Agent Cost Controller
Run AI agents in production without fearing the bill
Agent Cost Controller launched on July 9, 2026 with 3 votes, #8,766 of 24,623 launches that month and more than 50% of that year's launches.
What it does
Watch a runaway agent get caught live. AgentCC caps spend, stops rogue agents automatically, and opens the pull request that fixes the cause.
This is the whole product in twelve seconds: an agent gets stuck retrying the same request, spend races toward the budget, ACC kills it the moment the line is crossed — then opens the pull request that patches the loop for good. Connect the agents you already run, see where money leaks, stop it automatically, and fix the cause — without changing how your agents work. One line wraps the LLM client behind any agent in production — a support bot, a document pipeline, a screener. No rewrites, nothing about your agent changes. import { withCostControl } from "agentcc"; const client = withCostControl(new OpenAI(), { agentId, accKey }); 2 See where the money leaks Agents waste money in predictable…from agentcc.ca
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AgentGuard – Auto-kill AI agents before they burn through your budget2025 · github.com · ▲47Your 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…
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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