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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

* fix: refine json-first crew interactions

* fix: prioritize common json crew tools

* fix: make json crew more tools expandable

* fix: show json crew tools by category

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

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

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

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

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

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

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

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

* fix: address second round of PR review comments

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Addresses the latest Bugbot review round:

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

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

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

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

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

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

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

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

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

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

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

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

---------

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

797 lines
22 KiB
Python

from datetime import datetime
import time
import pytest
from crewai.events.event_bus import crewai_event_bus
from crewai.events.types.observation_events import (
GoalAchievedEarlyEvent,
PlanRefinementEvent,
PlanReplanTriggeredEvent,
PlanStepCompletedEvent,
PlanStepStartedEvent,
StepObservationCompletedEvent,
StepObservationFailedEvent,
StepObservationStartedEvent,
)
from crewai.events.types.tool_usage_events import (
ToolUsageErrorEvent,
ToolUsageFinishedEvent,
ToolUsageStartedEvent,
)
from crewai_cli import run_crew
from crewai_cli.crew_run_tui import CrewRunApp
def _app_with_plan() -> CrewRunApp:
app = CrewRunApp()
app._plan = {
"plan": "Demo plan",
"steps": [
{"step_number": 1, "description": "First"},
{"step_number": 2, "description": "Second"},
{"step_number": 3, "description": "Third"},
],
}
app._plan_step_status = {1: "pending", 2: "pending", 3: "pending"}
return app
def _log_entry(name: str) -> dict:
now = time.time()
return {
"tool_name": name,
"status": "success",
"args": None,
"result": f"{name} result",
"error": None,
"start_time": now,
"duration": 1.0,
"task_idx": 1,
}
def _emit_event(event: object) -> None:
future = crewai_event_bus.emit(None, event)
if future:
future.result(timeout=5)
def test_chain_deploy_skips_validation_after_auth_retry(monkeypatch) -> None:
create_calls: list[dict[str, object]] = []
login_calls: list[bool] = []
class FakeDeployCommand:
attempts = 0
def create_crew(self, **kwargs) -> None:
create_calls.append(kwargs)
FakeDeployCommand.attempts += 1
if FakeDeployCommand.attempts == 1:
raise SystemExit(1)
class FakeAuthenticationCommand:
def login(self) -> None:
login_calls.append(True)
monkeypatch.setattr("crewai_cli.deploy.main.DeployCommand", FakeDeployCommand)
monkeypatch.setattr(
"crewai_cli.authentication.main.AuthenticationCommand",
FakeAuthenticationCommand,
)
run_crew._chain_deploy()
assert create_calls == [
{"confirm": False, "skip_validate": True},
{"confirm": False, "skip_validate": True},
]
assert login_calls == [True]
def test_plan_step_status_updates_only_the_explicit_step() -> None:
app = _app_with_plan()
