Fix Duplicate IDs In @ai-sdk/anthropic Multi-Step Flows

by Alex Johnson 56 views

When building complex AI applications using the @ai-sdk/anthropic library, you might encounter an issue where the text-start and text-end chunks have duplicate IDs across multiple steps in your flow. This can lead to problems when trying to track and order message parts, especially in multi-step agentic flows. This article delves into the root cause of this issue, provides a reproduction case, and discusses the expected behavior.

Understanding the Problem: Duplicate Text IDs

The core issue lies in how @ai-sdk/anthropic handles the IDs for text-start chunks when using Anthropic models within multi-step agentic flows. Imagine a scenario where your AI agent needs to perform multiple actions, such as generating text, calling a tool, and then generating more text based on the tool's response. In such flows (TEXT → TOOL → TEXT), the text-start and text-end chunks may end up with duplicate IDs across these steps.

Root Cause Analysis

To understand why this happens, let's examine the relevant code snippet from @ai-sdk/anthropic. The text-start ID is set using the following logic:

case "text": {
 contentBlocks[value.index] = { type: "text" };
 controller.enqueue({
 type: "text-start",
 id: String(value.index) // <-- This resets to 0 for each LLM call
 });
}

Here, value.index corresponds to Anthropic's content_block_start index. The problem is that Anthropic resets this content block index to 0 for each new response. Consequently, in a multi-step scenario like this:

  1. Step 1: Text (id=0) → Tool call
  2. Step 2: Text (id=0) ← Duplicate!

Both text blocks end up with the same id="0", which can cause confusion and errors in downstream processing.

Why Unique IDs Matter

Unique IDs for each text-start event are crucial for maintaining the correct order and structure of messages, especially in complex, multi-step conversations. Frameworks rely on these IDs to accurately track and assemble message parts, ensuring that the final output is coherent and meaningful. When IDs are duplicated, it becomes difficult to determine the correct sequence of events, leading to potential data corruption or misinterpretation.

Reproduction: Demonstrating the Issue

To illustrate the problem, consider the following code snippet:

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!

This code defines a simple agentic flow that first generates some text ("Let me check"), then calls a tool to get the weather, and finally summarizes the information. The streamText function is used to stream the output, and the code logs the id of each text-start chunk. As the output demonstrates, the IDs are duplicated across steps, confirming the issue.

Explanation of the Code

  • The code imports necessary modules from @ai-sdk/anthropic, ai, and zod.
  • It initializes an array textIds to store the text-start events and their corresponding IDs and step numbers.
  • The streamText function is configured with:
    • An Anthropic model (claude-sonnet-4-5).
    • A system prompt that instructs the model to first say "Let me check", then call the tool, and then summarize.
    • A prompt asking for the weather.
    • A tool called get_weather that simulates fetching weather data.
    • A stopWhen condition that stops the stream after three steps.
  • The code iterates through the fullStream of the result and logs the id of each text-start chunk.
  • The output clearly shows that the text-start ID is duplicated across steps.

Expected Behavior: Unique Identification

The expected behavior is that each text-start event should have a unique ID, even across multiple steps in the same streaming session. This uniqueness is essential for frameworks that rely on these IDs to track and order message parts accurately. Without unique IDs, it becomes challenging to maintain the integrity and coherence of the conversation flow.

Importance of Unique IDs

  • Correct Ordering: Unique IDs ensure that message parts are assembled in the correct order, preventing confusion and misinterpretation.
  • Accurate Tracking: Frameworks can use unique IDs to track the progress of the conversation and identify any missing or out-of-order message parts.
  • Data Integrity: Unique IDs help maintain the integrity of the data by preventing conflicts and ensuring that each message part is correctly associated with its corresponding step.

Potential Solutions and Workarounds

While the issue lies within the @ai-sdk/anthropic library, there are several potential solutions and workarounds that you can implement to mitigate the problem.

1. Manual ID Generation

One approach is to manually generate unique IDs for each text-start event. You can achieve this by maintaining a counter that increments with each new text block. Here's an example:

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

let currentStep = 0;
let textIdCounter = 0; // Initialize a counter for text IDs

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') {
 const uniqueId = `text-${textIdCounter++}`;
 console.log(`[Step ${currentStep}] text-start id="${uniqueId}"`);
 // Use uniqueId for further processing
 }
}

In this solution, textIdCounter variable ensures that each text-start event receives a unique ID.

2. Modifying the Library (Advanced)

For those comfortable with modifying the @ai-sdk/anthropic library directly, you can adjust the code to generate unique IDs based on a combination of the step number and the content block index. This would require forking the repository and making changes to the relevant code.

3. Contributing to the Project

Consider contributing to the @ai-sdk/anthropic project by submitting a pull request with a fix for this issue. This would benefit the entire community and ensure that the library provides the expected behavior out of the box.

Conclusion

Duplicate text-start and text-end IDs in multi-step flows with @ai-sdk/anthropic can lead to significant challenges in tracking and ordering message parts. By understanding the root cause of this issue and implementing one of the proposed solutions or workarounds, you can ensure the integrity and coherence of your AI applications. Remember, unique IDs are crucial for maintaining the correct order and structure of messages, especially in complex, multi-step conversations.

For more information on AI SDKs and Anthropic models, visit the official Anthropic website. This comprehensive guide should assist you in resolving the duplicate ID issue and improving the reliability of your AI-powered applications.