future-agi

Why we included this project

Most teams building LLM agents end up stitching together separate tools for tracing, evals, and prompt tuning. future-agi folds those jobs into one self-hosted platform built around a single feedback loop: run simulations against synthetic users before launch, score outputs with built-in or custom metrics, trace live traffic to catch failures, then feed those observations back into optimization. Because every feature reads from the same traces, datasets, and scores, something caught in production becomes evaluation data and then a target for improvement, so the work compounds instead of scattering across tools. It starts from a single line of code, works with the stacks you already run, and supports a broad range of providers, which keeps it practical for engineers and product managers who want one source of truth. If keeping agents trustworthy is your real bottleneck, this is the kind of platform worth trying before you invest in more disjointed tooling.