AI

Telemetry

AI SDK Telemetry is experimental and may change in the future.

The AI SDK uses OpenTelemetry to collect telemetry data. OpenTelemetry is an open-source observability framework designed to provide standardized instrumentation for collecting telemetry data.

Check out the AI SDK Observability Integrations to see providers that offer monitoring and tracing for AI SDK applications.

Enabling telemetry

For Next.js applications, please follow the Next.js OpenTelemetry guide to enable telemetry first.

You can then use the experimental_telemetry option to enable telemetry on specific function calls while the feature is experimental:

const result = await generateText({
  model: openai('gpt-4-turbo'),
  prompt: 'Write a short story about a cat.',
  experimental_telemetry: { isEnabled: true },
});

When telemetry is enabled, you can also control if you want to record the input values and the output values for the function. By default, both are enabled. You can disable them by setting the recordInputs and recordOutputs options to false.

Disabling the recording of inputs and outputs can be useful for privacy, data transfer, and performance reasons. You might for example want to disable recording inputs if they contain sensitive information.

Telemetry Metadata

You can provide a functionId to identify the function that the telemetry data is for, and metadata to include additional information in the telemetry data.

const result = await generateText({
  model: openai('gpt-4-turbo'),
  prompt: 'Write a short story about a cat.',
  experimental_telemetry: {
    isEnabled: true,
    functionId: 'my-awesome-function',
    metadata: {
      something: 'custom',
      someOtherThing: 'other-value',
    },
  },
});

Custom Tracer

You may provide a tracer which must return an OpenTelemetry Tracer. This is useful in situations where you want your traces to use a TracerProvider other than the one provided by the @opentelemetry/api singleton.

const tracerProvider = new NodeTracerProvider();
const result = await generateText({
  model: openai('gpt-4-turbo'),
  prompt: 'Write a short story about a cat.',
  experimental_telemetry: {
    isEnabled: true,
    tracer: tracerProvider.getTracer('ai'),
  },
});

Collected Data

generateText function

generateText records 3 types of spans:

  • ai.generateText (span): the full length of the generateText call. It contains 1 or more ai.generateText.doGenerate spans. It contains the basic LLM span information and the following attributes:

    • operation.name: ai.generateText and the functionId that was set through telemetry.functionId
    • ai.operationId: "ai.generateText"
    • ai.prompt: the prompt that was used when calling generateText
    • ai.response.text: the text that was generated
    • ai.response.toolCalls: the tool calls that were made as part of the generation (stringified JSON)
    • ai.response.finishReason: the reason why the generation finished
    • ai.settings.maxSteps: the maximum number of steps that were set

  • ai.generateText.doGenerate (span): a provider doGenerate call. It can contain ai.toolCall spans. It contains the call LLM span information and the following attributes:

    • operation.name: ai.generateText.doGenerate and the functionId that was set through telemetry.functionId
    • ai.operationId: "ai.generateText.doGenerate"
    • ai.prompt.format: the format of the prompt
    • ai.prompt.messages: the messages that were passed into the provider
    • ai.prompt.tools: array of stringified tool definitions. The tools can be of type function or provider-defined. Function tools have a name, description (optional), and parameters (JSON schema). Provider-defined tools have a name, id, and args (Record).
    • ai.prompt.toolChoice: the stringified tool choice setting (JSON). It has a type property (auto, none, required, tool), and if the type is tool, a toolName property with the specific tool.
    • ai.response.text: the text that was generated
    • ai.response.toolCalls: the tool calls that were made as part of the generation (stringified JSON)
    • ai.response.finishReason: the reason why the generation finished

  • ai.toolCall (span): a tool call that is made as part of the generateText call. See Tool call spans for more details.

streamText function

streamText records 3 types of spans and 2 types of events:

  • ai.streamText (span): the full length of the streamText call. It contains a ai.streamText.doStream span. It contains the basic LLM span information and the following attributes:

    • operation.name: ai.streamText and the functionId that was set through telemetry.functionId
    • ai.operationId: "ai.streamText"
    • ai.prompt: the prompt that was used when calling streamText
    • ai.response.text: the text that was generated
    • ai.response.toolCalls: the tool calls that were made as part of the generation (stringified JSON)
    • ai.response.finishReason: the reason why the generation finished
    • ai.settings.maxSteps: the maximum number of steps that were set

  • ai.streamText.doStream (span): a provider doStream call. This span contains an ai.stream.firstChunk event and ai.toolCall spans. It contains the call LLM span information and the following attributes:

