fix: Replace remaining Type imports with built-in type annotations

Co-Authored-By: João <joao@crewai.com>

.github/workflows/linter.yml

@@ -15,8 +15,19 @@ jobs:
       - name: Fetch Target Branch
         run: git fetch origin $TARGET_BRANCH --depth=1
 
-      - name: Install Ruff
-        run: pip install ruff
+      - name: Install uv
+        uses: astral-sh/setup-uv@v6
+        with:
+          enable-cache: true
+          cache-dependency-glob: |
+            **/pyproject.toml
+            **/uv.lock
+
+      - name: Set up Python
+        run: uv python install 3.11
+
+      - name: Install dependencies
+        run: uv sync --dev --no-install-project
 
       - name: Get Changed Python Files
         id: changed-files
@@ -33,4 +44,4 @@ jobs:
           echo "${{ steps.changed-files.outputs.files }}" \
             | tr ' ' '\n' \
             | grep -v 'src/crewai/cli/templates/' \
-            | xargs -I{} ruff check "{}"
+            | xargs -I{} uv run ruff check "{}"

.github/workflows/security-checker.yml

@@ -10,14 +10,20 @@ jobs:
       - name: Checkout code
         uses: actions/checkout@v4
 
-      - name: Set up Python
-        uses: actions/setup-python@v5
+      - name: Install uv
+        uses: astral-sh/setup-uv@v6
         with:
-          python-version: "3.11.9"
+          enable-cache: true
+          cache-dependency-glob: |
+            **/pyproject.toml
+            **/uv.lock
+
+      - name: Set up Python
+        run: uv python install 3.11
 
       - name: Install dependencies
-        run: pip install bandit
+        run: uv sync --dev --no-install-project
 
       - name: Run Bandit
-        run: bandit -c pyproject.toml -r src/ -ll
+        run: uv run bandit -c pyproject.toml -r src/ -ll
 

.github/workflows/tests.yml

@@ -24,7 +24,7 @@ jobs:
         uses: actions/checkout@v4
 
       - name: Install uv
-        uses: astral-sh/setup-uv@v3
+        uses: astral-sh/setup-uv@v6
         with:
           enable-cache: true
           cache-dependency-glob: |

.github/workflows/type-checker.yml

@@ -6,21 +6,78 @@ permissions:
   contents: write
 
 jobs:
-  type-checker:
+  type-checker-matrix:
+    name: type-checker (${{ matrix.python-version }})
     runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: ["3.10", "3.11", "3.12", "3.13"]
 
     steps:
       - name: Checkout code
         uses: actions/checkout@v4
-
-      - name: Setup Python
-        uses: actions/setup-python@v5
         with:
-          python-version: "3.11.9"
+          fetch-depth: 0 # Fetch all history for proper diff
 
-      - name: Install Requirements
+      - name: Install uv
+        uses: astral-sh/setup-uv@v6
+        with:
+          enable-cache: true
+          cache-dependency-glob: |
+            **/pyproject.toml
+            **/uv.lock
+
+      - name: Set up Python ${{ matrix.python-version }}
+        run: uv python install ${{ matrix.python-version }}
+
+      - name: Install dependencies
+        run: uv sync --dev --no-install-project
+
+      - name: Get changed Python files
+        id: changed-files
         run: |
-          pip install mypy
+          # Get the list of changed Python files compared to the base branch
+          echo "Fetching changed files..."
+          git diff --name-only --diff-filter=ACMRT origin/${{ github.base_ref }}...HEAD -- '*.py' > changed_files.txt
 
-      - name: Run type checks
-        run: mypy src
+          # Filter for files in src/ and tests/ directories
+          grep -E "^(src/|tests/)" changed_files.txt > filtered_changed_files.txt || true
+
+          # Check if there are any changed files
+          if [ -s filtered_changed_files.txt ]; then
+            echo "Changed Python files in src/ and tests/:"
+            cat filtered_changed_files.txt
+            echo "has_changes=true" >> $GITHUB_OUTPUT
+            # Convert newlines to spaces for mypy command
+            echo "files=$(cat filtered_changed_files.txt | tr '\n' ' ')" >> $GITHUB_OUTPUT
+          else
+            echo "No Python files changed in src/ or tests/"
+            echo "has_changes=false" >> $GITHUB_OUTPUT
+          fi
+
+      - name: Run type checks on changed files
+        if: steps.changed-files.outputs.has_changes == 'true'
+        run: |
+          echo "Running mypy on changed files with Python ${{ matrix.python-version }}..."
+          uv run mypy ${{ steps.changed-files.outputs.files }}
+
+      - name: No files to check
+        if: steps.changed-files.outputs.has_changes == 'false'
+        run: echo "No Python files in src/ or tests/ were modified - skipping type checks"
+
+  # Summary job to provide single status for branch protection
+  type-checker:
+    name: type-checker
+    runs-on: ubuntu-latest
+    needs: type-checker-matrix
+    if: always()
+    steps:
+      - name: Check matrix results
+        run: |
+          if [ "${{ needs.type-checker-matrix.result }}" == "success" ] || [ "${{ needs.type-checker-matrix.result }}" == "skipped" ]; then
+            echo "✅ All type checks passed"
+          else
+            echo "❌ Type checks failed"
+            exit 1
+          fi

.gitignore

@@ -21,7 +21,6 @@ crew_tasks_output.json
 .mypy_cache
 .ruff_cache
 .venv
-agentops.log
 test_flow.html
 crewairules.mdc
 plan.md

.pre-commit-config.yaml

@@ -1,7 +1,14 @@
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.8.2
+    rev: v0.12.11
     hooks:
       - id: ruff
-        args: ["--fix"]
+        args: ["--config", "pyproject.toml"]
       - id: ruff-format
+        args: ["--config", "pyproject.toml"]
+  
+  - repo: https://github.com/pre-commit/mirrors-mypy
+    rev: v1.17.1
+    hooks:
+      - id: mypy
+        args: ["--config-file", "pyproject.toml"]

.ruff.toml

@@ -1,4 +0,0 @@
-exclude = [
-    "templates",
-    "__init__.py",
-]

README.md

@@ -418,10 +418,10 @@ Choose CrewAI to easily build powerful, adaptable, and production-ready AI autom
 
 You can test different real life examples of AI crews in the [CrewAI-examples repo](https://github.com/crewAIInc/crewAI-examples?tab=readme-ov-file):
 
-- [Landing Page Generator](https://github.com/crewAIInc/crewAI-examples/tree/main/landing_page_generator)
+- [Landing Page Generator](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/landing_page_generator)
 - [Having Human input on the execution](https://docs.crewai.com/how-to/Human-Input-on-Execution)
-- [Trip Planner](https://github.com/crewAIInc/crewAI-examples/tree/main/trip_planner)
-- [Stock Analysis](https://github.com/crewAIInc/crewAI-examples/tree/main/stock_analysis)
+- [Trip Planner](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/trip_planner)
+- [Stock Analysis](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/stock_analysis)
 
 ### Quick Tutorial
 
@@ -429,19 +429,19 @@ You can test different real life examples of AI crews in the [CrewAI-examples re
 
 ### Write Job Descriptions
 
-[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/job-posting) or watch a video below:
+[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/job-posting) or watch a video below:
 
 [![Jobs postings](https://img.youtube.com/vi/u98wEMz-9to/maxresdefault.jpg)](https://www.youtube.com/watch?v=u98wEMz-9to "Jobs postings")
 
 ### Trip Planner
 
-[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/trip_planner) or watch a video below:
+[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/trip_planner) or watch a video below:
 
 [![Trip Planner](https://img.youtube.com/vi/xis7rWp-hjs/maxresdefault.jpg)](https://www.youtube.com/watch?v=xis7rWp-hjs "Trip Planner")
 
 ### Stock Analysis
 
-[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/stock_analysis) or watch a video below:
+[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/stock_analysis) or watch a video below:
 
 [![Stock Analysis](https://img.youtube.com/vi/e0Uj4yWdaAg/maxresdefault.jpg)](https://www.youtube.com/watch?v=e0Uj4yWdaAg "Stock Analysis")
 

docs/docs.json

@@ -226,7 +226,6 @@
                 "group": "Observability",
                 "pages": [
                   "en/observability/overview",
-                  "en/observability/agentops",
                   "en/observability/arize-phoenix",
                   "en/observability/langdb",
                   "en/observability/langfuse",
@@ -321,6 +320,7 @@
                   "en/enterprise/guides/update-crew",
                   "en/enterprise/guides/enable-crew-studio",
                   "en/enterprise/guides/azure-openai-setup",
+                  "en/enterprise/guides/automation-triggers",
                   "en/enterprise/guides/hubspot-trigger",
                   "en/enterprise/guides/react-component-export",
                   "en/enterprise/guides/salesforce-trigger",
@@ -342,11 +342,12 @@
             "groups": [
               {
                 "group": "Getting Started",
-                "pages": ["en/api-reference/introduction"]
-              },
-              {
-                "group": "Endpoints",
-                "openapi": "https://raw.githubusercontent.com/crewAIInc/crewAI/main/docs/enterprise-api.en.yaml"
+                "pages": [
+                  "en/api-reference/introduction",
+                  "en/api-reference/inputs",
+                  "en/api-reference/kickoff",
+                  "en/api-reference/status"
+                ]
               }
             ]
           },
@@ -566,7 +567,6 @@
                 "group": "Observabilidade",
                 "pages": [
                   "pt-BR/observability/overview",
-                  "pt-BR/observability/agentops",
                   "pt-BR/observability/arize-phoenix",
                   "pt-BR/observability/langdb",
                   "pt-BR/observability/langfuse",
@@ -659,6 +659,7 @@
                   "pt-BR/enterprise/guides/update-crew",
                   "pt-BR/enterprise/guides/enable-crew-studio",
                   "pt-BR/enterprise/guides/azure-openai-setup",
+                  "pt-BR/enterprise/guides/automation-triggers",
                   "pt-BR/enterprise/guides/hubspot-trigger",
                   "pt-BR/enterprise/guides/react-component-export",
                   "pt-BR/enterprise/guides/salesforce-trigger",
@@ -682,11 +683,12 @@
             "groups": [
               {
                 "group": "Começando",
-                "pages": ["pt-BR/api-reference/introduction"]
-              },
-              {
-                "group": "Endpoints",
-                "openapi": "https://raw.githubusercontent.com/crewAIInc/crewAI/main/docs/enterprise-api.pt-BR.yaml"
+                "pages": [
+                  "pt-BR/api-reference/introduction",
+                  "pt-BR/api-reference/inputs",
+                  "pt-BR/api-reference/kickoff",
+                  "pt-BR/api-reference/status"
+                ]
               }
             ]
           },
@@ -711,7 +713,7 @@
               "icon": "globe"
             },
             {
-              "anchor": "법정",
+              "anchor": "포럼",
               "href": "https://community.crewai.com",
               "icon": "discourse"
             },
@@ -721,7 +723,7 @@
               "icon": "robot"
             },
             {
-              "anchor": "출시",
+              "anchor": "릴리스",
               "href": "https://github.com/crewAIInc/crewAI/releases",
               "icon": "tag"
             }
@@ -736,22 +738,22 @@
                 "pages": ["ko/introduction", "ko/installation", "ko/quickstart"]
               },
               {
-                "group": "안내서",
+                "group": "가이드",
                 "pages": [
                   {
                     "group": "전략",
                     "pages": ["ko/guides/concepts/evaluating-use-cases"]
                   },
                   {
-                    "group": "Agents",
+                    "group": "에이전트 (Agents)",
                     "pages": ["ko/guides/agents/crafting-effective-agents"]
                   },
                   {
-                    "group": "Crews",
+                    "group": "크루 (Crews)",
                     "pages": ["ko/guides/crews/first-crew"]
                   },
                   {
-                    "group": "Flows",
+                    "group": "플로우 (Flows)",
                     "pages": [
                       "ko/guides/flows/first-flow",
                       "ko/guides/flows/mastering-flow-state"
@@ -799,7 +801,7 @@
                 ]
               },
               {
-                "group": "도구",
+                "group": "도구 (Tools)",
                 "pages": [
                   "ko/tools/overview",
                   {
@@ -889,7 +891,7 @@
                     ]
                   },
                   {
-                    "group": "클라우드 & 저장",
+                    "group": "클라우드 & 스토리지",
                     "pages": [
                       "ko/tools/cloud-storage/overview",
                       "ko/tools/cloud-storage/s3readertool",
@@ -911,10 +913,9 @@
                 ]
               },
               {
-                "group": "오브저버빌리티",
+                "group": "Observability",
                 "pages": [
                   "ko/observability/overview",
-                  "ko/observability/agentops",
                   "ko/observability/arize-phoenix",
                   "ko/observability/langdb",
                   "ko/observability/langfuse",
@@ -930,7 +931,7 @@
                 ]
               },
               {
-                "group": "익히다",
+                "group": "학습",
                 "pages": [
                   "ko/learn/overview",
                   "ko/learn/llm-selection-guide",
@@ -954,13 +955,13 @@
                 ]
               },
               {
-                "group": "원격측정",
+                "group": "Telemetry",
                 "pages": ["ko/telemetry"]
               }
             ]
           },
           {
-            "tab": "기업",
+            "tab": "엔터프라이즈",
             "groups": [
               {
                 "group": "시작 안내",
@@ -1000,7 +1001,7 @@
                 ]
               },
               {
-                "group": "사용 안내서",
+                "group": "How-To Guides",
                 "pages": [
                   "ko/enterprise/guides/build-crew",
                   "ko/enterprise/guides/deploy-crew",
@@ -1008,6 +1009,7 @@
                   "ko/enterprise/guides/update-crew",
                   "ko/enterprise/guides/enable-crew-studio",
                   "ko/enterprise/guides/azure-openai-setup",
+                  "ko/enterprise/guides/automation-triggers",
                   "ko/enterprise/guides/hubspot-trigger",
                   "ko/enterprise/guides/react-component-export",
                   "ko/enterprise/guides/salesforce-trigger",
@@ -1029,11 +1031,12 @@
             "groups": [
               {
                 "group": "시작 안내",
-                "pages": ["ko/api-reference/introduction"]
-              },
-              {
-                "group": "Endpoints",
-                "openapi": "https://raw.githubusercontent.com/crewAIInc/crewAI/main/docs/enterprise-api.ko.yaml"
+                "pages": [
+                  "ko/api-reference/introduction",
+                  "ko/api-reference/inputs",
+                  "ko/api-reference/kickoff",
+                  "ko/api-reference/status"
+                ]
               }
             ]
           },
@@ -1084,6 +1087,10 @@
     "indexing": "all"
   },
   "redirects": [
+    {
+      "source": "/api-reference",
+      "destination": "/en/api-reference/introduction"
+    },
     {
       "source": "/introduction",
       "destination": "/en/introduction"
@@ -1136,6 +1143,18 @@
       "source": "/api-reference/:path*",
       "destination": "/en/api-reference/:path*"
     },
+    {
+      "source": "/en/api-reference",
+      "destination": "/en/api-reference/introduction"
+    },
+    {
+      "source": "/pt-BR/api-reference",
+      "destination": "/pt-BR/api-reference/introduction"
+    },
+    {
+      "source": "/ko/api-reference",
+      "destination": "/ko/api-reference/introduction"
+    },
     {
       "source": "/examples/:path*",
       "destination": "/en/examples/:path*"

