Add warning for models like deepseek-r1 using tools

Co-authored-by: Brandon Hancock (bhancock_ai) <109994880+bhancockio@users.noreply.github.com>

docs/changelog.mdx

@@ -0,0 +1,187 @@
+---
+title: Changelog
+description: View the latest updates and changes to CrewAI
+icon: timeline
+---
+
+<Update label="2024-03-17" description="v0.108.0">
+  **Features**
+  - Converted tabs to spaces in `crew.py` template
+  - Enhanced LLM Streaming Response Handling and Event System
+  - Included `model_name`
+  - Enhanced Event Listener with rich visualization and improved logging
+  - Added fingerprints
+
+  **Bug Fixes**
+  - Fixed Mistral issues
+  - Fixed a bug in documentation
+  - Fixed type check error in fingerprint property
+
+  **Documentation Updates**
+  - Improved tool documentation
+  - Updated installation guide for the `uv` tool package
+  - Added instructions for upgrading crewAI with the `uv` tool
+  - Added documentation for `ApifyActorsTool`
+</Update>
+
+<Update label="2024-03-10" description="v0.105.0">
+  **Core Improvements & Fixes**
+  - Fixed issues with missing template variables and user memory configuration
+  - Improved async flow support and addressed agent response formatting
+  - Enhanced memory reset functionality and fixed CLI memory commands
+  - Fixed type issues, tool calling properties, and telemetry decoupling
+
+  **New Features & Enhancements**
+  - Added Flow state export and improved state utilities
+  - Enhanced agent knowledge setup with optional crew embedder
+  - Introduced event emitter for better observability and LLM call tracking
+  - Added support for Python 3.10 and ChatOllama from langchain_ollama
+  - Integrated context window size support for the o3-mini model
+  - Added support for multiple router calls
+
+  **Documentation & Guides**
+  - Improved documentation layout and hierarchical structure
+  - Added QdrantVectorSearchTool guide and clarified event listener usage
+  - Fixed typos in prompts and updated Amazon Bedrock model listings
+</Update>
+
+<Update label="2024-02-12" description="v0.102.0">
+  **Core Improvements & Fixes**
+  - Enhanced LLM Support: Improved structured LLM output, parameter handling, and formatting for Anthropic models
+  - Crew & Agent Stability: Fixed issues with cloning agents/crews using knowledge sources, multiple task outputs in conditional tasks, and ignored Crew task callbacks
+  - Memory & Storage Fixes: Fixed short-term memory handling with Bedrock, ensured correct embedder initialization, and added a reset memories function in the crew class
+  - Training & Execution Reliability: Fixed broken training and interpolation issues with dict and list input types
+
+  **New Features & Enhancements**
+  - Advanced Knowledge Management: Improved naming conventions and enhanced embedding configuration with custom embedder support
+  - Expanded Logging & Observability: Added JSON format support for logging and integrated MLflow tracing documentation
+  - Data Handling Improvements: Updated excel_knowledge_source.py to process multi-tab files
+  - General Performance & Codebase Clean-Up: Streamlined enterprise code alignment and resolved linting issues
+  - Adding new tool: `QdrantVectorSearchTool`
+
+  **Documentation & Guides**
+  - Updated AI & Memory Docs: Improved Bedrock, Google AI, and long-term memory documentation
+  - Task & Workflow Clarity: Added "Human Input" row to Task Attributes, Langfuse guide, and FileWriterTool documentation
+  - Fixed Various Typos & Formatting Issues
+</Update>
+
+<Update label="2024-01-28" description="v0.100.0">
+  **Features**
+  - Add Composio docs
+  - Add SageMaker as a LLM provider
+  
+  **Fixes**
+  - Overall LLM connection issues
