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crewAI/lib/crewai/tests/utilities/test_planning_handler.py
João Moura bb477f8a91
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JSON first crews (#6131)
* feat(cli): introduce JSON crew project support and TUI enhancements

- Added support for creating and running JSON-defined crew projects, allowing users to scaffold projects with a new `create_json_crew.py` file.
- Implemented a full-screen Textual TUI for crew execution in `crew_run_tui.py`, enhancing user interaction with a two-column layout.
- Updated `run_crew.py` to prioritize JSON crew projects and added daemon mode for running without TUI.
- Introduced interactive pickers in `tui_picker.py` for improved CLI prompts.
- Enhanced validation for JSON crew files in `validate.py` to ensure proper structure and agent definitions.
- Updated `.gitignore` to exclude demo and crewai directories.

* feat: update LLM model references to gpt-5.4-mini

- Changed default LLM model from gpt-4o-mini to gpt-5.4-mini across various files, including CLI options, JSON crew configurations, and agent definitions.
- Enhanced benchmark and human feedback functionalities to utilize the new model.
- Improved user interface elements in the TUI for better interaction and feedback during execution.
- Added support for new skills directory in JSON crew project creation.

* feat(benchmark): add crew-level benchmarking functionality

- Introduced a new `benchmark` command in the CLI for crew-level benchmarking, allowing users to specify agents, models, and timeout settings.
- Implemented `CrewBenchmarkCase` to handle crew-level benchmark cases with inputs and criteria.
- Enhanced the benchmark runner to support progress tracking and detailed reporting of results for multiple models.
- Added tests for loading crew benchmark cases and validating their structure.
- Updated existing benchmark functions to accommodate the new crew-level execution model.

* feat(cli): enhance JSON crew project functionality and TUI improvements

- Added optional agent-level guardrails and advanced options in JSON crew configurations to improve output validation and flexibility.
- Updated the TUI to better handle plan step statuses, including visual indicators for task completion and failure.
- Introduced methods for parsing and managing step observation events, ensuring accurate updates to task statuses during execution.
- Enhanced validation for JSON crew projects, ensuring proper structure and error handling for agent and task definitions.
- Added comprehensive tests for new features and validation logic, ensuring robustness in JSON crew project handling.

* refactor(cli): streamline JSON crew project handling and improve validation

- Refactored JSON crew project loading and validation logic to enhance clarity and maintainability.
- Introduced utility functions for finding JSON crew files, improving code reuse across modules.
- Removed deprecated benchmark functionality and associated tests to simplify the codebase.
- Updated CLI commands to utilize the new JSON project structure, ensuring compatibility with recent changes.
- Enhanced test coverage for JSON crew project features, ensuring robust validation and error handling.

* feat(cli): enhance activity log navigation and focus management

- Added functionality to focus on the activity log when navigating through log entries.
- Implemented refresh logic for the log panel to ensure updates are displayed correctly during navigation.
- Improved keyboard navigation for log entries, allowing users to expand and scroll through logs seamlessly.
- Added tests to verify the correct behavior of log navigation and focus management in the TUI.

* feat(cli): enhance JSON crew project interaction and input handling

- Introduced a new function to enable prompt line editing for better user experience during input prompts.
- Updated the JSON crew project wizards to show interpolation hints for dynamic values, improving user guidance.
- Enhanced the handling of missing input placeholders by prompting users for required values during crew setup.
- Refactored the crew run logic to ensure proper loading and preparation of JSON-defined crews, including runtime input management.
- Added tests to verify the correct behavior of new input handling features and JSON crew project interactions.

* feat(cli): improve crew project input prompts and event handling

- Enhanced the `_prompt_text` function to allow for configurable spacing before prompts, improving user experience during input collection.
- Updated the wizards for agent and task creation to utilize the new prompt configuration, ensuring a more compact and streamlined interaction.
- Introduced new plan step lifecycle events (`PlanStepStartedEvent`, `PlanStepCompletedEvent`) to better track the execution status of plan steps.
- Refactored the step executor to emit these events during the execution of tasks, improving observability and debugging capabilities.
- Added tests to verify the correct behavior of new prompt handling and event emissions during crew project execution.