docs/en/api-reference/inputs.mdx

@@ -0,0 +1,7 @@
+---
+title: "GET /inputs"
+description: "Get required inputs for your crew"
+openapi: "/enterprise-api.en.yaml GET /inputs"
+---
+
+

docs/en/api-reference/kickoff.mdx

@@ -0,0 +1,7 @@
+---
+title: "POST /kickoff"
+description: "Start a crew execution"
+openapi: "/enterprise-api.en.yaml POST /kickoff"
+---
+
+

docs/en/api-reference/status.mdx

@@ -0,0 +1,7 @@
+---
+title: "GET /status/{kickoff_id}"
+description: "Get execution status"
+openapi: "/enterprise-api.en.yaml GET /status/{kickoff_id}"
+---
+
+

docs/en/concepts/cli.mdx

@@ -282,7 +282,25 @@ Watch this video tutorial for a step-by-step demonstration of deploying your cre
   allowfullscreen
 ></iframe>
 
-### 11. API Keys
+### 12. Login
+
+Authenticate with CrewAI Enterprise using a secure device code flow (no email entry required).
+
+```shell Terminal
+crewai login
+```
+
+What happens:
+- A verification URL and short code are displayed in your terminal
+- Your browser opens to the verification URL
+- Enter/confirm the code to complete authentication
+
+Notes:
+- The OAuth2 provider and domain are configured via `crewai config` (defaults use `login.crewai.com`)
+- After successful login, the CLI also attempts to authenticate to the Tool Repository automatically
+- If you reset your configuration, run `crewai login` again to re-authenticate
+
+### 13. API Keys
 
 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.
 
@@ -310,7 +328,7 @@ See the following link for each provider's key name:
 
 * [LiteLLM Providers](https://docs.litellm.ai/docs/providers)
 
-### 12. Configuration Management
+### 14. Configuration Management
 
 Manage CLI configuration settings for CrewAI.
 
@@ -385,6 +403,10 @@ Reset all configuration to defaults:
 crewai config reset
 ```
 
+<Tip>
+After resetting configuration, re-run `crewai login` to authenticate again.
+</Tip>
+
 <Note>
 Configuration settings are stored in `~/.config/crewai/settings.json`. Some settings like organization name and UUID are read-only and managed through authentication and organization commands. Tool repository related settings are hidden and cannot be set directly by users.
 </Note>

docs/en/concepts/event-listener.mdx

@@ -44,12 +44,12 @@ To create a custom event listener, you need to:
 Here's a simple example of a custom event listener class:
 
 ```python
-from crewai.utilities.events import (
+from crewai.events import (
     CrewKickoffStartedEvent,
     CrewKickoffCompletedEvent,
     AgentExecutionCompletedEvent,
 )
-from crewai.utilities.events.base_event_listener import BaseEventListener
+from crewai.events import BaseEventListener
 
 class MyCustomListener(BaseEventListener):
     def __init__(self):
@@ -146,7 +146,7 @@ my_project/
 
 ```python
 # my_custom_listener.py
-from crewai.utilities.events.base_event_listener import BaseEventListener
+from crewai.events import BaseEventListener
 # ... import events ...
 
 class MyCustomListener(BaseEventListener):
@@ -177,14 +177,7 @@ class MyCustomCrew:
     # Your crew implementation...
 ```
 
-This is exactly how CrewAI's built-in `agentops_listener` is registered. In the CrewAI codebase, you'll find:
-
-```python
-# src/crewai/utilities/events/third_party/__init__.py
-from .agentops_listener import agentops_listener
-```
-
-This ensures the `agentops_listener` is loaded when the `crewai.utilities.events` package is imported.
+This is how third-party event listeners are registered in the CrewAI codebase.
 
 ## Available Event Types
 
@@ -280,84 +273,13 @@ The structure of the event object depends on the event type, but all events inhe
 
 Additional fields vary by event type. For example, `CrewKickoffCompletedEvent` includes `crew_name` and `output` fields.
 
-## Real-World Example: Integration with AgentOps
-
-CrewAI includes an example of a third-party integration with [AgentOps](https://github.com/AgentOps-AI/agentops), a monitoring and observability platform for AI agents. Here's how it's implemented:
-
-```python
-from typing import Optional
-
-from crewai.utilities.events import (
-    CrewKickoffCompletedEvent,
-    ToolUsageErrorEvent,
-    ToolUsageStartedEvent,
-)
-from crewai.utilities.events.base_event_listener import BaseEventListener
-from crewai.utilities.events.crew_events import CrewKickoffStartedEvent
-from crewai.utilities.events.task_events import TaskEvaluationEvent
-
-try:
-    import agentops
-    AGENTOPS_INSTALLED = True
-except ImportError:
-    AGENTOPS_INSTALLED = False
-
-class AgentOpsListener(BaseEventListener):
-    tool_event: Optional["agentops.ToolEvent"] = None
-    session: Optional["agentops.Session"] = None
-
-    def __init__(self):
-        super().__init__()
-
-    def setup_listeners(self, crewai_event_bus):
-        if not AGENTOPS_INSTALLED:
-            return
-
-        @crewai_event_bus.on(CrewKickoffStartedEvent)
-        def on_crew_kickoff_started(source, event: CrewKickoffStartedEvent):
-            self.session = agentops.init()
-            for agent in source.agents:
-                if self.session:
-                    self.session.create_agent(
-                        name=agent.role,
-                        agent_id=str(agent.id),
-                    )
-
-        @crewai_event_bus.on(CrewKickoffCompletedEvent)
-        def on_crew_kickoff_completed(source, event: CrewKickoffCompletedEvent):
-            if self.session:
-                self.session.end_session(
-                    end_state="Success",
-                    end_state_reason="Finished Execution",
-                )
-
-        @crewai_event_bus.on(ToolUsageStartedEvent)
-        def on_tool_usage_started(source, event: ToolUsageStartedEvent):
-            self.tool_event = agentops.ToolEvent(name=event.tool_name)
-            if self.session:
-                self.session.record(self.tool_event)
-
-        @crewai_event_bus.on(ToolUsageErrorEvent)
-        def on_tool_usage_error(source, event: ToolUsageErrorEvent):
-            agentops.ErrorEvent(exception=event.error, trigger_event=self.tool_event)
-```
-
-This listener initializes an AgentOps session when a Crew starts, registers agents with AgentOps, tracks tool usage, and ends the session when the Crew completes.
-
-The AgentOps listener is registered in CrewAI's event system through the import in `src/crewai/utilities/events/third_party/__init__.py`:
-
-```python
-from .agentops_listener import agentops_listener
-```
-
-This ensures the `agentops_listener` is loaded when the `crewai.utilities.events` package is imported.
 
 ## Advanced Usage: Scoped Handlers
 
 For temporary event handling (useful for testing or specific operations), you can use the `scoped_handlers` context manager:
 
 ```python
-from crewai.utilities.events import crewai_event_bus, CrewKickoffStartedEvent
+from crewai.events import crewai_event_bus, CrewKickoffStartedEvent
 
 with crewai_event_bus.scoped_handlers():
     @crewai_event_bus.on(CrewKickoffStartedEvent)

docs/en/concepts/flows.mdx

@@ -97,7 +97,13 @@ The state's unique ID and stored data can be useful for tracking flow executions
 
 ### @start()
 
-The `@start()` decorator is used to mark a method as the starting point of a Flow. When a Flow is started, all the methods decorated with `@start()` are executed in parallel. You can have multiple start methods in a Flow, and they will all be executed when the Flow is started.
+The `@start()` decorator marks entry points for a Flow. You can:
+
+- Declare multiple unconditional starts: `@start()`
+- Gate a start on a prior method or router label: `@start("method_or_label")`
+- Provide a callable condition to control when a start should fire
+
+All satisfied `@start()` methods will execute (often in parallel) when the Flow begins or resumes.
 
 ### @listen()
 

docs/en/concepts/knowledge.mdx

@@ -24,6 +24,41 @@ For file-based Knowledge Sources, make sure to place your files in a `knowledge`
 Also, use relative paths from the `knowledge` directory when creating the source.
 </Tip>
 
+### Vector store (RAG) client configuration
+
+CrewAI exposes a provider-neutral RAG client abstraction for vector stores. The default provider is ChromaDB, and Qdrant is supported as well. You can switch providers using configuration utilities.
+
+Supported today:
+- ChromaDB (default)
+- Qdrant
+
+```python Code
+from crewai.rag.config.utils import set_rag_config, get_rag_client, clear_rag_config
+
+# ChromaDB (default)
+from crewai.rag.chromadb.config import ChromaDBConfig
+set_rag_config(ChromaDBConfig())
+chromadb_client = get_rag_client()
+
+# Qdrant
+from crewai.rag.qdrant.config import QdrantConfig
+set_rag_config(QdrantConfig())
+qdrant_client = get_rag_client()
+
+# Example operations (same API for any provider)
+client = qdrant_client  # or chromadb_client
+client.create_collection(collection_name="docs")
+client.add_documents(
+    collection_name="docs",
+    documents=[{"id": "1", "content": "CrewAI enables collaborative AI agents."}],
+)
+results = client.search(collection_name="docs", query="collaborative agents", limit=3)
+
+clear_rag_config()  # optional reset
+```
+
+This RAG client is separate from Knowledge’s built-in storage. Use it when you need direct vector-store control or custom retrieval pipelines.
+
 ### Basic String Knowledge Example
 
 ```python Code
@@ -681,11 +716,11 @@ CrewAI emits events during the knowledge retrieval process that you can listen f
 #### Example: Monitoring Knowledge Retrieval
 
 ```python
-from crewai.utilities.events import (
+from crewai.events import (
     KnowledgeRetrievalStartedEvent,
     KnowledgeRetrievalCompletedEvent,
+    BaseEventListener,
 )
-from crewai.utilities.events.base_event_listener import BaseEventListener
 
 class KnowledgeMonitorListener(BaseEventListener):
     def setup_listeners(self, crewai_event_bus):

docs/en/concepts/llms.mdx

@@ -733,10 +733,10 @@ CrewAI supports streaming responses from LLMs, allowing your application to rece
     CrewAI emits events for each chunk received during streaming:
 
     ```python
-    from crewai.utilities.events import (
+    from crewai.events import (
       LLMStreamChunkEvent
     )
-    from crewai.utilities.events.base_event_listener import BaseEventListener
+    from crewai.events import BaseEventListener
 
     class MyCustomListener(BaseEventListener):
         def setup_listeners(self, crewai_event_bus):
@@ -758,8 +758,8 @@ CrewAI supports streaming responses from LLMs, allowing your application to rece
 
     ```python
     from crewai import LLM, Agent, Task, Crew
-    from crewai.utilities.events import LLMStreamChunkEvent
-    from crewai.utilities.events.base_event_listener import BaseEventListener
+    from crewai.events import LLMStreamChunkEvent
+    from crewai.events import BaseEventListener
 
     class MyCustomListener(BaseEventListener):
         def setup_listeners(self, crewai_event_bus):

docs/en/concepts/memory.mdx

@@ -738,6 +738,17 @@ print(f"OpenAI: {openai_time:.2f}s")
 print(f"Ollama: {ollama_time:.2f}s")
 ```
 
+### Entity Memory batching behavior
+
+Entity Memory supports batching when saving multiple entities at once. When you pass a list of `EntityMemoryItem`, the system:
+
+- Emits a single MemorySaveStartedEvent with `entity_count`
+- Saves each entity internally, collecting any partial errors
+- Emits MemorySaveCompletedEvent with aggregate metadata (saved count, errors)
+- Raises a partial-save exception if some entities failed (includes counts)
+
+This improves performance and observability when writing many entities in one operation.
+
 ## 2. External Memory 
 External Memory provides a standalone memory system that operates independently from the crew's built-in memory. This is ideal for specialized memory providers or cross-application memory sharing.
 
