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]
Systematic debugging revealed the root cause of Pydantic validation errors:
- DeepSeek correctly returns tool_calls.arguments as JSON strings
- My wrapper was incorrectly converting strings to dicts
- This caused LangChain's parse_tool_call() to fail (json.loads(dict) error)
- Failure created invalid_tool_calls with dict args (should be string)
- Result: Pydantic validation error on invalid_tool_calls
Solution: Remove all conversion logic. DeepSeek format is already correct.
ToolCallArgsParsingWrapper now acts as a simple passthrough proxy.
Trading session completes successfully with no errors.
Fixes the systematic-debugging investigation that identified the
issue was in our fix attempt, not in the original API response.
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Co-Authored-By: Claude <noreply@anthropic.com>
Removed all argument conversion code to see what DeepSeek actually returns.
This will help identify if the problem is with our conversion or with the
original API response format.
Phase 1 continued - gathering evidence about original response structure.
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Co-Authored-By: Claude <noreply@anthropic.com>
Added detailed logging to patched_create_chat_result to investigate why
invalid_tool_calls.args conversion is not working. This will show:
- Response structure and keys
- Whether invalid_tool_calls exists
- Type and value of args before/after conversion
- Whether conversion is actually executing
This is Phase 1 (Root Cause Investigation) of systematic debugging.
Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Extended ToolCallArgsParsingWrapper to handle both tool_calls and
invalid_tool_calls args formatting inconsistencies from DeepSeek:
- tool_calls.args: string -> dict (for successful calls)
- invalid_tool_calls.args: dict -> string (for failed calls)
The wrapper now normalizes both types before AIMessage construction,
preventing Pydantic validation errors in both success and error cases.
Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Added ToolCallArgsParsingWrapper to handle AI providers (like DeepSeek)
that return tool_calls.args as JSON strings instead of dictionaries.
The wrapper monkey-patches ChatOpenAI's _create_chat_result method to
parse string arguments before AIMessage construction, preventing
Pydantic validation errors.
Changes:
- New: agent/chat_model_wrapper.py - Wrapper implementation
- Modified: agent/base_agent/base_agent.py - Wrap model during init
- Modified: CHANGELOG.md - Document fix as v0.4.1
- New: tests/unit/test_chat_model_wrapper.py - Unit tests
Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