app._set_plan_step_status(2, "done")
assert app._plan_step_status == {
1: "pending",
2: "done",
3: "pending",
}
def test_step_observation_events_update_the_explicit_step() -> None:
app = _app_with_plan()
app._subscribe()
try:
future = crewai_event_bus.emit(
None,
StepObservationStartedEvent(
agent_role="Agent",
step_number=2,
step_description="Second",
),
)
if future:
future.result(timeout=5)
assert app._plan_step_status == {
1: "pending",
2: "active",
3: "pending",
}
future = crewai_event_bus.emit(
None,
StepObservationCompletedEvent(
agent_role="Agent",
step_number=2,
step_description="Second",
step_completed_successfully=True,
),
)
if future:
future.result(timeout=5)
finally:
app._unsubscribe()
assert app._plan_step_status == {
1: "pending",
2: "done",
3: "pending",
}
def test_plan_step_lifecycle_events_update_the_explicit_step() -> None:
app = _app_with_plan()
app._subscribe()
try:
_emit_event(
PlanStepStartedEvent(
agent_role="Agent",
step_number=2,
step_description="Second",
)
)
assert app._plan_step_status == {
1: "pending",
2: "active",
3: "pending",
}
_emit_event(
PlanStepCompletedEvent(
agent_role="Agent",
step_number=2,
step_description="Second",
success=True,
result="done",
)
)
finally:
app._unsubscribe()
assert app._plan_step_status == {
1: "pending",
2: "done",
3: "pending",
}
def test_failed_plan_step_lifecycle_event_marks_exact_step_failed() -> None:
app = _app_with_plan()
app._subscribe()
try:
_emit_event(
PlanStepCompletedEvent(
agent_role="Agent",
step_number=2,
step_description="Second",
success=False,
error="Step failed",
)
)
finally:
app._unsubscribe()
assert app._plan_step_status == {
1: "pending",
2: "failed",
3: "pending",
}
def test_tool_usage_events_do_not_advance_plan_steps() -> None:
app = _app_with_plan()
app._subscribe()
try:
future = crewai_event_bus.emit(
None,
ToolUsageStartedEvent(tool_name="search", tool_args={"query": "CrewAI"}),
)
if future:
future.result(timeout=5)
now = datetime.now()
future = crewai_event_bus.emit(
None,
ToolUsageFinishedEvent(
tool_name="search",
tool_args={"query": "CrewAI"},
started_at=now,
finished_at=now,
output="result",
),
)
if future:
future.result(timeout=5)
finally:
app._unsubscribe()
assert app._plan_step_status == {
1: "pending",
2: "pending",
3: "pending",
}
def test_next_tool_does_not_mark_unfinished_tool_successful() -> None:
app = _app_with_plan()
app._subscribe()
try:
_emit_event(
ToolUsageStartedEvent(tool_name="search", tool_args={"query": "CrewAI"}),
)
_emit_event(
ToolUsageStartedEvent(tool_name="scrape", tool_args={"url": "https://x"}),
)
finally:
app._unsubscribe()
assert app._log_entries[0]["status"] == "timeout"
assert app._log_entries[0]["result"] is None
assert app._log_entries[0]["error"] == (
"No result received before the next tool started"
)
assert app._log_entries[1]["status"] == "running"
assert app._plan_step_status == {
1: "pending",
2: "pending",
3: "pending",
}
def test_internal_reasoning_function_call_is_hidden_from_activity_log() -> None:
app = _app_with_plan()
app._subscribe()
try:
future = crewai_event_bus.emit(
None,
ToolUsageStartedEvent(
tool_name="create_reasoning_plan",
tool_args={"plan": "Plan", "steps": [], "ready": True},
),
)
if future:
future.result(timeout=5)
now = datetime.now()
future = crewai_event_bus.emit(
None,
ToolUsageFinishedEvent(
tool_name="create_reasoning_plan",
tool_args={"plan": "Plan", "steps": [], "ready": True},
started_at=now,
finished_at=now,
output='{"plan":"Plan","steps":[],"ready":true}',
),
)
if future:
future.result(timeout=5)
future = crewai_event_bus.emit(
None,
ToolUsageErrorEvent(
tool_name="create_reasoning_plan",
tool_args={"plan": "Plan", "steps": [], "ready": True},
error="internal planning fallback",
),
)
if future:
