PHPMem v2.0.1

Version
1.6.45
Uptime
7 days 23 hours 11 minutes 31 seconds

Memory

Total
512MB
Used
7,68MB (1.5%)
Free
504,32MB

Keys

Current
5 910
Total (since start)
11 098
Evictions
0
Reclaimed
326
Expired Unfetched
0
Evicted Unfetched
0

Connections

Current
5 / 1 024 max
Total
75 155
Rejected
0
llm:c887edddf191e148a85abebfa248831c87f8b51c6ef9715c17054a65ef904629
TTL 5 days 22 hours 29 minutes 10 seconds Size 1,35KB Export
Edit
{
    "action": "tool_call",
    "reasoning": "I've reviewed the evidence and found several interesting patterns: (1) LLM usage shows claude-sonnet-4-5 dominates with 775 calls vs 222 for haiku despite both having 111 runs, (2) runs with 9 LLM calls have LOWER average cost than runs with 8 calls (counterintuitive), (3) 0% of relationships are join-viable in a platform designed to analyze data relationships (ironic), (4) ERROR-level logs have no duration_ms recorded while INFO logs average 24 seconds. The most surprising finding appears to be the inverse relationship between LLM call volume and cost efficiency. Let me investigate whether runs that make MORE LLM calls are actually MORE cost-efficient per call, which would be counterintuitive.",
    "tool": "query_sql",
    "args": {
        "sql": "SELECT total_llm_calls, COUNT(*) as run_count, AVG(total_cost_usd) as avg_total_cost, AVG(total_cost_usd / NULLIF(total_llm_calls, 0)) as avg_cost_per_call, AVG(total_input_tokens) as avg_input, AVG(total_output_tokens) as avg_output FROM raw.analysis_runs WHERE total_llm_calls > 0 GROUP BY total_llm_calls ORDER BY total_llm_calls",
        "purpose": "Calculate cost efficiency per LLM call to verify if higher call volumes correlate with lower per-call costs",
        "source": "raw"
    }
}