OpenTelemetry attributes and events

Braintrust maps OpenTelemetry span attributes and events into inputs, outputs, metrics, and other fields. Log records preserve their message bodies and attributes separately. Use this reference to understand span mappings and log storage. For export setup, see Send traces and logs.

Span attributes and events

Braintrust maps GenAI attributes and events, OpenInference attributes, and explicit braintrust.* attributes to span fields.

GenAI attributes

Braintrust implements the OpenTelemetry GenAI semantic conventions. When you send traces with these attributes, they are automatically mapped to Braintrust fields.

Attribute	Braintrust field	Description
`gen_ai.input.messages`	`input`	The chat history provided to the model as an input. Messages must be structured according to the OpenTelemetry GenAI input messages attribute specification.
`gen_ai.prompt`	`input`	User message (string). If you have an array of messages, you’ll need to use `gen_ai.prompt_json` (see below) or set flattened attributes like `gen_ai.prompt.0.role` or `gen_ai.prompt.0.content`.
`gen_ai.prompt_json`	`input`	A JSON-serialized string containing an array of OpenAI messages.
`gen_ai.output.messages`	`output`	Messages returned by the model. Messages must be structured according to the OpenTelemetry GenAI output messages attribute specification.
`gen_ai.completion`	`output`	Assistant message (string). Note that if you have an array of messages, you’ll need to use `gen_ai.completion_json` (see below) or set flattened attributes like `gen_ai.completion.0.role` or `gen_ai.completion.0.content`.
`gen_ai.completion_json`	`output`	A JSON-serialized string containing an array of OpenAI messages.
`gen_ai.request`	`metadata.*`	A JSON object or flattened attributes containing model parameters. The `model` parameter is cleaned of provider prefixes (e.g., “openai/gpt-4o” becomes “gpt-4o”).
`gen_ai.request.model`	`metadata.model`	The model name (e.g. “gpt-4o”). Provider prefixes like “openai/”, “anthropic/”, “google/” are automatically removed.
`gen_ai.request.max_tokens`	`metadata.max_tokens`	Maximum tokens to generate.
`gen_ai.request.temperature`	`metadata.temperature`	Sampling temperature.
`gen_ai.request.top_p`	`metadata.top_p`	Nucleus sampling parameter.
`gen_ai.operation.name`	`span_attributes.type`	The operation type. Value “chat” maps to type “llm”, “execute_tool” maps to type “tool”.
`gen_ai.agent.tools`	`metadata.tools`	A JSON-serialized array of tool names available to the agent. Tool names are automatically converted into tool definition objects with `type: "function"` and basic schemas.
`gen_ai.tool.name`	`metadata.tools`	The name of the tool being executed. Automatically converted into a tool definition object. Also sets `span_attributes.type` to “tool”.
`gen_ai.usage`	`metrics.*`	A JSON object containing token usage. Can include `prompt_tokens`, `completion_tokens`, `input_tokens`, `output_tokens`, and `total_tokens`.
`gen_ai.usage.prompt_tokens`	`metrics.prompt_tokens`	Input tokens (preferred field name).
`gen_ai.usage.completion_tokens`	`metrics.completion_tokens`	Output tokens (preferred field name).
`gen_ai.usage.input_tokens`	`metrics.prompt_tokens`	Input tokens (alternative field name, normalized to `prompt_tokens`).
`gen_ai.usage.output_tokens`	`metrics.completion_tokens`	Output tokens (alternative field name, normalized to `completion_tokens`).
`gen_ai.usage.total_tokens`	`metrics.tokens`	Total tokens (normalized to `tokens`). If not provided, automatically calculated from `prompt_tokens` + `completion_tokens`.
`gen_ai.usage.cache_creation.input_tokens`	`metrics.prompt_cache_creation_tokens`	Input tokens written to a provider-managed cache.
`gen_ai.usage.cache_read.input_tokens`	`metrics.prompt_cached_tokens`	Input tokens served from a provider-managed cache.

