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[BUG]If result_as_answer=true is set, then irrespective of tool's failure or success ,tool output which essentially is error returned will become final answer of agent #5156

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[BUG]If result_as_answer=true is set, then irrespective of tool's failure or success ,tool output which essentially is error returned will become final answer of agent#5156
#5290 (+2)
Labels
bugSomething isn't working
@Vamshi3130

Description

@Vamshi3130
Vamshi3130
opened on Mar 28, 2026

Description

If a tool with result_as_answer=True is given to agent, Agent ignores the success of tool and make the tool output it's own, which shouldn't happen.
result_as_answer=True should work for only successful tool calls ,This essentially removing the capability of agent reflecting on it's output

Steps to Reproduce

Any basic crew with tools where sucess or failure depends on agent(like code execution) set result_as_answer=True

Expected behavior

if tool output is failure then allow agent to reflect on the output ,even if result_as_answer=True

Screenshots/Code snippets

NA

Operating System

Windows 11

Python Version

3.11

crewAI Version

latest

crewAI Tools Version

latest

Virtual Environment

Venv

Evidence

╭─────────────────────────── 🔄 Flow Method Running ───────────────────────────╮
Γöé Γöé
Γöé Method: step3_assumption_testing Γöé
Γöé Status: Running Γöé
Γöé Γöé
Γöé Γöé
Γò░ΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓò»

╭───────────────────────── 🚀 Crew Execution Started ──────────────────────────╮
Γöé Γöé
Γöé Crew Execution Started Γöé
Γöé Name: crew Γöé
Γöé ID: cee6097c-1149-4c2b-aaf9-66ad5bdeac3e Γöé
Γöé Γöé
Γöé Γöé
Γò░ΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓò»

