# DeepSeek Harness Observability & Monitoring with OpenTelemetry > Source: > Published: 2026-08-19 00:00:00+00:00 ## What is DeepSeek Harness Observability? [DeepSeek Harness](https://github.com/deepseek-ai/deepseek-harness) (`dsh` ) is DeepSeek's open-source agent harness. It has no OpenTelemetry export of its own, so instrumenting it means installing one plugin. Once installed, every turn becomes a trace: the agent loop, each reasoning round, each model call with its token counts and time to first token, and each tool execution. With full DeepSeek Harness observability in SigNoz, you can see how much your team is spending in tokens and on which models, how many round trips a single request actually takes, which tools the agent reaches for, and where turns fail. ## Prerequisites - SigNoz setup (choose one): [SigNoz Cloud account](https://signoz.io/teams/)with an active ingestion key- Self-hosted SigNoz instance - DeepSeek Harness 0.1.0-rc.6 or later, below 0.2.0. See the [DeepSeek Harness repository](https://github.com/deepseek-ai/deepseek-harness) - Node.js 22.19.0 or later - A `DEEPSEEK_API_KEY` , or another provider configured in your profile ## Monitor DeepSeek Harness with OpenTelemetry Instrumentation comes from [ @loongsuite/dsh-plugin](https://github.com/loongsuite/dsh-plugin), an Apache-2.0 plugin that hooks the harness lifecycle and exports OTLP over HTTP. **Step 1:** Install the plugin into each profile you use ``` dsh plugin --profile headless add @loongsuite/dsh-plugin dsh plugin --profile web add @loongsuite/dsh-plugin ``` Plugins are installed per profile, so a plugin added to `headless` does nothing when you launch `web` . Confirm it loaded: ``` dsh --profile headless --dump-config | grep loongsuite ``` You should see `id: loongsuite-observability` . **Step 2:** Point the plugin at SigNoz ``` export OTEL_SERVICE_NAME=dsh-agent export OTEL_EXPORTER_OTLP_ENDPOINT="https://ingest..signoz.cloud:443" export OTEL_EXPORTER_OTLP_HEADERS="signoz-ingestion-key=" ``` **Verify these values:** `` : Your[SigNoz Cloud region](https://signoz.io/docs/ingestion/signoz-cloud/overview/#endpoint).`` : Your SigNoz[ingestion key](https://signoz.io/docs/ingestion/signoz-cloud/keys/). **Step 3:** Run the harness ``` dsh --profile headless "list the files in this directory and summarise the project" ``` Spans are batched, so allow a few seconds after the turn finishes before looking in SigNoz. ## View DeepSeek Harness Traces in SigNoz Open the Traces explorer and filter on `gen_ai.agent.system = 'deepseek-harness'` . Each turn arrives as its own trace. Open any `enter_ai_application_system` span to see the full turn. The waterfall shows each reasoning round, the model call inside it, and every tool the agent executed. The plugin tags every span with `gen_ai.span.kind` , which is the attribute to filter and group by: `gen_ai.span.kind` | Span name | What it covers | |---|---|---| `ENTRY` | `enter_ai_application_system` | One turn, the root span | `AGENT` | `invoke_agent deepseek-harness` | The agent loop, with turn-level token totals | `STEP` | `react step` | One reasoning round | `LLM` | `chat ` | One model call, with tokens and time to first token | `TOOL` | `execute_tool ` | One tool execution | Filter on `gen_ai.span.kind` rather than on the span name, since names embed the model and tool name and change per call. ## Prompts and Responses Are Not Captured by Default `captureContent` is `false` , so spans carry structure, timings, and token counts but no prompt or completion text. Enable it with `captureContent: true` in the profile config, or `OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT=SPAN_ONLY` . Two things to weigh before turning it on. Anything in the context window, including source code and file contents, is written to your backend. And the payload grows sharply: the same single-tool turn measured 6,350 bytes with capture off and 73,078 bytes with it on, mostly because `gen_ai.tool.definitions` repeats the full schema of every registered tool on each model call. Note that `dsh.session.cwd` records the absolute working directory on every span even with content capture off. ## DeepSeek Harness Observability Dashboard The [DeepSeek Harness dashboard](https://signoz.io/docs/dashboards/dashboard-templates/deepseek-harness-dashboard/) gives you token spend and cache efficiency, turn and session volume, model latency, tool activity, and error breakdown out of the box. ## Troubleshooting DeepSeek Harness Observability ### No data in SigNoz An expired or wrong ingestion key fails silently. The harness prints a normal answer and exits successfully while the exporter receives a 401, so nothing in the terminal tells you anything is wrong. Test the key directly: ``` curl -i -X POST "https://ingest..signoz.cloud/v1/traces" \ -H "content-type: application/json" \ -H "signoz-ingestion-key: " \ -d '{"resourceSpans":[]}' ``` A working key returns `200` with `{"partialSuccess":{}}` . An expired one returns `401` with `Expired key` . ### The plugin does not appear in the config Plugins are per profile. Run `dsh --profile --dump-config | grep loongsuite` for the profile you actually launch, and add the plugin to that profile if it is missing. ### Subagent work is missing from the parent trace A spawned subagent opens its own trace with its own root span, rather than nesting under the parent turn. There is no span link between them. Join them on attributes instead: the subagent's spans carry `dsh.session.parent_id` , `dsh.session.origin = 'subagent'` , and `dsh.session.delegation_depth` . ### A failing tool shows as a successful span Only tools that report failure explicitly, such as an MCP tool returning `isError` , set the span status to error and add `error.type = 'TOOL_ERROR'` . A shell command exiting non-zero still records a successful span, so tool error counts based on span status will undercount. ### Do not confuse this with the built-in telemetry plugin DeepSeek Harness ships its own `dsh-session-telemetry-otel` plugin, disabled by default through `DSH_TELEMETRY_MODE` , which sends OTLP logs to DeepSeek's own endpoint. That is DeepSeek's product analytics and is unrelated to this setup. ## Related integrations Instrument the other AI coding agents your team runs, using the same OpenTelemetry pipeline: [Monitor Claude Code with OpenTelemetry](https://signoz.io/docs/claude-code-monitoring/)- track token usage, cost, session activity, and tool decisions[OpenCode observability with OpenTelemetry](https://signoz.io/docs/opencode-observability/)- another agent that instruments through a plugin rather than natively[Monitor OpenAI Codex with OpenTelemetry](https://signoz.io/docs/codex-monitoring/)- trace Codex runs from the IDE extension and the CLI[Grok Build observability with OpenTelemetry](https://signoz.io/docs/grok-build-observability/)- token usage, tool activity, and startup latency from its native exporter[Monitor GitHub Copilot with OpenTelemetry](https://signoz.io/docs/github-copilot-monitoring/)- trace Copilot chat requests, models, and token spend Browse [all LLM observability integrations](https://signoz.io/docs/llm-observability/) to instrument the rest of your stack.