On Braintrust-hosted deployments, gen_ai.evaluation.name and gen_ai.evaluation.score.value map to scores.<name>. The name must be nonempty and the score must be a finite number from 0 to 1, inclusive. Numeric strings are accepted. Invalid values are not mapped to scores.

GenAI events

Braintrust also extracts messages and scores from GenAI span events. These events follow the OpenTelemetry GenAI semantic conventions for events:

Event name	Field	Description
`gen_ai.user.message`	`input`	User message event. Content is extracted from the `content` attribute (supports both string and JSON array format).
`gen_ai.choice`	`output`	Model response event. Message is extracted from the `message` attribute and can include both text content and tool calls.
`gen_ai.assistant.message`	`output`	Assistant message event. Content is extracted from the `content` attribute.
`gen_ai.tool.message`	`input`	Tool result event. Content is extracted from the `content` attribute and associated with the tool call via the `id` attribute.
`gen_ai.evaluation.result`	`scores`	Uses `gen_ai.evaluation.name` and `gen_ai.evaluation.score.value` with the same validation as span attributes.

The message events listed above are processed in the order they appear in the span. When they produce a nonempty input or output message list, that list takes precedence over the corresponding attribute-derived field. Each event is also stored as a separate log row associated with the span. Mapping a GenAI event to span fields does not remove the event record.

OpenInference attributes

On Braintrust-hosted deployments, spans with openinference.span.kind are mapped using OpenInference semantic conventions. These include traces from OpenInference instrumentors for LangChain, LangGraph, LlamaIndex, DSPy, and the OpenAI Agents SDK. Collections use flattened, indexed attributes, such as llm.input_messages.0.message.role and llm.input_messages.0.message.content.

Span fields

Attribute	Braintrust field	Mapping
`openinference.span.kind`	`span_attributes.type`	`LLM` and `EMBEDDING` → `llm`, `TOOL` → `tool`, `EVALUATOR` → `score`. All other kinds → `task`.
`llm.input_messages`, `llm.output_messages`	`input`, `output`	Chat messages on `LLM` spans, including tool calls, tool results, images, and reasoning. Falls back to `llm.prompts` and `llm.choices`, respectively.
`input.value`, `output.value`	`input`, `output`	Fallback when the corresponding messages, documents, or embeddings are unavailable. JSON is parsed when `input.mime_type` or `output.mime_type` is `application/json`, respectively.
`retrieval.documents`	`output`	Retrieved documents on `RETRIEVER` spans.
`reranker.query`, `reranker.input_documents`	`input`	Query and candidate documents on `RERANKER` spans.
`reranker.output_documents`	`output`	Reranked documents on `RERANKER` spans.
`embedding.embeddings`	`input`, `output.embedding_length`	Embedded texts and vector length on `EMBEDDING` spans.
`tag.tags`	`tags`	A string array or JSON-serialized string array.

When converting input messages, Braintrust prepends a system prompt from llm.invocation_parameters if no system or developer message is present. If media cannot be represented as chat messages, Braintrust preserves the original OpenInference messages in input or output instead.

Metadata

Attribute	Braintrust field	Mapping
`llm.model_name`	`metadata.model`	Model name with recognized provider prefixes removed, such as `openai/`.
`llm.provider`, `llm.system`	`metadata.provider`	Provider, preferring `llm.provider`.
`llm.invocation_parameters`	`metadata.*`	Model parameters, excluding message payloads, tool definitions, response identifiers, and credential fields.
`llm.tools`	`metadata.tools`	Tool definitions normalized to chat tool format.
`metadata`	`metadata.*`	Custom JSON metadata. Extracted model details take precedence over matching custom keys.

Unmapped attributes remain in metadata. Mapped attributes follow the attribute preservation settings.

Token usage

Token metrics are extracted only from LLM and EMBEDDING spans to avoid counting aggregate usage on chain or agent spans twice. prompt_tokens includes cached tokens.