╭────────────────────────────── 📋 Task Started ───────────────────────────────╮
Γöé Γöé
Γöé Task Started Γöé
Γöé Name: Γöé
Γöé Run formal statistical assumption tests on the prepared (transformed) Γöé
Γöé data. You MUST write and execute Python code using the Sandbox Python Code Γöé
Γöé Interpreter to run these tests before writing your report. Γöé
Γöé Research Context: - Topic: Correlation between Pulmonary Function And Γöé
Γöé C-Reactive Protein with HbA1c in Type 2 Diabetes Mellitus PatientsΓÇô A Γöé
Γöé Cross-Sectional Study (Dr.Anandeswari) - Objectives: 1. To determine the Γöé
Γöé association between Type 2 Diabetes Mellitus and pulmonary function test Γöé
Γöé 2. To explore the association between pulmonary function and blood Γöé
Γöé glucose, insulin resistance, and C-reactive protein (CRP) Γöé
Γöé Γöé
Γöé Transformations Applied: === ORIGINAL DATA SUMMARY === Γöé
Γöé Shape: (126, 6) Γöé
Γöé Γöé
Γöé Skewness: Γöé
Γöé Age -0.368212 Γöé
Γöé HbA1c 0.486706 Γöé
Γöé CRP 0.956464 Γöé
Γöé FEV1 -0.089411 Γöé
Γöé FVC 0.122733 Γöé
Γöé FEV1/FVC -0.439025 Γöé
Γöé dtype: float64 Γöé
Γöé Γöé
Γöé Describe: Γöé
Γöé Age HbA1c CRP FEV1 FVC Γöé
Γöé FEV1/FVC Γöé
Γöé count 126.000000 126.000000 126.000000 126.000000 126.000000 Γöé
Γöé 126.000000 Γöé
Γöé mean 50.404762 9.399206 9.235000 66.484127 68.976190 Γöé
Γöé 99.333333 Γöé
Γöé std 9.125944 1.741333 5.149231 17.206387 16.403153 Γöé
Γöé 15.958947 Γöé
Γöé min 23.000000 6.500000 2.100000 26.000000 28.000000 Γöé
Γöé 57.000000 Γöé
Γöé 25% 45.000000 8.000000 5.407500 53.000000 58.250000 Γöé
Γöé 89.000000 Γöé
Γöé 50% 51.000000 9.200000 7.860000 69.000000 70.500000 Γöé
Γöé 102.000000 Γöé
Γöé 75% 56.000000 10.600000 11.100000 77.000000 78.000000 Γöé
Γöé 108.750000 Γöé
Γöé max 78.000000 13.600000 24.780000 114.000000 119.000000 Γöé
Γöé 131.000000 Γöé
Γöé Γöé
Γöé --- TRANSFORMATION PLAN --- Γöé
Γöé Γöé
Γöé DECISIONS & STATISTICAL REASONING: Γöé
Γöé Γöé
Γöé 1. NO MISSING DATA: 0% missing across all variables - No imputation Γöé
Γöé needed. Γöé
Γöé Γöé
Γöé 2. SKEWNESS HANDLING: Γöé
│ - CRP: skewness = 0.945 (moderate right skew) → Log transformation │
Γöé Reason: Log reduces right skew for positive continuous variables with Γöé
Γöé outliers. Γöé
│ - HbA1c: skewness = 0.481 (mild skew) → Yeo-Johnson (Box-Cox variant) │
Γöé Reason: Handles mild skew safely, works with all positive values. Γöé
│ - Age, FEV1, FVC, FEV1/FVC: |skew| < 0.5 → No transformation needed │
Γöé Reason: Near-normal distribution, transformation unnecessary. Γöé
Γöé Γöé
Γöé 3. OUTLIER TREATMENT: Γöé
Γöé - Winsorize at 5th/95th percentiles for CRP, FEV1, FVC Γöé
Γöé Reason: Preserves data while capping extreme values (3-4% outliers), Γöé
Γöé better than removal for medical data. Γöé
Γöé Γöé
Γöé 4. SCALING: Γöé
Γöé - StandardScaler on ALL variables post-transformation Γöé
Γöé Reason: Variables have different scales/units (Age:23-78, CRP:2-25, Γöé
Γöé FEV1:26-114) Γöé
Γöé Essential for modeling (correlations, regressions). Γöé
Γöé Γöé
│ 5. NO CATEGORICAL VARIABLES: All float64 → No encoding needed. │
Γöé Γöé
Γöé 6. FEATURE ENGINEERING: Keep FEV1/FVC as ratio (already derived), monitor Γöé
Γöé multicollinearity. Γöé
Γöé Γöé
Γöé Γöé
Γöé --- Step 1: Winsorizing Outliers --- Γöé
Γöé CRP: Clipped 7 low, 7 high outliers Γöé
Γöé FEV1: Clipped 7 low, 7 high outliers Γöé
Γöé FVC: Clipped 7 low, 7 high outliers Γöé
Γöé --- Step 1 Output --- Γöé
Γöé Outliers after winsorization (IQR method on CRP example): Γöé
Γöé CRP outliers post-winsorize: 0 Γöé
Γöé Γöé
Γöé --- Step 2: Applying Skewness Transformations --- Γöé
│ CRP → log1p(): skew was 0.945 → -0.035886603974563905 │
│ HbA1c → Yeo-Johnson: skew was 0.481 → 0.029738155876168147 │
Γöé --- Step 2 Output --- Γöé
Γöé Skewness after transformations: Γöé
Γöé Age -0.368212 Γöé
Γöé FEV1 -0.310054 Γöé
Γöé FVC -0.064274 Γöé
Γöé FEV1/FVC -0.439025 Γöé
Γöé CRP -0.036320 Γöé
Γöé HbA1c 0.030098 Γöé
Γöé dtype: float64 Γöé
Γöé Γöé
Γöé --- Step 3: Standard Scaling --- Γöé
