test: fix llama converter tests to remove skip_external_api

docs: update docxsearchtool.mdx (#2767 )
- add `docx2txt` as a dependency requirement for the tool
2026-10-20 08:55:05 +02:00 · 2025-05-06 22:22:41 -03:00 · 2025-05-06 17:14:05 -04:00 · 2025-05-06 15:07:57 -04:00 · 2025-05-06 09:55:05 -04:00 · 2025-05-06 09:27:14 -04:00
31 changed files with 14259 additions and 1088 deletions
@@ -31,4 +31,4 @@ jobs:
        run: uv sync --dev --all-extras

      - name: Run tests
-        run: uv run pytest tests -vv
+        run: uv run pytest --block-network --timeout=60 -vv
@@ -27,23 +27,19 @@ Large Language Models (LLMs) are the core intelligence behind CrewAI agents. The
  </Card>
 </CardGroup>

-## Setting Up Your LLM
+## Setting up your LLM

-There are three ways to configure LLMs in CrewAI. Choose the method that best fits your workflow:
+There are different places in CrewAI code where you can specify the model to use. Once you specify the model you are using, you will need to provide the configuration (like an API key) for each of the model providers you use. See the [provider configuration examples](#provider-configuration-examples) section for your provider.

 <Tabs>
  <Tab title="1. Environment Variables">
-    The simplest way to get started. Set these variables in your environment:
+    The simplest way to get started. Set the model in your environment directly, through an `.env` file or in your app code. If you used `crewai create` to bootstrap your project, it will be set already.

-    ```bash
-    # Required: Your API key for authentication
-    OPENAI_API_KEY=<your-api-key>
+    ```bash .env
+    MODEL=model-id  # e.g. gpt-4o, gemini-2.0-flash, claude-3-sonnet-...

-    # Optional: Default model selection
-    OPENAI_MODEL_NAME=gpt-4o-mini  # Default if not set
-
-    # Optional: Organization ID (if applicable)
-    OPENAI_ORGANIZATION_ID=<your-org-id>
+    # Be sure to set your API keys here too. See the Provider
+    # section below.
    ```

    <Warning>
@@ -53,13 +49,13 @@ There are three ways to configure LLMs in CrewAI. Choose the method that best fi
  <Tab title="2. YAML Configuration">
    Create a YAML file to define your agent configurations. This method is great for version control and team collaboration:

-    ```yaml
+    ```yaml agents.yaml {6}
    researcher:
        role: Research Specialist
        goal: Conduct comprehensive research and analysis
        backstory: A dedicated research professional with years of experience
        verbose: true
-        llm: openai/gpt-4o-mini # your model here
+        llm: provider/model-id  # e.g. openai/gpt-4o, google/gemini-2.0-flash, anthropic/claude...
        # (see provider configuration examples below for more)
    ```

@@ -74,23 +70,23 @@ There are three ways to configure LLMs in CrewAI. Choose the method that best fi
  <Tab title="3. Direct Code">
    For maximum flexibility, configure LLMs directly in your Python code:

-    ```python
+    ```python {4,8}
    from crewai import LLM

    # Basic configuration
-    llm = LLM(model="gpt-4")
+    llm = LLM(model="model-id-here")  # gpt-4o, gemini-2.0-flash, anthropic/claude...

    # Advanced configuration with detailed parameters
    llm = LLM(
-        model="gpt-4o-mini",
+        model="model-id-here",  # gpt-4o, gemini-2.0-flash, anthropic/claude...
        temperature=0.7,        # Higher for more creative outputs
-        timeout=120,           # Seconds to wait for response
-        max_tokens=4000,       # Maximum length of response
-        top_p=0.9,            # Nucleus sampling parameter
-        frequency_penalty=0.1, # Reduce repetition
-        presence_penalty=0.1,  # Encourage topic diversity
+        timeout=120,            # Seconds to wait for response
+        max_tokens=4000,        # Maximum length of response
+        top_p=0.9,              # Nucleus sampling parameter
+        frequency_penalty=0.1 , # Reduce repetition
+        presence_penalty=0.1,   # Encourage topic diversity
        response_format={"type": "json"},  # For structured outputs
-        seed=42               # For reproducible results
+        seed=42                 # For reproducible results
    )
    ```

@@ -110,7 +106,6 @@ There are three ways to configure LLMs in CrewAI. Choose the method that best fi

 ## Provider Configuration Examples

-
 CrewAI supports a multitude of LLM providers, each offering unique features, authentication methods, and model capabilities.
 In this section, you'll find detailed examples that help you select, configure, and optimize the LLM that best fits your project's needs.

@@ -383,7 +378,7 @@ In this section, you'll find detailed examples that help you select, configure,
    | microsoft/phi-3-medium-4k-instruct                                  | 4,096 tokens   | Lightweight, state-of-the-art open LLM with strong math and logical reasoning skills. |
    | microsoft/phi-3-medium-128k-instruct                                | 128K tokens    | Lightweight, state-of-the-art open LLM with strong math and logical reasoning skills. |
    | microsoft/phi-3.5-mini-instruct                                     | 128K tokens    | Lightweight multilingual LLM powering AI applications in latency bound, memory/compute constrained environments |
-    | microsoft/phi-3.5-moe-instruct                                      | 128K tokens    | Advanced LLM based on Mixture of Experts architecure to deliver compute efficient content generation |
+    | microsoft/phi-3.5-moe-instruct                                      | 128K tokens    | Advanced LLM based on Mixture of Experts architecture to deliver compute efficient content generation |
    | microsoft/kosmos-2                                                  | 1,024 tokens   | Groundbreaking multimodal model designed to understand and reason about visual elements in images. |
    | microsoft/phi-3-vision-128k-instruct                               | 128k tokens    | Cutting-edge open multimodal model exceling in high-quality reasoning from images. |
    | microsoft/phi-3.5-vision-instruct                                  | 128k tokens    | Cutting-edge open multimodal model exceling in high-quality reasoning from images. |
@@ -407,19 +402,19 @@ In this section, you'll find detailed examples that help you select, configure,
  </Accordion>

  <Accordion title="Local NVIDIA NIM Deployed using WSL2">
-    
-    NVIDIA NIM enables you to run powerful LLMs locally on your Windows machine using WSL2 (Windows Subsystem for Linux). 
-    This approach allows you to leverage your NVIDIA GPU for private, secure, and cost-effective AI inference without relying on cloud services. 
+
+    NVIDIA NIM enables you to run powerful LLMs locally on your Windows machine using WSL2 (Windows Subsystem for Linux).
+    This approach allows you to leverage your NVIDIA GPU for private, secure, and cost-effective AI inference without relying on cloud services.
    Perfect for development, testing, or production scenarios where data privacy or offline capabilities are required.
-    
+
    Here is a step-by-step guide to setting up a local NVIDIA NIM model:
-    
+
    1. Follow installation instructions from [NVIDIA Website](https://docs.nvidia.com/nim/wsl2/latest/getting-started.html)

    2. Install the local model. For Llama 3.1-8b follow [instructions](https://build.nvidia.com/meta/llama-3_1-8b-instruct/deploy)

    3. Configure your crewai local models:
-  
+
    ```python Code
    from crewai.llm import LLM

@@ -441,7 +436,7 @@ In this section, you'll find detailed examples that help you select, configure,
                config=self.agents_config['researcher'], # type: ignore[index]
                llm=local_nvidia_nim_llm
            )
-        
+
        # ...
    ```
  </Accordion>
@@ -637,19 +632,19 @@ CrewAI supports streaming responses from LLMs, allowing your application to rece

    When streaming is enabled, responses are delivered in chunks as they're generated, creating a more responsive user experience.
  </Tab>
-  
+
  <Tab title="Event Handling">
    CrewAI emits events for each chunk received during streaming:
-    
+
    ```python
    from crewai import LLM
    from crewai.utilities.events import EventHandler, LLMStreamChunkEvent
-    
+
    class MyEventHandler(EventHandler):
        def on_llm_stream_chunk(self, event: LLMStreamChunkEvent):
            # Process each chunk as it arrives
            print(f"Received chunk: {event.chunk}")
-    
+
    # Register the event handler
    from crewai.utilities.events import crewai_event_bus
    crewai_event_bus.register_handler(MyEventHandler())
@@ -785,7 +780,7 @@ Learn how to get the most out of your LLM configuration:
    <Tip>
      Use larger context models for extensive tasks
    </Tip>
-    
+
    ```python
    # Large context model
    llm = LLM(model="openai/gpt-4o")  # 128K tokens
@@ -71,6 +71,10 @@ If you haven't installed `uv` yet, follow **step 1** to quickly get it set up on
            ```
          </Warning>

+          <Warning>
+            If you encounter the `chroma-hnswlib==0.7.6` build error (`fatal error C1083: Cannot open include file: 'float.h'`) on Windows, install (Visual Studio Build Tools)[https://visualstudio.microsoft.com/downloads/] with *Desktop development with C++*.
+          </Warning>
+
      - To verify that `crewai` is installed, run:
        ```shell
        uv tool list
@@ -22,7 +22,7 @@ streamlining the process of finding specific information within large document c
 Install the crewai_tools package by running the following command in your terminal:

 ```shell
-pip install 'crewai[tools]'
+uv pip install docx2txt 'crewai[tools]'
 ```

 ## Example
@@ -76,4 +76,4 @@ tool = DOCXSearchTool(
        ),
    )
 )
-```
+```
@@ -143,12 +143,30 @@ config = {
        "config": {
            "model": "text-embedding-ada-002"
        }
+    },
+    "vectordb": {
+        "provider": "elasticsearch",
+        "config": {
+            "collection_name": "my-collection",
+            "cloud_id": "deployment-name:xxxx",
+            "api_key": "your-key",
+            "verify_certs": False
+        }
+    },
+    "chunker": {
+        "chunk_size": 400,
+        "chunk_overlap": 100,
+        "length_function": "len",
+        "min_chunk_size": 0
    }
 }

 rag_tool = RagTool(config=config, summarize=True)
 ```

-## Conclusion
+The internal RAG tool utilizes the Embedchain adapter, allowing you to pass any configuration options that are supported by Embedchain.
+You can refer to the [Embedchain documentation](https://docs.embedchain.ai/components/introduction) for details.
+Make sure to review the configuration options available in the .yaml file.

+## Conclusion
 The `RagTool` provides a powerful way to create and query knowledge bases from various data sources. By leveraging Retrieval-Augmented Generation, it enables agents to access and retrieve relevant information efficiently, enhancing their ability to provide accurate and contextually appropriate responses.
@@ -11,7 +11,7 @@ dependencies = [
    # Core Dependencies
    "pydantic>=2.4.2",
    "openai>=1.13.3",
-    "litellm==1.67.1",
+    "litellm==1.68.0",
    "instructor>=1.3.3",
    # Text Processing
    "pdfplumber>=0.11.4",
@@ -85,6 +85,8 @@ dev-dependencies = [
    "pytest-asyncio>=0.23.7",
    "pytest-subprocess>=1.5.2",
    "pytest-recording>=0.13.2",
+    "pytest-randomly>=3.16.0",
+    "pytest-timeout>=2.3.1",
 ]

 [project.scripts]
@@ -4,7 +4,7 @@ import click


 # Be mindful about changing this.
-# on some enviorments we don't use this command but instead uv sync directly
+# on some environments we don't use this command but instead uv sync directly
 # so if you expect this to support more things you will need to replicate it there
 # ask @joaomdmoura if you are unsure
 def install_crew(proxy_options: list[str]) -> None:
@@ -2,7 +2,7 @@ import subprocess

 import click

-from crewai.cli.utils import get_crew
+from crewai.cli.utils import get_crews


 def reset_memories_command(
@@ -26,35 +26,47 @@ def reset_memories_command(
    """

    try:
-        crew = get_crew()
-        if not crew:
-            raise ValueError("No crew found.")
-        if all:
-            crew.reset_memories(command_type="all")
-            click.echo("All memories have been reset.")
-            return
-
-        if not any([long, short, entity, kickoff_outputs, knowledge]):
+        if not any([long, short, entity, kickoff_outputs, knowledge, all]):
            click.echo(
                "No memory type specified. Please specify at least one type to reset."
            )
            return

