llm-dashboard

name	LLM Dashboard
description	Open the MultiLLM dashboard showing sessions, token usage, costs, and backend status. Use when the user asks about LLM usage, costs, dashboard, or wants to see model statistics.
version	0.6.0

LLM Dashboard

Open the MultiLLM dashboard and provide a usage summary.

If the user asks for an hourly check, use hours=1 unless they specify another short window.

Steps

1. Fetch token usage summary:

curl -s 'http://localhost:8080/api/dashboard?hours=HOURS' | python3 -c "
import sys, json
d = json.load(sys.stdin)
t = d.get('totals', {})
total = (t.get('total_input',0) or 0) + (t.get('total_output',0) or 0)
reqs = t.get('total_requests',0) or 0
sessions = d.get('session_count',0) or 0
cost = t.get('total_cost',0) or 0
derived = d.get('derived', {})
print(f'Window:   {d.get(\"hours\", \"HOURS\")}h')
print(f'Requests: {reqs:,}  |  Tokens: {total:,} ({t.get(\"total_input\",0):,} in / {t.get(\"total_output\",0):,} out)  |  Cost: \${cost:.4f}')
print(f'Rates:    {derived.get(\"avg_requests_per_session\",0):.2f} req/session  |  {derived.get(\"avg_tokens_per_request\",0):.1f} tok/req  |  \${derived.get(\"avg_cost_per_request\",0):.6f}/req')
for m in d.get('by_model', [])[:10]:
    tok = (m.get('input_tokens',0) or 0) + (m.get('output_tokens',0) or 0)
    reqs = m.get('requests',0) or 0
    print(f'  {m[\"model_alias\"]:25s} {reqs:4d} reqs  {tok:>10,} tok  {(tok / reqs) if reqs else 0:>8.1f} tok/req  \${m.get(\"cost_usd\",0):.4f}')
"

2. Fetch recent sessions:

curl -s 'http://localhost:8080/api/sessions?hours=HOURS&limit=10' | python3 -c "
import sys, json
from datetime import datetime
for s in json.load(sys.stdin):
    started = datetime.fromtimestamp(s['started_at']).strftime('%b %d %H:%M')
    models = ', '.join(s.get('models_used', []))
    tok = (s.get('total_input_tokens',0) or 0) + (s.get('total_output_tokens',0) or 0)
    reqs = s.get('total_requests',0) or 0
    print(f'  {started} [{s[\"project\"]}] {reqs} reqs, {tok:,} tok, {(tok / reqs) if reqs else 0:.1f} tok/req, \${s.get(\"total_cost_usd\",0):.4f} -- {models}')
"

3. Present a formatted summary of:
   • Total requests, tokens, and estimated costs per model
   • Derived calculations such as request/session, token/request, and cost/request
   • Active backends and available models
   • Recent sessions with duration and models used
   • Hourly rates when the user asks for a short window such as 1h, 3h, 6h, or 12h
4. Direct the user to the full dashboard: http://localhost:8080/dashboard
5. If the gateway is not running, inform them to start it: python -m multillm.gateway