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