OpenTelemetry (OTel)

To set up Braintrust as an OpenTelemetry backend, you’ll need to route the traces to Braintrust’s OpenTelemetry endpoint, set your API key, and specify a parent project or experiment. Braintrust supports configuring OTel with our SDK, as well as libraries like OpenLLMetry and the Vercel AI SDK. You can also use OTel’s built-in exporters to send traces to Braintrust if you don’t want to install additional libraries or write code. OpenLLMetry supports a range of languages including Python, TypeScript, Java, and Go, so you can start logging to Braintrust from many different environments.

Python SDK configuration

Install the Braintrust Python SDK with OpenTelemetry support:

pip install "braintrust[otel]"

Configure these environment variables:

.env

BRAINTRUST_API_KEY=your-api-key
BRAINTRUST_PARENT=project_name:my-otel-project

# BRAINTRUST_API_URL=https://api.braintrust.dev
# - US data plane: Optional (defaults to https://api.braintrust.dev)
# - EU data plane: https://api-eu.braintrust.dev
# - Self-hosted data plane: Your data plane URL

For Python applications, use the BraintrustSpanProcessor for simplified configuration:

opentelemetry-braintrust.py

import os

from braintrust.otel import BraintrustSpanProcessor
from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider

# Configure the global OTel tracer provider
provider = TracerProvider()
trace.set_tracer_provider(provider)

# Send spans to Braintrust.
provider.add_span_processor(BraintrustSpanProcessor())

For more advanced configuration, you can pass in the following arguments to BraintrustSpanProcessor:

api_key: The API key to use for Braintrust. Defaults to the BRAINTRUST_API_KEY environment variable.
api_url: The URL of the Braintrust API. Defaults to the BRAINTRUST_API_URL environment variable or https://api.braintrust.dev if not set.
parent: The parent project or experiment to use for Braintrust. Defaults to the BRAINTRUST_PARENT environment variable.
filter_ai_spans: Defaults to False. If True, only AI-related spans will be sent to Braintrust.
custom_filter: A function that gives you fine-grained control over which spans are sent to Braintrust. It takes a span and returns a boolean. If True, the span will be sent to Braintrust. If False, the span will be dropped. If None, don’t influence the sampling decision.

TypeScript SDK configuration

Starting with v1.0, OpenTelemetry functionality has been moved to the separate @braintrust/otel npm package. This solves ESM build issues in Next.js (edge), Cloudflare Workers, Bun, and TanStack applications, and adds support for both OpenTelemetry v1 and v2. BraintrustSpanProcessor works with @opentelemetry/sdk-trace-base@1.x and v2 spans.If you’re upgrading from v0.x, see the upgrade guide for migration instructions.

Install the Braintrust TypeScript SDK with the following OpenTelemetry dependencies:

# pnpm
pnpm add braintrust @braintrust/otel @opentelemetry/api @opentelemetry/sdk-node @opentelemetry/sdk-trace-base
# npm
npm install braintrust @braintrust/otel @opentelemetry/api @opentelemetry/sdk-node @opentelemetry/sdk-trace-base

For TypeScript/JavaScript applications, use the BraintrustSpanProcessor with NodeSDK:

opentelemetry-braintrust.ts

import { NodeSDK } from "@opentelemetry/sdk-node";
import { BraintrustSpanProcessor } from "@braintrust/otel";

const sdk = new NodeSDK({
  serviceName: "my-service",
  spanProcessor: new BraintrustSpanProcessor({
    parent: "project_name:your-project-name",
  }),
});

sdk.start();

Or configure it manually with a custom tracer provider:

opentelemetry-braintrust.ts

import { BasicTracerProvider } from "@opentelemetry/sdk-trace-base";
import { trace } from "@opentelemetry/api";
import { BraintrustSpanProcessor } from "@braintrust/otel";

trace.setGlobalTracerProvider(
  new BasicTracerProvider({
    spanProcessors: [
      new BraintrustSpanProcessor({
        parent: "project_name:your-project-name",
      }),
    ],
  }),
);

For more advanced configuration, you can pass in the following arguments to BraintrustSpanProcessor:

apiKey: The API key to use for Braintrust. Defaults to the BRAINTRUST_API_KEY environment variable.
apiUrl: The URL of the Braintrust API. Defaults to the BRAINTRUST_API_URL environment variable or https://api.braintrust.dev if not set.
parent: The parent project or experiment to use for Braintrust. Defaults to the BRAINTRUST_PARENT environment variable.
filterAISpans: Defaults to false. If true, only AI-related spans will be sent to Braintrust.
customFilter: A function that gives you fine-grained control over which spans are sent to Braintrust. It takes a span and returns a boolean. If true, the span will be sent to Braintrust. If false, the span will be dropped. If null, don’t influence the sampling decision.

