• Actions Taken:

    • Extended the LLMClient interface to include a sendStreaming() method that accepts a callback for partial responses
    • Implemented streaming in OpenAIClient using Server-Sent Events (SSE) format with proper chunk parsing
    • Implemented streaming in OllamaClient using JSONL (newline-delimited JSON) format
    • Updated MorpheumBot to use streaming for better user experience:
      • Direct OpenAI commands (!openai) now show real-time thinking progress
      • Direct Ollama commands (!ollama) now show real-time thinking progress
      • Regular task processing shows iteration progress and LLM thinking status
    • Added comprehensive tests for streaming functionality in both clients
    • Updated bot tests to include streaming method mocks

  • Friction/Success Points:

    • Success: Streaming implementation provides immediate user feedback during long-running LLM operations
    • Success: Both OpenAI and Ollama streaming APIs work well with different formats (SSE vs JSONL)
    • Success: Test coverage maintained at 100% with proper streaming mocks
    • Friction: Had to update test mocks to include the new sendStreaming method to avoid test failures
    • Friction: Pre-commit hooks require DEVLOG updates, ensuring documentation stays current

  • Technical Learnings:

    • OpenAI Streaming: Uses Server-Sent Events with data: prefixed lines and [DONE] terminator
    • Ollama Streaming: Uses JSONL format with {"response": "chunk", "done": false} structure
    • ReadableStream Handling: Both APIs require proper stream reader management with TextDecoder
    • User Experience: Emojis (🤖, 🧠, âš¡, ✅) improve message readability and provide visual feedback
    • Error Handling: Streaming errors need special handling since they occur during data parsing
    • Test Strategy: Mocking streaming requires simulating async chunk delivery with callbacks