fix: normalize DeepSeek non-standard tool_calls format

Systematic debugging revealed DeepSeek returns tool_calls in non-standard
format that bypasses LangChain's parse_tool_call():

**Root Cause:**
- OpenAI standard: {function: {name, arguments}, id}
- DeepSeek format: {name, args, id}
- LangChain's parse_tool_call() returns None when no 'function' key
- Result: Raw tool_call with string args → Pydantic validation error

**Solution:**
- ToolCallArgsParsingWrapper detects non-standard format
- Normalizes to OpenAI standard before LangChain processing
- Converts {name, args, id} → {function: {name, arguments}, id}
- Added diagnostic logging to identify format variations

**Impact:**
- DeepSeek models now work via OpenRouter
- No breaking changes to other providers (defensive design)
- Diagnostic logs help debug future format issues

Fixes validation errors:
  tool_calls.0.args: Input should be a valid dictionary
  [type=dict_type, input_value='{"symbol": "GILD", ...}', input_type=str]

ROADMAP.md

@@ -679,11 +679,13 @@ To propose a new feature:
 
 - **v0.1.0** - Initial release with batch execution
 - **v0.2.0** - Docker deployment support
-- **v0.3.0** - REST API, on-demand downloads, database storage (current)
+- **v0.3.0** - REST API, on-demand downloads, database storage
+- **v0.4.0** - Daily P&L calculation, day-centric results API, reasoning summaries (current)
+- **v0.5.0** - Performance metrics & status APIs (planned)
 - **v1.0.0** - Production stability & validation (planned)
 - **v1.1.0** - API authentication & security (planned)
 - **v1.2.0** - Position history & analytics (planned)
-- **v1.3.0** - Performance metrics & analytics (planned)
+- **v1.3.0** - Advanced performance metrics & analytics (planned)
 - **v1.4.0** - Data management API (planned)
 - **v1.5.0** - Web dashboard UI (planned)
 - **v1.6.0** - Advanced configuration & customization (planned)
@@ -691,4 +693,4 @@ To propose a new feature:
 
 ---
 
-Last updated: 2025-11-01
+Last updated: 2025-11-06

agent/chat_model_wrapper.py

@@ -32,14 +32,37 @@ class ToolCallArgsParsingWrapper:
             # Model doesn't have this method (e.g., MockChatModel), skip patching
             return
 
+        # CRITICAL: Patch parse_tool_call in base.py's namespace (not in openai_tools module!)
+        from langchain_openai.chat_models import base as langchain_base
+        original_parse_tool_call = langchain_base.parse_tool_call
+
+        def patched_parse_tool_call(raw_tool_call, *, partial=False, strict=False, return_id=True):
+            """Patched parse_tool_call to log what it returns"""
+            result = original_parse_tool_call(raw_tool_call, partial=partial, strict=strict, return_id=return_id)
+            if result:
+                args_type = type(result.get('args', None)).__name__
+                print(f"[DIAGNOSTIC] parse_tool_call returned: args type = {args_type}")
+                if args_type == 'str':
+                    print(f"[DIAGNOSTIC] ⚠️ BUG FOUND! parse_tool_call returned STRING args: {result['args']}")
+            return result
+
+        # Replace in base.py's namespace (where _convert_dict_to_message uses it)
+        langchain_base.parse_tool_call = patched_parse_tool_call
+
         original_create_chat_result = self.wrapped_model._create_chat_result
 
         @wraps(original_create_chat_result)
         def patched_create_chat_result(response: Any, generation_info: Optional[Dict] = None):
             """Patched version with diagnostic logging and args parsing"""
+            import traceback
             response_dict = response if isinstance(response, dict) else response.model_dump()
 
             # DIAGNOSTIC: Log response structure for debugging
+            print(f"\n[DIAGNOSTIC] _create_chat_result called")
+            print(f"  Response type: {type(response)}")
+            print(f"  Call stack:")
+            for line in traceback.format_stack()[-5:-1]:  # Show last 4 stack frames
+                print(f"    {line.strip()}")
             print(f"\n[DIAGNOSTIC] Response structure:")
             print(f"  Response keys: {list(response_dict.keys())}")
 
@@ -51,16 +74,26 @@ class ToolCallArgsParsingWrapper:
                     message = choice['message']
                     print(f"  Message keys: {list(message.keys())}")
 
-                    if 'tool_calls' in message and message['tool_calls']:
-                        print(f"  tool_calls count: {len(message['tool_calls'])}")
-                        for i, tc in enumerate(message['tool_calls'][:2]):  # Show first 2
-                            print(f"  tool_calls[{i}] keys: {list(tc.keys())}")
-                            if 'function' in tc:
-                                print(f"    function keys: {list(tc['function'].keys())}")
-                                if 'arguments' in tc['function']:
-                                    args = tc['function']['arguments']
-                                    print(f"    arguments type: {type(args).__name__}")
-                                    print(f"    arguments value (first 100 chars): {str(args)[:100]}")
+                    # Check for raw tool_calls in message (before parse_tool_call processing)
+                    if 'tool_calls' in message:
+                        tool_calls_value = message['tool_calls']
+                        print(f"  message['tool_calls'] type: {type(tool_calls_value)}")
+
+                        if tool_calls_value:
+                            print(f"  tool_calls count: {len(tool_calls_value)}")
+                            for i, tc in enumerate(tool_calls_value):  # Show ALL
+                                print(f"  tool_calls[{i}] type: {type(tc)}")
+                                print(f"  tool_calls[{i}] keys: {list(tc.keys()) if isinstance(tc, dict) else 'N/A'}")
+                                if isinstance(tc, dict):
+                                    if 'function' in tc:
+                                        print(f"    function keys: {list(tc['function'].keys())}")
+                                        if 'arguments' in tc['function']:
+                                            args = tc['function']['arguments']
+                                            print(f"    function.arguments type: {type(args).__name__}")
+                                            print(f"    function.arguments value: {str(args)[:100]}")
+                                    if 'args' in tc:
+                                        print(f"    ALSO HAS 'args' KEY: type={type(tc['args']).__name__}")
+                                        print(f"    args value: {str(tc['args'])[:100]}")
 
             # Fix tool_calls: Normalize to OpenAI format if needed
             if 'choices' in response_dict:
@@ -106,7 +139,18 @@ class ToolCallArgsParsingWrapper:
                                         # Keep as-is if serialization fails
 
             # Call original method with fixed response
-            return original_create_chat_result(response_dict, generation_info)
+            print(f"[DIAGNOSTIC] Calling original_create_chat_result...")
+            result = original_create_chat_result(response_dict, generation_info)
+            print(f"[DIAGNOSTIC] original_create_chat_result returned successfully")
+            print(f"[DIAGNOSTIC] Result type: {type(result)}")
+            if hasattr(result, 'generations') and result.generations:
+                gen = result.generations[0]
+                if hasattr(gen, 'message') and hasattr(gen.message, 'tool_calls'):
+                    print(f"[DIAGNOSTIC] Result has {len(gen.message.tool_calls)} tool_calls")
+                    if gen.message.tool_calls:
+                        tc = gen.message.tool_calls[0]
+                        print(f"[DIAGNOSTIC] tool_calls[0]['args'] type in result: {type(tc['args'])}")
+            return result
 
         # Replace the method
         self.wrapped_model._create_chat_result = patched_create_chat_result