OTel compatibility

Braintrust interoperates with OpenTelemetry at two levels: an OTel-compatible ID and export format, and compatibility mode.

ID and export format

Braintrust can represent span and trace IDs as OTel-compatible hexadecimal values and serialize them in a shared export format, so exported spans interoperate with OpenTelemetry. Support for the OTel-compatible format, including reading spans propagated through the x-bt-parent header or other distributed-tracing channels, differs by SDK:

Python SDK: reads either format (auto-detecting) as of v0.3.0, and exports the OTel-compatible format by default as of v0.26.0. To export Braintrust’s legacy UUID-style format instead, set BRAINTRUST_LEGACY_IDS=true.
TypeScript SDK: reads and exports only the legacy format by default. Enable compatibility mode with @braintrust/otel’s setupOtelCompat() to read either format (auto-detecting) and export the OTel-compatible one.

For distributed tracing between Python and TypeScript services, make sure both services use the same format. Either set BRAINTRUST_LEGACY_IDS=true on the Python service, or enable compatibility mode on the TypeScript service.

Compatibility mode

In addition to using the compatible format, compatibility mode stores the active span in OpenTelemetry’s context, so Braintrust and OpenTelemetry instrumentation contribute to the same trace within a process. Use it when running evals around OpenTelemetry-instrumented code. How you enable compatibility mode differs by SDK: in Python, set an environment variable; in TypeScript, call a setup function. The examples below enable it and send spans to Braintrust.

Python
TypeScript

Set BRAINTRUST_OTEL_COMPAT=true before importing Braintrust:

Compatibility mode requires the braintrust[otel] package, v0.3.0 or later. As of v0.26.0, the OTel-compatible ID and export format is the default, but to also get compatibility mode’s context sharing, set BRAINTRUST_OTEL_COMPAT=true.

Python

import os

# Enable OTel compatibility before imports
os.environ["BRAINTRUST_OTEL_COMPAT"] = "true"
os.environ["BRAINTRUST_API_KEY"] = "<your-api-key>"

from braintrust import Eval
from braintrust.otel import BraintrustSpanProcessor
from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider

# Set up OTel tracing
provider = TracerProvider()
provider.add_span_processor(BraintrustSpanProcessor(parent="project_name:my-project"))
trace.set_tracer_provider(provider)

def task(input):
    tracer = trace.get_tracer(__name__)

    # This OTel span will nest under the Braintrust eval span
    with tracer.start_as_current_span("otel.task") as span:
        span.set_attribute("input", input)
        result = f"Processed: {input}"
        span.set_attribute("output", result)
        return result

Eval(
    "OTEL Integration Example",
    data=[
        {"input": "test1", "expected": "Processed: test1"},
        {"input": "test2", "expected": "Processed: test2"},
    ],
    task=task,
)

Call setupOtelCompat() before creating any loggers or spans, and use AsyncLocalStorageContextManager to nest spans under their parent evaluation spans:

Compatibility mode requires the @braintrust/otel package, v0.1.0 or later.

TypeScript

import { BasicTracerProvider } from "@opentelemetry/sdk-trace-base";
import { trace, context } from "@opentelemetry/api";
import { AsyncLocalStorageContextManager } from "@opentelemetry/context-async-hooks";
import { setupOtelCompat, BraintrustSpanProcessor } from "@braintrust/otel";

// Setup context manager to group span
const contextManager = new AsyncLocalStorageContextManager();
contextManager.enable();
context.setGlobalContextManager(contextManager);

const braintrustProcessor = new BraintrustSpanProcessor({
  parent: "project_name:my-braintrust-project",
  filterAISpans: true,
});

const provider = new BasicTracerProvider({
  spanProcessors: [braintrustProcessor]
});
trace.setGlobalTracerProvider(provider);

// Call this first, before any logger or span creation
setupOtelCompat();

async function task(input: string): Promise<string> {
  const tracer = trace.getTracer("my-service");

  return await tracer.startActiveSpan("otel.task", async (span) => {
    span.setAttribute("input", input);
    const result = `Processed: ${input}`;
    span.setAttribute("output", result);
    span.end();
    return result;
  });
}

await Eval("OTEL Integration Example", {
  data: [
    { input: "test1", expected: "Processed: test1" },
    { input: "test2", expected: "Processed: test2" },
  ],
  task,
});

Distributed tracing

You can do distributed tracing between services instrumented with the Braintrust SDK and OpenTelemetry, either to create OpenTelemetry spans as children of Braintrust spans or to create Braintrust spans as children of OpenTelemetry spans.

