Merge branch 'main' into bugfix/flow-persist-nested-models

docs: Update model reference in LLM configuration (#2267 )
Co-authored-by: Brandon Hancock (bhancock_ai) <109994880+bhancockio@users.noreply.github.com>
2025-12-18 13:28:31 +00:00 · 2025-03-21 17:03:57 -07:00 · 2025-03-21 15:12:26 -04:00 · 2025-03-21 15:10:08 -04:00 · 2025-03-21 11:59:11 -07:00 · 2025-03-21 14:48:25 -04:00
29 changed files with 1026 additions and 884 deletions
--- a/docs/changelog.mdx
+++ b/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>
--- a/docs/concepts/event-listener.mdx
+++ b/docs/concepts/event-listener.mdx
--- a/docs/concepts/knowledge.mdx
+++ b/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:
--- a/docs/concepts/llms.mdx
+++ b/docs/concepts/llms.mdx
@@ -59,7 +59,7 @@ There are three ways to configure LLMs in CrewAI. Choose the method that best fi
        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: openai/gpt-4o-mini # your model here
        # (see provider configuration examples below for more)
    ```

@@ -111,7 +111,7 @@ 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. 
+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.

 <AccordionGroup>
@@ -121,7 +121,7 @@ In this section, you'll find detailed examples that help you select, configure,
    ```toml Code
    # Required
    OPENAI_API_KEY=sk-...
-    
+
    # Optional
    OPENAI_API_BASE=<custom-base-url>
    OPENAI_ORGANIZATION=<your-org-id>
@@ -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:
@@ -222,7 +226,7 @@ In this section, you'll find detailed examples that help you select, configure,
    AZURE_API_KEY=<your-api-key>
    AZURE_API_BASE=<your-resource-url>
    AZURE_API_VERSION=<api-version>
-    
+
    # Optional
    AZURE_AD_TOKEN=<your-azure-ad-token>
    AZURE_API_TYPE=<your-azure-api-type>
@@ -250,8 +254,42 @@ 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">
    ```toml Code
    AWS_ACCESS_KEY_ID=<your-access-key>
@@ -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:

@@ -396,7 +474,7 @@ In this section, you'll find detailed examples that help you select, configure,
    WATSONX_URL=<your-url>
    WATSONX_APIKEY=<your-apikey>
    WATSONX_PROJECT_ID=<your-project-id>
-    
+
    # Optional
    WATSONX_TOKEN=<your-token>
    WATSONX_DEPLOYMENT_SPACE_ID=<your-space-id>
@@ -413,7 +491,7 @@ In this section, you'll find detailed examples that help you select, configure,

  <Accordion title="Ollama (Local LLMs)">
    1. Install Ollama: [ollama.ai](https://ollama.ai/)
-    2. Run a model: `ollama run llama2`
+    2. Run a model: `ollama run llama3`
    3. Configure:

    ```python Code
@@ -522,7 +600,7 @@ In this section, you'll find detailed examples that help you select, configure,
    ```toml Code
    OPENROUTER_API_KEY=<your-api-key>
    ```
-    
+
    Example usage in your CrewAI project:
    ```python Code
    llm = LLM(
@@ -645,7 +723,7 @@ Learn how to get the most out of your LLM configuration:
        - Small tasks (up to 4K tokens): Standard models
        - Medium tasks (between 4K-32K): Enhanced models
        - Large tasks (over 32K): Large context models
-        
+
        ```python
        # Configure model with appropriate settings
        llm = LLM(
@@ -682,11 +760,11 @@ Learn how to get the most out of your LLM configuration:
    <Warning>
      Most authentication issues can be resolved by checking API key format and environment variable names.
    </Warning>
-    
+
    ```bash
    # OpenAI
    OPENAI_API_KEY=sk-...
-    
+
    # Anthropic
    ANTHROPIC_API_KEY=sk-ant-...
    ```
@@ -695,11 +773,11 @@ Learn how to get the most out of your LLM configuration:
    <Check>
      Always include the provider prefix in model names
    </Check>
-    
+
    ```python
    # Correct
    llm = LLM(model="openai/gpt-4")
-    
+
    # Incorrect
    llm = LLM(model="gpt-4")
    ```
@@ -709,4 +787,9 @@ Learn how to get the most out of your LLM configuration:
      Use larger context models for extensive tasks
    </Tip>
    
+    ```python
+    # Large context model
+    llm = LLM(model="openai/gpt-4o")  # 128K tokens
    ```
+  </Tab>
+</Tabs>
--- a/docs/concepts/memory.mdx
+++ b/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(
--- a/docs/docs.json
+++ b/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/"
+    }
+  }
+}
--- a/docs/guides/advanced/customizing-prompts.mdx
+++ b/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
 ---
--- a/docs/mint.json
+++ b/docs/mint.json
@@ -1,224 +0,0 @@
-{
-  "name": "CrewAI",
-  "theme": "venus",
-  "logo": {
-    "dark": "crew_only_logo.png",
-    "light": "crew_only_logo.png"
-  },
-  "favicon": "favicon.svg",
-  "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",
-    "url": "https://github.com/crewAIInc/crewAI"
-  },
-  "primaryTab": {
-    "name": "Get Started"
-  },
-  "tabs": [
-    {
-      "name": "Examples",
-      "url": "examples"
-    }
-  ],
-  "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",
-        "quickstart"
-      ]
-    },
-    {
-      "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/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"
-  }
-}
--- a/docs/quickstart.mdx
+++ b/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
--- a/docs/tools/jsonsearchtool.mdx
+++ b/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(
        },
    }
 )
-```
+```
--- a/docs/tools/ragtool.mdx
+++ b/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.
--- a/pyproject.toml
+++ b/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",
--- a/src/crewai/agents/parser.py
+++ b/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 = ['""', "{}"]
--- a/src/crewai/cli/git.py
+++ b/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:
--- a/src/crewai/cli/templates/flow/pyproject.toml
+++ b/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]
--- a/src/crewai/flow/persistence/base.py
+++ b/src/crewai/flow/persistence/base.py
@@ -8,45 +8,45 @@ from pydantic import BaseModel

