chore: add commitizen to pre-commit hooks

Co-authored-by: Tony Kipkemboi <iamtonykipkemboi@gmail.com>

.github/workflows/tests.yml

@@ -7,14 +7,18 @@ permissions:
 
 env:
   OPENAI_API_KEY: fake-api-key
+  PYTHONUNBUFFERED: 1
 
 jobs:
   tests:
+    name: tests (${{ matrix.python-version }})
     runs-on: ubuntu-latest
     timeout-minutes: 15
     strategy:
+      fail-fast: true
       matrix:
         python-version: ['3.10', '3.11', '3.12', '3.13']
+        group: [1, 2, 3, 4, 5, 6, 7, 8]
     steps:
       - name: Checkout code
         uses: actions/checkout@v4
@@ -23,6 +27,9 @@ jobs:
         uses: astral-sh/setup-uv@v3
         with:
           enable-cache: true
+          cache-dependency-glob: |
+            **/pyproject.toml
+            **/uv.lock
 
       - name: Set up Python ${{ matrix.python-version }}
         run: uv python install ${{ matrix.python-version }}
@@ -30,5 +37,14 @@ jobs:
       - name: Install the project
         run: uv sync --dev --all-extras
 
-      - name: Run tests
-        run: uv run pytest --block-network --timeout=60 -vv
+      - name: Run tests (group ${{ matrix.group }} of 8)
+        run: |
+          uv run pytest \
+            --block-network \
+            --timeout=30 \
+            -vv \
+            --splits 8 \
+            --group ${{ matrix.group }} \
+            --durations=10 \
+            -n auto \
+            --maxfail=3

.pre-commit-config.yaml

@@ -5,3 +5,7 @@ repos:
       - id: ruff
         args: ["--fix"]
       - id: ruff-format
+  - repo: https://github.com/commitizen-tools/commitizen
+    rev: v3.13.0
+    hooks:
+      - id: commitizen

docs/docs.json

@@ -41,6 +41,11 @@
               "anchor": "Get Help",
               "href": "mailto:support@crewai.com",
               "icon": "headset"
+            },
+            {
+              "anchor": "Releases",
+              "href": "https://github.com/crewAIInc/crewAI/releases",
+              "icon": "tag"
             }
           ]
         },
@@ -89,6 +94,7 @@
                     "pages": [
                       "en/guides/advanced/customizing-prompts",
                       "en/guides/advanced/fingerprinting"
+
                     ]
                   }
                 ]
@@ -290,7 +296,8 @@
                   "en/enterprise/features/webhook-streaming",
                   "en/enterprise/features/traces",
                   "en/enterprise/features/hallucination-guardrail",
-                  "en/enterprise/features/integrations"
+                  "en/enterprise/features/integrations",
+                  "en/enterprise/features/agent-repositories"
                 ]
               },
               {
@@ -358,7 +365,7 @@
           },
           {
             "tab": "Examples",
-            "groups": [
+                        "groups": [
               {
                 "group": "Examples",
                 "pages": [
@@ -366,18 +373,8 @@
                 ]
               }
             ]
-          },
-          {
-            "tab": "Releases",
-            "groups": [
-              {
-                "group": "Releases",
-                "pages": [
-                  "en/changelog"
-                ]
-              }
-            ]
           }
+
         ]
       },
       {
@@ -403,6 +400,11 @@
               "anchor": "Obter Ajuda",
               "href": "mailto:support@crewai.com",
               "icon": "headset"
+            },
+            {
+              "anchor": "Lançamentos",
+              "href": "https://github.com/crewAIInc/crewAI/releases",
+              "icon": "tag"
             }
           ]
         },
@@ -720,7 +722,7 @@
           },
           {
             "tab": "Exemplos",
-            "groups": [
+                        "groups": [
               {
                 "group": "Exemplos",
                 "pages": [
@@ -728,18 +730,8 @@
                 ]
               }
             ]
-          },
-          {
-            "tab": "Lançamentos",
-            "groups": [
-              {
-                "group": "Lançamentos",
-                "pages": [
-                  "pt-BR/changelog"
-                ]
-              }
-            ]
           }
+
         ]
       }
     ]
@@ -777,6 +769,64 @@
   "seo": {
     "indexing": "all"
   },
+  "redirects": [
+    {
+      "source": "/introduction",
+      "destination": "/en/introduction"
+    },
+    {
+      "source": "/installation",
+      "destination": "/en/installation"
+    },
+    {
+      "source": "/quickstart",
+      "destination": "/en/quickstart"
+    },
+    {
+      "source": "/changelog",
+      "destination": "https://github.com/crewAIInc/crewAI/releases"
+    },
+    {
+      "source": "/telemetry",
+      "destination": "/en/telemetry"
+    },
+    {
+      "source": "/concepts/:path*",
+      "destination": "/en/concepts/:path*"
+    },
+    {
+      "source": "/guides/:path*",
+      "destination": "/en/guides/:path*"
+    },
+    {
+      "source": "/tools/:path*",
+      "destination": "/en/tools/:path*"
+    },
+    {
+      "source": "/learn/:path*",
+      "destination": "/en/learn/:path*"
+    },
+    {
+      "source": "/mcp/:path*",
+      "destination": "/en/mcp/:path*"
+    },
+    {
+      "source": "/observability/:path*",
+      "destination": "/en/observability/:path*"
+    },
+    {
+      "source": "/enterprise/:path*",
+      "destination": "/en/enterprise/:path*"
+    },
+    {
+      "source": "/api-reference/:path*",
+      "destination": "/en/api-reference/:path*"
+    },
+    {
+      "source": "/examples/:path*",
+      "destination": "/en/examples/:path*"
+    }
+  ],
   "errors": {
     "404": {
       "redirect": true

docs/en/changelog.mdx

@@ -1,473 +0,0 @@
----
-title: Changelog
-description: View the latest updates and changes to CrewAI
-icon: timeline
----
-
-<Update label="2024-05-22" description="v0.121.0" tags={["Latest"]}>
-  ## Release Highlights
-  <Frame>
-    <img src="/images/releases/v01210.png" />
-  </Frame>
-  
-  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
-    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.121.0">View on GitHub</a>
-  </div>
-
-  **Core Improvements & Fixes**
-  - Fixed encoding error when creating tools
-  - Fixed failing llama test
-  - Updated logging configuration for consistency
-  - Enhanced telemetry initialization and event handling
-
-  **New Features & Enhancements**
-  - Added **markdown attribute** to the Task class
-  - Added **reasoning attribute** to the Agent class
-  - Added **inject_date flag** to Agent for automatic date injection
-  - Implemented **HallucinationGuardrail** (no-op with test coverage)
-
-  **Documentation & Guides**
-  - Added documentation for **StagehandTool** and improved MDX structure
-  - Added documentation for **MCP integration** and updated enterprise docs
-  - Documented knowledge events and updated reasoning docs
-  - Added stop parameter documentation
-  - Fixed import references in doc examples (before_kickoff, after_kickoff)
-  - General docs updates and restructuring for clarity
-</Update>
-
-<Update label="2024-05-15" description="v0.120.1">
-  ## Release Highlights
-  <Frame>
-    <img src="/images/releases/v01201.png" />
-  </Frame>
-  
-  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
-    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.120.1">View on GitHub</a>
-  </div>
-
-  **Core Improvements & Fixes**
-  - Fixed **interpolation with hyphens**
-</Update>
-
-<Update label="2024-05-14" description="v0.120.0">
-  ## Release Highlights
-  <Frame>
-    <img src="/images/releases/v01200.png" />
-  </Frame>
-  
-  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
-    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.120.0">View on GitHub</a>
-  </div>
-
-  **Core Improvements & Fixes**
-  - Enabled **full Ruff rule set** by default for stricter linting
-  - Addressed race condition in FilteredStream using context managers
-  - Fixed agent knowledge reset issue
-  - Refactored agent fetching logic into utility module
-
-  **New Features & Enhancements**
-  - Added support for **loading an Agent directly from a repository**
-  - Enabled setting an empty context for Task
-  - Enhanced Agent repository feedback and fixed Tool auto-import behavior
-  - Introduced direct initialization of knowledge (bypassing knowledge_sources)
-
-  **Documentation & Guides**
-  - Updated security.md for current security practices
-  - Cleaned up Google setup section for clarity
-  - Added link to AI Studio when entering Gemini key
-  - Updated Arize Phoenix observability guide
-  - Refreshed flow documentation
-</Update>
-
-<Update label="2024-05-08" description="v0.119.0">
-  ## Release Highlights
-  <Frame>
-    <img src="/images/releases/v01190.png" />
-  </Frame>
-  
-  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
-    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.119.0">View on GitHub</a>
-  </div>
-
-  **Core Improvements & Fixes**
-  - Improved test reliability by enhancing pytest handling for flaky tests
-  - Fixed memory reset crash when embedding dimensions mismatch
-  - Enabled parent flow identification for Crew and LiteAgent
-  - Prevented telemetry-related crashes when unavailable
-  - Upgraded **LiteLLM version** for better compatibility
-  - Fixed llama converter tests by removing skip_external_api
-
-  **New Features & Enhancements**
-  - Introduced **knowledge retrieval prompt re-writing** in Agent for improved tracking and debugging
-  - Made LLM setup and quickstart guides model-agnostic
-
-  **Documentation & Guides**
-  - Added advanced configuration docs for the RAG tool
-  - Updated Windows troubleshooting guide
-  - Refined documentation examples for better clarity
-  - Fixed typos across docs and config files
-</Update>
-
-<Update label="2024-04-28" description="v0.118.0">
-  ## Release Highlights
-  <Frame>
-    <img src="/images/releases/v01180.png" />
-  </Frame>
-  
-  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
-    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.118.0">View on GitHub</a>
-  </div>
-
-  **Core Improvements & Fixes**
-  - Fixed issues with missing prompt or system templates
-  - Removed global logging configuration to avoid unintended overrides
-  - Renamed **TaskGuardrail to LLMGuardrail** for improved clarity
-  - Downgraded litellm to version 1.167.1 for compatibility
-  - Added missing init.py files to ensure proper module initialization
-
-  **New Features & Enhancements**
-  - Added support for **no-code Guardrail creation** to simplify AI behavior controls
-
-  **Documentation & Guides**
-  - Removed CrewStructuredTool from public documentation to reflect internal usage
-  - Updated enterprise documentation and YouTube embed for improved onboarding experience
-</Update>
-
-<Update label="2024-04-20" description="v0.117.0">
-  ## Release Highlights
-  <Frame>
-    <img src="/images/releases/v01170.png" />
-  </Frame>
-  
-  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
-    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.117.0">View on GitHub</a>
-  </div>
-
-  **New Features & Enhancements**
-  - Added `result_as_answer` parameter support in `@tool` decorator.
-  - Introduced support for new language models: GPT-4.1, Gemini-2.0, and Gemini-2.5 Pro.
-  - Enhanced knowledge management capabilities.
-  - Added Huggingface provider option in CLI.
-  - Improved compatibility and CI support for Python 3.10+.
-
-  **Core Improvements & Fixes**
-  - Fixed issues with incorrect template parameters and missing inputs.
-  - Improved asynchronous flow handling with coroutine condition checks.
-  - Enhanced memory management with isolated configuration and correct memory object copying.
-  - Fixed initialization of lite agents with correct references.
-  - Addressed Python type hint issues and removed redundant imports.
-  - Updated event placement for improved tool usage tracking.
-  - Raised explicit exceptions when flows fail.
-  - Removed unused code and redundant comments from various modules.
-  - Updated GitHub App token action to v2.
-
-  **Documentation & Guides**
-  - Enhanced documentation structure, including enterprise deployment instructions.
-  - Automatically create output folders for documentation generation.
-  - Fixed broken link in WeaviateVectorSearchTool documentation.
-  - Fixed guardrail documentation usage and import paths for JSON search tools.
-  - Updated documentation for CodeInterpreterTool.
-  - Improved SEO, contextual navigation, and error handling for documentation pages.
-</Update>
-
-<Update label="2024-04-25" description="v0.117.1">
-  ## Release Highlights
-  <Frame>
-    <img src="/images/releases/v01171.png" />
-  </Frame>
-  
-  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
-    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.117.1">View on GitHub</a>
-  </div>
-
-  **Core Improvements & Fixes**
-  - Upgraded **crewai-tools** to latest version
-  - Upgraded **liteLLM** to latest version
-  - Fixed **Mem0 OSS**
-</Update>
-
-<Update label="2024-04-07" description="v0.114.0">
-  ## Release Highlights
-  <Frame>
-    <img src="/images/releases/v01140.png" />
-  </Frame>
-
-  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
-    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.114.0">View on GitHub</a>
-  </div>
-
-  **New Features & Enhancements**
-  - Agents as an atomic unit. (`Agent(...).kickoff()`)
-  - Support for [Custom LLM implementations](https://docs.crewai.com/guides/advanced/custom-llm).
-  - Integrated External Memory and [Opik observability](https://docs.crewai.com/how-to/opik-observability).
-  - Enhanced YAML extraction.
-  - Multimodal agent validation.
-  - Added Secure fingerprints for agents and crews.
-
-  **Core Improvements & Fixes**
-  - Improved serialization, agent copying, and Python compatibility.
-  - Added wildcard support to `emit()`
-  - Added support for additional router calls and context window adjustments.
-  - Fixed typing issues, validation, and import statements.
-  - Improved method performance.
-  - Enhanced agent task handling, event emissions, and memory management.
-  - Fixed CLI issues, conditional tasks, cloning behavior, and tool outputs.
-
-  **Documentation & Guides**
-  - Improved documentation structure, theme, and organization.
-  - Added guides for Local NVIDIA NIM with WSL2, W&B Weave, and Arize Phoenix.
-  - Updated tool configuration examples, prompts, and observability docs.
-  - Guide on using singular agents within Flows.
-</Update>
-
-<Update label="2024-03-17" description="v0.108.0">
-  ## Release Highlights
-  <Frame>
-    <img src="/images/releases/v01080.png" />
-  </Frame>
-
-  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
-    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.108.0">View on GitHub</a>
-  </div>
-
-  **New Features & Enhancements**
-  - 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">
-  ## Release Highlights
-  <Frame>
-    <img src="/images/releases/v01050.png" />
-  </Frame>
-  
-  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
-    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.105.0">View on GitHub</a>
-  </div>
-
-  **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">
-  ## Release Highlights
-  <Frame>
-    <img src="/images/releases/v01020.png" />
-  </Frame>
-  
-  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
-    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.102.0">View on GitHub</a>
-  </div>
-
-  **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">
-  ## Release Highlights
-  <Frame>
-    <img src="/images/releases/v01000.png" />
-  </Frame>
-  
-  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
-    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.100.0">View on GitHub</a>
-  </div>
-
-  **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">
-  ## Release Highlights
-  <Frame>
-    <img src="/images/releases/v0980.png" />
-  </Frame>
-  
-  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
-    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.98.0">View on GitHub</a>
-  </div>
-
-  **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">
-  ## Release Highlights
-  <Frame>
-    <img src="/images/releases/v0950.png" />
-  </Frame>
-  
-  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
-    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.95.0">View on GitHub</a>
-  </div>
-
-  **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="2024-12-05" description="v0.86.0">
-  ## Release Highlights
-  <Frame>
-    <img src="/images/releases/v0860.png" />
-  </Frame>
-  
-  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
-    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.86.0">View on GitHub</a>
-  </div>
-  **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="2024-12-04" description="v0.85.0">
-  ## Release Highlights
-  <Frame>
-    <img src="/images/releases/v0850.png" />
-  </Frame>
-  
-  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
-    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.85.0">View on GitHub</a>
-  </div>
-  **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="2024-11-25" 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="2024-11-13" description="v0.80.0">
-  **Fixes**
-  - Fixing Tokens callback replacement bug
-  - Fixing Step callback issue
-  - Add cached prompt tokens info on usage metrics
-  - Fix crew_train_success test
-</Update>

docs/en/concepts/agents.mdx

@@ -526,6 +526,103 @@ agent = Agent(
 The context window management feature works automatically in the background. You don't need to call any special functions - just set `respect_context_window` to your preferred behavior and CrewAI handles the rest!
 </Note>
 
+## Direct Agent Interaction with `kickoff()`
+
+Agents can be used directly without going through a task or crew workflow using the `kickoff()` method. This provides a simpler way to interact with an agent when you don't need the full crew orchestration capabilities.
+
+### How `kickoff()` Works
+
+The `kickoff()` method allows you to send messages directly to an agent and get a response, similar to how you would interact with an LLM but with all the agent's capabilities (tools, reasoning, etc.).
+
+```python Code
+from crewai import Agent
+from crewai_tools import SerperDevTool
+
+# Create an agent
+researcher = Agent(
+    role="AI Technology Researcher",
+    goal="Research the latest AI developments",
+    tools=[SerperDevTool()],
+    verbose=True
+)
+
+# Use kickoff() to interact directly with the agent
+result = researcher.kickoff("What are the latest developments in language models?")
+
+# Access the raw response
+print(result.raw)
+```
+
+### Parameters and Return Values
+
+| Parameter         | Type                                | Description                                                               |
+| :---------------- | :---------------------------------- | :------------------------------------------------------------------------ |
+| `messages`        | `Union[str, List[Dict[str, str]]]`  | Either a string query or a list of message dictionaries with role/content |
+| `response_format` | `Optional[Type[Any]]`               | Optional Pydantic model for structured output                             |
+
+The method returns a `LiteAgentOutput` object with the following properties:
+
+- `raw`: String containing the raw output text
+- `pydantic`: Parsed Pydantic model (if a `response_format` was provided)
+- `agent_role`: Role of the agent that produced the output
+- `usage_metrics`: Token usage metrics for the execution
+
+### Structured Output
+
+You can get structured output by providing a Pydantic model as the `response_format`:
+
+```python Code
+from pydantic import BaseModel
+from typing import List
+
+class ResearchFindings(BaseModel):
+    main_points: List[str]
+    key_technologies: List[str]
+    future_predictions: str
+
+# Get structured output
+result = researcher.kickoff(
+    "Summarize the latest developments in AI for 2025",
+    response_format=ResearchFindings
+)
+
+# Access structured data
+print(result.pydantic.main_points)
+print(result.pydantic.future_predictions)
+```
+
+### Multiple Messages
+
+You can also provide a conversation history as a list of message dictionaries:
+
+```python Code
+messages = [
+    {"role": "user", "content": "I need information about large language models"},
+    {"role": "assistant", "content": "I'd be happy to help with that! What specifically would you like to know?"},
+    {"role": "user", "content": "What are the latest developments in 2025?"}
+]
+
+result = researcher.kickoff(messages)
+```
+
+### Async Support
+
+An asynchronous version is available via `kickoff_async()` with the same parameters:
+
+```python Code
+import asyncio
+
+async def main():
+    result = await researcher.kickoff_async("What are the latest developments in AI?")
+    print(result.raw)
+
+asyncio.run(main())
+```
+
+<Note>
+The `kickoff()` method uses a `LiteAgent` internally, which provides a simpler execution flow while preserving all of the agent's configuration (role, goal, backstory, tools, etc.).
+</Note>
+
 ## Important Considerations and Best Practices
 
 ### Security and Code Execution

docs/en/concepts/cli.mdx

@@ -4,6 +4,8 @@ description: Learn how to use the CrewAI CLI to interact with CrewAI.
 icon: terminal
 ---
 
+<Warning>Since release 0.140.0, CrewAI Enterprise started a process of migrating their login provider. As such, the authentication flow via CLI was updated. Users that use Google to login, or that created their account after July 3rd, 2025 will be unable to log in with older versions of the `crewai` library.</Warning>
+
 ## Overview
 
 The CrewAI CLI provides a set of commands to interact with CrewAI, allowing you to create, train, run, and manage crews & flows.
@@ -186,10 +188,7 @@ def crew(self) -> Crew:
 Deploy the crew or flow to [CrewAI Enterprise](https://app.crewai.com).
 
 - **Authentication**: You need to be authenticated to deploy to CrewAI Enterprise.
-    ```shell Terminal
-    crewai signup
-    ```
-    If you already have an account, you can login with:
+    You can login or create an account with:
     ```shell Terminal
     crewai login
     ```
@@ -285,13 +284,13 @@ Watch this video tutorial for a step-by-step demonstration of deploying your cre
 
 ### 11. API Keys
 
-When running ```crewai create crew``` command, the CLI will first show you the top 5 most common LLM providers and ask you to select one.
+When running ```crewai create crew``` command, the CLI will show you a list of available LLM providers to choose from, followed by model selection for your chosen provider.
 
-Once you've selected an LLM provider, you will be prompted for API keys.
+Once you've selected an LLM provider and model, you will be prompted for API keys.
 
-#### Initial API key providers
+#### Available LLM Providers
 
-The CLI will initially prompt for API keys for the following services:
+Here's a list of the most popular LLM providers suggested by the CLI:
 
 * OpenAI
 * Groq
@@ -299,17 +298,14 @@ The CLI will initially prompt for API keys for the following services:
 * Google Gemini
 * SambaNova
 
-When you select a provider, the CLI will prompt you to enter your API key.
+When you select a provider, the CLI will then show you available models for that provider and prompt you to enter your API key.
 
 #### Other Options
 
-If you select option 6, you will be able to select from a list of LiteLLM supported providers.
+If you select "other", you will be able to select from a list of LiteLLM supported providers.
 
 When you select a provider, the CLI will prompt you to enter the Key name and the API key.
 
 See the following link for each provider's key name:
 
 * [LiteLLM Providers](https://docs.litellm.ai/docs/providers)
-
-
-

docs/en/concepts/event-listener.mdx

@@ -255,6 +255,17 @@ CrewAI provides a wide range of events that you can listen for:
 - **LLMCallFailedEvent**: Emitted when an LLM call fails
 - **LLMStreamChunkEvent**: Emitted for each chunk received during streaming LLM responses
 
+### Memory Events
+
+- **MemoryQueryStartedEvent**: Emitted when a memory query is started. Contains the query, limit, and optional score threshold.
+- **MemoryQueryCompletedEvent**: Emitted when a memory query is completed successfully. Contains the query, results, limit, score threshold, and query execution time.
+- **MemoryQueryFailedEvent**: Emitted when a memory query fails. Contains the query, limit, score threshold, and error message.
+- **MemorySaveStartedEvent**: Emitted when a memory save operation is started. Contains the value to be saved, metadata, and optional agent role.
+- **MemorySaveCompletedEvent**: Emitted when a memory save operation is completed successfully. Contains the saved value, metadata, agent role, and save execution time.
+- **MemorySaveFailedEvent**: Emitted when a memory save operation fails. Contains the value, metadata, agent role, and error message.
+- **MemoryRetrievalStartedEvent**: Emitted when memory retrieval for a task prompt starts. Contains the optional task ID.
+- **MemoryRetrievalCompletedEvent**: Emitted when memory retrieval for a task prompt completes successfully. Contains the task ID, memory content, and retrieval execution time.
+
 ## Event Handler Structure
 
 Each event handler receives two parameters:

docs/en/concepts/llms.mdx

@@ -684,6 +684,28 @@ In this section, you'll find detailed examples that help you select, configure,
       - openrouter/deepseek/deepseek-chat
     </Info>
   </Accordion>
+
+  <Accordion title="Nebius AI Studio">
+    Set the following environment variables in your `.env` file:
+    ```toml Code
+    NEBIUS_API_KEY=<your-api-key>
+    ```
+
+    Example usage in your CrewAI project:
+    ```python Code
+    llm = LLM(
+        model="nebius/Qwen/Qwen3-30B-A3B"
+    )
+    ```
+
+    <Info>
+      Nebius AI Studio features:
+      - Large collection of open source models
+      - Higher rate limits
+      - Competitive pricing
+      - Good balance of speed and quality
+    </Info>
+  </Accordion>
 </AccordionGroup>
 
 ## Streaming Responses
@@ -727,9 +749,58 @@ CrewAI supports streaming responses from LLMs, allowing your application to rece
     ```
 
     <Tip>
-      [Click here](https://docs.crewai.com/concepts/event-listener#event-listeners) for more details 
+      [Click here](https://docs.crewai.com/concepts/event-listener#event-listeners) for more details
     </Tip>
   </Tab>
+
+  <Tab title="Agent & Task Tracking">
+    All LLM events in CrewAI include agent and task information, allowing you to track and filter LLM interactions by specific agents or tasks:
+
+    ```python
+    from crewai import LLM, Agent, Task, Crew
+    from crewai.utilities.events import LLMStreamChunkEvent
+    from crewai.utilities.events.base_event_listener import BaseEventListener
+
+    class MyCustomListener(BaseEventListener):
+        def setup_listeners(self, crewai_event_bus):
+            @crewai_event_bus.on(LLMStreamChunkEvent)
+            def on_llm_stream_chunk(source, event):
+                if researcher.id == event.agent_id:
+                    print("\n==============\n Got event:", event, "\n==============\n")
+
+
+    my_listener = MyCustomListener()
+
+    llm = LLM(model="gpt-4o-mini", temperature=0, stream=True)
+
+    researcher = Agent(
+        role="About User",
+        goal="You know everything about the user.",
+        backstory="""You are a master at understanding people and their preferences.""",
+        llm=llm,
+    )
+
+    search = Task(
+        description="Answer the following questions about the user: {question}",
+        expected_output="An answer to the question.",
+        agent=researcher,
+    )
+
+    crew = Crew(agents=[researcher], tasks=[search])
+
+    result = crew.kickoff(
+        inputs={"question": "..."}
+    )
+    ```
+
+    <Info>
+      This feature is particularly useful for:
+      - Debugging specific agent behaviors
+      - Logging LLM usage by task type
+      - Auditing which agents are making what types of LLM calls
+      - Performance monitoring of specific tasks
+    </Info>
+  </Tab>
 </Tabs>
 
 ## Structured LLM Calls
@@ -825,7 +896,7 @@ Learn how to get the most out of your LLM configuration:
       Remember to regularly monitor your token usage and adjust your configuration as needed to optimize costs and performance.
     </Info>
   </Accordion>
-  
+
   <Accordion title="Drop Additional Parameters">
     CrewAI internally uses Litellm for LLM calls, which allows you to drop additional parameters that are not needed for your specific use case. This can help simplify your code and reduce the complexity of your LLM configuration.
     For example, if you don't need to send the <code>stop</code> parameter, you can simply omit it from your LLM call:

docs/en/concepts/memory.mdx

@@ -9,7 +9,7 @@ icon: database
 The CrewAI framework provides a sophisticated memory system designed to significantly enhance AI agent capabilities. CrewAI offers **three distinct memory approaches** that serve different use cases:
 
 1. **Basic Memory System** - Built-in short-term, long-term, and entity memory
-2. **User Memory** - User-specific memory with Mem0 integration (legacy approach)  
+2. **User Memory** - User-specific memory with Mem0 integration (legacy approach)
 3. **External Memory** - Standalone external memory providers (new approach)
 
 ## Memory System Components
@@ -62,7 +62,7 @@ By default, CrewAI uses the `appdirs` library to determine storage locations fol
 ```
 ~/Library/Application Support/CrewAI/{project_name}/
 ├── knowledge/           # Knowledge base ChromaDB files
-├── short_term_memory/   # Short-term memory ChromaDB files  
+├── short_term_memory/   # Short-term memory ChromaDB files
 ├── long_term_memory/    # Long-term memory ChromaDB files
 ├── entities/            # Entity memory ChromaDB files
 └── long_term_memory_storage.db  # SQLite database
@@ -252,7 +252,7 @@ chroma_path = os.path.join(storage_path, "knowledge")
 if os.path.exists(chroma_path):
     client = chromadb.PersistentClient(path=chroma_path)
     collections = client.list_collections()
-    
+
     print("ChromaDB Collections:")
     for collection in collections:
         print(f"  - {collection.name}: {collection.count()} documents")
@@ -269,7 +269,7 @@ crew = Crew(agents=[...], tasks=[...], memory=True)
 
 # Reset specific memory types
 crew.reset_memories(command_type='short')     # Short-term memory
-crew.reset_memories(command_type='long')      # Long-term memory  
+crew.reset_memories(command_type='long')      # Long-term memory
 crew.reset_memories(command_type='entity')    # Entity memory
 crew.reset_memories(command_type='knowledge') # Knowledge storage
 ```
@@ -596,7 +596,7 @@ providers_to_test = [
     {
         "name": "Ollama",
         "config": {
-            "provider": "ollama", 
+            "provider": "ollama",
             "config": {"model": "mxbai-embed-large"}
         }
     }
@@ -604,7 +604,7 @@ providers_to_test = [
 
 for provider in providers_to_test:
     print(f"\nTesting {provider['name']} embeddings...")
-    
+
     # Create crew with specific embedder
     crew = Crew(
         agents=[...],
@@ -612,7 +612,7 @@ for provider in providers_to_test:
         memory=True,
         embedder=provider['config']
     )
-    
+
     # Run your test and measure performance
     result = crew.kickoff()
     print(f"{provider['name']} completed successfully")
@@ -655,17 +655,17 @@ import time
 
 def test_embedding_performance(embedder_config, test_text="This is a test document"):
     start_time = time.time()
-    
+
     crew = Crew(
         agents=[...],
         tasks=[...],
         memory=True,
         embedder=embedder_config
     )
-    
+
     # Simulate memory operation
     crew.kickoff()
-    
+
     end_time = time.time()
     return end_time - start_time
 
@@ -676,7 +676,7 @@ openai_time = test_embedding_performance({
 })
 
 ollama_time = test_embedding_performance({
-    "provider": "ollama", 
+    "provider": "ollama",
     "config": {"model": "mxbai-embed-large"}
 })
 
@@ -783,7 +783,7 @@ os.environ["MEM0_API_KEY"] = "your-api-key"
 # Create external memory instance
 external_memory = ExternalMemory(
     embedder_config={
-        "provider": "mem0", 
+        "provider": "mem0",
         "config": {"user_id": "U-123"}
     }
 )
@@ -808,8 +808,8 @@ class CustomStorage(Storage):
 
     def save(self, value, metadata=None, agent=None):
         self.memories.append({
-            "value": value, 
-            "metadata": metadata, 
+            "value": value,
+            "metadata": metadata,
             "agent": agent
         })
 
@@ -986,7 +986,201 @@ crew = Crew(
 - 🫡 **Enhanced Personalization:** Memory enables agents to remember user preferences and historical interactions, leading to personalized experiences.
 - 🧠 **Improved Problem Solving:** Access to a rich memory store aids agents in making more informed decisions, drawing on past learnings and contextual insights.
 