Γöé --- Step 3 Output --- Γöé
Γöé Means after scaling (should be ~0): Γöé
Γöé Age -2.973812e-17 Γöé
Γöé FEV1 -4.238232e-16 Γöé
Γöé FVC 2.083871e-16 Γöé
Γöé FEV1/FVC 3.004651e-16 Γöé
Γöé CRP -5.649278e-16 Γöé
Γöé HbA1c -1.173026e-14 Γöé
Γöé dtype: float64 Γöé
Γöé Γöé
Γöé Std after scaling (should be ~1): Γöé
Γöé Age 1.003992 Γöé
Γöé FEV1 1.003992 Γöé
Γöé FVC 1.003992 Γöé
Γöé FEV1/FVC 1.003992 Γöé
Γöé CRP 1.003992 Γöé
Γöé HbA1c 1.003992 Γöé
Γöé dtype: float64 Γöé
Γöé Γöé
Γöé === FINAL TRANSFORMED DATA SUMMARY === Γöé
Γöé Shape: (126, 6) Γöé
Γöé Γöé
Γöé Skewness: Γöé
Γöé Age -0.368 Γöé
Γöé FEV1 -0.310 Γöé
Γöé FVC -0.064 Γöé
Γöé FEV1/FVC -0.439 Γöé
Γöé CRP -0.036 Γöé
Γöé HbA1c 0.030 Γöé
Γöé dtype: float64 Γöé
Γöé Γöé
Γöé Describe: Γöé
Γöé Age FEV1 FVC FEV1/FVC CRP HbA1c Γöé
Γöé count 126.000 126.000 126.000 126.000 126.000 126.000 Γöé
Γöé mean -0.000 -0.000 0.000 0.000 -0.000 -0.000 Γöé
Γöé std 1.004 1.004 1.004 1.004 1.004 1.004 Γöé
Γöé min -3.015 -1.965 -1.892 -2.663 -1.740 -2.070 Γöé
Γöé 25% -0.595 -0.837 -0.726 -0.650 -0.729 -0.785 Γöé
Γöé 50% 0.065 0.181 0.115 0.168 -0.040 0.016 Γöé
Γöé 75% 0.616 0.689 0.629 0.592 0.623 0.776 Γöé
Γöé max 3.036 1.627 1.779 1.992 1.591 1.991 Γöé
Γöé Γöé
Γöé Correlation Matrix: Γöé
Γöé Age FEV1 FVC FEV1/FVC CRP HbA1c Γöé
Γöé Age 1.000 -0.109 -0.110 -0.025 -0.001 0.065 Γöé
Γöé FEV1 -0.109 1.000 0.813 0.414 -0.401 -0.302 Γöé
Γöé FVC -0.110 0.813 1.000 -0.025 -0.410 -0.352 Γöé
Γöé FEV1/FVC -0.025 0.414 -0.025 1.000 -0.101 -0.023 Γöé
Γöé CRP -0.001 -0.401 -0.410 -0.101 1.000 0.887 Γöé
Γöé HbA1c 0.065 -0.302 -0.352 -0.023 0.887 1.000 Γöé
Γöé Γöé
Γöé *** TRANSFORMATIONS COMPLETE *** Γöé
Γöé df is now model-ready with: Γöé
Γöé - Handled skewness (CRP log, HbA1c YJ) Γöé
Γöé - Winsorized outliers Γöé
Γöé - Standardized scales Γöé
Γöé - No missing data or categoricals Γöé
Γöé Run the following tests as appropriate for the data and planned Γöé
Γöé analyses: 1. Normality: Shapiro-Wilk test (for n < 50) or Γöé
Γöé Kolmogorov-Smirnov test (for n >= 50) 2. Homogeneity of Variance: Γöé
Γöé Levene's test or Bartlett's test 3. Independence: Chi-squared test of Γöé
Γöé independence (for categorical) 4. Linearity: Scatterplots / residual Γöé
Γöé analysis (for regression contexts) 5. Homoscedasticity: Breusch-Pagan Γöé
Γöé or White's test (for regression) Γöé
Γöé For each test, report: - Test name - Test statistic - p-value - Verdict Γöé
Γöé (Pass/Fail at ╬▒ = 0.05) - If failed: suggest a non-parametric or robust Γöé
Γöé alternative Γöé
Γöé Γöé
Γöé Γöé
Γöé ENVIRONMENT SETUP: Γöé
Γöé A Pandas DataFrame named df containing the cleaned dataset has Γöé
Γöé ALREADY been loaded into your environment. Γöé
Γöé Do NOT write code to read a CSV, Pickle, or Parquet file. Directly use Γöé
Γöé the df variable. Γöé
Γöé Γöé
Γöé CRITICAL RULE ΓÇö ALWAYS ASSIGN BACK TO df: Γöé
Γöé Any transformations, cleaning, or feature engineering you perform MUST Γöé
Γöé be assigned back to Γöé
Γöé the df variable (e.g. df = df.dropna(), df = pd.get_dummies(df, Γöé Γöé ...)). Γöé
Γöé Do NOT create new variable names like df_clean or df_engineered. Γöé
Γöé The environment saves the df variable automatically after your code Γöé
Γöé finishes. Γöé
Γöé Γöé
Γöé # --- Step 1 from Plan: [Description of Step 1] --- Γöé
Γöé # ... your code for step 1 ... Γöé
Γöé print("--- Step 1 Output ---") Γöé
Γöé # ... print results for step 1 ... Γöé
Γöé Γöé
Γöé ERROR HANDLING: Γöé
Γöé After generating the complete script, use the "Sandbox Python Code Γöé
Γöé Interpreter" tool to execute it. Γöé
Γöé - If the script fails, you MUST delegate to the "Python Code Γöé
Γöé Debugging Expert". Provide the debugger with the original plan, your full Γöé
Γöé faulty script, and the complete error message. Γöé
Γöé - After receiving corrected code, try executing it one more time. Γöé
Γöé - If it still fails, document the final error. Γöé
Γöé Γöé
Γöé ID: 97bbbb3b-7164-45da-9985-6719e1878e5a Γöé
Γöé Γöé
Γöé Γöé
Γò░ΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓò»