+  - Using safe accessors on training
+  - Add version check to crew_chat.py
+  
+  **Documentation**
+  - New docs for crewai chat
+  - Improve formatting and clarity in CLI and Composio Tool docs
+</Update>
+
+<Update label="2024-01-20" description="v0.98.0">
+  **Features**
+  - Conversation crew v1
+  - Add unique ID to flow states
+  - Add @persist decorator with FlowPersistence interface
+  
+  **Integrations**
+  - Add SambaNova integration
+  - Add NVIDIA NIM provider in cli
+  - Introducing VoyageAI
+  
+  **Fixes**
+  - Fix API Key Behavior and Entity Handling in Mem0 Integration
+  - Fixed core invoke loop logic and relevant tests
+  - Make tool inputs actual objects and not strings
+  - Add important missing parts to creating tools
+  - Drop litellm version to prevent windows issue
+  - Before kickoff if inputs are none
+  - Fixed typos, nested pydantic model issue, and docling issues
+</Update>
+
+<Update label="2024-01-04" description="v0.95.0">
+  **New Features**
+  - Adding Multimodal Abilities to Crew
+  - Programatic Guardrails
+  - HITL multiple rounds
+  - Gemini 2.0 Support
+  - CrewAI Flows Improvements
+  - Add Workflow Permissions
+  - Add support for langfuse with litellm
+  - Portkey Integration with CrewAI
+  - Add interpolate_only method and improve error handling
+  - Docling Support
+  - Weviate Support
+  
+  **Fixes**
+  - output_file not respecting system path
+  - disk I/O error when resetting short-term memory
+  - CrewJSONEncoder now accepts enums
+  - Python max version
+  - Interpolation for output_file in Task
+  - Handle coworker role name case/whitespace properly
+  - Add tiktoken as explicit dependency and document Rust requirement
+  - Include agent knowledge in planning process
+  - Change storage initialization to None for KnowledgeStorage
+  - Fix optional storage checks
+  - include event emitter in flows
+  - Docstring, Error Handling, and Type Hints Improvements
+  - Suppressed userWarnings from litellm pydantic issues
+</Update>
+
+<Update label="2023-12-05" description="v0.86.0">
+  **Changes**
+  - Remove all references to pipeline and pipeline router
+  - Add Nvidia NIM as provider in Custom LLM
+  - Add knowledge demo + improve knowledge docs
+  - Add HITL multiple rounds of followup
+  - New docs about yaml crew with decorators
+  - Simplify template crew
+</Update>
+
+<Update label="2023-12-04" description="v0.85.0">
+  **Features**
+  - Added knowledge to agent level
+  - Feat/remove langchain
+  - Improve typed task outputs
+  - Log in to Tool Repository on crewai login
+  
+  **Fixes**
+  - Fixes issues with result as answer not properly exiting LLM loop
+  - Fix missing key name when running with ollama provider
+  - Fix spelling issue found
+  
+  **Documentation**
+  - Update readme for running mypy
+  - Add knowledge to mint.json
+  - Update Github actions
+  - Update Agents docs to include two approaches for creating an agent
+  - Improvements to LLM Configuration and Usage
+</Update>
+
+<Update label="2023-11-25" description="v0.83.0">
+  **New Features**
+  - New before_kickoff and after_kickoff crew callbacks
+  - Support to pre-seed agents with Knowledge
+  - Add support for retrieving user preferences and memories using Mem0
+  
+  **Fixes**
+  - Fix Async Execution
+  - Upgrade chroma and adjust embedder function generator
+  - Update CLI Watson supported models + docs
+  - Reduce level for Bandit
+  - Fixing all tests
+  
+  **Documentation**
+  - Update Docs
+</Update>
+
+<Update label="2023-11-13" description="v0.80.0">
+  **Fixes**
+  - Fixing Tokens callback replacement bug
+  - Fixing Step callback issue
+  - Add cached prompt tokens info on usage metrics
+  - Fix crew_train_success test
+</Update>