@@ -1041,8 +1052,8 @@ CrewAI emits the following memory-related events:
 Track memory operation timing to optimize your application:
 
 ```python
-from crewai.utilities.events.base_event_listener import BaseEventListener
-from crewai.utilities.events import (
+from crewai.events import (
+    BaseEventListener,
     MemoryQueryCompletedEvent,
     MemorySaveCompletedEvent
 )
@@ -1076,8 +1087,8 @@ memory_monitor = MemoryPerformanceMonitor()
 Log memory operations for debugging and insights:
 
 ```python
-from crewai.utilities.events.base_event_listener import BaseEventListener
-from crewai.utilities.events import (
+from crewai.events import (
+    BaseEventListener,
     MemorySaveStartedEvent,
     MemoryQueryStartedEvent,
     MemoryRetrievalCompletedEvent
@@ -1117,8 +1128,8 @@ memory_logger = MemoryLogger()
 Capture and respond to memory errors:
 
 ```python
-from crewai.utilities.events.base_event_listener import BaseEventListener
-from crewai.utilities.events import (
+from crewai.events import (
+    BaseEventListener,
     MemorySaveFailedEvent,
     MemoryQueryFailedEvent
 )
@@ -1167,8 +1178,8 @@ error_tracker = MemoryErrorTracker(notify_email="admin@example.com")
 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 (
+from crewai.events import (
+    BaseEventListener,
     MemoryQueryCompletedEvent,
     MemorySaveCompletedEvent
 )

docs/en/concepts/tasks.mdx

@@ -59,6 +59,12 @@ crew = Crew(
 | **Output Pydantic** _(optional)_ | `output_pydantic` | `Optional[Type[BaseModel]]`   | A Pydantic model for task output.                                                                                    |
 | **Callback** _(optional)_        | `callback`        | `Optional[Any]`               | Function/object to be executed after task completion.                                                                |
 | **Guardrail** _(optional)_       | `guardrail`       | `Optional[Callable]`             | Function to validate task output before proceeding to next task.                                                  |
+| **Guardrail Max Retries** _(optional)_ | `guardrail_max_retries` | `Optional[int]`     | Maximum number of retries when guardrail validation fails. Defaults to 3.                                         |
+
+<Note type="warning" title="Deprecated: max_retries">
+  The task attribute `max_retries` is deprecated and will be removed in v1.0.0.
+  Use `guardrail_max_retries` instead to control retry attempts when a guardrail fails.
+</Note>
 
 ## Creating Tasks
 
@@ -431,7 +437,7 @@ When a guardrail returns `(False, error)`:
 2. The agent attempts to fix the issue
 3. The process repeats until:
    - The guardrail returns `(True, result)`
-   - Maximum retries are reached
+   - Maximum retries are reached (`guardrail_max_retries`)
 
 Example with retry handling:
 ```python Code
@@ -452,7 +458,7 @@ task = Task(
     expected_output="A valid JSON object",
     agent=analyst,
     guardrail=validate_json_output,
-    max_retries=3  # Limit retry attempts
+    guardrail_max_retries=3  # Limit retry attempts
 )
 ```
 

docs/en/concepts/training.mdx

@@ -21,13 +21,17 @@ To use the training feature, follow these steps:
 3. Run the following command:
 
 ```shell
-crewai train -n <n_iterations> <filename> (optional)
+crewai train -n <n_iterations> -f <filename.pkl>
 ```
 <Tip>
   Replace `<n_iterations>` with the desired number of training iterations and `<filename>` with the appropriate filename ending with `.pkl`.
 </Tip>
 
-### Training Your Crew Programmatically
+<Note>
+  If you omit `-f`, the output defaults to `trained_agents_data.pkl` in the current working directory. You can pass an absolute path to control where the file is written.
+</Note>
+
+### Training your Crew programmatically
 
 To train your crew programmatically, use the following steps:
 
@@ -51,19 +55,65 @@ except Exception as e:
     raise Exception(f"An error occurred while training the crew: {e}")
 ```
 
-### Key Points to Note
+## How trained data is used by agents
 
-- **Positive Integer Requirement:** Ensure that the number of iterations (`n_iterations`) is a positive integer. The code will raise a `ValueError` if this condition is not met.
-- **Filename Requirement:** Ensure that the filename ends with `.pkl`. The code will raise a `ValueError` if this condition is not met.
-- **Error Handling:** The code handles subprocess errors and unexpected exceptions, providing error messages to the user.
+CrewAI uses the training artifacts in two ways: during training to incorporate your human feedback, and after training to guide agents with consolidated suggestions.
 
-It is important to note that the training process may take some time, depending on the complexity of your agents and will also require your feedback on each iteration.
+### Training data flow
 
-Once the training is complete, your agents will be equipped with enhanced capabilities and knowledge, ready to tackle complex tasks and provide more consistent and valuable insights.
+```mermaid
+flowchart TD
+  A["Start training<br/>CLI: crewai train -n -f<br/>or Python: crew.train(...)"] --> B["Setup training mode<br/>- task.human_input = true<br/>- disable delegation<br/>- init training_data.pkl + trained file"]
 
-Remember to regularly update and retrain your agents to ensure they stay up-to-date with the latest information and advancements in the field.
+  subgraph "Iterations"
+    direction LR
+    C["Iteration i<br/>initial_output"] --> D["User human_feedback"]
+    D --> E["improved_output"]
+    E --> F["Append to training_data.pkl<br/>by agent_id and iteration"]
+  end
 
-Happy training with CrewAI! 🚀
+  B --> C
+  F --> G{"More iterations?"}
+  G -- "Yes" --> C
+  G -- "No" --> H["Evaluate per agent<br/>aggregate iterations"]
+
+  H --> I["Consolidate<br/>suggestions[] + quality + final_summary"]
+  I --> J["Save by agent role to trained file<br/>(default: trained_agents_data.pkl)"]
+
+  J --> K["Normal (non-training) runs"]
+  K --> L["Auto-load suggestions<br/>from trained_agents_data.pkl"]
+  L --> M["Append to prompt<br/>for consistent improvements"]
+```
+
+### During training runs
+
+- On each iteration, the system records for every agent:
+  - `initial_output`: the agent’s first answer
+  - `human_feedback`: your inline feedback when prompted
+  - `improved_output`: the agent’s follow-up answer after feedback
+- This data is stored in a working file named `training_data.pkl` keyed by the agent’s internal ID and iteration.
+- While training is active, the agent automatically appends your prior human feedback to its prompt to enforce those instructions on subsequent attempts within the training session.
+  Training is interactive: tasks set `human_input = true`, so running in a non-interactive environment will block on user input.
+
+### After training completes
+
+- When `train(...)` finishes, CrewAI evaluates the collected training data per agent and produces a consolidated result containing:
+  - `suggestions`: clear, actionable instructions distilled from your feedback and the difference between initial/improved outputs
+  - `quality`: a 0–10 score capturing improvement
+  - `final_summary`: a step-by-step set of action items for future tasks
+- These consolidated results are saved to the filename you pass to `train(...)` (default via CLI is `trained_agents_data.pkl`). Entries are keyed by the agent’s `role` so they can be applied across sessions.
+- During normal (non-training) execution, each agent automatically loads its consolidated `suggestions` and appends them to the task prompt as mandatory instructions. This gives you consistent improvements without changing your agent definitions.
+
+### File summary
+
+- `training_data.pkl` (ephemeral, per-session):
+  - Structure: `agent_id -> { iteration_number: { initial_output, human_feedback, improved_output } }`
+  - Purpose: capture raw data and human feedback during training
+  - Location: saved in the current working directory (CWD)
+- `trained_agents_data.pkl` (or your custom filename):
+  - Structure: `agent_role -> { suggestions: string[], quality: number, final_summary: string }`
+  - Purpose: persist consolidated guidance for future runs
+  - Location: written to the CWD by default; use `-f` to set a custom (including absolute) path
 
 ## Small Language Model Considerations
 
@@ -129,3 +179,18 @@ Happy training with CrewAI! 🚀
     </Warning>
   </Tab>
 </Tabs>
+
+### Key Points to Note
+
+- **Positive Integer Requirement:** Ensure that the number of iterations (`n_iterations`) is a positive integer. The code will raise a `ValueError` if this condition is not met.
+- **Filename Requirement:** Ensure that the filename ends with `.pkl`. The code will raise a `ValueError` if this condition is not met.
+- **Error Handling:** The code handles subprocess errors and unexpected exceptions, providing error messages to the user.
+- Trained guidance is applied at prompt time; it does not modify your Python/YAML agent configuration.
+- Agents automatically load trained suggestions from a file named `trained_agents_data.pkl` located in the current working directory. If you trained to a different filename, either rename it to `trained_agents_data.pkl` before running, or adjust the loader in code.
+- You can change the output filename when calling `crewai train` with `-f/--filename`. Absolute paths are supported if you want to save outside the CWD.
+
+It is important to note that the training process may take some time, depending on the complexity of your agents and will also require your feedback on each iteration.
+
+Once the training is complete, your agents will be equipped with enhanced capabilities and knowledge, ready to tackle complex tasks and provide more consistent and valuable insights.
+
+Remember to regularly update and retrain your agents to ensure they stay up-to-date with the latest information and advancements in the field.

docs/en/enterprise/features/integrations.mdx

@@ -59,7 +59,7 @@ Before using Authentication Integrations, ensure you have:
 3. Click **Connect** on your desired service from the Authentication Integrations section
 4. Complete the OAuth authentication flow
 5. Grant necessary permissions for your use case
-6. Get your Enterprise Token from your [CrewAI Enterprise](https://app.crewai.com) account page - https://app.crewai.com/crewai_plus/settings/account
+6. All set! Get your Enterprise Token from your [CrewAI Enterprise](https://app.crewai.com) in **Integration** tab
 
 <Frame>
   ![Integrations](/images/enterprise/enterprise_action_auth_token.png)

docs/en/enterprise/features/tool-repository.mdx

@@ -35,6 +35,22 @@ crewai tool install <tool-name>
 
 This installs the tool and adds it to `pyproject.toml`.
 
+You can use the tool by importing it and adding it to your agents:
+
+```python
+from your_tool.tool import YourTool
+
+custom_tool = YourTool()
+
+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.',
+    tools=[custom_tool],
+    verbose=True
+)
+```
+
 ## Creating and Publishing Tools
 
 To create a new tool project:

docs/en/enterprise/features/traces.mdx

@@ -141,6 +141,16 @@ Traces are invaluable for troubleshooting issues with your crews:
   </Step>
 </Steps>
 
+## Performance and batching
+
+CrewAI batches trace uploads to reduce overhead on high-volume runs:
+
+- A TraceBatchManager buffers events and sends them in batches via the Plus API client
+- Reduces network chatter and improves reliability on flaky connections
+- Automatically enabled in the default trace listener; no configuration needed
+
+This yields more stable tracing under load while preserving detailed task/agent telemetry.
+
 <Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
   Contact our support team for assistance with trace analysis or any other CrewAI Enterprise features.
 </Card>

docs/en/enterprise/guides/automation-triggers.mdx

@@ -0,0 +1,178 @@
+---
+title: "Automation Triggers"
+description: "Automatically execute your CrewAI workflows when specific events occur in connected integrations"
+icon: "bolt"
+---
+
+Automation triggers enable you to automatically run your CrewAI deployments when specific events occur in your connected integrations, creating powerful event-driven workflows that respond to real-time changes in your business systems.
+
+## Overview
+
+With automation triggers, you can:
+
+- **Respond to real-time events** - Automatically execute workflows when specific conditions are met
+- **Integrate with external systems** - Connect with platforms like Gmail, Outlook, OneDrive, JIRA, Slack, Stripe and more
+- **Scale your automation** - Handle high-volume events without manual intervention
+- **Maintain context** - Access trigger data within your crews and flows
+
+## Managing Automation Triggers
+
+### Viewing Available Triggers
+
+To access and manage your automation triggers:
+
+1. Navigate to your deployment in the CrewAI dashboard
+2. Click on the **Triggers** tab to view all available trigger integrations
+
+<Frame>
+  <img src="/images/enterprise/list-available-triggers.png" alt="List of available automation triggers" />
+</Frame>
+
+This view shows all the trigger integrations available for your deployment, along with their current connection status.
+
+### Enabling and Disabling Triggers
+
+Each trigger can be easily enabled or disabled using the toggle switch:
+
+<Frame>
+  <img src="/images/enterprise/trigger-selected.png" alt="Enable or disable triggers with toggle" />
+</Frame>
+
+- **Enabled (blue toggle)**: The trigger is active and will automatically execute your deployment when the specified events occur
+- **Disabled (gray toggle)**: The trigger is inactive and will not respond to events
+
+Simply click the toggle to change the trigger state. Changes take effect immediately.
+
+### Monitoring Trigger Executions
+
+Track the performance and history of your triggered executions:
+
+<Frame>
+  <img src="/images/enterprise/list-executions.png" alt="List of executions triggered by automation" />
+</Frame>
+
+## Building Automation
+
+Before building your automation, it's helpful to understand the structure of trigger payloads that your crews and flows will receive.
+
+### Payload Samples Repository
+
+We maintain a comprehensive repository with sample payloads from various trigger sources to help you build and test your automations:
+
+**🔗 [CrewAI Enterprise Trigger Payload Samples](https://github.com/crewAIInc/crewai-enterprise-trigger-payload-samples)**
+
+This repository contains:
+
+- **Real payload examples** from different trigger sources (Gmail, Google Drive, etc.)
+- **Payload structure documentation** showing the format and available fields
+
+### Triggers with Crew
+
+Your existing crew definitions work seamlessly with triggers, you just need to have a task to parse the received payload:
+
+```python
+@CrewBase
+class MyAutomatedCrew:
+    @agent
+    def researcher(self) -> Agent:
+        return Agent(
+            config=self.agents_config['researcher'],
+        )
+
+    @task
+    def parse_trigger_payload(self) -> Task:
+        return Task(
+            config=self.tasks_config['parse_trigger_payload'],
+            agent=self.researcher(),
+        )
+
+    @task
+    def analyze_trigger_content(self) -> Task:
+        return Task(
+            config=self.tasks_config['analyze_trigger_data'],
+            agent=self.researcher(),
+        )
+```
+
+The crew will automatically receive and can access the trigger payload through the standard CrewAI context mechanisms.
+
+<Note>
+  Crew and Flow inputs can include `crewai_trigger_payload`. CrewAI automatically injects this payload:
+  - Tasks: appended to the first task's description by default ("Trigger Payload: {crewai_trigger_payload}")
+  - Control via `allow_crewai_trigger_context`: set `True` to always inject, `False` to never inject
+  - Flows: any `@start()` method that accepts a `crewai_trigger_payload` parameter will receive it
+</Note>
+
+### Integration with Flows
+
+For flows, you have more control over how trigger data is handled:
+
+#### Accessing Trigger Payload
+
+All `@start()` methods in your flows will accept an additional parameter called `crewai_trigger_payload`:
+
+```python
+from crewai.flow import Flow, start, listen
+
+class MyAutomatedFlow(Flow):
+    @start()
+    def handle_trigger(self, crewai_trigger_payload: dict = None):
+        """
+        This start method can receive trigger data
+        """
+        if crewai_trigger_payload:
+            # Process the trigger data
+            trigger_id = crewai_trigger_payload.get('id')
+            event_data = crewai_trigger_payload.get('payload', {})
+
+            # Store in flow state for use by other methods
+            self.state.trigger_id = trigger_id
+            self.state.trigger_type = event_data
+
+            return event_data
+
+        # Handle manual execution
+        return None
+
+    @listen(handle_trigger)
+    def process_data(self, trigger_data):
+        """
+        Process the data from the trigger
+        """
+        # ... process the trigger
+```
+
+#### Triggering Crews from Flows
+
+When kicking off a crew within a flow that was triggered, pass the trigger payload as it:
+
+```python
+@start()
+def delegate_to_crew(self, crewai_trigger_payload: dict = None):
+    """
+    Delegate processing to a specialized crew
+    """
+    crew = MySpecializedCrew()
+
+    # Pass the trigger payload to the crew
+    result = crew.crew().kickoff(
+        inputs={
+            'a_custom_parameter': "custom_value",
+            'crewai_trigger_payload': crewai_trigger_payload
+        },
+    )
+
+    return result
+```
+
+## Troubleshooting
+
+**Trigger not firing:**
+- Verify the trigger is enabled
+- Check integration connection status
+
+**Execution failures:**
+- Check the execution logs for error details
+- If you are developing, make sure the inputs include the `crewai_trigger_payload` parameter with the correct payload
+
+Automation triggers transform your CrewAI deployments into responsive, event-driven systems that can seamlessly integrate with your existing business processes and tools.