+## Memory Events
+
+CrewAI's event system provides powerful insights into memory operations. By leveraging memory events, you can monitor, debug, and optimize your memory system's performance and behavior.
+
+### Available Memory Events
+
+CrewAI emits the following memory-related events:
+
+| Event | Description | Key Properties |
+| :---- | :---------- | :------------- |
+| **MemoryQueryStartedEvent** | Emitted when a memory query begins | `query`, `limit`, `score_threshold` |
+| **MemoryQueryCompletedEvent** | Emitted when a memory query completes successfully | `query`, `results`, `limit`, `score_threshold`, `query_time_ms` |
+| **MemoryQueryFailedEvent** | Emitted when a memory query fails | `query`, `limit`, `score_threshold`, `error` |
+| **MemorySaveStartedEvent** | Emitted when a memory save operation begins | `value`, `metadata`, `agent_role` |
+| **MemorySaveCompletedEvent** | Emitted when a memory save operation completes successfully | `value`, `metadata`, `agent_role`, `save_time_ms` |
+| **MemorySaveFailedEvent** | Emitted when a memory save operation fails | `value`, `metadata`, `agent_role`, `error` |
+| **MemoryRetrievalStartedEvent** | Emitted when memory retrieval for a task prompt starts | `task_id` |
+| **MemoryRetrievalCompletedEvent** | Emitted when memory retrieval completes successfully | `task_id`, `memory_content`, `retrieval_time_ms` |
+
+### Practical Applications
+
+#### 1. Memory Performance Monitoring
+
+Track memory operation timing to optimize your application:
+
+```python
+from crewai.utilities.events.base_event_listener import BaseEventListener
+from crewai.utilities.events import (
+    MemoryQueryCompletedEvent,
+    MemorySaveCompletedEvent
+)
+import time
+
+class MemoryPerformanceMonitor(BaseEventListener):
+    def __init__(self):
+        super().__init__()
+        self.query_times = []
+        self.save_times = []
+
+    def setup_listeners(self, crewai_event_bus):
+        @crewai_event_bus.on(MemoryQueryCompletedEvent)
+        def on_memory_query_completed(source, event: MemoryQueryCompletedEvent):
+            self.query_times.append(event.query_time_ms)
+            print(f"Memory query completed in {event.query_time_ms:.2f}ms. Query: '{event.query}'")
+            print(f"Average query time: {sum(self.query_times)/len(self.query_times):.2f}ms")
+
+        @crewai_event_bus.on(MemorySaveCompletedEvent)
+        def on_memory_save_completed(source, event: MemorySaveCompletedEvent):
+            self.save_times.append(event.save_time_ms)
+            print(f"Memory save completed in {event.save_time_ms:.2f}ms")
+            print(f"Average save time: {sum(self.save_times)/len(self.save_times):.2f}ms")
+
+# Create an instance of your listener
+memory_monitor = MemoryPerformanceMonitor()
+```
+
+#### 2. Memory Content Logging
+
+Log memory operations for debugging and insights:
+
+```python
+from crewai.utilities.events.base_event_listener import BaseEventListener
+from crewai.utilities.events import (
+    MemorySaveStartedEvent,
+    MemoryQueryStartedEvent,
+    MemoryRetrievalCompletedEvent
+)
+import logging
+
+# Configure logging
+logger = logging.getLogger('memory_events')
+
+class MemoryLogger(BaseEventListener):
+    def setup_listeners(self, crewai_event_bus):
+        @crewai_event_bus.on(MemorySaveStartedEvent)
+        def on_memory_save_started(source, event: MemorySaveStartedEvent):
+            if event.agent_role:
+                logger.info(f"Agent '{event.agent_role}' saving memory: {event.value[:50]}...")
+            else:
+                logger.info(f"Saving memory: {event.value[:50]}...")
+
+        @crewai_event_bus.on(MemoryQueryStartedEvent)
+        def on_memory_query_started(source, event: MemoryQueryStartedEvent):
+            logger.info(f"Memory query started: '{event.query}' (limit: {event.limit})")
+
+        @crewai_event_bus.on(MemoryRetrievalCompletedEvent)
+        def on_memory_retrieval_completed(source, event: MemoryRetrievalCompletedEvent):
+            if event.task_id:
+                logger.info(f"Memory retrieved for task {event.task_id} in {event.retrieval_time_ms:.2f}ms")
+            else:
+                logger.info(f"Memory retrieved in {event.retrieval_time_ms:.2f}ms")
+            logger.debug(f"Memory content: {event.memory_content}")
+
+# Create an instance of your listener
+memory_logger = MemoryLogger()
+```
+
+#### 3. Error Tracking and Notifications
+
+Capture and respond to memory errors:
+
+```python
+from crewai.utilities.events.base_event_listener import BaseEventListener
+from crewai.utilities.events import (
+    MemorySaveFailedEvent,
+    MemoryQueryFailedEvent
+)
+import logging
+from typing import Optional
+
+# Configure logging
+logger = logging.getLogger('memory_errors')
+
+class MemoryErrorTracker(BaseEventListener):
+    def __init__(self, notify_email: Optional[str] = None):
+        super().__init__()
+        self.notify_email = notify_email
+        self.error_count = 0
+
+    def setup_listeners(self, crewai_event_bus):
+        @crewai_event_bus.on(MemorySaveFailedEvent)
+        def on_memory_save_failed(source, event: MemorySaveFailedEvent):
+            self.error_count += 1
+            agent_info = f"Agent '{event.agent_role}'" if event.agent_role else "Unknown agent"
+            error_message = f"Memory save failed: {event.error}. {agent_info}"
+            logger.error(error_message)
+
+            if self.notify_email and self.error_count % 5 == 0:
+                self._send_notification(error_message)
+
+        @crewai_event_bus.on(MemoryQueryFailedEvent)
+        def on_memory_query_failed(source, event: MemoryQueryFailedEvent):
+            self.error_count += 1
+            error_message = f"Memory query failed: {event.error}. Query: '{event.query}'"
+            logger.error(error_message)
+
+            if self.notify_email and self.error_count % 5 == 0:
+                self._send_notification(error_message)
+
+    def _send_notification(self, message):
+        # Implement your notification system (email, Slack, etc.)
+        print(f"[NOTIFICATION] Would send to {self.notify_email}: {message}")
+
+# Create an instance of your listener
+error_tracker = MemoryErrorTracker(notify_email="admin@example.com")
+```
+
+### Integrating with Analytics Platforms
+
+Memory events can be forwarded to analytics and monitoring platforms to track performance metrics, detect anomalies, and visualize memory usage patterns:
+
+```python
+from crewai.utilities.events.base_event_listener import BaseEventListener
+from crewai.utilities.events import (
+    MemoryQueryCompletedEvent,
+    MemorySaveCompletedEvent
+)
+
+class MemoryAnalyticsForwarder(BaseEventListener):
+    def __init__(self, analytics_client):
+        super().__init__()
+        self.client = analytics_client
+
+    def setup_listeners(self, crewai_event_bus):
+        @crewai_event_bus.on(MemoryQueryCompletedEvent)
+        def on_memory_query_completed(source, event: MemoryQueryCompletedEvent):
+            # Forward query metrics to analytics platform
+            self.client.track_metric({
+                "event_type": "memory_query",
+                "query": event.query,
+                "duration_ms": event.query_time_ms,
+                "result_count": len(event.results) if hasattr(event.results, "__len__") else 0,
+                "timestamp": event.timestamp
+            })
+
+        @crewai_event_bus.on(MemorySaveCompletedEvent)
+        def on_memory_save_completed(source, event: MemorySaveCompletedEvent):
+            # Forward save metrics to analytics platform
+            self.client.track_metric({
+                "event_type": "memory_save",
+                "agent_role": event.agent_role,
+                "duration_ms": event.save_time_ms,
+                "timestamp": event.timestamp
+            })
+```
+
+### Best Practices for Memory Event Listeners
+
+1. **Keep handlers lightweight**: Avoid complex processing in event handlers to prevent performance impacts
+2. **Use appropriate logging levels**: Use INFO for normal operations, DEBUG for details, ERROR for issues
+3. **Batch metrics when possible**: Accumulate metrics before sending to external systems
+4. **Handle exceptions gracefully**: Ensure your event handlers don't crash due to unexpected data
+5. **Consider memory consumption**: Be mindful of storing large amounts of event data
+
 ## Conclusion
 
-Integrating CrewAI's memory system into your projects is straightforward. By leveraging the provided memory components and configurations, 
+Integrating CrewAI's memory system into your projects is straightforward. By leveraging the provided memory components and configurations,
 you can quickly empower your agents with the ability to remember, reason, and learn from their interactions, unlocking new levels of intelligence and capability.

docs/en/concepts/training.mdx

@@ -6,10 +6,10 @@ icon: dumbbell
 
 ## Overview
 
-The training feature in CrewAI allows you to train your AI agents using the command-line interface (CLI). 
+The training feature in CrewAI allows you to train your AI agents using the command-line interface (CLI).
 By running the command `crewai train -n <n_iterations>`, you can specify the number of iterations for the training process.
 
-During training, CrewAI utilizes techniques to optimize the performance of your agents along with human feedback. 
+During training, CrewAI utilizes techniques to optimize the performance of your agents along with human feedback.
 This helps the agents improve their understanding, decision-making, and problem-solving abilities.
 
 ### Training Your Crew Using the CLI
@@ -42,8 +42,8 @@ filename = "your_model.pkl"
 
 try:
     YourCrewName_Crew().crew().train(
-      n_iterations=n_iterations, 
-      inputs=inputs, 
+      n_iterations=n_iterations,
+      inputs=inputs,
       filename=filename
     )
 
@@ -64,4 +64,68 @@ Once the training is complete, your agents will be equipped with enhanced capabi
 Remember to regularly update and retrain your agents to ensure they stay up-to-date with the latest information and advancements in the field.
 
 Happy training with CrewAI! 🚀
-  
+
+## Small Language Model Considerations
+
+<Warning>
+  When using smaller language models (≤7B parameters) for training data evaluation, be aware that they may face challenges with generating structured outputs and following complex instructions.
+</Warning>
+
+### Limitations of Small Models in Training Evaluation
+
+<CardGroup cols={2}>
+  <Card title="JSON Output Accuracy" icon="triangle-exclamation">
+    Smaller models often struggle with producing valid JSON responses needed for structured training evaluations, leading to parsing errors and incomplete data.
+  </Card>
+  <Card title="Evaluation Quality" icon="chart-line">
+    Models under 7B parameters may provide less nuanced evaluations with limited reasoning depth compared to larger models.
+  </Card>
+  <Card title="Instruction Following" icon="list-check">
+    Complex training evaluation criteria may not be fully followed or considered by smaller models.
+  </Card>
+  <Card title="Consistency" icon="rotate">
+    Evaluations across multiple training iterations may lack consistency with smaller models.
+  </Card>
+</CardGroup>
+
+### Recommendations for Training
+
+<Tabs>
+  <Tab title="Best Practice">
+    For optimal training quality and reliable evaluations, we strongly recommend using models with at least 7B parameters or larger:
+
+    ```python
+    from crewai import Agent, Crew, Task, LLM
+
+    # Recommended minimum for training evaluation
+    llm = LLM(model="mistral/open-mistral-7b")
+
+    # Better options for reliable training evaluation
+    llm = LLM(model="anthropic/claude-3-sonnet-20240229-v1:0")
+    llm = LLM(model="gpt-4o")
+
+    # Use this LLM with your agents
+    agent = Agent(
+        role="Training Evaluator",
+        goal="Provide accurate training feedback",
+        llm=llm
+    )
+    ```
+
+    <Tip>
+      More powerful models provide higher quality feedback with better reasoning, leading to more effective training iterations.
+    </Tip>
+  </Tab>
+  <Tab title="Small Model Usage">
+    If you must use smaller models for training evaluation, be aware of these constraints:
+
+    ```python
+    # Using a smaller model (expect some limitations)
+    llm = LLM(model="huggingface/microsoft/Phi-3-mini-4k-instruct")
+    ```
+
+    <Warning>
+      While CrewAI includes optimizations for small models, expect less reliable and less nuanced evaluation results that may require more human intervention during training.
+    </Warning>
+  </Tab>
+</Tabs>

docs/en/enterprise/features/agent-repositories.mdx

@@ -0,0 +1,155 @@
+---
+title: 'Agent Repositories'
+description: 'Learn how to use Agent Repositories to share and reuse your agents across teams and projects'
+icon: 'database'
+---
+
+Agent Repositories allow enterprise users to store, share, and reuse agent definitions across teams and projects. This feature enables organizations to maintain a centralized library of standardized agents, promoting consistency and reducing duplication of effort.
+
+## Benefits of Agent Repositories
+
+- **Standardization**: Maintain consistent agent definitions across your organization
+- **Reusability**: Create an agent once and use it in multiple crews and projects
+- **Governance**: Implement organization-wide policies for agent configurations
+- **Collaboration**: Enable teams to share and build upon each other's work
+
+## Using Agent Repositories
+
+### Prerequisites
+
+1. You must have an account at CrewAI, try the [free plan](https://app.crewai.com).
+2. You need to be authenticated using the CrewAI CLI.
+3. If you have more than one organization, make sure you are switched to the correct organization using the CLI command:
+
+```bash
+crewai org switch <org_id>
+```
+
+### Creating and Managing Agents in Repositories
+
+To create and manage agents in repositories,Enterprise Dashboard.
+
+### Loading Agents from Repositories
+
+You can load agents from repositories in your code using the `from_repository` parameter:
+
+```python
+from crewai import Agent
+
+# Create an agent by loading it from a repository
+# The agent is loaded with all its predefined configurations
+researcher = Agent(
+    from_repository="market-research-agent"
+)
+
+```
+
+### Overriding Repository Settings
+
+You can override specific settings from the repository by providing them in the configuration:
+
+```python
+researcher = Agent(
+    from_repository="market-research-agent",
+    goal="Research the latest trends in AI development",  # Override the repository goal
+    verbose=True  # Add a setting not in the repository
+)
+```
+
+### Example: Creating a Crew with Repository Agents
+
+```python
+from crewai import Crew, Agent, Task
+
+# Load agents from repositories
+researcher = Agent(
+    from_repository="market-research-agent"
+)
+
+writer = Agent(
+    from_repository="content-writer-agent"
+)
+
+# Create tasks
+research_task = Task(
+    description="Research the latest trends in AI",
+    agent=researcher
+)
+
+writing_task = Task(
+    description="Write a comprehensive report based on the research",
+    agent=writer
+)
+
+# Create the crew
+crew = Crew(
+    agents=[researcher, writer],
+    tasks=[research_task, writing_task],
+    verbose=True
+)
+
+# Run the crew
+result = crew.kickoff()
+```
+
+### Example: Using `kickoff()` with Repository Agents
+
+You can also use repository agents directly with the `kickoff()` method for simpler interactions:
+
+```python
+from crewai import Agent
+from pydantic import BaseModel
+from typing import List
+
+# Define a structured output format
+class MarketAnalysis(BaseModel):
+    key_trends: List[str]
+    opportunities: List[str]
+    recommendation: str
+
+# Load an agent from repository
+analyst = Agent(
+    from_repository="market-analyst-agent",
+    verbose=True
+)
+
+# Get a free-form response
+result = analyst.kickoff("Analyze the AI market in 2025")
+print(result.raw)  # Access the raw response
+
+# Get structured output
+structured_result = analyst.kickoff(
+    "Provide a structured analysis of the AI market in 2025",
+    response_format=MarketAnalysis
+)
+
+# Access structured data
+print(f"Key Trends: {structured_result.pydantic.key_trends}")
+print(f"Recommendation: {structured_result.pydantic.recommendation}")
+```
+
+## Best Practices
+
+1. **Naming Convention**: Use clear, descriptive names for your repository agents
+2. **Documentation**: Include comprehensive descriptions for each agent
+3. **Tool Management**: Ensure that tools referenced by repository agents are available in your environment
+4. **Access Control**: Manage permissions to ensure only authorized team members can modify repository agents
+
+## Organization Management
+
+To switch between organizations or see your current organization, use the CrewAI CLI:
+
+```bash
+# View current organization
+crewai org current
+
+# Switch to a different organization
+crewai org switch <org_id>
+
+# List all available organizations
+crewai org list
+```
+
+<Note>
+When loading agents from repositories, you must be authenticated and switched to the correct organization. If you receive errors, check your authentication status and organization settings using the CLI commands above.
+</Note>

docs/en/enterprise/guides/deploy-crew.mdx

@@ -41,11 +41,8 @@ The CLI provides the fastest way to deploy locally developed crews to the Enterp
     First, you need to authenticate your CLI with the CrewAI Enterprise platform:
 
     ```bash
-    # If you already have a CrewAI Enterprise account
+    # If you already have a CrewAI Enterprise account, or want to create one:
     crewai login
-
-    # If you're creating a new account
-    crewai signup
     ```
 
     When you run either command, the CLI will:

docs/en/installation.mdx

@@ -72,7 +72,7 @@ If you haven't installed `uv` yet, follow **step 1** to quickly get it set up on
           </Warning>
 
           <Warning>
-            If you encounter the `chroma-hnswlib==0.7.6` build error (`fatal error C1083: Cannot open include file: 'float.h'`) on Windows, install (Visual Studio Build Tools)[https://visualstudio.microsoft.com/downloads/] with *Desktop development with C++*.
+            If you encounter the `chroma-hnswlib==0.7.6` build error (`fatal error C1083: Cannot open include file: 'float.h'`) on Windows, install [Visual Studio Build Tools](https://visualstudio.microsoft.com/downloads/) with *Desktop development with C++*.
           </Warning>
 
       - To verify that `crewai` is installed, run:
@@ -104,7 +104,6 @@ We recommend using the `YAML` template scaffolding for a structured approach to
       ```
 
     - This creates a new project with the following structure:
-      <Frame>
       ```
       my_project/
       ├── .gitignore
@@ -124,7 +123,6 @@ We recommend using the `YAML` template scaffolding for a structured approach to
                   ├── agents.yaml
                   └── tasks.yaml
       ```
-      </Frame>
   </Step>
 
   <Step title="Customize Your Project">
@@ -172,7 +170,7 @@ For teams and organizations, CrewAI offers enterprise deployment options that el
 
 ### CrewAI Factory (Self-hosted)
 - Containerized deployment for your infrastructure
-- Supports any hyperscaler including on prem depployments
+- Supports any hyperscaler including on prem deployments
 - Integration with your existing security systems
 
 <Card title="Explore Enterprise Options" icon="building" href="https://crewai.com/enterprise">

docs/en/learn/llm-connections.mdx

@@ -34,6 +34,7 @@ LiteLLM supports a wide range of providers, including but not limited to:
 - DeepInfra
 - Groq
 - SambaNova
+- Nebius AI Studio
 - [NVIDIA NIMs](https://docs.api.nvidia.com/nim/reference/models-1)
 - And many more!
 

docs/pt-BR/changelog.mdx

@@ -1,473 +0,0 @@
----
-title: Registro de Alterações
-description: Veja as atualizações e mudanças mais recentes do CrewAI
-icon: timeline
----
-
-<Update label="2024-05-22" description="v0.121.0" tags={["Latest"]}>
-  ## Destaques da Versão
-  <Frame>
-    <img src="/images/releases/v01210.png" />
-  </Frame>
-  
-  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
-    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.121.0">Ver no GitHub</a>
-  </div>
-
-  **Melhorias & Correções no Núcleo**
-  - Corrigido erro de codificação ao criar ferramentas
-  - Corrigido teste do llama com falha
-  - Configuração de logs atualizada para mais consistência
-  - Inicialização e tratamento de eventos de telemetria aprimorados
-
-  **Novas Funcionalidades & Aprimoramentos**
-  - Adicionado **atributo markdown** à classe Task
-  - Adicionado **atributo reasoning** à classe Agent
-  - Adicionada flag **inject_date** ao Agent para injeção automática de data
-  - Implementado **HallucinationGuardrail** (sem-operação, com cobertura de testes)
-
-  **Documentação & Guias**
-  - Documentação adicionada para **StagehandTool** e melhoria na estrutura MDX
-  - Inclusa documentação para integração **MCP** e atualização nos docs corporativos
-  - Eventos de conhecimento documentados e documentação de reasoning atualizada
-  - Adicionada explicação do parâmetro stop
-  - Corrigidas referências de import nos exemplos de documentação (before_kickoff, after_kickoff)
-  - Atualizações gerais e reestruturação nos docs para maior clareza
-</Update>
-
-<Update label="2024-05-15" description="v0.120.1">
-  ## Destaques da Versão
-  <Frame>
-    <img src="/images/releases/v01201.png" />
-  </Frame>
-  
-  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
-    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.120.1">Ver no GitHub</a>
-  </div>
-
-  **Melhorias & Correções no Núcleo**
-  - Corrigida **interpolação com hífens**
-</Update>
-
-<Update label="2024-05-14" description="v0.120.0">
-  ## Destaques da Versão
-  <Frame>
-    <img src="/images/releases/v01200.png" />
-  </Frame>
-  
-  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
-    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.120.0">Ver no GitHub</a>
-  </div>
-
-  **Melhorias & Correções no Núcleo**
-  - Conjunto completo de regras do Ruff habilitado por padrão para linting mais rigoroso
-  - Corrigida condição de corrida no FilteredStream usando context managers
-  - Corrigido problema de reset de conhecimento do agente
-  - Lógica de busca de agente refatorada para módulo utilitário
-
-  **Novas Funcionalidades & Aprimoramentos**
-  - Suporte adicionado para **carregar um Agent diretamente de um repositório**
-  - Permitida configuração de contexto vazio em Task
-  - Feedback do repositório de Agent aprimorado e correção no comportamento de auto-import de Tool
-  - Inicialização direta de conhecimento introduzida (bypassando knowledge_sources)
-
-  **Documentação & Guias**
-  - security.md atualizado para refletir práticas atuais de segurança
-  - Seção de configuração do Google revisada para mais clareza
-  - Adicionado link para AI Studio ao inserir chave Gemini
-  - Guia de observabilidade do Arize Phoenix atualizado
-  - Documentação de fluxo renovada
-</Update>
-
-<Update label="2024-05-08" description="v0.119.0">
-  ## Destaques da Versão
-  <Frame>
-    <img src="/images/releases/v01190.png" />
-  </Frame>
-  
-  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
-    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.119.0">Ver no GitHub</a>
-  </div>
-
-  **Melhorias & Correções no Núcleo**
-  - Confiabilidade de testes aprimorada melhorando o tratamento do pytest para testes instáveis
-  - Corrigido crash no reset de memória por incompatibilidade de dimensões de embeddings
-  - Identificação do fluxo pai habilitada para Crew e LiteAgent
-  - Crashes de telemetria prevenidos quando indisponíveis
-  - Atualização da versão do **LiteLLM** para melhor compatibilidade
-  - Corrigidos testes do conversor de llama removendo skip_external_api
-
-  **Novas Funcionalidades & Aprimoramentos**
-  - Introduzida **reescrita de prompt de recuperação de conhecimento** no Agent para melhor rastreamento e debug
-  - Guias de configuração do LLM e início rápido tornados independentes do modelo
-
-  **Documentação & Guias**
-  - Adicionada documentação de configuração avançada para o RAG tool
-  - Guia de resolução de problemas no Windows atualizado
-  - Exemplos na documentação refinados para maior clareza
-  - Correção de erros ortográficos em docs e arquivos de configuração
-</Update>
-
-<Update label="2024-04-28" description="v0.118.0">
-  ## Destaques da Versão
-  <Frame>
-    <img src="/images/releases/v01180.png" />
-  </Frame>
-  
-  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
-    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.118.0">Ver no GitHub</a>
-  </div>
-
-  **Melhorias & Correções no Núcleo**
-  - Corrigidos problemas com prompt ou templates de sistema ausentes
-  - Removida configuração global de logs para evitar sobreposição não intencional
-  - Renomeado **TaskGuardrail para LLMGuardrail** para maior clareza
-  - Versão do litellm rebaixada para 1.167.1 visando compatibilidade
-  - Adicionados arquivos init.py ausentes para garantir inicialização correta dos módulos
-
-  **Novas Funcionalidades & Aprimoramentos**
-  - Suporte adicionado para **criação de Guardrails sem código** facilitando o controle de comportamento da IA
-
-  **Documentação & Guias**
-  - CrewStructuredTool removido da documentação pública para refletir uso interno
-  - Documentação corporativa e embed do YouTube atualizados para melhor onboarding
-</Update>
-
-<Update label="2024-04-20" description="v0.117.0">
-  ## Destaques da Versão
-  <Frame>
-    <img src="/images/releases/v01170.png" />
-  </Frame>
-  
-  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
-    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.117.0">Ver no GitHub</a>
-  </div>
-
-  **Novas Funcionalidades & Aprimoramentos**
-  - Adicionado suporte ao parâmetro `result_as_answer` no decorator `@tool`.
-  - Suporte a novos modelos de linguagem: GPT-4.1, Gemini-2.0 e Gemini-2.5 Pro.
-  - Capacidades de gerenciamento de conhecimento aprimoradas.
-  - Adicionado provedor Huggingface na CLI.
-  - Compatibilidade e suporte CI melhorados para Python 3.10+.
-
-  **Melhorias & Correções no Núcleo**
-  - Corrigidos problemas com parâmetros de template incorretos e entradas ausentes.
-  - Fluxo assíncrono aprimorado com verificações de condição coroutine.
-  - Gerenciamento de memória aprimorado com configuração isolada e cópia correta dos objetos.
-  - Inicialização de lite agents corrigida com referências corretas.
-  - Corrigidos problemas de type hint em Python e remoção de imports redundantes.
-  - Atualização do posicionamento de eventos para rastreamento do uso de ferramentas.
-  - Exceções explícitas lançadas quando fluxos falham.
-  - Remoção de código e comentários redundantes em diversos módulos.
-  - Atualização da ação de token do GitHub App para v2.
-
-  **Documentação & Guias**
-  - Estrutura documental aprimorada, incluindo instruções para implantação corporativa.
-  - Criação automática de pastas de saída para geração de documentação.
-  - Link quebrado reparado na documentação do WeaviateVectorSearchTool.
-  - Correções na documentação do guardrail e nos caminhos de import dos search tools para JSON.
-  - Atualização na documentação do CodeInterpreterTool.
-  - Aprimoramento de SEO, navegação contextual e tratamento de erros nas páginas de documentação.
-</Update>
-
-<Update label="2024-04-25" description="v0.117.1">
-  ## Destaques da Versão
-  <Frame>
-    <img src="/images/releases/v01171.png" />
-  </Frame>
-  
-  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
-    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.117.1">Ver no GitHub</a>
-  </div>
-
-  **Melhorias & Correções no Núcleo**
-  - Versão do **crewai-tools** atualizada para a mais recente
-  - Versão do **liteLLM** atualizada para a mais recente
-  - Correção no **Mem0 OSS**
-</Update>
-
-<Update label="2024-04-07" description="v0.114.0">
-  ## Destaques da Versão
-  <Frame>
-    <img src="/images/releases/v01140.png" />
-  </Frame>
-
-  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
-    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.114.0">Ver no GitHub</a>
-  </div>
-
-  **Novas Funcionalidades & Aprimoramentos**
-  - Agents como unidade atômica. (`Agent(...).kickoff()`)
-  - Suporte para [implementações Custom LLM](https://docs.crewai.com/guides/advanced/custom-llm).
-  - Memória externa integrada e [Opik observability](https://docs.crewai.com/how-to/opik-observability).
-  - Extração de YAML aprimorada.
-  - Validação multimodal de agentes.
-  - Impressões digitais seguras adicionadas para agents e crews.
-
-  **Melhorias & Correções no Núcleo**
-  - Serialização, cópia de agent e compatibilidade Python aprimoradas.
-  - Suporte a curingas adicionado a `emit()`
-  - Suporte a chamadas adicionais do roteador e ajustes de janela de contexto.
-  - Correções em typing, validação e imports.
-  - Melhoria na performance de métodos.
-  - Manipulação de tasks do agent, emissão de eventos e gerenciamento de memória aprimorados.
-  - Correções na CLI, tarefas condicionais, comportamento de clonagem e saídas de ferramentas.
-
-  **Documentação & Guias**
-  - Estrutura, tema e organização da documentação aprimorados.
-  - Guias para Local NVIDIA NIM com WSL2, W&B Weave e Arize Phoenix adicionados.
-  - Exemplos de configuração de ferramentas, prompts e docs de observabilidade atualizados.
-  - Guia para usar agentes singulares nos Flows.
-</Update>
-
-<Update label="2024-03-17" description="v0.108.0">
-  ## Destaques da Versão
-  <Frame>
-    <img src="/images/releases/v01080.png" />
-  </Frame>
-
-  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
-    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.108.0">Ver no GitHub</a>
-  </div>
-
-  **Novas Funcionalidades & Aprimoramentos**
-  - Conversão de tabs para espaços no template `crew.py`
-  - Streaming de respostas LLM e sistema de eventos aprimorados
-  - Inclusão de `model_name`
-  - Event Listener aprimorado com visualização rica e logs melhorados
-  - Impressões digitais adicionadas
-
-  **Correções de Bugs**
-  - Correções de problemas com Mistral
-  - Correção de bug na documentação
-  - Correção de erro de type check na propriedade fingerprint
-
-  **Atualizações em Documentação**
-  - Documentação de ferramentas aprimorada
-  - Guia de instalação atualizado para o pacote `uv` tool
-  - Instruções adicionadas para upgrade do crewAI com o `uv` tool
-  - Documentação para `ApifyActorsTool` incluída
-</Update>
-
-<Update label="2024-03-10" description="v0.105.0">
-  ## Destaques da Versão
-  <Frame>
-    <img src="/images/releases/v01050.png" />
-  </Frame>
-  
-  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
-    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.105.0">Ver no GitHub</a>
-  </div>
-
-  **Melhorias & Correções no Núcleo**
-  - Correção de variáveis de template ausentes e configuração de memória de usuário
-  - Suporte a fluxo assíncrono melhorado e resolução para formatação de respostas do agente
-  - Função de reset de memória aprimorada e correção nos comandos de memória da CLI
-  - Correções em tipos, propriedades de chamadas de ferramenta e desacoplamento de telemetria
-
-  **Novas Funcionalidades & Aprimoramentos**
-  - Exportação de estado do Flow e utilitários de estado melhorados
-  - Configuração de conhecimento do agente aprimorada com embedder opcional para crew
-  - Emissor de eventos adicionado para melhor observabilidade e rastreamento de chamadas LLM
-  - Suporte para Python 3.10 e ChatOllama via langchain_ollama
-  - Suporte ao tamanho da janela de contexto para o modelo o3-mini
-  - Adicionada capacidade de múltiplas chamadas de roteador
-
-  **Documentação & Guias**
-  - Layout e estrutura hierárquica da documentação aprimorados
-  - Guia para QdrantVectorSearchTool incluído e uso de event listener esclarecido
-  - Correções de erros nos prompts e atualização da lista de modelos do Amazon Bedrock
-</Update>
-
-<Update label="2024-02-12" description="v0.102.0">
-  ## Destaques da Versão
-  <Frame>
-    <img src="/images/releases/v01020.png" />
-  </Frame>
-  
-  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
-    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.102.0">Ver no GitHub</a>
-  </div>
-
-  **Melhorias & Correções no Núcleo**
-  - Suporte LLM expandido: melhorar saída estruturada do LLM, manuseio de parâmetros e formatação para modelos Anthropic
-  - Estabilidade de Crew & Agent: corrigido clonagem de agents/crews com knowledge sources, múltiplas saídas de task em tarefas condicionais e callbacks de tarefa de Crew ignorados
-  - Correções de Memória & Armazenamento: melhora no gerenciamento de memória de curto prazo com Bedrock, inicialização correta do embedder e função reset memories adicionada na classe crew
-  - Confiabilidade em Treinamento & Execução: corrigidos treinamento quebrado e questões de interpolação com tipos de entrada dict e list
-
-  **Novas Funcionalidades & Aprimoramentos**
-  - Gerenciamento Avançado de Conhecimento: convencionamento de nomes melhorado e configuração de embedding aprimorada com suporte a embedder customizado
-  - Logging & Observabilidade Expandidos: suporte ao formato JSON para logging e documentação de rastreamento no MLflow integrada
-  - Melhorias no Tratamento de Dados: atualização do excel_knowledge_source.py para processar arquivos com múltipl abas
-  - Desempenho Geral & Limpeza do Código: alinhamento de código corporativo e resolução de problemas de linting
-  - Novo tool adicionado: `QdrantVectorSearchTool`
-
-  **Documentação & Guias**
-  - Docs de AI & Memória atualizados: melhorias em documentação do Bedrock, Google AI e memória de longo prazo
-  - Clareza em tarefas & fluxos: adicionada linha "Entrada Humana" em Task Attributes, guia para Langfuse e documentação para FileWriterTool
-  - Diversas correções de ortografia & formatação
-</Update>
-
-<Update label="2024-01-28" description="v0.100.0">
-  ## Destaques da Versão
-  <Frame>
-    <img src="/images/releases/v01000.png" />
-  </Frame>
-  
-  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
-    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.100.0">Ver no GitHub</a>
-  </div>
-
-  **Funcionalidades**
-  - Adição dos docs Composio
-  - Adicionado SageMaker como provedor LLM
-  
-  **Correções**
-  - Correção geral nas conexões LLM
-  - Uso de acessores seguros no treinamento
-  - Checagem de versão adicionada ao crew_chat.py
-  
-  **Documentação**
-  - Novos docs para crewai chat
-  - Melhorias no formato e clareza nos docs da CLI e da Composio Tool
-</Update>
-
-<Update label="2024-01-20" description="v0.98.0">
-  ## Destaques da Versão
-  <Frame>
-    <img src="/images/releases/v0980.png" />
-  </Frame>
-  
-  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
-    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.98.0">Ver no GitHub</a>
-  </div>
-
-  **Funcionalidades**
-  - Conversation crew v1
-  - Adição de ID único para estados de flow
-  - Adição do decorator @persist com a interface FlowPersistence
-  
-  **Integrações**
-  - Integração SambaNova adicionada
-  - Novo provedor NVIDIA NIM na CLI
-  - Apresentando VoyageAI
-  
-  **Correções**
-  - Correção de comportamento de chave API e tratamento de entidades na integração com Mem0
-  - Correção na lógica de invoke principal e nos testes relacionados
-  - Inputs de ferramentas agora são objetos reais em vez de strings
-  - Partes importantes adicionadas no processo de criação de ferramentas
-  - Versão do litellm foi rebaixada para prevenir problema no Windows
-  - Correção antes da execução caso inputs estejam nulos
-  - Corrigidos erros, modelo pydantic aninhado e falhas de docling
-</Update>
-
-<Update label="2024-01-04" description="v0.95.0">
-  ## Destaques da Versão
-  <Frame>
-    <img src="/images/releases/v0950.png" />
-  </Frame>
-  
-  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
-    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.95.0">Ver no GitHub</a>
-  </div>
-
-  **Novas Funcionalidades**
-  - Adição de habilidades multimodais ao Crew
-  - Guardrails programáticos
-  - HITL com múltiplas rodadas
-  - Suporte ao Gemini 2.0
-  - Melhorias em CrewAI Flows
-  - Permissões de workflow adicionadas
-  - Suporte a langfuse com litellm
-  - Integração Portkey com CrewAI
-  - Método interpolate_only e melhorias em tratamento de erros
-  - Suporte ao Docling
-  - Suporte ao Weviate
-  
-  **Correções**
-  - output_file não respeitava caminho do sistema
-  - Erro de I/O em disco ao resetar memória de curto prazo
-  - CrewJSONEncoder agora aceita enums
-  - Versão máxima do Python
-  - Interpolação de output_file em Task
-  - Manipulação adequada de nomes de funções coworker quanto a caixa e espaços
-  - tiktoken adicionado como dependência explícita e documentação do requisito Rust
-  - Inclusão do conhecimento do agent no processo de planejamento
-  - Inicialização do armazenamento definida como None em KnowledgeStorage
-  - Verificações opcionais de armazenamento corrigidas
-  - Emissor de eventos incluído nos flows
-  - Melhorias em docstring, tratamento de erros e type hints
-  - Suppressão de userWarnings de problemas pydantic no litellm
-</Update>
-
-<Update label="2024-12-05" description="v0.86.0">
-  ## Destaques da Versão
-  <Frame>
-    <img src="/images/releases/v0860.png" />
-  </Frame>
-  
-  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
-    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.86.0">Ver no GitHub</a>
-  </div>
-  **Alterações**
-  - Removidas todas referências a pipeline e roteador de pipeline
-  - Adicionado Nvidia NIM como provedor em Custom LLM
-  - Adicionado demo de knowledge + melhorias nos docs de knowledge
-  - Adicionado suporte a múltiplas rodadas de HITL
-  - Novos docs sobre crew em yaml com decorators
-  - Template de crew simplificado
-</Update>
-
-<Update label="2024-12-04" description="v0.85.0">
-  ## Destaques da Versão
-  <Frame>
-    <img src="/images/releases/v0850.png" />
-  </Frame>
-  
-  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
-    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.85.0">Ver no GitHub</a>
-  </div>
-  **Funcionalidades**
-  - Adicionado conhecimento em nível de agent
-  - Feat/remover langchain
-  - Aprimoradas saídas tipadas das tasks
-  - Login no Tool Repository ao fazer login no crewai
-  
-  **Correções**
-  - Correção em issues de result as answer não encerrando loop do LLM corretamente
-  - Correção de ausência de nome de chave ao rodar com o provedor ollama
-  - Correção em erro ortográfico identificado
-  
-  **Documentação**
-  - Atualização no readme para rodar mypy
-  - Adição do conhecimento em mint.json
-  - Atualização das ações do Github
-  - Atualização na documentação de Agents para incluir duas abordagens na criação de agent
-  - Melhorias na Configuração e Uso do LLM
-</Update>
-
-<Update label="2024-11-25" description="v0.83.0">
-  **Novas Funcionalidades**
-  - Novos callbacks before_kickoff e after_kickoff em crew
-  - Suporte para pre-seed de agents com Knowledge
-  - Adicionado suporte para recuperação de preferências de usuário e memórias usando Mem0
-  
-  **Correções**
-  - Correção em execução assíncrona
-  - Upgrade do chroma e ajuste no gerador de funções do embedder
-  - Atualização do CLI Watson com modelos suportados + docs
-  - Redução do nível para Bandit
-  - Correção de todos os testes
-  
-  **Documentação**
-  - Documentação atualizada
-</Update>
-
-<Update label="2024-11-13" description="v0.80.0">
-  **Correções**
-  - Correção em bug de substituição do callback Tokens
-  - Correção em issue de callback Step
-  - Informação de prompt tokens em cache adicionada nas métricas de uso
-  - Correção no teste crew_train_success
-</Update>