docs/concepts/knowledge.mdx

@@ -150,6 +150,8 @@ 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
@@ -460,12 +462,12 @@ class SpaceNewsKnowledgeSource(BaseKnowledgeSource):
             data = response.json()
             articles = data.get('results', [])
 
-            formatted_data = self._format_articles(articles)
+            formatted_data = self.validate_content(articles)
             return {self.api_endpoint: formatted_data}
         except Exception as e:
             raise ValueError(f"Failed to fetch space news: {str(e)}")
 
-    def _format_articles(self, articles: list) -> str:
+    def validate_content(self, articles: list) -> str:
         """Format articles into readable text."""
         formatted = "Space News Articles:\n\n"
         for article in articles:

docs/concepts/llms.mdx

@@ -158,7 +158,11 @@ 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:
@@ -250,6 +254,40 @@ 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 exceling 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">
@@ -368,6 +406,46 @@ In this section, you'll find detailed examples that help you select, configure,
     | baichuan-inc/baichuan2-13b-chat                                  | 4,096 tokens   | Support Chinese and English chat, coding, math, instruction following, solving quizzes |
   </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. 
+    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
+
+    local_nvidia_nim_llm = LLM(
+        model="openai/meta/llama-3.1-8b-instruct", # it's an openai-api compatible model
+        base_url="http://localhost:8000/v1",
+        api_key="<your_api_key|any text if you have not configured it>", # api_key is required, but you can use any text
+    )
+
+    # Then you can use it in your crew:
+
+    @CrewBase
+    class MyCrew():
+        # ...
+
+        @agent
+        def researcher(self) -> Agent:
+            return Agent(
+                config=self.agents_config['researcher'],
+                llm=local_nvidia_nim_llm
+            )
+        
+        # ...
+    ```
+  </Accordion>
+
   <Accordion title="Groq">
     Set the following environment variables in your `.env` file:
 
@@ -708,5 +786,5 @@ Learn how to get the most out of your LLM configuration:
     <Tip>
       Use larger context models for extensive tasks
     </Tip>
-    
-    ```
+  </Tab>
+</Tabs>

