diff --git a/lib/crewai/src/crewai/agent/planning_config.py b/lib/crewai/src/crewai/agent/planning_config.py index f93e6dd8d..957ecb33c 100644 --- a/lib/crewai/src/crewai/agent/planning_config.py +++ b/lib/crewai/src/crewai/agent/planning_config.py @@ -1,6 +1,6 @@ from __future__ import annotations -from typing import Any +from typing import Any, Literal from pydantic import BaseModel, Field @@ -9,12 +9,23 @@ class PlanningConfig(BaseModel): """Configuration for agent planning/reasoning before task execution. This allows users to customize the planning behavior including prompts, - iteration limits, and the LLM used for planning. + iteration limits, the LLM used for planning, and the reasoning effort + level that controls post-step observation and replanning behavior. Note: To disable planning, don't pass a planning_config or set planning=False on the Agent. The presence of a PlanningConfig enables planning. Attributes: + reasoning_effort: Controls observation and replanning after each step. + - "low": Observe each step (validates success), but skip the + decide/replan/refine pipeline. Steps are marked complete and + execution continues linearly. Fastest option. + - "medium": Observe each step. On failure, trigger replanning. + On success, skip refinement and continue. Balanced option. + - "high": Full observation pipeline — observe every step, then + route through decide_next_action which can trigger early goal + achievement, full replanning, or lightweight refinement. + Most adaptive but adds latency per step. max_attempts: Maximum number of planning refinement attempts. If None, will continue until the agent indicates readiness. max_steps: Maximum number of steps in the generated plan. @@ -28,7 +39,7 @@ class PlanningConfig(BaseModel): from crewai import Agent from crewai.agent.planning_config import PlanningConfig - # Simple usage + # Simple usage — fast, linear execution (default) agent = Agent( role="Researcher", goal="Research topics", @@ -36,12 +47,23 @@ class PlanningConfig(BaseModel): planning_config=PlanningConfig(), ) - # Customized planning + # Balanced — replan only when steps fail agent = Agent( role="Researcher", goal="Research topics", backstory="Expert researcher", planning_config=PlanningConfig( + reasoning_effort="medium", + ), + ) + + # Full adaptive planning with refinement and replanning + agent = Agent( + role="Researcher", + goal="Research topics", + backstory="Expert researcher", + planning_config=PlanningConfig( + reasoning_effort="high", max_attempts=3, max_steps=10, plan_prompt="Create a focused plan for: {description}", @@ -51,6 +73,16 @@ class PlanningConfig(BaseModel): ``` """ + reasoning_effort: Literal["low", "medium", "high"] = Field( + default="low", + description=( + "Controls post-step observation and replanning behavior. " + "'low' observes steps but skips replanning/refinement (fastest). " + "'medium' observes and replans only on step failure (balanced). " + "'high' runs full observation pipeline with replanning, refinement, " + "and early goal detection (most adaptive, highest latency)." + ), + ) max_attempts: int | None = Field( default=None, description=( diff --git a/lib/crewai/src/crewai/experimental/agent_executor.py b/lib/crewai/src/crewai/experimental/agent_executor.py index fdd5a5981..36163c4a1 100644 --- a/lib/crewai/src/crewai/experimental/agent_executor.py +++ b/lib/crewai/src/crewai/experimental/agent_executor.py @@ -468,6 +468,18 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin): ) return self._planner_observer + def _get_reasoning_effort(self) -> str: + """Get the reasoning effort level from the agent's planning config. + + Returns: + The reasoning effort level: "low", "medium", or "high". + Defaults to "low" if no planning config is set. + """ + config = getattr(self.agent, "planning_config", None) + if config is not None and hasattr(config, "reasoning_effort"): + return config.reasoning_effort + return "low" + def _build_context_for_todo(self, todo: TodoItem) -> StepExecutionContext: """Build an isolated execution context for a single todo. @@ -506,18 +518,27 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin): # ------------------------------------------------------------------------- @router("step_executed") - def observe_step_result(self) -> Literal["step_observed"]: - """THE OBSERVATION STEP — runs after EVERY step execution. + def observe_step_result( + self, + ) -> Literal["step_observed_low", "step_observed_medium", "step_observed_high"]: + """Observe step result and route based on reasoning_effort level. - This is the Planner's opportunity to incorporate new information - learned during execution. It is NOT an error handler — it runs on - every step, including successes. + Always runs PlannerObserver.observe() to validate whether the step + succeeded. Then routes to the appropriate handler based on the + agent's reasoning_effort setting: + + - "low": observe → mark complete → continue (no replan/refine) + - "medium": observe → replan on failure only (no refine) + - "high": observe → full decide pipeline (replan/refine/goal-achieved) Based on PLAN-AND-ACT Section 3.3. """ current_todo = self.state.todos.current_todo + effort = self._get_reasoning_effort() + if not current_todo: - return "step_observed" + # No todo — route to low handler which will just continue + return "step_observed_low" observer = self._ensure_planner_observer() all_completed = self.state.todos.get_completed_todos() @@ -542,13 +563,15 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin): "remaining_plan_still_valid": observation.remaining_plan_still_valid, "needs_full_replan": observation.needs_full_replan, "goal_already_achieved": observation.goal_already_achieved, + "reasoning_effort": effort, } ) if self.agent.verbose: self._printer.print( content=( - f"[Observe] Step {current_todo.step_number}: " + f"[Observe] Step {current_todo.step_number} " + f"(effort={effort}): " f"success={observation.step_completed_successfully}, " f"plan_valid={observation.remaining_plan_still_valid}, " f"learned={observation.key_information_learned[:80]}..." @@ -556,9 +579,85 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin): color="cyan", ) - return "step_observed" + if effort == "high": + return "step_observed_high" + if effort == "medium": + return "step_observed_medium" + return "step_observed_low" - @router("step_observed") + # -- Low effort: observe → mark complete → continue (no replan/refine) -- + + @router("step_observed_low") + def handle_step_observed_low( + self, + ) -> Literal["continue_plan"]: + """Low reasoning effort: mark step complete and continue linearly. + + Skips the entire decide/replan/refine pipeline. The observation + still validates success, but execution proceeds regardless. + """ + current_todo = self.state.todos.current_todo + if not current_todo: + return "continue_plan" + + self.state.todos.mark_completed( + current_todo.step_number, result=current_todo.result + ) + + if self.agent.verbose: + completed = self.state.todos.completed_count + total = len(self.state.todos.items) + self._printer.print( + content=f"[Low] Step {current_todo.step_number} done ({completed}/{total}) — continuing", + color="green", + ) + + return "continue_plan" + + # -- Medium effort: observe → replan on failure only (no refine) -- + + @router("step_observed_medium") + def handle_step_observed_medium( + self, + ) -> Literal["continue_plan", "replan_now"]: + """Medium reasoning effort: replan only when a step fails. + + On success, marks the step complete and continues without + refinement or early goal detection. On failure, triggers replanning + so the agent can recover. + """ + current_todo = self.state.todos.current_todo + if not current_todo: + return "continue_plan" + + observation = self.state.observations.get(current_todo.step_number) + + # If observation is missing or step succeeded — continue + if not observation or observation.step_completed_successfully: + self.state.todos.mark_completed( + current_todo.step_number, result=current_todo.result + ) + if self.agent.verbose: + completed = self.state.todos.completed_count + total = len(self.state.todos.items) + self._printer.print( + content=f"[Medium] Step {current_todo.step_number} succeeded ({completed}/{total}) — continuing", + color="green", + ) + return "continue_plan" + + # Step failed — trigger replan + if self.agent.verbose: + self._printer.print( + content=f"[Medium] Step {current_todo.step_number} failed — triggering replan", + color="yellow", + ) + self.state.last_replan_reason = "Step did not complete successfully" + return "replan_now" + + # -- High effort: full observation pipeline (existing behavior) -- + + @router("step_observed_high") def decide_next_action( self, ) -> Literal[ @@ -567,10 +666,11 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin): "refine_and_continue", "continue_plan", ]: - """Route based on the Planner's observation. + """High reasoning effort: full observation-driven routing. - This replaces the old reactive _should_replan() heuristics with - proactive, LLM-driven decisions. + Routes based on the Planner's observation. Can trigger early goal + achievement, full replanning, lightweight refinement, or simple + continuation. This is the most adaptive but highest-latency path. """ current_todo = self.state.todos.current_todo if not current_todo: diff --git a/lib/crewai/tests/agents/test_agent_executor.py b/lib/crewai/tests/agents/test_agent_executor.py index d0243350a..8c1695ccd 100644 --- a/lib/crewai/tests/agents/test_agent_executor.py +++ b/lib/crewai/tests/agents/test_agent_executor.py @@ -834,3 +834,293 @@ class TestResponseFormatWithKickoff: assert result is not None assert result.pydantic is None assert "20" in str(result) + + +class TestReasoningEffort: + """Test reasoning_effort levels