OpenTelemetry Attributes Truncated In AI SDK: A Deep Dive

by Alex Johnson 58 views

Are you wrestling with truncated OpenTelemetry span attributes when using the AI SDK, even after diligently setting environment variables to seemingly unlimited values? You're not alone. This article dives deep into the frustrating issue of OpenTelemetry span attribute truncation within the AI SDK, specifically when handling LLM messages and responses. We'll explore the problem, examine the environment variables involved, dissect the code examples, and suggest potential troubleshooting steps to get those crucial details logged in full.

The Core Problem: Truncation at the Source

The heart of the matter lies in the truncation of span attributes within the AI SDK's experimental_telemetry feature. Despite setting environment variables like OTEL_ATTRIBUTE_VALUE_LENGTH_LIMIT to seemingly generous values (e.g., 1MB or even unlimited with a value of 0), the attributes continue to be capped at a much lower limit – often around 1024 characters. This limitation directly impacts the debugging process of LLM conversations, as the full prompts, responses, and tool call details are not captured, leaving you with incomplete information. This truncation issue has a significant impact when you're trying to analyze the performance of your AI models, understand the nuances of the conversations, or pinpoint the source of errors.

This behavior is particularly problematic when dealing with complex prompts or extensive model outputs. The inability to see the complete input and output means you're missing critical context, hindering your ability to optimize prompts, fine-tune models, or troubleshoot unexpected behaviors. Without the full picture, identifying the root cause of issues becomes a guessing game, slowing down your development cycle and increasing frustration.

Environment Variables and Their Role

The cornerstone of controlling OpenTelemetry attribute lengths lies in the environment variables. The primary variable in question is OTEL_ATTRIBUTE_VALUE_LENGTH_LIMIT. When set correctly, this variable should dictate the maximum length of the string values stored within span attributes. In this scenario, users are setting this variable, along with TRIGGER_OTEL_SPAN_ATTRIBUTE_VALUE_LENGTH_LIMIT and TRIGGER_OTEL_LOG_ATTRIBUTE_VALUE_LENGTH_LIMIT, to values like 1048576 (1MB). These variables are verified as being set in the runtime environment. Despite these configurations, the truncation persists, indicating that the settings are not being honored or are being overridden elsewhere.

It's crucial to understand that OpenTelemetry implementations can have multiple layers. While these environment variables are the standard way to configure attribute length limits, the specific behavior can be influenced by the OpenTelemetry SDK used, the instrumentation library (in this case, the AI SDK), and the telemetry backend (Trigger.dev). The AI SDK's internal mechanisms or the way it integrates with OpenTelemetry might be the source of the truncation, overriding the environment variable settings.

To effectively troubleshoot this, you need to ensure the variables are correctly set within your runtime environment and that they are being correctly passed into the OpenTelemetry configuration used by the AI SDK.

Code Example Analysis

Let's examine the provided code example:

import { generateText } from "ai";
import { anthropic } from "@ai-sdk/anthropic";

const result = await generateText({
  model: anthropic("claude-sonnet-4-5"),
  prompt: "Very long prompt with lots of context...",
  experimental_telemetry: {
    isEnabled: true,
    functionId: "my-function",
    recordInputs: true,
    recordOutputs: true,
    metadata: {
      component: "my-component",
    },
  },
});

This code snippet demonstrates a typical use case of the AI SDK, where generateText is used to interact with a model. The experimental_telemetry option is enabled, and recordInputs and recordOutputs are set to true. This setup should, in theory, capture the full prompt and output text within the span attributes. However, because of the attribute truncation, only the initial portion of the prompt and the response will be logged, making it difficult to understand the complete interaction. The metadata field allows for more context to be added to the span attributes, enabling debugging with function and component names.

The critical aspect here is how the AI SDK internally handles the prompt and the model's response. The SDK might have its own internal limits on attribute length, which are overriding the environment variables. Alternatively, the OpenTelemetry instrumentation within the AI SDK might not be correctly configured to respect the OTEL_ATTRIBUTE_VALUE_LENGTH_LIMIT setting.

Troubleshooting Strategies and Workarounds

Since direct control over the AI SDK's internal telemetry might be limited, here are some troubleshooting strategies and potential workarounds:

  1. Verify Environment Variable Propagation: Double-check that the environment variables are being correctly passed to the runtime environment. Use console.log(process.env) to confirm that the OTEL_ATTRIBUTE_VALUE_LENGTH_LIMIT, TRIGGER_OTEL_SPAN_ATTRIBUTE_VALUE_LENGTH_LIMIT, and TRIGGER_OTEL_LOG_ATTRIBUTE_VALUE_LENGTH_LIMIT are correctly set with the desired values. Also, make sure that the environment variables are set before the application is initialized.
  2. Inspect the AI SDK's Code (if possible): If the source code of the AI SDK is accessible, examine how it handles OpenTelemetry integration. Look for any internal configurations or hardcoded limits on attribute lengths. This can help to identify the source of the truncation.
  3. Custom Tracer Configuration: Experiment with a custom NodeTracerProvider and configure the spanLimits to remove the attribute value limits. While the provided code example tried this, it might be worth revisiting to ensure proper implementation and integration with the AI SDK's telemetry. Ensure that the custom tracer is correctly initialized and passed to the experimental_telemetry option. Verify the configuration by inspecting the attributes of the spans generated by the custom tracer.
import { NodeTracerProvider } from "@opentelemetry/sdk-trace-node";
import {  SpanLimits } from '@opentelemetry/sdk-trace-base';
import {  registerInstrumentations } from '@opentelemetry/instrumentation';
import {  SemanticAttributes } from '@opentelemetry/semantic-conventions';


const provider = new NodeTracerProvider({
  spanLimits: {
    attributeValueLengthLimit: 0, // unlimited
  } as SpanLimits,
});
provider.register();

const result = await generateText({
  model, prompt,
  experimental_telemetry: {
    isEnabled: true,
    tracer: provider.getTracer("my-tracer"),
  },
});
  1. Check Trigger.dev Configuration: Since Trigger.dev is the telemetry backend, examine its configuration settings. There might be backend-side configurations that limit attribute lengths. Check the Trigger.dev documentation or support channels for any relevant settings.
  2. Alternative Logging Approaches: As a temporary workaround, you can implement a separate logging mechanism to capture the full prompts, responses, and tool call arguments. This could involve logging the data to a file, a database, or a dedicated logging service. This approach provides complete information, while the underlying OpenTelemetry issue is resolved.
  3. Update Dependencies: Ensure that you are using the latest versions of the AI SDK and the underlying OpenTelemetry packages. Updates may include bug fixes or improvements to the telemetry integration. Keep your development environment current to benefit from the latest features and fixes.

By systematically working through these troubleshooting steps, you can pinpoint the source of the truncation and implement effective workarounds or solutions.

Looking Ahead

The issue of OpenTelemetry span attribute truncation can be a significant hurdle when you are debugging LLM-based applications. While setting environment variables seems like the logical solution, it's not always effective due to complexities within the AI SDK, the OpenTelemetry libraries, or the telemetry backend. A thorough understanding of the OpenTelemetry configuration, the AI SDK's internal workings, and a systematic approach to troubleshooting are crucial to overcome this challenge and ensure you capture the full details of your LLM interactions.

By carefully checking environment variable propagation, delving into the code, and considering alternative logging strategies, you can minimize the impact of the truncation and maintain the clarity needed to debug, optimize, and improve your AI-powered applications. Furthermore, regular updates to the involved libraries and configurations will also help to address any bugs, or performance issues related to OpenTelemetry, which will reduce the impact of these issues on LLM-based applications.

Additional resources