Throttle-md

Throttle-md / spec

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THROTTLE.md — Open standard for AI agent rate limiting and cost control. Define token limits, API rate ceilings, and automatic slow-down before hard limits.

throttle.md agentic-ai ai-agent ai-agent-safety-stack ai-governance ai-safety
15
0
100% credibility
Found Mar 15, 2026 at 15 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
AI Summary

THROTTLE.md is a plain-text specification file for AI agent projects that defines rate limits, cost ceilings, and graceful slowdown strategies to prevent excessive resource use.

How It Works

1
🔍 Discover THROTTLE.md

While looking for ways to keep your AI helper from spending too much or going too fast, you find this helpful safety guide.

2
📖 Read the simple guide

You learn how it sets gentle speed limits and spending caps so your AI stays safe and predictable.

3
📋 Add it to your project

You copy one easy file right into your project's main folder, and it starts watching over your AI right away.

4
✏️ Tweak your limits

You adjust the numbers for your own speed preferences and budget to fit perfectly.

5
🛡️ Watch it work

Now your AI slows down nicely before hitting any trouble, keeping everything calm and under control.

🎉 Safer AI project

You feel confident with a project that respects your rules and avoids surprise costs or overloads.

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AI-Generated Review

What is spec?

Throttle-md/spec defines THROTTLE.md, a simple Markdown file you drop into any AI agent project's root to set soft limits on tokens per minute, API calls, costs, and file ops. It enforces graceful slowdowns, queues, and logging before hard caps kick in, preventing runaway bills or outages in autonomous agents. As a github spec driven standard, it's plain-text, AI-readable, and MIT-licensed for spec github ai agent workflows.

Why is it gaining traction?

Unlike ad-hoc scripts or vendor dashboards, this offers a standardized, auditable protocol that integrates with tools like spec github copilot or github spec kit alternatives, including claude code and codex setups. Developers dig the "safety stack" of companion specs for escalation, failsafes, and quality checks, making it a lightweight github spec kit for agent governance. It hooks on regulatory prep for EU AI Act compliance without complex tooling.

Who should use this?

AI engineers building production agents that hammer APIs or LLMs, like autonomous coders in cursor or copilot pipelines. Teams at startups scaling spec driven development, worried about opex spikes from unchecked spectre-like agent runs. Ops leads enforcing budgets in multi-agent systems, seeking speck-level precision without speckbohnen overhead.

Verdict

Worth watching for early AI agent projects—15 stars and 1.0% credibility score signal raw spec, not battle-tested code, but solid docs make it a no-risk experiment. Grab it as a github spec kit alternative if you're prototyping regulated agents; skip for mature prod without custom parsers.

(178 words)

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