 class FlowPersistence(abc.ABC):
    """Abstract base class for flow state persistence.
-    
+
    This class defines the interface that all persistence implementations must follow.
    It supports both structured (Pydantic BaseModel) and unstructured (dict) states.
    """
-    
+
    @abc.abstractmethod
    def init_db(self) -> None:
        """Initialize the persistence backend.
-        
+
        This method should handle any necessary setup, such as:
        - Creating tables
        - Establishing connections
        - Setting up indexes
        """
        pass
-    
+
    @abc.abstractmethod
    def save_state(
        self,
        flow_uuid: str,
        method_name: str,
-        state_data: Union[Dict[str, Any], BaseModel]
+        state_data: Union[Dict[str, Any], BaseModel],
    ) -> None:
        """Persist the flow state after method completion.
-        
+
        Args:
            flow_uuid: Unique identifier for the flow instance
            method_name: Name of the method that just completed
            state_data: Current state data (either dict or Pydantic model)
        """
        pass
-    
+
    @abc.abstractmethod
    def load_state(self, flow_uuid: str) -> Optional[Dict[str, Any]]:
        """Load the most recent state for a given flow UUID.
-        
+
        Args:
            flow_uuid: Unique identifier for the flow instance
-            
+
        Returns:
            The most recent state as a dictionary, or None if no state exists
        """
--- a/src/crewai/flow/persistence/sqlite.py
+++ b/src/crewai/flow/persistence/sqlite.py
@@ -11,6 +11,7 @@ from typing import Any, Dict, Optional, Union
 from pydantic import BaseModel

 from crewai.flow.persistence.base import FlowPersistence
+from crewai.flow.state_utils import to_serializable


 class SQLiteFlowPersistence(FlowPersistence):
@@ -78,34 +79,53 @@ class SQLiteFlowPersistence(FlowPersistence):
            flow_uuid: Unique identifier for the flow instance
            method_name: Name of the method that just completed
            state_data: Current state data (either dict or Pydantic model)
-        """
-        # Convert state_data to dict, handling both Pydantic and dict cases
-        if isinstance(state_data, BaseModel):
-            state_dict = dict(state_data)  # Use dict() for better type compatibility
-        elif isinstance(state_data, dict):
-            state_dict = state_data
-        else:
-            raise ValueError(
-                f"state_data must be either a Pydantic BaseModel or dict, got {type(state_data)}"
-            )

-        with sqlite3.connect(self.db_path) as conn:
-            conn.execute(
-                """
-            INSERT INTO flow_states (
-                flow_uuid,
-                method_name,
-                timestamp,
-                state_json
-            ) VALUES (?, ?, ?, ?)
-            """,
-                (
-                    flow_uuid,
-                    method_name,
-                    datetime.now(timezone.utc).isoformat(),
-                    json.dumps(state_dict),
-                ),
-            )
+        Raises:
+            ValueError: If state_data is neither a dict nor a BaseModel
+            RuntimeError: If database operations fail
+            TypeError: If JSON serialization fails
+        """
+        try:
+            # Convert state_data to a JSON-serializable dict using the helper method
+            state_dict = to_serializable(state_data)
+
+            # Try to serialize to JSON to catch any serialization issues early
+            try:
+                state_json = json.dumps(state_dict)
+            except (TypeError, ValueError, OverflowError) as json_err:
+                raise TypeError(
+                    f"Failed to serialize state to JSON: {json_err}"
+                ) from json_err
+
+            # Perform database operation with error handling
+            try:
+                with sqlite3.connect(self.db_path) as conn:
+                    conn.execute(
+                        """
+                    INSERT INTO flow_states (
+                        flow_uuid,
+                        method_name,
+                        timestamp,
+                        state_json
+                    ) VALUES (?, ?, ?, ?)
+                    """,
+                        (
+                            flow_uuid,
+                            method_name,
+                            datetime.now(timezone.utc).isoformat(),
+                            state_json,
+                        ),
+                    )
+            except sqlite3.Error as db_err:
+                raise RuntimeError(f"Database operation failed: {db_err}") from db_err
+
+        except Exception as e:
+            # Log the error but don't crash the application
+            import logging
+
+            logging.error(f"Failed to save flow state: {e}")
+            # Re-raise to allow caller to handle or ignore
+            raise

    def load_state(self, flow_uuid: str) -> Optional[Dict[str, Any]]:
        """Load the most recent state for a given flow UUID.
--- a/src/crewai/flow/state_utils.py
+++ b/src/crewai/flow/state_utils.py
@@ -1,36 +1,16 @@
 import json
 from datetime import date, datetime
+from enum import Enum
 from typing import Any, Dict, List, Union

 from pydantic import BaseModel

-from crewai.flow import Flow
-
 SerializablePrimitive = Union[str, int, float, bool, None]
 Serializable = Union[
    SerializablePrimitive, List["Serializable"], Dict[str, "Serializable"]
 ]


-def export_state(flow: Flow) -> dict[str, Serializable]:
-    """Exports the Flow's internal state as JSON-compatible data structures.
-
-    Performs a one-way transformation of a Flow's state into basic Python types
-    that can be safely serialized to JSON. To prevent infinite recursion with
-    circular references, the conversion is limited to a depth of 5 levels.
-
-    Args:
-        flow: The Flow object whose state needs to be exported
-
-    Returns:
-        dict[str, Any]: The transformed state using JSON-compatible Python
-            types.
-    """
-    result = to_serializable(flow._state)
-    assert isinstance(result, dict)
-    return result
-
-
 def to_serializable(
    obj: Any, max_depth: int = 5, _current_depth: int = 0
 ) -> Serializable:
@@ -52,6 +32,8 @@ def to_serializable(

