feat: adaptive replanning when task results deviate from plan

[Ricardo-M-L]

Summary

• Adds adaptive re-planning capability when planning=True crews encounter task results that deviate from the original plan's assumptions
• Introduces ReplanningEvaluator that runs a lightweight LLM check after each task to detect deviations (missing data, unexpected results, infeasible approaches)
• When deviation is detected, CrewPlanner.replan() generates revised plans for remaining tasks only, using actual results as context
• Fully backwards compatible: replan_on_failure defaults to False, existing crews are completely unaffected

New API

crew = Crew(
    agents=[...],
    tasks=[...],
    planning=True,
    replan_on_failure=True,     # enables adaptive replanning
    max_replans=3,              # prevents infinite loops (default: 3)
    evaluation_criteria=EvaluationCriteria(
        quality_threshold=7.0,  # configurable quality bar
        check_completeness=True,
        check_relevance=True,
        custom_criteria="Must include data sources",
    ),
    replanning_evaluator=custom_evaluator,  # pluggable evaluator
)

Files Changed

File	Change
lib/crewai/src/crewai/utilities/replanning_evaluator.py	New - ReplanningEvaluator, ReplanDecision, EvaluationCriteria
lib/crewai/src/crewai/utilities/planning_handler.py	Added replan() method to CrewPlanner
lib/crewai/src/crewai/crew.py	Added fields + _evaluate_and_replan() hook in _execute_tasks()
lib/crewai/tests/utilities/test_replanning_evaluator.py	New - 34 test cases

How It Works

1. After each synchronous task completes, _should_evaluate_for_replan() checks if replanning is enabled and budget remains
2. ReplanningEvaluator.evaluate() makes a structured LLM call: "Does this result deviate significantly from what the plan assumed?"
3. If ReplanDecision.should_replan=True, CrewPlanner.replan() generates revised plans for remaining tasks using completed results as context
4. Revised plans are injected into remaining task descriptions as [REVISED PLAN] sections (old revisions are replaced, not stacked)
5. _replan_count prevents runaway loops (capped at max_replans)

Test Plan

• 34 test cases covering all components (all passing)
• ReplanDecision model validation (bounds, defaults, all fields)
• EvaluationCriteria model validation (bounds, custom criteria)
• ReplanningEvaluator (init, no-remaining-tasks, replan/no-replan decisions, parse failure fallback, criteria text building)
• CrewPlanner.replan() (returns revised plans, raises on failure, remaining tasks summary)
• Crew integration (field defaults, custom config, should_evaluate guards, replan triggers, error handling, replan count, revision stacking, backwards compatibility)
• Existing test_planning_handler.py tests still pass (12/12)

Fixes #4983

🤖 Generated with Claude Code

---

Note

Medium Risk
Adds new LLM-driven evaluation and dynamic replanning into the core sequential execution loop, which can change task descriptions mid-run and affect determinism/cost. Guardrails exist (replan_on_failure default off, max_replans cap), but the behavior is complex and touches planning/execution paths.

Overview
Introduces adaptive replanning for planning=True crews via new Crew options (replan_on_failure, max_replans, evaluation_criteria, and pluggable replanning_evaluator). After each synchronous task, an LLM-based ReplanningEvaluator can decide whether outputs deviate from the original plan and, if so, CrewPlanner.replan() regenerates plans for remaining tasks and injects them into task descriptions as [REVISED PLAN].

Adds CrewPlanner.replan() to produce revised plans using completed TaskOutputs as context, plus a new utilities/replanning_evaluator.py module with structured decision/criteria models and robust fallback behavior. Includes a comprehensive new test suite covering evaluator behavior, replanning generation, and Crew integration/backwards-compatibility.

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Cursor Bugbot has reviewed your changes and found 1 potential issue.

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Replan count not incremented on replanning failure

High Severity

_replan_count += 1 is only reached on the success path inside the try block (after planner.replan() returns). When planner.replan() raises an exception, the increment is skipped and the except block doesn't increment it either. This means persistent replanning failures never consume the max_replans budget, so _should_evaluate_for_replan() keeps returning True and the system retries on every subsequent task — defeating the runaway-loop protection. The corresponding test also asserts _replan_count == 1 after a failure, which will fail against this code.

Additional Locations (1)

• lib/crewai/tests/utilities/test_replanning_evaluator.py#L565-L567

 

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[Ricardo-M-L]

Closing — branch has diverged significantly from upstream, and large features should be discussed first. Will resubmit properly if needed.