docs/pt-BR/concepts/agents.mdx

@@ -149,34 +149,33 @@ from crewai_tools import SerperDevTool
 
 # Crie um agente com todos os parâmetros disponíveis
 agent = Agent(
-    role="Senior Data Scientist",
-    goal="Analyze and interpret complex datasets to provide actionable insights",
-    backstory="With over 10 years of experience in data science and machine learning, "
-              "you excel at finding patterns in complex datasets.",
-    llm="gpt-4",  # Default: OPENAI_MODEL_NAME or "gpt-4"
-    function_calling_llm=None,  # Optional: Separate LLM for tool calling
-    verbose=False,  # Default: False
-    allow_delegation=False,  # Default: False
-    max_iter=20,  # Default: 20 iterations
-    max_rpm=None,  # Optional: Rate limit for API calls
-    max_execution_time=None,  # Optional: Maximum execution time in seconds
-    max_retry_limit=2,  # Default: 2 retries on error
-    allow_code_execution=False,  # Default: False
-    code_execution_mode="safe",  # Default: "safe" (options: "safe", "unsafe")
-    respect_context_window=True,  # Default: True
-    use_system_prompt=True,  # Default: True
-    multimodal=False,  # Default: False
-    inject_date=False,  # Default: False
-    date_format="%Y-%m-%d",  # Default: ISO format
-    reasoning=False,  # Default: False
-    max_reasoning_attempts=None,  # Default: None
-    tools=[SerperDevTool()],  # Optional: List of tools
-    knowledge_sources=None,  # Optional: List of knowledge sources
-    embedder=None,  # Optional: Custom embedder configuration
-    system_template=None,  # Optional: Custom system prompt template
-    prompt_template=None,  # Optional: Custom prompt template
-    response_template=None,  # Optional: Custom response template
-    step_callback=None,  # Optional: Callback function for monitoring
+    role="Cientista de Dados Sênior",
+    goal="Analisar e interpretar conjuntos de dados complexos para fornecer insights acionáveis",
+    backstory="Com mais de 10 anos de experiência em ciência de dados e aprendizado de máquina, você é especialista em encontrar padrões em grandes volumes de dados.",
+    llm="gpt-4",  # Padrão: OPENAI_MODEL_NAME ou "gpt-4"
+    function_calling_llm=None,  # Opcional: LLM separado para chamadas de ferramentas
+    verbose=False,  # Padrão: False
+    allow_delegation=False,  # Padrão: False
+    max_iter=20,  # Padrão: 20 iterações
+    max_rpm=None,  # Opcional: Limite de requisições por minuto
+    max_execution_time=None,  # Opcional: Tempo máximo de execução em segundos
+    max_retry_limit=2,  # Padrão: 2 tentativas em caso de erro
+    allow_code_execution=False,  # Padrão: False
+    code_execution_mode="safe",  # Padrão: "safe" (opções: "safe", "unsafe")
+    respect_context_window=True,  # Padrão: True
+    use_system_prompt=True,  # Padrão: True
+    multimodal=False,  # Padrão: False
+    inject_date=False,  # Padrão: False
+    date_format="%Y-%m-%d",  # Padrão: formato ISO
+    reasoning=False,  # Padrão: False
+    max_reasoning_attempts=None,  # Padrão: None
+    tools=[SerperDevTool()],  # Opcional: Lista de ferramentas
+    knowledge_sources=None,  # Opcional: Lista de fontes de conhecimento
+    embedder=None,  # Opcional: Configuração de embedder customizado
+    system_template=None,  # Opcional: Template de prompt de sistema
+    prompt_template=None,  # Opcional: Template de prompt customizado
+    response_template=None,  # Opcional: Template de resposta customizado
+    step_callback=None,  # Opcional: Função de callback para monitoramento
 )
 ```
 
@@ -185,65 +184,62 @@ Vamos detalhar algumas combinações de parâmetros-chave para casos de uso comu
 #### Agente de Pesquisa Básico
 ```python Code
 research_agent = Agent(
-    role="Research Analyst",
-    goal="Find and summarize information about specific topics",
-    backstory="You are an experienced researcher with attention to detail",
+    role="Analista de Pesquisa",
+    goal="Encontrar e resumir informações sobre tópicos específicos",
+    backstory="Você é um pesquisador experiente com atenção aos detalhes",
     tools=[SerperDevTool()],
-    verbose=True  # Enable logging for debugging
+    verbose=True  # Ativa logs para depuração
 )
 ```
 
 #### Agente de Desenvolvimento de Código
 ```python Code
 dev_agent = Agent(
-    role="Senior Python Developer",
-    goal="Write and debug Python code",
-    backstory="Expert Python developer with 10 years of experience",
+    role="Desenvolvedor Python Sênior",
+    goal="Escrever e depurar códigos Python",
+    backstory="Desenvolvedor Python especialista com 10 anos de experiência",
     allow_code_execution=True,
-    code_execution_mode="safe",  # Uses Docker for safety
-    max_execution_time=300,  # 5-minute timeout
-    max_retry_limit=3  # More retries for complex code tasks
+    code_execution_mode="safe",  # Usa Docker para segurança
+    max_execution_time=300,  # Limite de 5 minutos
+    max_retry_limit=3  # Mais tentativas para tarefas complexas
 )
 ```
 
 #### Agente de Análise de Longa Duração
 ```python Code
 analysis_agent = Agent(
-    role="Data Analyst",
-    goal="Perform deep analysis of large datasets",
-    backstory="Specialized in big data analysis and pattern recognition",
+    role="Analista de Dados",
+    goal="Realizar análise aprofundada de grandes conjuntos de dados",
+    backstory="Especialista em análise de big data e reconhecimento de padrões",
     memory=True,
     respect_context_window=True,
-    max_rpm=10,  # Limit API calls
-    function_calling_llm="gpt-4o-mini"  # Cheaper model for tool calls
+    max_rpm=10,  # Limite de requisições por minuto
+    function_calling_llm="gpt-4o-mini"  # Modelo mais econômico para chamadas de ferramentas
 )
 ```
 
 #### Agente com Template Personalizado
 ```python Code
 custom_agent = Agent(
-    role="Customer Service Representative",
-    goal="Assist customers with their inquiries",
-    backstory="Experienced in customer support with a focus on satisfaction",
-    system_template="""<|start_header_id|>system<|end_header_id|>
-                        {{ .System }}<|eot_id|>""",
-    prompt_template="""<|start_header_id|>user<|end_header_id|>
-                        {{ .Prompt }}<|eot_id|>""",
-    response_template="""<|start_header_id|>assistant<|end_header_id|>
-                        {{ .Response }}<|eot_id|>""",
+    role="Atendente de Suporte ao Cliente",
+    goal="Auxiliar clientes com suas dúvidas e solicitações",
+    backstory="Experiente em atendimento ao cliente com foco em satisfação",
+    system_template="""<|start_header_id|>system<|end_header_id|>\n                        {{ .System }}<|eot_id|>""",
+    prompt_template="""<|start_header_id|>user<|end_header_id|>\n                        {{ .Prompt }}<|eot_id|>""",
+    response_template="""<|start_header_id|>assistant<|end_header_id|>\n                        {{ .Response }}<|eot_id|>""",
 )
 ```
 
 #### Agente Ciente de Data, com Raciocínio
 ```python Code
 strategic_agent = Agent(
-    role="Market Analyst",
-    goal="Track market movements with precise date references and strategic planning",
-    backstory="Expert in time-sensitive financial analysis and strategic reporting",
-    inject_date=True,  # Automatically inject current date into tasks
-    date_format="%B %d, %Y",  # Format as "May 21, 2025"
-    reasoning=True,  # Enable strategic planning
-    max_reasoning_attempts=2,  # Limit planning iterations
+    role="Analista de Mercado",
+    goal="Acompanhar movimentos do mercado com referências de datas precisas e planejamento estratégico",
+    backstory="Especialista em análise financeira sensível ao tempo e relatórios estratégicos",
+    inject_date=True,  # Injeta automaticamente a data atual nas tarefas
+    date_format="%d de %B de %Y",  # Exemplo: "21 de maio de 2025"
+    reasoning=True,  # Ativa planejamento estratégico
+    max_reasoning_attempts=2,  # Limite de iterações de planejamento
     verbose=True
 )
 ```
@@ -251,12 +247,12 @@ strategic_agent = Agent(
 #### Agente de Raciocínio
 ```python Code
 reasoning_agent = Agent(
-    role="Strategic Planner",
-    goal="Analyze complex problems and create detailed execution plans",
-    backstory="Expert strategic planner who methodically breaks down complex challenges",
-    reasoning=True,  # Enable reasoning and planning
-    max_reasoning_attempts=3,  # Limit reasoning attempts
-    max_iter=30,  # Allow more iterations for complex planning
+    role="Planejador Estratégico",
+    goal="Analisar problemas complexos e criar planos de execução detalhados",
+    backstory="Especialista em planejamento estratégico que desmembra desafios complexos metodicamente",
+    reasoning=True,  # Ativa raciocínio e planejamento
+    max_reasoning_attempts=3,  # Limite de tentativas de raciocínio
+    max_iter=30,  # Permite mais iterações para planejamento complexo
     verbose=True
 )
 ```
@@ -264,10 +260,10 @@ reasoning_agent = Agent(
 #### Agente Multimodal
 ```python Code
 multimodal_agent = Agent(
-    role="Visual Content Analyst",
-    goal="Analyze and process both text and visual content",
-    backstory="Specialized in multimodal analysis combining text and image understanding",
-    multimodal=True,  # Enable multimodal capabilities
+    role="Analista de Conteúdo Visual",
+    goal="Analisar e processar tanto conteúdo textual quanto visual",
+    backstory="Especialista em análise multimodal combinando compreensão de texto e imagem",
+    multimodal=True,  # Ativa capacidades multimodais
     verbose=True
 )
 ```
@@ -336,8 +332,8 @@ wiki_tool = WikipediaTools()
 
 # Adicionar ferramentas ao agente
 researcher = Agent(
-    role="AI Technology Researcher",
-    goal="Research the latest AI developments",
+    role="Pesquisador de Tecnologia em IA",
+    goal="Pesquisar os últimos avanços em IA",
     tools=[search_tool, wiki_tool],
     verbose=True
 )
@@ -351,9 +347,9 @@ Agentes podem manter a memória de suas interações e usar contexto de tarefas
 from crewai import Agent
 
 analyst = Agent(
-    role="Data Analyst",
-    goal="Analyze and remember complex data patterns",
-    memory=True,  # Enable memory
+    role="Analista de Dados",
+    goal="Analisar e memorizar padrões complexos de dados",
+    memory=True,  # Ativa memória
     verbose=True
 )
 ```
@@ -380,10 +376,10 @@ Esta é a **configuração padrão e recomendada** para a maioria dos casos. Qua
 ```python Code
 # Agente com gerenciamento automático de contexto (padrão)
 smart_agent = Agent(
-    role="Research Analyst",
-    goal="Analyze large documents and datasets",
-    backstory="Expert at processing extensive information",
-    respect_context_window=True,  # 🔑 Default: auto-handle context limits
+    role="Analista de Pesquisa",
+    goal="Analisar grandes documentos e conjuntos de dados",
+    backstory="Especialista em processar informações extensas",
+    respect_context_window=True,  # 🔑 Padrão: gerencia limites de contexto automaticamente
     verbose=True
 )
 ```

docs/pt-BR/concepts/cli.mdx

@@ -3,6 +3,7 @@ title: CLI
 description: Aprenda a usar o CLI do CrewAI para interagir com o CrewAI.
 icon: terminal
 ---
+<Warning>A partir da versão 0.140.0, a plataforma CrewAI Enterprise iniciou um processo de migração de seu provedor de login. Como resultado, o fluxo de autenticação via CLI foi atualizado. Usuários que utlizam o Google para fazer login, ou que criaram conta após 3 de julho de 2025 não poderão fazer login com versões anteriores da biblioteca `crewai`.</Warning>
 
 ## Visão Geral
 
@@ -75,6 +76,20 @@ Exemplo:
 crewai train -n 10 -f my_training_data.pkl
 ```
 
+# Exemplo de uso programático do comando train
+n_iterations = 2
+inputs = {"topic": "Treinamento CrewAI"}
+filename = "seu_modelo.pkl"
+
+try:
+    SuaCrew().crew().train(
+      n_iterations=n_iterations, 
+      inputs=inputs, 
+      filename=filename
+    )
+except Exception as e:
+    raise Exception(f"Ocorreu um erro ao treinar a crew: {e}")
+
 ### 4. Replay
 
 Reexecute a execução do crew a partir de uma tarefa específica.

docs/pt-BR/concepts/collaboration.mdx

@@ -15,18 +15,18 @@ from crewai import Agent, Crew, Task
 
 # Enable collaboration for agents
 researcher = Agent(
-    role="Research Specialist",
-    goal="Conduct thorough research on any topic",
-    backstory="Expert researcher with access to various sources",
-    allow_delegation=True,  # 🔑 Key setting for collaboration
+    role="Especialista em Pesquisa",
+    goal="Realizar pesquisas aprofundadas sobre qualquer tema",
+    backstory="Pesquisador especialista com acesso a diversas fontes",
+    allow_delegation=True,  # 🔑 Configuração chave para colaboração
     verbose=True
 )
 
 writer = Agent(
-    role="Content Writer", 
-    goal="Create engaging content based on research",
-    backstory="Skilled writer who transforms research into compelling content",
-    allow_delegation=True,  # 🔑 Enables asking questions to other agents
+    role="Redator de Conteúdo",
+    goal="Criar conteúdo envolvente com base em pesquisas",
+    backstory="Redator habilidoso que transforma pesquisas em conteúdo atraente",
+    allow_delegation=True,  # 🔑 Permite fazer perguntas a outros agentes
     verbose=True
 )
 
@@ -67,19 +67,17 @@ from crewai import Agent, Crew, Task, Process
 
 # Create collaborative agents
 researcher = Agent(
-    role="Research Specialist",
-    goal="Find accurate, up-to-date information on any topic",
-    backstory="""You're a meticulous researcher with expertise in finding 
-    reliable sources and fact-checking information across various domains.""",
+    role="Especialista em Pesquisa",
+    goal="Realizar pesquisas aprofundadas sobre qualquer tema",
+    backstory="Pesquisador especialista com acesso a diversas fontes",
     allow_delegation=True,
     verbose=True
 )
 
 writer = Agent(
-    role="Content Writer",
-    goal="Create engaging, well-structured content",
-    backstory="""You're a skilled content writer who excels at transforming 
-    research into compelling, readable content for different audiences.""",
+    role="Redator de Conteúdo",
+    goal="Criar conteúdo envolvente com base em pesquisas",
+    backstory="Redator habilidoso que transforma pesquisas em conteúdo atraente",
     allow_delegation=True,
     verbose=True
 )
@@ -95,17 +93,17 @@ editor = Agent(
 
 # Create a task that encourages collaboration
 article_task = Task(
-    description="""Write a comprehensive 1000-word article about 'The Future of AI in Healthcare'.
-    
-    The article should include:
-    - Current AI applications in healthcare
-    - Emerging trends and technologies  
-    - Potential challenges and ethical considerations
-    - Expert predictions for the next 5 years
-    
-    Collaborate with your teammates to ensure accuracy and quality.""",
-    expected_output="A well-researched, engaging 1000-word article with proper structure and citations",
-    agent=writer  # Writer leads, but can delegate research to researcher
+    description="""Escreva um artigo abrangente de 1000 palavras sobre 'O Futuro da IA na Saúde'.
+
+O artigo deve incluir:
+- Aplicações atuais de IA na saúde
+- Tendências e tecnologias emergentes
+- Desafios potenciais e considerações éticas
+- Previsões de especialistas para os próximos 5 anos
+
+Colabore com seus colegas para garantir precisão e qualidade.""",
+    expected_output="Um artigo bem pesquisado, envolvente, com 1000 palavras, estrutura adequada e citações",
+    agent=writer  # O redator lidera, mas pode delegar pesquisa ao pesquisador
 )
 
 # Create collaborative crew
@@ -124,37 +122,37 @@ result = crew.kickoff()
 ### Padrão 1: Pesquisa → Redação → Edição
 ```python
 research_task = Task(
-    description="Research the latest developments in quantum computing",
-    expected_output="Comprehensive research summary with key findings and sources",
+    description="Pesquise os últimos avanços em computação quântica",
+    expected_output="Resumo abrangente da pesquisa com principais descobertas e fontes",
     agent=researcher
 )
 
 writing_task = Task(
-    description="Write an article based on the research findings",
-    expected_output="Engaging 800-word article about quantum computing",
+    description="Escreva um artigo com base nos achados da pesquisa",
+    expected_output="Artigo envolvente de 800 palavras sobre computação quântica",
     agent=writer,
-    context=[research_task]  # Gets research output as context
+    context=[research_task]  # Recebe a saída da pesquisa como contexto
 )
 
 editing_task = Task(
-    description="Edit and polish the article for publication",
-    expected_output="Publication-ready article with improved clarity and flow",
+    description="Edite e revise o artigo para publicação",
+    expected_output="Artigo pronto para publicação, com clareza e fluidez aprimoradas",
     agent=editor,
-    context=[writing_task]  # Gets article draft as context
+    context=[writing_task]  # Recebe o rascunho do artigo como contexto
 )
 ```
 
 ### Padrão 2: Tarefa Única Colaborativa
 ```python
 collaborative_task = Task(
-    description="""Create a marketing strategy for a new AI product.
-    
-    Writer: Focus on messaging and content strategy
-    Researcher: Provide market analysis and competitor insights
-    
-    Work together to create a comprehensive strategy.""",
-    expected_output="Complete marketing strategy with research backing",
-    agent=writer  # Lead agent, but can delegate to researcher
+    description="""Crie uma estratégia de marketing para um novo produto de IA.
+
+Redator: Foque em mensagens e estratégia de conteúdo
+Pesquisador: Forneça análise de mercado e insights de concorrentes
+
+Trabalhem juntos para criar uma estratégia abrangente.""",
+    expected_output="Estratégia de marketing completa com embasamento em pesquisa",
+    agent=writer  # Agente líder, mas pode delegar ao pesquisador
 )
 ```
 
@@ -167,35 +165,35 @@ from crewai import Agent, Crew, Task, Process
 
 # Manager agent coordinates the team
 manager = Agent(
-    role="Project Manager",
-    goal="Coordinate team efforts and ensure project success",
-    backstory="Experienced project manager skilled at delegation and quality control",
+    role="Gerente de Projetos",
+    goal="Coordenar esforços da equipe e garantir o sucesso do projeto",
+    backstory="Gerente de projetos experiente, habilidoso em delegação e controle de qualidade",
     allow_delegation=True,
     verbose=True
 )
 
 # Specialist agents
 researcher = Agent(
-    role="Researcher",
-    goal="Provide accurate research and analysis",
-    backstory="Expert researcher with deep analytical skills",
-    allow_delegation=False,  # Specialists focus on their expertise
+    role="Pesquisador",
+    goal="Fornecer pesquisa e análise precisas",
+    backstory="Pesquisador especialista com habilidades analíticas profundas",
+    allow_delegation=False,  # Especialistas focam em sua expertise
     verbose=True
 )
 
 writer = Agent(
-    role="Writer", 
-    goal="Create compelling content",
-    backstory="Skilled writer who creates engaging content",
+    role="Redator",
+    goal="Criar conteúdo envolvente",
+    backstory="Redator habilidoso que cria conteúdo atraente",
     allow_delegation=False,
     verbose=True
 )
 
 # Manager-led task
 project_task = Task(
-    description="Create a comprehensive market analysis report with recommendations",
-    expected_output="Executive summary, detailed analysis, and strategic recommendations",
-    agent=manager  # Manager will delegate to specialists
+    description="Crie um relatório de análise de mercado completo com recomendações",
+    expected_output="Resumo executivo, análise detalhada e recomendações estratégicas",
+    agent=manager  # O gerente delega para especialistas
 )
 
 # Hierarchical crew

docs/pt-BR/concepts/crews.mdx

@@ -153,32 +153,32 @@ from crewai_tools import YourCustomTool
 class YourCrewName:
     def agent_one(self) -> Agent:
         return Agent(
-            role="Data Analyst",
-            goal="Analyze data trends in the market",
-            backstory="An experienced data analyst with a background in economics",
+            role="Analista de Dados",
+            goal="Analisar tendências de dados no mercado brasileiro",
+            backstory="Analista experiente com formação em economia",
             verbose=True,
             tools=[YourCustomTool()]
         )
 
     def agent_two(self) -> Agent:
         return Agent(
-            role="Market Researcher",
-            goal="Gather information on market dynamics",
-            backstory="A diligent researcher with a keen eye for detail",
+            role="Pesquisador de Mercado",
+            goal="Coletar informações sobre a dinâmica do mercado nacional",
+            backstory="Pesquisador dedicado com olhar atento aos detalhes",
             verbose=True
         )
 
     def task_one(self) -> Task:
         return Task(
-            description="Collect recent market data and identify trends.",
-            expected_output="A report summarizing key trends in the market.",
+            description="Coletar dados recentes do mercado brasileiro e identificar tendências.",
+            expected_output="Um relatório resumido com as principais tendências do mercado.",
             agent=self.agent_one()
         )
 
     def task_two(self) -> Task:
         return Task(
-            description="Research factors affecting market dynamics.",
-            expected_output="An analysis of factors influencing the market.",
+            description="Pesquisar fatores que afetam a dinâmica do mercado nacional.",
+            expected_output="Uma análise dos fatores que influenciam o mercado.",
             agent=self.agent_two()
         )
 

docs/pt-BR/concepts/event-listener.mdx

@@ -51,24 +51,24 @@ from crewai.utilities.events import (
 )
 from crewai.utilities.events.base_event_listener import BaseEventListener
 
-class MyCustomListener(BaseEventListener):
+class MeuListenerPersonalizado(BaseEventListener):
     def __init__(self):
         super().__init__()
 
     def setup_listeners(self, crewai_event_bus):
         @crewai_event_bus.on(CrewKickoffStartedEvent)
-        def on_crew_started(source, event):
-            print(f"Crew '{event.crew_name}' has started execution!")
+        def ao_iniciar_crew(source, event):
+            print(f"Crew '{event.crew_name}' iniciou a execução!")
 
         @crewai_event_bus.on(CrewKickoffCompletedEvent)
-        def on_crew_completed(source, event):
-            print(f"Crew '{event.crew_name}' has completed execution!")
-            print(f"Output: {event.output}")
+        def ao_finalizar_crew(source, event):
+            print(f"Crew '{event.crew_name}' finalizou a execução!")
+            print(f"Saída: {event.output}")
 
         @crewai_event_bus.on(AgentExecutionCompletedEvent)
-        def on_agent_execution_completed(source, event):
-            print(f"Agent '{event.agent.role}' completed task")
-            print(f"Output: {event.output}")
+        def ao_finalizar_execucao_agente(source, event):
+            print(f"Agente '{event.agent.role}' concluiu a tarefa")
+            print(f"Saída: {event.output}")
 ```
 
 ## Registrando Corretamente Seu Listener

docs/pt-BR/concepts/flows.mdx

@@ -486,8 +486,9 @@ Existem duas formas de executar um flow:
 Você pode executar um flow programaticamente criando uma instância da sua classe de flow e chamando o método `kickoff()`:
 
 ```python
-flow = ExampleFlow()
-result = flow.kickoff()
+# Exemplo de execução de flow em português
+flow = ExemploFlow()
+resultado = flow.kickoff()
 ```
 
 ### Usando a CLI

docs/pt-BR/concepts/knowledge.mdx

@@ -39,17 +39,17 @@ llm = LLM(model="gpt-4o-mini", temperature=0)
 
 # Create an agent with the knowledge store
 agent = Agent(
-    role="About User",
-    goal="You know everything about the user.",
-    backstory="You are a master at understanding people and their preferences.",
+    role="Sobre o Usuário",
+    goal="Você sabe tudo sobre o usuário.",
+    backstory="Você é mestre em entender pessoas e suas preferências.",
     verbose=True,
     allow_delegation=False,
     llm=llm,
 )
 
 task = Task(
-    description="Answer the following questions about the user: {question}",
-    expected_output="An answer to the question.",
+    description="Responda às seguintes perguntas sobre o usuário: {question}",
+    expected_output="Uma resposta para a pergunta.",
     agent=agent,
 )
 
@@ -87,17 +87,17 @@ llm = LLM(model="gpt-4o-mini", temperature=0)
 
 # Create an agent with the knowledge store
 agent = Agent(
-    role="About papers",
-    goal="You know everything about the papers.",
-    backstory="You are a master at understanding papers and their content.",
+    role="Sobre artigos",
+    goal="Você sabe tudo sobre os artigos.",
+    backstory="Você é mestre em entender artigos e seus conteúdos.",
     verbose=True,
     allow_delegation=False,
     llm=llm,
 )
 
 task = Task(
-    description="Answer the following questions about the papers: {question}",
-    expected_output="An answer to the question.",
+    description="Responda às seguintes perguntas sobre os artigos: {question}",
+    expected_output="Uma resposta para a pergunta.",
     agent=agent,
 )
 
