llm-observability 8

OMK — Observe. Measure. Know. Evidence-backed knowledge changes for AI applications.

OpenTelemetry and Langfuse observability exporter for DeepSeek Harness: turn/step/tool/LLM spans, token and cost metrics, and sanitized LLM prompt/completion capture from the session/event stream, with async batching, bounded offline buffering, and retry

What a DeepSeek Harness run costs in tokens, money, and time. OpenTelemetry traces, metrics, and logs in GreptimeDB, with seven Grafana dashboards.

Self-contained token usage statistics for DeepSeek Harness.

Transparent, auditable token accounting for DeepSeek Harness: a restart-safe ledger over the durable session log, a CLI that recomputes it from raw logs and diffs the result, and a bill — by workspace, session, model or provider, exportable as CSV or JSON

Request Context Profiler for DeepSeek Harness — see what changed between model requests, and how cache reuse changed with it.

X-ray for your DeepSeek Harness — diagnostics for what's actually loaded, why, and what it costs: per-plugin context-tax attribution, per-request token ledger, skill catalog pricing, dependency cascades.

OpenTelemetry (OTLP/HTTP) + Langfuse trace exporter for DeepSeek Harness — GenAI semconv, sanitized before send, batched with retry, off by default.