finobs 0.0.1

CLI tool to monitor, analyze and optimize LLM agents in production

finobs — LLM Agent Observability Suite

Monitor, analyze and optimize LLM agents in production.

pip install finobs

Commands

finobs audit run_123           # Full observability audit (5 agents in parallel)
finobs trace run_123           # Visualize execution trace
finobs loops run_123           # Detect loops and redundant steps
finobs cost run_123            # Cost breakdown by run
finobs cost tenant_abc         # Cost breakdown by tenant
finobs performance run_123     # Latency analysis (p50/p95/p99)
finobs debug run_123           # Debug failed steps
finobs debug run_123 --llm     # Debug with Claude semantic analysis (Phase 4)

Instrument your agent

from finobs import traced, flush_trace

@traced("search_documents")
def search_documents(query: str) -> str:
    ...

@traced("llm_call")
def call_llm(prompt: str) -> str:
    ...

# At end of run:
flush_trace(run_id="run_123", tenant="my_tenant")

Generate test traces

from finobs.scripts.trace_generator import generate_trace, Scenario

generate_trace(Scenario.LOOP)           # loop detection
generate_trace(Scenario.LATENCY_SPIKE)  # p95 spike
generate_trace(Scenario.TOKEN_WASTE)    # cost waste
generate_trace(Scenario.MULTI_FAILURE)  # combined issues

Then audit:

finobs audit loop_xxxxxx

Scoring

Dimension	Weight
Performance	25%
Quality	25%
Cost	20%
Stability	15%
RAG	15%

Bands: 🟢 85–100 HEALTHY · 🟡 65–84 DEGRADED · 🔴 0–64 CRITICAL

Development

git clone https://github.com/tu-org/finobs
cd finobs
pip install -e ".[dev,fastapi]"
pytest tests/ -v

Roadmap

• Phase 1 — CLI + trace interceptor + 5 analysis agents
• Phase 2 — Scoring system + report generation
• Phase 3 — Deterministic scripts (loop detector, cost analyzer, metrics)
• Phase 4 — Claude LLM semantic analysis (--llm flag)
• Phase 5 — Self-healing + A/B prompt comparison + degradation detection