The examples below stitch the OpenTelemetry SDK and Braintrust SDK together using the @braintrust/otel package. If you only need to propagate Braintrust trace context between TypeScript services over HTTP, the core braintrust package has native W3C helpers — injectTraceContext and extractTraceContextFromHeaders — that emit and read standard traceparent headers without the extra package.

These examples require the OTel integration: the braintrust[otel] Python package (v0.3.5 or later) or the @braintrust/otel TypeScript package (v0.1.0 or later). The Python version requirement is higher than for compatibility mode (v0.3.0) because these examples use the distributed-tracing helper functions (context_from_span_export, add_span_parent_to_baggage, and parent_from_headers), which shipped in v0.3.5.

These examples use fetch and requests to make HTTP requests. The trace context can also be transmitted via message queue metadata, gRPC metadata, or any other inter-service communication mechanism that supports custom headers.

Create OpenTelemetry spans as children of Braintrust spans

Export the Braintrust span context and use it to create an OpenTelemetry context.

import braintrust
from braintrust.otel import context_from_span_export
from opentelemetry import context as otel_context
from opentelemetry import trace

# Service A: Create Braintrust span and export context
project = braintrust.init_logger(project="my-project")
with project.start_span(name="service_a") as span:
    exported = span.export()
    # Send to Service B via HTTP
    import requests

    requests.post("https://service-b/api", headers={"x-braintrust-context": exported})

# Service B: Receive request and create OTel span as child
exported = request.headers.get("x-braintrust-context")
ctx = context_from_span_export(exported)
token = otel_context.attach(ctx)
try:
    tracer = trace.get_tracer(__name__)
    with tracer.start_as_current_span("service_b") as span:
        # This span is now a child of the Braintrust span
        pass
finally:
    otel_context.detach(token)

import { initLogger } from "braintrust";
import { contextFromSpanExport } from "@braintrust/otel";
import * as api from "@opentelemetry/api";

// Service A: Create Braintrust span and export context
const logger = initLogger({ projectName: "my-project" });
await logger.traced(async (span) => {
  const exported = await span.export();
  // Send to Service B via HTTP
  await fetch("https://service-b/api", {
    headers: { "x-braintrust-context": exported },
  });
});

// Service B: Receive request and create OTel span as child
const exported = req.headers.get("x-braintrust-context");
const ctx = contextFromSpanExport(exported);
await api.context.with(ctx, async () => {
  const tracer = api.trace.getTracer("service-b");
  await tracer.startActiveSpan("service_b", async (span) => {
    // This span is now a child of the Braintrust span
    span.end();
  });
});

Create Braintrust spans as children of OpenTelemetry spans

Propagate the OpenTelemetry context using W3C Trace Context headers.

from braintrust.otel import add_span_parent_to_baggage, parent_from_headers
from opentelemetry import trace
from opentelemetry.propagate import inject

# Service A: Create OTel span and export headers
tracer = trace.get_tracer(__name__)
with tracer.start_as_current_span("service_a") as span:
    # Add Braintrust parent to baggage for propagation
    add_span_parent_to_baggage(span)

    # Export W3C trace context headers and send to Service B
    headers = {}
    inject(headers)
    import requests

    requests.post("https://service-b/api", headers=headers)

# Service B: Receive request and create Braintrust span as child
parent = parent_from_headers(request.headers)
with project.start_span(name="service_b", parent=parent) as span:
    # This span is now a child of the OTel span
    pass

import { addSpanParentToBaggage, parentFromHeaders } from "@braintrust/otel";
import * as api from "@opentelemetry/api";

// Service A: Create OTel span and export headers
const tracer = api.trace.getTracer("service-a");
await tracer.startActiveSpan("service_a", async (span) => {
  // Add Braintrust parent to baggage for propagation
  const ctx = addSpanParentToBaggage(span);

  // Export W3C trace context headers and send to Service B
  const headers: Record<string, string> = {};
  api.propagation.inject(ctx, headers);
  await fetch("https://service-b/api", { headers });

  span.end();
});

// Service B: Receive request and create Braintrust span as child
const parent = parentFromHeaders(req.headers);
await logger.traced(
  async (span) => {
    // This span is now a child of the OTel span
  },
  { name: "service_b", parent },
);

If you have the Braintrust parent as a string (for example, "project_name:my-project") rather than a span, use add_parent_to_baggage(parent) (Python) instead of add_span_parent_to_baggage(span).

