Merge branch 'main' into bugfix/event-listener

docs/concepts/knowledge.mdx

@@ -150,8 +150,6 @@ result = crew.kickoff(
 
 Here are examples of how to use different types of knowledge sources:
 
-Note: Please ensure that you create the ./knowldge folder. All source files (e.g., .txt, .pdf, .xlsx, .json) should be placed in this folder for centralized management.
-
 ### Text File Knowledge Source
 ```python
 from crewai.knowledge.source.text_file_knowledge_source import TextFileKnowledgeSource
@@ -462,12 +460,12 @@ class SpaceNewsKnowledgeSource(BaseKnowledgeSource):
             data = response.json()
             articles = data.get('results', [])
 
-            formatted_data = self.validate_content(articles)
+            formatted_data = self._format_articles(articles)
             return {self.api_endpoint: formatted_data}
         except Exception as e:
             raise ValueError(f"Failed to fetch space news: {str(e)}")
 
-    def validate_content(self, articles: list) -> str:
+    def _format_articles(self, articles: list) -> str:
         """Format articles into readable text."""
         formatted = "Space News Articles:\n\n"
         for article in articles:

docs/concepts/llms.mdx

@@ -158,11 +158,7 @@ In this section, you'll find detailed examples that help you select, configure,
 
   <Accordion title="Anthropic">
     ```toml Code
-    # Required
     ANTHROPIC_API_KEY=sk-ant-...
-
-    # Optional
-    ANTHROPIC_API_BASE=<custom-base-url>
     ```
 
     Example usage in your CrewAI project:
@@ -254,40 +250,6 @@ In this section, you'll find detailed examples that help you select, configure,
         model="bedrock/anthropic.claude-3-sonnet-20240229-v1:0"
     )
     ```
-
-    Before using Amazon Bedrock, make sure you have boto3 installed in your environment
-
-    [Amazon Bedrock](https://docs.aws.amazon.com/bedrock/latest/userguide/models-regions.html) is a managed service that provides access to multiple foundation models from top AI companies through a unified API, enabling secure and responsible AI application development.
-
-    | Model                   | Context Window       | Best For                                                          |
-    |-------------------------|----------------------|-------------------------------------------------------------------|
-    | Amazon Nova Pro         | Up to 300k tokens    | High-performance, model balancing accuracy, speed, and cost-effectiveness across diverse tasks. |
-    | Amazon Nova Micro       | Up to 128k tokens    | High-performance, cost-effective text-only model optimized for lowest latency responses. |
-    | Amazon Nova Lite        | Up to 300k tokens    | High-performance, affordable multimodal processing for images, video, and text with real-time capabilities. |
-    | Claude 3.7 Sonnet       | Up to 128k tokens    | High-performance, best for complex reasoning, coding & AI agents |
-    | Claude 3.5 Sonnet v2    | Up to 200k tokens    | State-of-the-art model specialized in software engineering, agentic capabilities, and computer interaction at optimized cost. |
-    | Claude 3.5 Sonnet       | Up to 200k tokens    | High-performance model delivering superior intelligence and reasoning across diverse tasks with optimal speed-cost balance. |
-    | Claude 3.5 Haiku        | Up to 200k tokens    | Fast, compact multimodal model optimized for quick responses and seamless human-like interactions |
-    | Claude 3 Sonnet         | Up to 200k tokens    | Multimodal model balancing intelligence and speed for high-volume deployments. |
-    | Claude 3 Haiku          | Up to 200k tokens    | Compact, high-speed multimodal model optimized for quick responses and natural conversational interactions |
-    | Claude 3 Opus           | Up to 200k tokens    | Most advanced multimodal model excelling at complex tasks with human-like reasoning and superior contextual understanding. |
-    | Claude 2.1              | Up to 200k tokens    | Enhanced version with expanded context window, improved reliability, and reduced hallucinations for long-form and RAG applications |
-    | Claude                  | Up to 100k tokens    | Versatile model excelling in sophisticated dialogue, creative content, and precise instruction following. |
