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dsh-observability

admin-security Updated 2026.08.15

Run the following command in DeepSeek Harness:

dsh plugin install CodePrometheus/dsh-observability

Paste the following prompt into your AI chat to install this plugin:

Run dsh plugin install CodePrometheus/dsh-observability in DeepSeek Harness to install this plugin (source: https://github.com/CodePrometheus/dsh-observability).

About this plugin

The official DeepSeek Harness telemetry Service Definition exports OTLP logs only, leaving the trace dimension unaddressed. dsh-observability is a community plugin that registers a second Service Provider on that same definition, collapsing every agent session into an OTLP span tree and shipping it to any collector that accepts OTLP/HTTP (Jaeger, Grafana Tempo, Datadog, etc.), filling the gap in distributed tracing for LLM agent workloads.

The span mapping is straightforward: each turn becomes the trace root span, each model step becomes a child span tagged gen_ai.operation.name=chat, and each tool call becomes a grandchild span tagged execute_tool. Every span carries the full GenAI semantic-convention attribute set including model name, provider, all five token usage counters (input, output, cache read, cache write, reasoning), time-to-first-chunk, error codes, and status. Three modes (FULL, FEEDBACK_ONLY, DISABLED) govern data egress; the default is DISABLED so nothing is constructed or sent. Trace and span IDs are derived from session.id, turn, and step rather than randomly generated, meaning a live capture and a FEEDBACK_ONLY canonical-log replay of the same events produce byte-identical trees and duplicate hand-offs are idempotent at the receiver. Spans left open by a missing terminal event are closed by a force-end sweep and marked dsh.force_ended. The plugin is purely observational: it never assembles or sends a provider request and has no KV-cache effect.

It suits teams that already run an OTLP collector and want to fold dsh agent sessions into their existing APM or compliance pipeline, developers who need per-turn token cost attribution and TTFB analysis, and contributors looking to extend the dsh observability stack at the community level. Keep in mind that the plugin ships no built-in redaction rules, so deployments exporting beyond a trusted boundary must mount their own waterfall listeners, and a context accepts exactly one telemetry backend: running both the official logs provider and this traces plugin simultaneously requires a multi-sink evolution upstream.

Use Cases

  • Export dsh agent sessions as OTLP span trees into Jaeger or Grafana Tempo
  • Analyze per-turn token usage, time-to-first-token, and tool-call latency at step granularity
  • Trace the full multi-step reasoning call chain during compliance audits
  • Attach GenAI semantic-convention attributes to every model and tool span

Best For

  • APM and SRE teams already running an OTLP collector pipeline
  • Application developers who need per-turn token-cost attribution and TTFB analysis
  • Community contributors extending the dsh telemetry stack