For agents

Reduce the repeated, expensive parts of agent workflows.

AI Optimizer is built for repeat-heavy agent workflows where retries, loops, recurring jobs, memory calls, and repeated tool paths can quietly burn API spend.

Quick answer

AI Optimizer helps reduce repeated agent costs by routing workflow traffic through a local proxy on localhost. It is strongest where retries, recurring jobs, repeated tool paths, and scheduled prompts keep revisiting the same API patterns.

Where agent waste usually shows up

Agent systems often repeat the same work more than people expect. A lot of the waste is not the “smart” step — it is retries, repeated summaries, recurring cron runs, repeated tool calls, duplicate context assembly, and loops that keep hitting the same API path.

A local control layer for agent traffic

AI Optimizer runs on localhost:3000 and sits between your workflow and the selected provider API. It helps cache repeated requests, reduce duplicate spend, and make agent behavior easier to reason about locally.

Provider support

AI Optimizer supports OpenAI, Anthropic, and a scoped Google Gemini lane, with one active provider selected at a time in the desktop app. Anthropic is focused on chat completions through the same local proxy path, while Gemini is intentionally scoped around repeat-heavy generateContent workflows.

Built for practical workflows

The strongest use cases are not flashy demos. They are the repeated, boring, expensive patterns that show up when agents, scripts, and automations keep revisiting the same API paths over time.

Why this matters now: as more teams move from chatting with models to running persistent agent workflows, “agent orchestration” and “agent economics” start becoming separate problems. AI Optimizer is built for the economics side: caching, repeated request control, and local workflow efficiency.

Adjustable cache TTL for recurring agent workflows

Agent workflows often run on schedules or repeat in cycles. AI Optimizer includes an adjustable cache TTL so recurring jobs, cron-style tasks, and repeat-heavy automations can keep useful results long enough to help without holding them longer than you want.

Good fit for

Local agent workflows, repeat-heavy automations, scheduled jobs, tool-driven assistants that revisit similar prompts, and developers testing agent loops locally.

Example for OpenClaw

Point OpenAI-compatible traffic at AI Optimizer locally.

"models": {
  "providers": {
    "openai": {
      "baseUrl": "http://localhost:3000/v1"
    }
  }
}

That gives OpenClaw one local path for prompts, responses, and embedding traffic instead of sending everything directly to OpenAI.

Example for Hermes-Agent

Use a custom endpoint and route it through the same proxy.

Select provider: Custom endpoint
API base URL: http://localhost:3000/v1
API key: your OpenAI API key
Model: gpt-5.4
Start chat: hermes --tui

That is enough to route Hermes-Agent through AI Optimizer and verify requests and cache hits in the local app.

How it works Cache OpenAI locally Reduce Gemini costs For developers FAQ

Make your agent workflows cheaper to repeat.

Route prompts, responses, and repeated agent traffic through one local optimizer path and keep the workflow you already rely on.

Start free trial