-        if long:
-            crew.reset_memories(command_type="long")
-            click.echo("Long term memory has been reset.")
-        if short:
-            crew.reset_memories(command_type="short")
-            click.echo("Short term memory has been reset.")
-        if entity:
-            crew.reset_memories(command_type="entity")
-            click.echo("Entity memory has been reset.")
-        if kickoff_outputs:
-            crew.reset_memories(command_type="kickoff_outputs")
-            click.echo("Latest Kickoff outputs stored has been reset.")
-        if knowledge:
-            crew.reset_memories(command_type="knowledge")
-            click.echo("Knowledge has been reset.")
+        crews = get_crews()
+        if not crews:
+            raise ValueError("No crew found.")
+        for crew in crews:
+            if all:
+                crew.reset_memories(command_type="all")
+                click.echo(
+                    f"[Crew ({crew.name if crew.name else crew.id})] Reset memories command has been completed."
+                )
+                continue
+            if long:
+                crew.reset_memories(command_type="long")
+                click.echo(
+                    f"[Crew ({crew.name if crew.name else crew.id})] Long term memory has been reset."
+                )
+            if short:
+                crew.reset_memories(command_type="short")
+                click.echo(
+                    f"[Crew ({crew.name if crew.name else crew.id})] Short term memory has been reset."
+                )
+            if entity:
+                crew.reset_memories(command_type="entity")
+                click.echo(
+                    f"[Crew ({crew.name if crew.name else crew.id})] Entity memory has been reset."
+                )
+            if kickoff_outputs:
+                crew.reset_memories(command_type="kickoff_outputs")
+                click.echo(
+                    f"[Crew ({crew.name if crew.name else crew.id})] Latest Kickoff outputs stored has been reset."
+                )
+            if knowledge:
+                crew.reset_memories(command_type="knowledge")
+                click.echo(
+                    f"[Crew ({crew.name if crew.name else crew.id})] Knowledge has been reset."
+                )

    except subprocess.CalledProcessError as e:
        click.echo(f"An error occurred while resetting the memories: {e}", err=True)
@@ -2,7 +2,8 @@ import os
 import shutil
 import sys
 from functools import reduce
-from typing import Any, Dict, List
+from inspect import isfunction, ismethod
+from typing import Any, Dict, List, get_type_hints

 import click
 import tomli
@@ -10,6 +11,7 @@ from rich.console import Console

 from crewai.cli.constants import ENV_VARS
 from crewai.crew import Crew
+from crewai.flow import Flow

 if sys.version_info >= (3, 11):
    import tomllib
@@ -250,11 +252,11 @@ def write_env_file(folder_path, env_vars):
            file.write(f"{key}={value}\n")


-def get_crew(crew_path: str = "crew.py", require: bool = False) -> Crew | None:
-    """Get the crew instance from the crew.py file."""
+def get_crews(crew_path: str = "crew.py", require: bool = False) -> list[Crew]:
+    """Get the crew instances from the a file."""
+    crew_instances = []
    try:
        import importlib.util
-        import os

        for root, _, files in os.walk("."):
            if crew_path in files:
@@ -271,12 +273,10 @@ def get_crew(crew_path: str = "crew.py", require: bool = False) -> Crew | None:
                        spec.loader.exec_module(module)

                        for attr_name in dir(module):
-                            attr = getattr(module, attr_name)
-                            try:
-                                if callable(attr) and hasattr(attr, "crew"):
-                                    crew_instance = attr().crew()
-                                    return crew_instance
+                            module_attr = getattr(module, attr_name)

+                            try:
+                                crew_instances.extend(fetch_crews(module_attr))
                            except Exception as e:
                                print(f"Error processing attribute {attr_name}: {e}")
                                continue
@@ -286,7 +286,6 @@ def get_crew(crew_path: str = "crew.py", require: bool = False) -> Crew | None:
                        import traceback

                        print(f"Traceback: {traceback.format_exc()}")
-
                except (ImportError, AttributeError) as e:
                    if require:
                        console.print(
@@ -300,7 +299,6 @@ def get_crew(crew_path: str = "crew.py", require: bool = False) -> Crew | None:
        if require:
            console.print("No valid Crew instance found in crew.py", style="bold red")
            raise SystemExit
-        return None

    except Exception as e:
        if require:
@@ -308,4 +306,36 @@ def get_crew(crew_path: str = "crew.py", require: bool = False) -> Crew | None:
                f"Unexpected error while loading crew: {str(e)}", style="bold red"
            )
            raise SystemExit
+    return crew_instances
+
+
+def get_crew_instance(module_attr) -> Crew | None:
+    if (
+        callable(module_attr)
+        and hasattr(module_attr, "is_crew_class")
+        and module_attr.is_crew_class
+    ):
+        return module_attr().crew()
+    if (ismethod(module_attr) or isfunction(module_attr)) and get_type_hints(
+        module_attr
+    ).get("return") is Crew:
+        return module_attr()
+    elif isinstance(module_attr, Crew):
+        return module_attr
+    else:
        return None
+
+
+def fetch_crews(module_attr) -> list[Crew]:
+    crew_instances: list[Crew] = []
+
+    if crew_instance := get_crew_instance(module_attr):
+        crew_instances.append(crew_instance)
+
+    if isinstance(module_attr, type) and issubclass(module_attr, Flow):
+        instance = module_attr()
+        for attr_name in dir(instance):
+            attr = getattr(instance, attr_name)
+            if crew_instance := get_crew_instance(attr):
+                crew_instances.append(crew_instance)
+    return crew_instances
@@ -6,7 +6,17 @@ import warnings
 from concurrent.futures import Future
 from copy import copy as shallow_copy
 from hashlib import md5
-from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Union, cast
+from typing import (
+    Any,
+    Callable,
+    Dict,
+    List,
+    Optional,
+    Set,
+    Tuple,
+    Union,
+    cast,
+)

 from pydantic import (
    UUID4,
@@ -24,6 +34,7 @@ from crewai.agent import Agent
 from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.agents.cache import CacheHandler
 from crewai.crews.crew_output import CrewOutput
+from crewai.flow.flow_trackable import FlowTrackable
 from crewai.knowledge.knowledge import Knowledge
 from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
 from crewai.llm import LLM, BaseLLM
@@ -69,7 +80,7 @@ from crewai.utilities.training_handler import CrewTrainingHandler
 warnings.filterwarnings("ignore", category=SyntaxWarning, module="pysbd")


-class Crew(BaseModel):
+class Crew(FlowTrackable, BaseModel):
    """
    Represents a group of agents, defining how they should collaborate and the tasks they should perform.

@@ -304,7 +315,9 @@ class Crew(BaseModel):
        """Initialize private memory attributes."""
        self._external_memory = (
            # External memory doesn’t support a default value since it was designed to be managed entirely externally
-            self.external_memory.set_crew(self) if self.external_memory else None
+            self.external_memory.set_crew(self)
+            if self.external_memory
+            else None
        )

        self._long_term_memory = self.long_term_memory
@@ -333,6 +346,7 @@ class Crew(BaseModel):
                        embedder=self.embedder,
                        collection_name="crew",
                    )
+                    self.knowledge.add_sources()

            except Exception as e:
                self._logger.log(
@@ -1369,8 +1383,6 @@ class Crew(BaseModel):
            else:
                self._reset_specific_memory(command_type)

-            self._logger.log("info", f"{command_type} memory has been reset")
-
        except Exception as e:
            error_msg = f"Failed to reset {command_type} memory: {str(e)}"
            self._logger.log("error", error_msg)
@@ -1391,8 +1403,14 @@ class Crew(BaseModel):
            if system is not None:
                try:
                    system.reset()
+                    self._logger.log(
+                        "info",
+                        f"[Crew ({self.name if self.name else self.id})] {name} memory has been reset",
+                    )
                except Exception as e:
-                    raise RuntimeError(f"Failed to reset {name} memory") from e
+                    raise RuntimeError(
+                        f"[Crew ({self.name if self.name else self.id})] Failed to reset {name} memory: {str(e)}"
+                    ) from e

    def _reset_specific_memory(self, memory_type: str) -> None:
        """Reset a specific memory system.
@@ -1421,5 +1439,11 @@ class Crew(BaseModel):

        try:
            memory_system.reset()
+            self._logger.log(
+                "info",
+                f"[Crew ({self.name if self.name else self.id})] {name} memory has been reset",
+            )
        except Exception as e:
-            raise RuntimeError(f"Failed to reset {name} memory") from e
+            raise RuntimeError(
+                f"[Crew ({self.name if self.name else self.id})] Failed to reset {name} memory: {str(e)}"
+            ) from e
@@ -0,0 +1,44 @@
+import inspect
+from typing import Optional
+
+from pydantic import BaseModel, Field, InstanceOf, model_validator
+
+from crewai.flow import Flow
+
+
+class FlowTrackable(BaseModel):
+    """Mixin that tracks the Flow instance that instantiated the object, e.g. a
+    Flow instance that created a Crew or Agent.
+
+    Automatically finds and stores a reference to the parent Flow instance by
+    inspecting the call stack.
+    """
+
+    parent_flow: Optional[InstanceOf[Flow]] = Field(
+        default=None,
+        description="The parent flow of the instance, if it was created inside a flow.",
+    )
+
+    @model_validator(mode="after")
+    def _set_parent_flow(self, max_depth: int = 5) -> "FlowTrackable":
+        frame = inspect.currentframe()
+
+        try:
+            if frame is None:
+                return self
+
+            frame = frame.f_back
+            for _ in range(max_depth):
+                if frame is None:
+                    break
+
+                candidate = frame.f_locals.get("self")
+                if isinstance(candidate, Flow):
+                    self.parent_flow = candidate
+                    break
+
+                frame = frame.f_back
+        finally:
+            del frame
+
+        return self
@@ -41,7 +41,6 @@ class Knowledge(BaseModel):
            )
        self.sources = sources
        self.storage.initialize_knowledge_storage()
-        self._add_sources()

    def query(
        self, query: List[str], results_limit: int = 3, score_threshold: float = 0.35
@@ -63,7 +62,7 @@ class Knowledge(BaseModel):
        )
        return results

-    def _add_sources(self):
+    def add_sources(self):
        try:
            for source in self.sources:
                source.storage = self.storage
@@ -13,6 +13,7 @@ from crewai.agents.parser import (
    AgentFinish,
    OutputParserException,
 )
+from crewai.flow.flow_trackable import FlowTrackable
 from crewai.llm import LLM
 from crewai.tools.base_tool import BaseTool
 from crewai.tools.structured_tool import CrewStructuredTool
@@ -80,7 +81,7 @@ class LiteAgentOutput(BaseModel):
        return self.raw


-class LiteAgent(BaseModel):
+class LiteAgent(FlowTrackable, BaseModel):
    """
    A lightweight agent that can process messages and use tools.

@@ -162,7 +163,7 @@ class LiteAgent(BaseModel):
    _messages: List[Dict[str, str]] = PrivateAttr(default_factory=list)
    _iterations: int = PrivateAttr(default=0)
    _printer: Printer = PrivateAttr(default_factory=Printer)
-    
+
    @model_validator(mode="after")
    def setup_llm(self):
        """Set up the LLM and other components after initialization."""
@@ -2,6 +2,7 @@ from __future__ import annotations

 import asyncio
 import json
+import logging
 import os
 import platform
 import warnings
@@ -14,6 +15,8 @@ from crewai.telemetry.constants import (
    CREWAI_TELEMETRY_SERVICE_NAME,
 )

+logger = logging.getLogger(__name__)
+

@contextmanager
 def suppress_warnings():
@@ -28,7 +31,10 @@ from opentelemetry.exporter.otlp.proto.http.trace_exporter import (
 )
 from opentelemetry.sdk.resources import SERVICE_NAME, Resource  # noqa: E402
 from opentelemetry.sdk.trace import TracerProvider  # noqa: E402
-from opentelemetry.sdk.trace.export import BatchSpanProcessor  # noqa: E402
+from opentelemetry.sdk.trace.export import (  # noqa: E402
+    BatchSpanProcessor,
+    SpanExportResult,
+)
 from opentelemetry.trace import Span, Status, StatusCode  # noqa: E402

 if TYPE_CHECKING:
@@ -36,6 +42,15 @@ if TYPE_CHECKING:
    from crewai.task import Task


+class SafeOTLPSpanExporter(OTLPSpanExporter):
+    def export(self, spans) -> SpanExportResult:
+        try:
+            return super().export(spans)
+        except Exception as e:
+            logger.error(e)
+            return SpanExportResult.FAILURE
+
+
 class Telemetry:
    """A class to handle anonymous telemetry for the crewai package.