@@ -201,16 +201,16 @@ specialist_knowledge = StringKnowledgeSource(
 )
 
 specialist_agent = Agent(
-    role="Technical Specialist",
-    goal="Provide technical expertise",
-    backstory="Expert in specialized technical domains",
-    knowledge_sources=[specialist_knowledge]  # Agent-specific knowledge
+    role="Especialista Técnico",
+    goal="Fornecer expertise técnica",
+    backstory="Especialista em domínios técnicos especializados",
+    knowledge_sources=[specialist_knowledge]  # Conhecimento específico do agente
 )
 
 task = Task(
-    description="Answer technical questions",
+    description="Responda perguntas técnicas",
     agent=specialist_agent,
-    expected_output="Technical answer"
+    expected_output="Resposta técnica"
 )
 
 # No crew-level knowledge required
@@ -240,7 +240,7 @@ Cada nível de knowledge usa coleções de armazenamento independentes:
 
 ```python
 # Agent knowledge storage
-agent_collection_name = agent.role  # e.g., "Technical Specialist"
+agent_collection_name = agent.role  # e.g., "Especialista Técnico"
 
 # Crew knowledge storage
 crew_collection_name = "crew"
@@ -248,7 +248,7 @@ crew_collection_name = "crew"
 # Both stored in same ChromaDB instance but different collections
 # Path: ~/.local/share/CrewAI/{project}/knowledge/
 #   ├── crew/                    # Crew knowledge collection
-#   ├── Technical Specialist/    # Agent knowledge collection
+#   ├── Especialista Técnico/    # Agent knowledge collection
 #   └── Another Agent Role/      # Another agent's collection
 ```
 
@@ -265,7 +265,7 @@ agent_knowledge = StringKnowledgeSource(
 )
 
 agent = Agent(
-    role="Specialist",
+    role="Especialista",
     goal="Use specialized knowledge",
     backstory="Expert with specific knowledge",
     knowledge_sources=[agent_knowledge],
@@ -299,10 +299,10 @@ specialist_knowledge = StringKnowledgeSource(
 )
 
 specialist = Agent(
-    role="Technical Specialist",
-    goal="Provide technical expertise",
-    backstory="Technical expert",
-    knowledge_sources=[specialist_knowledge]  # Agent-specific
+    role="Especialista Técnico",
+    goal="Fornecer expertise técnica",
+    backstory="Especialista em domínios técnicos especializados",
+    knowledge_sources=[specialist_knowledge]  # Conhecimento específico do agente
 )
 
 generalist = Agent(

docs/pt-BR/concepts/llms.mdx

@@ -78,15 +78,15 @@ Existem diferentes locais no código do CrewAI onde você pode especificar o mod
 
     # Configuração avançada com parâmetros detalhados
     llm = LLM(
-        model="model-id-here",  # gpt-4o, gemini-2.0-flash, anthropic/claude...
-        temperature=0.7,        # Mais alto para saídas criativas
-        timeout=120,            # Segundos para aguardar resposta
-        max_tokens=4000,        # Comprimento máximo da resposta
-        top_p=0.9,              # Parâmetro de amostragem nucleus
-        frequency_penalty=0.1 , # Reduz repetição
-        presence_penalty=0.1,   # Incentiva diversidade de tópicos
-        response_format={"type": "json"},  # Para respostas estruturadas
-        seed=42                 # Para resultados reproduzíveis
+        model="openai/gpt-4", 
+        temperature=0.8,
+        max_tokens=150,
+        top_p=0.9,
+        frequency_penalty=0.1,
+        presence_penalty=0.1,
+        response_format={"type":"json"},
+        stop=["FIM"],
+        seed=42
     )
     ```
 
@@ -127,13 +127,13 @@ Nesta seção, você encontrará exemplos detalhados que ajudam a selecionar, co
     from crewai import LLM
 
     llm = LLM(
-        model="openai/gpt-4", # chamar modelo por provider/model_name
+        model="openai/gpt-4",
         temperature=0.8,
         max_tokens=150,
         top_p=0.9,
         frequency_penalty=0.1,
         presence_penalty=0.1,
-        stop=["END"],
+        stop=["FIM"],
         seed=42
     )
     ```
@@ -169,7 +169,7 @@ Nesta seção, você encontrará exemplos detalhados que ajudam a selecionar, co
     llm = LLM(
         model="meta_llama/Llama-4-Scout-17B-16E-Instruct-FP8",
         temperature=0.8,
-        stop=["END"],
+        stop=["FIM"],
         seed=42
     )
     ```

docs/pt-BR/concepts/planning.mdx

@@ -17,7 +17,7 @@ Começar a usar o recurso de planejamento é muito simples, o único passo neces
 from crewai import Crew, Agent, Task, Process
 
 # Monte sua crew com capacidades de planejamento
-my_crew = Crew(
+minha_crew = Crew(
     agents=self.agents,
     tasks=self.tasks,
     process=Process.sequential,

docs/pt-BR/concepts/processes.mdx

@@ -28,23 +28,23 @@ from crewai import Crew, Process
 
 # Exemplo: Criando uma crew com processo sequencial
 crew = Crew(
-    agents=my_agents,
-    tasks=my_tasks,
+    agents=meus_agentes,
+    tasks=minhas_tarefas,
     process=Process.sequential
 )
 
 # Exemplo: Criando uma crew com processo hierárquico
 # Certifique-se de fornecer um manager_llm ou manager_agent
 crew = Crew(
-    agents=my_agents,
-    tasks=my_tasks,
+    agents=meus_agentes,
+    tasks=minhas_tarefas,
     process=Process.hierarchical,
     manager_llm="gpt-4o"
     # ou
-    # manager_agent=my_manager_agent
+    # manager_agent=meu_agente_gerente
 )
 ```
-**Nota:** Certifique-se de que `my_agents` e `my_tasks` estejam definidos antes de criar o objeto `Crew`, e para o processo hierárquico, é necessário também fornecer o `manager_llm` ou `manager_agent`.
+**Nota:** Certifique-se de que `meus_agentes` e `minhas_tarefas` estejam definidos antes de criar o objeto `Crew`, e para o processo hierárquico, é necessário também fornecer o `manager_llm` ou `manager_agent`.
 
 ## Processo Sequencial
 

docs/pt-BR/concepts/reasoning.mdx

@@ -15,12 +15,12 @@ Para habilitar o reasoning para um agente, basta definir `reasoning=True` ao cri
 ```python
 from crewai import Agent
 
-agent = Agent(
-    role="Data Analyst",
-    goal="Analyze complex datasets and provide insights",
-    backstory="You are an experienced data analyst with expertise in finding patterns in complex data.",
-    reasoning=True,  # Enable reasoning
-    max_reasoning_attempts=3  # Optional: Set a maximum number of reasoning attempts
+analista = Agent(
+    role="Analista de Dados",
+    goal="Analisar dados e fornecer insights",
+    backstory="Você é um analista de dados especialista.",
+    reasoning=True,
+    max_reasoning_attempts=3  # Opcional: Defina um limite de tentativas de reasoning
 )
 ```
 
@@ -53,23 +53,23 @@ Aqui está um exemplo completo:
 from crewai import Agent, Task, Crew
 
 # Create an agent with reasoning enabled
-analyst = Agent(
-    role="Data Analyst",
-    goal="Analyze data and provide insights",
-    backstory="You are an expert data analyst.",
+analista = Agent(
+    role="Analista de Dados",
+    goal="Analisar dados e fornecer insights",
+    backstory="Você é um analista de dados especialista.",
     reasoning=True,
-    max_reasoning_attempts=3  # Optional: Set a limit on reasoning attempts
+    max_reasoning_attempts=3  # Opcional: Defina um limite de tentativas de reasoning
 )
 
 # Create a task
 analysis_task = Task(
-    description="Analyze the provided sales data and identify key trends.",
-    expected_output="A report highlighting the top 3 sales trends.",
-    agent=analyst
+    description="Analise os dados de vendas fornecidos e identifique as principais tendências.",
+    expected_output="Um relatório destacando as 3 principais tendências de vendas.",
+    agent=analista
 )
 
 # Create a crew and run the task
-crew = Crew(agents=[analyst], tasks=[analysis_task])
+crew = Crew(agents=[analista], tasks=[analysis_task])
 result = crew.kickoff()
 
 print(result)
@@ -90,16 +90,16 @@ logging.basicConfig(level=logging.INFO)
 
 # Create an agent with reasoning enabled
 agent = Agent(
-    role="Data Analyst",
-    goal="Analyze data and provide insights",
+    role="Analista de Dados",
+    goal="Analisar dados e fornecer insights",
     reasoning=True,
     max_reasoning_attempts=3
 )
 
 # Create a task
 task = Task(
-    description="Analyze the provided sales data and identify key trends.",
-    expected_output="A report highlighting the top 3 sales trends.",
+    description="Analise os dados de vendas fornecidos e identifique as principais tendências.",
+    expected_output="Um relatório destacando as 3 principais tendências de vendas.",
     agent=agent
 )
 
@@ -113,7 +113,7 @@ result = agent.execute_task(task)
 Veja um exemplo de como pode ser um plano de reasoning para uma tarefa de análise de dados:
 
 ```
-Task: Analyze the provided sales data and identify key trends.
+Task: Analise os dados de vendas fornecidos e identifique as principais tendências.
 
 Reasoning Plan:
 I'll analyze the sales data to identify the top 3 trends.

docs/pt-BR/concepts/tasks.mdx

@@ -386,7 +386,7 @@ def validate_with_context(result: TaskOutput) -> Tuple[bool, Any]:
         validated_data = perform_validation(result)
         return (True, validated_data)
     except ValidationError as e:
-        return (False, f"VALIDATION_ERROR: {str(e)}")
+        return (False, f"ERRO_DE_VALIDACAO: {str(e)}")
     except Exception as e:
         return (False, str(e))
 ```

docs/pt-BR/concepts/tools.mdx

@@ -67,17 +67,17 @@ web_rag_tool = WebsiteSearchTool()
 
 # Criar agentes
 researcher = Agent(
-    role='Market Research Analyst',
-    goal='Provide up-to-date market analysis of the AI industry',
-    backstory='An expert analyst with a keen eye for market trends.',
+    role='Analista de Mercado',
+    goal='Fornecer análise de mercado atualizada da indústria de IA',
+    backstory='Analista especialista com olhar atento para tendências de mercado.',
     tools=[search_tool, web_rag_tool],
     verbose=True
 )
 
 writer = Agent(
-    role='Content Writer',
-    goal='Craft engaging blog posts about the AI industry',
-    backstory='A skilled writer with a passion for technology.',
+    role='Redator de Conteúdo',
+    goal='Criar posts de blog envolventes sobre a indústria de IA',
+    backstory='Redator habilidoso com paixão por tecnologia.',
     tools=[docs_tool, file_tool],
     verbose=True
 )

docs/pt-BR/concepts/training.mdx

@@ -36,19 +36,18 @@ Para treinar sua crew de forma programática, siga estes passos:
 3. Execute o comando de treinamento dentro de um bloco try-except para tratar possíveis erros.
 
 ```python Code
-n_iterations = 2
-inputs = {"topic": "CrewAI Training"}
-filename = "your_model.pkl"
+n_iteracoes = 2
+entradas = {"topic": "Treinamento CrewAI"}
+nome_arquivo = "seu_modelo.pkl"
 
 try:
-    YourCrewName_Crew().crew().train(
-      n_iterations=n_iterations, 
-      inputs=inputs, 
-      filename=filename
+    SuaCrew().crew().train(
+      n_iterations=n_iteracoes, 
+      inputs=entradas, 
+      filename=nome_arquivo
     )
-
 except Exception as e:
-    raise Exception(f"An error occurred while training the crew: {e}")
+    raise Exception(f"Ocorreu um erro ao treinar a crew: {e}")
 ```
 
 ### Pontos Importantes

docs/pt-BR/enterprise/features/hallucination-guardrail.mdx

@@ -26,13 +26,13 @@ from crewai.tasks.hallucination_guardrail import HallucinationGuardrail
 from crewai import LLM
 
 # Uso básico - utiliza o expected_output da tarefa como contexto
-guardrail = HallucinationGuardrail(
+protecao = HallucinationGuardrail(
     llm=LLM(model="gpt-4o-mini")
 )
 
 # Com contexto de referência explícito
-context_guardrail = HallucinationGuardrail(
-    context="AI helps with various tasks including analysis and generation.",
+protecao_com_contexto = HallucinationGuardrail(
+    context="IA ajuda em várias tarefas, incluindo análise e geração.",
     llm=LLM(model="gpt-4o-mini")
 )
 ```
@@ -43,11 +43,11 @@ context_guardrail = HallucinationGuardrail(
 from crewai import Task
 
 # Crie sua tarefa com a proteção
-task = Task(
-    description="Write a summary about AI capabilities",
-    expected_output="A factual summary based on the provided context",
-    agent=my_agent,
-    guardrail=guardrail  # Adiciona a proteção para validar a saída
+minha_tarefa = Task(
+    description="Escreva um resumo sobre as capacidades da IA",
+    expected_output="Um resumo factual baseado no contexto fornecido",
+    agent=meu_agente,
+    guardrail=protecao  # Adiciona a proteção para validar a saída
 )
 ```
 
@@ -59,8 +59,8 @@ Para validação mais rigorosa, é possível definir um limiar de fidelidade per
 
 ```python
 # Proteção rigorosa exigindo alta pontuação de fidelidade
-strict_guardrail = HallucinationGuardrail(
-    context="Quantum computing uses qubits that exist in superposition states.",
+protecao_rigorosa = HallucinationGuardrail(
+    context="Computação quântica utiliza qubits que existem em estados de superposição.",
     llm=LLM(model="gpt-4o-mini"),
     threshold=8.0  # Requer pontuação >= 8 para validar
 )
@@ -72,10 +72,10 @@ Se sua tarefa utiliza ferramentas, você pode incluir as respostas das ferrament
 
 ```python
 # Proteção com contexto de resposta da ferramenta
-weather_guardrail = HallucinationGuardrail(
-    context="Current weather information for the requested location",
+protecao_clima = HallucinationGuardrail(
+    context="Informações meteorológicas atuais para o local solicitado",
     llm=LLM(model="gpt-4o-mini"),
-    tool_response="Weather API returned: Temperature 22°C, Humidity 65%, Clear skies"
+    tool_response="API do Clima retornou: Temperatura 22°C, Umidade 65%, Céu limpo"
 )
 ```
 
@@ -123,15 +123,15 @@ Quando uma proteção é adicionada à tarefa, ela valida automaticamente a saí
 
 ```python
 # Fluxo de validação de saída da tarefa
-task_output = agent.execute_task(task)
-validation_result = guardrail(task_output)
+task_output = meu_agente.execute_task(minha_tarefa)
+resultado_validacao = protecao(task_output)
 
-if validation_result.valid:
+if resultado_validacao.valid:
     # Tarefa concluída com sucesso
     return task_output
 else:
     # Tarefa falha com feedback de validação
-    raise ValidationError(validation_result.feedback)
+    raise ValidationError(resultado_validacao.feedback)
 ```
 
 ### Rastreamento de Eventos
@@ -151,10 +151,10 @@ A proteção se integra ao sistema de eventos do CrewAI para fornecer observabil
     Inclua todas as informações factuais relevantes nas quais a IA deve basear sua saída:
 
     ```python
-    context = """
-    Company XYZ was founded in 2020 and specializes in renewable energy solutions.
-    They have 150 employees and generated $50M revenue in 2023.
-    Their main products include solar panels and wind turbines.
+    contexto = """
+    Empresa XYZ foi fundada em 2020 e é especializada em soluções de energia renovável.
+    Possui 150 funcionários e faturou R$ 50 milhões em 2023.
+    Seus principais produtos incluem painéis solares e turbinas eólicas.
     """
     ```
   </Step>
@@ -164,10 +164,10 @@ A proteção se integra ao sistema de eventos do CrewAI para fornecer observabil
 
     ```python
     # Bom: Contexto focado
-    context = "The current weather in New York is 18°C with light rain."
+    contexto = "O clima atual em Nova York é 18°C com chuva leve."
 
     # Evite: Informações irrelevantes
-    context = "The weather is 18°C. The city has 8 million people. Traffic is heavy."
+    contexto = "The weather is 18°C. The city has 8 million people. Traffic is heavy."
     ```
   </Step>
 

docs/pt-BR/enterprise/features/integrations.mdx

@@ -84,31 +84,31 @@ from crewai import Agent, Task, Crew
 from crewai_tools import CrewaiEnterpriseTools
 
 # Obtenha ferramentas enterprise (a ferramenta Gmail será incluída)
-enterprise_tools = CrewaiEnterpriseTools(
-    enterprise_token="your_enterprise_token"
+ferramentas_enterprise = CrewaiEnterpriseTools(
+    enterprise_token="seu_token_enterprise"
 )
 # imprima as ferramentas
-print(enterprise_tools)
+printf(ferramentas_enterprise)
 
 # Crie um agente com capacidades do Gmail
-email_agent = Agent(
-    role="Email Manager",
-    goal="Manage and organize email communications",
-    backstory="An AI assistant specialized in email management and communication.",
-    tools=enterprise_tools
+agente_email = Agent(
+    role="Gerente de E-mails",
+    goal="Gerenciar e organizar comunicações por e-mail",
+    backstory="Um assistente de IA especializado em gestão de e-mails e comunicação.",
+    tools=ferramentas_enterprise
 )
 
 # Tarefa para enviar um e-mail
-email_task = Task(
-    description="Draft and send a follow-up email to john@example.com about the project update",
-    agent=email_agent,
-    expected_output="Confirmation that email was sent successfully"
+tarefa_email = Task(
+    description="Redigir e enviar um e-mail de acompanhamento para john@example.com sobre a atualização do projeto",
+    agent=agente_email,
+    expected_output="Confirmação de que o e-mail foi enviado com sucesso"
 )
 
 # Execute a tarefa
 crew = Crew(
-    agents=[email_agent],
-    tasks=[email_task]
+    agents=[agente_email],
+    tasks=[tarefa_email]
 )
 
 # Execute o crew
@@ -125,23 +125,23 @@ enterprise_tools = CrewaiEnterpriseTools(
 )
 gmail_tool = enterprise_tools["gmail_find_email"]
 
-gmail_agent = Agent(
-    role="Gmail Manager",
-    goal="Manage gmail communications and notifications",
-    backstory="An AI assistant that helps coordinate gmail communications.",
+agente_gmail = Agent(
+    role="Gerente do Gmail",
+    goal="Gerenciar comunicações e notificações do gmail",
+    backstory="Um assistente de IA que ajuda a coordenar comunicações no gmail.",
     tools=[gmail_tool]
 )
 
-notification_task = Task(
-    description="Find the email from john@example.com",
-    agent=gmail_agent,
-    expected_output="Email found from john@example.com"
+tarefa_notificacao = Task(
+    description="Encontrar o e-mail de john@example.com",
+    agent=agente_gmail,
+    expected_output="E-mail encontrado de john@example.com"
 )
 
 # Execute a tarefa
 crew = Crew(
-    agents=[slack_agent],
-    tasks=[notification_task]
+    agents=[agente_gmail],
+    tasks=[tarefa_notificacao]
 )
 ```
 

docs/pt-BR/enterprise/features/tool-repository.mdx

@@ -30,7 +30,7 @@ Antes de usar o Repositório de Ferramentas, certifique-se de que você possui:
 Para instalar uma ferramenta:
 
 ```bash
-crewai tool install <tool-name>
+crewai tool install <nome-da-ferramenta>
 ```
 
 Isso instala a ferramenta e a adiciona ao `pyproject.toml`.
@@ -40,7 +40,7 @@ Isso instala a ferramenta e a adiciona ao `pyproject.toml`.
 Para criar um novo projeto de ferramenta:
 
 ```bash
-crewai tool create <tool-name>
+crewai tool create <nome-da-ferramenta>
 ```
 
 Isso gera um projeto de ferramenta estruturado localmente.
@@ -76,7 +76,7 @@ Para atualizar uma ferramenta publicada:
 3. Faça o commit das alterações e publique
 
 ```bash
-git commit -m "Update version to 0.1.1"
+git commit -m "Atualizar versão para 0.1.1"
 crewai tool publish
 ```
 

docs/pt-BR/enterprise/features/webhook-streaming.mdx

@@ -12,16 +12,17 @@ O Enterprise Event Streaming permite que você receba atualizações em tempo re
 
 Ao utilizar a API Kickoff, inclua um objeto `webhooks` em sua requisição, por exemplo:
 
+# Exemplo de uso da API Kickoff com webhooks
 ```json
 {
   "inputs": {"foo": "bar"},
   "webhooks": {
     "events": ["crew_kickoff_started", "llm_call_started"],
-    "url": "https://your.endpoint/webhook",
+    "url": "https://seu.endpoint/webhook",
     "realtime": false,
     "authentication": {
       "strategy": "bearer",
-      "token": "my-secret-token"
+      "token": "meu-token-secreto"
     }
   }
 }
@@ -33,19 +34,20 @@ Se `realtime` estiver definido como `true`, cada evento será entregue individua
 
 Cada webhook envia uma lista de eventos:
 
+# Exemplo de evento enviado pelo webhook
 ```json
 {
   "events": [
     {
-      "id": "event-id",
-      "execution_id": "crew-run-id",
+      "id": "id-do-evento",
+      "execution_id": "id-da-execucao-do-crew",
       "timestamp": "2025-02-16T10:58:44.965Z",
       "type": "llm_call_started",
       "data": {
         "model": "gpt-4",
         "messages": [
-          {"role": "system", "content": "You are an assistant."},
-          {"role": "user", "content": "Summarize this article."}
+          {"role": "system", "content": "Você é um assistente."},
+          {"role": "user", "content": "Resuma este artigo."}
         ]
       }
     }

docs/pt-BR/enterprise/guides/deploy-crew.mdx

@@ -41,11 +41,8 @@ A CLI fornece a maneira mais rápida de implantar crews desenvolvidos localmente
     Primeiro, você precisa autenticar sua CLI com a plataforma CrewAI Enterprise:
 
     ```bash
-    # Se já possui uma conta CrewAI Enterprise
+    # Se já possui uma conta CrewAI Enterprise, ou deseja criar uma:
     crewai login
-
-    # Se vai criar uma nova conta
-    crewai signup
     ```
 
     Ao executar qualquer um dos comandos, a CLI irá:

docs/pt-BR/learn/before-and-after-kickoff-hooks.mdx

@@ -16,17 +16,17 @@ from crewai import CrewBase
 from crewai.project import before_kickoff
 
 @CrewBase
-class MyCrew:
+class MinhaEquipe:
     @before_kickoff
-    def prepare_data(self, inputs):
-        # Preprocess or modify inputs
-        inputs['processed'] = True
-        return inputs
+    def preparar_dados(self, entradas):
+        # Pré-processa ou modifica as entradas
+        entradas['processado'] = True
+        return entradas
 
 #...
 ```
 
-Neste exemplo, a função prepare_data modifica as entradas adicionando um novo par chave-valor indicando que as entradas foram processadas.
+Neste exemplo, a função preparar_dados modifica as entradas adicionando um novo par chave-valor indicando que as entradas foram processadas.
 
 ## Hook Depois do Kickoff
 
@@ -39,17 +39,17 @@ from crewai import CrewBase
 from crewai.project import after_kickoff
 
 @CrewBase
-class MyCrew:
+class MinhaEquipe:
     @after_kickoff
-    def log_results(self, result):
-        # Log or modify the results
-        print("Crew execution completed with result:", result)
-        return result
+    def registrar_resultados(self, resultado):
+        # Registra ou modifica os resultados
+        print("Execução da equipe concluída com resultado:", resultado)
+        return resultado
 
 # ...
 ```
 
-Na função `log_results`, os resultados da execução da crew são simplesmente impressos. Você pode estender isso para realizar operações mais complexas, como enviar notificações ou integrar com outros serviços.
+Na função `registrar_resultados`, os resultados da execução da crew são simplesmente impressos. Você pode estender isso para realizar operações mais complexas, como enviar notificações ou integrar com outros serviços.
 
 ## Utilizando Ambos os Hooks
 

docs/pt-BR/learn/customizing-agents.mdx

@@ -77,9 +77,9 @@ search_tool = SerperDevTool()
 
 # Inicialize o agente com opções avançadas
 agent = Agent(
-  role='Research Analyst',
-  goal='Provide up-to-date market analysis',
-  backstory='An expert analyst with a keen eye for market trends.',
+  role='Analista de Pesquisa',
+  goal='Fornecer análises de mercado atualizadas',
+  backstory='Um analista especialista com olhar atento para tendências de mercado.',
   tools=[search_tool],
   memory=True, # Ativa memória
   verbose=True,
@@ -98,14 +98,9 @@ eficiência dentro do ecossistema CrewAI. Se necessário, a delegação pode ser
 
 ```python Code
 agent = Agent(
-  role='Content Writer',
-  goal='Write engaging content on market trends',
-  backstory='A seasoned writer with expertise in market analysis.',
+  role='Redator de Conteúdo',
+  goal='Escrever conteúdo envolvente sobre tendências de mercado',
+  backstory='Um redator experiente com expertise em análise de mercado.',
   allow_delegation=True # Habilitando delegação
 )
-```
-
-## Conclusão
-
-Personalizar agentes no CrewAI definindo seus papéis, objetivos, histórias e ferramentas, juntamente com opções avançadas como personalização de modelo de linguagem, memória, ajustes de performance e preferências de delegação, 
-proporciona uma equipe de IA sofisticada e preparada para enfrentar desafios complexos.
+```

docs/pt-BR/learn/kickoff-async.mdx

@@ -45,17 +45,17 @@ from crewai import Crew, Agent, Task
 
 # Create an agent with code execution enabled
 coding_agent = Agent(
-    role="Python Data Analyst",
-    goal="Analyze data and provide insights using Python",
-    backstory="You are an experienced data analyst with strong Python skills.",
+    role="Analista de Dados Python",
+    goal="Analisar dados e fornecer insights usando Python",
+    backstory="Você é um analista de dados experiente com fortes habilidades em Python.",
     allow_code_execution=True
 )
 
 # Create a task that requires code execution
 data_analysis_task = Task(
-    description="Analyze the given dataset and calculate the average age of participants. Ages: {ages}",
+    description="Analise o conjunto de dados fornecido e calcule a idade média dos participantes. Idades: {ages}",
     agent=coding_agent,
-    expected_output="The average age of the participants."
+    expected_output="A idade média dos participantes."
 )
 
 # Create a crew and add the task
@@ -83,23 +83,23 @@ from crewai import Crew, Agent, Task
 
 # Create an agent with code execution enabled
 coding_agent = Agent(
-    role="Python Data Analyst",
-    goal="Analyze data and provide insights using Python",
-    backstory="You are an experienced data analyst with strong Python skills.",
+    role="Analista de Dados Python",
+    goal="Analisar dados e fornecer insights usando Python",
+    backstory="Você é um analista de dados experiente com fortes habilidades em Python.",
     allow_code_execution=True
 )
 
 # Create tasks that require code execution
 task_1 = Task(
-    description="Analyze the first dataset and calculate the average age of participants. Ages: {ages}",
+    description="Analise o primeiro conjunto de dados e calcule a idade média dos participantes. Idades: {ages}",
     agent=coding_agent,
-    expected_output="The average age of the participants."
+    expected_output="A idade média dos participantes."
 )
 
 task_2 = Task(
-    description="Analyze the second dataset and calculate the average age of participants. Ages: {ages}",
+    description="Analise o segundo conjunto de dados e calcule a idade média dos participantes. Idades: {ages}",
     agent=coding_agent,
-    expected_output="The average age of the participants."
+    expected_output="A idade média dos participantes."
 )
 
 # Create two crews and add tasks

docs/pt-BR/mcp/multiple-servers.mdx

@@ -43,11 +43,11 @@ try:
     with MCPServerAdapter(server_params_list) as aggregated_tools:
         print(f"Available aggregated tools: {[tool.name for tool in aggregated_tools]}")
 
-        multi_server_agent = Agent(
-            role="Versatile Assistant",
-            goal="Utilize tools from local Stdio, remote SSE, and remote HTTP MCP servers.",
-            backstory="An AI agent capable of leveraging a diverse set of tools from multiple sources.",
-            tools=aggregated_tools, # All tools are available here
+        agente_multiservidor = Agent(
+            role="Assistente Versátil",
+            goal="Utilizar ferramentas de servidores MCP locais Stdio, remotos SSE e remotos HTTP.",
+            backstory="Um agente de IA capaz de aproveitar um conjunto diversificado de ferramentas de múltiplas fontes.",
+            tools=aggregated_tools, # Todas as ferramentas estão disponíveis aqui
             verbose=True,
         )
 

docs/pt-BR/mcp/overview.mdx

@@ -73,10 +73,10 @@ server_params = {
 with MCPServerAdapter(server_params) as mcp_tools:
     print(f"Available tools: {[tool.name for tool in mcp_tools]}")
 
-    my_agent = Agent(
-        role="MCP Tool User",
-        goal="Utilize tools from an MCP server.",
-        backstory="I can connect to MCP servers and use their tools.",
+    meu_agente = Agent(
+        role="Usuário de Ferramentas MCP",
+        goal="Utilizar ferramentas de um servidor MCP.",
+        backstory="Posso conectar a servidores MCP e usar suas ferramentas.",
         tools=mcp_tools, # Passe as ferramentas carregadas para o seu agente
         reasoning=True,
         verbose=True
@@ -91,10 +91,10 @@ Este padrão geral mostra como integrar ferramentas. Para exemplos específicos
 with MCPServerAdapter(server_params) as mcp_tools:
     print(f"Available tools: {[tool.name for tool in mcp_tools]}")
 
-    my_agent = Agent(
-        role="MCP Tool User",
-        goal="Utilize tools from an MCP server.",
-        backstory="I can connect to MCP servers and use their tools.",
+    meu_agente = Agent(
+        role="Usuário de Ferramentas MCP",
+        goal="Utilizar ferramentas de um servidor MCP.",
+        backstory="Posso conectar a servidores MCP e usar suas ferramentas.",
         tools=mcp_tools["tool_name"], # Passe as ferramentas filtradas para o seu agente
         reasoning=True,
         verbose=True

docs/pt-BR/mcp/sse.mdx

@@ -37,24 +37,24 @@ try:
         print(f"Available tools from SSE MCP server: {[tool.name for tool in tools]}")
 
