> ## Documentation Index > Fetch the complete documentation index at: https://docs.monocle.sh/llms.txt > Use this file to discover all available pages before exploring further. # AI Agents > Monitor LLM calls, token usage, costs, and conversation flows Monocle provides a dedicated AI Agents dashboard for monitoring your application's AI activity. Track LLM calls, tool usage, token consumption, estimated costs, and full conversation flows. ## What Monocle tracks The dashboard has four tabs: * **Overview**: total calls, token usage, estimated costs, and error rates at a glance * **Models**: per-model breakdown with latency, token consumption, and cost * **Tools**: tool call stats with success/error rates and duration * **Scorers**: evaluation and scorer results with score distributions and explanations * **Conversations**: multi-turn conversation flows with drill-down into individual runs ## Token tracking Monocle captures detailed token usage per LLM call, including provider-specific breakdowns when available: * Input and output tokens * Cached input tokens (cache hits) * Cache write tokens * Reasoning tokens (for models like o1 and o3) These metrics are used to estimate costs based on published model pricing. ## Conversation tracking You can group multiple LLM calls into a single conversation by setting a conversation ID. This enables the Conversations tab, where you can follow the full flow of an agentic loop: which models were called, which tools were invoked, and how many tokens were consumed at each step. ## Trace viewer integration When viewing a trace that contains AI spans, an **AI tab** appears in the span details sidebar. It shows token breakdowns, input/output previews, and model information for that specific call. ## Span attributes All AI instrumentations must emit these attributes for the AI dashboard to work correctly. ### Standard GenAI semconv attributes These follow the official [OpenTelemetry GenAI Semantic Conventions](https://opentelemetry.io/docs/specs/semconv/gen-ai/): | Attribute | Description | | -------------------------------- | ---------------------------------------------------------------------------------------------------- | | `gen_ai.operation.name` | Operation type (`chat`, `generate_content`, `text_completion`, `invoke_agent`, `execute_tool`, etc.) | | `gen_ai.system` | LLM provider name | | `gen_ai.request.model` | Requested model ID | | `gen_ai.response.model` | Model ID used in response | | `gen_ai.response.id` | Provider response ID | | `gen_ai.response.finish_reasons` | Finish reasons array | | `gen_ai.request.temperature` | Temperature setting | | `gen_ai.request.max_tokens` | Max tokens setting | | `gen_ai.request.top_p` | Top-p setting | ### Token usage attributes | Attribute | Description | | --------------------------------------- | ------------------------------- | | `gen_ai.usage.input_tokens` | Input tokens consumed | | `gen_ai.usage.output_tokens` | Output tokens generated | | `gen_ai.usage.total_tokens` | Computed total (input + output) | | `gen_ai.usage.input_tokens.cached` | Cached/cache-hit input tokens | | `gen_ai.usage.input_tokens.cache_write` | Cache write tokens | | `gen_ai.usage.output_tokens.reasoning` | Reasoning tokens | ### Tool call attributes | Attribute | Description | | --------------------- | ------------------- | | `gen_ai.tool.name` | Tool name | | `gen_ai.tool.call.id` | Tool call ID | | `gen_ai.tool.type` | Always `"function"` | ### Evaluation result attributes AI evaluations and scorer results should be emitted as span events named `gen_ai.evaluation.result`. The event name is a Monocle convention; the attributes below reuse the OpenTelemetry GenAI namespace. The core attributes follow the official [OpenTelemetry GenAI attribute registry](https://opentelemetry.io/docs/specs/semconv/registry/attributes/gen-ai/): | Attribute | Description | | ------------------------------- | --------------------------------------------------------- | | `gen_ai.evaluation.name` | Evaluation or scorer name | | `gen_ai.evaluation.score.value` | Numeric score value | | `gen_ai.evaluation.score.label` | Optional score label such as `pass`, `warning`, or `fail` | | `gen_ai.evaluation.explanation` | Human-readable explanation for the score | | `gen_ai.response.id` | Provider response ID being evaluated, when available | Monocle also recognizes these `gen_ai.evaluation.*` extensions for richer scorer dashboards: | Attribute | Description | | ----------------------------------- | --------------------------------------------------------------------------------- | | `gen_ai.evaluation.id` | Unique evaluation result ID | | `gen_ai.evaluation.scorer.id` | Stable scorer identifier | | `gen_ai.evaluation.scorer.name` | Display name for the scorer. Falls back to `gen_ai.evaluation.name` | | `gen_ai.evaluation.scorer.version` | Scorer version | | `gen_ai.evaluation.source` | Source of the score, for example `live`, `offline`, or `manual` | | `gen_ai.evaluation.target.trace_id` | Trace ID of the evaluated AI run | | `gen_ai.evaluation.target.span_id` | Span ID of the evaluated AI run | | `gen_ai.evaluation.target.type` | Type of target being evaluated, for example `agent`, `llm`, `tool`, or `workflow` | | `gen_ai.evaluation.target.name` | Name of the evaluated target | | `gen_ai.evaluation.trace_id` | Trace ID of the scoring/evaluation run, when scoring is traced separately | | `gen_ai.evaluation.metadata` | JSON-encoded scorer metadata | ### Monocle attributes These custom attributes power specific dashboard features: | Attribute | Dashboard Feature | | ---------------------------------- | --------------------------------------------------- | | `gen_ai.conversation.id` | Conversations tab, grouping multi-turn interactions | | `gen_ai.response.model` | Models tab, per-model cost and usage breakdown | | `gen_ai.tool.name` | Tools tab, per-tool call stats and error rates | | `gen_ai.usage.*_tokens` | Token consumption charts and cost estimation | | `gen_ai.usage.input_tokens.cached` | Cache hit rate display and cost savings calculation | | `gen_ai.function_id` | Pipeline name display | | `ai.streaming` | Distinguishing streaming vs non-streaming calls | ## Supported SDKs Auto-instruments generateText, streamText, generateObject, streamObject, embed, embedMany, and rerank ## Getting started See the instrumentation page above for setup instructions, or the [AdonisJS guide](/frameworks/adonisjs#ai-sdk) if you are using AdonisJS.