    if isinstance(obj, (str, int, float, bool, type(None))):
        return obj
+    elif isinstance(obj, Enum):
+        return obj.value
    elif isinstance(obj, (date, datetime)):
        return obj.isoformat()
    elif isinstance(obj, (list, tuple, set)):
--- a/src/crewai/llm.py
+++ b/src/crewai/llm.py
@@ -114,6 +114,60 @@ LLM_CONTEXT_WINDOW_SIZES = {
    "Llama-3.2-11B-Vision-Instruct": 16384,
    "Meta-Llama-3.2-3B-Instruct": 4096,
    "Meta-Llama-3.2-1B-Instruct": 16384,
+    # bedrock
+    "us.amazon.nova-pro-v1:0": 300000,
+    "us.amazon.nova-micro-v1:0": 128000,
+    "us.amazon.nova-lite-v1:0": 300000,
+    "us.anthropic.claude-3-5-sonnet-20240620-v1:0": 200000,
+    "us.anthropic.claude-3-5-haiku-20241022-v1:0": 200000,
+    "us.anthropic.claude-3-5-sonnet-20241022-v2:0": 200000,
+    "us.anthropic.claude-3-7-sonnet-20250219-v1:0": 200000,
+    "us.anthropic.claude-3-sonnet-20240229-v1:0": 200000,
+    "us.anthropic.claude-3-opus-20240229-v1:0": 200000,
+    "us.anthropic.claude-3-haiku-20240307-v1:0": 200000,
+    "us.meta.llama3-2-11b-instruct-v1:0": 128000,
+    "us.meta.llama3-2-3b-instruct-v1:0": 131000,
+    "us.meta.llama3-2-90b-instruct-v1:0": 128000,
+    "us.meta.llama3-2-1b-instruct-v1:0": 131000,
+    "us.meta.llama3-1-8b-instruct-v1:0": 128000,
+    "us.meta.llama3-1-70b-instruct-v1:0": 128000,
+    "us.meta.llama3-3-70b-instruct-v1:0": 128000,
+    "us.meta.llama3-1-405b-instruct-v1:0": 128000,
+    "eu.anthropic.claude-3-5-sonnet-20240620-v1:0": 200000,
+    "eu.anthropic.claude-3-sonnet-20240229-v1:0": 200000,
+    "eu.anthropic.claude-3-haiku-20240307-v1:0": 200000,
+    "eu.meta.llama3-2-3b-instruct-v1:0": 131000,
+    "eu.meta.llama3-2-1b-instruct-v1:0": 131000,
+    "apac.anthropic.claude-3-5-sonnet-20240620-v1:0": 200000,
+    "apac.anthropic.claude-3-5-sonnet-20241022-v2:0": 200000,
+    "apac.anthropic.claude-3-sonnet-20240229-v1:0": 200000,
+    "apac.anthropic.claude-3-haiku-20240307-v1:0": 200000,
+    "amazon.nova-pro-v1:0": 300000,
+    "amazon.nova-micro-v1:0": 128000,
+    "amazon.nova-lite-v1:0": 300000,
+    "anthropic.claude-3-5-sonnet-20240620-v1:0": 200000,
+    "anthropic.claude-3-5-haiku-20241022-v1:0": 200000,
+    "anthropic.claude-3-5-sonnet-20241022-v2:0": 200000,
+    "anthropic.claude-3-7-sonnet-20250219-v1:0": 200000,
+    "anthropic.claude-3-sonnet-20240229-v1:0": 200000,
+    "anthropic.claude-3-opus-20240229-v1:0": 200000,
+    "anthropic.claude-3-haiku-20240307-v1:0": 200000,
+    "anthropic.claude-v2:1": 200000,
+    "anthropic.claude-v2": 100000,
+    "anthropic.claude-instant-v1": 100000,
+    "meta.llama3-1-405b-instruct-v1:0": 128000,
+    "meta.llama3-1-70b-instruct-v1:0": 128000,
+    "meta.llama3-1-8b-instruct-v1:0": 128000,
+    "meta.llama3-70b-instruct-v1:0": 8000,
+    "meta.llama3-8b-instruct-v1:0": 8000,
+    "amazon.titan-text-lite-v1": 4000,
+    "amazon.titan-text-express-v1": 8000,
+    "cohere.command-text-v14": 4000,
+    "ai21.j2-mid-v1": 8191,
+    "ai21.j2-ultra-v1": 8191,
+    "ai21.jamba-instruct-v1:0": 256000,
+    "mistral.mistral-7b-instruct-v0:2": 32000,
+    "mistral.mixtral-8x7b-instruct-v0:1": 32000,
    # mistral
    "mistral-tiny": 32768,
    "mistral-small-latest": 32768,
--- a/src/crewai/memory/storage/mem0_storage.py
+++ b/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:
        """
--- a/src/crewai/task.py
+++ b/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]"
--- a/src/crewai/telemetry/telemetry.py
+++ b/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")
--- a/src/crewai/tools/tool_usage.py
+++ b/src/crewai/tools/tool_usage.py
@@ -455,7 +455,7 @@ class ToolUsage:

        # Attempt 4: Repair JSON
        try:
-            repaired_input = repair_json(tool_input)
+            repaired_input = repair_json(tool_input, skip_json_loads=True)
            self._printer.print(
                content=f"Repaired JSON: {repaired_input}", color="blue"
            )
--- a/src/crewai/utilities/events/crewai_event_bus.py
+++ b/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]
--- a/src/crewai/utilities/events/flow_events.py
+++ b/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"""
--- a/src/crewai/utilities/planning_handler.py
+++ b/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)
--- a/tests/task_test.py
+++ b/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):
--- a/tests/utilities/events/test_crewai_event_bus.py
+++ b/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)
--- a/tests/utilities/test_converter.py
+++ b/tests/utilities/test_converter.py
@@ -1,35 +1,17 @@
 import json
 import os
-from typing import Dict, List, Optional
+from datetime import date, datetime
+from enum import Enum
+from typing import Any, Dict, List, Optional, Union, cast
 from unittest.mock import MagicMock, Mock, patch

 import pytest
 from pydantic import BaseModel

-from crewai.llm import LLM
-from crewai.utilities.converter import (
-    Converter,
-    ConverterError,
-    convert_to_model,
-    convert_with_instructions,
-    create_converter,
-    generate_model_description,
-    get_conversion_instructions,
-    handle_partial_json,
-    validate_model,
-)
-from crewai.utilities.pydantic_schema_parser import PydanticSchemaParser
+from crewai.flow.state_utils import _to_serializable_key, to_serializable, to_string


 # Sample Pydantic models for testing
-class EmailResponse(BaseModel):
-    previous_message_content: str
-
-
-class EmailResponses(BaseModel):
-    responses: list[EmailResponse]
-
-
 class SimpleModel(BaseModel):
    name: str
    age: int
@@ -52,560 +34,190 @@ class Person(BaseModel):
    address: Address


-class CustomConverter(Converter):
-    pass
+class Color(Enum):
+    RED = "red"
+    GREEN = "green"
+    BLUE = "blue"


-# Fixtures
-@pytest.fixture
-def mock_agent():
-    agent = Mock()
-    agent.function_calling_llm = None
-    agent.llm = Mock()
-    return agent
+class EnumModel(BaseModel):
+    name: str
+    color: Color