future.result(timeout=5)
finally:
app._unsubscribe()
assert app._log_entries == []
assert app._current_task_steps == []
def test_tool_failure_does_not_override_successful_plan_step_completion() -> None:
app = _app_with_plan()
app._subscribe()
try:
_emit_event(
PlanStepStartedEvent(
agent_role="Agent",
step_number=1,
step_description="First",
)
)
_emit_event(
ToolUsageStartedEvent(
tool_name="search_the_internet_with_serper",
tool_args={"search_query": "CrewAI release"},
plan_step_number=1,
plan_step_description="First",
)
)
_emit_event(
ToolUsageErrorEvent(
tool_name="search_the_internet_with_serper",
tool_args={"search_query": "CrewAI release"},
plan_step_number=1,
plan_step_description="First",
error="No results",
)
)
_emit_event(
PlanStepCompletedEvent(
agent_role="Agent",
step_number=1,
step_description="First",
success=True,
result="Recovered with another source",
)
)
finally:
app._unsubscribe()
assert app._plan_step_status == {
1: "done",
2: "pending",
3: "pending",
}
def test_tool_event_step_metadata_is_stored_in_activity_log() -> None:
app = _app_with_plan()
app._subscribe()
try:
_emit_event(
ToolUsageStartedEvent(
tool_name="search_the_internet_with_serper",
tool_args={"search_query": "CrewAI release"},
plan_step_number=2,
plan_step_description="Second",
)
)
now = datetime.now()
_emit_event(
ToolUsageFinishedEvent(
tool_name="search_the_internet_with_serper",
tool_args={"search_query": "CrewAI release"},
plan_step_number=2,
plan_step_description="Second",
started_at=now,
finished_at=now,
output="Found official source",
)
)
finally:
app._unsubscribe()
assert app._log_entries[0]["plan_step_number"] == 2
assert app._plan_step_status == {
1: "pending",
2: "pending",
3: "pending",
}
def test_starting_next_tool_does_not_infer_plan_step_progress() -> None:
app = _app_with_plan()
app._subscribe()
try:
_emit_event(
ToolUsageStartedEvent(
tool_name="search_the_internet_with_serper",
tool_args={"search_query": "CrewAI release"},
)
)
_emit_event(
ToolUsageErrorEvent(
tool_name="search_the_internet_with_serper",
tool_args={"search_query": "CrewAI release"},
error="No results",
)
)
_emit_event(
ToolUsageStartedEvent(
tool_name="read_website_content",
tool_args={"url": "https://example.com"},
)
)
finally:
app._unsubscribe()
assert app._log_entries[0]["status"] == "error"
assert app._log_entries[1]["status"] == "running"
assert app._plan_step_status == {
1: "pending",
2: "pending",
3: "pending",
}
@pytest.mark.asyncio
async def test_crew_done_does_not_mark_unfinished_tool_successful() -> None:
app = _app_with_plan()
async with app.run_test(size=(100, 40)) as pilot:
app._plan_step_status = {1: "failed", 2: "done", 3: "pending"}
app._log_entries = [
{
"tool_name": "search",
"status": "running",
"args": '{"query": "CrewAI"}',
"result": None,
"error": None,
"start_time": time.time() - 2,
"duration": None,
"task_idx": 1,
}
]
app._on_crew_done("final output")
await pilot.pause()
assert app._log_entries[0]["status"] == "timeout"
assert app._log_entries[0]["result"] is None
assert app._log_entries[0]["error"] == "No result received before crew completed"
assert app._plan_step_status == {1: "failed", 2: "done", 3: "done"}
def test_streamed_step_observation_updates_named_step_only() -> None:
app = _app_with_plan()
updated = app._try_parse_step_observation(
'{"step_completed_successfully":true,'
'"key_information_learned":"Step 2 succeeded with the official source."}'
)
assert updated is True
assert app._plan_step_status == {
1: "pending",
2: "done",
3: "pending",
}
def test_failed_streamed_step_observation_marks_named_step_failed() -> None:
app = _app_with_plan()
updated = app._try_parse_step_observation(
'{"step_completed_successfully":false,'
