{"slug": "langflow-dashboard", "title": "Langflow Dashboard", "summary": "SigNoz released a new dashboard for monitoring Langflow applications using OpenTelemetry trace data, providing metrics on LLM token consumption, latency, model usage, agent activity, and errors to help optimize costs and performance.", "body_md": "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.\n\nThis dashboard provides a comprehensive view of the `Langflow`\n\nservice using trace data. It is built on the `gen_ai.*`\n\nOpenTelemetry 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.\n\nDashboard Preview\n\nDashboards → + New dashboard → Import JSON\n\nWhat This Dashboard Monitors\n\nThis dashboard tracks critical performance and cost metrics for your Langflow service using OpenTelemetry trace data to help you:\n\n**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.\n\nPanels Included\n\nUsage Summary (Top Row)\n\n| Panel | Type | What It Shows |\n|---|---|---|\nTotal LLM Tokens | Value | Sum of `gen_ai.usage.total_tokens` across all spans in the selected window |\nLLM Calls | Value | Count of spans where `gen_ai.request.model` exists, showing total LLM invocations |\nInput Tokens | Value | Sum of `gen_ai.usage.input_tokens` across all LLM calls |\nOutput Tokens | Value | Sum of `gen_ai.usage.output_tokens` across all LLM calls |\nAvg Tokens / Call | Value | Average of `gen_ai.usage.total_tokens` per LLM call |\nAgent / Flow Runs | Value | Count of `invoke_agent LangGraph` spans, representing agent and flow executions |\nTool Calls | Value | Count of spans whose name matches `execute_tool%` , representing tool invocations |\nErrors | Value | Count of spans with `hasError = true` ; highlights failures in the selected window |\n\nToken & Model Usage\n\n**Token Usage Over Time by Model**: Time-series graph of total token consumption grouped by`gen_ai.request.model`\n\n, 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`\n\n, 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`\n\nfor 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`\n\nversus`tool_call`\n\n), showing how often calls complete normally versus trigger a tool call.\n\nLatency\n\n**LLM Call Latency (p50 / p95 / p99)**: Time-series graph of p50, p95, and p99 latency for LLM calls to surface tail latency and regressions.\n\nAgent & Tool Activity\n\n**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`\n\nspan 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.\n\nRecent Activity\n\n**Recent LLM Calls**: List of the most recent spans where`gen_ai.request.model`\n\nexists, for quick inspection and debugging of individual LLM calls.", "url": "https://wpnews.pro/news/langflow-dashboard", "canonical_source": "https://signoz.io/docs/dashboards/dashboard-templates/langflow-dashboard", "published_at": "2026-07-08 00:00:00+00:00", "updated_at": "2026-07-09 02:43:02.115683+00:00", "lang": "en", "topics": ["developer-tools", "artificial-intelligence", "large-language-models", "ai-infrastructure"], "entities": ["SigNoz", "Langflow", "OpenTelemetry", "Traceloop"], "alternates": {"html": "https://wpnews.pro/news/langflow-dashboard", "markdown": "https://wpnews.pro/news/langflow-dashboard.md", "text": "https://wpnews.pro/news/langflow-dashboard.txt", "jsonld": "https://wpnews.pro/news/langflow-dashboard.jsonld"}}