free agent

Inspiration FreeAgent started from a simple frustration: local AI is powerful, but unstable. Long‑running agents crash, memory leaks build up, latency spikes appear without warning, and consumer hardware struggles under load. I wanted a system that didn’t just run local AI, but one that could adapt to the machine it lived on. That meant treating system health as a first‑class signal—something an agent runtime could monitor, interpret, and respond to in real time.

What it does FreeAgent is a local‑first adaptive agent runtime. It monitors CPU, RAM, disk I/O, and inference latency, then dynamically adjusts agent behavior to keep the system stable. It prevents crashes, reduces drift, manages cleanup cycles, and keeps long‑running agents reliable on everyday hardware.

How we built it FreeAgent is built around four core components: • Stress Scoring Engine — continuously measures system metrics and produces a stability score. • Adaptive Runtime Loop — adjusts concurrency, tool execution rate, and model behavior based on that score. • Rolling Cleanup System — removes zombie processes, clears stale caches, and prevents resource drift. • Agent Layer — a simple interface for building agents that automatically inherit adaptive behavior. It integrates with local providers like LM Studio and Ollama, while also supporting remote gateways. Everything is designed to run fully local without requiring cloud resources.

Challenges we ran into The hardest part was balancing responsiveness with stability. If the system reacted too quickly to stress, it oscillated; if it reacted too slowly, it failed to prevent crashes. Tuning the scoring function required dozens of iterations and real‑world stress tests. Another challenge was designing cleanup cycles that were aggressive enough to prevent drift but gentle enough not to interrupt active tasks. I also had to solve issues around scattered workspaces, inconsistent model providers, and ensuring the runtime could recover gracefully from partial failures.

Accomplishments that we're proud of FreeAgent can now run long‑running agents without crashing, even on modest hardware. It turns a consumer PC into a reliable AI workstation by treating system health as a dynamic signal rather than an afterthought. The adaptive runtime loop and scoring model both evolved into robust, production‑ready components. What we learned I learned how CPU saturation interacts with inference latency, how RAM fragmentation accumulates over multi‑hour sessions, and how disk I/O spikes cascade into model slowdowns. I also learned the math behind stability thresholds and how to design adaptive systems that feel almost alive. One key insight: system stress is not linear. A machine at 70% RAM behaves very differently from one at 90%, even though the difference is only . That shaped the scoring model and the runtime loop. What's next for FreeAgent Next steps include GPU‑aware scoring, a plugin system for custom metrics, multi‑agent orchestration, and a local‑node mesh for distributed inference. The long‑term vision is a fully offline, self‑optimizing agent ecosystem that can run anywhere.