OptexAI is a drop-in proxy for coding-agent workflows. It removes repeated context between turns so small and midsize teams can spend less on tokens and get steadier agent performance on real engineering tasks — up to 72% lower cost in our benchmark runs, and well ahead of the leading open-source compression proxy.
Coding agents replay context every turn. For a 12-person engineering team or a PM trying to unblock a launch, that means the same files, diffs, and tool output get resent over and over. Costs compound, runs slow down, and teams lose confidence in which agent work is actually worth scaling.
Refactors, test fixes, and bug hunts all create repeated context. It looks like progress, but a growing share of each turn is old information.
When every engineer is experimenting with agents, surprise usage and slower turns show up before you have a dedicated AI platform team.
Naive truncation or manual summarization can drop the line your agent needs. The fix loops, the review gets harder, and the bill grows.
Provider dashboards report tokens, not redundant context. It is hard to decide where agents help delivery and where they are just burning budget.
OptexAI wraps your existing agent CLI or in-house wrapper and rewrites context on the wire. Keep your editor, model provider, and workflow — add cleaner context between turns.
Set your provider base URL to OptexAI and keep using the agent CLI, editor, or wrapper you already have. No SDK migration, no new developer workflow.
OptexAI removes repeated context turn-by-turn while preserving what the model needs to keep working. Your agent sees a cleaner conversation; you see a smaller bill.
Every run reports input/output tokens and savings so you can tell which agent workflows are worth scaling. Per-workflow breakdowns and team views roll out during beta.
Measured turn-by-turn on real engineering tasks against a public compression baseline and an unmodified agent run. Use the controls below to see where context savings hold up by task, model, and metric.
| Configuration | Turns | Tokens | Cost | vs. base |
|---|---|---|---|---|
| Baseline | 49 | 114k | $0.219 | — |
| OptexAI | 12 | 20k | $0.061 | −72% |
| Public baseline | 70 | 132k | $0.274 | +25% |
Yes, and it's great at what it does — compressing noisy CLI output (cargo, git, find) before it hits your model. Use it.
We rewrite agent context between turns— conversation history, tool calls, file reads. On these runs, per-command compression doesn't help when the bottleneck is replayed conversation context. Use both if you want.
Your team is adopting coding agents faster than your tooling stack can govern them. Put OptexAI in front of the workflow to reduce repeated context and see where tokens are actually going.
You care less about the model demo and more about whether agents help ship the roadmap. OptexAI makes the hidden cost and performance tradeoff visible enough to choose the right workflows.
You're four turns into a refactor and watching tokens tick up. Wrap your CLI once, keep useful context flowing, and stop hand-pruning prompts just to finish a task.
OptexAI runs as a managed proxy in our cloud. Your agent's context turns transit our service, get rewritten to remove repetition, and forward on to your model provider. Here is what that means for your data.
We do not train models on your prompts, code, or tool output. Your context is used to serve the request in front of it — nothing more.
Request metadata used to operate the service auto-deletes after 30 days. Aggregate metrics (token counts, savings ratios) are retained without the underlying content.
TLS 1.3 in transit; provider API keys are encrypted at rest and scoped to your account. One tenant, one key — never logged, never shared.
Every rewritten turn is checked against an equivalence oracle before it reaches the model — if quality dips, the proxy falls back to the raw context silently. Built by a small team of senior researchers and infra engineers.
Every compression must reproduce the original turn's tool calls and final diff on a held-out test set, or it's discarded.
The proxy adds <1ms of latency per turn. The token savings dwarf the overhead by three orders of magnitude.
Enterprise customers run OptexAI inside their own VPC. Your code, prompts, and tool output never touch our infrastructure.
Every turn is logged with input tokens, output tokens, savings, and an equivalence score. Pipe it to your existing telemetry stack.
Wrap one workflow, compare real task runs, and decide where agent usage is worth scaling.