         # Example: Using a tool from the SSE MCP server
-        sse_agent = Agent(
-            role="Remote Service User",
-            goal="Utilize a tool provided by a remote SSE MCP server.",
-            backstory="An AI agent that connects to external services via SSE.",
+        agente_sse = Agent(
+            role="Usuário de Serviço Remoto",
+            goal="Utilizar uma ferramenta fornecida por um servidor MCP remoto via SSE.",
+            backstory="Um agente de IA que conecta a serviços externos via SSE.",
             tools=tools,
             reasoning=True,
             verbose=True,
         )
 
         sse_task = Task(
-            description="Fetch real-time stock updates for 'AAPL' using an SSE tool.",
-            expected_output="The latest stock price for AAPL.",
-            agent=sse_agent,
+            description="Buscar atualizações em tempo real das ações 'AAPL' usando uma ferramenta SSE.",
+            expected_output="O preço mais recente da ação AAPL.",
+            agent=agente_sse,
             markdown=True
         )
 
         sse_crew = Crew(
-            agents=[sse_agent],
+            agents=[agente_sse],
             tasks=[sse_task],
             verbose=True,
             process=Process.sequential
@@ -101,16 +101,16 @@ try:
     print(f"Available tools (manual SSE): {[tool.name for tool in tools]}")
 
     manual_sse_agent = Agent(
-        role="Remote Data Analyst",
-        goal="Analyze data fetched from a remote SSE MCP server using manual connection management.",
-        backstory="An AI skilled in handling SSE connections explicitly.",
+        role="Analista Remoto de Dados",
+        goal="Analisar dados obtidos de um servidor MCP remoto SSE usando gerenciamento manual de conexão.",
+        backstory="Um agente de IA especializado em gerenciar conexões SSE explicitamente.",
         tools=tools,
         verbose=True
     )
     
     analysis_task = Task(
-        description="Fetch and analyze the latest user activity trends from the SSE server.",
-        expected_output="A summary report of user activity trends.",
+        description="Buscar e analisar as tendências mais recentes de atividade de usuários do servidor SSE.",
+        expected_output="Um relatório resumido das tendências de atividade dos usuários.",
         agent=manual_sse_agent
     )
     

docs/pt-BR/mcp/stdio.mdx

@@ -38,24 +38,24 @@ with MCPServerAdapter(server_params) as tools:
     print(f"Available tools from Stdio MCP server: {[tool.name for tool in tools]}")
 
     # Exemplo: Usando as ferramentas do servidor MCP Stdio em um Agente CrewAI
-    research_agent = Agent(
-        role="Local Data Processor",
-        goal="Process data using a local Stdio-based tool.",
-        backstory="An AI that leverages local scripts via MCP for specialized tasks.",
+    pesquisador_local = Agent(
+        role="Processador Local de Dados",
+        goal="Processar dados usando uma ferramenta local baseada em Stdio.",
+        backstory="Uma IA que utiliza scripts locais via MCP para tarefas especializadas.",
         tools=tools,
         reasoning=True,
         verbose=True,
     )
     
     processing_task = Task(
-        description="Process the input data file 'data.txt' and summarize its contents.",
-        expected_output="A summary of the processed data.",
-        agent=research_agent,
+        description="Processar o arquivo de dados de entrada 'data.txt' e resumir seu conteúdo.",
+        expected_output="Um resumo dos dados processados.",
+        agent=pesquisador_local,
         markdown=True
     )
     
     data_crew = Crew(
-        agents=[research_agent],
+        agents=[pesquisador_local],
         tasks=[processing_task],
         verbose=True,
         process=Process.sequential 
@@ -95,16 +95,16 @@ try:
 
     # Exemplo: Usando as ferramentas com sua configuração de Agent, Task, Crew
     manual_agent = Agent(
-        role="Local Task Executor",
-        goal="Execute a specific local task using a manually managed Stdio tool.",
-        backstory="An AI proficient in controlling local processes via MCP.",
+        role="Executor Local de Tarefas",
+        goal="Executar uma tarefa local específica usando uma ferramenta Stdio gerenciada manualmente.",
+        backstory="Uma IA proficiente em controlar processos locais via MCP.",
         tools=tools,
         verbose=True
     )
     
     manual_task = Task(
-        description="Execute the 'perform_analysis' command via the Stdio tool.",
-        expected_output="Results of the analysis.",
+        description="Executar o comando 'perform_analysis' via ferramenta Stdio.",
+        expected_output="Resultados da análise.",
         agent=manual_agent
     )
     

docs/pt-BR/mcp/streamable-http.mdx

@@ -35,22 +35,22 @@ try:
     with MCPServerAdapter(server_params) as tools:
         print(f"Available tools from Streamable HTTP MCP server: {[tool.name for tool in tools]}")
 
-        http_agent = Agent(
-            role="HTTP Service Integrator",
-            goal="Utilize tools from a remote MCP server via Streamable HTTP.",
-            backstory="An AI agent adept at interacting with complex web services.",
+        agente_http = Agent(
+            role="Integrador de Serviços HTTP",
+            goal="Utilizar ferramentas de um servidor MCP remoto via Streamable HTTP.",
+            backstory="Um agente de IA especializado em interagir com serviços web complexos.",
             tools=tools,
             verbose=True,
         )
 
         http_task = Task(
-            description="Perform a complex data query using a tool from the Streamable HTTP server.",
-            expected_output="The result of the complex data query.",
-            agent=http_agent,
+            description="Realizar uma consulta de dados complexa usando uma ferramenta do servidor Streamable HTTP.",
+            expected_output="O resultado da consulta de dados complexa.",
+            agent=agente_http,
         )
 
         http_crew = Crew(
-            agents=[http_agent],
+            agents=[agente_http],
             tasks=[http_task],
             verbose=True,
             process=Process.sequential
@@ -91,16 +91,16 @@ try:
     print(f"Available tools (manual Streamable HTTP): {[tool.name for tool in tools]}")
 
     manual_http_agent = Agent(
-        role="Advanced Web Service User",
-        goal="Interact with an MCP server using manually managed Streamable HTTP connections.",
-        backstory="An AI specialist in fine-tuning HTTP-based service integrations.",
+        role="Usuário Avançado de Serviços Web",
+        goal="Interagir com um servidor MCP usando conexões HTTP Streamable gerenciadas manualmente.",
+        backstory="Um especialista em IA em ajustar integrações baseadas em HTTP.",
         tools=tools,
         verbose=True
     )
     
     data_processing_task = Task(
-        description="Submit data for processing and retrieve results via Streamable HTTP.",
-        expected_output="Processed data or confirmation.",
+        description="Enviar dados para processamento e recuperar resultados via Streamable HTTP.",
+        expected_output="Dados processados ou confirmação.",
         agent=manual_http_agent
     )
     

docs/pt-BR/observability/arize-phoenix.mdx

@@ -78,47 +78,40 @@ CrewAIInstrumentor().instrument(skip_dep_check=True, tracer_provider=tracer_prov
 search_tool = SerperDevTool()
 
 # Defina seus agentes com papéis e objetivos
-researcher = Agent(
-    role="Senior Research Analyst",
-    goal="Uncover cutting-edge developments in AI and data science",
-    backstory="""You work at a leading tech think tank.
-    Your expertise lies in identifying emerging trends.
-    You have a knack for dissecting complex data and presenting actionable insights.""",
+pesquisador = Agent(
+    role="Analista Sênior de Pesquisa",
+    goal="Descobrir os avanços mais recentes em IA e ciência de dados",
+    backstory="""
+Você trabalha em um importante think tank de tecnologia. Sua especialidade é identificar tendências emergentes. Você tem habilidade para dissecar dados complexos e apresentar insights acionáveis.
+""",
     verbose=True,
     allow_delegation=False,
-    # You can pass an optional llm attribute specifying what model you wanna use.
-    # llm=ChatOpenAI(model_name="gpt-3.5", temperature=0.7),
     tools=[search_tool],
 )
 writer = Agent(
-    role="Tech Content Strategist",
-    goal="Craft compelling content on tech advancements",
-    backstory="""You are a renowned Content Strategist, known for your insightful and engaging articles.
-    You transform complex concepts into compelling narratives.""",
+    role="Estrategista de Conteúdo Técnico",
+    goal="Criar conteúdo envolvente sobre avanços tecnológicos",
+    backstory="Você é um Estrategista de Conteúdo renomado, conhecido por seus artigos perspicazes e envolventes. Você transforma conceitos complexos em narrativas atraentes.",
     verbose=True,
     allow_delegation=True,
 )
 
 # Crie tarefas para seus agentes
 task1 = Task(
-    description="""Conduct a comprehensive analysis of the latest advancements in AI in 2024.
-    Identify key trends, breakthrough technologies, and potential industry impacts.""",
-    expected_output="Full analysis report in bullet points",
-    agent=researcher,
+    description="Realize uma análise abrangente dos avanços mais recentes em IA em 2024. Identifique tendências-chave, tecnologias inovadoras e impactos potenciais na indústria.",
+    expected_output="Relatório analítico completo em tópicos",
+    agent=pesquisador,
 )
 
 task2 = Task(
-    description="""Using the insights provided, develop an engaging blog
-    post that highlights the most significant AI advancements.
-    Your post should be informative yet accessible, catering to a tech-savvy audience.
-    Make it sound cool, avoid complex words so it doesn't sound like AI.""",
-    expected_output="Full blog post of at least 4 paragraphs",
+    description="Utilizando os insights fornecidos, desenvolva um blog envolvente destacando os avanços mais significativos em IA. O post deve ser informativo e acessível, voltado para um público técnico. Dê um tom interessante, evite palavras complexas para não soar como IA.",
+    expected_output="Post de blog completo com pelo menos 4 parágrafos",
     agent=writer,
 )
 
 # Instancie seu crew com um processo sequencial
 crew = Crew(
-    agents=[researcher, writer], tasks=[task1, task2], verbose=1, process=Process.sequential
+    agents=[pesquisador, writer], tasks=[task1, task2], verbose=1, process=Process.sequential
 )
 
 # Coloque seu crew para trabalhar!

docs/pt-BR/observability/langfuse.mdx

@@ -76,20 +76,20 @@ from crewai_tools import (
 
 web_rag_tool = WebsiteSearchTool()
 
-writer = Agent(
-        role="Writer",
-        goal="Você torna a matemática envolvente e compreensível para crianças pequenas através de poesias",
-        backstory="Você é especialista em escrever haicais mas não sabe nada de matemática.",
-        tools=[web_rag_tool],  
-    )
+escritor = Agent(
+    role="Escritor",
+    goal="Você torna a matemática envolvente e compreensível para crianças pequenas através de poesias",
+    backstory="Você é especialista em escrever haicais mas não sabe nada de matemática.",
+    tools=[web_rag_tool],  
+)
 
-task = Task(description=("O que é {multiplicação}?"),
-            expected_output=("Componha um haicai que inclua a resposta."),
-            agent=writer)
+tarefa = Task(description=("O que é {multiplicação}?"),
+              expected_output=("Componha um haicai que inclua a resposta."),
+              agent=escritor)
 
-crew = Crew(
-  agents=[writer],
-  tasks=[task],
+equipe = Crew(
+  agents=[escritor],
+  tasks=[tarefa],
   share_crew=False
 )
 ```

docs/pt-BR/observability/langtrace.mdx

@@ -35,7 +35,7 @@ Essa integração permite o registro de hiperparâmetros, o monitoramento de reg
 
     ```python
     from langtrace_python_sdk import langtrace
-    langtrace.init(api_key='<LANGTRACE_API_KEY>')
+    langtrace.init(api_key='<SUA_CHAVE_LANGTRACE>')
 
     # Agora importe os módulos do CrewAI
     from crewai import Agent, Task, Crew

docs/pt-BR/observability/maxim.mdx

@@ -73,26 +73,24 @@ instrument_crewai(logger)
 ### 4. Crie e execute sua aplicação CrewAI normalmente
 
 ```python
-
-# Crie seu agente
-researcher = Agent(
-    role='Senior Research Analyst',
-    goal='Uncover cutting-edge developments in AI',
-    backstory="You are an expert researcher at a tech think tank...",
+pesquisador = Agent(
+    role='Pesquisador Sênior',
+    goal='Descobrir os avanços mais recentes em IA',
+    backstory="Você é um pesquisador especialista em um think tank de tecnologia...",
     verbose=True,
     llm=llm
 )
 
 # Defina a tarefa
 research_task = Task(
-    description="Research the latest AI advancements...",
+    description="Pesquise os avanços mais recentes em IA...",
     expected_output="",
-    agent=researcher
+    agent=pesquisador
 )
 
 # Configure e execute a crew
 crew = Crew(
-    agents=[researcher],
+    agents=[pesquisador],
     tasks=[research_task],
     verbose=True
 )

docs/pt-BR/observability/mlflow.mdx

@@ -70,22 +70,19 @@ O tracing fornece uma forma de registrar os inputs, outputs e metadados associad
 
       class TripAgents:
           def city_selection_agent(self):
-              return Agent(
-                  role="City Selection Expert",
-                  goal="Select the best city based on weather, season, and prices",
-                  backstory="An expert in analyzing travel data to pick ideal destinations",
-                  tools=[
-                      search_tool,
-                  ],
+              especialista_cidades = Agent(
+                  role="Especialista em Seleção de Cidades",
+                  goal="Selecionar a melhor cidade com base no clima, estação e preços",
+                  backstory="Especialista em analisar dados de viagem para escolher destinos ideais",
+                  tools=[search_tool],
                   verbose=True,
               )
 
           def local_expert(self):
-              return Agent(
-                  role="Local Expert at this city",
-                  goal="Provide the BEST insights about the selected city",
-                  backstory="""A knowledgeable local guide with extensive information
-              about the city, it's attractions and customs""",
+              especialista_local = Agent(
+                  role="Especialista Local nesta cidade",
+                  goal="Fornecer as MELHORES informações sobre a cidade selecionada",
+                  backstory="Um guia local experiente com amplo conhecimento sobre a cidade, suas atrações e costumes",
                   tools=[search_tool],
                   verbose=True,
               )
@@ -96,53 +93,36 @@ O tracing fornece uma forma de registrar os inputs, outputs e metadados associad
               return Task(
                   description=dedent(
                       f"""
-                      Analyze and select the best city for the trip based
-                      on specific criteria such as weather patterns, seasonal
-                      events, and travel costs. This task involves comparing
-                      multiple cities, considering factors like current weather
-                      conditions, upcoming cultural or seasonal events, and
-                      overall travel expenses.
-                      Your final answer must be a detailed
-                      report on the chosen city, and everything you found out
-                      about it, including the actual flight costs, weather
-                      forecast and attractions.
+                      Analise e selecione a melhor cidade para a viagem com base em critérios específicos como padrões climáticos, eventos sazonais e custos de viagem. Esta tarefa envolve comparar várias cidades, considerando fatores como condições climáticas atuais, eventos culturais ou sazonais e despesas gerais de viagem.
+                      Sua resposta final deve ser um relatório detalhado sobre a cidade escolhida e tudo o que você descobriu sobre ela, incluindo custos reais de voo, previsão do tempo e atrações.
 
-                      Traveling from: {origin}
-                      City Options: {cities}
-                      Trip Date: {range}
-                      Traveler Interests: {interests}
+                      Saindo de: {origin}
+                      Opções de cidades: {cities}
+                      Data da viagem: {range}
+                      Interesses do viajante: {interests}
                   """
                   ),
                   agent=agent,
-                  expected_output="Detailed report on the chosen city including flight costs, weather forecast, and attractions",
+                  expected_output="Relatório detalhado sobre a cidade escolhida incluindo custos de voo, previsão do tempo e atrações",
               )
 
           def gather_task(self, agent, origin, interests, range):
               return Task(
                   description=dedent(
                       f"""
-                      As a local expert on this city you must compile an
-                      in-depth guide for someone traveling there and wanting
-                      to have THE BEST trip ever!
-                      Gather information about key attractions, local customs,
-                      special events, and daily activity recommendations.
-                      Find the best spots to go to, the kind of place only a
-                      local would know.
-                      This guide should provide a thorough overview of what
-                      the city has to offer, including hidden gems, cultural
-                      hotspots, must-visit landmarks, weather forecasts, and
-                      high level costs.
-                      The final answer must be a comprehensive city guide,
-                      rich in cultural insights and practical tips,
-                      tailored to enhance the travel experience.
+                      Como especialista local nesta cidade, você deve compilar um guia aprofundado para alguém que está viajando para lá e quer ter a MELHOR viagem possível!
+                      Reúna informações sobre principais atrações, costumes locais, eventos especiais e recomendações de atividades diárias.
+                      Encontre os melhores lugares para ir, aqueles que só um local conhece.
+                      Este guia deve fornecer uma visão abrangente do que a cidade tem a oferecer, incluindo joias escondidas, pontos culturais, marcos imperdíveis, previsão do tempo e custos gerais.
+                      A resposta final deve ser um guia completo da cidade, rico em insights culturais e dicas práticas, adaptado para aprimorar a experiência de viagem.
 
-                      Trip Date: {range}
-                      Traveling from: {origin}
-                      Traveler Interests: {interests}
+                      Data da viagem: {range}
+                      Saindo de: {origin}
+                      Interesses do viajante: {interests}
                   """
                   ),
                   agent=agent,
-                  expected_output="Comprehensive city guide including hidden gems, cultural hotspots, and practical travel tips",
+                  expected_output="Guia completo da cidade incluindo joias escondidas, pontos culturais e dicas práticas",
               )
 
 
@@ -189,7 +169,7 @@ O tracing fornece uma forma de registrar os inputs, outputs e metadados associad
       trip_crew = TripCrew("California", "Tokyo", "Dec 12 - Dec 20", "sports")
       result = trip_crew.run()
 
-      print(result)
+      print("Resultado da equipe:", result)
       ```
       Consulte a [Documentação de Tracing do MLflow](https://mlflow.org/docs/latest/llms/tracing/index.html) para mais configurações e casos de uso.
     </Step>

docs/pt-BR/observability/openlit.mdx

@@ -69,10 +69,10 @@ Essa configuração permite acompanhar hiperparâmetros e monitorar problemas de
 
           openlit.init(disable_metrics=True)
           # Definir seus agentes
-          researcher = Agent(
-              role="Researcher",
-              goal="Conduct thorough research and analysis on AI and AI agents",
-              backstory="You're an expert researcher, specialized in technology, software engineering, AI, and startups. You work as a freelancer and are currently researching for a new client.",
+          pesquisador = Agent(
+              role="Pesquisador",
+              goal="Realizar pesquisas e análises aprofundadas sobre IA e agentes de IA",
+              backstory="Você é um pesquisador especialista em tecnologia, engenharia de software, IA e startups. Trabalha como freelancer e está atualmente pesquisando para um novo cliente.",
               allow_delegation=False,
               llm='command-r'
           )
@@ -80,24 +80,24 @@ Essa configuração permite acompanhar hiperparâmetros e monitorar problemas de
 
           # Definir sua task
           task = Task(
-              description="Generate a list of 5 interesting ideas for an article, then write one captivating paragraph for each idea that showcases the potential of a full article on this topic. Return the list of ideas with their paragraphs and your notes.",
-              expected_output="5 bullet points, each with a paragraph and accompanying notes.",
+              description="Gere uma lista com 5 ideias interessantes para um artigo e escreva um parágrafo cativante para cada ideia, mostrando o potencial de um artigo completo sobre o tema. Retorne a lista de ideias com seus parágrafos e suas anotações.",
+              expected_output="5 tópicos, cada um com um parágrafo e notas complementares.",
           )
 
           # Definir o agente gerente
-          manager = Agent(
-              role="Project Manager",
-              goal="Efficiently manage the crew and ensure high-quality task completion",
-              backstory="You're an experienced project manager, skilled in overseeing complex projects and guiding teams to success. Your role is to coordinate the efforts of the crew members, ensuring that each task is completed on time and to the highest standard.",
+          gerente = Agent(
+              role="Gerente de Projeto",
+              goal="Gerenciar eficientemente a equipe e garantir a conclusão de tarefas de alta qualidade",
+              backstory="Você é um gerente de projetos experiente, habilidoso em supervisionar projetos complexos e guiar equipes para o sucesso. Sua função é coordenar os esforços dos membros da equipe, garantindo que cada tarefa seja concluída no prazo e com o mais alto padrão.",
               allow_delegation=True,
               llm='command-r'
           )
 
           # Instanciar sua crew com um manager personalizado
           crew = Crew(
-              agents=[researcher],
+              agents=[pesquisador],
               tasks=[task],
-              manager_agent=manager,
+              manager_agent=gerente,
               process=Process.hierarchical,
           )
 
@@ -132,18 +132,18 @@ Essa configuração permite acompanhar hiperparâmetros e monitorar problemas de
 
           # Criar um agente com execução de código habilitada
           coding_agent = Agent(
-            role="Python Data Analyst",
-            goal="Analyze data and provide insights using Python",
-            backstory="You are an experienced data analyst with strong Python skills.",
+            role="Analista de Dados Python",
+            goal="Analisar dados e fornecer insights usando Python",
+            backstory="Você é um analista de dados experiente com fortes habilidades em Python.",
             allow_code_execution=True,
             llm="command-r"
           )
 
           # Criar uma task que exige execução de código
           data_analysis_task = Task(
-            description="Analyze the given dataset and calculate the average age of participants. Ages: {ages}",
+            description="Analise o conjunto de dados fornecido e calcule a idade média dos participantes. Idades: {ages}",
             agent=coding_agent,
-            expected_output="5 bullet points, each with a paragraph and accompanying notes.",
+            expected_output="5 tópicos, cada um com um parágrafo e notas complementares.",
           )
 
           # Criar uma crew e adicionar a task

docs/pt-BR/observability/opik.mdx

@@ -58,43 +58,43 @@ Neste guia, utilizaremos o exemplo de início rápido da CrewAI.
       from crewai import Agent, Crew, Task, Process
 
 
-      class YourCrewName:
-          def agent_one(self) -> Agent:
+      class NomeDaEquipe:
+          def agente_um(self) -> Agent:
               return Agent(
-                  role="Data Analyst",
-                  goal="Analyze data trends in the market",
-                  backstory="An experienced data analyst with a background in economics",
+                  role="Analista de Dados",
+                  goal="Analisar tendências de dados no mercado",
+                  backstory="Analista de dados experiente com formação em economia",
                   verbose=True,
               )
 
-          def agent_two(self) -> Agent:
+          def agente_dois(self) -> Agent:
               return Agent(
-                  role="Market Researcher",
-                  goal="Gather information on market dynamics",
-                  backstory="A diligent researcher with a keen eye for detail",
+                  role="Pesquisador de Mercado",
+                  goal="Coletar informações sobre a dinâmica do mercado",
+                  backstory="Pesquisador dedicado com olhar atento para detalhes",
                   verbose=True,
               )
 
-          def task_one(self) -> Task:
+          def tarefa_um(self) -> Task:
               return Task(
-                  name="Collect Data Task",
-                  description="Collect recent market data and identify trends.",
-                  expected_output="A report summarizing key trends in the market.",
-                  agent=self.agent_one(),
+                  name="Tarefa de Coleta de Dados",
+                  description="Coletar dados recentes do mercado e identificar tendências.",
+                  expected_output="Um relatório resumindo as principais tendências do mercado.",
+                  agent=self.agente_um(),
               )
 
-          def task_two(self) -> Task:
+          def tarefa_dois(self) -> Task:
               return Task(
-                  name="Market Research Task",
-                  description="Research factors affecting market dynamics.",
-                  expected_output="An analysis of factors influencing the market.",
-                  agent=self.agent_two(),
+                  name="Tarefa de Pesquisa de Mercado",
+                  description="Pesquisar fatores que afetam a dinâmica do mercado.",
+                  expected_output="Uma análise dos fatores que influenciam o mercado.",
+                  agent=self.agente_dois(),
               )
 
-          def crew(self) -> Crew:
+          def equipe(self) -> Crew:
               return Crew(
-                  agents=[self.agent_one(), self.agent_two()],
-                  tasks=[self.task_one(), self.task_two()],
+                  agents=[self.agente_um(), self.agente_dois()],
+                  tasks=[self.tarefa_um(), self.tarefa_dois()],
                   process=Process.sequential,
                   verbose=True,
               )
@@ -108,7 +108,7 @@ Neste guia, utilizaremos o exemplo de início rápido da CrewAI.
 
       track_crewai(project_name="crewai-integration-demo")
 
-      my_crew = YourCrewName().crew()
+      my_crew = NomeDaEquipe().equipe()
       result = my_crew.kickoff()
 
       print(result)

docs/pt-BR/observability/patronus-evaluation.mdx

@@ -64,17 +64,17 @@ patronus_eval_tool = PatronusEvalTool()
 
 # Define an agent that uses the tool
 coding_agent = Agent(
-    role="Coding Agent",
-    goal="Generate high quality code and verify that the output is code",
-    backstory="An experienced coder who can generate high quality python code.",
+    role="Agente de Programação",
+    goal="Gerar código de alta qualidade e verificar se a saída é código",
+    backstory="Um programador experiente que pode gerar código Python de alta qualidade.",
     tools=[patronus_eval_tool],
     verbose=True,
 )
 
 # Example task to generate and evaluate code
 generate_code_task = Task(
-    description="Create a simple program to generate the first N numbers in the Fibonacci sequence. Select the most appropriate evaluator and criteria for evaluating your output.",
-    expected_output="Program that generates the first N numbers in the Fibonacci sequence.",
+    description="Crie um programa simples para gerar os N primeiros números da sequência de Fibonacci. Selecione o avaliador e os critérios mais apropriados para avaliar sua saída.",
+    expected_output="Programa que gera os N primeiros números da sequência de Fibonacci.",
     agent=coding_agent,
 )
 
@@ -98,17 +98,17 @@ patronus_eval_tool = PatronusPredefinedCriteriaEvalTool(
 
 # Define an agent that uses the tool
 coding_agent = Agent(
-    role="Coding Agent",
-    goal="Generate high quality code",
-    backstory="An experienced coder who can generate high quality python code.",
+    role="Agente de Programação",
+    goal="Gerar código de alta qualidade",
+    backstory="Um programador experiente que pode gerar código Python de alta qualidade.",
     tools=[patronus_eval_tool],
     verbose=True,
 )
 
 # Example task to generate code
 generate_code_task = Task(
-    description="Create a simple program to generate the first N numbers in the Fibonacci sequence.",
-    expected_output="Program that generates the first N numbers in the Fibonacci sequence.",
+    description="Crie um programa simples para gerar os N primeiros números da sequência de Fibonacci.",
+    expected_output="Programa que gera os N primeiros números da sequência de Fibonacci.",
     agent=coding_agent,
 )
 
@@ -149,17 +149,17 @@ patronus_eval_tool = PatronusLocalEvaluatorTool(
 
 # Define an agent that uses the tool
 coding_agent = Agent(
-    role="Coding Agent",
-    goal="Generate high quality code",
-    backstory="An experienced coder who can generate high quality python code.",
+    role="Agente de Programação",
+    goal="Gerar código de alta qualidade",
+    backstory="Um programador experiente que pode gerar código Python de alta qualidade.",
     tools=[patronus_eval_tool],
     verbose=True,
 )
 
 # Example task to generate code
 generate_code_task = Task(
-    description="Create a simple program to generate the first N numbers in the Fibonacci sequence.",
-    expected_output="Program that generates the first N numbers in the Fibonacci sequence.",
+    description="Crie um programa simples para gerar os N primeiros números da sequência de Fibonacci.",
+    expected_output="Programa que gera os N primeiros números da sequência de Fibonacci.",
     agent=coding_agent,
 )
 

docs/pt-BR/observability/weave.mdx

@@ -50,48 +50,48 @@ O Weave captura automaticamente rastreamentos (traces) de suas aplicações Crew
       llm = LLM(model="gpt-4o", temperature=0)
 
       # Crie os agentes
-      researcher = Agent(
-          role='Research Analyst',
-          goal='Find and analyze the best investment opportunities',
-          backstory='Expert in financial analysis and market research',
+      pesquisador = Agent(
+          role='Analista de Pesquisa',
+          goal='Encontrar e analisar as melhores oportunidades de investimento',
+          backstory='Especialista em análise financeira e pesquisa de mercado',
           llm=llm,
           verbose=True,
           allow_delegation=False,
       )
 
-      writer = Agent(
-          role='Report Writer',
-          goal='Write clear and concise investment reports',
-          backstory='Experienced in creating detailed financial reports',
+      redator = Agent(
+          role='Redator de Relatórios',
+          goal='Escrever relatórios de investimento claros e concisos',
+          backstory='Experiente na criação de relatórios financeiros detalhados',
           llm=llm,
           verbose=True,
           allow_delegation=False,
       )
 
       # Crie as tarefas
-      research_task = Task(
-          description='Deep research on the {topic}',
-          expected_output='Comprehensive market data including key players, market size, and growth trends.',
-          agent=researcher
+      pesquisa = Task(
+          description='Pesquisa aprofundada sobre o {tema}',
+          expected_output='Dados de mercado abrangentes incluindo principais players, tamanho de mercado e tendências de crescimento.',
+          agent=pesquisador
       )
 
-      writing_task = Task(
-          description='Write a detailed report based on the research',
-          expected_output='The report should be easy to read and understand. Use bullet points where applicable.',
-          agent=writer
+      redacao = Task(
+          description='Escreva um relatório detalhado com base na pesquisa',
+          expected_output='O relatório deve ser fácil de ler e entender. Use tópicos quando aplicável.',
+          agent=redator
       )
 
       # Crie o crew
-      crew = Crew(
-          agents=[researcher, writer],
-          tasks=[research_task, writing_task],
+      equipe = Crew(
+          agents=[pesquisador, redator],
+          tasks=[pesquisa, redacao],
           verbose=True,
           process=Process.sequential,
       )
 
       # Execute o crew
-      result = crew.kickoff(inputs={"topic": "AI in material science"})
-      print(result)
+      resultado = equipe.kickoff(inputs={"tema": "IA em ciência dos materiais"})
+      print(resultado)
       ```
     </Step>
     <Step title="Visualize rastreamentos no Weave">

docs/pt-BR/quickstart.mdx

@@ -39,23 +39,19 @@ Siga os passos abaixo para começar a tripular! 🚣‍♂️
     # src/latest_ai_development/config/agents.yaml
     researcher:
       role: >
-        {topic} Senior Data Researcher
+        Pesquisador Sênior de Dados em {topic}
       goal: >
-        Uncover cutting-edge developments in {topic}
+        Descobrir os avanços mais recentes em {topic}
       backstory: >
-        You're a seasoned researcher with a knack for uncovering the latest
-        developments in {topic}. Known for your ability to find the most relevant
-        information and present it in a clear and concise manner.
+        Você é um pesquisador experiente com talento para descobrir os últimos avanços em {topic}. Conhecido por sua habilidade em encontrar as informações mais relevantes e apresentá-las de forma clara e concisa.
 
     reporting_analyst:
       role: >
-        {topic} Reporting Analyst
+        Analista de Relatórios em {topic}
       goal: >
-        Create detailed reports based on {topic} data analysis and research findings
+        Criar relatórios detalhados com base na análise de dados e descobertas de pesquisa em {topic}
       backstory: >
-        You're a meticulous analyst with a keen eye for detail. You're known for
-        your ability to turn complex data into clear and concise reports, making
-        it easy for others to understand and act on the information you provide.
+        Você é um analista meticuloso com um olhar atento aos detalhes. É conhecido por sua capacidade de transformar dados complexos em relatórios claros e concisos, facilitando o entendimento e a tomada de decisão por parte dos outros.
     ```
   </Step>
   <Step title="Modifique seu arquivo `tasks.yaml`">
@@ -63,20 +59,19 @@ Siga os passos abaixo para começar a tripular! 🚣‍♂️
     # src/latest_ai_development/config/tasks.yaml
     research_task:
       description: >
-        Conduct a thorough research about {topic}
-        Make sure you find any interesting and relevant information given
-        the current year is 2025.
+        Realize uma pesquisa aprofundada sobre {topic}.
+        Certifique-se de encontrar informações interessantes e relevantes considerando que o ano atual é 2025.
       expected_output: >
-        A list with 10 bullet points of the most relevant information about {topic}
+        Uma lista com 10 tópicos dos dados mais relevantes sobre {topic}
       agent: researcher
 
     reporting_task:
       description: >
-        Review the context you got and expand each topic into a full section for a report.
-        Make sure the report is detailed and contains any and all relevant information.
+        Revise o contexto obtido e expanda cada tópico em uma seção completa para um relatório.
+        Certifique-se de que o relatório seja detalhado e contenha todas as informações relevantes.
       expected_output: >
-        A fully fledge reports with the mains topics, each with a full section of information.
-        Formatted as markdown without '```'
+        Um relatório completo com os principais tópicos, cada um com uma seção detalhada de informações.
+        Formate como markdown sem usar '```'
       agent: reporting_analyst
       output_file: report.md
     ```
@@ -122,15 +117,15 @@ Siga os passos abaixo para começar a tripular! 🚣‍♂️
       def reporting_task(self) -> Task:
         return Task(
           config=self.tasks_config['reporting_task'], # type: ignore[index]
-          output_file='output/report.md' # This is the file that will be contain the final report.
+          output_file='output/report.md' # Este é o arquivo que conterá o relatório final.
         )
 
