Observability, Monitoring, Feedback, and Cost

LARGESTACK provides local/self-hosted observability rather than requiring a SaaS-only platform.

Runtime flow

Agent/Workflow/Orchestrator run
  -> trace_id generated
  -> model/tool/runtime metrics recorded
  -> SQLite trace DB written best-effort
  -> /health, /metrics, dashboard APIs expose status
  -> Monitor facade reads traces and records feedback
  -> optional OTEL / Langfuse / Phoenix adapters export externally

Public API

from largestack import Monitor

monitor = Monitor()
print(monitor.summary())
traces = monitor.list_traces(limit=20)
if traces:
    monitor.record_feedback(traces[0]["trace_id"], rating=5, label="good")
    print(monitor.evaluate_trace(traces[0]["trace_id"]))

Positioning

LARGESTACK observability is strong for self-hosted/local operations:

trace listing
trace detail
feedback capture
lightweight quality evaluation
metrics endpoint
dashboard
optional OTEL/Langfuse/Phoenix export

It does not yet match LangSmith's managed-platform scope for projects, traces, datasets, evals, annotation queues, alerts, and automations.