sends text back. This works well for many use cases, but it creates a dependency on network availability, API rate limits, usage costs, and the privacy implications of sending your queries through someone else’s infrastructure.
Local Agent is an experiment in a different direction. It is a small orchestration layer that routes tasks to models running locally via Ollama, with a configuration-driven task definition format and a simple UI for watching work happen.
Current state
Work in progress. The core loop (define task, run against local model, collect output) is functional. The parts still in progress are multi-step task chaining, a useful results view, and configuration ergonomics.
The problem it is solving
Running a local model is not hard. Running a local model on a structured task, reviewing the output, and feeding it back into the next step is more friction than it should be. Local Agent is meant to make that loop feel like using a simple CLI tool, not like writing custom Python every time.
Why local
The same reason as everything else in this category: the data stays on your hardware. Queries about your own codebase, your own notes, your own documents. These are things I do not particularly want to route through external APIs. Running it locally means I do not have to think about what gets logged where.
The performance on modern consumer hardware is genuinely good now, for most tasks. The gap between “local model” and “cloud model” has narrowed to the point where the trade-off is worth making for routine work.