@@ -64,7 +79,7 @@ class Telemetry:
                self.provider = TracerProvider(resource=self.resource)

            processor = BatchSpanProcessor(
-                OTLPSpanExporter(
+                SafeOTLPSpanExporter(
                    endpoint=f"{CREWAI_TELEMETRY_BASE_URL}/v1/traces",
                    timeout=30,
                )
@@ -70,7 +70,12 @@ class CrewAIEventsBus:
        for event_type, handlers in self._handlers.items():
            if isinstance(event, event_type):
                for handler in handlers:
-                    handler(source, event)
+                    try:
+                        handler(source, event)
+                    except Exception as e:
+                        print(
+                            f"[EventBus Error] Handler '{handler.__name__}' failed for event '{event_type.__name__}': {e}"
+                        )

        self._signal.send(source, event=event)

@@ -16,7 +16,7 @@ interactions:
      answer MUST contain all the information requested in the following format: {\n  \"summary\":
      str,\n  \"confidence\": int\n}\n\nIMPORTANT: Ensure the final output does not
      include any code block markers like ```json or ```python."}, {"role": "user",
-      "content": "What is the population of Tokyo? Return your strucutred output in
+      "content": "What is the population of Tokyo? Return your structured output in
      JSON format with the following fields: summary, confidence"}], "model": "gpt-4o-mini",
      "stop": []}'
    headers:
@@ -0,0 +1,221 @@
+interactions:
+- request:
+    body: null
+    headers: {}
+    method: GET
+    uri: https://pypi.org/pypi/agentops/json
+  response:
+    body:
+      string: '{"info":{"author":null,"author_email":"Alex Reibman <areibman@gmail.com>,
+        Shawn Qiu <siyangqiu@gmail.com>, Braelyn Boynton <bboynton97@gmail.com>, Howard
+        Gil <howardbgil@gmail.com>, Constantin Teodorescu <teocns@gmail.com>, Pratyush
+        Shukla <ps4534@nyu.edu>, Travis Dent <tcdent@gmail.com>, Dwij Patel <dwijpatel1704@gmail.com>","bugtrack_url":null,"classifiers":["License
+        :: OSI Approved :: MIT License","Operating System :: OS Independent","Programming
+        Language :: Python :: 3","Programming Language :: Python :: 3.10","Programming
+        Language :: Python :: 3.11","Programming Language :: Python :: 3.12","Programming
+        Language :: Python :: 3.13","Programming Language :: Python :: 3.9"],"description":"","description_content_type":null,"docs_url":null,"download_url":null,"downloads":{"last_day":-1,"last_month":-1,"last_week":-1},"dynamic":null,"home_page":null,"keywords":null,"license":null,"license_expression":null,"license_files":["LICENSE"],"maintainer":null,"maintainer_email":null,"name":"agentops","package_url":"https://pypi.org/project/agentops/","platform":null,"project_url":"https://pypi.org/project/agentops/","project_urls":{"Homepage":"https://github.com/AgentOps-AI/agentops","Issues":"https://github.com/AgentOps-AI/agentops/issues"},"provides_extra":null,"release_url":"https://pypi.org/project/agentops/0.4.9/","requires_dist":["opentelemetry-api==1.29.0;
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+        python_version < \"3.10\"","opentelemetry-exporter-otlp-proto-http>1.29.0;
+        python_version >= \"3.10\"","opentelemetry-instrumentation==0.50b0; python_version
+        < \"3.10\"","opentelemetry-instrumentation>0.50b0; python_version >= \"3.10\"","opentelemetry-sdk==1.29.0;
+        python_version < \"3.10\"","opentelemetry-sdk>1.29.0; python_version >= \"3.10\"","opentelemetry-semantic-conventions==0.50b0;
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+version: 1
@@ -18,6 +18,7 @@ from crewai.cli.cli import (
    train,
    version,
 )
+from crewai.crew import Crew


@pytest.fixture
@@ -55,81 +56,133 @@ def test_train_invalid_string_iterations(train_crew, runner):
    )


-@mock.patch("crewai.cli.reset_memories_command.get_crew")
-def test_reset_all_memories(mock_get_crew, runner):
-    mock_crew = mock.Mock()
-    mock_get_crew.return_value = mock_crew
+@pytest.fixture
+def mock_crew():
+    _mock = mock.Mock(spec=Crew, name="test_crew")
+    _mock.name = "test_crew"
+    return _mock
+
+
+@pytest.fixture
+def mock_get_crews(mock_crew):
+    with mock.patch(
+        "crewai.cli.reset_memories_command.get_crews", return_value=[mock_crew]
+    ) as mock_get_crew:
+        yield mock_get_crew
+
+
+def test_reset_all_memories(mock_get_crews, runner):
    result = runner.invoke(reset_memories, ["-a"])

-    mock_crew.reset_memories.assert_called_once_with(command_type="all")
-    assert result.output == "All memories have been reset.\n"
+    call_count = 0
+    for crew in mock_get_crews.return_value:
+        crew.reset_memories.assert_called_once_with(command_type="all")
+        assert (
+            f"[Crew ({crew.name})] Reset memories command has been completed."
+            in result.output
+        )
+        call_count += 1
+
+    assert call_count == 1, "reset_memories should have been called once"


-@mock.patch("crewai.cli.reset_memories_command.get_crew")
-def test_reset_short_term_memories(mock_get_crew, runner):
-    mock_crew = mock.Mock()
-    mock_get_crew.return_value = mock_crew
+def test_reset_short_term_memories(mock_get_crews, runner):
    result = runner.invoke(reset_memories, ["-s"])
+    call_count = 0
+    for crew in mock_get_crews.return_value:
+        crew.reset_memories.assert_called_once_with(command_type="short")
+        assert (
+            f"[Crew ({crew.name})] Short term memory has been reset." in result.output
+        )
+        call_count += 1

-    mock_crew.reset_memories.assert_called_once_with(command_type="short")
-    assert result.output == "Short term memory has been reset.\n"
+    assert call_count == 1, "reset_memories should have been called once"


-@mock.patch("crewai.cli.reset_memories_command.get_crew")
-def test_reset_entity_memories(mock_get_crew, runner):
-    mock_crew = mock.Mock()
-    mock_get_crew.return_value = mock_crew
+def test_reset_entity_memories(mock_get_crews, runner):
    result = runner.invoke(reset_memories, ["-e"])
+    call_count = 0
+    for crew in mock_get_crews.return_value:
+        crew.reset_memories.assert_called_once_with(command_type="entity")
+        assert f"[Crew ({crew.name})] Entity memory has been reset." in result.output
+        call_count += 1

-    mock_crew.reset_memories.assert_called_once_with(command_type="entity")
-    assert result.output == "Entity memory has been reset.\n"
+    assert call_count == 1, "reset_memories should have been called once"


-@mock.patch("crewai.cli.reset_memories_command.get_crew")
-def test_reset_long_term_memories(mock_get_crew, runner):
-    mock_crew = mock.Mock()
-    mock_get_crew.return_value = mock_crew
+def test_reset_long_term_memories(mock_get_crews, runner):
    result = runner.invoke(reset_memories, ["-l"])
+    call_count = 0
+    for crew in mock_get_crews.return_value:
+        crew.reset_memories.assert_called_once_with(command_type="long")
+        assert f"[Crew ({crew.name})] Long term memory has been reset." in result.output
+        call_count += 1

-    mock_crew.reset_memories.assert_called_once_with(command_type="long")
-    assert result.output == "Long term memory has been reset.\n"
+    assert call_count == 1, "reset_memories should have been called once"


-@mock.patch("crewai.cli.reset_memories_command.get_crew")
-def test_reset_kickoff_outputs(mock_get_crew, runner):
-    mock_crew = mock.Mock()
-    mock_get_crew.return_value = mock_crew
+def test_reset_kickoff_outputs(mock_get_crews, runner):
    result = runner.invoke(reset_memories, ["-k"])
+    call_count = 0
+    for crew in mock_get_crews.return_value:
+        crew.reset_memories.assert_called_once_with(command_type="kickoff_outputs")
+        assert (
+            f"[Crew ({crew.name})] Latest Kickoff outputs stored has been reset."
+            in result.output
+        )
+        call_count += 1

-    mock_crew.reset_memories.assert_called_once_with(command_type="kickoff_outputs")
-    assert result.output == "Latest Kickoff outputs stored has been reset.\n"
+    assert call_count == 1, "reset_memories should have been called once"


-@mock.patch("crewai.cli.reset_memories_command.get_crew")
-def test_reset_multiple_memory_flags(mock_get_crew, runner):
-    mock_crew = mock.Mock()
-    mock_get_crew.return_value = mock_crew
+def test_reset_multiple_memory_flags(mock_get_crews, runner):
    result = runner.invoke(reset_memories, ["-s", "-l"])
+    call_count = 0
+    for crew in mock_get_crews.return_value:
+        crew.reset_memories.assert_has_calls(
+            [mock.call(command_type="long"), mock.call(command_type="short")]
+        )
+        assert (
+            f"[Crew ({crew.name})] Long term memory has been reset.\n"
+            f"[Crew ({crew.name})] Short term memory has been reset.\n" in result.output
+        )
+        call_count += 1

-    # Check that reset_memories was called twice with the correct arguments
-    assert mock_crew.reset_memories.call_count == 2
-    mock_crew.reset_memories.assert_has_calls(
-        [mock.call(command_type="long"), mock.call(command_type="short")]
-    )
-    assert (
-        result.output
-        == "Long term memory has been reset.\nShort term memory has been reset.\n"
-    )
+    assert call_count == 1, "reset_memories should have been called once"


-@mock.patch("crewai.cli.reset_memories_command.get_crew")
-def test_reset_knowledge(mock_get_crew, runner):
-    mock_crew = mock.Mock()
-    mock_get_crew.return_value = mock_crew
+def test_reset_knowledge(mock_get_crews, runner):
+    result = runner.invoke(reset_memories, ["--knowledge"])
+    call_count = 0
+    for crew in mock_get_crews.return_value:
+        crew.reset_memories.assert_called_once_with(command_type="knowledge")
+        assert f"[Crew ({crew.name})] Knowledge has been reset." in result.output
+        call_count += 1
+
+    assert call_count == 1, "reset_memories should have been called once"
+
+
+def test_reset_memory_from_many_crews(mock_get_crews, runner):
+
+    crews = []
+    for crew_id in ["id-1234", "id-5678"]:
+        mock_crew = mock.Mock(spec=Crew)
+        mock_crew.name = None
+        mock_crew.id = crew_id
+        crews.append(mock_crew)
+
+    mock_get_crews.return_value = crews
+
+    # Run the command
    result = runner.invoke(reset_memories, ["--knowledge"])

-    mock_crew.reset_memories.assert_called_once_with(command_type="knowledge")
-    assert result.output == "Knowledge has been reset.\n"
+    call_count = 0
+    for crew in crews:
+        call_count += 1
+        crew.reset_memories.assert_called_once_with(command_type="knowledge")
+        assert f"[Crew ({crew.id})] Knowledge has been reset." in result.output
+
+    assert call_count == 2, "reset_memories should have been called twice"


 def test_reset_no_memory_flags(runner):
@@ -3,12 +3,13 @@ import tempfile
 import unittest
 import unittest.mock
 from contextlib import contextmanager
-from io import StringIO
 from unittest import mock
 from unittest.mock import MagicMock, patch

+import pytest
 from pytest import raises

+from crewai.cli.authentication.utils import TokenManager
 from crewai.cli.tools.main import ToolCommand


@@ -23,17 +24,20 @@ def in_temp_dir():
            os.chdir(original_dir)


-@patch("crewai.cli.tools.main.subprocess.run")
-def test_create_success(mock_subprocess):
-    with in_temp_dir():
-        tool_command = ToolCommand()
+@pytest.fixture
+def tool_command():
+    TokenManager().save_tokens("test-token", 36000)
+    tool_command = ToolCommand()
+    with patch.object(tool_command, "login"):
+        yield tool_command

-        with (
-            patch.object(tool_command, "login") as mock_login,
-            patch("sys.stdout", new=StringIO()) as fake_out,
-        ):
-            tool_command.create("test-tool")
-            output = fake_out.getvalue()
+
+@patch("crewai.cli.tools.main.subprocess.run")
+def test_create_success(mock_subprocess, capsys, tool_command):
+    with in_temp_dir():
+        tool_command.create("test-tool")
+        output = capsys.readouterr().out
+        assert "Creating custom tool test_tool..." in output

        assert os.path.isdir("test_tool")
        assert os.path.isfile(os.path.join("test_tool", "README.md"))
@@ -47,15 +51,12 @@ def test_create_success(mock_subprocess):
            content = f.read()
            assert "class TestTool" in content

-        mock_login.assert_called_once()
        mock_subprocess.assert_called_once_with(["git", "init"], check=True)

