1. Type Mismatch and Likely TypeError Crash
• The Issue: The script's docstring explicitly states that the agents return "the typed Pydantic instance + the rendered markdown for each". However, near the bottom of the script, the checks loop attempts to find string markdown headers (such as "Recommendation:" or "Action:") directly inside the returned variables using the syntax ok = marker in text.
• Why it fails: If investment_plan, trader_plan, and final_decision are actually Pydantic model instances (as the docstring suggests), evaluating marker in text will raise a Python TypeError. You cannot use the in operator to search for a substring directly inside a Pydantic object; it requires a string or an iterable.
• Contradictory Type Hints: Compounding this issue, the helper function _make_pm_state uses string type hints for these parameters (investment_plan: str, trader_plan: str), which actively contradicts the docstring's claim that they are Pydantic objects.
2. Missing the Promised Pydantic Output
• The Issue: The script claims in its introductory documentation that it "prints the typed Pydantic instance + the rendered markdown for each".
• Why it fails: The code simply extracts a single key from each agent's result dictionary (e.g., investment_plan = rm_result["investment_plan"]) and passes it directly to _print_section. It never attempts to separate, extract, or independently print a Pydantic instance alongside a rendered markdown string. If the returned key merely holds a markdown string, the script fails its stated purpose of verifying the Pydantic instance.
3. Unused Standard Library Import
• The Issue: The statement import os is declared at the top of the file but the os module is never actually utilized anywhere in the code. While this won't cause the script to crash at runtime, it is poor practice and will be flagged as an error by any standard Python linter (like Flake8 or Ruff).
1. Type Mismatch and Likely TypeError Crash
• The Issue: The script's docstring explicitly states that the agents return "the typed Pydantic instance + the rendered markdown for each". However, near the bottom of the script, the checks loop attempts to find string markdown headers (such as "Recommendation:" or "Action:") directly inside the returned variables using the syntax ok = marker in text.
• Why it fails: If investment_plan, trader_plan, and final_decision are actually Pydantic model instances (as the docstring suggests), evaluating marker in text will raise a Python TypeError. You cannot use the in operator to search for a substring directly inside a Pydantic object; it requires a string or an iterable.
• Contradictory Type Hints: Compounding this issue, the helper function _make_pm_state uses string type hints for these parameters (investment_plan: str, trader_plan: str), which actively contradicts the docstring's claim that they are Pydantic objects.
2. Missing the Promised Pydantic Output
• The Issue: The script claims in its introductory documentation that it "prints the typed Pydantic instance + the rendered markdown for each".
• Why it fails: The code simply extracts a single key from each agent's result dictionary (e.g., investment_plan = rm_result["investment_plan"]) and passes it directly to _print_section. It never attempts to separate, extract, or independently print a Pydantic instance alongside a rendered markdown string. If the returned key merely holds a markdown string, the script fails its stated purpose of verifying the Pydantic instance.
3. Unused Standard Library Import
• The Issue: The statement import os is declared at the top of the file but the os module is never actually utilized anywhere in the code. While this won't cause the script to crash at runtime, it is poor practice and will be flagged as an error by any standard Python linter (like Flake8 or Ruff).