docs/concepts/memory.mdx

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

docs/docs.json

@@ -0,0 +1,223 @@
+{
+  "$schema": "https://mintlify.com/docs.json",
+  "theme": "palm",
+  "name": "CrewAI",
+  "colors": {
+    "primary": "#EB6658",
+    "light": "#F3A78B",
+    "dark": "#C94C3C"
+  },
+  "favicon": "favicon.svg",
+  "navigation": {
+    "tabs": [
+      {
+        "tab": "Get Started",
+        "groups": [
+          {
+            "group": "Get Started",
+            "pages": [
+              "introduction",
+              "installation",
+              "quickstart",
+              "changelog"
+            ]
+          },
+          {
+            "group": "Guides",
+            "pages": [
+              {
+                "group": "Concepts",
+                "pages": [
+                  "guides/concepts/evaluating-use-cases"
+                ]
+              },
+              {
+                "group": "Agents",
+                "pages": [
+                  "guides/agents/crafting-effective-agents"
+                ]
+              },
+              {
+                "group": "Crews",
+                "pages": [
+                  "guides/crews/first-crew"
+                ]
+              },
+              {
+                "group": "Flows",
+                "pages": [
+                  "guides/flows/first-flow",
+                  "guides/flows/mastering-flow-state"
+                ]
+              },
+              {
+                "group": "Advanced",
+                "pages": [
+                  "guides/advanced/customizing-prompts",
+                  "guides/advanced/fingerprinting"
+                ]
+              }
+            ]
+          },
+          {
+            "group": "Core Concepts",
+            "pages": [
+              "concepts/agents",
+              "concepts/tasks",
+              "concepts/crews",
+              "concepts/flows",
+              "concepts/knowledge",
+              "concepts/llms",
+              "concepts/processes",
+              "concepts/collaboration",
+              "concepts/training",
+              "concepts/memory",
+              "concepts/planning",
+              "concepts/testing",
+              "concepts/cli",
+              "concepts/tools",
+              "concepts/event-listener",
+              "concepts/langchain-tools",
+              "concepts/llamaindex-tools"
+            ]
+          },
+          {
+            "group": "How to Guides",
+            "pages": [
+              "how-to/create-custom-tools",
+              "how-to/sequential-process",
+              "how-to/hierarchical-process",
+              "how-to/custom-manager-agent",
+              "how-to/llm-connections",
+              "how-to/customizing-agents",
+              "how-to/multimodal-agents",
+              "how-to/coding-agents",
+              "how-to/force-tool-output-as-result",
+              "how-to/human-input-on-execution",
+              "how-to/kickoff-async",
+              "how-to/kickoff-for-each",
+              "how-to/replay-tasks-from-latest-crew-kickoff",
+              "how-to/conditional-tasks",
+              "how-to/agentops-observability",
+              "how-to/langtrace-observability",
+              "how-to/mlflow-observability",
+              "how-to/openlit-observability",
+              "how-to/portkey-observability",
+              "how-to/langfuse-observability"
+            ]
+          },
+          {
+            "group": "Tools",
+            "pages": [
+              "tools/aimindtool",
+              "tools/apifyactorstool",
+              "tools/bravesearchtool",
+              "tools/browserbaseloadtool",
+              "tools/codedocssearchtool",
+              "tools/codeinterpretertool",
+              "tools/composiotool",
+              "tools/csvsearchtool",
+              "tools/dalletool",
+              "tools/directorysearchtool",
+              "tools/directoryreadtool",
+              "tools/docxsearchtool",
+              "tools/exasearchtool",
+              "tools/filereadtool",
+              "tools/filewritetool",
+              "tools/firecrawlcrawlwebsitetool",
+              "tools/firecrawlscrapewebsitetool",
+              "tools/firecrawlsearchtool",
+              "tools/githubsearchtool",
+              "tools/hyperbrowserloadtool",
+              "tools/linkupsearchtool",
+              "tools/llamaindextool",
+              "tools/serperdevtool",
+              "tools/s3readertool",
+              "tools/s3writertool",
+              "tools/scrapegraphscrapetool",
+              "tools/scrapeelementfromwebsitetool",
+              "tools/jsonsearchtool",
+              "tools/mdxsearchtool",
+              "tools/mysqltool",
+              "tools/multiontool",
+              "tools/nl2sqltool",
+              "tools/patronustools",
+              "tools/pdfsearchtool",
+              "tools/pgsearchtool",
+              "tools/qdrantvectorsearchtool",
+              "tools/ragtool",
+              "tools/scrapewebsitetool",
+              "tools/scrapflyscrapetool",
+              "tools/seleniumscrapingtool",
+              "tools/snowflakesearchtool",
+              "tools/spidertool",
+              "tools/txtsearchtool",
+              "tools/visiontool",
+              "tools/weaviatevectorsearchtool",
+              "tools/websitesearchtool",
+              "tools/xmlsearchtool",
+              "tools/youtubechannelsearchtool",
+              "tools/youtubevideosearchtool"
+            ]
+          },
+          {
+            "group": "Telemetry",
+            "pages": [
+              "telemetry"
+            ]
+          }
+        ]
+      },
+      {
+        "tab": "Examples",
+        "groups": [
+          {
+            "group": "Examples",
+            "pages": [
+              "examples/example"
+            ]
+          }
+        ]
+      }
+    ],
+    "global": {
+      "anchors": [
+        {
+          "anchor": "Community",
+          "href": "https://community.crewai.com",
+          "icon": "discourse"
+        }
+      ]
+    }
+  },
+  "logo": {
+    "light": "crew_only_logo.png",
+    "dark": "crew_only_logo.png"
+  },
+  "appearance": {
+    "default": "dark",
+    "strict": false
+  },
+  "navbar": {
+    "primary": {
+      "type": "github",
+      "href": "https://github.com/crewAIInc/crewAI"
+    }
+  },
+  "search": {
+    "prompt": "Search CrewAI docs"
+  },
+  "seo": {
+    "indexing": "navigable"
+  },
+  "footer": {
+    "socials": {
+      "website": "https://crewai.com",
+      "x": "https://x.com/crewAIInc",
+      "github": "https://github.com/crewAIInc/crewAI",
+      "linkedin": "https://www.linkedin.com/company/crewai-inc",
+      "youtube": "https://youtube.com/@crewAIInc",
+      "reddit": "https://www.reddit.com/r/crewAIInc/"
+    }
+  }
+}