-        assert "Creating custom tool test_tool..." in output
-

@patch("crewai.cli.tools.main.subprocess.run")
@patch("crewai.cli.plus_api.PlusAPI.get_tool")
-def test_install_success(mock_get, mock_subprocess_run):
+def test_install_success(mock_get, mock_subprocess_run, capsys, tool_command):
    mock_get_response = MagicMock()
    mock_get_response.status_code = 200
    mock_get_response.json.return_value = {
@@ -65,11 +66,9 @@ def test_install_success(mock_get, mock_subprocess_run):
    mock_get.return_value = mock_get_response
    mock_subprocess_run.return_value = MagicMock(stderr=None)

-    tool_command = ToolCommand()
-
-    with patch("sys.stdout", new=StringIO()) as fake_out:
-        tool_command.install("sample-tool")
-        output = fake_out.getvalue()
+    tool_command.install("sample-tool")
+    output = capsys.readouterr().out
+    assert "Successfully installed sample-tool" in output

    mock_get.assert_has_calls([mock.call("sample-tool"), mock.call().json()])
    mock_subprocess_run.assert_any_call(
@@ -86,54 +85,42 @@ def test_install_success(mock_get, mock_subprocess_run):
        env=unittest.mock.ANY,
    )

-    assert "Successfully installed sample-tool" in output
-

@patch("crewai.cli.plus_api.PlusAPI.get_tool")
-def test_install_tool_not_found(mock_get):
+def test_install_tool_not_found(mock_get, capsys, tool_command):
    mock_get_response = MagicMock()
    mock_get_response.status_code = 404
    mock_get.return_value = mock_get_response

-    tool_command = ToolCommand()
-
-    with patch("sys.stdout", new=StringIO()) as fake_out:
-        try:
-            tool_command.install("non-existent-tool")
-        except SystemExit:
-            pass
-        output = fake_out.getvalue()
+    with raises(SystemExit):
+        tool_command.install("non-existent-tool")
+    output = capsys.readouterr().out
+    assert "No tool found with this name" in output

    mock_get.assert_called_once_with("non-existent-tool")
-    assert "No tool found with this name" in output


@patch("crewai.cli.plus_api.PlusAPI.get_tool")
-def test_install_api_error(mock_get):
+def test_install_api_error(mock_get, capsys, tool_command):
    mock_get_response = MagicMock()
    mock_get_response.status_code = 500
    mock_get.return_value = mock_get_response

-    tool_command = ToolCommand()
-
-    with patch("sys.stdout", new=StringIO()) as fake_out:
-        try:
-            tool_command.install("error-tool")
-        except SystemExit:
-            pass
-        output = fake_out.getvalue()
+    with raises(SystemExit):
+        tool_command.install("error-tool")
+    output = capsys.readouterr().out
+    assert "Failed to get tool details" in output

    mock_get.assert_called_once_with("error-tool")
-    assert "Failed to get tool details" in output


@patch("crewai.cli.tools.main.git.Repository.is_synced", return_value=False)
-def test_publish_when_not_in_sync(mock_is_synced):
-    with patch("sys.stdout", new=StringIO()) as fake_out, raises(SystemExit):
-        tool_command = ToolCommand()
+def test_publish_when_not_in_sync(mock_is_synced, capsys, tool_command):
+    with raises(SystemExit):
        tool_command.publish(is_public=True)

-    assert "Local changes need to be resolved before publishing" in fake_out.getvalue()
+    output = capsys.readouterr().out
+    assert "Local changes need to be resolved before publishing" in output


@patch("crewai.cli.tools.main.get_project_name", return_value="sample-tool")
@@ -157,13 +144,13 @@ def test_publish_when_not_in_sync_and_force(
    mock_get_project_description,
    mock_get_project_version,
    mock_get_project_name,
+    tool_command,
 ):
    mock_publish_response = MagicMock()
    mock_publish_response.status_code = 200
    mock_publish_response.json.return_value = {"handle": "sample-tool"}
    mock_publish.return_value = mock_publish_response

-    tool_command = ToolCommand()
    tool_command.publish(is_public=True, force=True)

    mock_get_project_name.assert_called_with(require=True)
@@ -205,13 +192,13 @@ def test_publish_success(
    mock_get_project_description,
    mock_get_project_version,
    mock_get_project_name,
+    tool_command,
 ):
    mock_publish_response = MagicMock()
    mock_publish_response.status_code = 200
    mock_publish_response.json.return_value = {"handle": "sample-tool"}
    mock_publish.return_value = mock_publish_response

-    tool_command = ToolCommand()
    tool_command.publish(is_public=True)

    mock_get_project_name.assert_called_with(require=True)
@@ -251,25 +238,22 @@ def test_publish_failure(
    mock_get_project_description,
    mock_get_project_version,
    mock_get_project_name,
+    capsys,
+    tool_command,
 ):
    mock_publish_response = MagicMock()
    mock_publish_response.status_code = 422
    mock_publish_response.json.return_value = {"name": ["is already taken"]}
    mock_publish.return_value = mock_publish_response

-    tool_command = ToolCommand()
-
-    with patch("sys.stdout", new=StringIO()) as fake_out:
-        try:
-            tool_command.publish(is_public=True)
-        except SystemExit:
-            pass
-        output = fake_out.getvalue()
-
-    mock_publish.assert_called_once()
+    with raises(SystemExit):
+        tool_command.publish(is_public=True)
+    output = capsys.readouterr().out
    assert "Failed to complete operation" in output
    assert "Name is already taken" in output

+    mock_publish.assert_called_once()
+

@patch("crewai.cli.tools.main.get_project_name", return_value="sample-tool")
@patch("crewai.cli.tools.main.get_project_version", return_value="1.0.0")
@@ -290,6 +274,8 @@ def test_publish_api_error(
    mock_get_project_description,
    mock_get_project_version,
    mock_get_project_name,
+    capsys,
+    tool_command,
 ):
    mock_response = MagicMock()
    mock_response.status_code = 500
@@ -297,14 +283,9 @@ def test_publish_api_error(
    mock_response.ok = False
    mock_publish.return_value = mock_response

-    tool_command = ToolCommand()
-
-    with patch("sys.stdout", new=StringIO()) as fake_out:
-        try:
-            tool_command.publish(is_public=True)
-        except SystemExit:
-            pass
-        output = fake_out.getvalue()
+    with raises(SystemExit):
+        tool_command.publish(is_public=True)
+    output = capsys.readouterr().out
+    assert "Request to Enterprise API failed" in output

    mock_publish.assert_called_once()
-    assert "Request to Enterprise API failed" in output
@@ -17,6 +17,7 @@ from crewai.agents.cache import CacheHandler
 from crewai.agents.crew_agent_executor import CrewAgentExecutor
 from crewai.crew import Crew
 from crewai.crews.crew_output import CrewOutput
+from crewai.flow import Flow, listen, start
 from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
 from crewai.llm import LLM
 from crewai.memory.contextual.contextual_memory import ContextualMemory
@@ -42,29 +43,38 @@ from crewai.utilities.events.event_listener import EventListener
 from crewai.utilities.rpm_controller import RPMController
 from crewai.utilities.task_output_storage_handler import TaskOutputStorageHandler

-ceo = Agent(
-    role="CEO",
-    goal="Make sure the writers in your company produce amazing content.",
-    backstory="You're an long time CEO of a content creation agency with a Senior Writer on the team. You're now working on a new project and want to make sure the content produced is amazing.",
-    allow_delegation=True,
-)

-researcher = Agent(
-    role="Researcher",
-    goal="Make the best research and analysis on content about AI and AI agents",
-    backstory="You're an expert researcher, specialized in technology, software engineering, AI and startups. You work as a freelancer and is now working on doing research and analysis for a new customer.",
-    allow_delegation=False,
-)
-
-writer = Agent(
-    role="Senior Writer",
-    goal="Write the best content about AI and AI agents.",
-    backstory="You're a senior writer, specialized in technology, software engineering, AI and startups. You work as a freelancer and are now working on writing content for a new customer.",
-    allow_delegation=False,
-)
+@pytest.fixture
+def ceo():
+    return Agent(
+        role="CEO",
+        goal="Make sure the writers in your company produce amazing content.",
+        backstory="You're an long time CEO of a content creation agency with a Senior Writer on the team. You're now working on a new project and want to make sure the content produced is amazing.",
+        allow_delegation=True,
+    )


-def test_crew_with_only_conditional_tasks_raises_error():
+@pytest.fixture
+def researcher():
+    return Agent(
+        role="Researcher",
+        goal="Make the best research and analysis on content about AI and AI agents",
+        backstory="You're an expert researcher, specialized in technology, software engineering, AI and startups. You work as a freelancer and is now working on doing research and analysis for a new customer.",
+        allow_delegation=False,
+    )
+
+
+@pytest.fixture
+def writer():
+    return Agent(
+        role="Senior Writer",
+        goal="Write the best content about AI and AI agents.",
+        backstory="You're a senior writer, specialized in technology, software engineering, AI and startups. You work as a freelancer and are now working on writing content for a new customer.",
+        allow_delegation=False,
+    )
+
+
+def test_crew_with_only_conditional_tasks_raises_error(researcher):
    """Test that creating a crew with only conditional tasks raises an error."""

    def condition_func(task_output: TaskOutput) -> bool:
@@ -146,7 +156,9 @@ def test_crew_config_conditional_requirement():
    ]


-def test_async_task_cannot_include_sequential_async_tasks_in_context():
+def test_async_task_cannot_include_sequential_async_tasks_in_context(
+    researcher, writer
+):
    task1 = Task(
        description="Task 1",
        async_execution=True,
@@ -194,7 +206,7 @@ def test_async_task_cannot_include_sequential_async_tasks_in_context():
        pytest.fail("Unexpected ValidationError raised")


-def test_context_no_future_tasks():
+def test_context_no_future_tasks(researcher, writer):
    task2 = Task(
        description="Task 2",
        expected_output="output",
@@ -258,7 +270,7 @@ def test_crew_config_with_wrong_keys():


@pytest.mark.vcr(filter_headers=["authorization"])
-def test_crew_creation():
+def test_crew_creation(researcher, writer):
    tasks = [
        Task(
            description="Give me a list of 5 interesting ideas to explore for na article, what makes them unique and interesting.",
@@ -290,7 +302,7 @@ def test_crew_creation():


@pytest.mark.vcr(filter_headers=["authorization"])
-def test_sync_task_execution():
+def test_sync_task_execution(researcher, writer):
    from unittest.mock import patch

    tasks = [
@@ -331,7 +343,7 @@ def test_sync_task_execution():


@pytest.mark.vcr(filter_headers=["authorization"])
-def test_hierarchical_process():
+def test_hierarchical_process(researcher, writer):
    task = Task(
        description="Come up with a list of 5 interesting ideas to explore for an article, then write one amazing paragraph highlight for each idea that showcases how good an article about this topic could be. Return the list of ideas with their paragraph and your notes.",
        expected_output="5 bullet points with a paragraph for each idea.",
@@ -352,7 +364,7 @@ def test_hierarchical_process():
    )


-def test_manager_llm_requirement_for_hierarchical_process():
+def test_manager_llm_requirement_for_hierarchical_process(researcher, writer):
    task = Task(
        description="Come up with a list of 5 interesting ideas to explore for an article, then write one amazing paragraph highlight for each idea that showcases how good an article about this topic could be. Return the list of ideas with their paragraph and your notes.",
        expected_output="5 bullet points with a paragraph for each idea.",
@@ -367,7 +379,7 @@ def test_manager_llm_requirement_for_hierarchical_process():


@pytest.mark.vcr(filter_headers=["authorization"])
-def test_manager_agent_delegating_to_assigned_task_agent():
+def test_manager_agent_delegating_to_assigned_task_agent(researcher, writer):
    """
    Test that the manager agent delegates to the assigned task agent.
    """
@@ -419,7 +431,7 @@ def test_manager_agent_delegating_to_assigned_task_agent():


@pytest.mark.vcr(filter_headers=["authorization"])
-def test_manager_agent_delegating_to_all_agents():
+def test_manager_agent_delegating_to_all_agents(researcher, writer):
    """
    Test that the manager agent delegates to all agents when none are specified.
    """
@@ -529,7 +541,7 @@ def test_manager_agent_delegates_with_varied_role_cases():


@pytest.mark.vcr(filter_headers=["authorization"])
-def test_crew_with_delegating_agents():
+def test_crew_with_delegating_agents(ceo, writer):
    tasks = [
        Task(
            description="Produce and amazing 1 paragraph draft of an article about AI Agents.",
@@ -553,7 +565,7 @@ def test_crew_with_delegating_agents():