Attribute	Braintrust metric
`llm.token_count.prompt`	`metrics.prompt_tokens`
`llm.token_count.completion`	`metrics.completion_tokens`
`llm.token_count.total`	`metrics.tokens`
`llm.token_count.prompt_details.cache_read`	`metrics.prompt_cached_tokens`
`llm.token_count.prompt_details.cache_write`	`metrics.prompt_cache_creation_tokens`
`llm.token_count.completion_details.reasoning`	`metrics.completion_reasoning_tokens`
`llm.token_count.prompt_details.audio`	`metrics.prompt_audio_tokens`
`llm.token_count.completion_details.audio`	`metrics.completion_audio_tokens`

Scores

Braintrust maps span-scoped annotations and evaluations to scores on the span carrying the attributes:

Name attribute	Score attribute	Braintrust field
`annotations.<n>.annotation.name`	`annotations.<n>.annotation.score`	`scores.<name>`
`evaluations.<n>.evaluation.name`	`evaluations.<n>.evaluation.score`	`scores.<name>`

Each entry must have a nonempty name and a finite score from 0 to 1, inclusive. Numeric strings are accepted. Missing, nonnumeric, or out-of-range values are not mapped to scores. Names, labels, and explanations remain in metadata. Trace- and session-scoped feedback, such as trace.annotations.* or session.evaluations.*, remains in metadata and is not mapped to span scores.

Braintrust attributes

You can also use the braintrust namespace to set fields in Braintrust directly:

Attribute	Braintrust field	Notes
`braintrust.input`	`input`	The input value. Plain strings are stored without conversion to chat messages. Use `braintrust.input_json` for JSON-encoded objects or arrays, or flattened attributes such as `braintrust.input.0.role`.
`braintrust.input_json`	`input`	A JSON-serialized input value, decoded without wrapping it in chat messages.
`braintrust.output`	`output`	The output value. Plain strings are stored without conversion to chat messages. Use `braintrust.output_json` for JSON-encoded objects or arrays, or flattened attributes such as `braintrust.output.0.role`.
`braintrust.output_json`	`output`	A JSON-serialized output value, decoded without wrapping it in chat messages.
`braintrust.metadata`	`metadata`	A JSON-serialized dictionary with string keys. Alternatively, you can use flattened attribute names, like `braintrust.metadata.model` or `braintrust.metadata.temperature`. If you include `tools`, you must provide full tool definition objects.
`braintrust.context_json`	`context`	A JSON-serialized dictionary that merges into the span’s context object. Braintrust automatically populates `context.span_origin` from OTEL resource attributes and instrumentation scope. Use this attribute to set additional context fields or override auto-derived values.
`braintrust.metrics`	`metrics`	A JSON-serialized dictionary with string keys. Alternatively, you can use flattened attribute names, like `braintrust.metrics.prompt_tokens` or `braintrust.metrics.completion_tokens`.
`braintrust.scores`	`scores`	A JSON-serialized dictionary with string keys, where values are scores for the span. Alternatively, you can use flattened attribute names, like `braintrust.scores.accuracy` or `braintrust.scores.relevance`.
`braintrust.expected`	`expected`	The expected output for the span. Can be any value (string, number, object, etc.).
`braintrust.expected_json`	`expected`	A JSON-serialized string containing the expected output. Use this when you need to pass complex objects or arrays as the expected value.
`braintrust.tags`	`tags`	An array of strings that can be set on any span. Tags from all spans in a trace are aggregated together.
`braintrust.span_attributes`	`span_attributes`	A JSON-serialized dictionary with string keys. Alternatively, you can use flattened attribute names, like `braintrust.span_attributes.type` or `braintrust.span_attributes.name`. The `type` field can be one of: `"llm"`, `"task"`, `"tool"`, `"eval"`, `"score"`, `"function"`, `"log"`.