-# Tests for convert_to_model
-def test_convert_to_model_with_valid_json():
-    result = '{"name": "John", "age": 30}'
-    output = convert_to_model(result, SimpleModel, None, None)
-    assert isinstance(output, SimpleModel)
-    assert output.name == "John"
-    assert output.age == 30
+class OptionalModel(BaseModel):
+    name: str
+    age: Optional[int]


-def test_convert_to_model_with_invalid_json():
-    result = '{"name": "John", "age": "thirty"}'
-    with patch("crewai.utilities.converter.handle_partial_json") as mock_handle:
-        mock_handle.return_value = "Fallback result"
-        output = convert_to_model(result, SimpleModel, None, None)
-        assert output == "Fallback result"
+class ListModel(BaseModel):
+    items: List[int]


-def test_convert_to_model_with_no_model():
-    result = "Plain text"
-    output = convert_to_model(result, None, None, None)
-    assert output == "Plain text"
+class UnionModel(BaseModel):
+    field: Union[int, str, None]


-def test_convert_to_model_with_special_characters():
-    json_string_test = """
-    {
-        "responses": [
-            {
-                "previous_message_content": "Hi Tom,\r\n\r\nNiamh has chosen the Mika phonics on"
-            }
-        ]
+# Tests for to_serializable function
+def test_to_serializable_primitives():
+    """Test serialization of primitive types."""
+    assert to_serializable("test string") == "test string"
+    assert to_serializable(42) == 42
+    assert to_serializable(3.14) == 3.14
+    assert to_serializable(True) == True
+    assert to_serializable(None) is None
+
+
+def test_to_serializable_dates():
+    """Test serialization of date and datetime objects."""
+    test_date = date(2023, 1, 15)
+    test_datetime = datetime(2023, 1, 15, 10, 30, 45)
+
+    assert to_serializable(test_date) == "2023-01-15"
+    assert to_serializable(test_datetime) == "2023-01-15T10:30:45"
+
+
+def test_to_serializable_collections():
+    """Test serialization of lists, tuples, and sets."""
+    test_list = [1, "two", 3.0]
+    test_tuple = (4, "five", 6.0)
+    test_set = {7, "eight", 9.0}
+
+    assert to_serializable(test_list) == [1, "two", 3.0]
+    assert to_serializable(test_tuple) == [4, "five", 6.0]
+
+    # For sets, we can't rely on order, so we'll verify differently
+    serialized_set = to_serializable(test_set)
+    assert isinstance(serialized_set, list)
+    assert len(serialized_set) == 3
+    assert 7 in serialized_set
+    assert "eight" in serialized_set
+    assert 9.0 in serialized_set
+
+
+def test_to_serializable_dict():
+    """Test serialization of dictionaries."""
+    test_dict = {"a": 1, "b": "two", "c": [3, 4, 5]}
+
+    assert to_serializable(test_dict) == {"a": 1, "b": "two", "c": [3, 4, 5]}
+
+
+def test_to_serializable_pydantic_models():
+    """Test serialization of Pydantic models."""
+    simple = SimpleModel(name="John", age=30)
+
+    assert to_serializable(simple) == {"name": "John", "age": 30}
+
+
+def test_to_serializable_nested_models():
+    """Test serialization of nested Pydantic models."""
+    simple = SimpleModel(name="John", age=30)
+    nested = NestedModel(id=1, data=simple)
+
+    assert to_serializable(nested) == {"id": 1, "data": {"name": "John", "age": 30}}
+
+
+def test_to_serializable_complex_model():
+    """Test serialization of a complex model with nested structures."""
+    person = Person(
+        name="Jane",
+        age=28,
+        address=Address(street="123 Main St", city="Anytown", zip_code="12345"),
+    )
+
+    assert to_serializable(person) == {
+        "name": "Jane",
+        "age": 28,
+        "address": {"street": "123 Main St", "city": "Anytown", "zip_code": "12345"},
    }
-    """
-    output = convert_to_model(json_string_test, EmailResponses, None, None)
-    assert isinstance(output, EmailResponses)
-    assert len(output.responses) == 1
-    assert (
-        output.responses[0].previous_message_content
-        == "Hi Tom,\r\n\r\nNiamh has chosen the Mika phonics on"
-    )


-def test_convert_to_model_with_escaped_special_characters():
-    json_string_test = json.dumps(
-        {
-            "responses": [
-                {
-                    "previous_message_content": "Hi Tom,\r\n\r\nNiamh has chosen the Mika phonics on"
-                }
-            ]
-        }
-    )
-    output = convert_to_model(json_string_test, EmailResponses, None, None)
-    assert isinstance(output, EmailResponses)
-    assert len(output.responses) == 1
-    assert (
-        output.responses[0].previous_message_content
-        == "Hi Tom,\r\n\r\nNiamh has chosen the Mika phonics on"
-    )
+def test_to_serializable_enum():
+    """Test serialization of Enum values."""
+    model = EnumModel(name="ColorTest", color=Color.RED)

+    assert to_serializable(model) == {"name": "ColorTest", "color": "red"}

-def test_convert_to_model_with_multiple_special_characters():
-    json_string_test = """
-    {
-        "responses": [
-            {
-                "previous_message_content": "Line 1\r\nLine 2\tTabbed\nLine 3\r\n\rEscaped newline"
-            }
-        ]
-    }
-    """
-    output = convert_to_model(json_string_test, EmailResponses, None, None)
-    assert isinstance(output, EmailResponses)
-    assert len(output.responses) == 1
-    assert (
-        output.responses[0].previous_message_content
-        == "Line 1\r\nLine 2\tTabbed\nLine 3\r\n\rEscaped newline"
-    )

+def test_to_serializable_optional_fields():
+    """Test serialization of models with optional fields."""
+    model_with_age = OptionalModel(name="WithAge", age=25)
+    model_without_age = OptionalModel(name="WithoutAge", age=None)

-# Tests for validate_model
-def test_validate_model_pydantic_output():
-    result = '{"name": "Alice", "age": 25}'
-    output = validate_model(result, SimpleModel, False)
-    assert isinstance(output, SimpleModel)
-    assert output.name == "Alice"
-    assert output.age == 25
+    assert to_serializable(model_with_age) == {"name": "WithAge", "age": 25}
+    assert to_serializable(model_without_age) == {"name": "WithoutAge", "age": None}