in PlanningConfig. + + - low: observe() runs (validates step success), but skip decide/replan/refine + - medium: observe() runs, replan on failure only (mocked) + - high: full observation pipeline with decide/replan/refine/goal-achieved + """ + + @pytest.mark.vcr() + def test_reasoning_effort_low_skips_decide_and_replan(self): + """Low effort: observe runs but decide/replan/refine are never called. + + Verifies that with reasoning_effort='low': + 1. The agent produces a correct result + 2. The observation phase still runs (observations are stored) + 3. The decide_next_action/refine/replan pipeline is bypassed + """ + from crewai import Agent, PlanningConfig + from crewai.llm import LLM + from crewai.experimental.agent_executor import AgentExecutor + + llm = LLM("gpt-4o-mini") + + agent = Agent( + role="Math Tutor", + goal="Solve multi-step math problems accurately", + backstory="An expert math tutor who breaks down problems step by step", + llm=llm, + planning_config=PlanningConfig( + reasoning_effort="low", + max_attempts=1, + max_steps=10, + ), + verbose=False, + ) + + # Capture the executor to inspect state after execution + executor_ref = [None] + original_invoke = AgentExecutor.invoke + + def capture_executor(self, inputs): + result = original_invoke(self, inputs) + executor_ref[0] = self + return result + + with patch.object(AgentExecutor, "invoke", capture_executor): + result = agent.kickoff( + "What is the sum of the first 3 prime numbers (2, 3, 5)?" + ) + + assert result is not None + assert "10" in str(result) + + # Verify observations were still collected (observe() ran) + executor = executor_ref[0] + if executor is not None and executor.state.todos.items: + assert len(executor.state.observations) > 0, ( + "Low effort should still run observe() to validate steps" + ) + + # Verify no replan was triggered + assert executor.state.replan_count == 0, ( + "Low effort should never trigger replanning" + ) + + # Check execution log for reasoning_effort annotation + observation_logs = [ + log for log in executor.state.execution_log + if log.get("type") == "observation" + ] + for log in observation_logs: + assert log.get("reasoning_effort") == "low" + + @pytest.mark.vcr() + def test_reasoning_effort_high_runs_full_observation_pipeline(self): + """High effort: full observation pipeline with decide/replan/refine. + + Verifies that with reasoning_effort='high': + 1. The agent produces a correct result + 2. Observations are stored + 3. The full decide_next_action pipeline runs (the observation-driven + routing is exercised, even if it just routes to continue_plan) + """ + from crewai import Agent, PlanningConfig + from crewai.llm import LLM + from crewai.experimental.agent_executor import AgentExecutor + + llm = LLM("gpt-4o-mini") + + agent = Agent( + role="Math Tutor", + goal="Solve multi-step math problems accurately", + backstory="An expert math tutor who breaks down problems step by step", + llm=llm, + planning_config=PlanningConfig( + reasoning_effort="high", + max_attempts=1, + max_steps=10, + ), + verbose=False, + ) + + executor_ref = [None] + original_invoke = AgentExecutor.invoke + + def capture_executor(self, inputs): + result = original_invoke(self, inputs) + executor_ref[0] = self + return result + + with patch.object(AgentExecutor, "invoke", capture_executor): + result = agent.kickoff( + "What is the sum of the first 3 prime numbers (2, 3, 5)?" + ) + + assert result is not None + assert "10" in str(result) + + # Verify observations were collected + executor = executor_ref[0] + if executor is not None and executor.state.todos.items: + assert len(executor.state.observations) > 0, ( + "High effort should run observe() on every step" + ) + + # Check execution log shows high reasoning_effort + observation_logs = [ + log for log in executor.state.execution_log + if log.get("type") == "observation" + ] + for log in observation_logs: + assert log.get("reasoning_effort") == "high" + + def test_reasoning_effort_medium_replans_on_failure(self): + """Medium effort: replan triggered when observation reports failure. + + This test mocks the PlannerObserver to simulate a failed step, + verifying that medium effort routes to replan_now on failure + but continues on success. + """ + from crewai.experimental.agent_executor import AgentExecutor + from crewai.utilities.planning_types import ( + StepObservation, + TodoItem, + TodoList, + ) + + # --- Build a minimal mock executor with medium effort --- + executor = Mock(spec=AgentExecutor) + executor.agent = Mock() + executor.agent.verbose = False + executor.agent.planning_config = Mock() + executor.agent.planning_config.reasoning_effort = "medium" + + # Provide the real method under test (bound to our mock) + executor.handle_step_observed_medium = ( + AgentExecutor.handle_step_observed_medium.