docs/en/guides/flows/mastering-flow-state.mdx

@@ -348,6 +348,31 @@ class SelectivePersistFlow(Flow):
 
 ## Advanced State Patterns
 
+### Conditional starts and resumable execution
+
+Flows support conditional `@start()` and resumable execution for HITL/cyclic scenarios:
+
+```python
+from crewai.flow.flow import Flow, start, listen, and_, or_
+
+class ResumableFlow(Flow):
+    @start()  # unconditional start
+    def init(self):
+        ...
+
+    # Conditional start: run after "init" or external trigger name
+    @start("init")
+    def maybe_begin(self):
+        ...
+
+    @listen(and_(init, maybe_begin))
+    def proceed(self):
+        ...
+```
+
+- Conditional `@start()` accepts a method name, a router label, or a callable condition.
+- During resume, listeners continue from prior checkpoints; cycle/router branches honor resumption flags.
+
 ### State-Based Conditional Logic
 
 You can use state to implement complex conditional logic in your flows:

docs/en/installation.mdx

@@ -30,6 +30,12 @@ Watch this video tutorial for a step-by-step demonstration of the installation p
   If you need to update Python, visit [python.org/downloads](https://python.org/downloads)
 </Note>
 
+<Note>
+  **OpenAI SDK Requirement**
+
+  CrewAI 0.175.0 requires `openai >= 1.13.3`. If you manage dependencies yourself, ensure your environment satisfies this constraint to avoid import/runtime issues.
+</Note>
+
 CrewAI uses the `uv` as its dependency management and package handling tool. It simplifies project setup and execution, offering a seamless experience.
 
 If you haven't installed `uv` yet, follow **step 1** to quickly get it set up on your system, else you can skip to **step 2**.

docs/en/observability/agentops.mdx

@@ -1,126 +0,0 @@
----
-title: AgentOps Integration
-description: Understanding and logging your agent performance with AgentOps.
-icon: paperclip
----
-
-# Introduction
-
-Observability is a key aspect of developing and deploying conversational AI agents. It allows developers to understand how their agents are performing, 
-how their agents are interacting with users, and how their agents use external tools and APIs. 
-AgentOps is a product independent of CrewAI that provides a comprehensive observability solution for agents.
-
-## AgentOps
-
-[AgentOps](https://agentops.ai/?=crew) provides session replays, metrics, and monitoring for agents.
-
-At a high level, AgentOps gives you the ability to monitor cost, token usage, latency, agent failures, session-wide statistics, and more. 
-For more info, check out the [AgentOps Repo](https://github.com/AgentOps-AI/agentops).
-
-### Overview
-
-AgentOps provides monitoring for agents in development and production. 
-It provides a dashboard for tracking agent performance, session replays, and custom reporting.
-
-Additionally, AgentOps provides session drilldowns for viewing Crew agent interactions, LLM calls, and tool usage in real-time. 
-This feature is useful for debugging and understanding how agents interact with users as well as other agents.
-
-![Overview of a select series of agent session runs](/images/agentops-overview.png)
-![Overview of session drilldowns for examining agent runs](/images/agentops-session.png)
-![Viewing a step-by-step agent replay execution graph](/images/agentops-replay.png)
-
-### Features
-
-- **LLM Cost Management and Tracking**: Track spend with foundation model providers.
-- **Replay Analytics**: Watch step-by-step agent execution graphs.
-- **Recursive Thought Detection**: Identify when agents fall into infinite loops.
-- **Custom Reporting**: Create custom analytics on agent performance.
-- **Analytics Dashboard**: Monitor high-level statistics about agents in development and production.
-- **Public Model Testing**: Test your agents against benchmarks and leaderboards.
-- **Custom Tests**: Run your agents against domain-specific tests.
-- **Time Travel Debugging**: Restart your sessions from checkpoints.
-- **Compliance and Security**: Create audit logs and detect potential threats such as profanity and PII leaks.
-- **Prompt Injection Detection**: Identify potential code injection and secret leaks.
-
-### Using AgentOps
-
-<Steps>
-   <Step title="Create an API Key">
-      Create a user API key here: [Create API Key](https://app.agentops.ai/account)
-   </Step>
-   <Step title="Configure Your Environment">
-      Add your API key to your environment variables:
-      ```bash
-      AGENTOPS_API_KEY=<YOUR_AGENTOPS_API_KEY>
-      ```
-   </Step>
-   <Step title="Install AgentOps">
-      Install AgentOps with:
-      ```bash
-      pip install 'crewai[agentops]'
-      ```
-      or
-      ```bash
-      pip install agentops
-      ```
-   </Step>
-   <Step title="Initialize AgentOps">
-      Before using `Crew` in your script, include these lines:
-
-      ```python
-      import agentops
-      agentops.init()
-      ```
-
-      This will initiate an AgentOps session as well as automatically track Crew agents. For further info on how to outfit more complex agentic systems, 
-      check out the [AgentOps documentation](https://docs.agentops.ai) or join the [Discord](https://discord.gg/j4f3KbeH).
-   </Step>
-</Steps>
-
-### Crew + AgentOps Examples
-
-<CardGroup cols={3}>
-  <Card
-  title="Job Posting"
-  color="#F3A78B"
-  href="https://github.com/joaomdmoura/crewAI-examples/tree/main/job-posting"
-  icon="briefcase"
-    iconType="solid"
-  >
-    Example of a Crew agent that generates job posts.
-  </Card>
-  <Card
-    title="Markdown Validator"
-    color="#F3A78B"
-    href="https://github.com/joaomdmoura/crewAI-examples/tree/main/markdown_validator"
-    icon="markdown"
-      iconType="solid"
-    >
-         Example of a Crew agent that validates Markdown files.
-    </Card>
-    <Card
-    title="Instagram Post"
-    color="#F3A78B"
-    href="https://github.com/joaomdmoura/crewAI-examples/tree/main/instagram_post"
-    icon="square-instagram"
-      iconType="brands"
-    >
-      Example of a Crew agent that generates Instagram posts.
-  </Card>
-</CardGroup>
-
-### Further Information
-
-To get started, create an [AgentOps account](https://agentops.ai/?=crew).
-
-For feature requests or bug reports, please reach out to the AgentOps team on the [AgentOps Repo](https://github.com/AgentOps-AI/agentops).
-
-#### Extra links
-
-<a href="https://twitter.com/agentopsai/">🐦 Twitter</a>
-<span>&nbsp;&nbsp;•&nbsp;&nbsp;</span>
-<a href="https://discord.gg/JHPt4C7r">📢 Discord</a>
-<span>&nbsp;&nbsp;•&nbsp;&nbsp;</span>
-<a href="https://app.agentops.ai/?=crew">🖇️ AgentOps Dashboard</a>
-<span>&nbsp;&nbsp;•&nbsp;&nbsp;</span>
-<a href="https://docs.agentops.ai/introduction">📙 Documentation</a>

docs/en/observability/overview.mdx

@@ -21,9 +21,6 @@ Observability is crucial for understanding how your CrewAI agents perform, ident
 ### Monitoring & Tracing Platforms
 
 <CardGroup cols={2}>
-  <Card title="AgentOps" icon="paperclip" href="/en/observability/agentops">
-    Session replays, metrics, and monitoring for agent development and production.
-  </Card>
 
   <Card title="LangDB" icon="database" href="/en/observability/langdb">
     End-to-end tracing for CrewAI workflows with automatic agent interaction capture.

docs/en/tools/database-data/weaviatevectorsearchtool.mdx

@@ -1,13 +1,13 @@
 ---
 title: Weaviate Vector Search
-description: The `WeaviateVectorSearchTool` is designed to search a Weaviate vector database for semantically similar documents.
+description: The `WeaviateVectorSearchTool` is designed to search a Weaviate vector database for semantically similar documents using hybrid search.
 icon: network-wired
 ---
 
 ## Overview
 
 
-The `WeaviateVectorSearchTool` is specifically crafted for conducting semantic searches within documents stored in a Weaviate vector database. This tool allows you to find semantically similar documents to a given query, leveraging the power of vector embeddings for more accurate and contextually relevant search results.
+The `WeaviateVectorSearchTool` is specifically crafted for conducting semantic searches within documents stored in a Weaviate vector database. This tool allows you to find semantically similar documents to a given query, leveraging the power of vector and keyword search for more accurate and contextually relevant search results.
 
 [Weaviate](https://weaviate.io/) is a vector database that stores and queries vector embeddings, enabling semantic search capabilities.
 
@@ -39,6 +39,7 @@ from crewai_tools import WeaviateVectorSearchTool
 tool = WeaviateVectorSearchTool(
     collection_name='example_collections',
     limit=3,
+    alpha=0.75,
     weaviate_cluster_url="https://your-weaviate-cluster-url.com",
     weaviate_api_key="your-weaviate-api-key",
 )
@@ -63,6 +64,7 @@ The `WeaviateVectorSearchTool` accepts the following parameters:
 - **weaviate_cluster_url**: Required. The URL of the Weaviate cluster.
 - **weaviate_api_key**: Required. The API key for the Weaviate cluster.
 - **limit**: Optional. The number of results to return. Default is `3`.
+- **alpha**: Optional. Controls the weighting between vector and keyword (BM25) search. alpha = 0 -> BM25 only, alpha = 1 -> vector search only. Default is `0.75`.
 - **vectorizer**: Optional. The vectorizer to use. If not provided, it will use `text2vec_openai` with the `nomic-embed-text` model.
 - **generative_model**: Optional. The generative model to use. If not provided, it will use OpenAI's `gpt-4o`.
 
@@ -78,6 +80,7 @@ from weaviate.classes.config import Configure
 tool = WeaviateVectorSearchTool(
     collection_name='example_collections',
     limit=3,
+    alpha=0.75,
     vectorizer=Configure.Vectorizer.text2vec_openai(model="nomic-embed-text"),
     generative_model=Configure.Generative.openai(model="gpt-4o-mini"),
     weaviate_cluster_url="https://your-weaviate-cluster-url.com",
@@ -128,6 +131,7 @@ with test_docs.batch.dynamic() as batch:
 tool = WeaviateVectorSearchTool(
     collection_name='example_collections', 
     limit=3,
+    alpha=0.75,
     weaviate_cluster_url="https://your-weaviate-cluster-url.com",
     weaviate_api_key="your-weaviate-api-key",
 )
@@ -145,6 +149,7 @@ from crewai_tools import WeaviateVectorSearchTool
 weaviate_tool = WeaviateVectorSearchTool(
     collection_name='example_collections',
     limit=3,
+    alpha=0.75,
     weaviate_cluster_url="https://your-weaviate-cluster-url.com",
     weaviate_api_key="your-weaviate-api-key",
 )

docs/en/tools/overview.mdx

@@ -117,4 +117,19 @@ agent = Agent(
 )
 ```
 
+## **Max Usage Count**
+
+You can set a maximum usage count for a tool to prevent it from being used more than a certain number of times.
+By default, the max usage count is unlimited.
+
+
+
+```python
+from crewai_tools import FileReadTool
+
+tool = FileReadTool(max_usage_count=5, ...)
+```
+
+
+
 Ready to explore? Pick a category above to discover tools that fit your use case!

docs/ko/api-reference/inputs.mdx

@@ -0,0 +1,7 @@
+---
+title: "GET /inputs"
+description: "크루가 필요로 하는 입력 확인"
+openapi: "/enterprise-api.ko.yaml GET /inputs"
+---
+
+

docs/ko/api-reference/kickoff.mdx

@@ -0,0 +1,7 @@
+---
+title: "POST /kickoff"
+description: "크루 실행 시작"
+openapi: "/enterprise-api.ko.yaml POST /kickoff"
+---
+
+

docs/ko/api-reference/status.mdx

@@ -0,0 +1,7 @@
+---
+title: "GET /status/{kickoff_id}"
+description: "실행 상태 조회"
+openapi: "/enterprise-api.ko.yaml GET /status/{kickoff_id}"
+---
+
+

docs/ko/concepts/event-listener.mdx

@@ -44,12 +44,12 @@ Prompt Tracing을 통해 다음과 같은 작업이 가능합니다:
 아래는 커스텀 이벤트 리스너 클래스의 간단한 예시입니다:
 
 ```python
-from crewai.utilities.events import (
+from crewai.events import (
     CrewKickoffStartedEvent,
     CrewKickoffCompletedEvent,
     AgentExecutionCompletedEvent,
 )
-from crewai.utilities.events.base_event_listener import BaseEventListener
+from crewai.events import BaseEventListener
 
 class MyCustomListener(BaseEventListener):
     def __init__(self):
@@ -146,7 +146,7 @@ my_project/
 
 ```python
 # my_custom_listener.py
-from crewai.utilities.events.base_event_listener import BaseEventListener
+from crewai.events import BaseEventListener
 # ... import events ...
 
 class MyCustomListener(BaseEventListener):
@@ -177,14 +177,7 @@ class MyCustomCrew:
     # Your crew implementation...
 ```
 
-이것이 바로 CrewAI의 내장 `agentops_listener`가 등록되는 방식과 동일합니다. CrewAI 코드베이스에서는 다음과 같이 되어 있습니다:
-
-```python
-# src/crewai/utilities/events/third_party/__init__.py
-from .agentops_listener import agentops_listener
-```
-
-이렇게 하면 `crewai.utilities.events` 패키지가 임포트될 때 `agentops_listener`가 자동으로 로드됩니다.
+이것이 CrewAI 코드베이스에서 서드파티 이벤트 리스너가 등록되는 방식입니다.
 