docs/guides/advanced/customizing-prompts.mdx

@@ -1,4 +1,5 @@
----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/mint.json

@@ -1,225 +0,0 @@
-{
-  "name": "CrewAI",
-  "theme": "venus",
-  "logo": {
-    "dark": "crew_only_logo.png",
-    "light": "crew_only_logo.png"
-  },
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-  "colors": {
-    "primary": "#EB6658",
-    "light": "#F3A78B",
-    "dark": "#C94C3C",
-    "anchors": {
-      "from": "#737373",
-      "to": "#EB6658"
-    }
-  },
-  "seo": {
-    "indexHiddenPages": false
-  },
-  "modeToggle": {
-    "default": "dark",
-    "isHidden": false
-  },
-  "feedback": {
-    "suggestEdit": true,
-    "raiseIssue": true,
-    "thumbsRating": true
-  },
-  "topbarCtaButton": {
-    "type": "github",
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-  },
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-    "name": "Get Started"
-  },
-  "tabs": [
-    {
-      "name": "Examples",
-      "url": "examples"
-    }
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-  "anchors": [
-    {
-      "name": "Community",
-      "icon": "discourse",
-      "url": "https://community.crewai.com"
-    },
-    {
-      "name": "Changelog",
-      "icon": "timeline",
-      "url": "https://github.com/crewAIInc/crewAI/releases"
-    }
-  ],
-  "navigation": [
-    {
-      "group": "Get Started",
-      "pages": [
-        "introduction",
-        "installation",
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-      "group": "Guides",
-      "pages": [
-        {
-          "group": "Concepts",
-          "pages": [
-            "guides/concepts/evaluating-use-cases"
-          ]
-        },
-        {
-          "group": "Agents",
-          "pages": [
-            "guides/agents/crafting-effective-agents"
-          ]
-        },
-        {
-          "group": "Crews",
-          "pages": [
-            "guides/crews/first-crew"
-          ]
-        },
-        {
-          "group": "Flows",
-          "pages": [
-            "guides/flows/first-flow",
-            "guides/flows/mastering-flow-state"
-          ]
-        },
-        {
-          "group": "Advanced",
-          "pages": [
-            "guides/advanced/customizing-prompts",
-            "guides/advanced/fingerprinting"
-          ]
-        }
-      ]
-    },
-    {
-      "group": "Core Concepts",
-      "pages": [
-        "concepts/agents",
-        "concepts/tasks",
-        "concepts/crews",
-        "concepts/flows",
-        "concepts/knowledge",
-        "concepts/llms",
-        "concepts/processes",
-        "concepts/collaboration",
-        "concepts/training",
-        "concepts/memory",
-        "concepts/planning",
-        "concepts/testing",
-        "concepts/cli",
-        "concepts/tools",
-        "concepts/event-listener",
-        "concepts/langchain-tools",
-        "concepts/llamaindex-tools"
-      ]
-    },
-    {
-      "group": "How to Guides",
-      "pages": [
-        "how-to/create-custom-tools",
-        "how-to/sequential-process",
-        "how-to/hierarchical-process",
-        "how-to/custom-manager-agent",
-        "how-to/llm-connections",
-        "how-to/customizing-agents",
-        "how-to/multimodal-agents",
-        "how-to/coding-agents",
-        "how-to/force-tool-output-as-result",
-        "how-to/human-input-on-execution",
-        "how-to/kickoff-async",
-        "how-to/kickoff-for-each",
-        "how-to/replay-tasks-from-latest-crew-kickoff",
-        "how-to/conditional-tasks",
-        "how-to/agentops-observability",
-        "how-to/langtrace-observability",
-        "how-to/mlflow-observability",
-        "how-to/openlit-observability",
-        "how-to/portkey-observability",
-        "how-to/langfuse-observability"
-      ]
-    },
-    {
-      "group": "Examples",
-      "pages": [
-        "examples/example"
-      ]
-    },
-    {
-      "group": "Tools",
-      "pages": [
-        "tools/aimindtool",
-        "tools/apifyactorstool",
-        "tools/bravesearchtool",
-        "tools/browserbaseloadtool",
-        "tools/codedocssearchtool",
-        "tools/codeinterpretertool",
-        "tools/composiotool",
-        "tools/csvsearchtool",
-        "tools/dalletool",
-        "tools/directorysearchtool",
-        "tools/directoryreadtool",
-        "tools/docxsearchtool",
-        "tools/exasearchtool",
-        "tools/filereadtool",
-        "tools/filewritetool",
-        "tools/firecrawlcrawlwebsitetool",
-        "tools/firecrawlscrapewebsitetool",
-        "tools/firecrawlsearchtool",
-        "tools/githubsearchtool",
-        "tools/hyperbrowserloadtool",
-        "tools/linkupsearchtool",
-        "tools/llamaindextool",
-        "tools/serperdevtool",
-        "tools/s3readertool",
-        "tools/s3writertool",
-        "tools/scrapegraphscrapetool",
-        "tools/scrapeelementfromwebsitetool",
-        "tools/jsonsearchtool",
-        "tools/mdxsearchtool",
-        "tools/mysqltool",
-        "tools/multiontool",
-        "tools/nl2sqltool",
-        "tools/patronustools",
-        "tools/pdfsearchtool",
-        "tools/pgsearchtool",
-        "tools/qdrantvectorsearchtool",
-        "tools/ragtool",
-        "tools/scrapewebsitetool",
-        "tools/scrapflyscrapetool",
-        "tools/seleniumscrapingtool",
-        "tools/snowflakesearchtool",
-        "tools/spidertool",
-        "tools/txtsearchtool",
-        "tools/visiontool",
-        "tools/weaviatevectorsearchtool",
-        "tools/websitesearchtool",
-        "tools/xmlsearchtool",
-        "tools/youtubechannelsearchtool",
-        "tools/youtubevideosearchtool"
-      ]
-    },
-    {
-      "group": "Telemetry",
-      "pages": [
-        "telemetry"
-      ]
-    }
-  ],
-  "search": {
-    "prompt": "Search CrewAI docs"
-  },
-  "footerSocials": {
-    "website": "https://crewai.com",
-    "x": "https://x.com/crewAIInc",
-    "github": "https://github.com/crewAIInc/crewAI",
-    "linkedin": "https://www.linkedin.com/company/crewai-inc",
-    "youtube": "https://youtube.com/@crewAIInc"
-  }
-}

docs/quickstart.mdx

@@ -300,7 +300,7 @@ email_summarizer:
 ```
 
 <Tip>
-  Note how we use the same name for the agent in the `tasks.yaml` (`email_summarizer_task`) file as the method name in the `crew.py` (`email_summarizer_task`) file.
+  Note how we use the same name for the task in the `tasks.yaml` (`email_summarizer_task`) file as the method name in the `crew.py` (`email_summarizer_task`) file.
 </Tip>
 
 ```yaml tasks.yaml

docs/tools/jsonsearchtool.mdx

@@ -7,8 +7,10 @@ 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
@@ -60,7 +62,7 @@ tool = JSONSearchTool(
                 # stream=true,
             },
         },
-        "embedder": {
+        "embedding_model": {
             "provider": "google", # or openai, ollama, ...
             "config": {
                 "model": "models/embedding-001",
@@ -70,4 +72,4 @@ 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",
         }
     },
-    "embedder": {
+    "embedding_model": {
         "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.