2026-03-28 15:46:22,360 - LiteLLM - INFO -
LiteLLM completion() model= grok-4-1-fast-non-reasoning; provider = xai
╭────────────────────────────── 🤖 Agent Started ──────────────────────────────╮
Γöé Γöé
Γöé Agent: Statistical Assumption Testing Specialist Γöé
Γöé Γöé
Γöé Task: Γöé
Γöé Run formal statistical assumption tests on the prepared (transformed) Γöé
Γöé data. You MUST write and execute Python code using the Sandbox Python Code Γöé
Γöé Interpreter to run these tests before writing your report. Γöé
Γöé Research Context: - Topic: Correlation between Pulmonary Function And Γöé
Γöé C-Reactive Protein with HbA1c in Type 2 Diabetes Mellitus PatientsΓÇô A Γöé
Γöé Cross-Sectional Study (Dr.Anandeswari) - Objectives: 1. To determine the Γöé
Γöé association between Type 2 Diabetes Mellitus and pulmonary function test Γöé
Γöé 2. To explore the association between pulmonary function and blood Γöé
Γöé glucose, insulin resistance, and C-reactive protein (CRP) Γöé
Γöé Γöé
Γöé Transformations Applied: === ORIGINAL DATA SUMMARY === Γöé
Γöé Shape: (126, 6) Γöé
Γöé Γöé
Γöé Skewness: Γöé
Γöé Age -0.368212 Γöé
Γöé HbA1c 0.486706 Γöé
Γöé CRP 0.956464 Γöé
Γöé FEV1 -0.089411 Γöé
Γöé FVC 0.122733 Γöé
Γöé FEV1/FVC -0.439025 Γöé
Γöé dtype: float64 Γöé
Γöé Γöé
Γöé Describe: Γöé
Γöé Age HbA1c CRP FEV1 FVC Γöé
Γöé FEV1/FVC Γöé
Γöé count 126.000000 126.000000 126.000000 126.000000 126.000000 Γöé
Γöé 126.000000 Γöé
Γöé mean 50.404762 9.399206 9.235000 66.484127 68.976190 Γöé
Γöé 99.333333 Γöé
Γöé std 9.125944 1.741333 5.149231 17.206387 16.403153 Γöé
Γöé 15.958947 Γöé
Γöé min 23.000000 6.500000 2.100000 26.000000 28.000000 Γöé
Γöé 57.000000 Γöé
Γöé 25% 45.000000 8.000000 5.407500 53.000000 58.250000 Γöé
Γöé 89.000000 Γöé
Γöé 50% 51.000000 9.200000 7.860000 69.000000 70.500000 Γöé
Γöé 102.000000 Γöé
Γöé 75% 56.000000 10.600000 11.100000 77.000000 78.000000 Γöé
Γöé 108.750000 Γöé
Γöé max 78.000000 13.600000 24.780000 114.000000 119.000000 Γöé
Γöé 131.000000 Γöé
Γöé Γöé
Γöé --- TRANSFORMATION PLAN --- Γöé
Γöé Γöé
Γöé DECISIONS & STATISTICAL REASONING: Γöé
Γöé Γöé
Γöé 1. NO MISSING DATA: 0% missing across all variables - No imputation Γöé
Γöé needed. Γöé
Γöé Γöé
Γöé 2. SKEWNESS HANDLING: Γöé
│ - CRP: skewness = 0.945 (moderate right skew) → Log transformation │
Γöé Reason: Log reduces right skew for positive continuous variables with Γöé
Γöé outliers. Γöé
│ - HbA1c: skewness = 0.481 (mild skew) → Yeo-Johnson (Box-Cox variant) │
Γöé Reason: Handles mild skew safely, works with all positive values. Γöé
│ - Age, FEV1, FVC, FEV1/FVC: |skew| < 0.5 → No transformation needed │
Γöé Reason: Near-normal distribution, transformation unnecessary. Γöé
Γöé Γöé
Γöé 3. OUTLIER TREATMENT: Γöé
Γöé - Winsorize at 5th/95th percentiles for CRP, FEV1, FVC Γöé
Γöé Reason: Preserves data while capping extreme values (3-4% outliers), Γöé
Γöé better than removal for medical data. Γöé
Γöé Γöé
Γöé 4. SCALING: Γöé
Γöé - StandardScaler on ALL variables post-transformation Γöé
Γöé Reason: Variables have different scales/units (Age:23-78, CRP:2-25, Γöé
Γöé FEV1:26-114) Γöé
Γöé Essential for modeling (correlations, regressions). Γöé
Γöé Γöé
│ 5. NO CATEGORICAL VARIABLES: All float64 → No encoding needed. │
Γöé Γöé
Γöé 6. FEATURE ENGINEERING: Keep FEV1/FVC as ratio (already derived), monitor Γöé
Γöé multicollinearity. Γöé
Γöé Γöé
Γöé Γöé
Γöé --- Step 1: Winsorizing Outliers --- Γöé
Γöé CRP: Clipped 7 low, 7 high outliers Γöé
Γöé FEV1: Clipped 7 low, 7 high outliers Γöé
Γöé FVC: Clipped 7 low, 7 high outliers Γöé
Γöé --- Step 1 Output --- Γöé
Γöé Outliers after winsorization (IQR method on CRP example): Γöé
Γöé CRP outliers post-winsorize: 0 Γöé
Γöé Γöé
Γöé --- Step 2: Applying Skewness Transformations --- Γöé
│ CRP → log1p(): skew was 0.945 → -0.035886603974563905 │
│ HbA1c → Yeo-Johnson: skew was 0.481 → 0.029738155876168147 │
Γöé --- Step 2 Output --- Γöé
Γöé Skewness after transformations: Γöé
Γöé Age -0.368212 Γöé
Γöé FEV1 -0.310054 Γöé
Γöé FVC -0.064274 Γöé
Γöé FEV1/FVC -0.439025 Γöé
Γöé CRP -0.036320 Γöé
Γöé HbA1c 0.030098 Γöé
Γöé dtype: float64 Γöé
Γöé Γöé
Γöé --- Step 3: Standard Scaling --- Γöé
Γöé --- Step 3 Output --- Γöé
Γöé Means after scaling (should be ~0): Γöé
Γöé Age -2.973812e-17 Γöé
Γöé FEV1 -4.238232e-16 Γöé