@pytest.mark.vcr(filter_headers=["authorization"])
-def test_crew_with_delegating_agents_should_not_override_task_tools():
+def test_crew_with_delegating_agents_should_not_override_task_tools(ceo, writer):
    from typing import Type

    from pydantic import BaseModel, Field
@@ -615,7 +627,7 @@ def test_crew_with_delegating_agents_should_not_override_task_tools():


@pytest.mark.vcr(filter_headers=["authorization"])
-def test_crew_with_delegating_agents_should_not_override_agent_tools():
+def test_crew_with_delegating_agents_should_not_override_agent_tools(ceo, writer):
    from typing import Type

    from pydantic import BaseModel, Field
@@ -679,7 +691,7 @@ def test_crew_with_delegating_agents_should_not_override_agent_tools():


@pytest.mark.vcr(filter_headers=["authorization"])
-def test_task_tools_override_agent_tools():
+def test_task_tools_override_agent_tools(researcher):
    from typing import Type

    from pydantic import BaseModel, Field
@@ -734,7 +746,7 @@ def test_task_tools_override_agent_tools():


@pytest.mark.vcr(filter_headers=["authorization"])
-def test_task_tools_override_agent_tools_with_allow_delegation():
+def test_task_tools_override_agent_tools_with_allow_delegation(researcher, writer):
    """
    Test that task tools override agent tools while preserving delegation tools when allow_delegation=True
    """
@@ -817,7 +829,7 @@ def test_task_tools_override_agent_tools_with_allow_delegation():


@pytest.mark.vcr(filter_headers=["authorization"])
-def test_crew_verbose_output(capsys):
+def test_crew_verbose_output(researcher, writer, capsys):
    tasks = [
        Task(
            description="Research AI advancements.",
@@ -877,7 +889,7 @@ def test_crew_verbose_output(capsys):


@pytest.mark.vcr(filter_headers=["authorization"])
-def test_cache_hitting_between_agents():
+def test_cache_hitting_between_agents(researcher, writer, ceo):
    from unittest.mock import call, patch

    from crewai.tools import tool
@@ -1050,7 +1062,7 @@ def test_agents_rpm_is_never_set_if_crew_max_RPM_is_not_set():


@pytest.mark.vcr(filter_headers=["authorization"])
-def test_sequential_async_task_execution_completion():
+def test_sequential_async_task_execution_completion(researcher, writer):
    list_ideas = Task(
        description="Give me a list of 5 interesting ideas to explore for an article, what makes them unique and interesting.",
        expected_output="Bullet point list of 5 important events.",
@@ -1204,7 +1216,7 @@ async def test_crew_async_kickoff():

@pytest.mark.asyncio
@pytest.mark.vcr(filter_headers=["authorization"])
-async def test_async_task_execution_call_count():
+async def test_async_task_execution_call_count(researcher, writer):
    from unittest.mock import MagicMock, patch

    list_ideas = Task(
@@ -1707,7 +1719,7 @@ def test_agents_do_not_get_delegation_tools_with_there_is_only_one_agent():


@pytest.mark.vcr(filter_headers=["authorization"])
-def test_sequential_crew_creation_tasks_without_agents():
+def test_sequential_crew_creation_tasks_without_agents(researcher):
    task = Task(
        description="Come up with a list of 5 interesting ideas to explore for an article, then write one amazing paragraph highlight for each idea that showcases how good an article about this topic could be. Return the list of ideas with their paragraph and your notes.",
        expected_output="5 bullet points with a paragraph for each idea.",
@@ -1757,7 +1769,7 @@ def test_agent_usage_metrics_are_captured_for_hierarchical_process():


@pytest.mark.vcr(filter_headers=["authorization"])
-def test_hierarchical_crew_creation_tasks_with_agents():
+def test_hierarchical_crew_creation_tasks_with_agents(researcher, writer):
    """
    Agents are not required for tasks in a hierarchical process but sometimes they are still added
    This test makes sure that the manager still delegates the task to the agent even if the agent is passed in the task
@@ -1810,7 +1822,7 @@ def test_hierarchical_crew_creation_tasks_with_agents():


@pytest.mark.vcr(filter_headers=["authorization"])
-def test_hierarchical_crew_creation_tasks_with_async_execution():
+def test_hierarchical_crew_creation_tasks_with_async_execution(researcher, writer, ceo):
    """
    Tests that async tasks in hierarchical crews are handled correctly with proper delegation tools
    """
@@ -1867,7 +1879,7 @@ def test_hierarchical_crew_creation_tasks_with_async_execution():


@pytest.mark.vcr(filter_headers=["authorization"])
-def test_hierarchical_crew_creation_tasks_with_sync_last():
+def test_hierarchical_crew_creation_tasks_with_sync_last(researcher, writer, ceo):
    """
    Agents are not required for tasks in a hierarchical process but sometimes they are still added
    This test makes sure that the manager still delegates the task to the agent even if the agent is passed in the task
@@ -2153,7 +2165,6 @@ def test_tools_with_custom_caching():
    with patch.object(
        CacheHandler, "add", wraps=crew._cache_handler.add
    ) as add_to_cache:
-
        result = crew.kickoff()

        # Check that add_to_cache was called exactly twice
@@ -2170,7 +2181,7 @@ def test_tools_with_custom_caching():


@pytest.mark.vcr(filter_headers=["authorization"])
-def test_conditional_task_uses_last_output():
+def test_conditional_task_uses_last_output(researcher, writer):
    """Test that conditional tasks use the last task output for condition evaluation."""
    task1 = Task(
        description="First task",
@@ -2244,7 +2255,7 @@ def test_conditional_task_uses_last_output():


@pytest.mark.vcr(filter_headers=["authorization"])
-def test_conditional_tasks_result_collection():
+def test_conditional_tasks_result_collection(researcher, writer):
    """Test that task outputs are properly collected based on execution status."""
    task1 = Task(
        description="Normal task that always executes",
@@ -2325,7 +2336,7 @@ def test_conditional_tasks_result_collection():


@pytest.mark.vcr(filter_headers=["authorization"])
-def test_multiple_conditional_tasks():
+def test_multiple_conditional_tasks(researcher, writer):
    """Test that having multiple conditional tasks in sequence works correctly."""
    task1 = Task(
        description="Initial research task",
@@ -2560,7 +2571,7 @@ def test_disabled_memory_using_contextual_memory():


@pytest.mark.vcr(filter_headers=["authorization"])
-def test_crew_log_file_output(tmp_path):
+def test_crew_log_file_output(tmp_path, researcher):
    test_file = tmp_path / "logs.txt"
    tasks = [
        Task(
@@ -2658,7 +2669,7 @@ def test_crew_output_file_validation_failures():
        Crew(agents=[agent], tasks=[task]).kickoff()


-def test_manager_agent():
+def test_manager_agent(researcher, writer):
    from unittest.mock import patch

    task = Task(
@@ -2696,7 +2707,7 @@ def test_manager_agent():
        mock_execute_sync.assert_called()


-def test_manager_agent_in_agents_raises_exception():
+def test_manager_agent_in_agents_raises_exception(researcher, writer):
    task = Task(
        description="Come up with a list of 5 interesting ideas to explore for an article, then write one amazing paragraph highlight for each idea that showcases how good an article about this topic could be. Return the list of ideas with their paragraph and your notes.",
        expected_output="5 bullet points with a paragraph for each idea.",
@@ -2718,7 +2729,7 @@ def test_manager_agent_in_agents_raises_exception():
        )


-def test_manager_agent_with_tools_raises_exception():
+def test_manager_agent_with_tools_raises_exception(researcher, writer):
    from crewai.tools import tool

    @tool
@@ -2755,7 +2766,7 @@ def test_manager_agent_with_tools_raises_exception():
@patch("crewai.crew.TaskEvaluator")
@patch("crewai.crew.Crew.copy")
 def test_crew_train_success(
-    copy_mock, task_evaluator, crew_training_handler, kickoff_mock
+    copy_mock, task_evaluator, crew_training_handler, kickoff_mock, researcher, writer
 ):
    task = Task(
        description="Come up with a list of 5 interesting ideas to explore for an article, then write one amazing paragraph highlight for each idea that showcases how good an article about this topic could be. Return the list of ideas with their paragraph and your notes.",
@@ -2831,7 +2842,7 @@ def test_crew_train_success(
    assert isinstance(received_events[1], CrewTrainCompletedEvent)


-def test_crew_train_error():
+def test_crew_train_error(researcher, writer):
    task = Task(
        description="Come up with a list of 5 interesting ideas to explore for an article",
        expected_output="5 bullet points with a paragraph for each idea.",
@@ -2850,7 +2861,7 @@ def test_crew_train_error():
        )


-def test__setup_for_training():
+def test__setup_for_training(researcher, writer):
    researcher.allow_delegation = True
    writer.allow_delegation = True
    agents = [researcher, writer]
@@ -2881,7 +2892,7 @@ def test__setup_for_training():


@pytest.mark.vcr(filter_headers=["authorization"])
-def test_replay_feature():
+def test_replay_feature(researcher, writer):
    list_ideas = Task(
        description="Generate a list of 5 interesting ideas to explore for an article, where each bulletpoint is under 15 words.",
        expected_output="Bullet point list of 5 important events. No additional commentary.",
@@ -2918,7 +2929,7 @@ def test_replay_feature():


@pytest.mark.vcr(filter_headers=["authorization"])
-def test_crew_replay_error():
+def test_crew_replay_error(researcher, writer):
    task = Task(
        description="Come up with a list of 5 interesting ideas to explore for an article",
        expected_output="5 bullet points with a paragraph for each idea.",
@@ -3314,7 +3325,7 @@ def test_replay_setup_context():
        assert crew.tasks[1].prompt_context == "context raw output"


-def test_key():
+def test_key(researcher, writer):
    tasks = [
        Task(
            description="Give me a list of 5 interesting ideas to explore for na article, what makes them unique and interesting.",
@@ -3383,7 +3394,9 @@ def test_key_with_interpolated_inputs():
    assert crew.key == curr_key


-def test_conditional_task_requirement_breaks_when_singular_conditional_task():
+def test_conditional_task_requirement_breaks_when_singular_conditional_task(
+    researcher, writer
+):
    def condition_fn(output) -> bool:
        return output.raw.startswith("Andrew Ng has!!")

@@ -3401,7 +3414,7 @@ def test_conditional_task_requirement_breaks_when_singular_conditional_task():


@pytest.mark.vcr(filter_headers=["authorization"])
-def test_conditional_task_last_task_when_conditional_is_true():
+def test_conditional_task_last_task_when_conditional_is_true(researcher, writer):
    def condition_fn(output) -> bool:
        return True

@@ -3428,7 +3441,7 @@ def test_conditional_task_last_task_when_conditional_is_true():


@pytest.mark.vcr(filter_headers=["authorization"])
-def test_conditional_task_last_task_when_conditional_is_false():
+def test_conditional_task_last_task_when_conditional_is_false(researcher, writer):
    def condition_fn(output) -> bool:
        return False

@@ -3452,7 +3465,7 @@ def test_conditional_task_last_task_when_conditional_is_false():
    assert result.raw == "Hi"


-def test_conditional_task_requirement_breaks_when_task_async():
+def test_conditional_task_requirement_breaks_when_task_async(researcher, writer):
    def my_condition(context):
        return context.get("some_value") > 10

@@ -3477,7 +3490,7 @@ def test_conditional_task_requirement_breaks_when_task_async():


@pytest.mark.vcr(filter_headers=["authorization"])
-def test_conditional_should_skip():
+def test_conditional_should_skip(researcher, writer):
    task1 = Task(description="Return hello", expected_output="say hi", agent=researcher)

    condition_mock = MagicMock(return_value=False)
@@ -3509,7 +3522,7 @@ def test_conditional_should_skip():


@pytest.mark.vcr(filter_headers=["authorization"])
-def test_conditional_should_execute():
+def test_conditional_should_execute(researcher, writer):
    task1 = Task(description="Return hello", expected_output="say hi", agent=researcher)

    condition_mock = MagicMock(
@@ -3542,7 +3555,7 @@ def test_conditional_should_execute():
@mock.patch("crewai.crew.CrewEvaluator")
@mock.patch("crewai.crew.Crew.copy")
@mock.patch("crewai.crew.Crew.kickoff")
-def test_crew_testing_function(kickoff_mock, copy_mock, crew_evaluator):
+def test_crew_testing_function(kickoff_mock, copy_mock, crew_evaluator, researcher):
    task = Task(
        description="Come up with a list of 5 interesting ideas to explore for an article, then write one amazing paragraph highlight for each idea that showcases how good an article about this topic could be. Return the list of ideas with their paragraph and your notes.",
        expected_output="5 bullet points with a paragraph for each idea.",
@@ -3592,7 +3605,7 @@ def test_crew_testing_function(kickoff_mock, copy_mock, crew_evaluator):