Fields mapped from braintrust.* attributes are removed from metadata and stored in the corresponding Braintrust fields. Nonempty input or output arrays whose elements have valid chat roles are recognized as LLM message arrays.

Attribute example

This example sets GenAI and Braintrust attributes using flattened names and JSON-encoded values:

Python

import json
import os

from opentelemetry import trace
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor

BRAINTRUST_API_URL = os.environ.get("BRAINTRUST_API_URL", "https://api.braintrust.dev")
BRAINTRUST_API_KEY = os.environ.get("BRAINTRUST_API_KEY", "<Your API Key>")
PROJECT_ID = "<Your Project ID>"

provider = TracerProvider()
processor = BatchSpanProcessor(
    OTLPSpanExporter(
        endpoint=f"{BRAINTRUST_API_URL}/otel/v1/traces",
        headers={"Authorization": f"Bearer {BRAINTRUST_API_KEY}", "x-bt-parent": f"project_id:{PROJECT_ID}"},
    )
)
provider.add_span_processor(processor)
trace.set_tracer_provider(provider)
tracer = trace.get_tracer(__name__)

# Export a span with flattened attribute names.
with tracer.start_as_current_span("GenAI Attributes") as span:
    span.set_attribute("gen_ai.prompt.0.role", "system")
    span.set_attribute("gen_ai.prompt.0.content", "You are a helpful assistant.")
    span.set_attribute("gen_ai.prompt.1.role", "user")
    span.set_attribute("gen_ai.prompt.1.content", "What is the capital of France?")

    span.set_attribute("gen_ai.completion.0.role", "assistant")
    span.set_attribute("gen_ai.completion.0.content", "The capital of France is Paris.")

    span.set_attribute("gen_ai.request.model", "gpt-4o-mini")
    span.set_attribute("gen_ai.request.temperature", 0.5)
    span.set_attribute("gen_ai.usage.prompt_tokens", 10)
    span.set_attribute("gen_ai.usage.completion_tokens", 30)

# Export a span using JSON-serialized attributes.
with tracer.start_as_current_span("GenAI JSON-Serialized Attributes") as span:
    span.set_attribute(
        "gen_ai.prompt_json",
        json.dumps(
            [
                {"role": "system", "content": "You are a helpful assistant."},
                {"role": "user", "content": "What is the capital of Italy?"},
            ]
        ),
    )
    span.set_attribute(
        "gen_ai.completion_json",
        json.dumps(
            [
                {"role": "assistant", "content": "The capital of Italy is Rome."},
            ]
        ),
    )

# Export a span using the `braintrust` namespace.
with tracer.start_as_current_span("Braintrust Attributes") as span:
    span.set_attribute("braintrust.input.0.role", "system")
    span.set_attribute("braintrust.input.0.content", "You are a helpful assistant.")
    span.set_attribute("braintrust.input.1.role", "user")
    span.set_attribute("braintrust.input.1.content", "What is the capital of Libya?")

    span.set_attribute("braintrust.output.0.role", "assistant")
    span.set_attribute("braintrust.output.0.content", "The capital of Libya is Tripoli.")

    span.set_attribute("braintrust.metadata.model", "gpt-4o-mini")
    span.set_attribute("braintrust.metadata.country", "Libya")
    span.set_attribute("braintrust.metrics.prompt_tokens", 10)
    span.set_attribute("braintrust.metrics.completion_tokens", 20)