OTLP configuration

If you are using a different language or want to use pure OTel code, you can set up the OpenTelemetry Protocol Exporter (OTLP) to send traces to Braintrust. Once you set up an OTLP exporter to send traces to Braintrust, we automatically convert LLM calls into Braintrust LLM spans, which can be saved as prompts and evaluated in the playground. For JavaScript/TypeScript applications, you can use the BraintrustExporter directly:

import { BatchSpanProcessor } from "@opentelemetry/sdk-trace-base";
import { BraintrustExporter } from "@braintrust/otel";

const exporter = new BraintrustExporter({
  apiKey: "your-api-key",
  parent: "project_name:your-project",
  filterAISpans: true,
});

const processor = new BatchSpanProcessor(exporter);

For collectors that use the OpenTelemetry SDK to export traces, set the following environment variables:

OTEL_EXPORTER_OTLP_ENDPOINT=https://api.braintrust.dev/otel
OTEL_EXPORTER_OTLP_HEADERS="Authorization=Bearer <Your API Key>, x-bt-parent=project_id:<Your Project ID>"

The trace endpoint URL is https://api.braintrust.dev/otel/v1/traces. If your exporter uses signal-specific environment variables, you’ll need to set the full path: OTEL_EXPORTER_OTLP_TRACES_ENDPOINT=https://api.braintrust.dev/otel/v1/traces

If your organization is on the EU data plane, use https://api-eu.braintrust.dev/otel instead. If you’re self-hosting Braintrust, substitute your stack’s Universal API URL. For example: OTEL_EXPORTER_OTLP_ENDPOINT=https://dfwhllz61x709.cloudfront.net/otelSee Data plane region.

The x-bt-parent header sets the trace’s parent project or experiment. You can use a prefix like project_id:, project_name:, or experiment_id: here, or pass in a span slug (span.export()) to nest the trace under a span within the parent object.

To find your project ID, navigate to your project’s configuration page and find the Copy Project ID button at the bottom of the page.

Vercel AI SDK

The Vercel AI SDK natively supports OpenTelemetry and works out of the box with Braintrust, either via Next.js or Node.js.

Next.js

If you are using Next.js, use the Braintrust exporter with @vercel/otel:

import { registerOTel } from "@vercel/otel";
import { BraintrustExporter } from "@braintrust/otel";

// In your instrumentation.ts file
export function register() {
  registerOTel({
    serviceName: "my-braintrust-app",
    traceExporter: new BraintrustExporter({
      parent: "project_name:your-project-name",
      filterAISpans: true, // Only send AI-related spans
    }),
  });
}

Traced LLM calls will appear under the Braintrust project or experiment provided in the parent field. When you call the AI SDK, make sure to set experimental_telemetry:

const result = await generateText({
  model: openai("gpt-4o-mini"),
  prompt: "What is 2 + 2?",
  experimental_telemetry: {
    isEnabled: true,
    metadata: {
      query: "weather",
      location: "San Francisco",
    },
  },
});

The integration supports streaming functions like streamText. Each streamed call will produce ai.streamText spans in Braintrust.

import { openai } from "@ai-sdk/openai";
import { streamText } from "ai";

export async function POST(req: Request) {
  const { prompt } = await req.json();

  const result = await streamText({
    model: openai("gpt-4o-mini"),
    prompt,
    experimental_telemetry: { isEnabled: true },
  });

  return result.toDataStreamResponse();
}

Node.js

If you are using Node.js without a framework, you must configure the NodeSDK directly. Here, it’s more straightforward to use the BraintrustSpanProcessor. First, install the necessary dependencies:

npm install ai @ai-sdk/openai braintrust @braintrust/otel @opentelemetry/sdk-node @opentelemetry/sdk-trace-base zod

