In this commit, we add the Python harness that drives GEPA's
optimize_anything over the routesim evaluator. Two modes are supported:
parameter mode tunes the existing heuristic's knobs as a JSON candidate,
while code mode evolves the entire routing algorithm as the full Go
source of candidate_impl.go, compiled per-eval via go build -overlay
with compiler errors fed back to the proposer as reflection signal.
The evaluator scores success rate with small saturating penalties for
retry attempts and fees, and guards against reward hacking by rejecting
candidates that reach for unsafe, reflect, or exec surfaces. The
reflection LM runs through the Codex CLI in headless mode via a small
LM-protocol wrapper, and omni-style two-phase composition (parallel
explore, then a fresh engine seeded with the winner) is available
alongside plain and adaptive runs. The background prompt encodes the
insights discovered by prior champion runs so follow-up evolution builds
on them rather than rediscovering them.