@pytest.mark.vcr(filter_headers=["authorization"])
-def test_hierarchical_verbose_manager_agent():
+def test_hierarchical_verbose_manager_agent(researcher, writer):
    task = Task(
        description="Come up with a list of 5 interesting ideas to explore for an article, then write one amazing paragraph highlight for each idea that showcases how good an article about this topic could be. Return the list of ideas with their paragraph and your notes.",
        expected_output="5 bullet points with a paragraph for each idea.",
@@ -3613,7 +3626,7 @@ def test_hierarchical_verbose_manager_agent():


@pytest.mark.vcr(filter_headers=["authorization"])
-def test_hierarchical_verbose_false_manager_agent():
+def test_hierarchical_verbose_false_manager_agent(researcher, writer):
    task = Task(
        description="Come up with a list of 5 interesting ideas to explore for an article, then write one amazing paragraph highlight for each idea that showcases how good an article about this topic could be. Return the list of ideas with their paragraph and your notes.",
        expected_output="5 bullet points with a paragraph for each idea.",
@@ -4186,7 +4199,7 @@ def test_before_kickoff_without_inputs():


@pytest.mark.vcr(filter_headers=["authorization"])
-def test_crew_with_knowledge_sources_works_with_copy():
+def test_crew_with_knowledge_sources_works_with_copy(researcher, writer):
    content = "Brandon's favorite color is red and he likes Mexican food."
    string_source = StringKnowledgeSource(content=content)

@@ -4195,7 +4208,6 @@ def test_crew_with_knowledge_sources_works_with_copy():
        tasks=[Task(description="test", expected_output="test", agent=researcher)],
        knowledge_sources=[string_source],
    )
-
    crew_copy = crew.copy()

    assert crew_copy.knowledge_sources == crew.knowledge_sources
@@ -4339,3 +4351,35 @@ def test_crew_copy_with_memory():
            raise e  # Re-raise other validation errors
    except Exception as e:
        pytest.fail(f"Copying crew raised an unexpected exception: {e}")
+
+
+def test_sets_parent_flow_when_outside_flow(researcher, writer):
+    crew = Crew(
+        agents=[researcher, writer],
+        process=Process.sequential,
+        tasks=[
+            Task(description="Task 1", expected_output="output", agent=researcher),
+            Task(description="Task 2", expected_output="output", agent=writer),
+        ],
+    )
+    assert crew.parent_flow is None
+
+
+def test_sets_parent_flow_when_inside_flow(researcher, writer):
+    class MyFlow(Flow):
+        @start()
+        def start(self):
+            return Crew(
+                agents=[researcher, writer],
+                process=Process.sequential,
+                tasks=[
+                    Task(
+                        description="Task 1", expected_output="output", agent=researcher
+                    ),
+                    Task(description="Task 2", expected_output="output", agent=writer),
+                ],
+            )
+
+    flow = MyFlow()
+    result = flow.kickoff()
+    assert result.parent_flow is flow
@@ -547,6 +547,7 @@ def test_excel_knowledge_source(mock_vector_db, tmpdir):
    mock_vector_db.query.assert_called_once()


+@pytest.mark.vcr
 def test_docling_source(mock_vector_db):
    docling_source = CrewDoclingSource(
        file_paths=[
@@ -567,6 +568,7 @@ def test_docling_source(mock_vector_db):
    mock_vector_db.query.assert_called_once()


+@pytest.mark.vcr
 def test_multiple_docling_sources():
    urls: List[Union[Path, str]] = [
        "https://lilianweng.github.io/posts/2024-11-28-reward-hacking/",
@@ -0,0 +1,69 @@
+import os
+from unittest.mock import patch
+
+import pytest
+
+from crewai import Agent, Crew, Task
+from crewai.telemetry import Telemetry
+
+
+@pytest.mark.parametrize(
+    "env_var,value,expected_ready",
+    [
+        ("OTEL_SDK_DISABLED", "true", False),
+        ("OTEL_SDK_DISABLED", "TRUE", False),
+        ("CREWAI_DISABLE_TELEMETRY", "true", False),
+        ("CREWAI_DISABLE_TELEMETRY", "TRUE", False),
+        ("OTEL_SDK_DISABLED", "false", True),
+        ("CREWAI_DISABLE_TELEMETRY", "false", True),
+    ],
+)
+def test_telemetry_environment_variables(env_var, value, expected_ready):
+    """Test telemetry state with different environment variable configurations."""
+    with patch.dict(os.environ, {env_var: value}):
+        with patch("crewai.telemetry.telemetry.TracerProvider"):
+            telemetry = Telemetry()
+            assert telemetry.ready is expected_ready
+
+
+def test_telemetry_enabled_by_default():
+    """Test that telemetry is enabled by default."""
+    with patch.dict(os.environ, {}, clear=True):
+        with patch("crewai.telemetry.telemetry.TracerProvider"):
+            telemetry = Telemetry()
+            assert telemetry.ready is True
+
+
+from opentelemetry import trace
+
+
+@patch("crewai.telemetry.telemetry.logger.error")
+@patch(
+    "opentelemetry.exporter.otlp.proto.http.trace_exporter.OTLPSpanExporter.export",
+    side_effect=Exception("Test exception"),
+)
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_telemetry_fails_due_connect_timeout(export_mock, logger_mock):
+    error = Exception("Test exception")
+    export_mock.side_effect = error
+
+    tracer = trace.get_tracer(__name__)
+    with tracer.start_as_current_span("test-span"):
+        agent = Agent(
+            role="agent",
+            llm="gpt-4o-mini",
+            goal="Just say hi",
+            backstory="You are a helpful assistant that just says hi",
+        )
+        task = Task(
+            description="Just say hi",
+            expected_output="hi",
+            agent=agent,
+        )
+        crew = Crew(agents=[agent], tasks=[task], name="TestCrew")
+        crew.kickoff()
+
+    trace.get_tracer_provider().force_flush()
+
+    export_mock.assert_called_once()
+    logger_mock.assert_called_once_with(error)
@@ -1,13 +1,16 @@
 import asyncio
 from typing import cast
+from unittest.mock import Mock

 import pytest
 from pydantic import BaseModel, Field

 from crewai import LLM, Agent
+from crewai.flow import Flow, start
 from crewai.lite_agent import LiteAgent, LiteAgentOutput
 from crewai.tools import BaseTool
 from crewai.utilities.events import crewai_event_bus
+from crewai.utilities.events.agent_events import LiteAgentExecutionStartedEvent
 from crewai.utilities.events.tool_usage_events import ToolUsageStartedEvent


