Evolver

Evolver is an autonomous agent swarm that runs an evolutionary loop over an objective: agents propose candidate programs, build them, verify them in a sandbox, and only the winners survive into the next generation. The first objective is GPU kernel and inference speedup, with KernelBench-style tasks — in early runs, a bundled softmax task hit a ~960× speedup over its reference implementation on an AMD RX 7900 XTX (via ROCm under WSL2).

Two design decisions do most of the work:

• A hard cost governor. Every backend has a per-backend USD cap, a race-safe kill switch, and DB-backed rate limiting shared across all workers — the swarm can run overnight without ever silently draining a premium API quota. This was the gap in every off-the-shelf option and the reason the project exists.
• A two-tier, game-proof verifier. Candidates are scored on public seeds, then re-verified on private held-out inputs with paired in-process timing — because agents will reward-hack a naive benchmark, and several real verifier exploits were found and regression-locked along the way.

The swarm is genuinely heterogeneous: ~14 LLM providers behind one adapter, per-role routing (proposer / builder / verifier / auditor drawn from distinct backends), personas for division of labor, and a shared playbook that transfers accepted wins between agents. Coordination lives in a single SQLite file; a zero-dependency web dashboard watches it live.

Python 3.12, uv, SQLite, Typer + Rich, PyTorch/ROCm. Private for now.