'"key_information_learned":"Step 2 failed because the tool failed."}'
)
assert updated is True
assert app._plan_step_status == {
1: "pending",
2: "failed",
3: "pending",
}
def test_streamed_goal_achieved_observation_collapses_remaining_steps_done() -> None:
app = _app_with_plan()
updated = app._try_parse_step_observation(
'{"step_number":2,'
'"step_completed_successfully":true,'
'"key_information_learned":"Goal is already satisfied.",'
'"goal_already_achieved":true}'
)
assert updated is True
assert app._plan_step_status == {
1: "done",
2: "done",
3: "done",
}
def test_task_completion_collapses_pending_plan_steps_but_preserves_failed() -> None:
app = _app_with_plan()
app._plan_step_status = {1: "failed", 2: "done", 3: "pending"}
app._collapse_plan_on_task_done()
assert app._plan_step_status == {1: "failed", 2: "done", 3: "done"}
def test_observation_failure_collapses_to_done_because_executor_continues() -> None:
app = _app_with_plan()
app._plan_step_status = {1: "done", 2: "active", 3: "pending"}
app._subscribe()
try:
future = crewai_event_bus.emit(
None,
StepObservationFailedEvent(
agent_role="Agent",
step_number=2,
step_description="Second",
error="observer timeout",
),
)
if future:
future.result(timeout=5)
finally:
app._unsubscribe()
assert app._plan_step_status == {
1: "done",
2: "done",
3: "pending",
}
def test_goal_achieved_event_collapses_remaining_steps_done() -> None:
app = _app_with_plan()
app._plan_step_status = {1: "done", 2: "active", 3: "pending"}
app._subscribe()
try:
future = crewai_event_bus.emit(
None,
GoalAchievedEarlyEvent(
agent_role="Agent",
step_number=2,
steps_completed=2,
steps_remaining=1,
),
)
if future:
future.result(timeout=5)
finally:
app._unsubscribe()
assert app._plan_step_status == {
1: "done",
2: "done",
3: "done",
}
def test_replan_event_keeps_old_plan_until_next_streamed_plan_replaces_it() -> None:
app = _app_with_plan()
app._subscribe()
try:
future = crewai_event_bus.emit(
None,
PlanReplanTriggeredEvent(
agent_role="Agent",
step_number=2,
replan_reason="Need updated sources",
replan_count=1,
completed_steps_preserved=1,
),
)
if future:
future.result(timeout=5)
finally:
app._unsubscribe()
assert app._plan is not None
assert app._plan_step_status == {1: "pending", 2: "pending", 3: "pending"}
assert app._awaiting_replan is True
app._try_parse_plan(
'{"plan":"Updated plan","steps":['
'{"step_number":1,"description":"Updated first"},'
'{"step_number":2,"description":"Updated second"}]}'
)
assert app._plan == {
"plan": "Updated plan",
"steps": [
{"step_number": 1, "description": "Updated first"},
{"step_number": 2, "description": "Updated second"},
],
}
assert app._plan_step_status == {1: "pending", 2: "pending"}
assert app._awaiting_replan is False
def test_plan_refinement_updates_descriptions_without_new_statuses() -> None:
app = _app_with_plan()
app._plan_step_status = {1: "done", 2: "active", 3: "pending"}
app._subscribe()
try:
future = crewai_event_bus.emit(
None,
PlanRefinementEvent(
agent_role="Agent",
step_number=2,
refined_step_count=1,
refinements=["Step 3: Write the final answer from verified facts"],
),
)
if future:
future.result(timeout=5)
finally:
app._unsubscribe()
assert app._plan_step_status == {
1: "done",
2: "done",
3: "pending",
}
assert app._plan["steps"][2]["description"] == (
"Write the final answer from verified facts"
)
def test_step_observation_json_is_hidden_from_streaming_text() -> None:
app = _app_with_plan()
assert (
app._strip_step_observation_json(
'Visible before {"step_completed_successfully":true,'
'"key_information_learned":"Step 2 succeeded."} visible after'
)
== "Visible before visible after"
)
@pytest.mark.asyncio
async def test_completed_run_keeps_activity_log_keyboard_navigation_active() -> None:
app = CrewRunApp()