 ## 사용 가능한 이벤트 유형
 
@@ -280,84 +273,13 @@ CrewAI는 여러분이 청취할 수 있는 다양한 이벤트를 제공합니
 
 추가 필드는 이벤트 타입에 따라 다릅니다. 예를 들어, `CrewKickoffCompletedEvent`에는 `crew_name`과 `output` 필드가 포함됩니다.
 
-## 실제 예시: AgentOps와의 통합
-
-CrewAI는 AI 에이전트를 위한 모니터링 및 관찰 플랫폼인 [AgentOps](https://github.com/AgentOps-AI/agentops)와의 서드파티 통합 예시를 포함하고 있습니다. 구현 방식은 다음과 같습니다:
-
-```python
-from typing import Optional
-
-from crewai.utilities.events import (
-    CrewKickoffCompletedEvent,
-    ToolUsageErrorEvent,
-    ToolUsageStartedEvent,
-)
-from crewai.utilities.events.base_event_listener import BaseEventListener
-from crewai.utilities.events.crew_events import CrewKickoffStartedEvent
-from crewai.utilities.events.task_events import TaskEvaluationEvent
-
-try:
-    import agentops
-    AGENTOPS_INSTALLED = True
-except ImportError:
-    AGENTOPS_INSTALLED = False
-
-class AgentOpsListener(BaseEventListener):
-    tool_event: Optional["agentops.ToolEvent"] = None
-    session: Optional["agentops.Session"] = None
-
-    def __init__(self):
-        super().__init__()
-
-    def setup_listeners(self, crewai_event_bus):
-        if not AGENTOPS_INSTALLED:
-            return
-
-        @crewai_event_bus.on(CrewKickoffStartedEvent)
-        def on_crew_kickoff_started(source, event: CrewKickoffStartedEvent):
-            self.session = agentops.init()
-            for agent in source.agents:
-                if self.session:
-                    self.session.create_agent(
-                        name=agent.role,
-                        agent_id=str(agent.id),
-                    )
-
-        @crewai_event_bus.on(CrewKickoffCompletedEvent)
-        def on_crew_kickoff_completed(source, event: CrewKickoffCompletedEvent):
-            if self.session:
-                self.session.end_session(
-                    end_state="Success",
-                    end_state_reason="Finished Execution",
-                )
-
-        @crewai_event_bus.on(ToolUsageStartedEvent)
-        def on_tool_usage_started(source, event: ToolUsageStartedEvent):
-            self.tool_event = agentops.ToolEvent(name=event.tool_name)
-            if self.session:
-                self.session.record(self.tool_event)
-
-        @crewai_event_bus.on(ToolUsageErrorEvent)
-        def on_tool_usage_error(source, event: ToolUsageErrorEvent):
-            agentops.ErrorEvent(exception=event.error, trigger_event=self.tool_event)
-```
-
-이 listener는 crew가 시작될 때 AgentOps 세션을 초기화하고, agent를 AgentOps에 등록하며, 도구 사용을 추적하고, crew가 완료되면 세션을 종료합니다.
-
-AgentOps listener는 `src/crewai/utilities/events/third_party/__init__.py` 파일의 import를 통해 CrewAI 이벤트 시스템에 등록됩니다:
-
-```python
-from .agentops_listener import agentops_listener
-```
-
-이렇게 하면 `crewai.utilities.events` 패키지가 import될 때 `agentops_listener`가 로드되는 것이 보장됩니다.
 
 ## 고급 사용법: Scoped Handlers
 
 임시 이벤트 처리가 필요한 경우(테스트 또는 특정 작업에 유용함), `scoped_handlers` 컨텍스트 관리자를 사용할 수 있습니다:
 
 ```python
-from crewai.utilities.events import crewai_event_bus, CrewKickoffStartedEvent
+from crewai.events import crewai_event_bus, CrewKickoffStartedEvent
 
 with crewai_event_bus.scoped_handlers():
     @crewai_event_bus.on(CrewKickoffStartedEvent)

docs/ko/concepts/knowledge.mdx

@@ -683,11 +683,11 @@ CrewAI는 knowledge 검색 과정에서 이벤트를 발생시키며, 이벤트
 #### 예시: Knowledge Retrieval 모니터링
 
 ```python
-from crewai.utilities.events import (
+from crewai.events import (
     KnowledgeRetrievalStartedEvent,
     KnowledgeRetrievalCompletedEvent,
+    BaseEventListener,
 )
-from crewai.utilities.events.base_event_listener import BaseEventListener
 
 class KnowledgeMonitorListener(BaseEventListener):
     def setup_listeners(self, crewai_event_bus):

docs/ko/concepts/llms.mdx

@@ -731,10 +731,10 @@ CrewAI는 LLM의 스트리밍 응답을 지원하여, 애플리케이션이 출
     CrewAI는 스트리밍 중 수신되는 각 청크에 대해 이벤트를 발생시킵니다:
 
     ```python
-    from crewai.utilities.events import (
+    from crewai.events import (
       LLMStreamChunkEvent
     )
-    from crewai.utilities.events.base_event_listener import BaseEventListener
+    from crewai.events import BaseEventListener
 
     class MyCustomListener(BaseEventListener):
         def setup_listeners(self, crewai_event_bus):
@@ -756,8 +756,8 @@ CrewAI는 LLM의 스트리밍 응답을 지원하여, 애플리케이션이 출
 
     ```python
     from crewai import LLM, Agent, Task, Crew
-    from crewai.utilities.events import LLMStreamChunkEvent
-    from crewai.utilities.events.base_event_listener import BaseEventListener
+    from crewai.events import LLMStreamChunkEvent
+    from crewai.events import BaseEventListener
 
     class MyCustomListener(BaseEventListener):
         def setup_listeners(self, crewai_event_bus):

docs/ko/concepts/memory.mdx

@@ -985,8 +985,8 @@ CrewAI는 다음과 같은 메모리 관련 이벤트를 발생시킵니다:
 애플리케이션을 최적화하기 위해 메모리 작업 타이밍을 추적하세요:
 
 ```python
-from crewai.utilities.events.base_event_listener import BaseEventListener
-from crewai.utilities.events import (
+from crewai.events import (
+    BaseEventListener,
     MemoryQueryCompletedEvent,
     MemorySaveCompletedEvent
 )
@@ -1020,8 +1020,8 @@ memory_monitor = MemoryPerformanceMonitor()
 디버깅 및 인사이트를 위해 메모리 작업을 로깅합니다:
 
 ```python
-from crewai.utilities.events.base_event_listener import BaseEventListener
-from crewai.utilities.events import (
+from crewai.events import (
+    BaseEventListener,
     MemorySaveStartedEvent,
     MemoryQueryStartedEvent,
     MemoryRetrievalCompletedEvent
@@ -1061,8 +1061,8 @@ memory_logger = MemoryLogger()
 메모리 오류를 캡처하고 대응합니다:
 
 ```python
-from crewai.utilities.events.base_event_listener import BaseEventListener
-from crewai.utilities.events import (
+from crewai.events import (
+    BaseEventListener,
     MemorySaveFailedEvent,
     MemoryQueryFailedEvent
 )
@@ -1111,8 +1111,8 @@ error_tracker = MemoryErrorTracker(notify_email="admin@example.com")
 메모리 이벤트는 분석 및 모니터링 플랫폼으로 전달되어 성능 지표를 추적하고, 이상 징후를 감지하며, 메모리 사용 패턴을 시각화할 수 있습니다:
 
 ```python
-from crewai.utilities.events.base_event_listener import BaseEventListener
-from crewai.utilities.events import (
+from crewai.events import (
+    BaseEventListener,
     MemoryQueryCompletedEvent,
     MemorySaveCompletedEvent
 )

docs/ko/concepts/tasks.mdx

@@ -59,6 +59,7 @@ crew = Crew(
 | **Pydantic 출력** _(선택 사항)_   | `output_pydantic`     | `Optional[Type[BaseModel]]`       | 태스크 출력용 Pydantic 모델입니다.                                                                             |
 | **콜백** _(선택 사항)_            | `callback`            | `Optional[Any]`                  | 태스크 완료 후 실행할 함수/객체입니다.                                                                         |
 | **가드레일** _(선택 사항)_        | `guardrail`           | `Optional[Callable]`                | 다음 태스크로 진행하기 전에 태스크 출력을 검증하는 함수입니다.                                               |
+| **가드레일 최대 재시도** _(선택 사항)_ | `guardrail_max_retries` | `Optional[int]`          | 가드레일 검증 실패 시 최대 재시도 횟수입니다. 기본값은 3입니다.                                                |
 