       @crew
       def crew(self) -> Crew:
         """Creates the LatestAiDevelopment crew"""
         return Crew(
-          agents=self.agents, # Automatically created by the @agent decorator
-          tasks=self.tasks, # Automatically created by the @task decorator
+          agents=self.agents, # Criado automaticamente pelo decorador @agent
+          tasks=self.tasks, # Criado automaticamente pelo decorador @task
           process=Process.sequential,
           verbose=True,
         )
@@ -229,7 +224,7 @@ Siga os passos abaixo para começar a tripular! 🚣‍♂️
 
   <CodeGroup>
     ```markdown output/report.md
-    # Comprehensive Report on the Rise and Impact of AI Agents in 2025
+    # Relatório Abrangente sobre a Ascensão e o Impacto dos Agentes de IA em 2025
 
     ## 1. Introduction to AI Agents
     In 2025, Artificial Intelligence (AI) agents are at the forefront of innovation across various industries. As intelligent systems that can perform tasks typically requiring human cognition, AI agents are paving the way for significant advancements in operational efficiency, decision-making, and overall productivity within sectors like Human Resources (HR) and Finance. This report aims to detail the rise of AI agents, their frameworks, applications, and potential implications on the workforce.

docs/pt-BR/tools/search-research/linkupsearchtool.mdx

@@ -35,78 +35,18 @@ from crewai_tools import LinkupSearchTool
 from crewai import Agent
 import os
 
-# Initialize the tool with your API key
-linkup_tool = LinkupSearchTool(api_key=os.getenv("LINKUP_API_KEY"))
+# Inicialize a ferramenta com sua chave de API
+linkup_ferramenta = LinkupSearchTool(api_key=os.getenv("LINKUP_API_KEY"))
 
-# Define an agent that uses the tool
+# Defina um agente que usa a ferramenta
 @agent
-def researcher(self) -> Agent:
+def pesquisador(self) -> Agent:
     '''
-    This agent uses the LinkupSearchTool to retrieve contextual information
-    from the Linkup API.
+    Este agente usa o LinkupSearchTool para recuperar informações contextuais
+    da API do Linkup.
     '''
     return Agent(
-        config=self.agents_config["researcher"],
-        tools=[linkup_tool]
+        config=self.agentes_config["pesquisador"],
+        tools=[linkup_ferramenta]
     )
-```
-
-## Parâmetros
-
-O `LinkupSearchTool` aceita os seguintes parâmetros:
-
-### Parâmetros do Construtor
-- **api_key**: Obrigatório. Sua chave de API do Linkup.
-
-### Parâmetros de Execução
-- **query**: Obrigatório. O termo ou frase de busca.
-- **depth**: Opcional. A profundidade da busca. O padrão é "standard".
-- **output_type**: Opcional. O tipo de saída. O padrão é "searchResults".
-
-## Uso Avançado
-
-Você pode personalizar os parâmetros de busca para resultados mais específicos:
-
-```python Code
-# Perform a search with custom parameters
-results = linkup_tool.run(
-    query="Women Nobel Prize Physics",
-    depth="deep",
-    output_type="searchResults"
-)
-```
-
-## Formato de Retorno
-
-A ferramenta retorna resultados no seguinte formato:
-
-```json
-{
-  "success": true,
-  "results": [
-    {
-      "name": "Result Title",
-      "url": "https://example.com/result",
-      "content": "Content of the result..."
-    },
-    // Additional results...
-  ]
-}
-```
-
-Se ocorrer um erro, a resposta será:
-
-```json
-{
-  "success": false,
-  "error": "Error message"
-}
-```
-
-## Tratamento de Erros
-
-A ferramenta lida com erros de API de forma amigável e fornece feedback estruturado. Se a requisição à API falhar, a ferramenta retornará um dicionário com `success: false` e uma mensagem de erro.
-
-## Conclusão
-
-O `LinkupSearchTool` oferece uma forma integrada de incorporar as capacidades de busca de informações contextuais do Linkup aos seus agentes CrewAI. Ao utilizar esta ferramenta, os agentes podem acessar informações relevantes e atualizadas para aprimorar sua tomada de decisão e execução de tarefas.
+```

docs/reo-tracking.js

@@ -0,0 +1,16 @@
+(function() {
+  var clientID = 'e1256ea7e23318f';
+  
+  var initReo = function() {
+    Reo.init({
+      clientID: clientID
+    });
+  };
+  
+  var script = document.createElement('script');
+  script.src = 'https://static.reo.dev/' + clientID + '/reo.js';
+  script.defer = true;
+  script.onload = initReo;
+  
+  document.head.appendChild(script);
+})(); 

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "crewai"
-version = "0.134.0"
+dynamic = ["version"]
 description = "Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks."
 readme = "README.md"
 requires-python = ">=3.10,<3.14"
@@ -11,7 +11,7 @@ dependencies = [
     # Core Dependencies
     "pydantic>=2.4.2",
     "openai>=1.13.3",
-    "litellm==1.72.0",
+    "litellm==1.72.6",
     "instructor>=1.3.3",
     # Text Processing
     "pdfplumber>=0.11.4",
@@ -27,13 +27,13 @@ dependencies = [
     "openpyxl>=3.1.5",
     "pyvis>=0.3.2",
     # Authentication and Security
-    "auth0-python>=4.7.1",
     "python-dotenv>=1.0.0",
+    "pyjwt>=2.9.0",
     # Configuration and Utils
     "click>=8.1.7",
     "appdirs>=1.4.4",
     "jsonref>=1.1.0",
-    "json-repair>=0.25.2",
+    "json-repair==0.25.2",
     "uv>=0.4.25",
     "tomli-w>=1.1.0",
     "tomli>=2.0.2",
@@ -47,11 +47,11 @@ Documentation = "https://docs.crewai.com"
 Repository = "https://github.com/crewAIInc/crewAI"
 
 [project.optional-dependencies]
-tools = ["crewai-tools~=0.48.0"]
+tools = ["crewai-tools~=0.49.0"]
 embeddings = [
     "tiktoken~=0.8.0"
 ]
-agentops = ["agentops>=0.3.0"]
+agentops = ["agentops==0.3.18"]
 pdfplumber = [
     "pdfplumber>=0.11.4",
 ]
@@ -83,6 +83,8 @@ dev-dependencies = [
     "pytest-recording>=0.13.2",
     "pytest-randomly>=3.16.0",
     "pytest-timeout>=2.3.1",
+    "pytest-xdist>=3.6.1",
+    "pytest-split>=0.9.0",
 ]
 
 [project.scripts]
@@ -117,6 +119,21 @@ torchvision = [
   { index = "pytorch", marker = "python_version < '3.13'" },
 ]
 
+[tool.hatch.version]
+path = "src/crewai/__init__.py"
+
 [build-system]
 requires = ["hatchling"]
 build-backend = "hatchling.build"
+
+[tool.hatch.build.targets.wheel]
+exclude = [
+"docs/**",
+"docs/",
+]
+
+[tool.hatch.build.targets.sdist]
+exclude = [
+"docs/**",
+"docs/",
+]

src/crewai/__init__.py

@@ -1,4 +1,6 @@
 import warnings
+import threading
+import urllib.request
 
 from crewai.agent import Agent
 from crewai.crew import Crew
@@ -11,6 +13,7 @@ from crewai.process import Process
 from crewai.task import Task
 from crewai.tasks.llm_guardrail import LLMGuardrail
 from crewai.tasks.task_output import TaskOutput
+from crewai.telemetry.telemetry import Telemetry
 
 warnings.filterwarnings(
     "ignore",
@@ -18,7 +21,40 @@ warnings.filterwarnings(
     category=UserWarning,
     module="pydantic.main",
 )
-__version__ = "0.134.0"
+
+_telemetry_submitted = False
+
+
+def _track_install():
+    """Track package installation/first-use via Scarf analytics."""
+    global _telemetry_submitted
+
+    if _telemetry_submitted or Telemetry._is_telemetry_disabled():
+        return
+
+    try:
+        pixel_url = "https://api.scarf.sh/v2/packages/CrewAI/crewai/docs/00f2dad1-8334-4a39-934e-003b2e1146db"
+
+        req = urllib.request.Request(pixel_url)
+        req.add_header('User-Agent', f'CrewAI-Python/{__version__}')
+
+        with urllib.request.urlopen(req, timeout=2):  # nosec B310
+            _telemetry_submitted = True
+
+    except Exception:
+        pass
+
+
+def _track_install_async():
+    """Track installation in background thread to avoid blocking imports."""
+    if not Telemetry._is_telemetry_disabled():
+        thread = threading.Thread(target=_track_install, daemon=True)
+        thread.start()
+
+
+_track_install_async()
+
+__version__ = "0.140.0"
 __all__ = [
     "Agent",
     "Crew",
@@ -31,4 +67,5 @@ __all__ = [
     "Knowledge",
     "TaskOutput",
     "LLMGuardrail",
+    "__version__",
 ]

src/crewai/agent.py

@@ -1,5 +1,6 @@
 import shutil
 import subprocess
+import time
 from typing import Any, Callable, Dict, List, Literal, Optional, Sequence, Tuple, Type, Union
 
 from pydantic import Field, InstanceOf, PrivateAttr, model_validator
@@ -32,6 +33,10 @@ from crewai.utilities.events.agent_events import (
     AgentExecutionStartedEvent,
 )
 from crewai.utilities.events.crewai_event_bus import crewai_event_bus
+from crewai.utilities.events.memory_events import (
+    MemoryRetrievalStartedEvent,
+    MemoryRetrievalCompletedEvent,
+)
 from crewai.utilities.events.knowledge_events import (
     KnowledgeQueryCompletedEvent,
     KnowledgeQueryFailedEvent,
@@ -302,6 +307,15 @@ class Agent(BaseAgent):
             )
 
         if self._is_any_available_memory():
+            crewai_event_bus.emit(
+                self,
+                event=MemoryRetrievalStartedEvent(
+                    task_id=str(task.id) if task else None,
+                    source_type="agent",
+                ),
+            )
+
+            start_time = time.time()
             contextual_memory = ContextualMemory(
                 self.crew.memory_config,
                 self.crew._short_term_memory,
@@ -313,6 +327,16 @@ class Agent(BaseAgent):
             memory = contextual_memory.build_context_for_task(task, context)
             if memory.strip() != "":
                 task_prompt += self.i18n.slice("memory").format(memory=memory)
+
+            crewai_event_bus.emit(
+                self,
+                event=MemoryRetrievalCompletedEvent(
+                    task_id=str(task.id) if task else None,
+                    memory_content=memory,
+                    retrieval_time_ms=(time.time() - start_time) * 1000,
+                    source_type="agent",
+                ),
+            )
         knowledge_config = (
             self.knowledge_config.model_dump() if self.knowledge_config else {}
         )
@@ -775,6 +799,7 @@ class Agent(BaseAgent):
             LiteAgentOutput: The result of the agent execution.
         """
         lite_agent = LiteAgent(
+            id=self.id,
             role=self.role,
             goal=self.goal,
             backstory=self.backstory,

src/crewai/agents/crew_agent_executor.py

@@ -159,6 +159,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                     messages=self.messages,
                     callbacks=self.callbacks,
                     printer=self._printer,
+                    from_task=self.task
                 )
                 formatted_answer = process_llm_response(answer, self.use_stop_words)
 

src/crewai/cli/authentication/constants.py

@@ -2,3 +2,7 @@ ALGORITHMS = ["RS256"]
 AUTH0_DOMAIN = "crewai.us.auth0.com"
 AUTH0_CLIENT_ID = "DEVC5Fw6NlRoSzmDCcOhVq85EfLBjKa8"
 AUTH0_AUDIENCE = "https://crewai.us.auth0.com/api/v2/"
+
+WORKOS_DOMAIN = "login.crewai.com"
+WORKOS_CLI_CONNECT_APP_ID = "client_01JYT06R59SP0NXYGD994NFXXX"
+WORKOS_ENVIRONMENT_ID = "client_01JNJQWB4HG8T5980R5VHP057C"

src/crewai/cli/authentication/main.py

@@ -5,39 +5,72 @@ from typing import Any, Dict
 import requests
 from rich.console import Console
 
-from crewai.cli.tools.main import ToolCommand
+from .constants import (
+    AUTH0_AUDIENCE,
+    AUTH0_CLIENT_ID,
+    AUTH0_DOMAIN,
+    WORKOS_DOMAIN,
+    WORKOS_CLI_CONNECT_APP_ID,
+    WORKOS_ENVIRONMENT_ID,
+)
 
-from .constants import AUTH0_AUDIENCE, AUTH0_CLIENT_ID, AUTH0_DOMAIN
-from .utils import TokenManager, validate_token
+from .utils import TokenManager, validate_jwt_token
+from urllib.parse import quote
+from crewai.cli.plus_api import PlusAPI
+from crewai.cli.config import Settings
 
 console = Console()
 
 
 class AuthenticationCommand:
-    DEVICE_CODE_URL = f"https://{AUTH0_DOMAIN}/oauth/device/code"
-    TOKEN_URL = f"https://{AUTH0_DOMAIN}/oauth/token"
+    AUTH0_DEVICE_CODE_URL = f"https://{AUTH0_DOMAIN}/oauth/device/code"
+    AUTH0_TOKEN_URL = f"https://{AUTH0_DOMAIN}/oauth/token"
+
+    WORKOS_DEVICE_CODE_URL = f"https://{WORKOS_DOMAIN}/oauth2/device_authorization"
+    WORKOS_TOKEN_URL = f"https://{WORKOS_DOMAIN}/oauth2/token"
 
     def __init__(self):
         self.token_manager = TokenManager()
+        # TODO: WORKOS - This variable is temporary until migration to WorkOS is complete.
+        self.user_provider = "workos"
 
-    def signup(self) -> None:
+    def login(self) -> None:
         """Sign up to CrewAI+"""
-        console.print("Signing Up to CrewAI+ \n", style="bold blue")
-        device_code_data = self._get_device_code()
+
+        device_code_url = self.WORKOS_DEVICE_CODE_URL
+        token_url = self.WORKOS_TOKEN_URL
+        client_id = WORKOS_CLI_CONNECT_APP_ID
+        audience = None
+
+        console.print("Signing in to CrewAI Enterprise...\n", style="bold blue")
+
+        # TODO: WORKOS - Next line and conditional are temporary until migration to WorkOS is complete.
+        user_provider = self._determine_user_provider()
+        if user_provider == "auth0":
+            device_code_url = self.AUTH0_DEVICE_CODE_URL
+            token_url = self.AUTH0_TOKEN_URL
+            client_id = AUTH0_CLIENT_ID
+            audience = AUTH0_AUDIENCE
+            self.user_provider = "auth0"
+        # End of temporary code.
+
+        device_code_data = self._get_device_code(client_id, device_code_url, audience)
         self._display_auth_instructions(device_code_data)
 
-        return self._poll_for_token(device_code_data)
+        return self._poll_for_token(device_code_data, client_id, token_url)
 
-    def _get_device_code(self) -> Dict[str, Any]:
+    def _get_device_code(
+        self, client_id: str, device_code_url: str, audience: str | None = None
+    ) -> Dict[str, Any]:
         """Get the device code to authenticate the user."""
 
         device_code_payload = {
-            "client_id": AUTH0_CLIENT_ID,
+            "client_id": client_id,
             "scope": "openid",
-            "audience": AUTH0_AUDIENCE,
+            "audience": audience,
         }
         response = requests.post(
-            url=self.DEVICE_CODE_URL, data=device_code_payload, timeout=20
+            url=device_code_url, data=device_code_payload, timeout=20
         )
         response.raise_for_status()
         return response.json()
@@ -48,37 +81,33 @@ class AuthenticationCommand:
         console.print("2. Enter the following code: ", device_code_data["user_code"])
         webbrowser.open(device_code_data["verification_uri_complete"])
 
-    def _poll_for_token(self, device_code_data: Dict[str, Any]) -> None:
-        """Poll the server for the token."""
+    def _poll_for_token(
+        self, device_code_data: Dict[str, Any], client_id: str, token_poll_url: str
+    ) -> None:
+        """Polls the server for the token until it is received, or max attempts are reached."""
+
         token_payload = {
             "grant_type": "urn:ietf:params:oauth:grant-type:device_code",
             "device_code": device_code_data["device_code"],
-            "client_id": AUTH0_CLIENT_ID,
+            "client_id": client_id,
         }
 
+        console.print("\nWaiting for authentication... ", style="bold blue", end="")
+
         attempts = 0
-        while True and attempts < 5:
-            response = requests.post(self.TOKEN_URL, data=token_payload, timeout=30)
+        while True and attempts < 10:
+            response = requests.post(token_poll_url, data=token_payload, timeout=30)
             token_data = response.json()
 
             if response.status_code == 200:
-                validate_token(token_data["id_token"])
-                expires_in = 360000  # Token expiration time in seconds
-                self.token_manager.save_tokens(token_data["access_token"], expires_in)
+                self._validate_and_save_token(token_data)
 
-                try:
-                    ToolCommand().login()
-                except Exception:
-                    console.print(
-                        "\n[bold yellow]Warning:[/bold yellow] Authentication with the Tool Repository failed.",
-                        style="yellow",
-                    )
-                    console.print(
-                        "Other features will work normally, but you may experience limitations "
-                        "with downloading and publishing tools."
-                        "\nRun [bold]crewai login[/bold] to try logging in again.\n",
-                        style="yellow",
-                    )
+                console.print(
+                    "Success!",
+                    style="bold green",
+                )
+
+                self._login_to_tool_repository()
 
                 console.print(
                     "\n[bold green]Welcome to CrewAI Enterprise![/bold green]\n"
@@ -94,3 +123,88 @@ class AuthenticationCommand:
         console.print(
             "Timeout: Failed to get the token. Please try again.", style="bold red"
         )
+
+    def _validate_and_save_token(self, token_data: Dict[str, Any]) -> None:
+        """Validates the JWT token and saves the token to the token manager."""
+
+        jwt_token = token_data["access_token"]
+        jwt_token_data = {
+            "jwt_token": jwt_token,
+            "jwks_url": f"https://{WORKOS_DOMAIN}/oauth2/jwks",
+            "issuer": f"https://{WORKOS_DOMAIN}",
+            "audience": WORKOS_ENVIRONMENT_ID,
+        }
+
+        # TODO: WORKOS - The following conditional is temporary until migration to WorkOS is complete.
+        if self.user_provider == "auth0":
+            jwt_token_data["jwks_url"] = f"https://{AUTH0_DOMAIN}/.well-known/jwks.json"
+            jwt_token_data["issuer"] = f"https://{AUTH0_DOMAIN}/"
+            jwt_token_data["audience"] = AUTH0_AUDIENCE
+
+        decoded_token = validate_jwt_token(**jwt_token_data)
+
+        expires_at = decoded_token.get("exp", 0)
+        self.token_manager.save_tokens(jwt_token, expires_at)
+
+    def _login_to_tool_repository(self) -> None:
+        """Login to the tool repository."""
+
+        from crewai.cli.tools.main import ToolCommand
+
+        try:
+            console.print(
+                "Now logging you in to the Tool Repository... ",
+                style="bold blue",
+                end="",
+            )
+
+            ToolCommand().login()
+
+            console.print(
+                "Success!\n",
+                style="bold green",
+            )
+
+            settings = Settings()
+            console.print(
+                f"You are authenticated to the tool repository as [bold cyan]'{settings.org_name}'[/bold cyan] ({settings.org_uuid})",
+                style="green",
+            )
+        except Exception:
+            console.print(
+                "\n[bold yellow]Warning:[/bold yellow] Authentication with the Tool Repository failed.",
+                style="yellow",
+            )
+            console.print(
+                "Other features will work normally, but you may experience limitations "
+                "with downloading and publishing tools."
+                "\nRun [bold]crewai login[/bold] to try logging in again.\n",
+                style="yellow",
+            )
+
+    # TODO: WORKOS - This method is temporary until migration to WorkOS is complete.
+    def _determine_user_provider(self) -> str:
+        """Determine which provider to use for authentication."""
+
+        console.print(
+            "Enter your CrewAI Enterprise account email: ", style="bold blue", end=""
+        )
+        email = input()
+        email_encoded = quote(email)
+
+        # It's not correct to call this method directly, but it's temporary until migration is complete.
+        response = PlusAPI("")._make_request(
+            "GET", f"/crewai_plus/api/v1/me/provider?email={email_encoded}"
+        )
+
+        if response.status_code == 200:
+            if response.json().get("provider") == "auth0":
+                return "auth0"
+            else:
+                return "workos"
+        else:
+            console.print(
+                "Error: Failed to authenticate with crewai enterprise. Ensure that you are using the latest crewai version and please try again. If the problem persists, contact support@crewai.com.",
+                style="red",
+            )
+            raise SystemExit

src/crewai/cli/authentication/utils.py

@@ -1,32 +1,63 @@
 import json
 import os
 import sys
-from datetime import datetime, timedelta
+from datetime import datetime
 from pathlib import Path
 from typing import Optional
-
-from auth0.authentication.token_verifier import (
-    AsymmetricSignatureVerifier,
-    TokenVerifier,
-)
+import jwt
+from jwt import PyJWKClient
 from cryptography.fernet import Fernet
 
-from .constants import AUTH0_CLIENT_ID, AUTH0_DOMAIN
 
-
-def validate_token(id_token: str) -> None:
+def validate_jwt_token(
+    jwt_token: str, jwks_url: str, issuer: str, audience: str
+) -> dict:
     """
-    Verify the token and its precedence
-
-    :param id_token:
+    Verify the token's signature and claims using PyJWT.
+    :param jwt_token: The JWT (JWS) string to validate.
+    :param jwks_url: The URL of the JWKS endpoint.
+    :param issuer: The expected issuer of the token.
+    :param audience: The expected audience of the token.
+    :return: The decoded token.
+    :raises Exception: If the token is invalid for any reason (e.g., signature mismatch,
+                       expired, incorrect issuer/audience, JWKS fetching error,
+                       missing required claims).
     """
-    jwks_url = f"https://{AUTH0_DOMAIN}/.well-known/jwks.json"
-    issuer = f"https://{AUTH0_DOMAIN}/"
-    signature_verifier = AsymmetricSignatureVerifier(jwks_url)
-    token_verifier = TokenVerifier(
-        signature_verifier=signature_verifier, issuer=issuer, audience=AUTH0_CLIENT_ID
-    )
-    token_verifier.verify(id_token)
+
+    decoded_token = None
+
+    try:
+        jwk_client = PyJWKClient(jwks_url)
+        signing_key = jwk_client.get_signing_key_from_jwt(jwt_token)
+
+        decoded_token = jwt.decode(
+            jwt_token,
+            signing_key.key,
+            algorithms=["RS256"],
+            audience=audience,
+            issuer=issuer,
+            options={
+                "verify_signature": True,
+                "verify_exp": True,
+                "verify_nbf": True,
+                "verify_iat": True,
+                "require": ["exp", "iat", "iss", "aud", "sub"],
+            },
+        )
+        return decoded_token
+
+    except jwt.ExpiredSignatureError:
+        raise Exception("Token has expired.")
+    except jwt.InvalidAudienceError:
+        raise Exception(f"Invalid token audience. Expected: '{audience}'")
+    except jwt.InvalidIssuerError:
+        raise Exception(f"Invalid token issuer. Expected: '{issuer}'")
+    except jwt.MissingRequiredClaimError as e:
+        raise Exception(f"Token is missing required claims: {str(e)}")
+    except jwt.exceptions.PyJWKClientError as e:
+        raise Exception(f"JWKS or key processing error: {str(e)}")
+    except jwt.InvalidTokenError as e:
+        raise Exception(f"Invalid token: {str(e)}")
 
 
 class TokenManager:
@@ -56,14 +87,14 @@ class TokenManager:
         self.save_secure_file(key_filename, new_key)
         return new_key
 
-    def save_tokens(self, access_token: str, expires_in: int) -> None:
+    def save_tokens(self, access_token: str, expires_at: int) -> None:
         """
         Save the access token and its expiration time.
 
         :param access_token: The access token to save.
-        :param expires_in: The expiration time of the access token in seconds.
+        :param expires_at: The UNIX timestamp of the expiration time.
         """
-        expiration_time = datetime.now() + timedelta(seconds=expires_in)
+        expiration_time = datetime.fromtimestamp(expires_at)
         data = {
             "access_token": access_token,
             "expiration": expiration_time.isoformat(),

src/crewai/cli/cli.py

@@ -2,7 +2,7 @@ from importlib.metadata import version as get_version
 from typing import Optional
 
 import click
-
+from crewai.cli.config import Settings
 from crewai.cli.add_crew_to_flow import add_crew_to_flow
 from crewai.cli.create_crew import create_crew
 from crewai.cli.create_flow import create_flow
@@ -138,8 +138,12 @@ def log_tasks_outputs() -> None:
 @click.option("-s", "--short", is_flag=True, help="Reset SHORT TERM memory")
 @click.option("-e", "--entities", is_flag=True, help="Reset ENTITIES memory")
 @click.option("-kn", "--knowledge", is_flag=True, help="Reset KNOWLEDGE storage")
-@click.option("-akn", "--agent-knowledge", is_flag=True, help="Reset AGENT KNOWLEDGE storage")
-@click.option("-k","--kickoff-outputs",is_flag=True,help="Reset LATEST KICKOFF TASK OUTPUTS")
+@click.option(
+    "-akn", "--agent-knowledge", is_flag=True, help="Reset AGENT KNOWLEDGE storage"
+)
+@click.option(
+    "-k", "--kickoff-outputs", is_flag=True, help="Reset LATEST KICKOFF TASK OUTPUTS"
+)
 @click.option("-a", "--all", is_flag=True, help="Reset ALL memories")
 def reset_memories(
     long: bool,
@@ -154,13 +158,23 @@ def reset_memories(
     Reset the crew memories (long, short, entity, latest_crew_kickoff_ouputs, knowledge, agent_knowledge). This will delete all the data saved.
     """
     try:
-        memory_types = [long, short, entities, knowledge, agent_knowledge, kickoff_outputs, all]
+        memory_types = [
+            long,
+            short,
+            entities,
+            knowledge,
+            agent_knowledge,
+            kickoff_outputs,
+            all,
+        ]
         if not any(memory_types):
             click.echo(
                 "Please specify at least one memory type to reset using the appropriate flags."
             )
             return
-        reset_memories_command(long, short, entities, knowledge, agent_knowledge, kickoff_outputs, all)
+        reset_memories_command(
+            long, short, entities, knowledge, agent_knowledge, kickoff_outputs, all
+        )
     except Exception as e:
         click.echo(f"An error occurred while resetting memories: {e}", err=True)
 
@@ -210,16 +224,11 @@ def update():
     update_crew()
 
 
-@crewai.command()
-def signup():
-    """Sign Up/Login to CrewAI+."""
-    AuthenticationCommand().signup()
-
-
 @crewai.command()
 def login():
-    """Sign Up/Login to CrewAI+."""
-    AuthenticationCommand().signup()
+    """Sign Up/Login to CrewAI Enterprise."""
+    Settings().clear()
+    AuthenticationCommand().login()
 
 
 # DEPLOY CREWAI+ COMMANDS

src/crewai/cli/config.py

@@ -37,6 +37,10 @@ class Settings(BaseModel):
         merged_data = {**file_data, **data}
         super().__init__(config_path=config_path, **merged_data)
 
+    def clear(self) -> None:
+        """Clear all settings"""
+        self.config_path.unlink(missing_ok=True)
+
     def dump(self) -> None:
         """Save current settings to settings.json"""
         if self.config_path.is_file():

src/crewai/cli/create_crew.py

@@ -14,8 +14,50 @@ from crewai.cli.utils import copy_template, load_env_vars, write_env_file
 
 
 def create_folder_structure(name, parent_folder=None):
+    import keyword
+    import re
+    
+    name = name.rstrip('/')
+    
+    if not name.strip():
+        raise ValueError("Project name cannot be empty or contain only whitespace")
+    
     folder_name = name.replace(" ", "_").replace("-", "_").lower()
+    folder_name = re.sub(r'[^a-zA-Z0-9_]', '', folder_name)
+    
+    # Check if the name starts with invalid characters or is primarily invalid
+    if re.match(r'^[^a-zA-Z0-9_-]+', name):
+        raise ValueError(f"Project name '{name}' contains no valid characters for a Python module name")
+    
+    if not folder_name:
+        raise ValueError(f"Project name '{name}' contains no valid characters for a Python module name")
+    
+    if folder_name[0].isdigit():
+        raise ValueError(f"Project name '{name}' would generate folder name '{folder_name}' which cannot start with a digit (invalid Python module name)")
+    
+    if keyword.iskeyword(folder_name):
+        raise ValueError(f"Project name '{name}' would generate folder name '{folder_name}' which is a reserved Python keyword")
+    
+    if not folder_name.isidentifier():
+        raise ValueError(f"Project name '{name}' would generate invalid Python module name '{folder_name}'")
+    
     class_name = name.replace("_", " ").replace("-", " ").title().replace(" ", "")
+    
+    class_name = re.sub(r'[^a-zA-Z0-9_]', '', class_name)
+    
+    if not class_name:
+        raise ValueError(f"Project name '{name}' contains no valid characters for a Python class name")
+    
+    if class_name[0].isdigit():
+        raise ValueError(f"Project name '{name}' would generate class name '{class_name}' which cannot start with a digit")
+    
+    # Check if the original name (before title casing) is a keyword
+    original_name_clean = re.sub(r'[^a-zA-Z0-9_]', '', name.replace("_", "").replace("-", "").lower())
+    if keyword.iskeyword(original_name_clean) or keyword.iskeyword(class_name) or class_name in ('True', 'False', 'None'):
+        raise ValueError(f"Project name '{name}' would generate class name '{class_name}' which is a reserved Python keyword")
+    
+    if not class_name.isidentifier():
+        raise ValueError(f"Project name '{name}' would generate invalid Python class name '{class_name}'")
 
     if parent_folder:
         folder_path = Path(parent_folder) / folder_name

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

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.14"
 dependencies = [
-    "crewai[tools]>=0.134.0,<1.0.0"
+    "crewai[tools]>=0.140.0,<1.0.0"
 ]
 
 [project.scripts]

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

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.14"
 dependencies = [
-    "crewai[tools]>=0.134.0,<1.0.0",
+    "crewai[tools]>=0.140.0,<1.0.0",
 ]
 
 [project.scripts]

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

@@ -5,7 +5,7 @@ description = "Power up your crews with {{folder_name}}"
 readme = "README.md"
 requires-python = ">=3.10,<3.14"
 dependencies = [
-    "crewai[tools]>=0.134.0"
+    "crewai[tools]>=0.140.0"
 ]
 
 [tool.crewai]

src/crewai/cli/tools/main.py

@@ -156,7 +156,7 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
 
         console.print(f"Successfully installed {handle}", style="bold green")
 