-def test_validate_model_json_output():
-    result = '{"name": "Bob", "age": 40}'
-    output = validate_model(result, SimpleModel, True)
-    assert isinstance(output, dict)
-    assert output == {"name": "Bob", "age": 40}
+def test_to_serializable_list_field():
+    """Test serialization of models with list fields."""
+    model = ListModel(items=[1, 2, 3, 4, 5])

+    assert to_serializable(model) == {"items": [1, 2, 3, 4, 5]}

-# Tests for handle_partial_json
-def test_handle_partial_json_with_valid_partial():
-    result = 'Some text {"name": "Charlie", "age": 35} more text'
-    output = handle_partial_json(result, SimpleModel, False, None)
-    assert isinstance(output, SimpleModel)
-    assert output.name == "Charlie"
-    assert output.age == 35

+def test_to_serializable_union_field():
+    """Test serialization of models with union fields."""
+    model_int = UnionModel(field=42)
+    model_str = UnionModel(field="test")
+    model_none = UnionModel(field=None)

-def test_handle_partial_json_with_invalid_partial(mock_agent):
-    result = "No valid JSON here"
-    with patch("crewai.utilities.converter.convert_with_instructions") as mock_convert:
-        mock_convert.return_value = "Converted result"
-        output = handle_partial_json(result, SimpleModel, False, mock_agent)
-        assert output == "Converted result"
+    assert to_serializable(model_int) == {"field": 42}
+    assert to_serializable(model_str) == {"field": "test"}
+    assert to_serializable(model_none) == {"field": None}


-# Tests for convert_with_instructions
-@patch("crewai.utilities.converter.create_converter")
-@patch("crewai.utilities.converter.get_conversion_instructions")
-def test_convert_with_instructions_success(
-    mock_get_instructions, mock_create_converter, mock_agent
-):
-    mock_get_instructions.return_value = "Instructions"
-    mock_converter = Mock()
-    mock_converter.to_pydantic.return_value = SimpleModel(name="David", age=50)
-    mock_create_converter.return_value = mock_converter
+def test_to_serializable_max_depth():
+    """Test max depth parameter to prevent infinite recursion."""
+    # Create recursive structure
+    a: Dict[str, Any] = {"name": "a"}
+    b: Dict[str, Any] = {"name": "b", "ref": a}
+    a["ref"] = b  # Create circular reference

-    result = "Some text to convert"
-    output = convert_with_instructions(result, SimpleModel, False, mock_agent)
+    result = to_serializable(a, max_depth=3)

-    assert isinstance(output, SimpleModel)
-    assert output.name == "David"
-    assert output.age == 50
+    assert isinstance(result, dict)
+    assert "name" in result
+    assert "ref" in result
+    assert isinstance(result["ref"], dict)
+    assert "ref" in result["ref"]
+    assert isinstance(result["ref"]["ref"], dict)
+    # At depth 3, it should convert to string
+    assert isinstance(result["ref"]["ref"]["ref"], str)


-@patch("crewai.utilities.converter.create_converter")
-@patch("crewai.utilities.converter.get_conversion_instructions")
-def test_convert_with_instructions_failure(
-    mock_get_instructions, mock_create_converter, mock_agent
-):
-    mock_get_instructions.return_value = "Instructions"
-    mock_converter = Mock()
-    mock_converter.to_pydantic.return_value = ConverterError("Conversion failed")
-    mock_create_converter.return_value = mock_converter
+def test_to_serializable_non_serializable():
+    """Test serialization of objects that aren't directly JSON serializable."""

-    result = "Some text to convert"
-    with patch("crewai.utilities.converter.Printer") as mock_printer:
-        output = convert_with_instructions(result, SimpleModel, False, mock_agent)
-        assert output == result
-        mock_printer.return_value.print.assert_called_once()
+    class CustomObject:
+        def __repr__(self):
+            return "CustomObject()"

+    obj = CustomObject()

-# Tests for get_conversion_instructions
-def test_get_conversion_instructions_gpt():
-    llm = LLM(model="gpt-4o-mini")
-    with patch.object(LLM, "supports_function_calling") as supports_function_calling:
-        supports_function_calling.return_value = True
-        instructions = get_conversion_instructions(SimpleModel, llm)
-        model_schema = PydanticSchemaParser(model=SimpleModel).get_schema()
-        expected_instructions = (
-            "Please convert the following text into valid JSON.\n\n"
-            "Output ONLY the valid JSON and nothing else.\n\n"
-            "The JSON must follow this schema exactly:\n```json\n"
-            f"{model_schema}\n```"
-        )
-        assert instructions == expected_instructions
+    # Should convert to string representation
+    assert to_serializable(obj) == "CustomObject()"


-def test_get_conversion_instructions_non_gpt():
-    llm = LLM(model="ollama/llama3.1", base_url="http://localhost:11434")
-    with patch.object(LLM, "supports_function_calling", return_value=False):
-        instructions = get_conversion_instructions(SimpleModel, llm)
-        assert '"name": str' in instructions
-        assert '"age": int' in instructions
+def test_to_string_conversion():
+    """Test the to_string function."""
+    test_dict = {"name": "Test", "values": [1, 2, 3]}

+    # Should convert to a JSON string
+    assert to_string(test_dict) == '{"name": "Test", "values": [1, 2, 3]}'

-# Tests for is_gpt
-def test_supports_function_calling_true():
-    llm = LLM(model="gpt-4o")
-    assert llm.supports_function_calling() is True
+    # None should return None
+    assert to_string(None) is None


-def test_supports_function_calling_false():
-    llm = LLM(model="non-existent-model")
-    assert llm.supports_function_calling() is False
+def test_to_serializable_key():
+    """Test serialization of dictionary keys."""
+    # String and int keys are converted to strings
+    assert _to_serializable_key("test") == "test"
+    assert _to_serializable_key(42) == "42"

