AI Chat Messages Vanish Mid-Stream: A Deep Dive

Have you ever been in the middle of an AI-powered chat, watching a response unfold word by word, only for it to suddenly shrink or vanish? It’s a frustrating experience, to say the least, and one that users of the useAIConversation hook in AWS Amplify, particularly with streaming responses, have encountered. This isn't just a minor glitch; it's a significant usability issue that can leave users confused and the AI interaction feeling broken. We're going to dive deep into this phenomenon, exploring why it might be happening, how it impacts the user experience, and what steps are being taken to fix it.

The Frustrating Phenomenon: Messages That Don't Stick

Imagine you’ve asked your AI assistant a complex question, perhaps one that requires it to access and analyze data you’ve previously provided or fetched. You see the response begin to stream in, characters appearing steadily, building the answer you're waiting for. Suddenly, mid-stream, the text you were just reading gets replaced by an earlier, shorter version of the message. It’s like watching a movie where a crucial scene gets swapped out for an earlier draft. This is precisely the bug reported by developers using React, Vite, and Chrome, with specific issues noted in the eu-west-2 region. The core of the problem lies within the useAIConversation hook when handling streaming responses, especially when those responses are lengthy and rely on context derived from custom tools and aiContext callbacks.

The expected behavior is straightforward: once a message starts streaming, it should continue to append content until the AI has finished its thought. It should never revert to a previous state or lose characters mid-stream. The actual behavior, however, is jarring. The message content might grow from, say, 1188 characters to 1513, and then abruptly shrink back to 1229 characters. This happens while the message count remains the same, but the content itself is corrupted. This instability is particularly noticeable when the AI is processing and referencing data provided via custom tools, which is a common and powerful use case for AI conversations. The bug doesn't always manifest immediately after a tool call; it might appear on the second, third, or even fourth subsequent message that leverages that contextual data, especially if the AI is generating a lengthy analysis or comparison. This variability makes it tricky to pinpoint but deeply impacts the reliability of the feature.

Unpacking the Complexity: Backend and Frontend Interactions

To truly understand this bug, we need to look at how the system is set up. The backend involves defining an AIConversation using tools like Claude 3.7 Sonnet, configuring custom tools for data retrieval, and registering response components via the responseComponents prop. A crucial part of this setup is the aiContext callback, which is designed to provide data from tool results back to the AI for subsequent turns in the conversation. For instance, a tool might fetch a list of items, and this data is then made available to the AI to answer questions like "Compare item A to item B."

On the frontend, the useAIConversation hook is employed. Developers are using custom response components, like SomeCustomComponent, and passing an aiContext callback that feeds tool data into the conversation. The AIConversation component itself is then used to render these messages. The provided code snippet showcases a detailed implementation using useMemo and useCallback to stabilize components and callbacks, aiming to prevent unnecessary re-renders that could theoretically cause state issues. They’ve even implemented logic to stabilize the messages array by comparing stringified content, a testament to the effort to rule out frontend rendering as the root cause. Despite these meticulous efforts, the bug persists, pointing towards a deeper issue within the hook’s handling of streaming updates.

Key Factors Contributing to the Bug

Several key factors seem to be consistently present when this bug surfaces:

• Tool Call Integration: The bug appears more frequently when a tool call successfully populates the aiContext with data. This suggests that the process of fetching and integrating external data into the AI's working memory is a critical juncture.
• Contextual Data Usage: Subsequent messages that require the AI to use this aiContext data are prime candidates for triggering the bug. It's not just about fetching data, but about the AI actively reasoning with it.
• Long Streaming Responses: The issue is more pronounced with longer responses, typically those exceeding 500 tokens. This implies that the duration and complexity of the streaming process might be exposing a race condition or a state management flaw.
• Delayed Manifestation: The bug often doesn't occur on the very next message but on follow-up questions. This suggests that the state might degrade over multiple turns, or a specific sequence of operations is required.
• Response Complexity: Responses involving detailed reasoning, comparisons, or analyses based on the provided context seem to be more susceptible. This points towards the AI generating a significant amount of new information that needs to be streamed and integrated.

The console logs provided with the bug report offer further evidence. They show the aiContextCallback being invoked and returning the relevant data, and then the useAIConversation hook processing messages. However, at a certain point, the character count of the streamed message abruptly decreases, indicating that the frontend has received and rendered an incomplete or older version of the message, overwriting the progress made during the stream.

Pinpointing the Problem: Frontend State Management Under Scrutiny

Our analysis strongly suggests that the issue lies within the frontend implementation, specifically how the useAIConversation hook manages its state during active, streaming responses. The backend Lambda functions have been confirmed to complete successfully, ruling out timeouts as the culprit. The raw messages array, as returned by the hook, contains the corrupted, shrunk data. This points to a potential problem in one of several areas:

1. AppSync Real-Time Subscriptions: AWS Amplify often uses AppSync for real-time data updates. If the subscription mechanism that pushes new message chunks to the client experiences issues – perhaps delivering stale data, duplicates, or corrupting data in transit – it could explain the phenomenon.
2. DynamoDB Data Fetching/Caching: While less likely to directly affect streaming mid-response, underlying issues with how data is fetched or cached from DynamoDB, if interleaved with the streaming process, could theoretically lead to inconsistencies. However, the direct observation of shrunk messages during streaming makes this less probable as the primary cause.
3. Hook's Internal State Management During Streaming: This is the most probable area of concern. The useAIConversation hook needs to robustly handle a continuous flow of updates. If there's a race condition where an older state update accidentally overwrites a newer, partially streamed message, or if the hook's internal buffers aren't being managed correctly during concurrent updates from the streaming API and potential other state changes, this could lead to the observed data corruption. For example, if a new message chunk arrives while the hook is still processing a previous chunk, and the processing order is incorrect, it might lead to a state where an incomplete message is considered the