 ## 작업 생성하기
 
@@ -448,7 +449,7 @@ task = Task(
     expected_output="A valid JSON object",
     agent=analyst,
     guardrail=validate_json_output,
-    max_retries=3  # Limit retry attempts
+    guardrail_max_retries=3  # 재시도 횟수 제한
 )
 ```
 
@@ -899,4 +900,4 @@ except RuntimeError as e:
 작업(task)은 CrewAI 에이전트의 행동을 이끄는 원동력입니다.
 작업과 그 결과를 적절하게 정의함으로써, 에이전트가 독립적으로 또는 협업 단위로 효과적으로 작동할 수 있는 기반을 마련할 수 있습니다.
 작업에 적합한 도구를 장착하고, 실행 과정을 이해하며, 견고한 검증 절차를 따르는 것은 CrewAI의 잠재력을 극대화하는 데 필수적입니다.
-이를 통해 에이전트가 할당된 작업에 효과적으로 준비되고, 작업이 의도대로 수행될 수 있습니다.
+이를 통해 에이전트가 할당된 작업에 효과적으로 준비되고, 작업이 의도대로 수행될 수 있습니다.

docs/ko/enterprise/features/integrations.mdx

@@ -58,7 +58,7 @@ Authentication Integrations를 사용하기 전에 다음이 준비되어 있는
 3. Authentication Integrations 섹션에서 원하는 서비스의 **Connect** 버튼을 클릭합니다.
 4. OAuth 인증 과정을 완료합니다.
 5. 사용 사례에 필요한 권한을 부여합니다.
-6. [CrewAI Enterprise](https://app.crewai.com) 계정 페이지 - https://app.crewai.com/crewai_plus/settings/account 에서 Enterprise Token을 받습니다.
+6. 완료! [CrewAI Enterprise](https://app.crewai.com)의 **Integration** 탭에서 Enterprise Token을 받습니다.
 
 <Frame>
   ![Integrations](/images/enterprise/enterprise_action_auth_token.png)
@@ -176,4 +176,4 @@ crew를 배포하고 각 통합을 특정 사용자에게 범위 지정할 수
 
 <Card title="도움이 필요하신가요?" icon="headset" href="mailto:support@crewai.com">
   통합 설정이나 문제 해결에 대한 지원이 필요하시면 저희 지원팀에 문의하세요.
-</Card>
+</Card>

docs/ko/enterprise/guides/automation-triggers.mdx

@@ -0,0 +1,171 @@
+---
+title: "자동화 트리거"
+description: "연결된 통합에서 특정 이벤트가 발생할 때 CrewAI 워크플로우를 자동으로 실행합니다"
+icon: "bolt"
+---
+
+자동화 트리거를 사용하면 연결된 통합에서 특정 이벤트가 발생할 때 CrewAI 배포를 자동으로 실행할 수 있어, 비즈니스 시스템의 실시간 변화에 반응하는 강력한 이벤트 기반 워크플로우를 만들 수 있습니다.
+
+## 개요
+
+자동화 트리거를 사용하면 다음을 수행할 수 있습니다:
+
+- **실시간 이벤트에 응답** - 특정 조건이 충족될 때 워크플로우를 자동으로 실행
+- **외부 시스템과 통합** - Gmail, Outlook, OneDrive, JIRA, Slack, Stripe 등의 플랫폼과 연결
+- **자동화 확장** - 수동 개입 없이 대용량 이벤트 처리
+- **컨텍스트 유지** - crew와 flow 내에서 트리거 데이터에 액세스
+
+## 자동화 트리거 관리
+
+### 사용 가능한 트리거 보기
+
+자동화 트리거에 액세스하고 관리하려면:
+
+1. CrewAI 대시보드에서 배포로 이동
+2. **트리거** 탭을 클릭하여 사용 가능한 모든 트리거 통합 보기
+
+<Frame>
+  <img src="/images/enterprise/list-available-triggers.png" alt="사용 가능한 자동화 트리거 목록" />
+</Frame>
+
+이 보기는 배포에 사용 가능한 모든 트리거 통합과 현재 연결 상태를 보여줍니다.
+
+### 트리거 활성화 및 비활성화
+
+각 트리거는 토글 스위치를 사용하여 쉽게 활성화하거나 비활성화할 수 있습니다:
+
+<Frame>
+  <img src="/images/enterprise/trigger-selected.png" alt="토글로 트리거 활성화 또는 비활성화" />
+</Frame>
+
+- **활성화됨 (파란색 토글)**: 트리거가 활성 상태이며 지정된 이벤트가 발생할 때 배포를 자동으로 실행합니다
+- **비활성화됨 (회색 토글)**: 트리거가 비활성 상태이며 이벤트에 응답하지 않습니다
+
+토글을 클릭하기만 하면 트리거 상태를 변경할 수 있습니다. 변경 사항은 즉시 적용됩니다.
+
+### 트리거 실행 모니터링
+
+트리거된 실행의 성능과 기록을 추적합니다:
+
+<Frame>
+  <img src="/images/enterprise/list-executions.png" alt="자동화에 의해 트리거된 실행 목록" />
+</Frame>
+
+## 자동화 구축
+
+자동화를 구축하기 전에 crew와 flow가 받을 트리거 페이로드의 구조를 이해하는 것이 도움이 됩니다.
+
+### 페이로드 샘플 저장소
+
+자동화를 구축하고 테스트하는 데 도움이 되도록 다양한 트리거 소스의 샘플 페이로드가 포함된 포괄적인 저장소를 유지 관리하고 있습니다:
+
+**🔗 [CrewAI Enterprise 트리거 페이로드 샘플](https://github.com/crewAIInc/crewai-enterprise-trigger-payload-samples)**
+
+이 저장소에는 다음이 포함되어 있습니다:
+
+- **실제 페이로드 예제** - 다양한 트리거 소스(Gmail, Google Drive 등)에서 가져온 예제
+- **페이로드 구조 문서** - 형식과 사용 가능한 필드를 보여주는 문서
+
+### Crew와 트리거
+
+기존 crew 정의는 트리거와 완벽하게 작동하며, 받은 페이로드를 분석하는 작업만 있으면 됩니다:
+
+```python
+@CrewBase
+class MyAutomatedCrew:
+    @agent
+    def researcher(self) -> Agent:
+        return Agent(
+            config=self.agents_config['researcher'],
+        )
+
+    @task
+    def parse_trigger_payload(self) -> Task:
+        return Task(
+            config=self.tasks_config['parse_trigger_payload'],
+            agent=self.researcher(),
+        )
+
+    @task
+    def analyze_trigger_content(self) -> Task:
+        return Task(
+            config=self.tasks_config['analyze_trigger_data'],
+            agent=self.researcher(),
+        )
+```
+
+crew는 자동으로 트리거 페이로드를 받고 표준 CrewAI 컨텍스트 메커니즘을 통해 액세스할 수 있습니다.
+
+### Flow와의 통합
+
+flow의 경우 트리거 데이터 처리 방법을 더 세밀하게 제어할 수 있습니다:
+
+#### 트리거 페이로드 액세스
+
+flow의 모든 `@start()` 메서드는 `crewai_trigger_payload`라는 추가 매개변수를 허용합니다:
+
+```python
+from crewai.flow import Flow, start, listen
+
+class MyAutomatedFlow(Flow):
+    @start()
+    def handle_trigger(self, crewai_trigger_payload: dict = None):
+        """
+        이 start 메서드는 트리거 데이터를 받을 수 있습니다
+        """
+        if crewai_trigger_payload:
+            # 트리거 데이터 처리
+            trigger_id = crewai_trigger_payload.get('id')
+            event_data = crewai_trigger_payload.get('payload', {})
+
+            # 다른 메서드에서 사용할 수 있도록 flow 상태에 저장
+            self.state.trigger_id = trigger_id
+            self.state.trigger_type = event_data
+
+            return event_data
+
+        # 수동 실행 처리
+        return None
+
+    @listen(handle_trigger)
+    def process_data(self, trigger_data):
+        """
+        트리거 데이터 처리
+        """
+        # ... 트리거 처리
+```
+
+#### Flow에서 Crew 트리거하기
+
+트리거된 flow 내에서 crew를 시작할 때 트리거 페이로드를 전달합니다:
+
+```python
+@start()
+def delegate_to_crew(self, crewai_trigger_payload: dict = None):
+    """
+    전문 crew에 처리 위임
+    """
+    crew = MySpecializedCrew()
+
+    # crew에 트리거 페이로드 전달
+    result = crew.crew().kickoff(
+        inputs={
+            'a_custom_parameter': "custom_value",
+            'crewai_trigger_payload': crewai_trigger_payload
+        },
+    )
+
+    return result
+```
+
+## 문제 해결
+
+**트리거가 작동하지 않는 경우:**
+- 트리거가 활성화되어 있는지 확인
+- 통합 연결 상태 확인
+
+**실행 실패:**
+- 오류 세부 정보는 실행 로그 확인
+- 개발 중인 경우 입력에 올바른 페이로드가 포함된 `crewai_trigger_payload` 매개변수가 포함되어 있는지 확인
+
+자동화 트리거는 CrewAI 배포를 기존 비즈니스 프로세스 및 도구와 완벽하게 통합할 수 있는 반응형 이벤트 기반 시스템으로 변환합니다.

docs/ko/introduction.mdx

@@ -1,65 +1,65 @@
 ---
 title: 소개
-description: 함께 협력하여 복잡한 작업을 해결하는 AI 에이전트 팀 구축
+description: 함께 협력하여 복잡한 작업을 해결하는 AI agent 팀 구축
 icon: handshake
 ---
 
 # CrewAI란 무엇인가?
 
-**CrewAI는 완전히 독립적으로, LangChain이나 기타 agent 프레임워크에 의존하지 않고 처음부터 스크래치로 개발된 가볍고 매우 빠른 Python 프레임워크입니다.**
+**CrewAI는 LangChain이나 기타 agent 프레임워크에 의존하지 않고, 완전히 독립적으로 처음부터 스크래치로 개발된 가볍고 매우 빠른 Python 프레임워크입니다.**
 
 CrewAI는 고수준의 간편함과 정밀한 저수준 제어를 모두 제공하여, 어떤 시나리오에도 맞춤화된 자율 AI agent를 만드는 데 이상적입니다:
 
 - **[CrewAI Crews](/ko/guides/crews/first-crew)**: 자율성과 협업 지능을 극대화하여, 각 agent가 특정 역할, 도구, 목표를 가진 AI 팀을 만들 수 있습니다.
-- **[CrewAI Flows](/ko/guides/flows/first-flow)**: 세밀한 이벤트 기반 제어와 단일 LLM 호출을 통한 정확한 작업 오케스트레이션을 가능하게 하며 Crews를 네이티브로 지원합니다.
+- **[CrewAI Flows](/ko/guides/flows/first-flow)**: 이벤트 기반의 세밀한 제어와 단일 LLM 호출을 통한 정확한 작업 orchestration을 지원하며, Crews와 네이티브로 통합됩니다.
 
 10만 명이 넘는 개발자가 커뮤니티 과정을 통해 인증을 받았으며, CrewAI는 기업용 AI 자동화의 표준으로 빠르게 자리잡고 있습니다.
 
-## 크루 작동 방식
+## Crew의 작동 방식
 
 <Note>
-  회사가 비즈니스 목표를 달성하기 위해 여러 부서(영업, 엔지니어링, 마케팅 등)가 리더십 아래에서 함께 일하는 것처럼, CrewAI는 복잡한 작업을 달성하기 위해 전문화된 역할의 AI 에이전트들이 협력하는 조직을 만들 수 있도록 도와줍니다.
+  회사가 비즈니스 목표를 달성하기 위해 여러 부서(영업, 엔지니어링, 마케팅 등)가 리더십 아래에서 함께 일하는 것처럼, CrewAI는 복잡한 작업을 달성하기 위해 전문화된 역할의 AI agent들이 협력하는 조직을 만들 수 있도록 도와줍니다.
 </Note>
 
-<Frame caption="CrewAI 프레임워크 개요">
+<Frame caption="CrewAI Framework Overview">
   <img src="/images/crews.png" alt="CrewAI Framework Overview" />
 </Frame>
 
-| 구성 요소     | 설명                   | 주요 특징 |
-|:--------------|:---------------------:|:----------|
-| **크루**     | 최상위 조직           | • AI 에이전트 팀 관리<br/>• 워크플로우 감독<br/>• 협업 보장<br/>• 결과 전달 |
-| **AI 에이전트** | 전문 팀원             | • 특정 역할 보유(연구원, 작가 등)<br/>• 지정된 도구 사용<br/>• 작업 위임 가능<br/>• 자율적 의사결정 가능 |
-| **프로세스**  | 워크플로우 관리 시스템 | • 협업 패턴 정의<br/>• 작업 할당 제어<br/>• 상호작용 관리<br/>• 효율적 실행 보장 |
-| **작업**     | 개별 할당             | • 명확한 목표 보유<br/>• 특정 도구 사용<br/>• 더 큰 프로세스에 기여<br/>• 실행 가능한 결과 도출 |
+| 구성 요소 | 설명 | 주요 특징 |
+|:----------|:----:|:----------|
+| **Crew** | 최상위 조직 | • AI agent 팀 관리<br/>• workflow 감독<br/>• 협업 보장<br/>• 결과 전달 |
+| **AI agents** | 전문 팀원 | • 특정 역할 보유(Researcher, Writer 등)<br/>• 지정된 도구 사용<br/>• 작업 위임 가능<br/>• 자율적 의사결정 가능 |
+| **Process** | workflow 관리 시스템 | • 협업 패턴 정의<br/>• 작업 할당 제어<br/>• 상호작용 관리<br/>• 효율적 실행 보장 |
+| **Task** | 개별 할당 | • 명확한 목표 보유<br/>• 특정 도구 사용<br/>• 더 큰 프로세스에 기여<br/>• 실행 가능한 결과 도출 |
 
-### 어떻게 모두 함께 작동하는가
+### 전체 구조의 동작 방식
 
 1. **Crew**가 전체 운영을 조직합니다
-2. **AI Agents**가 자신들의 전문 작업을 수행합니다
+2. **AI agents**가 자신들의 전문 작업을 수행합니다
 3. **Process**가 원활한 협업을 보장합니다
 4. **Tasks**가 완료되어 목표를 달성합니다
 
 ## 주요 기능
 
 <CardGroup cols={2}>
-  <Card title="역할 기반 에이전트" icon="users">
-    연구원, 분석가, 작가 등 다양한 역할, 전문성, 목표를 가진 전문 에이전트를 생성할 수 있습니다
+  <Card title="역할 기반 agent" icon="users">
+    Researcher, Analyst, Writer 등 다양한 역할과 전문성, 목표를 가진 agent를 생성할 수 있습니다
   </Card>
   <Card title="유연한 도구" icon="screwdriver-wrench">
-    에이전트에게 외부 서비스 및 데이터 소스와 상호작용할 수 있는 맞춤형 도구와 API를 제공합니다
+    agent에게 외부 서비스 및 데이터 소스와 상호작용할 수 있는 맞춤형 도구와 API를 제공합니다
   </Card>
   <Card title="지능형 협업" icon="people-arrows">
-    에이전트가 함께 작업하며, 인사이트를 공유하고 작업을 조율하여 복잡한 목표를 달성합니다
+    agent들이 함께 작업하며, 인사이트를 공유하고 작업을 조율하여 복잡한 목표를 달성합니다
   </Card>
   <Card title="작업 관리" icon="list-check">
-    순차적 또는 병렬 워크플로우를 정의할 수 있으며, 에이전트가 작업 의존성을 자동으로 처리합니다
+    순차적 또는 병렬 workflow를 정의할 수 있으며, agent가 작업 의존성을 자동으로 처리합니다
   </Card>
 </CardGroup>
 
-## 플로우의 작동 원리
+## Flow의 작동 원리
 
 <Note>
-  crew는 자율 협업에 탁월한 반면, 플로우는 구조화된 자동화를 제공하여 워크플로우 실행에 대한 세밀한 제어를 제공합니다. 플로우는 조건부 로직, 반복문, 동적 상태 관리를 정확하게 처리하면서 작업이 신뢰성 있게, 안전하게, 효율적으로 실행되도록 보장합니다. 플로우는 crew와 원활하게 통합되어 높은 자율성과 엄격한 제어의 균형을 이룰 수 있게 해줍니다.