-    | Claude Instant          | Up to 100k tokens    | Fast, cost-effective model for everyday tasks like dialogue, analysis, summarization, and document Q&A |
-    | Llama 3.1 405B Instruct | Up to 128k tokens    | Advanced LLM for synthetic data generation, distillation, and inference for chatbots, coding, and domain-specific tasks. |
-    | Llama 3.1 70B Instruct  | Up to 128k tokens    | Powers complex conversations with superior contextual understanding, reasoning and text generation. |
-    | Llama 3.1 8B Instruct   | Up to 128k tokens    | Advanced state-of-the-art model with language understanding, superior reasoning, and text generation. |
-    | Llama 3 70B Instruct    | Up to 8k tokens      | Powers complex conversations with superior contextual understanding, reasoning and text generation. |
-    | Llama 3 8B Instruct     | Up to 8k tokens      | Advanced state-of-the-art LLM with language understanding, superior reasoning, and text generation. |
-    | Titan Text G1 - Lite    | Up to 4k tokens      | Lightweight, cost-effective model optimized for English tasks and fine-tuning with focus on summarization and content generation. |
-    | Titan Text G1 - Express | Up to 8k tokens      | Versatile model for general language tasks, chat, and RAG applications with support for English and 100+ languages. |
-    | Cohere Command          | Up to 4k tokens      | Model specialized in following user commands and delivering practical enterprise solutions. |
-    | Jurassic-2 Mid          | Up to 8,191 tokens   | Cost-effective model balancing quality and affordability for diverse language tasks like Q&A, summarization, and content generation. |
-    | Jurassic-2 Ultra        | Up to 8,191 tokens   | Model for advanced text generation and comprehension, excelling in complex tasks like analysis and content creation. |
-    | Jamba-Instruct          | Up to 256k tokens    | Model with extended context window optimized for cost-effective text generation, summarization, and Q&A. |
-    | Mistral 7B Instruct     | Up to 32k tokens     | This LLM follows instructions, completes requests, and generates creative text. |
-    | Mistral 8x7B Instruct   | Up to 32k tokens     | An MOE LLM that follows instructions, completes requests, and generates creative text. |
-
   </Accordion>
   
   <Accordion title="Amazon SageMaker">

docs/concepts/memory.mdx

@@ -60,8 +60,7 @@ my_crew = Crew(
 ```python Code
 from crewai import Crew, Process
 from crewai.memory import LongTermMemory, ShortTermMemory, EntityMemory
-from crewai.memory.storage.rag_storage import RAGStorage
-from crewai.memory.storage.ltm_sqlite_storage import LTMSQLiteStorage
+from crewai.memory.storage import LTMSQLiteStorage, RAGStorage
 from typing import List, Optional
 
 # Assemble your crew with memory capabilities
@@ -120,7 +119,7 @@ Example using environment variables:
 import os
 from crewai import Crew
 from crewai.memory import LongTermMemory
-from crewai.memory.storage.ltm_sqlite_storage import LTMSQLiteStorage
+from crewai.memory.storage import LTMSQLiteStorage
 
 # Configure storage path using environment variable
 storage_path = os.getenv("CREWAI_STORAGE_DIR", "./storage")
@@ -149,7 +148,7 @@ crew = Crew(memory=True)  # Uses default storage locations
 ```python
 from crewai import Crew
 from crewai.memory import LongTermMemory
-from crewai.memory.storage.ltm_sqlite_storage import LTMSQLiteStorage
+from crewai.memory.storage import LTMSQLiteStorage
 
 # Configure custom storage paths
 crew = Crew(

docs/guides/advanced/customizing-prompts.mdx

@@ -1,5 +1,4 @@
----
-title: Customizing Prompts
+---title: Customizing Prompts
 description: Dive deeper into low-level prompt customization for CrewAI, enabling super custom and complex use cases for different models and languages.
 icon: message-pen
 ---

docs/tools/jsonsearchtool.mdx

@@ -7,10 +7,8 @@ icon: file-code
 # `JSONSearchTool`
 
 <Note>
-  The JSONSearchTool is currently in an experimental phase. This means the tool
-  is under active development, and users might encounter unexpected behavior or
-  changes. We highly encourage feedback on any issues or suggestions for
-  improvements.
