AgentGuard – Auto-kill AI agents before they burn through your budget
Your AI agent hits an infinite loop and racks up $2000 in API charges overnight. This happens weekly to AI developers. AgentGuard monitors API calls in real-time and automatically kills your process when it hits your budget limit. How it works: Add 2 lines to any AI project: const agentGuard = require('agent-guard'); await agentGuard.init({ limit: 50 }); // $50 budget // Your existing code runs unchanged const response = await openai.chat.completions.create({...}); // AgentGuard tracks costs automatically When your code hits $50 in API costs, AgentGuard stops…
AgentGuard – Auto-kill AI agents before they burn through your budget launched on July 31, 2025 with 47 votes, #585 of 9,223 launches that month and more than 90% of that year's launches. The site was still online in September 2026, 1 year on.
In plain words
AgentGuard is a monitoring tool for AI developers that automatically halts code execution when API spending reaches a user-defined budget limit. Designed to prevent runaway costs from infinite loops or malfunctioning agents, it integrates with existing projects through two lines of code and tracks API calls in real-time. When the budget threshold is hit, the process stops and displays detailed information about what caused the spending, functioning as a circuit breaker for AI applications.
written from the facts on this page · September 2026
From the sources
Real-time guardrail that shows token spend & kills runaway LLM/agent loops. - dipampaul17/AgentGuard
In the maker’s words, at launch
Your AI agent hits an infinite loop and racks up $2000 in API charges overnight. This happens weekly to AI developers. AgentGuard monitors API calls in real-time and automatically kills your process when it hits your budget limit. How it works: Add 2 lines to any AI project: const agentGuard = require('agent-guard')…
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AgentWatchJun 2026 · agent-watch.dev · ▲12Hi HN, I’m a solo developer and built AgentWatch to solve a problem I kept running into while building AI agents: preventing runaway loops and unexpected LLM spend before requests reach the model. AgentWatch sits in front of OpenAI, Anthropic, Gemini, Bedrock, Azure OpenAI, Groq, and others to enforce budgets and runtime policies. I’d really appreciate your feedback. If you’re building AI agents, does this solve a problem you’ve experienced? I’d also love to hear what you’d improve or challenge.
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