* fix: refine json-first crew interactions

* fix: prioritize common json crew tools

* fix: make json crew more tools expandable

* fix: show json crew tools by category

* feat(memory): update default embedder to OpenAI text-embedding-3-large and enhance memory compatibility

- Changed the default embedding model for Memory to OpenAI text-embedding-3-large, which uses 3072-dimensional vectors.
- Added warnings regarding compatibility issues with existing local memory stores created with 1536-dimensional embeddings.
- Updated documentation to reflect the new default embedder and its configuration options.
- Enhanced the CLI and codebase to support the new embedding model across various components, ensuring a seamless transition for users.

* fix: address PR review feedback for JSON-first crews

Review blockers:
- Forward trained_agents_file to JSON crews: crewai run -f now exports
  CREWAI_TRAINED_AGENTS_FILE for the in-process JSON crew path
- Wizard agent picker: Esc/cancel now reprompts instead of silently
  assigning the first agent
- JSON tool resolution hard-fails: unknown tool names, missing custom
  tool files, and invalid custom tool modules raise JSONProjectError
  with actionable messages instead of warn-and-continue
- Embedding dimension mismatch: LanceDB and Qdrant Edge storages raise
  EmbeddingDimensionMismatchError with reset/pin guidance instead of
  silently zero-filling vectors or returning empty search results
- Custom tool code execution documented in loader docstring and the
  scaffolded project README

CI fixes:
- ruff format across lib/
- All 133 PR-introduced mypy errors fixed (llm.py lazy-litellm and
  cli.py lazy command shims now use TYPE_CHECKING imports; textual
  is_mounted misuse fixed; pick_many overloads; misc annotations)

Bot review comments:
- Empty except blocks now have explanatory comments or debug logging
- Removed unused _C_BG/_C_PANEL/_C_BORDER globals and redundant
  import re; tests use a single import style for create_json_crew

Tests: trained-agents propagation, wizard cancel, tool resolution
failures, and dimension mismatch guidance.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* fix: address second round of PR review comments

Cursor Bugbot:
- Wizard agent slugs: strip to [a-z0-9_] and fall back to agent_<n> so
  symbol-only roles can't produce an empty agents/.jsonc filename
- Wizard task names: dedupe against prior task names and fall back to
  task_<n> for symbol-only descriptions

CodeRabbit:
- Agent.message(): import Task explicitly at runtime instead of relying
  on the namespace injection done by crewai/__init__
- Async executor: move the native-tools-unsupported fallback from
  _ainvoke_loop_react (self-recursion) to _ainvoke_loop_native_tools,
  mirroring the sync implementation
- StepExecutor downgrade: keep the in-step conversation and append the
  text-tooling instructions instead of rebuilding messages, so completed
  native tool calls are not re-executed
- crewai-files: extension-based MIME lookup now runs before byte
  sniffing so csv/xml types are not degraded to text/plain
- Memory storages: validate every record in a save() batch against a
  consistent embedding dimension (LanceDB previously checked only the
  first record); added mixed-batch tests
- _print_post_tui_summary now typed against CrewRunApp
- Docs: Azure OpenAI default embedder change called out in the memory
  migration warning and provider table

Code quality bots:
- Removed unused _C_YELLOW/_C_CYAN (crew_run_tui) and _GREEN (tui_picker)

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* feat(cli): accordion tool picker in JSON crew wizard

The flat tool list had grown to ~90 rows. The picker now shows:
- Common tools always visible at the top
- Every other category as a single expandable row with tool and
  selection counts (e.g. "Search & Research  (27 tools, 2 selected)")
- Expanding a category collapses the previously expanded one
- Selections persist across expand/collapse via new preselected
  support in pick_many; cursor follows the toggled category row

tui_picker gains preselected + initial_cursor options on pick_many,
and Esc in multi-select now confirms the current selection instead of
discarding it (required so collapsing can't silently drop choices).

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* refactor(cli): remove --daemon flag from crewai run

The flag only affected JSON crew projects — classic and flow projects
ignored it entirely, which made the behavior inconsistent. Removed the
option, the daemon code path (_run_json_crew_daemon), and its helper
(_load_json_crew_with_inputs).