-
-def test_create_converter_with_mock_agent():
-    mock_agent = MagicMock()
-    mock_agent.get_output_converter.return_value = MagicMock(spec=Converter)
-
-    converter = create_converter(
-        agent=mock_agent,
-        llm=Mock(),
-        text="Sample",
-        model=SimpleModel,
-        instructions="Convert",
-    )
-
-    assert isinstance(converter, Converter)
-    mock_agent.get_output_converter.assert_called_once()
-
-
-def test_create_converter_with_custom_converter():
-    converter = create_converter(
-        converter_cls=CustomConverter,
-        llm=LLM(model="gpt-4o-mini"),
-        text="Sample",
-        model=SimpleModel,
-        instructions="Convert",
-    )
-
-    assert isinstance(converter, CustomConverter)
-
-
-def test_create_converter_fails_without_agent_or_converter_cls():
-    with pytest.raises(
-        ValueError, match="Either agent or converter_cls must be provided"
-    ):
-        create_converter(
-            llm=Mock(), text="Sample", model=SimpleModel, instructions="Convert"
-        )
-
-
-def test_generate_model_description_simple_model():
-    description = generate_model_description(SimpleModel)
-    expected_description = '{\n  "name": str,\n  "age": int\n}'
-    assert description == expected_description
-
-
-def test_generate_model_description_nested_model():
-    description = generate_model_description(NestedModel)
-    expected_description = (
-        '{\n  "id": int,\n  "data": {\n  "name": str,\n  "age": int\n}\n}'
-    )
-    assert description == expected_description
-
-
-def test_generate_model_description_optional_field():
-    class ModelWithOptionalField(BaseModel):
-        name: Optional[str]
-        age: int
-
-    description = generate_model_description(ModelWithOptionalField)
-    expected_description = '{\n  "name": Optional[str],\n  "age": int\n}'
-    assert description == expected_description
-
-
-def test_generate_model_description_list_field():
-    class ModelWithListField(BaseModel):
-        items: List[int]
-
-    description = generate_model_description(ModelWithListField)
-    expected_description = '{\n  "items": List[int]\n}'
-    assert description == expected_description
-
-
-def test_generate_model_description_dict_field():
-    class ModelWithDictField(BaseModel):
-        attributes: Dict[str, int]
-
-    description = generate_model_description(ModelWithDictField)
-    expected_description = '{\n  "attributes": Dict[str, int]\n}'
-    assert description == expected_description
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_convert_with_instructions():
-    llm = LLM(model="gpt-4o-mini")
-    sample_text = "Name: Alice, Age: 30"
-
-    instructions = get_conversion_instructions(SimpleModel, llm)
-    converter = Converter(
-        llm=llm,
-        text=sample_text,
-        model=SimpleModel,
-        instructions=instructions,
-    )
-
-    # Act
-    output = converter.to_pydantic()
-
-    # Assert
-    assert isinstance(output, SimpleModel)
-    assert output.name == "Alice"
-    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")
-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"
-    instructions = get_conversion_instructions(SimpleModel, llm)
-    converter = Converter(
-        llm=llm,
-        text=sample_text,
-        model=SimpleModel,
-        instructions=instructions,
-    )
-    output = converter.to_pydantic()
-    assert isinstance(output, SimpleModel)
-    assert output.name == "Alice Llama"
-    assert output.age == 30
-
-
-@skip_external_api
-@pytest.mark.vcr(filter_headers=["authorization"], record_mode="once")
-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"
-    instructions = get_conversion_instructions(SimpleModel, llm)
-    converter = Converter(
-        llm=llm,
-        text=sample_text,
-        model=SimpleModel,
-        instructions=instructions,
-    )
-    output = converter.to_pydantic()
-    assert isinstance(output, SimpleModel)
-    assert output.name == "Alice Llama"
-    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")
-    sample_text = "Name: John Doe\nAge: 30\nAddress: 123 Main St, Anytown, 12345"
-
-    instructions = get_conversion_instructions(Person, llm)
-    converter = Converter(
-        llm=llm,
-        text=sample_text,
-        model=Person,
-        instructions=instructions,
-    )
-
-    output = converter.to_pydantic()
-
-    assert isinstance(output, Person)
-    assert output.name == "John Doe"
-    assert output.age == 30
-    assert isinstance(output.address, Address)
-    assert output.address.street == "123 Main St"
-    assert output.address.city == "Anytown"
-    assert output.address.zip_code == "12345"
-
-
-# Tests for error handling
-def test_converter_error_handling():
-    llm = Mock(spec=LLM)
-    llm.supports_function_calling.return_value = False
-    llm.call.return_value = "Invalid JSON"
-    sample_text = "Name: Alice, Age: 30"
-
-    instructions = get_conversion_instructions(SimpleModel, llm)
-    converter = Converter(
-        llm=llm,
-        text=sample_text,
-        model=SimpleModel,
-        instructions=instructions,
-    )
-
-    with pytest.raises(ConverterError) as exc_info:
-        output = converter.to_pydantic()
-
-    assert "Failed to convert text into a Pydantic model" in str(exc_info.value)
-
-
-# Tests for retry logic
-def test_converter_retry_logic():
-    llm = Mock(spec=LLM)
-    llm.supports_function_calling.return_value = False
-    llm.call.side_effect = [
-        "Invalid JSON",
-        "Still invalid",
-        '{"name": "Retry Alice", "age": 30}',
-    ]
-    sample_text = "Name: Retry Alice, Age: 30"
-
-    instructions = get_conversion_instructions(SimpleModel, llm)
-    converter = Converter(
-        llm=llm,
-        text=sample_text,
-        model=SimpleModel,
-        instructions=instructions,
-        max_attempts=3,
-    )
-
-    output = converter.to_pydantic()
-
-    assert isinstance(output, SimpleModel)