pyproject.toml

@@ -17,9 +17,9 @@ dependencies = [
     "pdfplumber>=0.11.4",
     "regex>=2024.9.11",
     # Telemetry and Monitoring
-    "opentelemetry-api>=1.22.0",
-    "opentelemetry-sdk>=1.22.0",
-    "opentelemetry-exporter-otlp-proto-http>=1.22.0",
+    "opentelemetry-api>=1.30.0",
+    "opentelemetry-sdk>=1.30.0",
+    "opentelemetry-exporter-otlp-proto-http>=1.30.0",
     # Data Handling
     "chromadb>=0.5.23",
     "openpyxl>=3.1.5",

src/crewai/agents/parser.py

@@ -124,9 +124,9 @@ class CrewAgentParser:
             )
 
     def _extract_thought(self, text: str) -> str:
-        thought_index = text.find("\n\nAction")
+        thought_index = text.find("\nAction")
         if thought_index == -1:
-            thought_index = text.find("\n\nFinal Answer")
+            thought_index = text.find("\nFinal Answer")
         if thought_index == -1:
             return ""
         thought = text[:thought_index].strip()
@@ -136,7 +136,7 @@ class CrewAgentParser:
 
     def _clean_action(self, text: str) -> str:
         """Clean action string by removing non-essential formatting characters."""
-        return re.sub(r"^\s*\*+\s*|\s*\*+\s*$", "", text).strip()
+        return text.strip().strip("*").strip()
 
     def _safe_repair_json(self, tool_input: str) -> str:
         UNABLE_TO_REPAIR_JSON_RESULTS = ['""', "{}"]

src/crewai/cli/git.py

@@ -1,4 +1,5 @@
 import subprocess
+from functools import lru_cache
 
 
 class Repository:
@@ -35,6 +36,7 @@ class Repository:
             encoding="utf-8",
         ).strip()
 
+    @lru_cache(maxsize=None)
     def is_git_repo(self) -> bool:
         """Check if the current directory is a git repository."""
         try:

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

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

src/crewai/memory/storage/mem0_storage.py

@@ -1,7 +1,7 @@
 import os
 from typing import Any, Dict, List
 
-from mem0 import MemoryClient
+from mem0 import Memory, MemoryClient
 
 from crewai.memory.storage.interface import Storage
 
@@ -32,13 +32,16 @@ class Mem0Storage(Storage):
         mem0_org_id = config.get("org_id")
         mem0_project_id = config.get("project_id")
 
-        # Initialize MemoryClient with available parameters
-        if mem0_org_id and mem0_project_id:
-            self.memory = MemoryClient(
-                api_key=mem0_api_key, org_id=mem0_org_id, project_id=mem0_project_id
-            )
+        # Initialize MemoryClient or Memory based on the presence of the mem0_api_key
+        if mem0_api_key:
+            if mem0_org_id and mem0_project_id:
+                self.memory = MemoryClient(
+                    api_key=mem0_api_key, org_id=mem0_org_id, project_id=mem0_project_id
+                )
+            else:
+                self.memory = MemoryClient(api_key=mem0_api_key)
         else:
-            self.memory = MemoryClient(api_key=mem0_api_key)
+            self.memory = Memory()  # Fallback to Memory if no Mem0 API key is provided
 
     def _sanitize_role(self, role: str) -> str:
         """

src/crewai/task.py

@@ -19,6 +19,8 @@ from typing import (
     Tuple,
     Type,
     Union,
+    get_args,
+    get_origin,
 )
 
 from pydantic import (
@@ -178,15 +180,29 @@ class Task(BaseModel):
         """
         if v is not None:
             sig = inspect.signature(v)
-            if len(sig.parameters) != 1:
+            positional_args = [
+                param
+                for param in sig.parameters.values()
+                if param.default is inspect.Parameter.empty
+            ]
+            if len(positional_args) != 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 (
-                    return_annotation == Tuple[bool, Any]
-                    or str(return_annotation) == "Tuple[bool, Any]"
+                    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]
+                    )
                 ):
                     raise ValueError(
                         "If return type is annotated, it must be Tuple[bool, Any]"

src/crewai/telemetry/telemetry.py

@@ -281,8 +281,16 @@ class Telemetry:
         return self._safe_telemetry_operation(operation)
 
     def task_ended(self, span: Span, task: Task, crew: Crew):
-        """Records task execution in a crew."""
+        """Records the completion of a task execution in a crew.
 
+        Args:
+            span (Span): The OpenTelemetry span tracking the task execution
+            task (Task): The task that was completed
+            crew (Crew): The crew context in which the task was executed
+
+        Note:
+            If share_crew is enabled, this will also record the task output
+        """
         def operation():
             if crew.share_crew:
                 self._add_attribute(
@@ -297,8 +305,13 @@ class Telemetry:
         self._safe_telemetry_operation(operation)
 
     def tool_repeated_usage(self, llm: Any, tool_name: str, attempts: int):
-        """Records the repeated usage 'error' of a tool by an agent."""
+        """Records when a tool is used repeatedly, which might indicate an issue.
 