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* test: update run command tests after --daemon removal

lib/crewai/tests/cli/test_run_crew.py still asserted the old
run_crew(trained_agents_file=..., daemon=False) call signature.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* fix(cli): exit codes, mid-run quit, async statuses, hyphen placeholders

Addresses the latest Bugbot review round:

- Failed JSON crew runs now exit non-zero (SystemExit(1)) so scripts
  and CI don't treat failures as success, mirroring the classic path
- Quitting the TUI mid-run now ends the process (os._exit(130));
  kickoff runs in a thread worker that cannot be force-cancelled, so
  letting the CLI return would leave LLM/tool work burning tokens in
  the background
- Sidebar task statuses are now async-safe: completion/failure events
  resolve the task's own row via identity instead of assuming the most
  recently started task, and starting a task no longer blanket-marks
  earlier active rows as done
- The runtime-input prompt regex now accepts hyphenated placeholder
  names ({my-topic}), matching kickoff's interpolation pattern

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* fix: validation safety, custom tool sandboxing, TUI log integrity, memory error surfacing

- Deploy validation no longer executes project code: validation mode
  checks tool declarations structurally (well-formed entries, custom
  tool file exists) without importing or instantiating anything.
  custom:<name> resolution only happens on the actual run path.
- custom:<name> is constrained to [A-Za-z_][A-Za-z0-9_]* and the
  resolved path must stay inside the project's tools/ directory, so
  custom:../foo or absolute-path names cannot execute code outside it.
  Tool paths resolve relative to the crew project root, not cwd.
- TUI task logs are built from per-task state captured at task start
  (idx, description, agent, start time); an out-of-order completion
  takes its output from the event and no longer steals or resets the
  current task's streamed steps/output.
- EmbeddingDimensionMismatchError now inherits ValueError instead of
  RuntimeError so background saves surface it through
  MemorySaveFailedEvent instead of silently dropping the save; the
  shutdown catch in _background_encode_batch is narrowed to the
  "cannot schedule new futures" case.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* fix(cli): declared project type wins over crew.json presence

A flow project that also contains a crew.json(c) file now runs and
validates as the flow it declares in pyproject.toml instead of being
hijacked by the JSON crew path. Both crewai run (_has_json_crew) and
deploy validation (_is_json_crew) check tool.crewai.type; a missing or
unreadable pyproject still means a bare JSON crew project.

Also documents why StepObservationFailedEvent intentionally marks the
plan step "done": the event signals an observer failure, not a step
failure, and the executor continues past it.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* fix(cli): type the declared_type locals so mypy stays clean

Comparing an Any-typed .get() chain returns Any, which tripped
no-any-return on the previous commit.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

