token_time_bridge
Read-onlyIdempotent
Map LLM token budgets to estimated wall-clock time using model-specific calibration. Plan agentic tasks by factoring reasoning depth and tool-call latency.
Instructions
Map LLM token budgets to estimated wall-clock time.
Uses model-specific calibration data (tokens/second, reasoning overhead, tool-call latency) to estimate how long a task will actually take. Bridges the gap between token-space (how agents reason) and time-space (what humans need).
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| tokens | Yes | Total number of tokens in the LLM request (prompt + completion). | |
| model | Yes | LLM model identifier. Unknown models fall back to generic estimates. | |
| tool_calls | No | Number of tool calls expected in the agentic loop. Each adds overhead latency. | |
| reasoning_depth | No | Expected depth of chain-of-thought reasoning. Deep reasoning adds significant per-token latency. | moderate |
| task_type | No | Optional task type for feedback matching. |