agent-fuse

⚡ AgentFuse — Financial Circuit Breaker for AI Agents

Stop your AI agent before it burns your budget. Drop AgentFuse into any agentic workflow and get hard spending limits — not soft warnings.

Why This Exists

AI agents don’t crash. They spend.

Real incidents that triggered this:

Reddit r/AI_Agents: “$30K agent loop — implementing financial circuit breakers”
LinkedIn viral post: “AI agent burned $47,000 in a recursive loop for 11 days”
Reddit r/SaaS: “My agent built a runaway retry loop that cost me $700”
Towards AI: “The $12,000 Infinite Loop: How My AI Agent Bankrupted a Sandbox”
sanj.dev: “AI Agents Don’t Crash, They Spend” — Claude Code consumed 1.67B tokens in a loop

Standard LLM providers have no spending guardrails. When an agent loops, you find out on your billing statement.

AgentFuse is an MCP server your agents call directly. When the budget runs out, they get a halt: true response — not a suggestion, a hard signal to stop.

What’s Inside

agent-fuse/
├── index.js          # MCP server (stdio transport)
├── sessions.json     # State file — auto-created on first use
├── dashboard.html    # Visual budget dashboard (drag-drop sessions.json)
├── test.js           # Smoke tests (6/6 passing)
└── package.json

Quick Start

# Clone
git clone https://github.com/RLASAF12/agent-fuse.git
cd agent-fuse

# No npm install needed — zero dependencies, pure Node.js
node index.js

Add to your MCP config (Claude Desktop / Cursor / Continue)

{
  "mcpServers": {
    "agent-fuse": {
      "command": "node",
      "args": ["/absolute/path/to/agent-fuse/index.js"]
    }
  }
}

The 4 Tools

`create_budget_session`

Open a budget envelope before starting an agent run.

{
  "session_id": "weekly-research-agent",
  "budget_usd": 0.50,
  "model": "claude-sonnet-4",
  "description": "Scrapes 20 competitor pricing pages"
}

`track_tokens`

Record every API call. Returns halt: true the moment the budget is exceeded.

{
  "session_id": "weekly-research-agent",
  "input_tokens": 1500,
  "output_tokens": 300,
  "call_label": "competitor_scrape_step_3"
}

Response when over budget:

{
  "halt": true,
  "message": "🚨 BUDGET EXCEEDED — HARD HALT. Spent $0.51 of $0.50..."
}

`check_budget`

Check current spend without recording new usage. Call before expensive operations.

`get_session_report`

Full breakdown: total cost, token counts, call-by-call log, top 5 most expensive calls.

Supported Models (Built-in Pricing)

Model	Input (per 1M)	Output (per 1M)
`gpt-4o`	$2.50	$10.00
`gpt-4o-mini`	$0.15	$0.60
`claude-sonnet-4`	$3.00	$15.00
`claude-haiku-4`	$0.80	$4.00
`claude-opus-4`	$15.00	$75.00
`gemini-2.5-flash`	$0.075	$0.30
`gemini-2.5-pro`	$1.25	$10.00
`deepseek-v3`	$0.27	$1.10
`llama-3.3-70b`	$0.23	$0.40

Unknown model → falls back to a conservative default. Pass any model name — partial matching works (claude → picks closest Claude model).

Dashboard

Open dashboard.html in any browser.

Drag-drop your sessions.json to visualize:

Total spend across all sessions
Budget usage per session (progress bar)
Halted vs active sessions
Per-session call counts

Or run a local server: python3 -m http.server 8080 then visit http://localhost:8080/dashboard.html — auto-loads sessions.json.

Agent Integration Pattern

// 1. At the start of your agent loop
await mcp.call('create_budget_session', {
  session_id: `run-${Date.now()}`,
  budget_usd: 1.00,
  model: 'claude-sonnet-4',
});

// 2. After every LLM call
const guard = await mcp.call('track_tokens', {
  session_id: currentSessionId,
  input_tokens: response.usage.input_tokens,
  output_tokens: response.usage.output_tokens,
});

if (guard.halt) {
  console.error('Budget exceeded — stopping agent');
  break; // or throw
}

// 3. Before expensive operations
const status = await mcp.call('check_budget', { session_id: currentSessionId });
if (status.halt) return;

Run Tests

node test.js
# ✅ tools/list returns 4 tools
# ✅ create_budget_session OK
# ✅ track_tokens OK
# ✅ check_budget OK
# ✅ HALT triggered correctly at overrun
# ✅ get_session_report OK
# 6 passed, 0 failed

License

MIT — use it however you want.

Built by @RLASAF12