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## Changes 🏗️ <img width="800" height="621" alt="Screenshot 2026-02-11 at 19 32 39" src="https://github.com/user-attachments/assets/e97be1a7-972e-4ae0-8dfa-6ade63cf287b" /> When the BE API has an error, prevent it from leaking into the stream and instead handle it gracefully via toast. ## Checklist 📋 ### For code changes: - [x] I have clearly listed my changes in the PR description - [x] I have made a test plan - [x] I have tested my changes according to the test plan: - [x] Run the app locally and trust the changes <!-- greptile_comment --> <h2>Greptile Overview</h2> <details><summary><h3>Greptile Summary</h3></summary> This PR fixes an issue where backend API stream errors were leaking into the chat prompt instead of being handled gracefully. The fix involves both backend and frontend changes to ensure error events conform to the AI SDK's strict schema. **Key Changes:** - **Backend (`response_model.py`)**: Added custom `to_sse()` method for `StreamError` that only emits `type` and `errorText` fields, stripping extra fields like `code` and `details` that cause AI SDK validation failures - **Backend (`prompt.py`)**: Added validation step after context compression to remove orphaned tool responses without matching tool calls, preventing "unexpected tool_use_id" API errors - **Frontend (`route.ts`)**: Implemented SSE stream normalization with `normalizeSSEStream()` and `normalizeSSEEvent()` functions to strip non-conforming fields from error events before they reach the AI SDK - **Frontend (`ChatMessagesContainer.tsx`)**: Added toast notifications for errors and improved error display UI with deduplication logic The changes ensure a clean separation between internal error metadata (useful for logging/debugging) and the strict schema required by the AI SDK on the frontend. </details> <details><summary><h3>Confidence Score: 4/5</h3></summary> - This PR is safe to merge with low risk - The changes are well-structured and address a specific bug with proper error handling. The dual-layer approach (backend filtering in `to_sse()` + frontend normalization) provides defense-in-depth. However, the lack of automated tests for the new error normalization logic and the potential for edge cases in SSE parsing prevent a perfect score. - Pay close attention to `autogpt_platform/frontend/src/app/api/chat/sessions/[sessionId]/stream/route.ts` - the SSE normalization logic should be tested with various error scenarios </details> <details><summary><h3>Sequence Diagram</h3></summary> ```mermaid sequenceDiagram participant User participant Frontend as ChatMessagesContainer participant Proxy as /api/chat/.../stream participant Backend as Backend API participant AISDK as AI SDK User->>Frontend: Send message Frontend->>Proxy: POST with message Proxy->>Backend: Forward request with auth Backend->>Backend: Process message alt Success Path Backend->>Proxy: SSE stream (text-delta, etc.) Proxy->>Proxy: normalizeSSEStream (pass through) Proxy->>AISDK: Forward SSE events AISDK->>Frontend: Update messages Frontend->>User: Display response else Error Path Backend->>Backend: StreamError.to_sse() Note over Backend: Only emit {type, errorText} Backend->>Proxy: SSE error event Proxy->>Proxy: normalizeSSEEvent() Note over Proxy: Strip extra fields (code, details) Proxy->>AISDK: {type: "error", errorText: "..."} AISDK->>Frontend: error state updated Frontend->>Frontend: Toast notification (deduplicated) Frontend->>User: Show error UI + toast end ``` </details> <!-- greptile_other_comments_section --> <!-- /greptile_comment --> --------- Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com> Co-authored-by: Otto-AGPT <otto@agpt.co>