+        Args:
+            llm (Any): The language model being used
+            tool_name (str): Name of the tool being repeatedly used
+            attempts (int): Number of attempts made with this tool
+        """
         def operation():
             tracer = trace.get_tracer("crewai.telemetry")
             span = tracer.start_span("Tool Repeated Usage")
@@ -317,8 +330,13 @@ class Telemetry:
         self._safe_telemetry_operation(operation)
 
     def tool_usage(self, llm: Any, tool_name: str, attempts: int):
-        """Records the usage of a tool by an agent."""
+        """Records the usage of a tool by an agent.
 
+        Args:
+            llm (Any): The language model being used
+            tool_name (str): Name of the tool being used
+            attempts (int): Number of attempts made with this tool
+        """
         def operation():
             tracer = trace.get_tracer("crewai.telemetry")
             span = tracer.start_span("Tool Usage")
@@ -337,8 +355,11 @@ class Telemetry:
         self._safe_telemetry_operation(operation)
 
     def tool_usage_error(self, llm: Any):
-        """Records the usage of a tool by an agent."""
+        """Records when a tool usage results in an error.
 
+        Args:
+            llm (Any): The language model being used when the error occurred
+        """
         def operation():
             tracer = trace.get_tracer("crewai.telemetry")
             span = tracer.start_span("Tool Usage Error")
@@ -357,6 +378,14 @@ class Telemetry:
     def individual_test_result_span(
         self, crew: Crew, quality: float, exec_time: int, model_name: str
     ):
+        """Records individual test results for a crew execution.
+
+        Args:
+            crew (Crew): The crew being tested
+            quality (float): Quality score of the execution
+            exec_time (int): Execution time in seconds
+            model_name (str): Name of the model used
+        """
         def operation():
             tracer = trace.get_tracer("crewai.telemetry")
             span = tracer.start_span("Crew Individual Test Result")
@@ -383,6 +412,14 @@ class Telemetry:
         inputs: dict[str, Any] | None,
         model_name: str,
     ):
+        """Records the execution of a test suite for a crew.
+
+        Args:
+            crew (Crew): The crew being tested
+            iterations (int): Number of test iterations
+            inputs (dict[str, Any] | None): Input parameters for the test
+            model_name (str): Name of the model used in testing
+        """
         def operation():
             tracer = trace.get_tracer("crewai.telemetry")
             span = tracer.start_span("Crew Test Execution")
@@ -408,6 +445,7 @@ class Telemetry:
         self._safe_telemetry_operation(operation)
 
     def deploy_signup_error_span(self):
+        """Records when an error occurs during the deployment signup process."""
         def operation():
             tracer = trace.get_tracer("crewai.telemetry")
             span = tracer.start_span("Deploy Signup Error")
@@ -417,6 +455,11 @@ class Telemetry:
         self._safe_telemetry_operation(operation)
 
     def start_deployment_span(self, uuid: Optional[str] = None):
+        """Records the start of a deployment process.
+
+        Args:
+            uuid (Optional[str]): Unique identifier for the deployment
+        """
         def operation():
             tracer = trace.get_tracer("crewai.telemetry")
             span = tracer.start_span("Start Deployment")
@@ -428,6 +471,7 @@ class Telemetry:
         self._safe_telemetry_operation(operation)
 
     def create_crew_deployment_span(self):
+        """Records the creation of a new crew deployment."""
         def operation():
             tracer = trace.get_tracer("crewai.telemetry")
             span = tracer.start_span("Create Crew Deployment")
@@ -437,6 +481,12 @@ class Telemetry:
         self._safe_telemetry_operation(operation)
 
     def get_crew_logs_span(self, uuid: Optional[str], log_type: str = "deployment"):
+        """Records the retrieval of crew logs.
+
+        Args:
+            uuid (Optional[str]): Unique identifier for the crew
+            log_type (str, optional): Type of logs being retrieved. Defaults to "deployment".
+        """
         def operation():
             tracer = trace.get_tracer("crewai.telemetry")
             span = tracer.start_span("Get Crew Logs")
@@ -449,6 +499,11 @@ class Telemetry:
         self._safe_telemetry_operation(operation)
 
     def remove_crew_span(self, uuid: Optional[str] = None):
+        """Records the removal of a crew.
+
+        Args:
+            uuid (Optional[str]): Unique identifier for the crew being removed
+        """
         def operation():
             tracer = trace.get_tracer("crewai.telemetry")
             span = tracer.start_span("Remove Crew")
@@ -574,6 +629,11 @@ class Telemetry:
         self._safe_telemetry_operation(operation)
 
