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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>
292 lines
8.3 KiB
Python
292 lines
8.3 KiB
Python
"""Integration tests for streaming with real LLM interactions using cassettes."""
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import pytest
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from crewai import Agent, Crew, Task
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from crewai.flow.flow import Flow, start
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from crewai.types.streaming import CrewStreamingOutput, FlowStreamingOutput
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@pytest.fixture
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def researcher() -> Agent:
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"""Create a researcher agent for testing."""
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return Agent(
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role="Research Analyst",
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goal="Gather comprehensive information on topics",
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backstory="You are an experienced researcher with excellent analytical skills.",
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allow_delegation=False,
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)
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@pytest.fixture
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def simple_task(researcher: Agent) -> Task:
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"""Create a simple research task."""
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return Task(
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description="Research the latest developments in {topic}",
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expected_output="A brief summary of recent developments",
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agent=researcher,
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)
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class TestStreamingCrewIntegration:
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"""Integration tests for crew streaming that match documentation examples."""
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@pytest.mark.vcr()
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def test_basic_crew_streaming_from_docs(
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self, researcher: Agent, simple_task: Task
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) -> None:
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"""Test basic streaming example from documentation."""
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crew = Crew(
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agents=[researcher],
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tasks=[simple_task],
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stream=True,
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verbose=False,
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)
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streaming = crew.kickoff(inputs={"topic": "artificial intelligence"})
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assert isinstance(streaming, CrewStreamingOutput)
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chunks = []
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for chunk in streaming:
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chunks.append(chunk.content)
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assert len(chunks) > 0
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result = streaming.result
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assert result.raw is not None
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assert len(result.raw) > 0
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@pytest.mark.vcr()
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def test_streaming_with_chunk_context_from_docs(
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self, researcher: Agent, simple_task: Task
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) -> None:
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"""Test streaming with chunk context example from documentation."""
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crew = Crew(
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agents=[researcher],
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tasks=[simple_task],
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stream=True,
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verbose=False,
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)
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streaming = crew.kickoff(inputs={"topic": "AI"})
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chunk_contexts = []
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for chunk in streaming:
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chunk_contexts.append(
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{
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"task_name": chunk.task_name,
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"task_index": chunk.task_index,
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"agent_role": chunk.agent_role,
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"content": chunk.content,
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"type": chunk.chunk_type,
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}
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)
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assert len(chunk_contexts) > 0
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assert all("agent_role" in ctx for ctx in chunk_contexts)
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result = streaming.result
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assert result is not None
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@pytest.mark.vcr()
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def test_streaming_properties_from_docs(
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self, researcher: Agent, simple_task: Task
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) -> None:
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"""Test streaming properties example from documentation."""
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crew = Crew(
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agents=[researcher],
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tasks=[simple_task],
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stream=True,
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verbose=False,
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)
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streaming = crew.kickoff(inputs={"topic": "AI"})
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for _ in streaming:
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pass
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assert streaming.is_completed is True
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full_text = streaming.get_full_text()
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assert len(full_text) > 0
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assert len(streaming.chunks) > 0
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result = streaming.result
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assert result.raw is not None
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@pytest.mark.vcr()
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@pytest.mark.asyncio
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async def test_async_streaming_from_docs(
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self, researcher: Agent, simple_task: Task
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) -> None:
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"""Test async streaming example from documentation."""
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crew = Crew(
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agents=[researcher],
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tasks=[simple_task],
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stream=True,
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verbose=False,
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)
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streaming = await crew.kickoff_async(inputs={"topic": "AI"})
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assert isinstance(streaming, CrewStreamingOutput)
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chunks = []
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async for chunk in streaming:
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chunks.append(chunk.content)
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assert len(chunks) > 0
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result = streaming.result
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assert result.raw is not None
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@pytest.mark.vcr()
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def test_kickoff_for_each_streaming_from_docs(
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self, researcher: Agent, simple_task: Task
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) -> None:
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"""Test kickoff_for_each streaming example from documentation."""
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crew = Crew(
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agents=[researcher],
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tasks=[simple_task],
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stream=True,
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verbose=False,
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)
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inputs_list = [{"topic": "AI in healthcare"}, {"topic": "AI in finance"}]
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streaming_outputs = crew.kickoff_for_each(inputs=inputs_list)
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assert len(streaming_outputs) == 2
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assert all(isinstance(s, CrewStreamingOutput) for s in streaming_outputs)
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results = []
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for streaming in streaming_outputs:
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for _ in streaming:
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pass
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result = streaming.result
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results.append(result)
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assert len(results) == 2
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assert all(r.raw is not None for r in results)
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class TestStreamingFlowIntegration:
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"""Integration tests for flow streaming that match documentation examples."""
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@pytest.mark.vcr()
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def test_basic_flow_streaming_from_docs(self) -> None:
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"""Test basic flow streaming example from documentation."""
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class ResearchFlow(Flow):
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stream = True
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@start()
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def research_topic(self) -> str:
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researcher = Agent(
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role="Research Analyst",
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goal="Research topics thoroughly",
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backstory="Expert researcher with analytical skills",
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allow_delegation=False,
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)
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task = Task(
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description="Research AI trends and provide insights",
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expected_output="Detailed research findings",
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agent=researcher,
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)
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crew = Crew(
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agents=[researcher],
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tasks=[task],
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stream=True,
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verbose=False,
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)
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streaming = crew.kickoff()
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for _ in streaming:
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pass
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return streaming.result.raw
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flow = ResearchFlow()
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streaming = flow.kickoff()
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assert isinstance(streaming, FlowStreamingOutput)
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chunks = []
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for chunk in streaming:
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chunks.append(chunk.content)
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assert len(chunks) > 0
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result = streaming.result
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assert result is not None
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@pytest.mark.vcr()
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def test_flow_streaming_properties_from_docs(self) -> None:
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"""Test flow streaming properties example from documentation."""
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class SimpleFlow(Flow):
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stream = True
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@start()
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def execute(self) -> str:
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return "Flow result"
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flow = SimpleFlow()
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streaming = flow.kickoff()
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for _ in streaming:
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pass
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assert streaming.is_completed is True
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streaming.get_full_text()
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assert len(streaming.chunks) >= 0
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result = streaming.result
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assert result is not None
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@pytest.mark.asyncio
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@pytest.mark.timeout(180)
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@pytest.mark.vcr()
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async def test_async_flow_streaming_from_docs(self) -> None:
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"""Test async flow streaming example from documentation."""
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class AsyncResearchFlow(Flow):
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stream = True
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@start()
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def research(self) -> str:
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researcher = Agent(
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role="Researcher",
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goal="Research topics",
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backstory="Expert researcher",
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allow_delegation=False,
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)
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task = Task(
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description="Research AI",
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expected_output="Research findings",
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agent=researcher,
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)
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crew = Crew(agents=[researcher], tasks=[task], stream=True, verbose=False)
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streaming = crew.kickoff()
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for _ in streaming:
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pass
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return streaming.result.raw
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flow = AsyncResearchFlow()
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streaming = await flow.kickoff_async()
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assert isinstance(streaming, FlowStreamingOutput)
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chunks = []
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async for chunk in streaming:
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chunks.append(chunk.content)
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result = streaming.result
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assert result is not None
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