TokenFence
Per-workflow budget caps and kill switch for AI agents
TokenFence launched on April 1, 2026.
What it does
LLM providers give you account-level limits. TokenFence gives you per-workflow limits. Set a dollar cap, pick what happens when it's hit (kill or downgrade model), add two lines of code. Works with OpenAI, Anthropic, and any provider.
Set per-workflow budgets, auto-downgrade models at 80%, and kill runaway spend before your API bill explodes.
Does a similar job
all alternatives →
Tokencap – Token budget enforcement across your AI agentsApr 2026 · github.com · ▲7I 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…
OpenClawCloudMar 2026 · open.claw.cloud · ▲85The turn key OpenClaw solution with unlimited LLM tokens
OpenBrowser-AIApr 2026 · openbrowser.me · ▲76Your 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…
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…
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