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Citadel Predict

Stop AI agents from blowing your token budget

Details

External ID
1243408
Source
PH
Company
—
Product
Citadel Predict
Website domain
producthunt.com
Launched
Sept. 15, 2026
Cohort
—
Upvotes
2
Upvotes percentile
0.7405388069275176
Tags
Open Source, Developer Tools, Artificial Intelligence, GitHub
Fetched at
Sept. 16, 2026, 1:16 a.m.
Updated at
Sept. 16, 2026, 1:16 a.m.

Description

Citadel Predict forecasts AI agent token usage and cost before the agent runs — not after, like existing tools (Langfuse, Helicone, LangSmith). It analyzes your task and tool list, then uses a calibrated ML model to give you an expected token range, confidence level, and out-of-distribution risk flag — now converted into an estimated USD cost too. Ships as a pip package + CLI, an MCP server for Claude Desktop/Code, and a hosted API. Tested on Linux, Windows, and macOS.

Enrichment

Theme
AI trading bots and financial intelligence
Vertical
Horizontal
Function
Observability & eval
Audience
Developer
AI stance
AI-native
Project type
Commercial product
Normalized one-liner
token budget guardrails for ai agents
Manually corrected
False

Could you build this?

Partial The Python SDK and client wrapper are simple, but accurately predicting multi-step agent token usage prior to execution requires proprietary training datasets of complex agent execution traces.

What it would actually take: The backend requires collecting millions of real-world multi-step agent traces across diverse tools and reasoning architectures (ReAct, LangGraph, CrewAI). An ML model (such as a gradient-boosted tree or fine-tuned transformer with conformal prediction) must be trained on tool-call topologies and task embeddings to output well-calibrated confidence intervals and out-of-distribution flags for non-deterministic LLM loops.

Competitors

Other products that read as similar to this one — 140 launches clear the similarity bar, closest 8 shown.

Attention rank: #36 of 141 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 315 days after the earliest competitor.

Other launches for this product

Same idea, different domain

Nobody's really built a observability & eval tool for Media & entertainment yet.