Token Forecaster

Product Introduction

1. Definition: Token Forecaster is an open-source, local-first AI token prediction tool and developer utility. It is a technical forecasting engine that provides real-time, probabilistic estimates for Large Language Model (LLM) output token consumption, specifically optimized for Anthropic's Claude Code and agentic workflows.
2. Core Value Proposition: It exists to solve the critical problem of unpredictable LLM context window usage and cost overruns. By forecasting a range (typical and worst-case) for token output before a request is sent and monitoring live usage, it enables developers and teams to manage AI context windows efficiently, prevent agent loops, and optimize AI operational costs with data-driven precision.

Main Features

1. Multi-Surface Real-Time Monitoring: The core forecasting daemon powers four integrated surfaces. The Claude Code status line provides live metrics during generation. The macOS menu bar app offers an ambient visual glyph (smiling/frowning face) and a detailed overlay. A local web dashboard serves diagnostic data on 127.0.0.1. A Chrome extension delivers forecasts directly on claude.ai/code. All surfaces sync to a single local data source.
2. Quantile-Based Forecasting Engine: The system predicts three key horizons using quantile regression (p50, p90, p99). It employs a hierarchical lookup table that groups historical calls by model type and extended thinking flag, walking from the most specific to the broadest group. A secondary gradient-boosted quantile tree model (depth 3, 48 rounds) applies a small, trained correction based on agent-loop state (session depth, prior call count, Write events).
3. Privacy-Preserving Local Learning: The tool performs local post-training on your own Claude API call history. It reduces prompts to non-invertible aggregate counts locally, ensuring no raw text data is stored or transmitted. The system starts from a bundled prior (trained on one user's Claude Code history) and continuously self-calibrates, only admitting predictor updates that statistically improve performance on held-out data.

Problems Solved

1. Pain Point: Unpredictable and costly LLM token usage, leading to context window overflows, failed agentic tasks, and surprise API bills. Developers lack visibility into whether an LLM reply or multi-turn agent loop will run short or exhaust the context budget mid-task.
2. Target Audience: AI Engineers and Developer Teams building with Claude API, especially those implementing agentic workflows and Claude Code power users. Technical Leads managing AI operational budgets and MLOps engineers responsible for cost optimization and pipeline reliability.
3. Use Cases: Reserving context window space for long-running coding sessions. Spotting and terminating runaway agent loops in real-time. Making informed task-splitting decisions based on projected token consumption. Providing developer foresight into the potential cost and length of an LLM interaction before committing to the API call.

Unique Advantages

1. Differentiation: Unlike simple token counters or static cost calculators, Token Forecaster provides a probabilistic, live forecast rather than a point estimate. It focuses exclusively on output token prediction—the most variable and costly factor—using a methodologically rigorous quantile approach. It is open-source (MIT licensed) and local-first, contrasting with cloud-based analytics services.
2. Key Innovation: Its "ladder" predictor architecture that combines a hierarchical empirical lookup with a lightweight, context-aware machine learning nudge. The rigorous statistical gating for model updates, requiring the entire confidence interval of improvement to be below zero in a session-block bootstrap test, ensures deployed forecasts are robust and non-regressive.

Frequently Asked Questions (FAQ)

1. How accurate is the Token Forecaster prediction? The bundled model's p90 forecast (worst-case) covered 90.6% of 4,146 unseen API calls in evaluation. The system is designed to be conservatively calibrated, leaning high because the cost of a shortfall is weighted nine times higher than slack at the p90 level.
2. Does Token Forecaster work with OpenAI models or other LLMs? The current implementation and bundled prior are specifically trained and optimized for Anthropic's Claude models, particularly within the Claude Code environment. The architecture is model-aware, but accuracy for other LLMs would require retraining on relevant call history.
3. How does Token Forecaster handle my private code and prompts? It maintains strict local data privacy. Prompt text is never stored; it is immediately reduced to aggregate counts (like token length) locally. All learning and history are kept on your machine, with no data sent to external servers.
4. What is the difference between the forecast for a "call" and a "turn"? A call forecast predicts the token output of the next single LLM API response. A turn forecast predicts the total output of an entire agentic loop (which may involve multiple internal LLM calls, like "Write" actions) before it returns a final answer to the user. This is crucial for managing complex workflows.
5. Can I use Token Forecaster to set hard limits or stop a running query? No. Token Forecaster is a monitoring and forecasting tool only. It "watches and never changes the request." It provides the insight for you to make manual decisions about interrupting a process or adjusting your task, but does not automatically enforce limits.