__get__(executor) + ) + executor._printer = Mock() + + # --- Case 1: step succeeded → should return "continue_plan" --- + success_todo = TodoItem( + step_number=1, + description="Calculate something", + status="running", + result="42", + ) + success_observation = StepObservation( + step_completed_successfully=True, + key_information_learned="Got the answer", + remaining_plan_still_valid=True, + ) + + # Set up state + todo_list = TodoList(items=[success_todo]) + executor.state = Mock() + executor.state.todos = todo_list + executor.state.observations = {1: success_observation} + + route = executor.handle_step_observed_medium() + assert route == "continue_plan", ( + "Medium effort should continue on successful step" + ) + assert success_todo.status == "completed" + + # --- Case 2: step failed → should return "replan_now" --- + failed_todo = TodoItem( + step_number=2, + description="Divide by zero", + status="running", + result="Error: division by zero", + ) + failed_observation = StepObservation( + step_completed_successfully=False, + key_information_learned="Division failed", + remaining_plan_still_valid=False, + needs_full_replan=True, + replan_reason="Step failed with error", + ) + + todo_list_2 = TodoList(items=[failed_todo]) + executor.state.todos = todo_list_2 + executor.state.observations = {2: failed_observation} + executor.state.last_replan_reason = None + + route = executor.handle_step_observed_medium() + assert route == "replan_now", ( + "Medium effort should trigger replan on failed step" + ) + assert executor.state.last_replan_reason == "Step did not complete successfully" + + def test_reasoning_effort_low_marks_complete_without_deciding(self): + """Low effort: mark_completed is called, decide_next_action is not. + + Unit test verifying the low handler's behavior directly. + """ + from crewai.experimental.agent_executor import AgentExecutor + from crewai.utilities.planning_types import TodoItem, TodoList + + executor = Mock(spec=AgentExecutor) + executor.agent = Mock() + executor.agent.verbose = False + executor.agent.planning_config = Mock() + executor.agent.planning_config.reasoning_effort = "low" + + # Bind the real method + executor.handle_step_observed_low = ( + AgentExecutor.handle_step_observed_low.__get__(executor) + ) + executor._printer = Mock() + + todo = TodoItem( + step_number=1, + description="Do something", + status="running", + result="Done successfully", + ) + todo_list = TodoList(items=[todo]) + executor.state = Mock() + executor.state.todos = todo_list + + route = executor.handle_step_observed_low() + assert route == "continue_plan" + assert todo.status == "completed" + assert todo.result == "Done successfully" + + def test_planning_config_reasoning_effort_default_is_low(self): + """Verify PlanningConfig defaults reasoning_effort to 'low'.""" + from crewai.agent.planning_config import PlanningConfig + + config = PlanningConfig() + assert config.reasoning_effort == "low" + + def test_planning_config_reasoning_effort_validation(self): + """Verify PlanningConfig rejects invalid reasoning_effort values.""" + from pydantic import ValidationError + from crewai.agent.planning_config import PlanningConfig + + with pytest.raises(ValidationError): + PlanningConfig(reasoning_effort="ultra") + + # Valid values should work + for level in ("low", "medium", "high"): + config = PlanningConfig(reasoning_effort=level) + assert config.reasoning_effort == level + + def test_get_reasoning_effort_reads_from_config(self): + """Verify _get_reasoning_effort reads from agent.planning_config.""" + from crewai.experimental.agent_executor import AgentExecutor + + executor = Mock(spec=AgentExecutor) + executor._get_reasoning_effort = ( + AgentExecutor._get_reasoning_effort.__get__(executor) + ) + + # Case 1: planning_config with reasoning_effort set + executor.agent = Mock() + executor.agent.planning_config = Mock() + executor.agent.planning_config.reasoning_effort = "high" + assert executor._get_reasoning_effort() == "high" + + # Case 2: no planning_config → defaults to "low" + executor.agent.planning_config = None + assert executor._get_reasoning_effort() == "low" + + # Case 3: planning_config without reasoning_effort attr → defaults to "low" + executor.agent.planning_config = Mock(spec=[]) + assert executor._get_reasoning_effort() == "low" diff --git a/lib/crewai/tests/cassettes/agents/TestReasoningEffort.test_reasoning_effort_high_runs_full_observation_pipeline.yaml b/lib/crewai/tests/cassettes/agents/TestReasoningEffort.test_reasoning_effort_high_runs_full_observation_pipeline.yaml new file mode 100644 index 000000000..0e2ffe46c --- /dev/null +++ b/lib/crewai/tests/cassettes/agents/TestReasoningEffort.test_reasoning_effort_high_runs_full_observation_pipeline.yaml @@ -0,0 +1,531 @@ +interactions: +- request: + body: '{"messages":[{"role":"system","content":"You are a strategic planning assistant. + Create minimal, effective execution plans. 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