Then, set up the OpenTelemetry SDK:

import { NodeSDK } from "@opentelemetry/sdk-node";
import { generateText, tool } from "ai";
import { openai } from "@ai-sdk/openai";
import { z } from "zod";
import { BraintrustSpanProcessor } from "@braintrust/otel";

const sdk = new NodeSDK({
  spanProcessors: [
    new BraintrustSpanProcessor({
      parent: "project_name:your-project-name",
      filterAISpans: true,
    }),
  ],
});

sdk.start();

async function main() {
  const result = await generateText({
    model: openai("gpt-4o-mini"),
    messages: [
      {
        role: "user",
        content: "What are my orders and where are they? My user ID is 123",
      },
    ],
    tools: {
      listOrders: tool({
        description: "list all orders",
        parameters: z.object({ userId: z.string() }),
        execute: async ({ userId }) =>
          `User ${userId} has the following orders: 1`,
      }),
      viewTrackingInformation: tool({
        description: "view tracking information for a specific order",
        parameters: z.object({ orderId: z.string() }),
        execute: async ({ orderId }) =>
          `Here is the tracking information for ${orderId}`,
      }),
    },
    experimental_telemetry: {
      isEnabled: true,
      functionId: "my-awesome-function",
      metadata: {
        something: "custom",
        someOtherThing: "other-value",
      },
    },
    maxSteps: 10,
  });

  await sdk.shutdown();
}

main().catch(console.error);

Manual tracing

If you want to log LLM calls directly to the OTel endpoint, you can set up a custom OpenTelemetry tracer and add the appropriate attributes to your spans. This gives you fine-grained control over what data gets logged.

For attaching custom application metadata and tags to spans — like user IDs or request context — see Add metadata and tags.

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.

Braintrust attributes

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

Attribute	Braintrust Field	Notes
`braintrust.input`	`input`	Typically a single user message (string). If you have an array of messages, use `braintrust.input_json` instead (see below) or set flattened attributes like `braintrust.input.0.role` or `braintrust.input.0.content`.
`braintrust.input_json`	`input`	A JSON-serialized string containing an array of OpenAI messages.
`braintrust.output`	`output`	Typically a single assistant message (string). If you have an array of messages, use `braintrust.output_json` instead (see below) or set flattened attributes like `braintrust.output.0.role` or `braintrust.output.0.content`.
`braintrust.output_json`	`output`	A JSON-serialized string containing an array of OpenAI 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.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"`.

Fields mapped from braintrust.* attributes are deleted and translated into Braintrust’s native format.

GenAI events

In addition to attributes, Braintrust also processes GenAI events on spans to extract input/output messages. 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.system.message`	`input`	System message event. Content is extracted from the `content` attribute.
`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.

Events are processed in chronological order and combined with attribute-based messages to provide a complete view of the conversation flow. Here’s an example of how to set up manual tracing:

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}),
    )

Troubleshooting

Why are my traces not showing up?

There are a few common reasons why your traces may not show up in Braintrust:

Braintrust’s logs table only shows traces that have a root span (i.e. span_parents is empty). If you only send children spans, they will not appear in the logs table. A common reason for this is only sending spans to Braintrust which have a traceparent header. To fix this, make sure to send a root span for every trace you want to appear in the UI.
Make sure the OTEL_EXPORTER_OTLP_ENDPOINT matches your organization’s data plane region. Organizations on the EU data plane should use https://api-eu.braintrust.dev/otel. Self-hosted deployments should use their custom API URL, for example https://dfwhllz61x709.cloudfront.net/otel.
You must explicitly set up OpenTelemetry in your application. If you’re using Next.js, then follow the Next.js OpenTelemetry guide. If you are using Node.js without a framework, then follow this example to set up a basic exporter.

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.

​Python SDK configuration

​TypeScript SDK configuration

​OTel compatibility

​ID and export format

​Compatibility mode

​Distributed tracing

​Create OpenTelemetry spans as children of Braintrust spans

​Create Braintrust spans as children of OpenTelemetry spans

​OTLP configuration

​Vercel AI SDK

​Next.js

​Node.js

​Manual tracing

​GenAI attributes

​Braintrust attributes

​GenAI events

​Troubleshooting

​Why are my traces not showing up?

​Why are some attributes missing from metadata?

Python SDK configuration

TypeScript SDK configuration

OTel compatibility

ID and export format

Compatibility mode

Distributed tracing

Create OpenTelemetry spans as children of Braintrust spans

Create Braintrust spans as children of OpenTelemetry spans

OTLP configuration

Vercel AI SDK

Next.js

Node.js

Manual tracing

GenAI attributes

Braintrust attributes

GenAI events

Troubleshooting

Why are my traces not showing up?

Why are some attributes missing from metadata?