# Langflow Dashboard

> Source: <https://signoz.io/docs/dashboards/dashboard-templates/langflow-dashboard>
> Published: 2026-07-08 00:00:00+00:00

Before using this dashboard, instrument your Langflow application with OpenTelemetry and configure export to SigNoz. See the [Langflow observability guide](https://signoz.io/docs/langflow-observability/) for complete setup instructions.

This dashboard provides a comprehensive view of the `Langflow`

service using trace data. It is built on the `gen_ai.*`

OpenTelemetry span attributes exported by Langflow's built-in Traceloop tracer and focuses on LLM usage: token consumption (input, output, and total), per-model breakdown, LLM call latency, tool calls, agent and flow runs, and errors.

Dashboard Preview

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What This Dashboard Monitors

This dashboard tracks critical performance and cost metrics for your Langflow service using OpenTelemetry trace data to help you:

**Optimize LLM Cost**: Break down input, output, and total token consumption per model to understand cost drivers and track usage trends over time.**Track Model Usage**: Compare call volumes and token consumption across every model in use to guide model selection.** Monitor LLM Latency**: Watch p50, p95, and p99 latency for LLM calls to surface slow responses and regressions.** Understand Agent Activity**: See how many agent and flow runs execute over time and how long they take.** Track Tool Usage**: Identify which tools your agents call most frequently.** Catch Errors Early**: Surface the count of spans with errors so you can detect incidents immediately.** Inspect Recent Calls**: Drill into the most recent LLM calls for quick debugging.

Panels Included

Usage Summary (Top Row)

| Panel | Type | What It Shows |
|---|---|---|
Total LLM Tokens | Value | Sum of `gen_ai.usage.total_tokens` across all spans in the selected window |
LLM Calls | Value | Count of spans where `gen_ai.request.model` exists, showing total LLM invocations |
Input Tokens | Value | Sum of `gen_ai.usage.input_tokens` across all LLM calls |
Output Tokens | Value | Sum of `gen_ai.usage.output_tokens` across all LLM calls |
Avg Tokens / Call | Value | Average of `gen_ai.usage.total_tokens` per LLM call |
Agent / Flow Runs | Value | Count of `invoke_agent LangGraph` spans, representing agent and flow executions |
Tool Calls | Value | Count of spans whose name matches `execute_tool%` , representing tool invocations |
Errors | Value | Count of spans with `hasError = true` ; highlights failures in the selected window |

Token & Model Usage

**Token Usage Over Time by Model**: Time-series graph of total token consumption grouped by`gen_ai.request.model`

, showing which models drive usage over time.**Input vs Output Tokens Over Time**: Time-series graph comparing input and output token consumption to understand the balance between prompt and completion sizes.**LLM Calls Over Time by Model**: Time-series graph of LLM invocation counts grouped by`gen_ai.request.model`

, revealing model adoption and call-volume trends.**Per-Model Usage Breakdown**: Table of call count, input tokens, output tokens, total tokens, and average duration per`gen_ai.request.model`

for per-model cost and performance tracking.**Tokens by Model**: Pie chart of total token consumption per model, showing the proportion of usage across models at a glance.** Response Finish Reasons**: Donut chart of LLM responses grouped by finish reason (for example,`stop`

versus`tool_call`

), showing how often calls complete normally versus trigger a tool call.

Latency

**LLM Call Latency (p50 / p95 / p99)**: Time-series graph of p50, p95, and p99 latency for LLM calls to surface tail latency and regressions.

Agent & Tool Activity

**Tool Calls by Tool**: Bar chart of tool-invocation counts grouped by span name, revealing which tools your agents rely on most.** Agent / Flow Runs Over Time**: Time-series graph of`invoke_agent LangGraph`

span counts, showing agent and flow execution volume over time.**Agent Run Latency (p95)**: Time-series graph of p95 latency for agent and flow runs to catch slow executions.

Recent Activity

**Recent LLM Calls**: List of the most recent spans where`gen_ai.request.model`

exists, for quick inspection and debugging of individual LLM calls.