AI · alternatives · 2026
24 alternatives to TokenFence
Per-workflow budget caps and kill switch for AI agents
Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes.
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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 · its alternatives →
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Tokenwise▲143A smart LLM proxy that shows where you're overpaying
Jun 2026 · tokenwisehq.com · its alternatives →
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AgentReady▲118Compression API that reduces prompt size by 40-60% before sending to your LLM. Your API key never leaves your environment.
Feb 2026 · agentready.cloud · its alternatives →
- 5
The turn key OpenClaw solution with unlimited LLM tokens
Mar 2026 · open.claw.cloud · its alternatives →
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TokenCounter▲117Count tokens and estimate costs for any AI model
2024 · tokencounter.co · its alternatives →
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TokenZip▲82Open protocol for AI agents to share memory, not tokens
Mar 2026 · tokenzip.org · its alternatives →
- 8
Your AI agent is burning 6x more tokens than it needs to just to browse the web. We built OpenBrowser MCP to fix that. Most browser MCPs give the LLM dozens of tools: click, scroll, type, extract, navigate. Each call dumps the entire page accessibility tree into the context window. One Wikipedia page? 124K+ tokens. Every. Single. Call. OpenBrowser works differently. It exposes one tool. Your agent writes Python code, and OpenBrowser executes it in a persistent runtime with full browser access. The agent controls what comes back. No bloated page dumps. No wasted tokens. Just the data your…
Apr 2026 · openbrowser.me · its alternatives →
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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 · its alternatives →
- 11
Run GPT-OSS, Qwen, Llama, and DeepSeek through one OpenAI-compatible API with streaming and clear usage-based pricing.
May 2026 · adola.app · its alternatives →
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Tokenlimits▲64Check if your input satisfies the token limit of an AI model
2023 · tokenlimits.com · its alternatives →
- 13
Skip the setup and run OpenClaw & Hermes, fully managed
Aug 2026 · cloudways.com · its alternatives →
- 14
Airbolt AI▲79Hi HN! We love the "backend-less" stack: Stripe for payments, Supabase for data, Clerk for auth. But the moment we add AI features, we're forced to spin up a backend to hide API keys, implement per-user token-based rate limits and graceful degradation, etc. So we built Airbolt. What it does: Drop in our SDK and start making OpenAI calls directly from your frontend. Your keys are AES-256-GCM encrypted on our servers, never exposed to the client. We provide token-based per-user rate limits and origin allow lists to address inference abuse. Short-lived JWTs and bring-your-own-auth are coming…
2025 · airbolt.ai · its alternatives →
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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 · its alternatives →
- 17OS
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 · its alternatives →
- 18
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 · its alternatives →
- 19AA
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 · its alternatives →
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TokenCap▲2Give AI coding agents the context they actually need.
Aug 2026 · tokencap.vansharora.app · its alternatives →
- 21
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 · its alternatives →
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Run your coding agents in one workspace. Maxxwell helps you follow their progress, coordinate their work, and focus on what needs your attention.
Sep 2026 · maxxwell.dev · its alternatives →
- 24
RiskKernel▲25Deterministic cost / loop / time budgets · full observability · crash-resumable runs · human-approval gates · a memory you own. Self-hosted. Your keys. No telemetry. Point it at your existing agents - one env var. - prashar32/riskkernel
Jun 2026 · github.com · its alternatives →
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Ranked by how close each launch is in meaning, then by votes. Prices were read from each product’s own site when checked and can change. Refine with your own description →