@@ -255,3 +258,60 @@ async def test_lite_agent_returns_usage_metrics_async():
    assert "21 million" in result.raw or "37 million" in result.raw
    assert result.usage_metrics is not None
    assert result.usage_metrics["total_tokens"] > 0
+
+
+class TestFlow(Flow):
+    """A test flow that creates and runs an agent."""
+
+    def __init__(self, llm, tools):
+        self.llm = llm
+        self.tools = tools
+        super().__init__()
+
+    @start()
+    def start(self):
+        agent = Agent(
+            role="Test Agent",
+            goal="Test Goal",
+            backstory="Test Backstory",
+            llm=self.llm,
+            tools=self.tools,
+        )
+        return agent.kickoff("Test query")
+
+
+def verify_agent_parent_flow(result, agent, flow):
+    """Verify that both the result and agent have the correct parent flow."""
+    assert result.parent_flow is flow
+    assert agent is not None
+    assert agent.parent_flow is flow
+
+
+def test_sets_parent_flow_when_inside_flow():
+    captured_agent = None
+
+    mock_llm = Mock(spec=LLM)
+    mock_llm.call.return_value = "Test response"
+
+    class MyFlow(Flow):
+        @start()
+        def start(self):
+            agent = Agent(
+                role="Test Agent",
+                goal="Test Goal",
+                backstory="Test Backstory",
+                llm=mock_llm,
+                tools=[WebSearchTool()],
+            )
+            return agent.kickoff("Test query")
+
+    flow = MyFlow()
+    with crewai_event_bus.scoped_handlers():
+
+        @crewai_event_bus.on(LiteAgentExecutionStartedEvent)
+        def capture_agent(source, event):
+            nonlocal captured_agent
+            captured_agent = source
+
+        result = flow.kickoff()
+        assert captured_agent.parent_flow is flow
@@ -113,4 +113,434 @@ interactions:
      - req_2f9d1e3f0ace4944891dde05093486aa
    http_version: HTTP/1.1
    status_code: 200
+- request:
+    body: '{"name": "llama3.2:3b"}'
+    headers:
+      accept:
+      - '*/*'
+      accept-encoding:
+      - gzip, deflate
+      connection:
+      - keep-alive
+      content-length:
+      - '23'
+      content-type:
+      - application/json
+      host:
+      - localhost:11434
+      user-agent:
+      - litellm/1.68.0
+    method: POST
+    uri: http://localhost:11434/api/show
+  response:
+    body:
+      string: "{\"license\":\"LLAMA 3.2 COMMUNITY LICENSE AGREEMENT\\nLlama 3.2 Version
+        Release Date: September 25, 2024\\n\\n\u201CAgreement\u201D means the terms
+        and conditions for use, reproduction, distribution \\nand modification of
+        the Llama Materials set forth herein.\\n\\n\u201CDocumentation\u201D means
+        the specifications, manuals and documentation accompanying Llama 3.2\\ndistributed
+        by Meta at https://llama.meta.com/doc/overview.\\n\\n\u201CLicensee\u201D
+        or \u201Cyou\u201D means you, or your employer or any other person or entity
+        (if you are \\nentering into this Agreement on such person or entity\u2019s
+        behalf), of the age required under\\napplicable laws, rules or regulations
+        to provide legal consent and that has legal authority\\nto bind your employer
+        or such other person or entity if you are entering in this Agreement\\non
+        their behalf.\\n\\n\u201CLlama 3.2\u201D means the foundational large language
+        models and software and algorithms, including\\nmachine-learning model code,
+        trained model weights, inference-enabling code, training-enabling code,\\nfine-tuning
+        enabling code and other elements of the foregoing distributed by Meta at \\nhttps://www.llama.com/llama-downloads.\\n\\n\u201CLlama
+        Materials\u201D means, collectively, Meta\u2019s proprietary Llama 3.2 and
+        Documentation (and \\nany portion thereof) made available under this Agreement.\\n\\n\u201CMeta\u201D
+        or \u201Cwe\u201D means Meta Platforms Ireland Limited (if you are located
+        in or, \\nif you are an entity, your principal place of business is in the
+        EEA or Switzerland) \\nand Meta Platforms, Inc. (if you are located outside
+        of the EEA or Switzerland). \\n\\n\\nBy clicking \u201CI Accept\u201D below
+        or by using or distributing any portion or element of the Llama Materials,\\nyou
+        agree to be bound by this Agreement.\\n\\n\\n1. License Rights and Redistribution.\\n\\n
+        \   a. Grant of Rights. You are granted a non-exclusive, worldwide, \\nnon-transferable
+        and royalty-free limited license under Meta\u2019s intellectual property or
+        other rights \\nowned by Meta embodied in the Llama Materials to use, reproduce,
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+        the Llama Materials.  \\n\\n    b. Redistribution and Use.  \\n\\n        i.
+        If you distribute or make available the Llama Materials (or any derivative
+        works thereof), \\nor a product or service (including another AI model) that
+        contains any of them, you shall (A) provide\\na copy of this Agreement with
+        any such Llama Materials; and (B) prominently display \u201CBuilt with Llama\u201D\\non
+        a related website, user interface, blogpost, about page, or product documentation.
+        If you use the\\nLlama Materials or any outputs or results of the Llama Materials
+        to create, train, fine tune, or\\notherwise improve an AI model, which is
+        distributed or made available, you shall also include \u201CLlama\u201D\\nat
+        the beginning of any such AI model name.\\n\\n        ii. If you receive Llama
+        Materials, or any derivative works thereof, from a Licensee as part\\nof an
+        integrated end user product, then Section 2 of this Agreement will not apply
+        to you. \\n\\n        iii. You must retain in all copies of the Llama Materials
+        that you distribute the \\nfollowing attribution notice within a \u201CNotice\u201D
+        text file distributed as a part of such copies: \\n\u201CLlama 3.2 is licensed
+        under the Llama 3.2 Community License, Copyright \xA9 Meta Platforms,\\nInc.
+        All Rights Reserved.\u201D\\n\\n        iv. Your use of the Llama Materials
+        must comply with applicable laws and regulations\\n(including trade compliance
+        laws and regulations) and adhere to the Acceptable Use Policy for\\nthe Llama
+        Materials (available at https://www.llama.com/llama3_2/use-policy), which
+        is hereby \\nincorporated by reference into this Agreement.\\n  \\n2. Additional
+        Commercial Terms. If, on the Llama 3.2 version release date, the monthly active
+        users\\nof the products or services made available by or for Licensee, or
+        Licensee\u2019s affiliates, \\nis greater than 700 million monthly active
+        users in the preceding calendar month, you must request \\na license from
+        Meta, which Meta may grant to you in its sole discretion, and you are not
+        authorized to\\nexercise any of the rights under this Agreement unless or
+        until Meta otherwise expressly grants you such rights.\\n\\n3. Disclaimer
+        of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY
+        OUTPUT AND \\nRESULTS THEREFROM ARE PROVIDED ON AN \u201CAS IS\u201D BASIS,
+        WITHOUT WARRANTIES OF ANY KIND, AND META DISCLAIMS\\nALL WARRANTIES OF ANY
+        KIND, BOTH EXPRESS AND IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES\\nOF
+        TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE.
+        YOU ARE SOLELY RESPONSIBLE\\nFOR DETERMINING THE APPROPRIATENESS OF USING
+        OR REDISTRIBUTING THE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED\\nWITH
+        YOUR USE OF THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS.\\n\\n4. Limitation
+        of Liability. IN NO EVENT WILL META OR ITS AFFILIATES BE LIABLE UNDER ANY
+        THEORY OF LIABILITY, \\nWHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY,
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+        INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES,
+        EVEN \\nIF META OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF
+        ANY OF THE FOREGOING.\\n\\n5. Intellectual Property.\\n\\n    a. No trademark
+        licenses are granted under this Agreement, and in connection with the Llama
+        Materials, \\nneither Meta nor Licensee may use any name or mark owned by
+        or associated with the other or any of its affiliates, \\nexcept as required
+        for reasonable and customary use in describing and redistributing the Llama
+        Materials or as \\nset forth in this Section 5(a). Meta hereby grants you
+        a license to use \u201CLlama\u201D (the \u201CMark\u201D) solely as required
+        \\nto comply with the last sentence of Section 1.b.i. You will comply with
+        Meta\u2019s brand guidelines (currently accessible \\nat https://about.meta.com/brand/resources/meta/company-brand/).
+        All goodwill arising out of your use of the Mark \\nwill inure to the benefit
+        of Meta.\\n\\n    b. Subject to Meta\u2019s ownership of Llama Materials and
+        derivatives made by or for Meta, with respect to any\\n    derivative works
+        and modifications of the Llama Materials that are made by you, as between
+        you and Meta,\\n    you are and will be the owner of such derivative works
+        and modifications.\\n\\n    c. If you institute litigation or other proceedings
+        against Meta or any entity (including a cross-claim or\\n    counterclaim
+        in a lawsuit) alleging that the Llama Materials or Llama 3.2 outputs or results,
+        or any portion\\n    of any of the foregoing, constitutes infringement of
+        intellectual property or other rights owned or licensable\\n    by you, then
+        any licenses granted to you under this Agreement shall terminate as of the
+        date such litigation or\\n    claim is filed or instituted. You will indemnify
+        and hold harmless Meta from and against any claim by any third\\n    party
+        arising out of or related to your use or distribution of the Llama Materials.\\n\\n6.
+        Term and Termination. The term of this Agreement will commence upon your acceptance
+        of this Agreement or access\\nto the Llama Materials and will continue in
+        full force and effect until terminated in accordance with the terms\\nand
+        conditions herein. Meta may terminate this Agreement if you are in breach
+        of any term or condition of this\\nAgreement. Upon termination of this Agreement,
+        you shall delete and cease use of the Llama Materials. Sections 3,\\n4 and
+        7 shall survive the termination of this Agreement. \\n\\n7. Governing Law
+        and Jurisdiction. This Agreement will be governed and construed under the
+        laws of the State of \\nCalifornia without regard to choice of law principles,
+        and the UN Convention on Contracts for the International\\nSale of Goods does
+        not apply to this Agreement. The courts of California shall have exclusive
+        jurisdiction of\\nany dispute arising out of this Agreement.\\n**Llama 3.2**
+        **Acceptable Use Policy**\\n\\nMeta is committed to promoting safe and fair
+        use of its tools and features, including Llama 3.2. If you access or use Llama
+        3.2, you agree to this Acceptable Use Policy (\u201C**Policy**\u201D). The
+        most recent copy of this policy can be found at [https://www.llama.com/llama3_2/use-policy](https://www.llama.com/llama3_2/use-policy).\\n\\n**Prohibited
+        Uses**\\n\\nWe want everyone to use Llama 3.2 safely and responsibly. You
+        agree you will not use, or allow others to use, Llama 3.2 to:\\n\\n\\n\\n1.
+        Violate the law or others\u2019 rights, including to:\\n    1. Engage in,
+        promote, generate, contribute to, encourage, plan, incite, or further illegal
+        or unlawful activity or content, such as:\\n        1. Violence or terrorism\\n
+        \       2. Exploitation or harm to children, including the solicitation, creation,
+        acquisition, or dissemination of child exploitative content or failure to
+        report Child Sexual Abuse Material\\n        3. Human trafficking, exploitation,
+        and sexual violence\\n        4. The illegal distribution of information or
+        materials to minors, including obscene materials, or failure to employ legally
+        required age-gating in connection with such information or materials.\\n        5.
+        Sexual solicitation\\n        6. Any other criminal activity\\n    1. Engage
+        in, promote, incite, or facilitate the harassment, abuse, threatening, or
+        bullying of individuals or groups of individuals\\n    2. Engage in, promote,
+        incite, or facilitate discrimination or other unlawful or harmful conduct
+        in the provision of employment, employment benefits, credit, housing, other
+        economic benefits, or other essential goods and services\\n    3. Engage in
+        the unauthorized or unlicensed practice of any profession including, but not
+        limited to, financial, legal, medical/health, or related professional practices\\n
+        \   4. Collect, process, disclose, generate, or infer private or sensitive
+        information about individuals, including information about individuals\u2019
+        identity, health, or demographic information, unless you have obtained the
+        right to do so in accordance with applicable law\\n    5. Engage in or facilitate
+        any action or generate any content that infringes, misappropriates, or otherwise
+        violates any third-party rights, including the outputs or results of any products
+        or services using the Llama Materials\\n    6. Create, generate, or facilitate
+        the creation of malicious code, malware, computer viruses or do anything else
+        that could disable, overburden, interfere with or impair the proper working,
+        integrity, operation or appearance of a website or computer system\\n    7.
+        Engage in any action, or facilitate any action, to intentionally circumvent
+        or remove usage restrictions or other safety measures, or to enable functionality
+        disabled by Meta\\n2. Engage in, promote, incite, facilitate, or assist in
+        the planning or development of activities that present a risk of death or
+        bodily harm to individuals, including use of Llama 3.2 related to the following:\\n
+        \   8. Military, warfare, nuclear industries or applications, espionage, use
+        for materials or activities that are subject to the International Traffic
+        Arms Regulations (ITAR) maintained by the United States Department of State
+        or to the U.S. Biological Weapons Anti-Terrorism Act of 1989 or the Chemical
+        Weapons Convention Implementation Act of 1997\\n    9. Guns and illegal weapons
+        (including weapon development)\\n    10. Illegal drugs and regulated/controlled
+        substances\\n    11. Operation of critical infrastructure, transportation
+        technologies, or heavy machinery\\n    12. Self-harm or harm to others, including
+        suicide, cutting, and eating disorders\\n    13. Any content intended to incite
+        or promote violence, abuse, or any infliction of bodily harm to an individual\\n3.
+        Intentionally deceive or mislead others, including use of Llama 3.2 related
+        to the following:\\n    14. Generating, promoting, or furthering fraud or
+        the creation or promotion of disinformation\\n    15. Generating, promoting,
+        or furthering defamatory content, including the creation of defamatory statements,
+        images, or other content\\n    16. Generating, promoting, or further distributing
+        spam\\n    17. Impersonating another individual without consent, authorization,
+        or legal right\\n    18. Representing that the use of Llama 3.2 or outputs
+        are human-generated\\n    19. Generating or facilitating false online engagement,
+        including fake reviews and other means of fake online engagement\\n4. Fail
+        to appropriately disclose to end users any known dangers of your AI system\\n5.
+        Interact with third party tools, models, or software designed to generate
+        unlawful content or engage in unlawful or harmful conduct and/or represent
+        that the outputs of such tools, models, or software are associated with Meta
+        or Llama 3.2\\n\\nWith respect to any multimodal models included in Llama
+        3.2, the rights granted under Section 1(a) of the Llama 3.2 Community License
+        Agreement are not being granted to you if you are an individual domiciled
+        in, or a company with a principal place of business in, the European Union.
+        This restriction does not apply to end users of a product or service that
+        incorporates any such multimodal models.\\n\\nPlease report any violation
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+        Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://l.workplace.com/l.php?u=https%3A%2F%2Fgithub.com%2Fmeta-llama%2Fllama-models%2Fissues\\u0026h=AT0qV8W9BFT6NwihiOHRuKYQM_UnkzN_NmHMy91OT55gkLpgi4kQupHUl0ssR4dQsIQ8n3tfd0vtkobvsEvt1l4Ic6GXI2EeuHV8N08OG2WnbAmm0FL4ObkazC6G_256vN0lN9DsykCvCqGZ)\\n*
+        Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)\\n*
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+        Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama
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+        September 25, 2024\\n\\n\u201CAgreement\u201D means the terms and conditions
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+        that has legal authority\\nto bind your employer or such other person or entity
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+        other rights \\nowned by Meta embodied in the Llama Materials to use, reproduce,
+        distribute, copy, create derivative works \\nof, and make modifications to
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+        If you use the\\nLlama Materials or any outputs or results of the Llama Materials
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+        that you distribute the \\nfollowing attribution notice within a \u201CNotice\u201D
+        text file distributed as a part of such copies: \\n\u201CLlama 3.2 is licensed
+        under the Llama 3.2 Community License, Copyright \xA9 Meta Platforms,\\nInc.
+        All Rights Reserved.\u201D\\n\\n        iv. Your use of the Llama Materials
+        must comply with applicable laws and regulations\\n(including trade compliance
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+        until Meta otherwise expressly grants you such rights.\\n\\n3. Disclaimer
+        of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY
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+        WITHOUT WARRANTIES OF ANY KIND, AND META DISCLAIMS\\nALL WARRANTIES OF ANY
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+        \\\"**Llama 3.2** **Acceptable Use Policy**\\n\\nMeta is committed to promoting
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+        or Llama 3.2\\n\\nWith respect to any multimodal models included in Llama
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+        of this Policy, software \u201Cbug,\u201D or other problems that could lead
+        to a violation of this Policy through one of the following means:\\n\\n\\n\\n*
+        Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://l.workplace.com/l.php?u=https%3A%2F%2Fgithub.com%2Fmeta-llama%2Fllama-models%2Fissues\\u0026h=AT0qV8W9BFT6NwihiOHRuKYQM_UnkzN_NmHMy91OT55gkLpgi4kQupHUl0ssR4dQsIQ8n3tfd0vtkobvsEvt1l4Ic6GXI2EeuHV8N08OG2WnbAmm0FL4ObkazC6G_256vN0lN9DsykCvCqGZ)\\n*
+        Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)\\n*
+        Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)\\n*
+        Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama
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truncated
+    headers:
+      Content-Type:
+      - application/json; charset=utf-8
+      Date:
+      - Wed, 07 May 2025 01:18:39 GMT
+      Transfer-Encoding:
+      - chunked
+    status:
+      code: 200
+      message: OK
 version: 1
@@ -32,3 +32,16 @@ def test_wildcard_event_handler():
    crewai_event_bus.emit("source_object", event)

    mock_handler.assert_called_once_with("source_object", event)
+
+
+def test_event_bus_error_handling(capfd):
+    @crewai_event_bus.on(BaseEvent)
+    def broken_handler(source, event):
+        raise ValueError("Simulated handler failure")
+
+    event = TestEvent(type="test_event")
+    crewai_event_bus.emit("source_object", event)
+
+    out, err = capfd.readouterr()
+    assert "Simulated handler failure" in out
+    assert "Handler 'broken_handler' failed" in out
@@ -357,14 +357,7 @@ def test_convert_with_instructions():
    assert output.age == 30


