Technical overview
Built for parallel agents, not solo copilots.
Reploy is the coordination layer between engineering intent, real infrastructure, and model inference. The product is team-first, model-agnostic, and cost-optimized because every session benefits from what previous sessions already learned.
The Caching Proxy
Reploy puts a proxy in front of agent traffic so repeated context stops becoming repeated spend. Static cache handles durable repository knowledge, active cache keeps the current session’s working set warm, and hot cache captures repeated requests across agents working in the same monorepo.
static → active → hot
Smart Model Routing
Not every subtask needs the biggest model. Reploy classifies work at negligible cost, routes simple inspection to Haiku-tier models, implementation to Sonnet-tier models, and ambiguous architecture or review tasks to Opus-tier models when the extra reasoning is worth it.
Haiku / Sonnet / Opus
Request Deduplication
Parallel agents often ask the same question in slightly different places. Reploy uses structural hashes to identify equivalent requests, reuse prior answers, and avoid inference entirely when a cached result is safe to return.
structural hash → zero inference
Rate Limit Management
Cached tokens do not count against input-token-per-minute limits. At an 80% cache hit rate, a team gets roughly five times the effective throughput without negotiating higher rate limits or slowing down parallel execution.
80% cache hit → 5x throughput
Credible because the savings compound.
A single agent run can be expensive. Ten parallel agent runs against the same monorepo can be wasteful without shared memory, routing, and deduplication. Reploy treats each run as part of a team system, so every cache hit and every learned codebase fact improves the next session.