Γöé FVC 2.083871e-16 Γöé
Γöé FEV1/FVC 3.004651e-16 Γöé
Γöé CRP -5.649278e-16 Γöé
Γöé HbA1c -1.173026e-14 Γöé
Γöé dtype: float64 Γöé
Γöé Γöé
Γöé Std after scaling (should be ~1): Γöé
Γöé Age 1.003992 Γöé
Γöé FEV1 1.003992 Γöé
Γöé FVC 1.003992 Γöé
Γöé FEV1/FVC 1.003992 Γöé
Γöé CRP 1.003992 Γöé
Γöé HbA1c 1.003992 Γöé
Γöé dtype: float64 Γöé
Γöé Γöé
Γöé === FINAL TRANSFORMED DATA SUMMARY === Γöé
Γöé Shape: (126, 6) Γöé
Γöé Γöé
Γöé Skewness: Γöé
Γöé Age -0.368 Γöé
Γöé FEV1 -0.310 Γöé
Γöé FVC -0.064 Γöé
Γöé FEV1/FVC -0.439 Γöé
Γöé CRP -0.036 Γöé
Γöé HbA1c 0.030 Γöé
Γöé dtype: float64 Γöé
Γöé Γöé
Γöé Describe: Γöé
Γöé Age FEV1 FVC FEV1/FVC CRP HbA1c Γöé
Γöé count 126.000 126.000 126.000 126.000 126.000 126.000 Γöé
Γöé mean -0.000 -0.000 0.000 0.000 -0.000 -0.000 Γöé
Γöé std 1.004 1.004 1.004 1.004 1.004 1.004 Γöé
Γöé min -3.015 -1.965 -1.892 -2.663 -1.740 -2.070 Γöé
Γöé 25% -0.595 -0.837 -0.726 -0.650 -0.729 -0.785 Γöé
Γöé 50% 0.065 0.181 0.115 0.168 -0.040 0.016 Γöé
Γöé 75% 0.616 0.689 0.629 0.592 0.623 0.776 Γöé
Γöé max 3.036 1.627 1.779 1.992 1.591 1.991 Γöé
Γöé Γöé
Γöé Correlation Matrix: Γöé
Γöé Age FEV1 FVC FEV1/FVC CRP HbA1c Γöé
Γöé Age 1.000 -0.109 -0.110 -0.025 -0.001 0.065 Γöé
Γöé FEV1 -0.109 1.000 0.813 0.414 -0.401 -0.302 Γöé
Γöé FVC -0.110 0.813 1.000 -0.025 -0.410 -0.352 Γöé
Γöé FEV1/FVC -0.025 0.414 -0.025 1.000 -0.101 -0.023 Γöé
Γöé CRP -0.001 -0.401 -0.410 -0.101 1.000 0.887 Γöé
Γöé HbA1c 0.065 -0.302 -0.352 -0.023 0.887 1.000 Γöé
Γöé Γöé
Γöé *** TRANSFORMATIONS COMPLETE *** Γöé
Γöé df is now model-ready with: Γöé
Γöé - Handled skewness (CRP log, HbA1c YJ) Γöé
Γöé - Winsorized outliers Γöé
Γöé - Standardized scales Γöé
Γöé - No missing data or categoricals Γöé
Γöé Run the following tests as appropriate for the data and planned Γöé
Γöé analyses: 1. Normality: Shapiro-Wilk test (for n < 50) or Γöé
Γöé Kolmogorov-Smirnov test (for n >= 50) 2. Homogeneity of Variance: Γöé
Γöé Levene's test or Bartlett's test 3. Independence: Chi-squared test of Γöé
Γöé independence (for categorical) 4. Linearity: Scatterplots / residual Γöé
Γöé analysis (for regression contexts) 5. Homoscedasticity: Breusch-Pagan Γöé
Γöé or White's test (for regression) Γöé
Γöé For each test, report: - Test name - Test statistic - p-value - Verdict Γöé
Γöé (Pass/Fail at ╬▒ = 0.05) - If failed: suggest a non-parametric or robust Γöé
Γöé alternative Γöé
Γöé Γöé
Γöé Γöé
Γöé ENVIRONMENT SETUP: Γöé
Γöé A Pandas DataFrame named df containing the cleaned dataset has Γöé
Γöé ALREADY been loaded into your environment. Γöé
Γöé Do NOT write code to read a CSV, Pickle, or Parquet file. Directly use Γöé
Γöé the df variable. Γöé
Γöé Γöé
Γöé CRITICAL RULE ΓÇö ALWAYS ASSIGN BACK TO df: Γöé
Γöé Any transformations, cleaning, or feature engineering you perform MUST Γöé
Γöé be assigned back to Γöé
Γöé the df variable (e.g. df = df.dropna(), df = pd.get_dummies(df, Γöé Γöé ...)). Γöé
Γöé Do NOT create new variable names like df_clean or df_engineered. Γöé
Γöé The environment saves the df variable automatically after your code Γöé
Γöé finishes. Γöé
Γöé Γöé
Γöé # --- Step 1 from Plan: [Description of Step 1] --- Γöé
Γöé # ... your code for step 1 ... Γöé
Γöé print("--- Step 1 Output ---") Γöé
Γöé # ... print results for step 1 ... Γöé
Γöé Γöé
Γöé ERROR HANDLING: Γöé
Γöé After generating the complete script, use the "Sandbox Python Code Γöé
Γöé Interpreter" tool to execute it. Γöé
Γöé - If the script fails, you MUST delegate to the "Python Code Γöé
Γöé Debugging Expert". Provide the debugger with the original plan, your full Γöé
Γöé faulty script, and the complete error message. Γöé
Γöé - After receiving corrected code, try executing it one more time. Γöé
Γöé - If it still fails, document the final error. Γöé
Γöé Γöé
Γöé Γöé
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2026-03-28 15:46:35,277 - LiteLLM - INFO - Wrapper: Completed Call, calling success_handler
╭─────────────────────── 🔧 Tool Execution Started (#3) ───────────────────────╮
Γöé Γöé
Γöé Tool: sandbox_python_code_interpreter Γöé
Γöé Args: {'code': 'import pandas as pd\nimport numpy as np\nfrom scipy import Γöé
Γöé stats\nfrom scipy.stats import shapiro, kstest_normal, levene, bartlett, Γöé
Γöé jarque_bera\nfrom statsmodels.stats.diagnostic import het_... Γöé