-# Skip tests that call external APIs when running in CI/CD
-skip_external_api = pytest.mark.skipif(
-    os.getenv("CI") is not None, reason="Skipping tests that call external API in CI/CD"
-)
-
-
-@skip_external_api
-@pytest.mark.vcr(filter_headers=["authorization"], record_mode="once")
+@pytest.mark.vcr(filter_headers=["authorization"])
 def test_converter_with_llama3_2_model():
    llm = LLM(model="ollama/llama3.2:3b", base_url="http://localhost:11434")
    sample_text = "Name: Alice Llama, Age: 30"
@@ -381,8 +374,7 @@ def test_converter_with_llama3_2_model():
    assert output.age == 30


-@skip_external_api
-@pytest.mark.vcr(filter_headers=["authorization"], record_mode="once")
+@pytest.mark.vcr(filter_headers=["authorization"])
 def test_converter_with_llama3_1_model():
    llm = LLM(model="ollama/llama3.1", base_url="http://localhost:11434")
    sample_text = "Name: Alice Llama, Age: 30"
@@ -399,13 +391,6 @@ def test_converter_with_llama3_1_model():
    assert output.age == 30


-# Skip tests that call external APIs when running in CI/CD
-skip_external_api = pytest.mark.skipif(
-    os.getenv("CI") is not None, reason="Skipping tests that call external API in CI/CD"
-)
-
-
-@skip_external_api
@pytest.mark.vcr(filter_headers=["authorization"])
 def test_converter_with_nested_model():
    llm = LLM(model="gpt-4o-mini")
@@ -811,8 +811,10 @@ dev = [
    { name = "pre-commit" },
    { name = "pytest" },
    { name = "pytest-asyncio" },
+    { name = "pytest-randomly" },
    { name = "pytest-recording" },
    { name = "pytest-subprocess" },
+    { name = "pytest-timeout" },
    { name = "python-dotenv" },
    { name = "ruff" },
 ]
@@ -833,7 +835,7 @@ requires-dist = [
    { name = "json-repair", specifier = ">=0.25.2" },
    { name = "json5", specifier = ">=0.10.0" },
    { name = "jsonref", specifier = ">=1.1.0" },
-    { name = "litellm", specifier = "==1.67.1" },
+    { name = "litellm", specifier = "==1.68.0" },
    { name = "mem0ai", marker = "extra == 'mem0'", specifier = ">=0.1.94" },
    { name = "openai", specifier = ">=1.13.3" },
    { name = "openpyxl", specifier = ">=3.1.5" },
@@ -867,8 +869,10 @@ dev = [
    { name = "pre-commit", specifier = ">=3.6.0" },
    { name = "pytest", specifier = ">=8.0.0" },
    { name = "pytest-asyncio", specifier = ">=0.23.7" },
+    { name = "pytest-randomly", specifier = ">=3.16.0" },
    { name = "pytest-recording", specifier = ">=0.13.2" },
    { name = "pytest-subprocess", specifier = ">=1.5.2" },
+    { name = "pytest-timeout", specifier = ">=2.3.1" },
    { name = "python-dotenv", specifier = ">=1.0.0" },
    { name = "ruff", specifier = ">=0.8.2" },
 ]
@@ -2383,7 +2387,7 @@ wheels = [

 [[package]]
 name = "litellm"
-version = "1.67.1"
+version = "1.68.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
    { name = "aiohttp" },
@@ -2398,9 +2402,9 @@ dependencies = [
    { name = "tiktoken" },
    { name = "tokenizers" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/54/a4/bb3e9ae59e5a9857443448de7c04752630dc84cddcbd8cee037c0976f44f/litellm-1.67.1.tar.gz", hash = "sha256:78eab1bd3d759ec13aa4a05864356a4a4725634e78501db609d451bf72150ee7", size = 7242044 }
+sdist = { url = "https://files.pythonhosted.org/packages/ba/22/138545b646303ca3f4841b69613c697b9d696322a1386083bb70bcbba60b/litellm-1.68.0.tar.gz", hash = "sha256:9fb24643db84dfda339b64bafca505a2eef857477afbc6e98fb56512c24dbbfa", size = 7314051 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/88/86/c14d3c24ae13c08296d068e6f79fd4bd17a0a07bddbda94990b87c35d20e/litellm-1.67.1-py3-none-any.whl", hash = "sha256:8fff5b2a16b63bb594b94d6c071ad0f27d3d8cd4348bd5acea2fd40c8e0c11e8", size = 7607266 },
+    { url = "https://files.pythonhosted.org/packages/10/af/1e344bc8aee41445272e677d802b774b1f8b34bdc3bb5697ba30f0fb5d52/litellm-1.68.0-py3-none-any.whl", hash = "sha256:3bca38848b1a5236b11aa6b70afa4393b60880198c939e582273f51a542d4759", size = 7684460 },
 ]

 [[package]]
@@ -4228,6 +4232,18 @@ wheels = [
    { url = "https://files.pythonhosted.org/packages/96/31/6607dab48616902f76885dfcf62c08d929796fc3b2d2318faf9fd54dbed9/pytest_asyncio-0.24.0-py3-none-any.whl", hash = "sha256:a811296ed596b69bf0b6f3dc40f83bcaf341b155a269052d82efa2b25ac7037b", size = 18024 },
 ]

+[[package]]
+name = "pytest-randomly"
+version = "3.16.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "pytest" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/c0/68/d221ed7f4a2a49a664da721b8e87b52af6dd317af2a6cb51549cf17ac4b8/pytest_randomly-3.16.0.tar.gz", hash = "sha256:11bf4d23a26484de7860d82f726c0629837cf4064b79157bd18ec9d41d7feb26", size = 13367 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/22/70/b31577d7c46d8e2f9baccfed5067dd8475262a2331ffb0bfdf19361c9bde/pytest_randomly-3.16.0-py3-none-any.whl", hash = "sha256:8633d332635a1a0983d3bba19342196807f6afb17c3eef78e02c2f85dade45d6", size = 8396 },
+]
+
 [[package]]
 name = "pytest-recording"
 version = "0.13.2"
@@ -4254,6 +4270,18 @@ wheels = [
    { url = "https://files.pythonhosted.org/packages/10/77/a80e8f9126b95ffd5ad4d04bd14005c68dcbf0d88f53b2b14893f6cc7232/pytest_subprocess-1.5.2-py3-none-any.whl", hash = "sha256:23ac7732aa8bd45f1757265b1316eb72a7f55b41fb21e2ca22e149ba3629fa46", size = 20886 },
 ]

+[[package]]
+name = "pytest-timeout"
+version = "2.3.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "pytest" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/93/0d/04719abc7a4bdb3a7a1f968f24b0f5253d698c9cc94975330e9d3145befb/pytest-timeout-2.3.1.tar.gz", hash = "sha256:12397729125c6ecbdaca01035b9e5239d4db97352320af155b3f5de1ba5165d9", size = 17697 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/03/27/14af9ef8321f5edc7527e47def2a21d8118c6f329a9342cc61387a0c0599/pytest_timeout-2.3.1-py3-none-any.whl", hash = "sha256:68188cb703edfc6a18fad98dc25a3c61e9f24d644b0b70f33af545219fc7813e", size = 14148 },
+]
+
 [[package]]
 name = "python-bidi"
 version = "0.6.3"
Author	SHA1	Message	Date
Lucas Gomide	b02e3e1b57	test: fix llama converter tests to remove skip_external_api	2025-05-06 22:22:41 -03:00
Tony Kipkemboi	cac06adc6c	docs: update docxsearchtool.mdx (#2767 ) Notify Downstream / notify-downstream (push) Has been cancelled Details Mark stale issues and pull requests / stale (push) Has been cancelled Details - add `docx2txt` as a dependency requirement for the tool	2025-05-06 17:14:05 -04:00
leopardracerandLucas Gomide	c8ec03424a	Fix typos in documentation and configuration files (#2712 ) Notify Downstream / notify-downstream (push) Has been cancelled Details * Update test_lite_agent_structured_output.yaml * Update install_crew.py * Update llms.mdx --------- Co-authored-by: Lucas Gomide <lucaslg200@gmail.com>	2025-05-06 15:07:57 -04:00
Henrique BrancoandTony Kipkemboi	bfea85d22c	docs: added Windows bug solving to docs (#2764 ) Co-authored-by: Tony Kipkemboi <iamtonykipkemboi@gmail.com>	2025-05-06 09:55:05 -04:00
Mark McDonaldandTony Kipkemboi	836e9fc545	Removes model provider defaults from LLM Setup (#2766 ) This removes any specific model from the "Setting up your LLM" guide, but provides examples for the top-3 providers. This section also conflated "model selection" with "model configuration", where configuration is provider-specific, so I've focused this first section on just model selection, deferring the config to the "provider" section that follows. Co-authored-by: Tony Kipkemboi <iamtonykipkemboi@gmail.com>	2025-05-06 09:27:14 -04:00
Vidit Ostwal Lucas Gomide Lorenze Jay	c3726092fd	Added Advance Configuration Docs for Rag Tool (#2713 ) * Added Advance Configuration Docs for Rag Tool * Re-run test cases * Change doc * prepping new version (#2733) --------- Co-authored-by: Lucas Gomide <lucaslg200@gmail.com> Co-authored-by: Lorenze Jay <63378463+lorenzejay@users.noreply.github.com>	2025-05-06 09:07:52 -04:00
Lucas Gomide	dabf02a90d	build(LiteLLM): upgrade LiteLLM version (#2757 ) Notify Downstream / notify-downstream (push) Has been cancelled Details Mark stale issues and pull requests / stale (push) Has been cancelled Details	2025-05-05 17:07:29 -04:00
Lucas Gomide	2912c93d77	feat: prevent crash once Telemetry is not available (#2758 ) * feat: prevent crash once Telemetry is not available * tests: adding missing cassettes	2025-05-05 15:22:32 -04:00
Vini Brasil	17474a3a0c	Identify `parent_flow` of `Crew` and `LiteAgent` (#2723 ) Notify Downstream / notify-downstream (push) Has been cancelled Details Mark stale issues and pull requests / stale (push) Has been cancelled Details This commit adds a new crew field called parent_flow, evaluated when the Crew instance is instantiated. The stacktrace is traversed to look up if the caller is an instance of Flow, and if so, it fills in the field. Other alternatives were considered, such as a global context or even a new field to be manually filled, however, this is the most magical solution that was thread-safe and did not require public API changes.	2025-05-02 14:40:39 -03:00
Lucas Gomide	f89c2bfb7e	Fix crewai reset-memories when Embedding dimension mismatch (#2737 ) Notify Downstream / notify-downstream (push) Has been cancelled Details * fix: support to reset memories after changing Crew's embedder The sources must not be added while initializing the Knowledge otherwise we could not reset it * chore: improve reset memory feedback Previously, even when no memories were actually erased, we logged that they had been. From now on, the log will specify which memory has been reset. * feat: improve get_crew discovery from a single file Crew instances can now be discovered from any function or method with a return type annotation of -> Crew, as well as from module-level attributes assigned to a Crew instance. Additionally, crews can be retrieved from within a Flow * refactor: make add_sources a public method from Knowledge	2025-05-02 12:40:42 -04:00
Lucas Gomide	2902201bfa	pytest improvements to handle flaky test (#2726 ) Mark stale issues and pull requests / stale (push) Has been cancelled Details Notify Downstream / notify-downstream (push) Has been cancelled Details * build(dev): add pytest-randomly dependency By randomizing the test execution order, this helps identify tests that unintentionally depend on shared state or specific execution order, which can lead to flaky or unreliable test behavior. * build(dev): add pytest-timeout This will prevent a test from running indefinitely * test: block external requests in CI and set default 10s timeout per test * test: adding missing cassettes We notice that those cassettes are missing after enabling block-network on CI * test: increase tests timeout on CI * test: fix flaky test ValueError: Circular reference detected (id repeated) * fix: prevent crash when event handler raises exception Previously, if a registered event handler raised an exception during execution, it could crash the entire application or interrupt the event dispatch process. This change wraps handler execution in a try/except block within the `emit` method, ensuring that exceptions are caught and logged without affecting other handlers or flow. This improves the resilience of the event bus, especially when handling third-party or temporary listeners.	2025-05-01 15:48:29 -04:00