Fixing Duplicate Text IDs In @ai-sdk/anthropic

Dec 2, 2025 by Alex Johnson 47 views

Fixing Duplicate Text IDs in Multi-Step Flows with @ai-SDK/Anthropic

When building complex applications that use AI, especially those involving multi-step flows, ensuring that data is handled correctly across different steps is crucial. A recent issue highlighted in the @ai-sdk/anthropic library reveals a problem with duplicate text-start and text-end IDs in such flows. This article delves into the root cause of this issue, demonstrates how to reproduce it, and discusses the expected behavior for a robust AI application.

Understanding the Issue: Duplicate IDs in Multi-Step Flows

In the realm of AI-driven applications, multi-step flows are common. Think of scenarios where a user input triggers a series of actions, such as a chatbot that first asks for clarification, then fetches information, and finally provides a summary. In these flows, maintaining unique identifiers for different text blocks is essential for tracking and ordering message parts correctly.

The @ai-sdk/anthropic library, which facilitates interaction with Anthropic's language models, encountered an issue where the text-start and text-end chunks produced duplicate IDs across steps when using the streamText function. This function is particularly useful for streaming responses from the language model, providing a more interactive and responsive user experience. However, the duplication of IDs can lead to confusion and errors in downstream processing, making it difficult to accurately track and manage the flow of information.

The Root Cause: Anthropic's Content Block Index

The root cause of the problem lies in how @ai-sdk/anthropic handles Anthropic's content_block_start index. Within the library's code, the text-start ID is set to String(value.index), where value.index corresponds to Anthropic's content block index. This approach works well for single-step interactions, but it falls short in multi-step scenarios.

Anthropic's language models reset the content block index to 0 for each new response. This means that in a multi-step flow, where an agent performs multiple calls to the language model, the text-start ID will be reset to 0 at the beginning of each step. Consider the following example:

Step 1: The agent generates text (with id=0) and then calls a tool.
Step 2: The agent receives the tool's response and generates more text. Crucially, the text-start ID is again set to 0, leading to a duplicate ID.

This duplication can cause significant issues in applications that rely on these IDs to maintain context and order, highlighting the need for a solution that ensures unique identifiers across all steps in a flow.

Reproducing the Issue: A Step-by-Step Guide

To illustrate the problem, let's walk through a practical example. The following code snippet demonstrates how to reproduce the duplicate ID issue using @ai-sdk/anthropic:

import { anthropic } from '@ai-sdk/anthropic';
import { streamText, stepCountIs } from 'ai';
import { z } from 'zod';

const textIds: { type: string; id: string; step: number }[] = [];
let currentStep = 0;

const result = streamText({
    model: anthropic('claude-sonnet-4-5'),
    system: 'First say "Let me check", then call the tool, then summarize.',
    prompt: 'What is the weather?',
    tools: {
        get_weather: {
            description: 'Get the current weather',
            inputSchema: z.object({ city: z.string().optional() }),
            execute: async () => ({ temperature: 72, condition: 'sunny' }),
        },
    },
    stopWhen: stepCountIs(3),
});

for await (const chunk of result.fullStream) {
    if (chunk.type === 'step-start') {
        currentStep++;
    } else if (chunk.type === 'text-start') {
        console.log(`[Step ${currentStep}] text-start id="${chunk.id}"`);
        textIds.push({ type: 'text-start', id: chunk.id, step: currentStep });
    }
}

// Output shows duplicate IDs:
// [Step 1] text-start id="0"
// [Step 2] text-start id="0"  <-- Duplicate!

In this example, we set up a simple agent that interacts with a weather tool. The agent is instructed to first acknowledge the request, then call the get_weather tool, and finally summarize the information. The streamText function is used to stream the agent's responses, and we track the text-start events to observe their IDs.

The output clearly shows that the text-start ID is duplicated across steps. In both Step 1 and Step 2, the text-start event has an ID of "0". This duplication confirms the issue and highlights the need for a solution that ensures unique IDs across multi-step flows.

Expected Behavior: Unique Identifiers Across Steps

The expected behavior for a robust AI application is that each text-start event should have a unique ID, even across multiple steps in the same streaming session. This uniqueness is crucial for frameworks and applications that rely on these IDs to track and order message parts correctly.

Imagine a scenario where an application uses these IDs to reconstruct the conversation flow or to highlight specific parts of the text. If the IDs are duplicated, it becomes impossible to reliably identify and manipulate the individual text blocks. This can lead to a degraded user experience and potentially incorrect application behavior.

Importance of Unique IDs

Tracking Message Parts: Unique IDs allow applications to accurately track and manage different parts of the message stream.
Ordering Text Blocks: Proper ordering is essential for maintaining the correct context and flow of the conversation.
Reliable Manipulation: Applications can confidently manipulate specific text blocks without affecting others.
Enhanced User Experience: A consistent and reliable flow of information leads to a better user experience.

Addressing the Issue: Potential Solutions

Several approaches can be taken to address the duplicate ID issue in @ai-sdk/anthropic. One potential solution is to generate unique IDs that are not tied to Anthropic's content block index. This could involve using a counter that increments with each text-start event or generating UUIDs (Universally Unique Identifiers) for each text block.

Implementing a Counter

A simple counter-based approach could look like this:

let textIdCounter = 0;

// ...

else if (chunk.type === 'text-start') {
    const uniqueId = String(textIdCounter++);
    console.log(`[Step ${currentStep}] text-start id="${uniqueId}"`);
    textIds.push({ type: 'text-start', id: uniqueId, step: currentStep });
}

In this example, the textIdCounter variable is incremented each time a text-start event is encountered, ensuring that each text block receives a unique ID.

Using UUIDs

Another approach is to use UUIDs, which are guaranteed to be unique across different systems and time. Libraries like uuid can be used to generate these identifiers:

import { v4 as uuidv4 } from 'uuid';

// ...

else if (chunk.type === 'text-start') {
    const uniqueId = uuidv4();
    console.log(`[Step ${currentStep}] text-start id="${uniqueId}"`);
    textIds.push({ type: 'text-start', id: uniqueId, step: currentStep });
}

Both of these solutions would ensure that each text-start event has a unique ID, resolving the duplication issue and allowing for more reliable tracking and manipulation of text blocks in multi-step flows.

Conclusion

The duplicate text-start and text-end ID issue in @ai-sdk/anthropic highlights the importance of careful attention to detail when building AI-driven applications. While the library provides powerful tools for interacting with Anthropic's language models, it's crucial to understand the underlying mechanisms and potential pitfalls.

By understanding the root cause of the issue, reproducing it in a controlled environment, and exploring potential solutions, developers can ensure the reliability and robustness of their applications. Implementing unique identifiers for text blocks across multi-step flows is essential for maintaining context, ordering information, and providing a seamless user experience.

For more information on AI SDKs and best practices in AI development, consider exploring resources like the Vercel AI SDK Documentation. This documentation provides valuable insights and guidance on building high-quality AI applications.