Alternatives
Products that do what TokenFence does
Per-workflow budget caps and kill switch for AI agents
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I built this after hitting the same wall repeatedly — no good way to enforce token budgets in application code. Provider caps are account-level and tell you what happened, not what is happening. Two ways to add it: # Direct client wrapper client = tokencap.wrap(anthropic.Anthropic(), limit=50_000) # LangChain, CrewAI, AutoGen, etc. tokencap.patch(limit=50_000) Four actions at configurable thresholds: WARN, DEGRADE (transparent model swap), BLOCK, and WEBHOOK. SQLite out of the box, Redis for multi-agent setups. One design decision worth mentioning: tokencap tracks tokens, not dollars. Token…
Apr 2026 · github.com
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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…
2025 · github.com
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Skip the setup and run OpenClaw & Hermes, fully managed
18d ago · cloudways.com
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Hi HN! I want to share our latest project at NEXA AI. We developed AI agent foundation models designed to transform how developers create AI agent powered apps. One major challenge we've observed with current human-computer interactions is that many simple, one-step tasks become unnecessarily complex, multi-step workflows due to limitations of current GUIs. AI agents can solve this, but existing AI agent models are slow and costly. To tackle these issues, we built lightweight AI agent models based on our Octopus V2, small language models for function calling (You can learn more about our…
2024 · nexa4ai.com
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Hello HN, I’ve been building AI agents lately and ran into a common "Context Bloat" problem. When an agent has 20+ skills, stuffing every system prompt, reference doc, and tool definition into a single request quickly hits token limits and degrades model performance (the "lost in the middle" problem). To solve this, I built OpenSkills, an open-source SDK that implements a Progressive Disclosure Architecture for agent skills. The Core Concept: Instead of loading everything upfront, OpenSkills splits a skill into three layers: Layer 1 (Metadata): Light-weight tags and triggers (always loaded…
Jan 2026
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May 2026 · adola.app
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Hi HN, I was once given the advice: Don't waste expensive frontier model credits (GPT/Claude/etc.) on bulk work. Send the boring, repetitive, high-volume jobs to a smaller model, and save the expensive prompts for when you actually need frontier-level reasoning. I complained and told my manager that I shouldnt have to think about using certain models for certain coding tasks, and that one model should handle everything. Well, here we are anyway. If anyone needs a place to absolutely abuse an LLM with high-volume tasks, come beat ours up at https://yolo-auto.com. Here are…
Jul 2026 · yolo-auto.com
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I'm a solo dev in Taiwan. I built 4 AI agents that handle content, sales leads, security scanning, and ops for my tech agency — all on Gemini 2.5 Flash free tier (1,500 req/day). I use ~105. Monthly LLM cost: $0. Architecture: 4 agents on OpenClaw (open source), running on WSL2 at home with 25 systemd timers. What they do every day: - Generate 8 social posts across platforms (quality-gated: generate → self-review → rewrite if score < 7/10) - Engage with community posts and auto-reply to comments (context-aware, max 2 rounds) - Research via RSS + HN API + Jina Reader → feed…
Mar 2026
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Prompt instructions like 'never do X' don't hold up in production. LLMs ignore them when context gets long or users push hard. Limits sits between your agent and the real world. Every action — database writes, API calls, refunds — gets intercepted and checked against your rules before it executes. Deterministically. No LLM involved in enforcement. Three modes: Conditions: hard rules on structured data Guideance: validate LLM output before it reaches the user and give the agent chance to reason and retry Guardrails: scan for PII, toxicity, prompt injection etc One line to integrate: npm…
Feb 2026 · limits.dev
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