Γöé Γöé
Γöé Γöé
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�[32mTool sandbox_python_code_interpreter executed with result: Error executing tool: exceptions must derive from BaseException...�[0m
╭───────────────────────────── 🔧 Tool Error (#3) ─────────────────────────────╮
Γöé Γöé
Γöé Tool Failed Γöé
Γöé Tool: sandbox_python_code_interpreter Γöé
Γöé Iteration: 3 Γöé
Γöé Attempt: 0 Γöé
Γöé Error: exceptions must derive from BaseException Γöé
Γöé Γöé
Γöé Γöé
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╭───────────────────────────── 📋 Task Completion ─────────────────────────────╮
Γöé Γöé
Γöé Task Completed Γöé
Γöé Name: Γöé
Γöé Run formal statistical assumption tests on the prepared (transformed) Γöé
Γöé data. You MUST write and execute Python code using the Sandbox Python Code Γöé
Γöé Interpreter to run these tests before writing your report. Γöé
Γöé Research Context: - Topic: Correlation between Pulmonary Function And Γöé
Γöé C-Reactive Protein with HbA1c in Type 2 Diabetes Mellitus PatientsΓÇô A Γöé
Γöé Cross-Sectional Study (Dr.Anandeswari) - Objectives: 1. To determine the Γöé
Γöé association between Type 2 Diabetes Mellitus and pulmonary function test Γöé
Γöé 2. To explore the association between pulmonary function and blood Γöé
Γöé glucose, insulin resistance, and C-reactive protein (CRP) Γöé
Γöé Γöé
Γöé Transformations Applied: === ORIGINAL DATA SUMMARY === Γöé
Γöé Shape: (126, 6) Γöé
Γöé Γöé
Γöé Skewness: Γöé
Γöé Age -0.368212 Γöé
Γöé HbA1c 0.486706 Γöé
Γöé CRP 0.956464 Γöé
Γöé FEV1 -0.089411 Γöé
Γöé FVC 0.122733 Γöé
Γöé FEV1/FVC -0.439025 Γöé
Γöé dtype: float64 Γöé
Γöé Γöé
Γöé Describe: Γöé
Γöé Age HbA1c CRP FEV1 FVC Γöé
Γöé FEV1/FVC Γöé
Γöé count 126.000000 126.000000 126.000000 126.000000 126.000000 Γöé
Γöé 126.000000 Γöé
Γöé mean 50.404762 9.399206 9.235000 66.484127 68.976190 Γöé
Γöé 99.333333 Γöé
Γöé std 9.125944 1.741333 5.149231 17.206387 16.403153 Γöé
Γöé 15.958947 Γöé
Γöé min 23.000000 6.500000 2.100000 26.000000 28.000000 Γöé
Γöé 57.000000 Γöé
Γöé 25% 45.000000 8.000000 5.407500 53.000000 58.250000 Γöé
Γöé 89.000000 Γöé
Γöé 50% 51.000000 9.200000 7.860000 69.000000 70.500000 Γöé
Γöé 102.000000 Γöé
Γöé 75% 56.000000 10.600000 11.100000 77.000000 78.000000 Γöé
Γöé 108.750000 Γöé
Γöé max 78.000000 13.600000 24.780000 114.000000 119.000000 Γöé
Γöé 131.000000 Γöé
Γöé Γöé
Γöé --- TRANSFORMATION PLAN --- Γöé
Γöé Γöé
Γöé DECISIONS & STATISTICAL REASONING: Γöé
Γöé Γöé
Γöé 1. NO MISSING DATA: 0% missing across all variables - No imputation Γöé
Γöé needed. Γöé
Γöé Γöé
Γöé 2. SKEWNESS HANDLING: Γöé
│ - CRP: skewness = 0.945 (moderate right skew) → Log transformation │
Γöé Reason: Log reduces right skew for positive continuous variables with Γöé
Γöé outliers. Γöé
│ - HbA1c: skewness = 0.481 (mild skew) → Yeo-Johnson (Box-Cox variant) │
Γöé Reason: Handles mild skew safely, works with all positive values. Γöé
│ - Age, FEV1, FVC, FEV1/FVC: |skew| < 0.5 → No transformation needed │
Γöé Reason: Near-normal distribution, transformation unnecessary. Γöé
Γöé Γöé
Γöé 3. OUTLIER TREATMENT: Γöé
Γöé - Winsorize at 5th/95th percentiles for CRP, FEV1, FVC Γöé
Γöé Reason: Preserves data while capping extreme values (3-4% outliers), Γöé
Γöé better than removal for medical data. Γöé
Γöé Γöé
Γöé 4. SCALING: Γöé
Γöé - StandardScaler on ALL variables post-transformation Γöé
Γöé Reason: Variables have different scales/units (Age:23-78, CRP:2-25, Γöé
Γöé FEV1:26-114) Γöé
Γöé Essential for modeling (correlations, regressions). Γöé
Γöé Γöé
│ 5. NO CATEGORICAL VARIABLES: All float64 → No encoding needed. │
Γöé Γöé
Γöé 6. FEATURE ENGINEERING: Keep FEV1/FVC as ratio (already derived), monitor Γöé
Γöé multicollinearity. Γöé
Γöé Γöé
Γöé Γöé
Γöé --- Step 1: Winsorizing Outliers --- Γöé
Γöé CRP: Clipped 7 low, 7 high outliers Γöé
Γöé FEV1: Clipped 7 low, 7 high outliers Γöé
Γöé FVC: Clipped 7 low, 7 high outliers Γöé
Γöé --- Step 1 Output --- Γöé
Γöé Outliers after winsorization (IQR method on CRP example): Γöé
Γöé CRP outliers post-winsorize: 0 Γöé
Γöé Γöé
Γöé --- Step 2: Applying Skewness Transformations --- Γöé
│ CRP → log1p(): skew was 0.945 → -0.035886603974563905 │
│ HbA1c → Yeo-Johnson: skew was 0.481 → 0.029738155876168147 │
Γöé --- Step 2 Output --- Γöé
