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crewai - ✅(Solved) Fix [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 [1 pull requests, 1 participants]

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crewAIInc/crewAI#5156•Fetched 2026-04-08 01:48:18
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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

Error Message

│ ERROR HANDLING: │ │ faulty script, and the complete error message. │ │ - If it still fails, document the final error. │ │ ERROR HANDLING: │ │ faulty script, and the complete error message. │ │ - If it still fails, document the final error. │ Tool sandbox_python_code_interpreter executed with result: Error executing tool: exceptions must derive from BaseException... ╭───────────────────────────── 🔧 Tool Error (#3) ─────────────────────────────╮ │ Error: exceptions must derive from BaseException │ │ ERROR HANDLING: │ │ faulty script, and the complete error message. │ │ - If it still fails, document the final error. │ │ Final Output: Error executing tool: exceptions must derive from │ result.raw tail: ...Error executing tool: exceptions must derive from BaseException

Root Cause

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

Fix Action

Fixed

PR fix notes

PR #5157: fix: don't honor result_as_answer when tool execution errors

Description (problem / solution / changelog)

Summary

Fixes #5156. When a tool with result_as_answer=True raises an exception, the error message was being treated as the agent's final answer, preventing the agent from reflecting on the failure and retrying.

The fix adds error tracking across all tool execution code paths so that result_as_answer is only honored on successful tool executions:

  • tool_usage.py: Added _last_execution_errored flag, set in all error branches (ToolUsageError, tool selection failure, runtime exception in _use/_ause)
  • tool_utils.py: Both execute_tool_and_check_finality and aexecute_tool_and_check_finality check the flag before returning result_as_answer=True
  • crew_agent_executor.py: Propagates error_occurred through execution result dict; _append_tool_result_and_check_finality gates on it
  • agent_utils.py: Uses existing error_event_emitted to gate result_as_answer
  • experimental/agent_executor.py: Same pattern applied to sequential loop, parallel results loop, and parallel error fallback

Review & Testing Checklist for Human

  • Verify step_executor.py coverage: This file was not modified. Confirm that its native tool path delegates to one of the fixed executors and doesn't have its own independent result_as_answer check that bypasses the fix.
  • Verify _last_execution_errored reliability: The flag is a mutable instance attribute on ToolUsage, reset at the top of use()/ause() and read immediately after by tool_utils.py. Confirm no intermediate call can reset it before it's read.
  • End-to-end test: Create an agent with a result_as_answer=True tool that intentionally fails, and confirm the agent continues reasoning rather than returning the error as its final answer.
  • Verify parallel execution path in experimental executor: The parallel error fallback sets "original_tool": None alongside "error_occurred": True — the result_as_answer guard is technically unreachable here since original_tool is falsy. Confirm this is acceptable.

Notes

  • Six new unit tests were added covering the ToolUsage flag, execute_tool_and_check_finality (both error and success), and native tool execution in AgentExecutor (both error and success).
  • The fix uses two different error-tracking mechanisms depending on the code path: a _last_execution_errored flag on ToolUsage (for text/ReAct pattern), and an error_occurred dict key / error_event_emitted local variable (for native tool calling). This follows existing conventions in each module rather than introducing a new abstraction.

Link to Devin session: https://app.devin.ai/sessions/a7393abd35bf4141bf23fe9e1b86b364

<!-- CURSOR_SUMMARY -->

[!NOTE] Medium Risk Changes tool-execution finality logic across multiple executors and hook wrappers; behavior around result_as_answer now depends on new error-tracking flags, which could alter when agents short-circuit after tool calls.

Overview Prevents tools marked result_as_answer=True from prematurely short-circuiting the agent when the tool execution fails, allowing the model to see the error and continue reasoning/retrying.

This propagates explicit error state through native tool execution results (including parallel paths) in CrewAgentExecutor and the experimental AgentExecutor, and adds _last_execution_errored tracking in ToolUsage so tool_utils.execute_*_tool_and_check_finality only returns result_as_answer on successful runs. Adds regression tests covering both success/error cases for native tool execution and ToolUsage/execute_tool_and_check_finality behavior.

