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This commit fixes the Azure OpenAI backend architecture to use native tool_calls from the Azure API, making it consistent with Claude, ChatCompletions, and Grok backends. Problem: - Azure backend was converting native tool_calls to plain text - Then trying to extract them back using regex patterns - Custom system prompts were injected asking for specific JSON format - This caused tool call detection to fail, especially for Ministral-3B - Led to infinite "coordination in progress" loops without consensus Solution: 1. Fixed _convert_chunk_to_stream_chunk() to yield native tool_calls - Changed from converting to text to returning proper StreamChunk - Lines 746-753: Now returns StreamChunk(type="tool_calls") 2. Added tool call accumulation in stream_with_tools() - Added accumulated_tool_calls dictionary (line 226) - Accumulates tool call deltas during streaming (lines 260-279) - Yields complete tool_calls after streaming (lines 311-323) - Replaced text extraction with native tool call yielding 3. Removed custom system prompt injection - Line 153: Now uses messages as-is - Tools are passed to API natively via api_params["tools"] Benefits: - Works with ALL Azure models (GPT-4o, GPT-4.1-mini, Ministral-3B, etc.) - No model-specific code needed - More reliable (native API vs regex parsing) - Simpler architecture (net removal of ~30 lines) - Future-proof (works with new models automatically) Testing: - Tested with 3-agent setup (GPT-4o, GPT-4.1-mini, Ministral-3B) - All agents successfully use workflow tools - Consensus reached without infinite loops - No regression for existing agents
feat: Fix Logging for Web Search (MAS - 165)
…-main fix: Fix/azure native tool calls from main
feat: Structured logging
docs: docs for v0.1.31
ncrispino
approved these changes
Dec 29, 2025
a5507203
approved these changes
Dec 29, 2025
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