+  Crew가 자율 협업에 탁월하다면, Flow는 구조화된 자동화를 제공하여 workflow 실행에 대한 세밀한 제어를 제공합니다. Flow는 조건부 로직, 반복문, 동적 상태 관리를 정확하게 처리하면서 작업이 신뢰성 있게, 안전하게, 효율적으로 실행되도록 보장합니다. Flow는 Crew와 원활하게 통합되어 높은 자율성과 엄격한 제어의 균형을 이룰 수 있게 해줍니다.
 </Note>
 
 <Frame caption="CrewAI Framework Overview">
@@ -68,41 +68,41 @@ CrewAI는 고수준의 간편함과 정밀한 저수준 제어를 모두 제공
 
 | 구성 요소 | 설명 | 주요 기능 |
 |:----------|:-----------:|:------------|
-| **Flow** | 구조화된 워크플로우 오케스트레이션 | • 실행 경로 관리<br/>• 상태 전환 처리<br/>• 작업 순서 제어<br/>• 신뢰성 있는 실행 보장 |
-| **Events** | 워크플로우 액션 트리거 | • 특정 프로세스 시작<br/>• 동적 응답 가능<br/>• 조건부 분기 지원<br/>• 실시간 적응 허용 |
-| **States** | 워크플로우 실행 컨텍스트 | • 실행 데이터 유지<br/>• 데이터 영속성 지원<br/>• 재개 가능성 보장<br/>• 실행 무결성 확보 |
-| **Crew Support** | 워크플로우 자동화 강화 | • 필요할 때 agency 삽입<br/>• 구조화된 워크플로우 보완<br/>• 자동화와 인텔리전스의 균형<br/>• 적응적 의사결정 지원 |
+| **Flow** | 구조화된 workflow orchestration | • 실행 경로 관리<br/>• 상태 전환 처리<br/>• 작업 순서 제어<br/>• 신뢰성 있는 실행 보장 |
+| **Events** | workflow 액션 트리거 | • 특정 프로세스 시작<br/>• 동적 응답 가능<br/>• 조건부 분기 지원<br/>• 실시간 적응 허용 |
+| **States** | workflow 실행 컨텍스트 | • 실행 데이터 유지<br/>• 데이터 영속성 지원<br/>• 재개 가능성 보장<br/>• 실행 무결성 확보 |
+| **Crew Support** | workflow 자동화 강화 | • 필요할 때 agency 삽입<br/>• 구조화된 workflow 보완<br/>• 자동화와 인텔리전스의 균형<br/>• 적응적 의사결정 지원 |
 
 ### 주요 기능
 
 <CardGroup cols={2}>
-  <Card title="이벤트 기반 오케스트레이션" icon="bolt">
-    이벤트에 동적으로 반응하여 정밀한 실행 경로 정의
+  <Card title="이벤트 기반 orchestration" icon="bolt">
+    이벤트에 동적으로 반응하여 정밀한 실행 경로를 정의합니다
   </Card>
   <Card title="세밀한 제어" icon="sliders">
-    워크플로우 상태와 조건부 실행을 안전하고 효율적으로 관리
+    workflow 상태와 조건부 실행을 안전하고 효율적으로 관리합니다
   </Card>
   <Card title="네이티브 Crew 통합" icon="puzzle-piece">
-    Crews와 손쉽게 결합하여 자율성과 지능 강화
+    Crews와 손쉽게 결합하여 자율성과 지능을 강화합니다
   </Card>
   <Card title="결정론적 실행" icon="route">
-    명시적 제어 흐름과 오류 처리로 예측 가능한 결과 보장
+    명시적 제어 흐름과 오류 처리로 예측 가능한 결과를 보장합니다
   </Card>
 </CardGroup>
 
-## 크루(Crews)와 플로우(Flows)를 언제 사용할까
+## Crew와 Flow를 언제 사용할까
 
 <Note>
-  [크루](/ko/guides/crews/first-crew)와 [플로우](/ko/guides/flows/first-flow)를 언제 사용할지 이해하는 것은 CrewAI의 잠재력을 애플리케이션에서 극대화하는 데 핵심적입니다.
+  [Crew](/ko/guides/crews/first-crew)와 [Flow](/ko/guides/flows/first-flow)를 언제 사용할지 이해하는 것은 CrewAI의 잠재력을 애플리케이션에서 극대화하는 데 핵심적입니다.
 </Note>
 
 | 사용 사례 | 권장 접근 방식 | 이유 |
 |:---------|:---------------------|:-----|
-| **개방형 연구** | [크루](/ko/guides/crews/first-crew) | 과제가 창의적인 사고, 탐색, 적응이 필요할 때 |
-| **콘텐츠 생성** | [크루](/ko/guides/crews/first-crew) | 기사, 보고서, 마케팅 자료 등 협업형 생성 시 |
-| **의사결정 워크플로우** | [플로우](/ko/guides/flows/first-flow) | 예측 가능하고 감사 가능한 의사결정 경로 및 정밀 제어가 필요할 때 |
-| **API 오케스트레이션** | [플로우](/ko/guides/flows/first-flow) | 특정 순서로 여러 외부 서비스에 신뢰성 있게 통합할 때 |
-| **하이브리드 애플리케이션** | 혼합 접근 방식 | [플로우](/ko/guides/flows/first-flow)로 전체 프로세스를 오케스트레이션하고, [크루](/ko/guides/crews/first-crew)로 복잡한 하위 작업을 처리 |
+| **개방형 연구** | [Crew](/ko/guides/crews/first-crew) | 창의적 사고, 탐색, 적응이 필요한 작업에 적합 |
+| **콘텐츠 생성** | [Crew](/ko/guides/crews/first-crew) | 기사, 보고서, 마케팅 자료 등 협업형 생성에 적합 |
+| **의사결정 workflow** | [Flow](/ko/guides/flows/first-flow) | 예측 가능하고 감사 가능한 의사결정 경로 및 정밀 제어가 필요할 때 |
+| **API orchestration** | [Flow](/ko/guides/flows/first-flow) | 특정 순서로 여러 외부 서비스에 신뢰성 있게 통합할 때 |
+| **하이브리드 애플리케이션** | 혼합 접근 방식 | [Flow](/ko/guides/flows/first-flow)로 전체 프로세스를 orchestration하고, [Crew](/ko/guides/crews/first-crew)로 복잡한 하위 작업을 처리 |
 
 ### 의사결정 프레임워크
 
@@ -112,8 +112,8 @@ CrewAI는 고수준의 간편함과 정밀한 저수준 제어를 모두 제공
 
 ## CrewAI를 선택해야 하는 이유?
 
-- 🧠 **자율적 운영**: 에이전트가 자신의 역할과 사용 가능한 도구를 바탕으로 지능적인 결정을 내립니다
-- 📝 **자연스러운 상호작용**: 에이전트가 인간 팀원처럼 소통하고 협업합니다
+- 🧠 **자율적 운영**: agent가 자신의 역할과 사용 가능한 도구를 바탕으로 지능적인 결정을 내립니다
+- 📝 **자연스러운 상호작용**: agent가 인간 팀원처럼 소통하고 협업합니다
 - 🛠️ **확장 가능한 설계**: 새로운 도구, 역할, 기능을 쉽게 추가할 수 있습니다
 - 🚀 **프로덕션 준비 완료**: 실제 환경에서의 신뢰성과 확장성을 고려하여 구축되었습니다
 - 🔒 **보안 중심**: 엔터프라이즈 보안 요구 사항을 고려하여 설계되었습니다
@@ -134,7 +134,7 @@ CrewAI는 고수준의 간편함과 정밀한 저수준 제어를 모두 제공
     icon="diagram-project"
     href="/ko/guides/flows/first-flow"
   >
-    실행을 정밀하게 제어할 수 있는 구조화된, 이벤트 기반 워크플로우를 만드는 방법을 배워보세요.
+    실행을 정밀하게 제어할 수 있는 구조화된, 이벤트 기반 workflow를 만드는 방법을 배워보세요.
   </Card>
 </CardGroup>
 
@@ -151,7 +151,7 @@ CrewAI는 고수준의 간편함과 정밀한 저수준 제어를 모두 제공
     icon="bolt"
     href="ko/quickstart"
   >
-    빠른 시작 가이드를 따라 첫 번째 CrewAI 에이전트를 만들고 직접 경험해 보세요.
+    빠른 시작 가이드를 따라 첫 번째 CrewAI agent를 만들고 직접 경험해 보세요.
   </Card>
   <Card
     title="커뮤니티 가입하기"

docs/ko/observability/agentops.mdx

@@ -1,124 +0,0 @@
----
-title: AgentOps 통합
-description: AgentOps를 사용하여 에이전트 성능을 이해하고 로깅하기
-icon: paperclip
----
-
-# 소개
-
-Observability는 대화형 AI 에이전트를 개발하고 배포하는 데 있어 핵심적인 요소입니다. 이는 개발자가 에이전트의 성능을 이해하고, 에이전트가 사용자와 어떻게 상호작용하는지, 그리고 에이전트가 외부 도구와 API를 어떻게 사용하는지를 파악할 수 있게 해줍니다.  
-AgentOps는 CrewAI와 독립적인 제품으로, 에이전트를 위한 종합적인 observability 솔루션을 제공합니다.
-
-## AgentOps
-
-[AgentOps](https://agentops.ai/?=crew)은 에이전트에 대한 세션 리플레이, 메트릭, 모니터링을 제공합니다.
-
-AgentOps는 높은 수준에서 비용, 토큰 사용량, 대기 시간, 에이전트 실패, 세션 전체 통계 등 다양한 항목을 모니터링할 수 있는 기능을 제공합니다.  
-더 자세한 내용은 [AgentOps Repo](https://github.com/AgentOps-AI/agentops)를 확인하세요.
-
-### 개요
-
-AgentOps는 개발 및 프로덕션 환경에서 에이전트에 대한 모니터링을 제공합니다.  
-에이전트 성능, 세션 리플레이, 맞춤형 리포팅을 추적할 수 있는 대시보드를 제공합니다.
-
-또한, AgentOps는 Crew 에이전트 상호작용, LLM 호출, 툴 사용을 실시간으로 볼 수 있는 세션 드릴다운 기능을 제공합니다.  
-이 기능은 에이전트가 사용자 및 다른 에이전트와 어떻게 상호작용하는지 디버깅하고 이해하는 데 유용합니다.
-
-![선택된 에이전트 세션 실행 시리즈의 개요](/images/agentops-overview.png)
-![에이전트 실행을 조사하기 위한 세션 드릴다운 개요](/images/agentops-session.png)
-![단계별 에이전트 리플레이 실행 그래프 보기](/images/agentops-replay.png)
-
-### 특징
-
-- **LLM 비용 관리 및 추적**: 기반 모델 공급자와의 지출을 추적합니다.
-- **재생 분석**: 단계별 에이전트 실행 그래프를 시청할 수 있습니다.
-- **재귀적 사고 감지**: 에이전트가 무한 루프에 빠졌는지 식별합니다.
-- **맞춤형 보고서**: 에이전트 성능에 대한 맞춤형 분석을 생성합니다.
-- **분석 대시보드**: 개발 및 운영 중인 에이전트에 대한 상위 수준 통계를 모니터링합니다.
-- **공개 모델 테스트**: 벤치마크 및 리더보드를 통해 에이전트를 테스트할 수 있습니다.
-- **맞춤형 테스트**: 도메인별 테스트로 에이전트를 실행합니다.
-- **타임 트래블 디버깅**: 체크포인트에서 세션을 재시작합니다.
-- **컴플라이언스 및 보안**: 감사 로그를 생성하고 욕설 및 PII 유출과 같은 잠재적 위협을 감지합니다.
-- **프롬프트 인젝션 감지**: 잠재적 코드 인젝션 및 시크릿 유출을 식별합니다.
-
-### AgentOps 사용하기
-
-<Steps>
-   <Step title="API 키 생성">
-      사용자 API 키를 여기서 생성하세요: [API 키 생성](https://app.agentops.ai/account)
-   </Step>
-   <Step title="환경 설정">
-      API 키를 환경 변수에 추가하세요:
-      ```bash
-      AGENTOPS_API_KEY=<YOUR_AGENTOPS_API_KEY>
-      ```
-   </Step>
-   <Step title="AgentOps 설치">
-      다음 명령어로 AgentOps를 설치하세요:
-      ```bash
-      pip install 'crewai[agentops]'
-      ```
-      또는
-      ```bash
-      pip install agentops
-      ```
-   </Step>
-   <Step title="AgentOps 초기화">
-      스크립트에서 `Crew`를 사용하기 전에 다음 코드를 포함하세요:
-
-      ```python
-      import agentops
-      agentops.init()
-      ```
-
-      이렇게 하면 AgentOps 세션이 시작되고 Crew 에이전트가 자동으로 추적됩니다. 더 복잡한 agentic 시스템을 구성하는 방법에 대한 자세한 정보는 [AgentOps 문서](https://docs.agentops.ai) 또는 [Discord](https://discord.gg/j4f3KbeH)를 참조하세요.
-   </Step>
-</Steps>
-
-### Crew + AgentOps 예시
-
-<CardGroup cols={3}>
-  <Card
-  title="Job Posting"
-  color="#F3A78B"
-  href="https://github.com/joaomdmoura/crewAI-examples/tree/main/job-posting"
-  icon="briefcase"
-    iconType="solid"
-  >
-    채용 공고를 생성하는 Crew agent의 예시입니다.
-  </Card>
-  <Card
-    title="Markdown Validator"
-    color="#F3A78B"
-    href="https://github.com/joaomdmoura/crewAI-examples/tree/main/markdown_validator"
-    icon="markdown"
-      iconType="solid"
-    >
-         Markdown 파일을 검증하는 Crew agent의 예시입니다.
-    </Card>
-    <Card
-    title="Instagram Post"
-    color="#F3A78B"
-    href="https://github.com/joaomdmoura/crewAI-examples/tree/main/instagram_post"
-    icon="square-instagram"
-      iconType="brands"
-    >
-      Instagram 게시물을 생성하는 Crew agent의 예시입니다.
-  </Card>
-</CardGroup>
-
-### 추가 정보
-
-시작하려면 [AgentOps 계정](https://agentops.ai/?=crew)을 생성하세요.
-
-기능 요청이나 버그 보고가 필요하시면 [AgentOps Repo](https://github.com/AgentOps-AI/agentops)에서 AgentOps 팀에 문의해 주세요.
-
-#### 추가 링크
-
-<a href="https://twitter.com/agentopsai/">🐦 트위터</a>
-<span>&nbsp;&nbsp;•&nbsp;&nbsp;</span>
-<a href="https://discord.gg/JHPt4C7r">📢 디스코드</a>
-<span>&nbsp;&nbsp;•&nbsp;&nbsp;</span>
-<a href="https://app.agentops.ai/?=crew">🖇️ AgentOps 대시보드</a>
-<span>&nbsp;&nbsp;•&nbsp;&nbsp;</span>
-<a href="https://docs.agentops.ai/introduction">📙 문서화</a>

docs/ko/observability/overview.mdx

@@ -21,9 +21,6 @@ icon: "face-smile"
 ### 모니터링 & 트레이싱 플랫폼
 
 <CardGroup cols={2}>
-  <Card title="AgentOps" icon="paperclip" href="/ko/observability/agentops">
-    에이전트 개발 및 운영을 위한 세션 리플레이, 메트릭, 모니터링 제공.
-  </Card>
 
   <Card title="LangDB" icon="database" href="/ko/observability/langdb">
     자동 에이전트 상호작용 캡처를 포함한 CrewAI 워크플로의 엔드-투-엔드 트레이싱.

docs/pt-BR/api-reference/inputs.mdx

@@ -0,0 +1,7 @@
+---
+title: "GET /inputs"
+description: "Obter entradas necessárias para sua crew"
+openapi: "/enterprise-api.pt-BR.yaml GET /inputs"
+---
+
+

docs/pt-BR/api-reference/kickoff.mdx

@@ -0,0 +1,7 @@
+---
+title: "POST /kickoff"
+description: "Iniciar a execução da crew"
+openapi: "/enterprise-api.pt-BR.yaml POST /kickoff"
+---
+
+

docs/pt-BR/api-reference/status.mdx

@@ -0,0 +1,7 @@
+---
+title: "GET /status/{kickoff_id}"
+description: "Obter o status da execução"
+openapi: "/enterprise-api.pt-BR.yaml GET /status/{kickoff_id}"
+---
+
+

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

@@ -44,12 +44,12 @@ Para criar um listener de evento personalizado, você precisa:
 Veja um exemplo simples de uma classe de listener de evento personalizado:
 