     def flow_creation_span(self, flow_name: str):
+        """Records the creation of a new flow.
+
+        Args:
+            flow_name (str): Name of the flow being created
+        """
         def operation():
             tracer = trace.get_tracer("crewai.telemetry")
             span = tracer.start_span("Flow Creation")
@@ -584,6 +644,12 @@ class Telemetry:
         self._safe_telemetry_operation(operation)
 
     def flow_plotting_span(self, flow_name: str, node_names: list[str]):
+        """Records flow visualization/plotting activity.
+
+        Args:
+            flow_name (str): Name of the flow being plotted
+            node_names (list[str]): List of node names in the flow
+        """
         def operation():
             tracer = trace.get_tracer("crewai.telemetry")
             span = tracer.start_span("Flow Plotting")
@@ -595,6 +661,12 @@ class Telemetry:
         self._safe_telemetry_operation(operation)
 
     def flow_execution_span(self, flow_name: str, node_names: list[str]):
+        """Records the execution of a flow.
+
+        Args:
+            flow_name (str): Name of the flow being executed
+            node_names (list[str]): List of nodes being executed in the flow
+        """
         def operation():
             tracer = trace.get_tracer("crewai.telemetry")
             span = tracer.start_span("Flow Execution")

src/crewai/tools/tool_usage.py

@@ -337,11 +337,23 @@ class ToolUsage:
         return "\n--\n".join(descriptions)
 
     def _function_calling(self, tool_string: str):
-        model = (
-            InstructorToolCalling
-            if self.function_calling_llm.supports_function_calling()
-            else ToolCalling
+        supports_function_calling = (
+            self.function_calling_llm.supports_function_calling()
         )
+
+        if not supports_function_calling:
+            import warnings
+
+            warnings.warn(
+                "The model you're using doesn't natively support function calling. "
+                "CrewAI will attempt to use a workaround, but this may be less reliable. "
+                "Consider using a model with native function calling support for better results.",
+                UserWarning,
+                stacklevel=2,
+            )
+
+        model = InstructorToolCalling if supports_function_calling else ToolCalling
+
         converter = Converter(
             text=f"Only tools available:\n###\n{self._render()}\n\nReturn a valid schema for the tool, the tool name must be exactly equal one of the options, use this text to inform the valid output schema:\n\n### TEXT \n{tool_string}",
             llm=self.function_calling_llm,

src/crewai/utilities/events/crewai_event_bus.py

@@ -67,15 +67,12 @@ class CrewAIEventsBus:
             source: The object emitting the event
             event: The event instance to emit
         """
-        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)
+        for event_type, handlers in self._handlers.items():
+            if isinstance(event, event_type):
+                for handler in handlers:
+                    handler(source, event)
 
-    def clear_handlers(self) -> None:
-        """Clear all registered event handlers - useful for testing"""
-        self._handlers.clear()
+        self._signal.send(source, event=event)
 
     def register_handler(
         self, event_type: Type[EventTypes], handler: Callable[[Any, EventTypes], None]

src/crewai/utilities/events/flow_events.py

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

src/crewai/utilities/planning_handler.py

@@ -96,6 +96,10 @@ class CrewPlanner:
         tasks_summary = []
         for idx, task in enumerate(self.tasks):
             knowledge_list = self._get_agent_knowledge(task)
+            agent_tools = (
+                f"[{', '.join(str(tool) for tool in task.agent.tools)}]" if task.agent and task.agent.tools else '"agent has no tools"',
+                f',\n                "agent_knowledge": "[\\"{knowledge_list[0]}\\"]"' if knowledge_list and str(knowledge_list) != "None" else ""
+            )
             task_summary = f"""
                 Task Number {idx + 1} - {task.description}
                 "task_description": {task.description}
@@ -103,10 +107,7 @@ class CrewPlanner:
                 "agent": {task.agent.role if task.agent else "None"}
                 "agent_goal": {task.agent.goal if task.agent else "None"}
                 "task_tools": {task.tools}
-                "agent_tools": %s%s""" % (
-                    f"[{', '.join(str(tool) for tool in task.agent.tools)}]" if task.agent and task.agent.tools else '"agent has no tools"',
-                    f',\n                "agent_knowledge": "[\\"{knowledge_list[0]}\\"]"' if knowledge_list and str(knowledge_list) != "None" else ""
-                )
+                "agent_tools": {"".join(agent_tools)}"""
 
             tasks_summary.append(task_summary)
         return " ".join(tasks_summary)

tests/task_test.py

@@ -3,6 +3,8 @@
 import hashlib
 import json
 import os
+from functools import partial
+from typing import Tuple, Union
 from unittest.mock import MagicMock, patch
 
 import pytest
@@ -215,6 +217,75 @@ 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

@@ -0,0 +1,34 @@
+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)