-    def login(self):
+    def login(self) -> None:
         login_response = self.plus_api_client.login_to_tool_repository()
 
         if login_response.status_code != 200:
@@ -175,18 +175,10 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
         settings.tool_repository_password = login_response_json["credential"][
             "password"
         ]
-        settings.org_uuid = login_response_json["current_organization"][
-            "uuid"
-        ]
-        settings.org_name = login_response_json["current_organization"][
-            "name"
-        ]
+        settings.org_uuid = login_response_json["current_organization"]["uuid"]
+        settings.org_name = login_response_json["current_organization"]["name"]
         settings.dump()
 
-        console.print(
-            f"Successfully authenticated to the tool repository as {settings.org_name} ({settings.org_uuid}).", style="bold green"
-        )
-
     def _add_package(self, tool_details: dict[str, Any]):
         is_from_pypi = tool_details.get("source", None) == "pypi"
         tool_handle = tool_details["handle"]
@@ -243,9 +235,15 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
 
         return env
 
-    def _print_current_organization(self):
+    def _print_current_organization(self) -> None:
         settings = Settings()
         if settings.org_uuid:
-            console.print(f"Current organization: {settings.org_name} ({settings.org_uuid})", style="bold blue")
+            console.print(
+                f"Current organization: {settings.org_name} ({settings.org_uuid})",
+                style="bold blue",
+            )
         else:
-            console.print("No organization currently set. We recommend setting one before using: `crewai org switch <org_id>` command.", style="yellow")
+            console.print(
+                "No organization currently set. We recommend setting one before using: `crewai org switch <org_id>` command.",
+                style="yellow",
+            )

src/crewai/cli/utils.py

@@ -252,7 +252,7 @@ def write_env_file(folder_path, env_vars):
     env_file_path = folder_path / ".env"
     with open(env_file_path, "w") as file:
         for key, value in env_vars.items():
-            file.write(f"{key}={value}\n")
+            file.write(f"{key.upper()}={value}\n")
 
 
 def get_crews(crew_path: str = "crew.py", require: bool = False) -> list[Crew]:

src/crewai/crew.py

@@ -18,6 +18,11 @@ from typing import (
     cast,
 )
 
+from opentelemetry import baggage
+from opentelemetry.context import attach, detach
+
+from crewai.utilities.crew.models import CrewContext
+
 from pydantic import (
     UUID4,
     BaseModel,
@@ -616,6 +621,11 @@ class Crew(FlowTrackable, BaseModel):
         self,
         inputs: Optional[Dict[str, Any]] = None,
     ) -> CrewOutput:
+        ctx = baggage.set_baggage(
+            "crew_context", CrewContext(id=str(self.id), key=self.key)
+        )
+        token = attach(ctx)
+
         try:
             for before_callback in self.before_kickoff_callbacks:
                 if inputs is None:
@@ -676,6 +686,8 @@ class Crew(FlowTrackable, BaseModel):
                 CrewKickoffFailedEvent(error=str(e), crew_name=self.name or "crew"),
             )
             raise
+        finally:
+            detach(token)
 
     def kickoff_for_each(self, inputs: List[Dict[str, Any]]) -> List[CrewOutput]:
         """Executes the Crew's workflow for each input in the list and aggregates results."""

src/crewai/lite_agent.py

@@ -15,12 +15,14 @@ from typing import (
     get_origin,
 )
 
+
 try:
     from typing import Self
 except ImportError:
     from typing_extensions import Self
 
 from pydantic import (
+    UUID4,
     BaseModel,
     Field,
     InstanceOf,
@@ -129,6 +131,7 @@ class LiteAgent(FlowTrackable, BaseModel):
     model_config = {"arbitrary_types_allowed": True}
 
     # Core Agent Properties
+    id: UUID4 = Field(default_factory=uuid.uuid4, frozen=True)
     role: str = Field(description="Role of the agent")
     goal: str = Field(description="Goal of the agent")
     backstory: str = Field(description="Backstory of the agent")
@@ -517,6 +520,7 @@ class LiteAgent(FlowTrackable, BaseModel):
                         messages=self._messages,
                         tools=None,
                         callbacks=self._callbacks,
+                        from_agent=self,
                     ),
                 )
 
@@ -526,6 +530,7 @@ class LiteAgent(FlowTrackable, BaseModel):
                         messages=self._messages,
                         callbacks=self._callbacks,
                         printer=self._printer,
+                        from_agent=self,
                     )
 
                     # Emit LLM call completed event
@@ -534,13 +539,14 @@ class LiteAgent(FlowTrackable, BaseModel):
                         event=LLMCallCompletedEvent(
                             response=answer,
                             call_type=LLMCallType.LLM_CALL,
+                            from_agent=self,
                         ),
                     )
                 except Exception as e:
                     # Emit LLM call failed event
                     crewai_event_bus.emit(
                         self,
-                        event=LLMCallFailedEvent(error=str(e)),
+                        event=LLMCallFailedEvent(error=str(e), from_agent=self),
                     )
                     raise e
 

src/crewai/llm.py

@@ -419,6 +419,8 @@ class LLM(BaseLLM):
         params: Dict[str, Any],
         callbacks: Optional[List[Any]] = None,
         available_functions: Optional[Dict[str, Any]] = None,
+        from_task: Optional[Any] = None,
+        from_agent: Optional[Any] = None,
     ) -> str:
         """Handle a streaming response from the LLM.
 
@@ -426,6 +428,8 @@ class LLM(BaseLLM):
             params: Parameters for the completion call
             callbacks: Optional list of callback functions
             available_functions: Dict of available functions
+            from_task: Optional task object
+            from_agent: Optional agent object
 
         Returns:
             str: The complete response text
@@ -510,6 +514,8 @@ class LLM(BaseLLM):
                                         tool_calls=tool_calls,
                                         accumulated_tool_args=accumulated_tool_args,
                                         available_functions=available_functions,
+                                        from_task=from_task,
+                                        from_agent=from_agent,
                                     )
                                     if result is not None:
                                         chunk_content = result
@@ -527,7 +533,7 @@ class LLM(BaseLLM):
                     assert hasattr(crewai_event_bus, "emit")
                     crewai_event_bus.emit(
                         self,
-                        event=LLMStreamChunkEvent(chunk=chunk_content),
+                        event=LLMStreamChunkEvent(chunk=chunk_content, from_task=from_task, from_agent=from_agent),
                     )
             # --- 4) Fallback to non-streaming if no content received
             if not full_response.strip() and chunk_count == 0:
@@ -540,7 +546,7 @@ class LLM(BaseLLM):
                     "stream_options", None
                 )  # Remove stream_options for non-streaming call
                 return self._handle_non_streaming_response(
-                    non_streaming_params, callbacks, available_functions
+                    non_streaming_params, callbacks, available_functions, from_task, from_agent
                 )
 
             # --- 5) Handle empty response with chunks
@@ -625,7 +631,7 @@ class LLM(BaseLLM):
                 # Log token usage if available in streaming mode
                 self._handle_streaming_callbacks(callbacks, usage_info, last_chunk)
                 # Emit completion event and return response
-                self._handle_emit_call_events(full_response, LLMCallType.LLM_CALL)
+                self._handle_emit_call_events(full_response, LLMCallType.LLM_CALL, from_task, from_agent)
                 return full_response
 
             # --- 9) Handle tool calls if present
@@ -637,7 +643,7 @@ class LLM(BaseLLM):
             self._handle_streaming_callbacks(callbacks, usage_info, last_chunk)
 
             # --- 11) Emit completion event and return response
-            self._handle_emit_call_events(full_response, LLMCallType.LLM_CALL)
+            self._handle_emit_call_events(full_response, LLMCallType.LLM_CALL, from_task, from_agent)
             return full_response
 
         except ContextWindowExceededError as e:
@@ -649,14 +655,14 @@ class LLM(BaseLLM):
             logging.error(f"Error in streaming response: {str(e)}")
             if full_response.strip():
                 logging.warning(f"Returning partial response despite error: {str(e)}")
-                self._handle_emit_call_events(full_response, LLMCallType.LLM_CALL)
+                self._handle_emit_call_events(full_response, LLMCallType.LLM_CALL, from_task, from_agent)
                 return full_response
 
             # Emit failed event and re-raise the exception
             assert hasattr(crewai_event_bus, "emit")
             crewai_event_bus.emit(
                 self,
-                event=LLMCallFailedEvent(error=str(e)),
+                event=LLMCallFailedEvent(error=str(e), from_task=from_task, from_agent=from_agent),
             )
             raise Exception(f"Failed to get streaming response: {str(e)}")
 
@@ -665,6 +671,8 @@ class LLM(BaseLLM):
         tool_calls: List[ChatCompletionDeltaToolCall],
         accumulated_tool_args: DefaultDict[int, AccumulatedToolArgs],
         available_functions: Optional[Dict[str, Any]] = None,
+        from_task: Optional[Any] = None,
+        from_agent: Optional[Any] = None,
     ) -> None | str:
         for tool_call in tool_calls:
             current_tool_accumulator = accumulated_tool_args[tool_call.index]
@@ -682,6 +690,8 @@ class LLM(BaseLLM):
                 event=LLMStreamChunkEvent(
                     tool_call=tool_call.to_dict(),
                     chunk=tool_call.function.arguments,
+                    from_task=from_task,
+                    from_agent=from_agent,
                 ),
             )
 
@@ -748,6 +758,8 @@ class LLM(BaseLLM):
         params: Dict[str, Any],
         callbacks: Optional[List[Any]] = None,
         available_functions: Optional[Dict[str, Any]] = None,
+        from_task: Optional[Any] = None,
+        from_agent: Optional[Any] = None,
     ) -> str:
         """Handle a non-streaming response from the LLM.
 
@@ -755,6 +767,8 @@ class LLM(BaseLLM):
             params: Parameters for the completion call
             callbacks: Optional list of callback functions
             available_functions: Dict of available functions
+            from_task: Optional Task that invoked the LLM
+            from_agent: Optional Agent that invoked the LLM
 
         Returns:
             str: The response text
@@ -795,7 +809,7 @@ class LLM(BaseLLM):
 
         # --- 5) If no tool calls or no available functions, return the text response directly
         if not tool_calls or not available_functions:
-            self._handle_emit_call_events(text_response, LLMCallType.LLM_CALL)
+            self._handle_emit_call_events(text_response, LLMCallType.LLM_CALL, from_task, from_agent)
             return text_response
 
         # --- 6) Handle tool calls if present
@@ -804,7 +818,7 @@ class LLM(BaseLLM):
             return tool_result
 
         # --- 7) If tool call handling didn't return a result, emit completion event and return text response
-        self._handle_emit_call_events(text_response, LLMCallType.LLM_CALL)
+        self._handle_emit_call_events(text_response, LLMCallType.LLM_CALL, from_task, from_agent)
         return text_response
 
     def _handle_tool_call(
@@ -889,6 +903,8 @@ class LLM(BaseLLM):
         tools: Optional[List[dict]] = None,
         callbacks: Optional[List[Any]] = None,
         available_functions: Optional[Dict[str, Any]] = None,
+        from_task: Optional[Any] = None,
+        from_agent: Optional[Any] = None,
     ) -> Union[str, Any]:
         """High-level LLM call method.
 
@@ -903,6 +919,8 @@ class LLM(BaseLLM):
                       during and after the LLM call.
             available_functions: Optional dict mapping function names to callables
                                that can be invoked by the LLM.
+            from_task: Optional Task that invoked the LLM
+            from_agent: Optional Agent that invoked the LLM
 
         Returns:
             Union[str, Any]: Either a text response from the LLM (str) or
@@ -922,6 +940,8 @@ class LLM(BaseLLM):
                 tools=tools,
                 callbacks=callbacks,
                 available_functions=available_functions,
+                from_task=from_task,
+                from_agent=from_agent,
             ),
         )
 
@@ -950,11 +970,11 @@ class LLM(BaseLLM):
                 # --- 7) Make the completion call and handle response
                 if self.stream:
                     return self._handle_streaming_response(
-                        params, callbacks, available_functions
+                        params, callbacks, available_functions, from_task, from_agent
                     )
                 else:
                     return self._handle_non_streaming_response(
-                        params, callbacks, available_functions
+                        params, callbacks, available_functions, from_task, from_agent
                     )
 
             except LLMContextLengthExceededException:
@@ -966,12 +986,12 @@ class LLM(BaseLLM):
                 assert hasattr(crewai_event_bus, "emit")
                 crewai_event_bus.emit(
                     self,
-                    event=LLMCallFailedEvent(error=str(e)),
+                    event=LLMCallFailedEvent(error=str(e), from_task=from_task, from_agent=from_agent),
                 )
                 logging.error(f"LiteLLM call failed: {str(e)}")
                 raise
 
-    def _handle_emit_call_events(self, response: Any, call_type: LLMCallType):
+    def _handle_emit_call_events(self, response: Any, call_type: LLMCallType, from_task: Optional[Any] = None, from_agent: Optional[Any] = None):
         """Handle the events for the LLM call.
 
         Args:
@@ -981,7 +1001,7 @@ class LLM(BaseLLM):
         assert hasattr(crewai_event_bus, "emit")
         crewai_event_bus.emit(
             self,
-            event=LLMCallCompletedEvent(response=response, call_type=call_type),
+            event=LLMCallCompletedEvent(response=response, call_type=call_type, from_task=from_task, from_agent=from_agent),
         )
 
     def _format_messages_for_provider(

src/crewai/llms/base_llm.py

@@ -1,5 +1,5 @@
 from abc import ABC, abstractmethod
-from typing import Any, Callable, Dict, List, Optional, Union
+from typing import Any, Dict, List, Optional, Union
 
 
 class BaseLLM(ABC):
@@ -47,6 +47,8 @@ class BaseLLM(ABC):
         tools: Optional[List[dict]] = None,
         callbacks: Optional[List[Any]] = None,
         available_functions: Optional[Dict[str, Any]] = None,
+        from_task: Optional[Any] = None,
+        from_agent: Optional[Any] = None,
     ) -> Union[str, Any]:
         """Call the LLM with the given messages.
 
@@ -61,6 +63,7 @@ class BaseLLM(ABC):
                       during and after the LLM call.
             available_functions: Optional dict mapping function names to callables
                                that can be invoked by the LLM.
+            from_task: Optional task caller to be used for the LLM call.
 
         Returns:
             Either a text response from the LLM (str) or

src/crewai/llms/third_party/ai_suite.py

@@ -16,6 +16,8 @@ class AISuiteLLM(BaseLLM):
         tools: Optional[List[dict]] = None,
         callbacks: Optional[List[Any]] = None,
         available_functions: Optional[Dict[str, Any]] = None,
+        from_task: Optional[Any] = None,
+        from_agent: Optional[Any] = None,
     ) -> Union[str, Any]:
         completion_params = self._prepare_completion_params(messages, tools)
         response = self.client.chat.completions.create(**completion_params)

src/crewai/memory/entity/entity_memory.py

@@ -1,10 +1,20 @@
 from typing import Optional
+import time
 
 from pydantic import PrivateAttr
 
 from crewai.memory.entity.entity_memory_item import EntityMemoryItem
 from crewai.memory.memory import Memory
 from crewai.memory.storage.rag_storage import RAGStorage
+from crewai.utilities.events.crewai_event_bus import crewai_event_bus
+from crewai.utilities.events.memory_events import (
+    MemoryQueryStartedEvent,
+    MemoryQueryCompletedEvent,
+    MemoryQueryFailedEvent,
+    MemorySaveStartedEvent,
+    MemorySaveCompletedEvent,
+    MemorySaveFailedEvent,
+)
 
 
 class EntityMemory(Memory):
@@ -48,16 +58,96 @@ class EntityMemory(Memory):
 
     def save(self, item: EntityMemoryItem) -> None:  # type: ignore # BUG?: Signature of "save" incompatible with supertype "Memory"
         """Saves an entity item into the SQLite storage."""
-        if self._memory_provider == "mem0":
-            data = f"""
-            Remember details about the following entity:
-            Name: {item.name}
-            Type: {item.type}
-            Entity Description: {item.description}
-            """
-        else:
-            data = f"{item.name}({item.type}): {item.description}"
-        super().save(data, item.metadata)
+        crewai_event_bus.emit(
+            self,
+            event=MemorySaveStartedEvent(
+                metadata=item.metadata,
+                source_type="entity_memory",
+            ),
+        )
+
+        start_time = time.time()
+        try:
+            if self._memory_provider == "mem0":
+                data = f"""
+                Remember details about the following entity:
+                Name: {item.name}
+                Type: {item.type}
+                Entity Description: {item.description}
+                """
+            else:
+                data = f"{item.name}({item.type}): {item.description}"
+
+            super().save(data, item.metadata)
+
+            # Emit memory save completed event
+            crewai_event_bus.emit(
+                self,
+                event=MemorySaveCompletedEvent(
+                    value=data,
+                    metadata=item.metadata,
+                    save_time_ms=(time.time() - start_time) * 1000,
+                    source_type="entity_memory",
+                ),
+            )
+        except Exception as e:
+            crewai_event_bus.emit(
+                self,
+                event=MemorySaveFailedEvent(
+                    metadata=item.metadata,
+                    error=str(e),
+                    source_type="entity_memory",
+                ),
+            )
+            raise
+
+    def search(
+        self,
+        query: str,
+        limit: int = 3,
+        score_threshold: float = 0.35,
+    ):
+        crewai_event_bus.emit(
+            self,
+            event=MemoryQueryStartedEvent(
+                query=query,
+                limit=limit,
+                score_threshold=score_threshold,
+                source_type="entity_memory",
+            ),
+        )
+
+        start_time = time.time()
+        try:
+            results = super().search(
+                query=query, limit=limit, score_threshold=score_threshold
+            )
+
+            crewai_event_bus.emit(
+                self,
+                event=MemoryQueryCompletedEvent(
+                    query=query,
+                    results=results,
+                    limit=limit,
+                    score_threshold=score_threshold,
+                    query_time_ms=(time.time() - start_time) * 1000,
+                    source_type="entity_memory",
+                ),
+            )
+
+            return results
+        except Exception as e:
+            crewai_event_bus.emit(
+                self,
+                event=MemoryQueryFailedEvent(
+                    query=query,
+                    limit=limit,
+                    score_threshold=score_threshold,
+                    error=str(e),
+                    source_type="entity_memory",
+                ),
+            )
+            raise
 
     def reset(self) -> None:
         try:

src/crewai/memory/external/external_memory.py

@@ -1,8 +1,18 @@
 from typing import TYPE_CHECKING, Any, Dict, Optional
+import time
 
 from crewai.memory.external.external_memory_item import ExternalMemoryItem
 from crewai.memory.memory import Memory
 from crewai.memory.storage.interface import Storage
+from crewai.utilities.events.crewai_event_bus import crewai_event_bus
+from crewai.utilities.events.memory_events import (
+    MemoryQueryStartedEvent,
+    MemoryQueryCompletedEvent,
+    MemoryQueryFailedEvent,
+    MemorySaveStartedEvent,
+    MemorySaveCompletedEvent,
+    MemorySaveFailedEvent,
+)
 
 if TYPE_CHECKING:
     from crewai.memory.storage.mem0_storage import Mem0Storage
@@ -46,8 +56,91 @@ class ExternalMemory(Memory):
         agent: Optional[str] = None,
     ) -> None:
         """Saves a value into the external storage."""
-        item = ExternalMemoryItem(value=value, metadata=metadata, agent=agent)
-        super().save(value=item.value, metadata=item.metadata, agent=item.agent)
+        crewai_event_bus.emit(
+            self,
+            event=MemorySaveStartedEvent(
+                value=value,
+                metadata=metadata,
+                agent_role=agent,
+                source_type="external_memory",
+            ),
+        )
+
+        start_time = time.time()
+        try:
+            item = ExternalMemoryItem(value=value, metadata=metadata, agent=agent)
+            super().save(value=item.value, metadata=item.metadata, agent=item.agent)
+
+            crewai_event_bus.emit(
+                self,
+                event=MemorySaveCompletedEvent(
+                    value=value,
+                    metadata=metadata,
+                    agent_role=agent,
+                    save_time_ms=(time.time() - start_time) * 1000,
+                    source_type="external_memory",
+                ),
+            )
+        except Exception as e:
+            crewai_event_bus.emit(
+                self,
+                event=MemorySaveFailedEvent(
+                    value=value,
+                    metadata=metadata,
+                    agent_role=agent,
+                    error=str(e),
+                    source_type="external_memory",
+                ),
+            )
+            raise
+
+    def search(
+        self,
+        query: str,
+        limit: int = 3,
+        score_threshold: float = 0.35,
+    ):
+        crewai_event_bus.emit(
+            self,
+            event=MemoryQueryStartedEvent(
+                query=query,
+                limit=limit,
+                score_threshold=score_threshold,
+                source_type="external_memory",
+            ),
+        )
+
+        start_time = time.time()
+        try:
+            results = super().search(
+                query=query, limit=limit, score_threshold=score_threshold
+            )
+
+            crewai_event_bus.emit(
+                self,
+                event=MemoryQueryCompletedEvent(
+                    query=query,
+                    results=results,
+                    limit=limit,
+                    score_threshold=score_threshold,
+                    query_time_ms=(time.time() - start_time) * 1000,
+                    source_type="external_memory",
+                ),
+            )
+
+            return results
+        except Exception as e:
+            crewai_event_bus.emit(
+                self,
+                event=MemoryQueryFailedEvent(
+                    query=query,
+                    limit=limit,
+                    score_threshold=score_threshold,
+                    error=str(e),
+                    source_type="external_memory",
+                ),
+            )
+            raise
 
     def reset(self) -> None:
         self.storage.reset()

src/crewai/memory/long_term/long_term_memory.py

@@ -1,7 +1,17 @@
 from typing import Any, Dict, List
+import time
 
 from crewai.memory.long_term.long_term_memory_item import LongTermMemoryItem
 from crewai.memory.memory import Memory
+from crewai.utilities.events.crewai_event_bus import crewai_event_bus
+from crewai.utilities.events.memory_events import (
+    MemoryQueryStartedEvent,
+    MemoryQueryCompletedEvent,
+    MemoryQueryFailedEvent,
+    MemorySaveStartedEvent,
+    MemorySaveCompletedEvent,
+    MemorySaveFailedEvent,
+)
 from crewai.memory.storage.ltm_sqlite_storage import LTMSQLiteStorage
 
 
@@ -20,17 +30,87 @@ class LongTermMemory(Memory):
         super().__init__(storage=storage)
 
     def save(self, item: LongTermMemoryItem) -> None:  # type: ignore # BUG?: Signature of "save" incompatible with supertype "Memory"
-        metadata = item.metadata
-        metadata.update({"agent": item.agent, "expected_output": item.expected_output})
-        self.storage.save(  # type: ignore # BUG?: Unexpected keyword argument "task_description","score","datetime" for "save" of "Storage"
-            task_description=item.task,
-            score=metadata["quality"],
-            metadata=metadata,
-            datetime=item.datetime,
+        crewai_event_bus.emit(
+            self,
+            event=MemorySaveStartedEvent(
+                value=item.task,
+                metadata=item.metadata,
+                agent_role=item.agent,
+                source_type="long_term_memory",
+            ),
         )
 
+        start_time = time.time()
+        try:
+            metadata = item.metadata
+            metadata.update({"agent": item.agent, "expected_output": item.expected_output})
+            self.storage.save(  # type: ignore # BUG?: Unexpected keyword argument "task_description","score","datetime" for "save" of "Storage"
+                task_description=item.task,
+                score=metadata["quality"],
+                metadata=metadata,
+                datetime=item.datetime,
+            )
+
+            crewai_event_bus.emit(
+                self,
+                event=MemorySaveCompletedEvent(
+                    value=item.task,
+                    metadata=item.metadata,
+                    agent_role=item.agent,
+                    save_time_ms=(time.time() - start_time) * 1000,
+                    source_type="long_term_memory",
+                ),
+            )
+        except Exception as e:
+            crewai_event_bus.emit(
+                self,
+                event=MemorySaveFailedEvent(
+                    value=item.task,
+                    metadata=item.metadata,
+                    agent_role=item.agent,
+                    error=str(e),
+                    source_type="long_term_memory",
+                ),
+            )
+            raise
+
     def search(self, task: str, latest_n: int = 3) -> List[Dict[str, Any]]:  # type: ignore # signature of "search" incompatible with supertype "Memory"
-        return self.storage.load(task, latest_n)  # type: ignore # BUG?: "Storage" has no attribute "load"
+        crewai_event_bus.emit(
+            self,
+            event=MemoryQueryStartedEvent(
+                query=task,
+                limit=latest_n,
+                source_type="long_term_memory",
+            ),
+        )
+
+        start_time = time.time()
+        try:
+            results = self.storage.load(task, latest_n)  # type: ignore # BUG?: "Storage" has no attribute "load"
+
+            crewai_event_bus.emit(
+                self,
+                event=MemoryQueryCompletedEvent(
+                    query=task,
+                    results=results,
+                    limit=latest_n,
+                    query_time_ms=(time.time() - start_time) * 1000,
+                    source_type="long_term_memory",
+                ),
+            )
+
+            return results
+        except Exception as e:
+            crewai_event_bus.emit(
+                self,
+                event=MemoryQueryFailedEvent(
+                    query=task,
+                    limit=latest_n,
+                    error=str(e),
+                    source_type="long_term_memory",
+                ),
+            )
+            raise
 
     def reset(self) -> None:
         self.storage.reset()

src/crewai/memory/short_term/short_term_memory.py

@@ -1,10 +1,20 @@
 from typing import Any, Dict, Optional
+import time
 
 from pydantic import PrivateAttr
 
 from crewai.memory.memory import Memory
 from crewai.memory.short_term.short_term_memory_item import ShortTermMemoryItem
 from crewai.memory.storage.rag_storage import RAGStorage
+from crewai.utilities.events.crewai_event_bus import crewai_event_bus
+from crewai.utilities.events.memory_events import (
+    MemoryQueryStartedEvent,
+    MemoryQueryCompletedEvent,
+    MemoryQueryFailedEvent,
+    MemorySaveStartedEvent,
+    MemorySaveCompletedEvent,
+    MemorySaveFailedEvent,
+)
 
 
 class ShortTermMemory(Memory):
@@ -52,11 +62,46 @@ class ShortTermMemory(Memory):
         metadata: Optional[Dict[str, Any]] = None,
         agent: Optional[str] = None,
     ) -> None:
-        item = ShortTermMemoryItem(data=value, metadata=metadata, agent=agent)
-        if self._memory_provider == "mem0":
-            item.data = f"Remember the following insights from Agent run: {item.data}"
+        crewai_event_bus.emit(
+            self,
+            event=MemorySaveStartedEvent(
+                value=value,
+                metadata=metadata,
+                agent_role=agent,
+                source_type="short_term_memory",
+            ),
+        )
 
-        super().save(value=item.data, metadata=item.metadata, agent=item.agent)
+        start_time = time.time()
+        try:
+            item = ShortTermMemoryItem(data=value, metadata=metadata, agent=agent)
+            if self._memory_provider == "mem0":
+                item.data = f"Remember the following insights from Agent run: {item.data}"
+
+            super().save(value=item.data, metadata=item.metadata, agent=item.agent)
+
+            crewai_event_bus.emit(
+                self,
+                event=MemorySaveCompletedEvent(
+                    value=value,
+                    metadata=metadata,
+                    agent_role=agent,
+                    save_time_ms=(time.time() - start_time) * 1000,
+                    source_type="short_term_memory",
+                ),
+            )
+        except Exception as e:
+            crewai_event_bus.emit(
+                self,
+                event=MemorySaveFailedEvent(
+                    value=value,
+                    metadata=metadata,
+                    agent_role=agent,
+                    error=str(e),
+                    source_type="short_term_memory",
+                ),
+            )
+            raise
 
     def search(
         self,
@@ -64,9 +109,47 @@ class ShortTermMemory(Memory):
         limit: int = 3,
         score_threshold: float = 0.35,
     ):
-        return self.storage.search(
-            query=query, limit=limit, score_threshold=score_threshold
-        )  # type: ignore # BUG? The reference is to the parent class, but the parent class does not have this parameters
+        crewai_event_bus.emit(
+            self,
+            event=MemoryQueryStartedEvent(
+                query=query,
+                limit=limit,
+                score_threshold=score_threshold,
+                source_type="short_term_memory",
+            ),
+        )
+
+        start_time = time.time()
+        try:
+            results = self.storage.search(
+                query=query, limit=limit, score_threshold=score_threshold
+            )  # type: ignore # BUG? The reference is to the parent class, but the parent class does not have this parameters
+
+            crewai_event_bus.emit(
+                self,
+                event=MemoryQueryCompletedEvent(
+                    query=query,
+                    results=results,
+                    limit=limit,
+                    score_threshold=score_threshold,
+                    query_time_ms=(time.time() - start_time) * 1000,
+                    source_type="short_term_memory",
+                ),
+            )
+
+            return results
+        except Exception as e:
+            crewai_event_bus.emit(
+                self,
+                event=MemoryQueryFailedEvent(
+                    query=query,
+                    limit=limit,
+                    score_threshold=score_threshold,
+                    error=str(e),
+                    source_type="short_term_memory",
+                ),
+            )
+            raise
 
     def reset(self) -> None:
         try:

src/crewai/memory/storage/rag_storage.py

@@ -71,14 +71,10 @@ class RAGStorage(BaseRAGStorage):
 
         self.app = chroma_client
 
-        try:
-            self.collection = self.app.get_collection(
-                name=self.type, embedding_function=self.embedder_config
-            )
-        except Exception:
-            self.collection = self.app.create_collection(
-                name=self.type, embedding_function=self.embedder_config
-            )
+        self.collection = self.app.get_or_create_collection(
+            name=self.type, embedding_function=self.embedder_config
+        )
+        logging.info(f"Collection found or created: {self.collection}")
 
     def _sanitize_role(self, role: str) -> str:
         """

src/crewai/task.py

@@ -97,7 +97,7 @@ class Task(BaseModel):
     )
     context: Union[List["Task"], None, _NotSpecified] = Field(
         description="Other tasks that will have their output used as context for this task.",
-        default=NOT_SPECIFIED
+        default=NOT_SPECIFIED,
     )
     async_execution: Optional[bool] = Field(
         description="Whether the task should be executed asynchronously or not.",
@@ -158,9 +158,7 @@ class Task(BaseModel):
     end_time: Optional[datetime.datetime] = Field(
         default=None, description="End time of the task execution"
     )
-    model_config = {
-        "arbitrary_types_allowed": True
-    }
+    model_config = {"arbitrary_types_allowed": True}
 
     @field_validator("guardrail")
     @classmethod
@@ -204,7 +202,6 @@ class Task(BaseModel):
             # Check return annotation if present, but don't require it
             return_annotation = sig.return_annotation
             if return_annotation != inspect.Signature.empty:
-
                 return_annotation_args = get_args(return_annotation)
                 if not (
                     get_origin(return_annotation) is tuple
@@ -437,7 +434,7 @@ class Task(BaseModel):
                 guardrail_result = process_guardrail(
                     output=task_output,
                     guardrail=self._guardrail,
-                    retry_count=self.retry_count
+                    retry_count=self.retry_count,
                 )
                 if not guardrail_result.success:
                     if self.retry_count >= self.max_retries:
@@ -510,8 +507,6 @@ class Task(BaseModel):
         )
         from crewai.utilities.events.crewai_event_bus import crewai_event_bus
 