    • operation.name: ai.streamText.doStream and the functionId that was set through telemetry.functionId
    • ai.operationId: "ai.streamText.doStream"
    • ai.prompt.format: the format of the prompt
    • ai.prompt.messages: the messages that were passed into the provider
    • ai.prompt.tools: array of stringified tool definitions. The tools can be of type function or provider-defined. Function tools have a name, description (optional), and parameters (JSON schema). Provider-defined tools have a name, id, and args (Record).
    • ai.prompt.toolChoice: the stringified tool choice setting (JSON). It has a type property (auto, none, required, tool), and if the type is tool, a toolName property with the specific tool.
    • ai.response.text: the text that was generated
    • ai.response.toolCalls: the tool calls that were made as part of the generation (stringified JSON)
    • ai.response.msToFirstChunk: the time it took to receive the first chunk in milliseconds
    • ai.response.msToFinish: the time it took to receive the finish part of the LLM stream in milliseconds
    • ai.response.avgCompletionTokensPerSecond: the average number of completion tokens per second
    • ai.response.finishReason: the reason why the generation finished

  • ai.toolCall (span): a tool call that is made as part of the generateText call. See Tool call spans for more details.

  • ai.stream.firstChunk (event): an event that is emitted when the first chunk of the stream is received.

    • ai.response.msToFirstChunk: the time it took to receive the first chunk

  • ai.stream.finish (event): an event that is emitted when the finish part of the LLM stream is received.

It also records a ai.stream.firstChunk event when the first chunk of the stream is received.

generateObject function

generateObject records 2 types of spans:

  • ai.generateObject (span): the full length of the generateObject call. It contains 1 or more ai.generateObject.doGenerate spans. It contains the basic LLM span information and the following attributes:

    • operation.name: ai.generateObject and the functionId that was set through telemetry.functionId
    • ai.operationId: "ai.generateObject"
    • ai.prompt: the prompt that was used when calling generateObject
    • ai.schema: Stringified JSON schema version of the schema that was passed into the generateObject function
    • ai.schema.name: the name of the schema that was passed into the generateObject function
    • ai.schema.description: the description of the schema that was passed into the generateObject function
    • ai.response.object: the object that was generated (stringified JSON)
    • ai.settings.mode: the object generation mode, e.g. json
    • ai.settings.output: the output type that was used, e.g. object or no-schema

  • ai.generateObject.doGenerate (span): a provider doGenerate call. It contains the call LLM span information and the following attributes:

    • operation.name: ai.generateObject.doGenerate and the functionId that was set through telemetry.functionId
    • ai.operationId: "ai.generateObject.doGenerate"
    • ai.prompt.format: the format of the prompt
    • ai.prompt.messages: the messages that were passed into the provider
    • ai.response.object: the object that was generated (stringified JSON)
    • ai.settings.mode: the object generation mode
    • ai.response.finishReason: the reason why the generation finished

streamObject function

streamObject records 2 types of spans and 1 type of event:

  • ai.streamObject (span): the full length of the streamObject call. It contains 1 or more ai.streamObject.doStream spans. It contains the basic LLM span information and the following attributes:

    • operation.name: ai.streamObject and the functionId that was set through telemetry.functionId
    • ai.operationId: "ai.streamObject"
    • ai.prompt: the prompt that was used when calling streamObject
    • ai.schema: Stringified JSON schema version of the schema that was passed into the streamObject function
    • ai.schema.name: the name of the schema that was passed into the streamObject function
    • ai.schema.description: the description of the schema that was passed into the streamObject function
    • ai.response.object: the object that was generated (stringified JSON)
    • ai.settings.mode: the object generation mode, e.g. json
    • ai.settings.output: the output type that was used, e.g. object or no-schema

  • ai.streamObject.doStream (span): a provider doStream call. This span contains an ai.stream.firstChunk event. It contains the call LLM span information and the following attributes:

    • operation.name: ai.streamObject.doStream and the functionId that was set through telemetry.functionId
    • ai.operationId: "ai.streamObject.doStream"
    • ai.prompt.format: the format of the prompt
    • ai.prompt.messages: the messages that were passed into the provider
    • ai.settings.mode: the object generation mode
    • ai.response.object: the object that was generated (stringified JSON)
    • ai.response.msToFirstChunk: the time it took to receive the first chunk
    • ai.response.finishReason: the reason why the generation finished

  • ai.stream.firstChunk (event): an event that is emitted when the first chunk of the stream is received.