Γöé Skewness after transformations: Γöé
Γöé Age -0.368212 Γöé
Γöé FEV1 -0.310054 Γöé
Γöé FVC -0.064274 Γöé
Γöé FEV1/FVC -0.439025 Γöé
Γöé CRP -0.036320 Γöé
Γöé HbA1c 0.030098 Γöé
Γöé dtype: float64 Γöé
Γöé Γöé
Γöé --- Step 3: Standard Scaling --- Γöé
Γöé --- Step 3 Output --- Γöé
Γöé Means after scaling (should be ~0): Γöé
Γöé Age -2.973812e-17 Γöé
Γöé FEV1 -4.238232e-16 Γöé
Γöé FVC 2.083871e-16 Γöé
Γöé FEV1/FVC 3.004651e-16 Γöé
Γöé CRP -5.649278e-16 Γöé
Γöé HbA1c -1.173026e-14 Γöé
Γöé dtype: float64 Γöé
Γöé Γöé
Γöé Std after scaling (should be ~1): Γöé
Γöé Age 1.003992 Γöé
Γöé FEV1 1.003992 Γöé
Γöé FVC 1.003992 Γöé
Γöé FEV1/FVC 1.003992 Γöé
Γöé CRP 1.003992 Γöé
Γöé HbA1c 1.003992 Γöé
Γöé dtype: float64 Γöé
Γöé Γöé
Γöé === FINAL TRANSFORMED DATA SUMMARY === Γöé
Γöé Shape: (126, 6) Γöé
Γöé Γöé
Γöé Skewness: Γöé
Γöé Age -0.368 Γöé
Γöé FEV1 -0.310 Γöé
Γöé FVC -0.064 Γöé
Γöé FEV1/FVC -0.439 Γöé
Γöé CRP -0.036 Γöé
Γöé HbA1c 0.030 Γöé
Γöé dtype: float64 Γöé
Γöé Γöé
Γöé Describe: Γöé
Γöé Age FEV1 FVC FEV1/FVC CRP HbA1c Γöé
Γöé count 126.000 126.000 126.000 126.000 126.000 126.000 Γöé
Γöé mean -0.000 -0.000 0.000 0.000 -0.000 -0.000 Γöé
Γöé std 1.004 1.004 1.004 1.004 1.004 1.004 Γöé
Γöé min -3.015 -1.965 -1.892 -2.663 -1.740 -2.070 Γöé
Γöé 25% -0.595 -0.837 -0.726 -0.650 -0.729 -0.785 Γöé
Γöé 50% 0.065 0.181 0.115 0.168 -0.040 0.016 Γöé
Γöé 75% 0.616 0.689 0.629 0.592 0.623 0.776 Γöé
Γöé max 3.036 1.627 1.779 1.992 1.591 1.991 Γöé
Γöé Γöé
Γöé Correlation Matrix: Γöé
Γöé Age FEV1 FVC FEV1/FVC CRP HbA1c Γöé
Γöé Age 1.000 -0.109 -0.110 -0.025 -0.001 0.065 Γöé
Γöé FEV1 -0.109 1.000 0.813 0.414 -0.401 -0.302 Γöé
Γöé FVC -0.110 0.813 1.000 -0.025 -0.410 -0.352 Γöé
Γöé FEV1/FVC -0.025 0.414 -0.025 1.000 -0.101 -0.023 Γöé
Γöé CRP -0.001 -0.401 -0.410 -0.101 1.000 0.887 Γöé
Γöé HbA1c 0.065 -0.302 -0.352 -0.023 0.887 1.000 Γöé
Γöé Γöé
Γöé *** TRANSFORMATIONS COMPLETE *** Γöé
Γöé df is now model-ready with: Γöé
Γöé - Handled skewness (CRP log, HbA1c YJ) Γöé
Γöé - Winsorized outliers Γöé
Γöé - Standardized scales Γöé
Γöé - No missing data or categoricals Γöé
Γöé Run the following tests as appropriate for the data and planned Γöé
Γöé analyses: 1. Normality: Shapiro-Wilk test (for n < 50) or Γöé
Γöé Kolmogorov-Smirnov test (for n >= 50) 2. Homogeneity of Variance: Γöé
Γöé Levene's test or Bartlett's test 3. Independence: Chi-squared test of Γöé
Γöé independence (for categorical) 4. Linearity: Scatterplots / residual Γöé
Γöé analysis (for regression contexts) 5. Homoscedasticity: Breusch-Pagan Γöé
Γöé or White's test (for regression) Γöé
Γöé For each test, report: - Test name - Test statistic - p-value - Verdict Γöé
Γöé (Pass/Fail at ╬▒ = 0.05) - If failed: suggest a non-parametric or robust Γöé
Γöé alternative Γöé
Γöé Γöé
Γöé Γöé
Γöé ENVIRONMENT SETUP: Γöé
Γöé A Pandas DataFrame named df containing the cleaned dataset has Γöé
Γöé ALREADY been loaded into your environment. Γöé
Γöé Do NOT write code to read a CSV, Pickle, or Parquet file. Directly use Γöé
Γöé the df variable. Γöé
Γöé Γöé
Γöé CRITICAL RULE ΓÇö ALWAYS ASSIGN BACK TO df: Γöé
Γöé Any transformations, cleaning, or feature engineering you perform MUST Γöé
Γöé be assigned back to Γöé
Γöé the df variable (e.g. df = df.dropna(), df = pd.get_dummies(df, Γöé Γöé ...)). Γöé
Γöé Do NOT create new variable names like df_clean or df_engineered. Γöé
Γöé The environment saves the df variable automatically after your code Γöé
Γöé finishes. Γöé
Γöé Γöé
Γöé # --- Step 1 from Plan: [Description of Step 1] --- Γöé
Γöé # ... your code for step 1 ... Γöé
Γöé print("--- Step 1 Output ---") Γöé
Γöé # ... print results for step 1 ... Γöé
Γöé Γöé
Γöé ERROR HANDLING: Γöé
Γöé After generating the complete script, use the "Sandbox Python Code Γöé
Γöé Interpreter" tool to execute it. Γöé
Γöé - If the script fails, you MUST delegate to the "Python Code Γöé
Γöé Debugging Expert". Provide the debugger with the original plan, your full Γöé
Γöé faulty script, and the complete error message. Γöé
Γöé - After receiving corrected code, try executing it one more time. Γöé
Γöé - If it still fails, document the final error. Γöé
Γöé Γöé
Γöé Agent: Statistical Assumption Testing Specialist Γöé
Γöé Γöé
Γöé Γöé
Γò░ΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓò»