+    The JSONSearchTool is currently in an experimental phase. This means the tool is under active development, and users might encounter unexpected behavior or changes. 
+    We highly encourage feedback on any issues or suggestions for improvements.
 </Note>
 
 ## Description
@@ -62,7 +60,7 @@ tool = JSONSearchTool(
                 # stream=true,
             },
         },
-        "embedding_model": {
+        "embedder": {
             "provider": "google", # or openai, ollama, ...
             "config": {
                 "model": "models/embedding-001",
@@ -72,4 +70,4 @@ tool = JSONSearchTool(
         },
     }
 )
-```
+```

docs/tools/ragtool.mdx

@@ -8,8 +8,8 @@ icon: vector-square
 
 ## Description
 
-The `RagTool` is designed to answer questions by leveraging the power of Retrieval-Augmented Generation (RAG) through EmbedChain.
-It provides a dynamic knowledge base that can be queried to retrieve relevant information from various data sources.
+The `RagTool` is designed to answer questions by leveraging the power of Retrieval-Augmented Generation (RAG) through EmbedChain. 
+It provides a dynamic knowledge base that can be queried to retrieve relevant information from various data sources. 
 This tool is particularly useful for applications that require access to a vast array of information and need to provide contextually relevant answers.
 
 ## Example
@@ -138,7 +138,7 @@ config = {
             "model": "gpt-4",
         }
     },
-    "embedding_model": {
+    "embedder": {
         "provider": "openai",
         "config": {
             "model": "text-embedding-ada-002"
@@ -151,4 +151,4 @@ rag_tool = RagTool(config=config, summarize=True)
 
 ## 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.
+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. 

src/crewai/cli/templates/flow/pyproject.toml

@@ -10,7 +10,6 @@ dependencies = [
 
 [project.scripts]
 kickoff = "{{folder_name}}.main:kickoff"
-run_crew = "{{folder_name}}.main:kickoff"
 plot = "{{folder_name}}.main:plot"
 
 [build-system]

src/crewai/task.py

@@ -19,8 +19,6 @@ from typing import (
     Tuple,
     Type,
     Union,
-    get_args,
-    get_origin,
 )
 
 from pydantic import (
@@ -180,29 +178,15 @@ class Task(BaseModel):
         """
         if v is not None:
             sig = inspect.signature(v)
-            positional_args = [
-                param
-                for param in sig.parameters.values()
-                if param.default is inspect.Parameter.empty
-            ]
-            if len(positional_args) != 1:
+            if len(sig.parameters) != 1:
                 raise ValueError("Guardrail function must accept exactly one parameter")
 
             # Check return annotation if present, but don't require it
             return_annotation = sig.return_annotation
             if return_annotation != inspect.Signature.empty:
-
-                return_annotation_args = get_args(return_annotation)
                 if not (
-                    get_origin(return_annotation) is tuple
-                    and len(return_annotation_args) == 2
-                    and return_annotation_args[0] is bool
-                    and (
-                        return_annotation_args[1] is Any
-                        or return_annotation_args[1] is str
-                        or return_annotation_args[1] is TaskOutput
-                        or return_annotation_args[1] == Union[str, TaskOutput]
-                    )
+                    return_annotation == Tuple[bool, Any]
+                    or str(return_annotation) == "Tuple[bool, Any]"
                 ):
                     raise ValueError(
                         "If return type is annotated, it must be Tuple[bool, Any]"

src/crewai/utilities/events/crewai_event_bus.py

@@ -67,13 +67,16 @@ class CrewAIEventsBus:
             source: The object emitting the event
             event: The event instance to emit
         """
-        for event_type, handlers in self._handlers.items():
-            if isinstance(event, event_type):
-                for handler in handlers:
-                    handler(source, event)
-
+        event_type = type(event)
+        if event_type in self._handlers:
+            for handler in self._handlers[event_type]:
+                handler(source, event)
         self._signal.send(source, event=event)
 
+    def clear_handlers(self) -> None:
+        """Clear all registered event handlers - useful for testing"""
+        self._handlers.clear()
+
     def register_handler(
         self, event_type: Type[EventTypes], handler: Callable[[Any, EventTypes], None]
     ) -> None:

src/crewai/utilities/events/flow_events.py

@@ -1,6 +1,6 @@
 from typing import Any, Dict, Optional, Union
 
-from pydantic import BaseModel, ConfigDict
+from pydantic import BaseModel
 
 from .base_events import CrewEvent
 
@@ -52,11 +52,9 @@ class MethodExecutionFailedEvent(FlowEvent):
 
     flow_name: str
     method_name: str
-    error: Exception
+    error: Any
     type: str = "method_execution_failed"
 
-    model_config = ConfigDict(arbitrary_types_allowed=True)
-
 
 class FlowFinishedEvent(FlowEvent):
     """Event emitted when a flow completes execution"""

tests/task_test.py

@@ -3,8 +3,6 @@
 import hashlib
 import json
 import os
-from functools import partial
-from typing import Tuple, Union
 from unittest.mock import MagicMock, patch
 
 import pytest
@@ -217,75 +215,6 @@ def test_multiple_output_type_error():
         )
 
 
-def test_guardrail_type_error():
-    desc = "Give me a list of 5 interesting ideas to explore for na article, what makes them unique and interesting."