---------

Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
2026-06-14 04:19:48 -03:00

359 lines
13 KiB
Python

"""Tests for the planning handler module."""
from unittest.mock import MagicMock, patch
import pytest
from crewai.agent import Agent
from crewai.crew import Crew
from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
from crewai.task import Task
from crewai.tasks.task_output import TaskOutput
from crewai.tools.base_tool import BaseTool
from crewai.utilities.planning_handler import (
CrewPlanner,
PlannerTaskPydanticOutput,
PlanPerTask,
)
class TestInternalCrewPlanner:
@pytest.fixture
def crew_planner(self):
tasks = [
Task(
description="Task 1",
expected_output="Output 1",
agent=Agent(role="Agent 1", goal="Goal 1", backstory="Backstory 1"),
),
Task(
description="Task 2",
expected_output="Output 2",
agent=Agent(role="Agent 2", goal="Goal 2", backstory="Backstory 2"),
),
Task(
description="Task 3",
expected_output="Output 3",
agent=Agent(role="Agent 3", goal="Goal 3", backstory="Backstory 3"),
),
]
return CrewPlanner(tasks, None)
@pytest.fixture
def crew_planner_different_llm(self):
tasks = [
Task(
description="Task 1",
expected_output="Output 1",
agent=Agent(role="Agent 1", goal="Goal 1", backstory="Backstory 1"),
)
]
planning_agent_llm = "gpt-3.5-turbo"
return CrewPlanner(tasks, planning_agent_llm)
def test_handle_crew_planning(self, crew_planner):
list_of_plans_per_task = [
PlanPerTask(task_number=1, task="Task1", plan="Plan 1"),
PlanPerTask(task_number=2, task="Task2", plan="Plan 2"),
PlanPerTask(task_number=3, task="Task3", plan="Plan 3"),
]
with patch.object(Task, "execute_sync") as execute:
execute.return_value = TaskOutput(
description="Description",
agent="agent",
pydantic=PlannerTaskPydanticOutput(
list_of_plans_per_task=list_of_plans_per_task
),
)
result = crew_planner._handle_crew_planning()
assert crew_planner.planning_agent_llm == "gpt-5.4-mini"
assert isinstance(result, PlannerTaskPydanticOutput)
assert len(result.list_of_plans_per_task) == len(crew_planner.tasks)
execute.assert_called_once()
def test_create_planning_agent(self, crew_planner):
agent = crew_planner._create_planning_agent()
assert isinstance(agent, Agent)
assert agent.role == "Task Execution Planner"
def test_create_planner_task(self, crew_planner):
planning_agent = Agent(
role="Planning Agent",
goal="Plan Step by Step Plan",
backstory="Master in Planning",
)
tasks_summary = "Summary of tasks"
task = crew_planner._create_planner_task(planning_agent, tasks_summary)
assert isinstance(task, Task)
assert task.description.startswith("Based on these tasks summary")
assert task.agent == planning_agent
assert (
task.expected_output
== "Step by step plan on how the agents can execute their tasks using the available tools with mastery"
)
def test_create_tasks_summary(self, crew_planner):
tasks_summary = crew_planner._create_tasks_summary()
assert isinstance(tasks_summary, str)
assert tasks_summary.startswith("\n Task Number 1 - Task 1")
assert '"agent_tools": "agent has no tools"' in tasks_summary
# Knowledge field should not be present when empty
assert '"agent_knowledge"' not in tasks_summary
@patch("crewai.knowledge.knowledge.Knowledge.add_sources")
@patch("crewai.knowledge.storage.knowledge_storage.KnowledgeStorage")
def test_create_tasks_summary_with_knowledge_and_tools(
self, mock_storage, mock_add_sources
):
"""Test task summary generation with both knowledge and tools present."""
class MockTool(BaseTool):
name: str
description: str
def __init__(self, name: str, description: str):
tool_data = {"name": name, "description": description}
super().__init__(**tool_data)
def __str__(self):
return self.name
def __repr__(self):
return self.name
def to_structured_tool(self):
return self
def _run(self, *args, **kwargs):
pass
def _generate_description(self) -> str:
"""Override _generate_description to avoid args_schema handling."""
return self.description
tool1 = MockTool("tool1", "Tool 1 description")
tool2 = MockTool("tool2", "Tool 2 description")
task = Task(
description="Task with knowledge and tools",
expected_output="Expected output",
agent=Agent(
role="Test Agent",
goal="Test Goal",
backstory="Test Backstory",
tools=[tool1, tool2],
knowledge_sources=[
StringKnowledgeSource(content="Test knowledge content")
],
),
)
planner = CrewPlanner([task], None)
tasks_summary = planner._create_tasks_summary()
assert isinstance(tasks_summary, str)
assert task.description in tasks_summary
assert task.expected_output in tasks_summary
assert '"agent_tools": [tool1, tool2]' in tasks_summary
assert '"agent_knowledge": "[\\"Test knowledge content\\"]"' in tasks_summary
assert task.agent.role in tasks_summary
assert task.agent.goal in tasks_summary
def test_handle_crew_planning_different_llm(self, crew_planner_different_llm):
with patch.object(Task, "execute_sync") as execute:
execute.return_value = TaskOutput(
description="Description",
agent="agent",
pydantic=PlannerTaskPydanticOutput(
list_of_plans_per_task=[
PlanPerTask(task_number=1, task="Task1", plan="Plan 1")
]
),
)
result = crew_planner_different_llm._handle_crew_planning()