Γò¡ΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇ Crew Completion ΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓò«
Γöé Γöé
Γöé Crew Execution Completed Γöé
Γöé Name: crew Γöé
Γöé ID: cee6097c-1149-4c2b-aaf9-66ad5bdeac3e Γöé
Γöé Final Output: Error executing tool: exceptions must derive from Γöé
Γöé BaseException Γöé
Γöé Γöé
Γöé Γöé
Γò░ΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓò»

2026-03-28 15:46:37,558 - repository.data_analysis_crew.flow - INFO - ============================================================
Step 3 Assumption Testing TOKEN DIAGNOSTIC
prompt_tokens: 7300
completion_tokens: 4077
full usage: {'prompt_tokens': 7300, 'completion_tokens': 4077, 'total_tokens': 11377}
result.raw length: 63 chars
result.raw tail: ...Error executing tool: exceptions must derive from BaseException

2026-03-28 15:46:37,558 - repository.data_analysis_crew.flow - INFO - Step 3 complete: Assumption test output stored
2026-03-28 15:46:37,566 - repository.data_analysis_crew.flow - INFO - Step 4: Model Selection
╭────────────────────────── ✅ Flow Method Completed ──────────────────────────╮
Γöé Γöé
Γöé Method: step3_assumption_testing Γöé
Γöé Status: Completed Γöé
Γöé Γöé
Γöé Γöé
Γò░ΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓöÇΓò»

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