-    assert output.name == "Retry Alice"
-    assert output.age == 30
-    assert llm.call.call_count == 3
-
-
-# Tests for optional fields
-def test_converter_with_optional_fields():
-    class OptionalModel(BaseModel):
-        name: str
-        age: Optional[int]
-
-    llm = Mock(spec=LLM)
-    llm.supports_function_calling.return_value = False
-    # Simulate the LLM's response with 'age' explicitly set to null
-    llm.call.return_value = '{"name": "Bob", "age": null}'
-    sample_text = "Name: Bob, age: None"
-
-    instructions = get_conversion_instructions(OptionalModel, llm)
-    converter = Converter(
-        llm=llm,
-        text=sample_text,
-        model=OptionalModel,
-        instructions=instructions,
-    )
-
-    output = converter.to_pydantic()
-
-    assert isinstance(output, OptionalModel)
-    assert output.name == "Bob"
-    assert output.age is None
-
-
-# Tests for list fields
-def test_converter_with_list_field():
-    class ListModel(BaseModel):
-        items: List[int]
-
-    llm = Mock(spec=LLM)
-    llm.supports_function_calling.return_value = False
-    llm.call.return_value = '{"items": [1, 2, 3]}'
-    sample_text = "Items: 1, 2, 3"
-
-    instructions = get_conversion_instructions(ListModel, llm)
-    converter = Converter(
-        llm=llm,
-        text=sample_text,
-        model=ListModel,
-        instructions=instructions,
-    )
-
-    output = converter.to_pydantic()
-
-    assert isinstance(output, ListModel)
-    assert output.items == [1, 2, 3]
-
-
-# Tests for enums
-from enum import Enum
-
-
-def test_converter_with_enum():
-    class Color(Enum):
-        RED = "red"
-        GREEN = "green"
-        BLUE = "blue"
-
-    class EnumModel(BaseModel):
-        name: str
-        color: Color
-
-    llm = Mock(spec=LLM)
-    llm.supports_function_calling.return_value = False
-    llm.call.return_value = '{"name": "Alice", "color": "red"}'
-    sample_text = "Name: Alice, Color: Red"
-
-    instructions = get_conversion_instructions(EnumModel, llm)
-    converter = Converter(
-        llm=llm,
-        text=sample_text,
-        model=EnumModel,
-        instructions=instructions,
-    )
-
-    output = converter.to_pydantic()
-
-    assert isinstance(output, EnumModel)
-    assert output.name == "Alice"
-    assert output.color == Color.RED
-
-
-# Tests for ambiguous input
-def test_converter_with_ambiguous_input():
-    llm = Mock(spec=LLM)
-    llm.supports_function_calling.return_value = False
-    llm.call.return_value = '{"name": "Charlie", "age": "Not an age"}'
-    sample_text = "Charlie is thirty years old"
-
-    instructions = get_conversion_instructions(SimpleModel, llm)
-    converter = Converter(
-        llm=llm,
-        text=sample_text,
-        model=SimpleModel,
-        instructions=instructions,
-    )
-
-    with pytest.raises(ConverterError) as exc_info:
-        output = converter.to_pydantic()
-
-    assert "failed to convert text into a pydantic model" in str(exc_info.value).lower()
-
-
-# Tests for function calling support
-def test_converter_with_function_calling():
-    llm = Mock(spec=LLM)
-    llm.supports_function_calling.return_value = True
-
-    instructor = Mock()
-    instructor.to_pydantic.return_value = SimpleModel(name="Eve", age=35)
-
-    converter = Converter(
-        llm=llm,
-        text="Name: Eve, Age: 35",
-        model=SimpleModel,
-        instructions="Convert this text.",
-    )
-    converter._create_instructor = Mock(return_value=instructor)
-
-    output = converter.to_pydantic()
-
-    assert isinstance(output, SimpleModel)
-    assert output.name == "Eve"
-    assert output.age == 35
-    instructor.to_pydantic.assert_called_once()
-
-
-def test_generate_model_description_union_field():
-    class UnionModel(BaseModel):
-        field: int | str | None
-
-    description = generate_model_description(UnionModel)
-    expected_description = '{\n  "field": int | str | None\n}'
-    assert description == expected_description
+    # Complex objects are converted to a unique string
+    obj = object()
+    key_str = _to_serializable_key(obj)
+    assert isinstance(key_str, str)
+    assert "key_" in key_str
+    assert "object" in key_str
Author	SHA1	Message	Date
Lorenze Jay	38735cba99	Merge branch 'main' into bugfix/flow-persist-nested-models	2025-03-21 17:03:57 -07:00
Matisse	bb3829a9ed	docs: Update model reference in LLM configuration (#2267 ) Co-authored-by: Brandon Hancock (bhancock_ai) <109994880+bhancockio@users.noreply.github.com>	2025-03-21 15:12:26 -04:00
Brandon Hancock	cde67882b4	resuse existing code and address PRs	2025-03-21 15:10:08 -04:00
Lorenze Jay	d3df545f1e	Merge branch 'main' into bugfix/flow-persist-nested-models	2025-03-21 11:59:11 -07:00
Fernando Galves	0a116202f0	Update the context window size for Amazon Bedrock FM- llm.py (#2304 ) Update the context window size for Amazon Bedrock Foundation Models. Co-authored-by: Brandon Hancock (bhancock_ai) <109994880+bhancockio@users.noreply.github.com> Co-authored-by: Lorenze Jay <63378463+lorenzejay@users.noreply.github.com>	2025-03-21 14:48:25 -04:00
Stefano Baccianella	4daa88fa59	As explained in https://github.com/mangiucugna/json_repair?tab=readme-ov-file#performance-considerations we can skip a wasteful json.loads() here and save quite some time (#2397 ) Co-authored-by: Brandon Hancock (bhancock_ai) <109994880+bhancockio@users.noreply.github.com> Co-authored-by: Lorenze Jay <63378463+lorenzejay@users.noreply.github.com>	2025-03-21 14:25:19 -04:00
Parth Patel	53067f8b92	add Mem0 OSS support (#2429 ) Co-authored-by: Brandon Hancock (bhancock_ai) <109994880+bhancockio@users.noreply.github.com>	2025-03-21 13:57:24 -04:00
Saurabh Misra	d3a09c3180	⚡️ Speed up method `CrewAgentParser._clean_action` by 427,565% (#2382 ) Here is the optimized version of the program. Co-authored-by: codeflash-ai[bot] <148906541+codeflash-ai[bot]@users.noreply.github.com> Co-authored-by: Brandon Hancock (bhancock_ai) <109994880+bhancockio@users.noreply.github.com>	2025-03-21 13:51:14 -04:00