<sup>Written by Cursor Bugbot for commit f5dc745669d0827fb0f3450858f790b9229b71b5. This will update automatically on new commits. Configure here.</sup>

<!-- /CURSOR_SUMMARY -->

Changed files

  • lib/crewai/src/crewai/agents/crew_agent_executor.py (modified, +5/-0)
  • lib/crewai/src/crewai/experimental/agent_executor.py (modified, +10/-0)
  • lib/crewai/src/crewai/tools/tool_usage.py (modified, +11/-0)
  • lib/crewai/src/crewai/utilities/agent_utils.py (modified, +3/-1)
  • lib/crewai/src/crewai/utilities/tool_utils.py (modified, +12/-2)
  • lib/crewai/tests/agents/test_agent_executor.py (modified, +74/-0)
  • lib/crewai/tests/tools/test_tool_usage.py (modified, +240/-0)
RAW_BUFFERClick to expand / collapse

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 ───────────────────────────╮ │ │ │ Method: step3_assumption_testing │ │ Status: Running │ │ │ │ │ ╰──────────────────────────────────────────────────────────────────────────────╯

╭───────────────────────── 🚀 Crew Execution Started ──────────────────────────╮ │ │ │ Crew Execution Started │ │ Name: crew │ │ ID: cee6097c-1149-4c2b-aaf9-66ad5bdeac3e │ │ │ │ │ ╰──────────────────────────────────────────────────────────────────────────────╯

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

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

2026-03-28 15:46:35,277 - LiteLLM - INFO - Wrapper: Completed Call, calling success_handler ╭─────────────────────── 🔧 Tool Execution Started (#3) ───────────────────────╮ │ │ │ Tool: sandbox_python_code_interpreter │ │ Args: {'code': 'import pandas as pd\nimport numpy as np\nfrom scipy import │ │ stats\nfrom scipy.stats import shapiro, kstest_normal, levene, bartlett, │ │ jarque_bera\nfrom statsmodels.stats.diagnostic import het_... │ │ │ │ │ ╰──────────────────────────────────────────────────────────────────────────────╯

Tool sandbox_python_code_interpreter executed with result: Error executing tool: exceptions must derive from BaseException... ╭───────────────────────────── 🔧 Tool Error (#3) ─────────────────────────────╮ │ │ │ Tool Failed │ │ Tool: sandbox_python_code_interpreter │ │ Iteration: 3 │ │ Attempt: 0 │ │ Error: exceptions must derive from BaseException │ │ │ │ │ ╰──────────────────────────────────────────────────────────────────────────────╯

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

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 ──────────────────────────╮ │ │ │ Method: step3_assumption_testing │ │ Status: Completed │ │ │ │ │ ╰──────────────────────────────────────────────────────────────────────────────╯

Possible Solution

NA

Additional context

NA

extent analysis

Fix Plan

To address the issue where the Agent ignores the success of a tool and makes the tool output its own when result_as_answer=True, we need to modify the logic of the Agent to handle tool outputs differently based on the result_as_answer flag.

Step 1: Modify Agent Logic

We need to check if result_as_answer=True and if the tool call was successful. If both conditions are met, the Agent should use the tool's output as its own. Otherwise, it should reflect on the output.

if result_as_answer and tool_call_successful:
    # Use tool output as Agent output
    agent_output = tool_output
else:
    # Reflect on tool output
    agent_output = reflect_on_output(tool_output)

Step 2: Handle Tool Failure

When a tool fails, the Agent should not ignore the failure. Instead, it should handle the failure and provide a meaningful output.

if not tool_call_successful:
    # Handle tool failure
    agent_output = handle_failure(tool_output)

Step 3: Implement Reflection Logic

The reflection logic should be implemented to handle the tool output when result_as_answer=False or the tool call fails.

def reflect_on_output(tool_output):
    # Implement reflection logic here
    pass

def handle_failure(tool_output):
    # Implement failure handling logic here
    pass

Verification

To verify the fix, we can test the Agent with different scenarios:

  • Test with result_as_answer=True and a successful tool call.
  • Test with result_as_answer=True and a failed tool call.
  • Test with result_as_answer=False and a successful tool call.
  • Test with result_as_answer=False and a failed tool call.

Extra Tips

  • Make sure to handle edge cases, such as when the tool output is empty or null.
  • Consider adding logging or monitoring to track the Agent's behavior and tool outputs.
  • Review the reflection logic and failure handling to ensure they meet the requirements and are robust.

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FAQ

Expected behavior

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

#api#environment setup#SSR setup#ISR setup#authentication setup

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crewai - ✅(Solved) Fix [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 [1 pull requests, 1 participants]