- Add LLMBatcher for grouping similar LLM requests
- Automatic grouping by request type and model
- Enqueue requests with optional result channels
- Auto-flush on max batch size
- Manual flush on demand
- Time-based flush (max batch age)
- Result delivery via channels
- Batch status tracking and error handling
- API cost reduction through request consolidation
- 29 LLM batching tests, all passing
Features:
- Enqueue() for adding LLM requests
- Flush() for manual batch creation
- GetPendingBatch() for next batch
- MarkBatchExecuting/Completed/Failed()
- GroupByTypeAndModel() - automatic grouping
- ResultDelivery() via channels
- GetStats() for batching statistics
- Token counting and tracking
Performance Benefits:
- 3 Implementer requests → 1 API call
- N requests in M batches saves N-M API calls
- Example: 30 requests in 3 batches saves 27 API calls (90% reduction)
- Configurable batch size (default 10)
- Configurable max age (default 2s)
Grouping Strategy:
- Requests grouped by (Type, Model)
- Implementer + claude-opus → separate batch from Implementer + gpt-4
- Judge requests grouped separately from Implementer
- Enables provider-specific optimizations
Result Delivery:
- Each request gets async result channel
- Results delivered to channels on completion
- Error results on batch failure
- Non-blocking result delivery
Statistics:
- Total requests tracked
- Total batches created
- Average requests per batch
- API calls saved calculation
- Total tokens used
- Total execution time
Test Coverage:
- 29 LLM batching tests (enqueue, flush, grouping, delivery)
- Result delivery verification
- Token counting tested
- Auto-flush and manual flush
- Error handling
- Multi-type grouping
- Concurrent safety (RWMutex)
Next: T2.7 (Workflow history pruning)
Test
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