Saurabh Misra	4d7aacb5f2	⚡️ Speed up method `Repository.is_git_repo` by 72,270% (#2381 ) Here is the optimized version of the `Repository` class. Co-authored-by: codeflash-ai[bot] <148906541+codeflash-ai[bot]@users.noreply.github.com> Co-authored-by: Brandon Hancock (bhancock_ai) <109994880+bhancockio@users.noreply.github.com>	2025-03-21 13:43:48 -04:00
Julio Peixoto	6b1cf78e41	docs: add detailed docstrings to Telemetry class methods (#2377 ) Co-authored-by: Brandon Hancock (bhancock_ai) <109994880+bhancockio@users.noreply.github.com>	2025-03-21 13:34:16 -04:00
Patcher	80f1a88b63	Upgrade OTel SDK version to 1.30.0 (#2375 ) Co-authored-by: Brandon Hancock (bhancock_ai) <109994880+bhancockio@users.noreply.github.com>	2025-03-21 13:26:50 -04:00
Jorge Gonzalez	32da76a2ca	Use task in the note about how methods names need to match task names (#2355 ) The note is about the task but mentions the agent incorrectly. Co-authored-by: Brandon Hancock (bhancock_ai) <109994880+bhancockio@users.noreply.github.com>	2025-03-21 13:17:43 -04:00
Gustavo Satheler	3aa48dcd58	fix: move agent tools for a variable instead of use format (#2319 ) Co-authored-by: Brandon Hancock (bhancock_ai) <109994880+bhancockio@users.noreply.github.com>	2025-03-21 12:32:54 -04:00
Tony Kipkemboi	03f1d57463	Merge pull request #2430 from crewAIInc/update-llm-docs docs: add documentation for Local NVIDIA NIM with WSL2	2025-03-20 12:57:37 -07:00
Tony Kipkemboi	4725d0de0d	Merge branch 'main' into update-llm-docs	2025-03-20 12:50:06 -07:00
Arthur Chien	b766af75f2	fix the _extract_thought (#2398 ) * fix the _extract_thought the regex string should be same with prompt in en.json:129 ...\nThought: I now know the final answer\nFinal Answer: the... * fix Action match --------- Co-authored-by: Brandon Hancock (bhancock_ai) <109994880+bhancockio@users.noreply.github.com>	2025-03-20 15:44:44 -04:00
Tony Kipkemboi	b2c8779f4c	Add documentation for Local NVIDIA NIM with WSL2	2025-03-20 12:39:37 -07:00
Tony Kipkemboi	df266bda01	Update documentation: Add changelog, fix formatting issues, replace mint.json with docs.json (#2400 )	2025-03-20 14:44:21 -04:00
Lorenze Jay	2155acb3a3	docs: Update JSONSearchTool and RagTool configuration parameter from 'embedder' to 'embedding_model' (#2311 ) Co-authored-by: Brandon Hancock (bhancock_ai) <109994880+bhancockio@users.noreply.github.com>	2025-03-20 13:11:37 -04:00
Sir Qasim	794574957e	Add note to create ./knowldge folder for source file management (#2297 ) This update includes a note in the documentation instructing users to create a ./knowldge folder. All source files (such as .txt, .pdf, .xlsx, .json) should be placed in this folder for centralized management. This change aims to streamline file organization and improve accessibility across projects. Co-authored-by: Brandon Hancock (bhancock_ai) <109994880+bhancockio@users.noreply.github.com>	2025-03-20 12:54:17 -04:00
Sir Qasim	66b19311a7	Fix crewai run Command Issue for Flow Projects and Cloud Deployment (#2291 ) This PR addresses an issue with the crewai run command following the creation of a flow project. Previously, the update command interfered with execution, causing it not to work as expected. With these changes, the command now runs according to the instructions in the readme.md, and it also improves deployment support when using CrewAI Cloud. Co-authored-by: Brandon Hancock (bhancock_ai) <109994880+bhancockio@users.noreply.github.com>	2025-03-20 12:48:02 -04:00
devin-ai-integration[bot]	9fc84fc1ac	Fix incorrect import statement in memory examples documentation (fixes #2395 ) (#2396 ) Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> Co-authored-by: Joe Moura <joao@crewai.com> Co-authored-by: Brandon Hancock (bhancock_ai) <109994880+bhancockio@users.noreply.github.com>	2025-03-20 12:17:26 -04:00
Amine Saihi	f8f9df6d1d	update doc SpaceNewsKnowledgeSource code snippet (#2275 ) Co-authored-by: Brandon Hancock (bhancock_ai) <109994880+bhancockio@users.noreply.github.com>	2025-03-20 12:06:21 -04:00
João Moura	6e94edb777	TYPO	2025-03-20 08:21:17 -07:00
Vini Brasil	bbe896d48c	Support wildcard handling in `emit()` (#2424 ) * Support wildcard handling in `emit()` Change `emit()` to call handlers registered for parent classes using `isinstance()`. Ensures that base event handlers receive derived events. * Fix failing test * Remove unused variable	2025-03-20 09:59:17 -04:00
Seyed Mostafa Meshkati	9298054436	docs: add base_url env for anthropic llm example (#2204 ) Co-authored-by: Brandon Hancock (bhancock_ai) <109994880+bhancockio@users.noreply.github.com>	2025-03-20 09:48:11 -04:00
Fernando Galves	90b7937796	Update documentation (#2199 ) * Update llms.mdx Update Amazon Bedrock section with more information about the foundation models available. * Update llms.mdx fix the description of Amazon Bedrock section * Update llms.mdx Remove the incorrect </tab> tag * Update llms.mdx Add Claude 3.7 Sonnet to the Amazon Bedrock list --------- Co-authored-by: Brandon Hancock (bhancock_ai) <109994880+bhancockio@users.noreply.github.com>	2025-03-20 09:42:23 -04:00
elda27	520933b4c5	Fix: More comfortable validation #2177 (#2178 ) * Fix: More confortable validation * Fix: union type support --------- Co-authored-by: Brandon Hancock (bhancock_ai) <109994880+bhancockio@users.noreply.github.com>	2025-03-20 09:28:31 -04:00
Vini Brasil	fe0813e831	Improve `MethodExecutionFailedEvent.error` typing (#2401 )	2025-03-18 12:52:23 -04:00
Brandon Hancock (bhancock_ai)	33cebea15b	spelling and tab fix (#2394 )	2025-03-17 16:31:23 -04:00
Brandon Hancock (bhancock_ai)	b5067a2689	Merge branch 'main' into bugfix/flow-persist-nested-models	2025-03-10 12:05:13 -04:00
Brandon Hancock (bhancock_ai)	362b20f052	Merge branch 'main' into bugfix/flow-persist-nested-models	2025-03-07 12:55:05 -05:00
Brandon Hancock	d5408ec461	Drop file	2025-03-06 16:40:36 -05:00
Brandon Hancock	6677c9c192	nested models in flow persist	2025-03-05 16:14:50 -05:00