    • ai.response.msToFirstChunk: the time it took to receive the first chunk

embed function

embed records 2 types of spans:

  • ai.embed (span): the full length of the embed call. It contains 1 ai.embed.doEmbed spans. It contains the basic embedding span information and the following attributes:

    • operation.name: ai.embed and the functionId that was set through telemetry.functionId
    • ai.operationId: "ai.embed"
    • ai.value: the value that was passed into the embed function
    • ai.embedding: a JSON-stringified embedding

  • ai.embed.doEmbed (span): a provider doEmbed call. It contains the basic embedding span information and the following attributes:

    • operation.name: ai.embed.doEmbed and the functionId that was set through telemetry.functionId
    • ai.operationId: "ai.embed.doEmbed"
    • ai.values: the values that were passed into the provider (array)
    • ai.embeddings: an array of JSON-stringified embeddings

embedMany function

embedMany records 2 types of spans:

  • ai.embedMany (span): the full length of the embedMany call. It contains 1 or more ai.embedMany.doEmbed spans. It contains the basic embedding span information and the following attributes:

    • operation.name: ai.embedMany and the functionId that was set through telemetry.functionId
    • ai.operationId: "ai.embedMany"
    • ai.values: the values that were passed into the embedMany function
    • ai.embeddings: an array of JSON-stringified embedding

  • ai.embedMany.doEmbed (span): a provider doEmbed call. It contains the basic embedding span information and the following attributes:

    • operation.name: ai.embedMany.doEmbed and the functionId that was set through telemetry.functionId
    • ai.operationId: "ai.embedMany.doEmbed"
    • ai.values: the values that were sent to the provider
    • ai.embeddings: an array of JSON-stringified embeddings for each value

Span Details

Basic LLM span information

Many spans that use LLMs (ai.generateText, ai.generateText.doGenerate, ai.streamText, ai.streamText.doStream, ai.generateObject, ai.generateObject.doGenerate, ai.streamObject, ai.streamObject.doStream) contain the following attributes:

  • resource.name: the functionId that was set through telemetry.functionId
  • ai.model.id: the id of the model
  • ai.model.provider: the provider of the model
  • ai.request.headers.*: the request headers that were passed in through headers
  • ai.settings.maxRetries: the maximum number of retries that were set
  • ai.telemetry.functionId: the functionId that was set through telemetry.functionId
  • ai.telemetry.metadata.*: the metadata that was passed in through telemetry.metadata
  • ai.usage.completionTokens: the number of completion tokens that were used
  • ai.usage.promptTokens: the number of prompt tokens that were used

Call LLM span information

Spans that correspond to individual LLM calls (ai.generateText.doGenerate, ai.streamText.doStream, ai.generateObject.doGenerate, ai.streamObject.doStream) contain basic LLM span information and the following attributes:

  • ai.response.model: the model that was used to generate the response. This can be different from the model that was requested if the provider supports aliases.
  • ai.response.id: the id of the response. Uses the ID from the provider when available.
  • ai.response.timestamp: the timestamp of the response. Uses the timestamp from the provider when available.
  • Semantic Conventions for GenAI operations
    • gen_ai.system: the provider that was used
    • gen_ai.request.model: the model that was requested
    • gen_ai.request.temperature: the temperature that was set
    • gen_ai.request.max_tokens: the maximum number of tokens that were set
    • gen_ai.request.frequency_penalty: the frequency penalty that was set
    • gen_ai.request.presence_penalty: the presence penalty that was set
    • gen_ai.request.top_k: the topK parameter value that was set
    • gen_ai.request.top_p: the topP parameter value that was set
    • gen_ai.request.stop_sequences: the stop sequences
    • gen_ai.response.finish_reasons: the finish reasons that were returned by the provider
    • gen_ai.response.model: the model that was used to generate the response. This can be different from the model that was requested if the provider supports aliases.
    • gen_ai.response.id: the id of the response. Uses the ID from the provider when available.
    • gen_ai.usage.input_tokens: the number of prompt tokens that were used
    • gen_ai.usage.output_tokens: the number of completion tokens that were used

Basic embedding span information

Many spans that use embedding models (ai.embed, ai.embed.doEmbed, ai.embedMany, ai.embedMany.doEmbed) contain the following attributes:

  • ai.model.id: the id of the model
  • ai.model.provider: the provider of the model
  • ai.request.headers.*: the request headers that were passed in through headers
  • ai.settings.maxRetries: the maximum number of retries that were set
  • ai.telemetry.functionId: the functionId that was set through telemetry.functionId
  • ai.telemetry.metadata.*: the metadata that was passed in through telemetry.metadata
  • ai.usage.tokens: the number of tokens that were used
  • resource.name: the functionId that was set through telemetry.functionId

Tool call spans

Tool call spans (ai.toolCall) contain the following attributes:

  • operation.name: "ai.toolCall"
  • ai.operationId: "ai.toolCall"
  • ai.toolCall.name: the name of the tool
  • ai.toolCall.id: the id of the tool call
  • ai.toolCall.args: the parameters of the tool call
  • ai.toolCall.result: the result of the tool call. Only available if the tool call is successful and the result is serializable.