-        result = self._guardrail(task_output)
-
         crewai_event_bus.emit(
             self,
             LLMGuardrailStartedEvent(
@@ -519,7 +514,13 @@ class Task(BaseModel):
             ),
         )
 
-        guardrail_result = GuardrailResult.from_tuple(result)
+        try:
+            result = self._guardrail(task_output)
+            guardrail_result = GuardrailResult.from_tuple(result)
+        except Exception as e:
+            guardrail_result = GuardrailResult(
+                success=False, result=None, error=f"Guardrail execution error: {str(e)}"
+            )
 
         crewai_event_bus.emit(
             self,

src/crewai/telemetry/telemetry.py

@@ -111,11 +111,13 @@ class Telemetry:
                 raise  # Re-raise the exception to not interfere with system signals
             self.ready = False
 
-    def _is_telemetry_disabled(self) -> bool:
+    @classmethod
+    def _is_telemetry_disabled(cls) -> bool:
         """Check if telemetry should be disabled based on environment variables."""
         return (
             os.getenv("OTEL_SDK_DISABLED", "false").lower() == "true"
             or os.getenv("CREWAI_DISABLE_TELEMETRY", "false").lower() == "true"
+            or os.getenv("CREWAI_DISABLE_TRACKING", "false").lower() == "true"
         )
 
     def _should_execute_telemetry(self) -> bool:

src/crewai/utilities/agent_utils.py

@@ -145,12 +145,16 @@ def get_llm_response(
     messages: List[Dict[str, str]],
     callbacks: List[Any],
     printer: Printer,
+    from_task: Optional[Any] = None,
+    from_agent: Optional[Any] = None,
 ) -> str:
     """Call the LLM and return the response, handling any invalid responses."""
     try:
         answer = llm.call(
             messages,
             callbacks=callbacks,
+            from_task=from_task,
+            from_agent=from_agent,
         )
     except Exception as e:
         printer.print(

src/crewai/utilities/crew/__init__.py

@@ -0,0 +1 @@
+"""Crew-specific utilities."""

src/crewai/utilities/crew/crew_context.py

@@ -0,0 +1,16 @@
+"""Context management utilities for tracking crew and task execution context using OpenTelemetry baggage."""
+
+from typing import Optional
+
+from opentelemetry import baggage
+
+from crewai.utilities.crew.models import CrewContext
+
+
+def get_crew_context() -> Optional[CrewContext]:
+    """Get the current crew context from OpenTelemetry baggage.
+
+    Returns:
+        CrewContext instance containing crew context information, or None if no context is set
+    """
+    return baggage.get_baggage("crew_context")

src/crewai/utilities/crew/models.py

@@ -0,0 +1,16 @@
+"""Models for crew-related data structures."""
+
+from typing import Optional
+
+from pydantic import BaseModel, Field
+
+
+class CrewContext(BaseModel):
+    """Model representing crew context information."""
+
+    id: Optional[str] = Field(
+        default=None, description="Unique identifier for the crew"
+    )
+    key: Optional[str] = Field(
+        default=None, description="Optional crew key/name for identification"
+    )

src/crewai/utilities/evaluators/task_evaluator.py

@@ -5,6 +5,7 @@ from pydantic import BaseModel, Field
 from crewai.utilities import Converter
 from crewai.utilities.events import TaskEvaluationEvent, crewai_event_bus
 from crewai.utilities.pydantic_schema_parser import PydanticSchemaParser
+from crewai.utilities.training_converter import TrainingConverter
 
 
 class Entity(BaseModel):
@@ -133,7 +134,7 @@ class TaskEvaluator:
             ).get_schema()
             instructions = f"{instructions}\n\nThe json should have the following structure, with the following keys:\n{model_schema}"
 
-        converter = Converter(
+        converter = TrainingConverter(
             llm=self.llm,
             text=evaluation_query,
             model=TrainingTaskEvaluation,

src/crewai/utilities/events/__init__.py

@@ -51,6 +51,71 @@ from .llm_events import (
     LLMStreamChunkEvent,
 )
 
+from .memory_events import (
+    MemorySaveStartedEvent,
+    MemorySaveCompletedEvent,
+    MemorySaveFailedEvent,
+    MemoryQueryStartedEvent,
+    MemoryQueryCompletedEvent,
+    MemoryQueryFailedEvent,
+    MemoryRetrievalStartedEvent,
+    MemoryRetrievalCompletedEvent,
+)
+
 # events
 from .event_listener import EventListener
 from .third_party.agentops_listener import agentops_listener
+
+__all__ = [
+    "EventListener",
+    "agentops_listener",
+    "CrewAIEventsBus",
+    "crewai_event_bus",
+    "AgentExecutionStartedEvent",
+    "AgentExecutionCompletedEvent",
+    "AgentExecutionErrorEvent",
+    "TaskStartedEvent",
+    "TaskCompletedEvent",
+    "TaskFailedEvent",
+    "TaskEvaluationEvent",
+    "FlowCreatedEvent",
+    "FlowStartedEvent",
+    "FlowFinishedEvent",
+    "FlowPlotEvent",
+    "MethodExecutionStartedEvent",
+    "MethodExecutionFinishedEvent",
+    "MethodExecutionFailedEvent",
+    "LLMCallCompletedEvent",
+    "LLMCallFailedEvent",
+    "LLMCallStartedEvent",
+    "LLMCallType",
+    "LLMStreamChunkEvent",
+    "MemorySaveStartedEvent",
+    "MemorySaveCompletedEvent",
+    "MemorySaveFailedEvent",
+    "MemoryQueryStartedEvent",
+    "MemoryQueryCompletedEvent",
+    "MemoryQueryFailedEvent",
+    "MemoryRetrievalStartedEvent",
+    "MemoryRetrievalCompletedEvent",
+    "EventListener",
+    "agentops_listener",
+    "CrewKickoffStartedEvent",
+    "CrewKickoffCompletedEvent",
+    "CrewKickoffFailedEvent",
+    "CrewTrainStartedEvent",
+    "CrewTrainCompletedEvent",
+    "CrewTrainFailedEvent",
+    "CrewTestStartedEvent",
+    "CrewTestCompletedEvent",
+    "CrewTestFailedEvent",
+    "LLMGuardrailCompletedEvent",
+    "LLMGuardrailStartedEvent",
+    "ToolUsageFinishedEvent",
+    "ToolUsageErrorEvent",
+    "ToolUsageStartedEvent",
+    "ToolExecutionErrorEvent",
+    "ToolSelectionErrorEvent",
+    "ToolUsageEvent",
+    "ToolValidateInputErrorEvent",
+]

src/crewai/utilities/events/base_events.py

@@ -12,7 +12,7 @@ class BaseEvent(BaseModel):
     timestamp: datetime = Field(default_factory=datetime.now)
     type: str
     source_fingerprint: Optional[str] = None  # UUID string of the source entity
-    source_type: Optional[str] = None  # "agent", "task", "crew"
+    source_type: Optional[str] = None  # "agent", "task", "crew", "memory", "entity_memory", "short_term_memory", "long_term_memory", "external_memory"
     fingerprint_metadata: Optional[Dict[str, Any]] = None  # Any relevant metadata
 
     def to_json(self, exclude: set[str] | None = None):

src/crewai/utilities/events/event_listener.py

@@ -22,6 +22,10 @@ from crewai.utilities.events.llm_events import (
     LLMCallStartedEvent,
     LLMStreamChunkEvent,
 )
+from crewai.utilities.events.llm_guardrail_events import (
+    LLMGuardrailStartedEvent,
+    LLMGuardrailCompletedEvent,
+)
 from crewai.utilities.events.utils.console_formatter import ConsoleFormatter
 
 from .agent_events import (
@@ -65,6 +69,8 @@ from .reasoning_events import (
     AgentReasoningFailedEvent,
 )
 
+from .listeners.memory_listener import MemoryListener
+
 
 class EventListener(BaseEventListener):
     _instance = None
@@ -91,6 +97,8 @@ class EventListener(BaseEventListener):
             self._initialized = True
             self.formatter = ConsoleFormatter(verbose=True)
 
+            MemoryListener(formatter=self.formatter)
+
     # ----------- CREW EVENTS -----------
 
     def setup_listeners(self, crewai_event_bus):
@@ -366,6 +374,23 @@ class EventListener(BaseEventListener):
             print(content, end="", flush=True)
             self.next_chunk = self.text_stream.tell()
 
+        # ----------- LLM GUARDRAIL EVENTS -----------
+
+        @crewai_event_bus.on(LLMGuardrailStartedEvent)
+        def on_llm_guardrail_started(source, event: LLMGuardrailStartedEvent):
+            guardrail_str = str(event.guardrail)
+            guardrail_name = (
+                guardrail_str[:50] + "..." if len(guardrail_str) > 50 else guardrail_str
+            )
+
+            self.formatter.handle_guardrail_started(guardrail_name, event.retry_count)
+
+        @crewai_event_bus.on(LLMGuardrailCompletedEvent)
+        def on_llm_guardrail_completed(source, event: LLMGuardrailCompletedEvent):
+            self.formatter.handle_guardrail_completed(
+                event.success, event.error, event.retry_count
+            )
+
         @crewai_event_bus.on(CrewTestStartedEvent)
         def on_crew_test_started(source, event: CrewTestStartedEvent):
             cloned_crew = source.copy()

src/crewai/utilities/events/event_types.py

@@ -57,6 +57,17 @@ from .knowledge_events import (
     KnowledgeSearchQueryFailedEvent,
 )
 
+from .memory_events import (
+    MemorySaveStartedEvent,
+    MemorySaveCompletedEvent,
+    MemorySaveFailedEvent,
+    MemoryQueryStartedEvent,
+    MemoryQueryCompletedEvent,
+    MemoryQueryFailedEvent,
+    MemoryRetrievalStartedEvent,
+    MemoryRetrievalCompletedEvent,
+)
+
 EventTypes = Union[
     CrewKickoffStartedEvent,
     CrewKickoffCompletedEvent,
@@ -96,4 +107,12 @@ EventTypes = Union[
     KnowledgeQueryCompletedEvent,
     KnowledgeQueryFailedEvent,
     KnowledgeSearchQueryFailedEvent,
+    MemorySaveStartedEvent,
+    MemorySaveCompletedEvent,
+    MemorySaveFailedEvent,
+    MemoryQueryStartedEvent,
+    MemoryQueryCompletedEvent,
+    MemoryQueryFailedEvent,
+    MemoryRetrievalStartedEvent,
+    MemoryRetrievalCompletedEvent,
 ]

src/crewai/utilities/events/listeners/memory_listener.py

@@ -0,0 +1,110 @@
+from crewai.utilities.events.base_event_listener import BaseEventListener
+from crewai.utilities.events.memory_events import (
+    MemoryRetrievalCompletedEvent,
+    MemoryRetrievalStartedEvent,
+    MemoryQueryFailedEvent,
+    MemoryQueryCompletedEvent,
+    MemorySaveStartedEvent,
+    MemorySaveCompletedEvent,
+    MemorySaveFailedEvent,
+)
+
+class MemoryListener(BaseEventListener):
+
+    def __init__(self, formatter):
+        super().__init__()
+        self.formatter = formatter
+        self.memory_retrieval_in_progress = False
+        self.memory_save_in_progress = False
+
+    def setup_listeners(self, crewai_event_bus):
+        @crewai_event_bus.on(MemoryRetrievalStartedEvent)
+        def on_memory_retrieval_started(
+            source, event: MemoryRetrievalStartedEvent
+        ):
+            if self.memory_retrieval_in_progress:
+                return
+
+            self.memory_retrieval_in_progress = True
+
+            self.formatter.handle_memory_retrieval_started(
+                self.formatter.current_agent_branch,
+                self.formatter.current_crew_tree,
+            )
+
+        @crewai_event_bus.on(MemoryRetrievalCompletedEvent)
+        def on_memory_retrieval_completed(
+            source, event: MemoryRetrievalCompletedEvent
+        ):
+            if not self.memory_retrieval_in_progress:
+                return
+
+            self.memory_retrieval_in_progress = False
+            self.formatter.handle_memory_retrieval_completed(
+                self.formatter.current_agent_branch,
+                self.formatter.current_crew_tree,
+                event.memory_content,
+                event.retrieval_time_ms
+            )
+
+        @crewai_event_bus.on(MemoryQueryCompletedEvent)
+        def on_memory_query_completed(source, event: MemoryQueryCompletedEvent):
+            if not self.memory_retrieval_in_progress:
+                return
+
+            self.formatter.handle_memory_query_completed(
+                self.formatter.current_agent_branch,
+                event.source_type,
+                event.query_time_ms,
+                self.formatter.current_crew_tree,
+            )
+
+        @crewai_event_bus.on(MemoryQueryFailedEvent)
+        def on_memory_query_failed(source, event: MemoryQueryFailedEvent):
+            if not self.memory_retrieval_in_progress:
+                return
+
+            self.formatter.handle_memory_query_failed(
+                self.formatter.current_agent_branch,
+                self.formatter.current_crew_tree,
+                event.error,
+                event.source_type,
+            )
+
+        @crewai_event_bus.on(MemorySaveStartedEvent)
+        def on_memory_save_started(source, event: MemorySaveStartedEvent):
+            if self.memory_save_in_progress:
+                return
+
+            self.memory_save_in_progress = True
+
+            self.formatter.handle_memory_save_started(
+                self.formatter.current_agent_branch,
+                self.formatter.current_crew_tree,
+            )
+
+        @crewai_event_bus.on(MemorySaveCompletedEvent)
+        def on_memory_save_completed(source, event: MemorySaveCompletedEvent):
+            if not self.memory_save_in_progress:
+                return
+
+            self.memory_save_in_progress = False
+
+            self.formatter.handle_memory_save_completed(
+                self.formatter.current_agent_branch,
+                self.formatter.current_crew_tree,
+                event.save_time_ms,
+                event.source_type,
+            )
+
+        @crewai_event_bus.on(MemorySaveFailedEvent)
+        def on_memory_save_failed(source, event: MemorySaveFailedEvent):
+            if not self.memory_save_in_progress:
+                return
+
+            self.formatter.handle_memory_save_failed(
+                self.formatter.current_agent_branch,
+                event.error,
+                event.source_type,
+                self.formatter.current_crew_tree,
+            )

src/crewai/utilities/events/llm_events.py

@@ -5,6 +5,32 @@ from pydantic import BaseModel
 
 from crewai.utilities.events.base_events import BaseEvent
 
+class LLMEventBase(BaseEvent):
+    task_name: Optional[str] = None
+    task_id: Optional[str] = None
+
+    agent_id: Optional[str] = None
+    agent_role: Optional[str] = None
+
+    def __init__(self, **data):
+        super().__init__(**data)
+        self._set_agent_params(data)
+        self._set_task_params(data)
+
+    def _set_agent_params(self, data: Dict[str, Any]):
+        task = data.get("from_task", None)
+        agent = task.agent if task else data.get("from_agent", None)
+
+        if not agent:
+            return
+
+        self.agent_id = agent.id
+        self.agent_role = agent.role
+
+    def _set_task_params(self, data: Dict[str, Any]):
+        if "from_task" in data and (task := data["from_task"]):
+            self.task_id = task.id
+            self.task_name = task.name
 
 class LLMCallType(Enum):
     """Type of LLM call being made"""
@@ -13,7 +39,7 @@ class LLMCallType(Enum):
     LLM_CALL = "llm_call"
 
 
-class LLMCallStartedEvent(BaseEvent):
+class LLMCallStartedEvent(LLMEventBase):
     """Event emitted when a LLM call starts
 
     Attributes:
@@ -28,7 +54,7 @@ class LLMCallStartedEvent(BaseEvent):
     available_functions: Optional[Dict[str, Any]] = None
 
 
-class LLMCallCompletedEvent(BaseEvent):
+class LLMCallCompletedEvent(LLMEventBase):
     """Event emitted when a LLM call completes"""
 
     type: str = "llm_call_completed"
@@ -36,7 +62,7 @@ class LLMCallCompletedEvent(BaseEvent):
     call_type: LLMCallType
 
 
-class LLMCallFailedEvent(BaseEvent):
+class LLMCallFailedEvent(LLMEventBase):
     """Event emitted when a LLM call fails"""
 
     error: str
@@ -55,7 +81,7 @@ class ToolCall(BaseModel):
     index: int
 
 
-class LLMStreamChunkEvent(BaseEvent):
+class LLMStreamChunkEvent(LLMEventBase):
     """Event emitted when a streaming chunk is received"""
 
     type: str = "llm_stream_chunk"

src/crewai/utilities/events/llm_guardrail_events.py

@@ -1,3 +1,4 @@
+from inspect import getsource
 from typing import Any, Callable, Optional, Union
 
 from crewai.utilities.events.base_events import BaseEvent
@@ -16,23 +17,26 @@ class LLMGuardrailStartedEvent(BaseEvent):
     retry_count: int
 
     def __init__(self, **data):
-        from inspect import getsource
-
         from crewai.tasks.llm_guardrail import LLMGuardrail
         from crewai.tasks.hallucination_guardrail import HallucinationGuardrail
 
         super().__init__(**data)
 
-        if isinstance(self.guardrail, LLMGuardrail) or isinstance(
-            self.guardrail, HallucinationGuardrail
-        ):
+        if isinstance(self.guardrail, (LLMGuardrail, HallucinationGuardrail)):
             self.guardrail = self.guardrail.description.strip()
         elif isinstance(self.guardrail, Callable):
             self.guardrail = getsource(self.guardrail).strip()
 
 
 class LLMGuardrailCompletedEvent(BaseEvent):
-    """Event emitted when a guardrail task completes"""
+    """Event emitted when a guardrail task completes
+
+    Attributes:
+        success: Whether the guardrail validation passed
+        result: The validation result
+        error: Error message if validation failed
+        retry_count: The number of times the guardrail has been retried
+    """
 
     type: str = "llm_guardrail_completed"
     success: bool

src/crewai/utilities/events/memory_events.py

@@ -0,0 +1,78 @@
+from typing import Any, Dict, Optional
+
+from crewai.utilities.events.base_events import BaseEvent
+
+
+class MemoryQueryStartedEvent(BaseEvent):
+    """Event emitted when a memory query is started"""
+
+    type: str = "memory_query_started"
+    query: str
+    limit: int
+    score_threshold: Optional[float] = None
+
+
+class MemoryQueryCompletedEvent(BaseEvent):
+    """Event emitted when a memory query is completed successfully"""
+
+    type: str = "memory_query_completed"
+    query: str
+    results: Any
+    limit: int
+    score_threshold: Optional[float] = None
+    query_time_ms: float
+
+
+class MemoryQueryFailedEvent(BaseEvent):
+    """Event emitted when a memory query fails"""
+
+    type: str = "memory_query_failed"
+    query: str
+    limit: int
+    score_threshold: Optional[float] = None
+    error: str
+
+
+class MemorySaveStartedEvent(BaseEvent):
+    """Event emitted when a memory save operation is started"""
+
+    type: str = "memory_save_started"
+    value: Optional[str] = None
+    metadata: Optional[Dict[str, Any]] = None
+    agent_role: Optional[str] = None
+
+
+class MemorySaveCompletedEvent(BaseEvent):
+    """Event emitted when a memory save operation is completed successfully"""
+
+    type: str = "memory_save_completed"
+    value: str
+    metadata: Optional[Dict[str, Any]] = None
+    agent_role: Optional[str] = None
+    save_time_ms: float
+
+
+class MemorySaveFailedEvent(BaseEvent):
+    """Event emitted when a memory save operation fails"""
+
+    type: str = "memory_save_failed"
+    value: Optional[str] = None
+    metadata: Optional[Dict[str, Any]] = None
+    agent_role: Optional[str] = None
+    error: str
+
+
+class MemoryRetrievalStartedEvent(BaseEvent):
+    """Event emitted when memory retrieval for a task prompt starts"""
+
+    type: str = "memory_retrieval_started"
+    task_id: Optional[str] = None
+
+
+class MemoryRetrievalCompletedEvent(BaseEvent):
+    """Event emitted when memory retrieval for a task prompt completes successfully"""
+
+    type: str = "memory_retrieval_completed"
+    task_id: Optional[str] = None
+    memory_content: str
+    retrieval_time_ms: float

src/crewai/utilities/events/utils/console_formatter.py

@@ -1454,3 +1454,302 @@ class ConsoleFormatter:
             )
             self.print(finish_panel)
             self.print()
+
+    def handle_memory_retrieval_started(
+        self,
+        agent_branch: Optional[Tree],
+        crew_tree: Optional[Tree],
+    ) -> Optional[Tree]:
+        if not self.verbose:
+            return None
+
+        branch_to_use = agent_branch or self.current_lite_agent_branch
+        tree_to_use = branch_to_use or crew_tree
+
+        if branch_to_use is None or tree_to_use is None:
+            if crew_tree is not None:
+                branch_to_use = tree_to_use = crew_tree
+            else:
+                return None
+
+        memory_branch = branch_to_use.add("")
+        self.update_tree_label(memory_branch, "🧠", "Memory Retrieval Started", "blue")
+
+        self.print(tree_to_use)
+        self.print()
+        return memory_branch
+
+    def handle_memory_retrieval_completed(
+        self,
+        agent_branch: Optional[Tree],
+        crew_tree: Optional[Tree],
+        memory_content: str,
+        retrieval_time_ms: float,
+    ) -> None:
+        if not self.verbose:
+            return None
+
+        branch_to_use = self.current_lite_agent_branch or agent_branch
+        tree_to_use = branch_to_use or crew_tree
+
+        if branch_to_use is None and tree_to_use is not None:
+            branch_to_use = tree_to_use
+
+        def add_panel():
+            memory_text = str(memory_content)
+            if len(memory_text) > 500:
+                memory_text = memory_text[:497] + "..."
+
+            memory_panel = Panel(
+                Text(memory_text, style="white"),
+                title="🧠 Retrieved Memory",
+                subtitle=f"Retrieval Time: {retrieval_time_ms:.2f}ms",
+                border_style="green",
+                padding=(1, 2),
+            )
+            self.print(memory_panel)
+            self.print()
+
+        if branch_to_use is None or tree_to_use is None:
+            add_panel()
+            return None
+
+        memory_branch_found = False
+        for child in branch_to_use.children:
+            if "Memory Retrieval Started" in str(child.label):
+                self.update_tree_label(
+                    child, "✅", "Memory Retrieval Completed", "green"
+                )
+                memory_branch_found = True
+                break
+
+        if not memory_branch_found:
+            for child in branch_to_use.children:
+                if (
+                    "Memory Retrieval" in str(child.label)
+                    and "Started" not in str(child.label)
+                    and "Completed" not in str(child.label)
+                ):
+                    self.update_tree_label(
+                        child, "✅", "Memory Retrieval Completed", "green"
+                    )
+                    memory_branch_found = True
+                    break
+
+        if not memory_branch_found:
+            memory_branch = branch_to_use.add("")
+            self.update_tree_label(
+                memory_branch, "✅", "Memory Retrieval Completed", "green"
+            )
+
+        self.print(tree_to_use)
+
+        if memory_content:
+            add_panel()
+
+    def handle_memory_query_completed(
+        self,
+        agent_branch: Optional[Tree],
+        source_type: str,
+        query_time_ms: float,
+        crew_tree: Optional[Tree],
+    ) -> None:
+        if not self.verbose:
+            return None
+
+        branch_to_use = self.current_lite_agent_branch or agent_branch
+        tree_to_use = branch_to_use or crew_tree
+
+        if branch_to_use is None and tree_to_use is not None:
+            branch_to_use = tree_to_use
+
+        if branch_to_use is None:
+            return None
+
+        memory_type = source_type.replace("_", " ").title()
+
+        for child in branch_to_use.children:
+            if "Memory Retrieval" in str(child.label):
+                for child in child.children:
+                    sources_branch = child
+                    if "Sources Used" in str(child.label):
+                        sources_branch.add(f"✅ {memory_type} ({query_time_ms:.2f}ms)")
+                        break
+                else:
+                    sources_branch = child.add("Sources Used")
+                    sources_branch.add(f"✅ {memory_type} ({query_time_ms:.2f}ms)")
+                    break
+
+    def handle_memory_query_failed(
+        self,
+        agent_branch: Optional[Tree],
+        crew_tree: Optional[Tree],
+        error: str,
+        source_type: str,
+    ) -> None:
+        if not self.verbose:
+            return None
+
+        branch_to_use = self.current_lite_agent_branch or agent_branch
+        tree_to_use = branch_to_use or crew_tree
+
+        if branch_to_use is None and tree_to_use is not None:
+            branch_to_use = tree_to_use
+
+        if branch_to_use is None:
+            return None
+
+        memory_type = source_type.replace("_", " ").title()
+
+        for child in branch_to_use.children:
+            if "Memory Retrieval" in str(child.label):
+                for child in child.children:
+                    sources_branch = child
+                    if "Sources Used" in str(child.label):
+                        sources_branch.add(f"❌ {memory_type} - Error: {error}")
+                        break
+                else:
+                    sources_branch = child.add("🧠 Sources Used")
+                    sources_branch.add(f"❌ {memory_type} - Error: {error}")
+                    break
+
+    def handle_memory_save_started(
+        self, agent_branch: Optional[Tree], crew_tree: Optional[Tree]
+    ) -> None:
+        if not self.verbose:
+            return None
+
+        branch_to_use = agent_branch or self.current_lite_agent_branch
+        tree_to_use = branch_to_use or crew_tree
+
+        if tree_to_use is None:
+            return None
+
+        for child in tree_to_use.children:
+            if "Memory Update" in str(child.label):
+                break
+        else:
+            memory_branch = tree_to_use.add("")
+            self.update_tree_label(
+                memory_branch, "🧠", "Memory Update Overall", "white"
+            )
+
+        self.print(tree_to_use)
+        self.print()
+
+    def handle_memory_save_completed(
+        self,
+        agent_branch: Optional[Tree],
+        crew_tree: Optional[Tree],
+        save_time_ms: float,
+        source_type: str,
+    ) -> None:
+        if not self.verbose:
+            return None
+
+        branch_to_use = agent_branch or self.current_lite_agent_branch
+        tree_to_use = branch_to_use or crew_tree
+
+        if tree_to_use is None:
+            return None
+
+        memory_type = source_type.replace("_", " ").title()
+        content = f"✅ {memory_type} Memory Saved ({save_time_ms:.2f}ms)"
+
+        for child in tree_to_use.children:
+            if "Memory Update" in str(child.label):
+                child.add(content)
+                break
+        else:
+            memory_branch = tree_to_use.add("")
+            memory_branch.add(content)
+
+        self.print(tree_to_use)
+        self.print()
+
+    def handle_memory_save_failed(
+        self,
+        agent_branch: Optional[Tree],
+        error: str,
+        source_type: str,
+        crew_tree: Optional[Tree],
+    ) -> None:
+        if not self.verbose:
+            return None
+
+        branch_to_use = agent_branch or self.current_lite_agent_branch
+        tree_to_use = branch_to_use or crew_tree
+
+        if branch_to_use is None or tree_to_use is None:
+            return None
+
+        memory_type = source_type.replace("_", " ").title()
+        content = f"❌ {memory_type} Memory Save Failed"
+        for child in branch_to_use.children:
+            if "Memory Update" in str(child.label):
+                child.add(content)
+                break
+        else:
+            memory_branch = branch_to_use.add("")
+            memory_branch.add(content)
+
+        self.print(tree_to_use)
+        self.print()
+
+    def handle_guardrail_started(
+        self,
+        guardrail_name: str,
+        retry_count: int,
+    ) -> None:
+        """Display guardrail evaluation started status.
+
+        Args:
+            guardrail_name: Name/description of the guardrail being evaluated.
+            retry_count: Zero-based retry count (0 = first attempt).
+        """
+        if not self.verbose:
+            return
+
+        content = self.create_status_content(
+            "Guardrail Evaluation Started",
+            guardrail_name,
+            "yellow",
+            Status="🔄 Evaluating",
+            Attempt=f"{retry_count + 1}",
+        )
+        self.print_panel(content, "🛡️ Guardrail Check", "yellow")
+
+    def handle_guardrail_completed(
+        self,
+        success: bool,
+        error: Optional[str],
+        retry_count: int,
+    ) -> None:
+        """Display guardrail evaluation result.
+
+        Args:
+            success: Whether validation passed.
+            error: Error message if validation failed.
+            retry_count: Zero-based retry count.
+        """
+        if not self.verbose:
+            return
+
+        if success:
+            content = self.create_status_content(
+                "Guardrail Passed",
+                "Validation Successful",
+                "green",
+                Status="✅ Validated",
+                Attempts=f"{retry_count + 1}",
+            )
+            self.print_panel(content, "🛡️ Guardrail Success", "green")
+        else:
+            content = self.create_status_content(
+                "Guardrail Failed",
+                "Validation Error",
+                "red",
+                Error=str(error) if error else "Unknown error",
+                Attempts=f"{retry_count + 1}",
+            )
+            self.print_panel(content, "🛡️ Guardrail Failed", "red")

src/crewai/utilities/training_converter.py

@@ -0,0 +1,89 @@
+import json
+import re
+from typing import Any, get_origin
+
+from pydantic import BaseModel, ValidationError
+
+from crewai.utilities.converter import Converter, ConverterError
+
+
+class TrainingConverter(Converter):
+    """
+    A specialized converter for smaller LLMs (up to 7B parameters) that handles validation errors
+    by breaking down the model into individual fields and querying the LLM for each field separately.
+    """
+
+    def to_pydantic(self, current_attempt=1) -> BaseModel:
+        try:
+            return super().to_pydantic(current_attempt)
+        except ConverterError:
+            return self._convert_field_by_field()
+
+    def _convert_field_by_field(self) -> BaseModel:
+        field_values = {}
+
+        for field_name, field_info in self.model.model_fields.items():
+            field_description = field_info.description
+            field_type = field_info.annotation
+
+            response = self._ask_llm_for_field(field_name, field_description)
+            value = self._process_field_value(response, field_type)
+            field_values[field_name] = value
+
+        try:
+            return self.model(**field_values)
+        except ValidationError as e:
+            raise ConverterError(f"Failed to create model from individually collected fields: {e}")
+
+    def _ask_llm_for_field(self, field_name: str, field_description: str) -> str:
+        prompt = f"""
+Based on the following information:
+{self.text}
+
+Please provide ONLY the {field_name} field value as described:
+"{field_description}"
+
+Respond with ONLY the requested information, nothing else.
+"""
+        return self.llm.call([
+            {"role": "system", "content": f"Extract the {field_name} from the previous information."},
+            {"role": "user", "content": prompt}
+        ])
+
+    def _process_field_value(self, response: str, field_type: Any) -> Any:
+        response = response.strip()
+        origin = get_origin(field_type)
+
+        if origin is list:
+            return self._parse_list(response)
+
+        if field_type is float:
+            return self._parse_float(response)
+
+        if field_type is str:
+            return response
+
+        return response
+
+    def _parse_list(self, response: str) -> list:
+        try:
+            if response.startswith('['):
+                return json.loads(response)
+
+            items = [item.strip() for item in response.split('\n') if item.strip()]
+            return [self._strip_bullet(item) for item in items]
+
+        except json.JSONDecodeError:
+            return [response]
+
+    def _parse_float(self, response: str) -> float:
+        try:
+            match = re.search(r'(\d+(\.\d+)?)', response)
+            return float(match.group(1)) if match else 0.0
+        except Exception:
+            return 0.0
+
+    def _strip_bullet(self, item: str) -> str:
+        if item.startswith(('- ', '* ')):
+            return item[2:].strip()
+        return item.strip()