assert crew_planner_different_llm.planning_agent_llm == "gpt-3.5-turbo"
assert isinstance(result, PlannerTaskPydanticOutput)
assert len(result.list_of_plans_per_task) == len(
crew_planner_different_llm.tasks
)
execute.assert_called_once()
def test_plan_per_task_requires_task_number(self):
"""Test that PlanPerTask model requires task_number field."""
with pytest.raises(ValueError):
PlanPerTask(task="Task1", plan="Plan 1")
def test_plan_per_task_with_task_number(self):
"""Test PlanPerTask model with task_number field."""
plan = PlanPerTask(task_number=5, task="Task5", plan="Plan for task 5")
assert plan.task_number == 5
assert plan.task == "Task5"
assert plan.plan == "Plan for task 5"
class TestCrewPlanningIntegration:
"""Tests for Crew._handle_crew_planning integration with task_number matching."""
def test_crew_planning_with_out_of_order_plans(self):
"""Test that plans are correctly matched to tasks even when returned out of order.
This test verifies the fix for issue #3953 where plans returned by the LLM
in a different order than the tasks would be incorrectly assigned.
"""
agent1 = Agent(role="Agent 1", goal="Goal 1", backstory="Backstory 1")
agent2 = Agent(role="Agent 2", goal="Goal 2", backstory="Backstory 2")
agent3 = Agent(role="Agent 3", goal="Goal 3", backstory="Backstory 3")
task1 = Task(
description="First task description",
expected_output="Output 1",
agent=agent1,
)
task2 = Task(
description="Second task description",
expected_output="Output 2",
agent=agent2,
)
task3 = Task(
description="Third task description",
expected_output="Output 3",
agent=agent3,
)
crew = Crew(
agents=[agent1, agent2, agent3],
tasks=[task1, task2, task3],
planning=True,
)
out_of_order_plans = [
PlanPerTask(task_number=3, task="Task 3", plan=" [PLAN FOR TASK 3]"),
PlanPerTask(task_number=1, task="Task 1", plan=" [PLAN FOR TASK 1]"),
PlanPerTask(task_number=2, task="Task 2", plan=" [PLAN FOR TASK 2]"),
]
mock_planner_result = PlannerTaskPydanticOutput(
list_of_plans_per_task=out_of_order_plans
)
with patch.object(
CrewPlanner, "_handle_crew_planning", return_value=mock_planner_result
):
crew._handle_crew_planning()
assert "[PLAN FOR TASK 1]" in task1.description
assert "[PLAN FOR TASK 2]" in task2.description
assert "[PLAN FOR TASK 3]" in task3.description
assert "[PLAN FOR TASK 3]" not in task1.description
assert "[PLAN FOR TASK 1]" not in task2.description
assert "[PLAN FOR TASK 2]" not in task3.description
def test_crew_planning_with_missing_plan(self):
"""Test that missing plans are handled gracefully with a warning."""
agent1 = Agent(role="Agent 1", goal="Goal 1", backstory="Backstory 1")
agent2 = Agent(role="Agent 2", goal="Goal 2", backstory="Backstory 2")
task1 = Task(
description="First task description",
expected_output="Output 1",
agent=agent1,
)
task2 = Task(
description="Second task description",
expected_output="Output 2",
agent=agent2,
)
crew = Crew(
agents=[agent1, agent2],
tasks=[task1, task2],
planning=True,
)
original_task1_desc = task1.description
original_task2_desc = task2.description
incomplete_plans = [
PlanPerTask(task_number=1, task="Task 1", plan=" [PLAN FOR TASK 1]"),
]
mock_planner_result = PlannerTaskPydanticOutput(
list_of_plans_per_task=incomplete_plans
)
with patch.object(
CrewPlanner, "_handle_crew_planning", return_value=mock_planner_result
):
crew._handle_crew_planning()
assert "[PLAN FOR TASK 1]" in task1.description
assert task2.description == original_task2_desc
def test_crew_planning_preserves_original_description(self):
"""Test that planning appends to the original task description."""
agent = Agent(role="Agent 1", goal="Goal 1", backstory="Backstory 1")
task = Task(
description="Original task description",
expected_output="Output 1",
agent=agent,
)
crew = Crew(
agents=[agent],
tasks=[task],
planning=True,
)
plans = [
PlanPerTask(task_number=1, task="Task 1", plan=" - Additional plan steps"),
]
mock_planner_result = PlannerTaskPydanticOutput(list_of_plans_per_task=plans)
with patch.object(
CrewPlanner, "_handle_crew_planning", return_value=mock_planner_result
):
crew._handle_crew_planning()
assert "Original task description" in task.description
assert "Additional plan steps" in task.description
def test_crew_planning_with_duplicate_task_numbers(self):
"""Test that duplicate task numbers use the first plan and log a warning."""
agent = Agent(role="Agent 1", goal="Goal 1", backstory="Backstory 1")
task = Task(
description="Task description",
expected_output="Output 1",
agent=agent,
)
crew = Crew(
agents=[agent],
tasks=[task],
planning=True,
)
# Two plans with the same task_number - should use the first one
duplicate_plans = [
PlanPerTask(task_number=1, task="Task 1", plan=" [FIRST PLAN]"),
PlanPerTask(task_number=1, task="Task 1", plan=" [SECOND PLAN]"),
]
mock_planner_result = PlannerTaskPydanticOutput(
list_of_plans_per_task=duplicate_plans
)
with patch.object(
CrewPlanner, "_handle_crew_planning", return_value=mock_planner_result
):
crew._handle_crew_planning()
assert "[FIRST PLAN]" in task.description
assert "[SECOND PLAN]" not in task.description