# DSPy Dashboard > Source: > Published: 2026-07-21 00:00:00+00:00 This dashboard provides a comprehensive view of your `DSPy` programs using trace data. It is built on the DSPy-native span attributes emitted by `openinference-instrumentation-dspy` (`openinference.span.kind` , `llm.model_name` , `llm.provider` ), so every panel keys off the span kinds DSPy produces: `CHAIN` (module and pipeline steps), `LLM` (model requests), and `TOOL` (ReAct tool calls). Use the `service_name` picker at the top to filter the panels to one or more DSPy services. Dashboard Preview Dashboards → + New dashboard → Import JSON What This Dashboard Monitors This dashboard tracks the performance, cost, and reliability of your DSPy programs using OpenTelemetry trace data to help you: **Understand Program Activity**: See total DSPy span volume and how CHAIN, LLM, and TOOL work is distributed over time.** Track Model Usage**: Compare LLM call volume and average latency across every model and provider in use to guide model selection.** Monitor LLM Latency**: Watch p50, p90, p95, and p99 latency for model calls to surface slow responses and regressions.** Measure Call Fan-Out**: Track average LLM calls per program run to spot changes in how many model calls each run makes.** Inspect Module Behavior**: Break down CHAIN spans by module and operation to compare call counts and latency across a program.** Track Tool Usage**: Identify which tools your ReAct agents call most and which are slowest.** Catch Errors Early**: Watch the error rate and drill into recent failures the moment incidents begin. Panels Included Summary (Top Row) | Panel | Type | What It Shows | |---|---|---| DSPy operations | Value | Count of all instrumented DSPy spans (any span carrying `openinference.span.kind` ) in the selected window | LLM calls | Value | Count of `LLM` span-kind spans, representing the actual model requests DSPy makes | Tool calls | Value | Count of `TOOL` span-kind spans, representing ReAct tool invocations | Error rate | Value | Fraction of selected services' spans with an error status, shown as a percentage | Avg LLM calls / program run | Value | `LLM` spans divided by `dspy.program.run` root spans, the model-call fan-out per program run | LLM latency (p95) | Value | p95 duration of `LLM` span-kind spans | Activity & Distribution **DSPy operations by span kind (over time)**: Time-series graph of CHAIN, LLM, and TOOL span counts grouped by`openinference.span.kind` , revealing traffic patterns and which span kinds dominate.**Span kind distribution**: Pie chart showing each span kind's share of total activity, complementing the trend graph with a share-of-activity view. Model Usage & Latency **LLM calls by model (over time)**: Time-series graph of model-request volume grouped by`llm.model_name` , showing which models drive traffic and how it trends.**LLM latency percentiles (over time)**: Time-series graph of p50, p90, and p99 duration for`LLM` spans, surfacing both typical performance and tail latency over time.**Model usage**: Table of`LLM` spans grouped by`llm.model_name` and`llm.provider` , with call count and average latency, showing how usage is distributed across models and providers. Modules & Tools **DSPy module breakdown**: Table of`CHAIN` spans grouped by module and operation name, with call count and average latency, for a side-by-side comparison across the modules in a program.**Tool usage**: Table of`TOOL` spans grouped by tool name, with call count and average latency, identifying which tools are used most and which are slowest. Recent Activity **Recent DSPy operations**: List of the latest instrumented DSPy spans ordered by timestamp, useful for drilling into individual operations when investigating a latency spike.**Errors**: List of recent errored spans across the selected DSPy services, for jumping straight to failures when the error rate climbs.