-    expected_output = "Bullet point list of 5 interesting ideas."
-    # Lambda function
-    Task(
-        description=desc,
-        expected_output=expected_output,
-        guardrail=lambda x: (True, x),
-    )
-
-    # Function
-    def guardrail_fn(x: TaskOutput) -> tuple[bool, TaskOutput]:
-        return (True, x)
-
-    Task(
-        description=desc,
-        expected_output=expected_output,
-        guardrail=guardrail_fn,
-    )
-
-    class Object:
-        def guardrail_fn(self, x: TaskOutput) -> tuple[bool, TaskOutput]:
-            return (True, x)
-
-        @classmethod
-        def guardrail_class_fn(cls, x: TaskOutput) -> tuple[bool, str]:
-            return (True, x)
-
-        @staticmethod
-        def guardrail_static_fn(x: TaskOutput) -> tuple[bool, Union[str, TaskOutput]]:
-            return (True, x)
-
-    obj = Object()
-    # Method
-    Task(
-        description=desc,
-        expected_output=expected_output,
-        guardrail=obj.guardrail_fn,
-    )
-    # Class method
-    Task(
-        description=desc,
-        expected_output=expected_output,
-        guardrail=Object.guardrail_class_fn,
-    )
-    # Static method
-    Task(
-        description=desc,
-        expected_output=expected_output,
-        guardrail=Object.guardrail_static_fn,
-    )
-
-    def error_fn(x: TaskOutput, y: bool) -> Tuple[bool, TaskOutput]:
-        return (y, x)
-
-    Task(
-        description=desc,
-        expected_output=expected_output,
-        guardrail=partial(error_fn, y=True),
-    )
-
-    with pytest.raises(ValidationError):
-        Task(
-            description=desc,
-            expected_output=expected_output,
-            guardrail=error_fn,
-        )
-
-
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_output_pydantic_sequential():
     class ScoreOutput(BaseModel):

tests/utilities/events/test_crewai_event_bus.py

@@ -1,34 +0,0 @@
-from unittest.mock import Mock
-
-from crewai.utilities.events.base_events import CrewEvent
-from crewai.utilities.events.crewai_event_bus import crewai_event_bus
-
-
-class TestEvent(CrewEvent):
-    pass
-
-
-def test_specific_event_handler():
-    mock_handler = Mock()
-
-    @crewai_event_bus.on(TestEvent)
-    def handler(source, event):
-        mock_handler(source, event)
-
-    event = TestEvent(type="test_event")
-    crewai_event_bus.emit("source_object", event)
-
-    mock_handler.assert_called_once_with("source_object", event)
-
-
-def test_wildcard_event_handler():
-    mock_handler = Mock()
-
-    @crewai_event_bus.on(CrewEvent)
-    def handler(source, event):
-        mock_handler(source, event)
-
-    event = TestEvent(type="test_event")
-    crewai_event_bus.emit("source_object", event)
-
-    mock_handler.assert_called_once_with("source_object", event)