async with app.run_test(size=(100, 40)) as pilot:
app._log_entries = [_log_entry("search"), _log_entry("scrape")]
app._on_crew_done("final output")
await pilot.pause()
assert app.focused is app.query_one("#log-panel")
await pilot.press("down", "enter")
await pilot.pause()
assert app._log_cursor == 1
assert app._log_expanded == {1}
await pilot.press("up")
await pilot.pause()
assert app._log_cursor == 0
class _FakeTask:
fingerprint = None
def __init__(self, task_id: str, name: str) -> None:
self.id = task_id
self.name = name
self.description = name
def test_async_task_completion_marks_the_right_sidebar_row() -> None:
"""Overlapping tasks: completing task 1 while task 2 runs must not
mark task 2 done, and starting task 2 must not mark task 1 done."""
from crewai.events.types.task_events import TaskCompletedEvent, TaskStartedEvent
from crewai.tasks.task_output import TaskOutput
app = CrewRunApp(total_tasks=2, task_names=["first", "second"])
app._subscribe()
try:
task1 = _FakeTask("id-1", "first")
task2 = _FakeTask("id-2", "second")
for task in (task1, task2):
future = crewai_event_bus.emit(
None, TaskStartedEvent(context=None, task=task)
)
if future:
future.result(timeout=5)
# Both started: neither prematurely done
assert app._task_statuses == {1: "active", 2: "active"}
future = crewai_event_bus.emit(
None,
TaskCompletedEvent(
output=TaskOutput(description="first", raw="done", agent="a"),
task=task1,
),
)
if future:
future.result(timeout=5)
assert app._task_statuses == {1: "done", 2: "active"}
finally:
app._unsubscribe()
def test_pop_task_state_falls_back_to_current_task() -> None:
app = CrewRunApp(total_tasks=2, task_names=["first", "second"])
app._current_task_idx = 2
app._current_task_desc = "second"
class _Evt:
task = None
task_name = "unknown"
state = app._pop_task_state(_Evt())
assert state["idx"] == 2
assert state["desc"] == "second"
def test_overlapping_task_logs_keep_their_own_state() -> None:
"""Task 1 completing after task 2 started must log its own description,
agent, and output — and must not steal or reset task 2's stream state."""
from crewai.events.types.task_events import TaskCompletedEvent, TaskStartedEvent
from crewai.tasks.task_output import TaskOutput
app = CrewRunApp(total_tasks=2, task_names=["first", "second"])
app._subscribe()
try:
task1 = _FakeTask("id-1", "first")
task2 = _FakeTask("id-2", "second")
for task in (task1, task2):
future = crewai_event_bus.emit(
None, TaskStartedEvent(context=None, task=task)
)
if future:
future.result(timeout=5)
# Task 2 is current and has streamed state in flight
app._task_full_output = "task two streaming output"
app._current_task_steps = [{"type": "llm", "summary": "thinking"}]
future = crewai_event_bus.emit(
None,
TaskCompletedEvent(
output=TaskOutput(
description="first", raw="task one result", agent="a1"
),
task=task1,
),
)
if future:
future.result(timeout=5)
# Task 1's entry carries its own identity and output
entry1 = app._task_logs[-1]
assert entry1["idx"] == 1
assert entry1["desc"] == "first"
assert entry1["output"] == "task one result"
assert entry1["steps"] == []
# Task 2's in-flight stream state was not consumed or reset
assert app._task_full_output == "task two streaming output"
assert app._current_task_steps == [{"type": "llm", "summary": "thinking"}]
future = crewai_event_bus.emit(
None,
TaskCompletedEvent(
output=TaskOutput(
description="second", raw="task two result", agent="a2"
),
task=task2,
),
)
if future:
future.result(timeout=5)
entry2 = app._task_logs[-1]
assert entry2["idx"] == 2
assert entry2["desc"] == "second"
assert entry2["output"] == "task two streaming output"
assert any(step.get("summary") == "thinking" for step in entry2["steps"])
finally:
app._unsubscribe()