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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>
178 lines
5.6 KiB
Python
178 lines
5.6 KiB
Python
import os
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import threading
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from unittest.mock import patch
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import pytest
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from crewai import Agent, Crew, Task
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from crewai.telemetry import Telemetry
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from opentelemetry.sdk.trace import TracerProvider
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@pytest.fixture(autouse=True)
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def cleanup_telemetry():
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Telemetry._instance = None
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if hasattr(Telemetry, "_lock"):
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Telemetry._lock = threading.Lock()
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yield
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Telemetry._instance = None
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if hasattr(Telemetry, "_lock"):
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Telemetry._lock = threading.Lock()
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@pytest.mark.parametrize(
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"env_var,value,expected_ready",
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[
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("OTEL_SDK_DISABLED", "true", False),
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("OTEL_SDK_DISABLED", "TRUE", False),
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("CREWAI_DISABLE_TELEMETRY", "true", False),
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("CREWAI_DISABLE_TELEMETRY", "TRUE", False),
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("OTEL_SDK_DISABLED", "false", True),
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("CREWAI_DISABLE_TELEMETRY", "false", True),
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],
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)
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def test_telemetry_environment_variables(env_var, value, expected_ready):
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"""Test telemetry state with different environment variable configurations."""
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# Clear all telemetry-related env vars first, then set only the one being tested
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env_overrides = {
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"OTEL_SDK_DISABLED": "false",
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"CREWAI_DISABLE_TELEMETRY": "false",
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"CREWAI_DISABLE_TRACKING": "false",
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env_var: value,
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}
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with patch.dict(os.environ, env_overrides):
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with patch("crewai.telemetry.telemetry.TracerProvider"):
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telemetry = Telemetry()
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assert telemetry.ready is expected_ready
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def test_telemetry_enabled_by_default():
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"""Test that telemetry is enabled by default."""
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with patch.dict(os.environ, {}, clear=True):
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with patch("crewai.telemetry.telemetry.TracerProvider"):
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telemetry = Telemetry()
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assert telemetry.ready is True
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def test_set_tracer_skips_when_provider_already_configured():
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"""A second telemetry instance must not re-install the global provider."""
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with (
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patch.dict(os.environ, {}, clear=True),
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patch(
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"crewai.telemetry.telemetry.trace.get_tracer_provider",
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return_value=TracerProvider(),
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),
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patch("crewai.telemetry.telemetry.trace.set_tracer_provider") as mock_set,
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):
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telemetry = Telemetry()
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telemetry.set_tracer()
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mock_set.assert_not_called()
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assert telemetry.trace_set is True
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@patch("crewai.telemetry.telemetry.logger.error")
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@patch(
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"opentelemetry.exporter.otlp.proto.http.trace_exporter.OTLPSpanExporter.export",
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side_effect=Exception("Test exception"),
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)
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@pytest.mark.vcr()
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def test_telemetry_fails_due_connect_timeout(export_mock, logger_mock):
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error = Exception("Test exception")
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export_mock.side_effect = error
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with patch.dict(
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os.environ, {"CREWAI_DISABLE_TELEMETRY": "false", "OTEL_SDK_DISABLED": "false"}
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):
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telemetry = Telemetry()
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tracer = telemetry.provider.get_tracer(__name__)
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with tracer.start_as_current_span("test-span"):
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agent = Agent(
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role="agent",
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llm="gpt-4o-mini",
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goal="Just say hi",
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backstory="You are a helpful assistant that just says hi",
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)
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task = Task(
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description="Just say hi",
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expected_output="hi",
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agent=agent,
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)
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crew = Crew(agents=[agent], tasks=[task], name="TestCrew")
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crew.kickoff()
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telemetry.provider.force_flush()
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assert export_mock.called
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assert logger_mock.call_count == export_mock.call_count
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for call in logger_mock.call_args_list:
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assert call[0][0] == error
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@pytest.mark.telemetry
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def test_telemetry_singleton_pattern():
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"""Test that Telemetry uses the singleton pattern correctly."""
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Telemetry._instance = None
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telemetry1 = Telemetry()
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telemetry2 = Telemetry()
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assert telemetry1 is telemetry2
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telemetry1.test_attribute = "test_value"
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assert hasattr(telemetry2, "test_attribute")
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assert telemetry2.test_attribute == "test_value"
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import threading
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instances = []
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def create_instance():
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instances.append(Telemetry())
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threads = [threading.Thread(target=create_instance) for _ in range(5)]
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for thread in threads:
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thread.start()
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for thread in threads:
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thread.join()
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assert all(instance is telemetry1 for instance in instances)
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def test_no_signal_handler_traceback_in_non_main_thread():
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"""Signal handler registration should be silently skipped in non-main threads.
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Regression test for https://github.com/crewAIInc/crewAI/issues/4289
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"""
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errors: list[Exception] = []
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mock_holder: dict = {}
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def init_in_thread():
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try:
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Telemetry._instance = None
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with (
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patch.dict(
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os.environ,
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{"CREWAI_DISABLE_TELEMETRY": "false", "OTEL_SDK_DISABLED": "false"},
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),
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patch("crewai.telemetry.telemetry.TracerProvider"),
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patch("signal.signal") as mock_signal,
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patch("crewai.telemetry.telemetry.logger") as mock_logger,
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):
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Telemetry()
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mock_holder["signal"] = mock_signal
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mock_holder["logger"] = mock_logger
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except Exception as exc:
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errors.append(exc)
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thread = threading.Thread(target=init_in_thread)
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thread.start()
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thread.join()
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assert not errors, f"Unexpected error: {errors}"
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assert mock_holder, "Thread did not execute"
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mock_holder["signal"].assert_not_called()
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mock_holder["logger"].debug.assert_any_call(
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"Skipping signal handler registration: not running in main thread"
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)
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