# Export a span using JSON-serialized `braintrust` attributes.
with tracer.start_as_current_span("Braintrust JSON-Serialized Attributes") as span:
    span.set_attribute(
        "braintrust.input_json",
        json.dumps(
            [
                {"role": "system", "content": "You are a helpful assistant."},
                {"role": "user", "content": "What is the capital of Argentina?"},
            ]
        ),
    )
    span.set_attribute(
        "braintrust.output_json",
        json.dumps(
            [
                {"role": "assistant", "content": "The capital of Argentina is Buenos Aires."},
            ]
        ),
    )
    span.set_attribute(
        "braintrust.metadata",
        json.dumps({"model": "gpt-4o-mini", "country": "Argentina"}),
    )
    span.set_attribute(
        "braintrust.metrics",
        json.dumps({"prompt_tokens": 15, "completion_tokens": 45}),
    )
    span.set_attribute(
        "braintrust.expected_json",
        json.dumps([{"role": "assistant", "content": "The capital of Argentina is Buenos Aires."}]),
    )
    span.set_attribute(
        "braintrust.scores",
        json.dumps({"accuracy": 1.0, "relevance": 0.95}),
    )

Why are some attributes missing from metadata?

For Braintrust-hosted organizations, source attributes such as ai.prompt, gen_ai.input.messages, and llm.input_messages are removed from metadata once Braintrust has mapped them to a structured field (input, output, metrics, etc.). The data itself can be found on the structured field it was mapped into.Self-hosted deployments behave the same way when the STRIP_OTEL_ATTRIBUTES_FROM_METADATA environment variable is set on the api-ts service.To keep the raw attributes on metadata for a specific span, set the braintrust.otel.preserve_attributes attribute to true on that span.For details, see Strip OTel attributes from metadata.

Log records

Each OTLP record becomes a separate row with span_attributes.type set to log:

OTLP data	Braintrust field
Message body	`output`
Record attributes	`metadata`, except attributes consumed for routing.
Resource and scope	`context.otel.resource` and `context.otel.scope`
Original severity number and text	`context.otel.log.severity_number` and `context.otel.log.severity_text`

Log attributes do not undergo the GenAI mapping used for trace spans. For example, a gen_ai.usage.input_tokens attribute on a log remains in metadata rather than becoming a token metric. A record with valid trace and span IDs shares its owning span’s span_id and trace’s root_span_id, while retaining its own unique row id. A record with a trace ID only joins that trace with a generated span ID. A record without a trace ID receives its own span and root IDs. OpenTelemetry span events received through /otel/v1/traces, including GenAI events, are also stored as separate log rows. Each event row shares its span’s span_id and root_span_id and has its own row id. The event name is stored in output, and its attributes are stored in metadata. Recognized GenAI events can also populate fields on the owning span, as described in GenAI events.

Log levels and exceptions

On Braintrust-hosted deployments, the severity number maps to span_attributes.log_level:

OTel severity number	Braintrust log level
1–4	`trace`
5–8	`debug`
9–12	`info`
13–16	`warn`
17–20	`error`
21–24	`fatal`

If the severity number is absent, 0, or outside these ranges, Braintrust does not derive a log level. The original severity number and text remain in context.otel.log. On Braintrust-hosted deployments, the top-level error field is populated from nonempty exception attributes: exception.stacktrace, exception.type, or exception.message. Severity and message body alone do not populate it. Use span_attributes.log_level to find error-level messages, and error to find records carrying exception details.

The canonical log-level mapping and exception-only error behavior require data plane v2.15.0 or later. On earlier versions, inspect the original severity fields in context.otel.log. Error-level severity can populate error even without exception attributes.

AI providers

SDKs

Developer tools

Runtime environments

OpenTelemetry attributes and events

Span attributes and events

GenAI attributes

GenAI events

OpenInference attributes

Span fields

Metadata

Token usage

Scores

Braintrust attributes

Attribute example

Log records

Log levels and exceptions

Resources

​Span attributes and events

​GenAI attributes

​GenAI events

​OpenInference attributes

​Span fields

​Metadata

​Token usage

​Scores

​Braintrust attributes

​Attribute example

​Log records

​Log levels and exceptions

​Resources

Span attributes and events

GenAI attributes

GenAI events

OpenInference attributes

Span fields

Metadata

Token usage

Scores

Braintrust attributes

Attribute example

Log records

Log levels and exceptions

Resources