 ```python
-from crewai.utilities.events import (
+from crewai.events import (
     CrewKickoffStartedEvent,
     CrewKickoffCompletedEvent,
     AgentExecutionCompletedEvent,
 )
-from crewai.utilities.events.base_event_listener import BaseEventListener
+from crewai.events import BaseEventListener
 
 class MeuListenerPersonalizado(BaseEventListener):
     def __init__(self):
@@ -146,7 +146,7 @@ my_project/
 
 ```python
 # my_custom_listener.py
-from crewai.utilities.events.base_event_listener import BaseEventListener
+from crewai.events import BaseEventListener
 # ... importe events ...
 
 class MyCustomListener(BaseEventListener):
@@ -177,14 +177,7 @@ class MyCustomCrew:
     # Sua implementação do crew...
 ```
 
-É exatamente assim que o `agentops_listener` integrado do CrewAI é registrado. No código-fonte do CrewAI, você encontrará:
-
-```python
-# src/crewai/utilities/events/third_party/__init__.py
-from .agentops_listener import agentops_listener
-```
-
-Isso garante que o `agentops_listener` seja carregado quando o pacote `crewai.utilities.events` for importado.
+É assim que listeners de eventos de terceiros são registrados no código do CrewAI.
 
 ## Tipos de Eventos Disponíveis
 
@@ -269,84 +262,13 @@ A estrutura do objeto de evento depende do tipo do evento, mas todos herdam de `
 
 Campos adicionais variam pelo tipo de evento. Por exemplo, `CrewKickoffCompletedEvent` inclui os campos `crew_name` e `output`.
 
-## Exemplo Real: Integração com AgentOps
-
-O CrewAI inclui um exemplo de integração com [AgentOps](https://github.com/AgentOps-AI/agentops), uma plataforma de monitoramento e observabilidade para agentes de IA. Veja como é implementado:
-
-```python
-from typing import Optional
-
-from crewai.utilities.events import (
-    CrewKickoffCompletedEvent,
-    ToolUsageErrorEvent,
-    ToolUsageStartedEvent,
-)
-from crewai.utilities.events.base_event_listener import BaseEventListener
-from crewai.utilities.events.crew_events import CrewKickoffStartedEvent
-from crewai.utilities.events.task_events import TaskEvaluationEvent
-
-try:
-    import agentops
-    AGENTOPS_INSTALLED = True
-except ImportError:
-    AGENTOPS_INSTALLED = False
-
-class AgentOpsListener(BaseEventListener):
-    tool_event: Optional["agentops.ToolEvent"] = None
-    session: Optional["agentops.Session"] = None
-
-    def __init__(self):
-        super().__init__()
-
-    def setup_listeners(self, crewai_event_bus):
-        if not AGENTOPS_INSTALLED:
-            return
-
-        @crewai_event_bus.on(CrewKickoffStartedEvent)
-        def on_crew_kickoff_started(source, event: CrewKickoffStartedEvent):
-            self.session = agentops.init()
-            for agent in source.agents:
-                if self.session:
-                    self.session.create_agent(
-                        name=agent.role,
-                        agent_id=str(agent.id),
-                    )
-
-        @crewai_event_bus.on(CrewKickoffCompletedEvent)
-        def on_crew_kickoff_completed(source, event: CrewKickoffCompletedEvent):
-            if self.session:
-                self.session.end_session(
-                    end_state="Success",
-                    end_state_reason="Finished Execution",
-                )
-
-        @crewai_event_bus.on(ToolUsageStartedEvent)
-        def on_tool_usage_started(source, event: ToolUsageStartedEvent):
-            self.tool_event = agentops.ToolEvent(name=event.tool_name)
-            if self.session:
-                self.session.record(self.tool_event)
-
-        @crewai_event_bus.on(ToolUsageErrorEvent)
-        def on_tool_usage_error(source, event: ToolUsageErrorEvent):
-            agentops.ErrorEvent(exception=event.error, trigger_event=self.tool_event)
-```
-
-Esse listener inicializa uma sessão do AgentOps quando um Crew inicia, cadastra agentes no AgentOps, rastreia o uso de ferramentas e finaliza a sessão quando o Crew é concluído.
-
-O listener AgentOps é registrado no sistema de eventos do CrewAI via importação em `src/crewai/utilities/events/third_party/__init__.py`:
-
-```python
-from .agentops_listener import agentops_listener
-```
-
-Isso garante que o `agentops_listener` seja carregado quando o pacote `crewai.utilities.events` for importado.
 
 ## Uso Avançado: Handlers Escopados
 
 Para lidar temporariamente com eventos (útil para testes ou operações específicas), você pode usar o context manager `scoped_handlers`:
 
 ```python
-from crewai.utilities.events import crewai_event_bus, CrewKickoffStartedEvent
+from crewai.events import crewai_event_bus, CrewKickoffStartedEvent
 
 with crewai_event_bus.scoped_handlers():
     @crewai_event_bus.on(CrewKickoffStartedEvent)

docs/pt-BR/concepts/knowledge.mdx

@@ -681,11 +681,11 @@ O CrewAI emite eventos durante o processo de recuperação de knowledge que voc
 #### Exemplo: Monitorando Recuperação de Knowledge
 
 ```python
-from crewai.utilities.events import (
+from crewai.events import (
     KnowledgeRetrievalStartedEvent,
     KnowledgeRetrievalCompletedEvent,
+    BaseEventListener,
 )
-from crewai.utilities.events.base_event_listener import BaseEventListener
 
 class KnowledgeMonitorListener(BaseEventListener):
     def setup_listeners(self, crewai_event_bus):

docs/pt-BR/concepts/llms.mdx

@@ -708,10 +708,10 @@ O CrewAI suporta respostas em streaming de LLMs, permitindo que sua aplicação
     O CrewAI emite eventos para cada chunk recebido durante o streaming:
 
     ```python
-    from crewai.utilities.events import (
+    from crewai.events import (
       LLMStreamChunkEvent
     )
-    from crewai.utilities.events.base_event_listener import BaseEventListener
+    from crewai.events import BaseEventListener
 
     class MyCustomListener(BaseEventListener):
         def setup_listeners(self, crewai_event_bus):

docs/pt-BR/concepts/tasks.mdx

@@ -59,6 +59,7 @@ crew = Crew(
 | **Output Pydantic** _(opcional)_ | `output_pydantic` | `Optional[Type[BaseModel]]`  | Um modelo Pydantic para a saída da tarefa.                                                                         |
 | **Callback** _(opcional)_        | `callback`        | `Optional[Any]`              | Função/objeto a ser executado após a conclusão da tarefa.                                                          |
 | **Guardrail** _(opcional)_       | `guardrail`       | `Optional[Callable]`             | Função para validar a saída da tarefa antes de prosseguir para a próxima tarefa.                                |
+| **Max Tentativas Guardrail** _(opcional)_ | `guardrail_max_retries` | `Optional[int]`    | Número máximo de tentativas quando a validação do guardrail falha. Padrão é 3.                                   |
 
 ## Criando Tarefas
 
@@ -450,7 +451,7 @@ task = Task(
     expected_output="Um objeto JSON válido",
     agent=analyst,
     guardrail=validate_json_output,
-    max_retries=3  # Limite de tentativas
+    guardrail_max_retries=3  # Limite de tentativas
 )
 ```
 
@@ -935,7 +936,7 @@ task = Task(
     description="Gerar dados",
     expected_output="Dados válidos",
     guardrail=validate_data,
-    max_retries=5  # Sobrescreve o limite padrão de tentativas
+    guardrail_max_retries=5  # Sobrescreve o limite padrão de tentativas
 )
 ```
 

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

@@ -58,7 +58,7 @@ Antes de usar as Integrações de Autenticação, certifique-se de que você pos
 3. Clique em **Conectar** no serviço desejado na seção Integrações de Autenticação
 4. Complete o fluxo de autenticação OAuth
 5. Conceda as permissões necessárias para seu caso de uso
-6. Obtenha seu Token Enterprise na sua página de conta do [CrewAI Enterprise](https://app.crewai.com) - https://app.crewai.com/crewai_plus/settings/account
+6. Pronto! Obtenha seu Token Enterprise do [CrewAI Enterprise](https://app.crewai.com) na aba **Integration**
 
 <Frame>
   ![Integrações](/images/enterprise/enterprise_action_auth_token.png)
@@ -176,4 +176,4 @@ Use o `user_bearer_token` para direcionar a integração a um usuário específi
 
 <Card title="Precisa de ajuda?" icon="headset" href="mailto:support@crewai.com">
   Entre em contato com nosso time de suporte para assistência com a configuração de integrações ou solução de problemas.
-</Card>
+</Card>

docs/pt-BR/enterprise/guides/automation-triggers.mdx

@@ -0,0 +1,171 @@
+---
+title: "Triggers de Automação"
+description: "Execute automaticamente seus workflows CrewAI quando eventos específicos ocorrem em integrações conectadas"
+icon: "bolt"
+---
+
+Os triggers de automação permitem executar automaticamente suas implantações CrewAI quando eventos específicos ocorrem em suas integrações conectadas, criando workflows poderosos orientados por eventos que respondem a mudanças em tempo real em seus sistemas de negócio.
+
+## Visão Geral
+
+Com triggers de automação, você pode:
+
+- **Responder a eventos em tempo real** - Execute workflows automaticamente quando condições específicas forem atendidas
+- **Integrar com sistemas externos** - Conecte com plataformas como Gmail, Outlook, OneDrive, JIRA, Slack, Stripe e muito mais
+- **Escalar sua automação** - Lide com eventos de alto volume sem intervenção manual
+- **Manter contexto** - Acesse dados do trigger dentro de suas crews e flows
+
+## Gerenciando Triggers de Automação
+
+### Visualizando Triggers Disponíveis
+
+Para acessar e gerenciar seus triggers de automação:
+
+1. Navegue até sua implantação no painel do CrewAI
+2. Clique na aba **Triggers** para visualizar todas as integrações de trigger disponíveis
+
+<Frame>
+  <img src="/images/enterprise/list-available-triggers.png" alt="Lista de triggers de automação disponíveis" />
+</Frame>
+
+Esta visualização mostra todas as integrações de trigger disponíveis para sua implantação, junto com seus status de conexão atuais.
+
+### Habilitando e Desabilitando Triggers
+
+Cada trigger pode ser facilmente habilitado ou desabilitado usando o botão de alternância:
+
+<Frame>
+  <img src="/images/enterprise/trigger-selected.png" alt="Habilitar ou desabilitar triggers com alternância" />
+</Frame>
+
+- **Habilitado (alternância azul)**: O trigger está ativo e executará automaticamente sua implantação quando os eventos especificados ocorrerem
+- **Desabilitado (alternância cinza)**: O trigger está inativo e não responderá a eventos
+
+Simplesmente clique na alternância para mudar o estado do trigger. As alterações entram em vigor imediatamente.
+
+### Monitorando Execuções de Trigger
+
+Acompanhe o desempenho e histórico de suas execuções acionadas:
+
+<Frame>
+  <img src="/images/enterprise/list-executions.png" alt="Lista de execuções acionadas por automação" />
+</Frame>
+
+## Construindo Automação
+
+Antes de construir sua automação, é útil entender a estrutura dos payloads de trigger que suas crews e flows receberão.
+
+### Repositório de Amostras de Payload
+
+Mantemos um repositório abrangente com amostras de payload de várias fontes de trigger para ajudá-lo a construir e testar suas automações:
+
+**🔗 [Amostras de Payload de Trigger CrewAI Enterprise](https://github.com/crewAIInc/crewai-enterprise-trigger-payload-samples)**
+
+Este repositório contém:
+
+- **Exemplos reais de payload** de diferentes fontes de trigger (Gmail, Google Drive, etc.)
+- **Documentação da estrutura de payload** mostrando o formato e campos disponíveis
+
+### Triggers com Crew
+
+Suas definições de crew existentes funcionam perfeitamente com triggers, você só precisa ter uma tarefa para analisar o payload recebido:
+
+```python
+@CrewBase
+class MinhaCrewAutomatizada:
+    @agent
+    def pesquisador(self) -> Agent:
+        return Agent(
+            config=self.agents_config['pesquisador'],
+        )
+
+    @task
+    def analisar_payload_trigger(self) -> Task:
+        return Task(
+            config=self.tasks_config['analisar_payload_trigger'],
+            agent=self.pesquisador(),
+        )
+
+    @task
+    def analisar_conteudo_trigger(self) -> Task:
+        return Task(
+            config=self.tasks_config['analisar_dados_trigger'],
+            agent=self.pesquisador(),
+        )
+```
+
+A crew receberá automaticamente e pode acessar o payload do trigger através dos mecanismos de contexto padrão do CrewAI.
+
+### Integração com Flows
+
+Para flows, você tem mais controle sobre como os dados do trigger são tratados:
+
+#### Acessando Payload do Trigger
+
+Todos os métodos `@start()` em seus flows aceitarão um parâmetro adicional chamado `crewai_trigger_payload`:
+
+```python
+from crewai.flow import Flow, start, listen
+
+class MeuFlowAutomatizado(Flow):
+    @start()
+    def lidar_com_trigger(self, crewai_trigger_payload: dict = None):
+        """
+        Este método start pode receber dados do trigger
+        """
+        if crewai_trigger_payload:
+            # Processa os dados do trigger
+            trigger_id = crewai_trigger_payload.get('id')
+            dados_evento = crewai_trigger_payload.get('payload', {})
+
+            # Armazena no estado do flow para uso por outros métodos
+            self.state.trigger_id = trigger_id
+            self.state.trigger_type = dados_evento
+
+            return dados_evento
+
+        # Lida com execução manual
+        return None
+
+    @listen(lidar_com_trigger)
+    def processar_dados(self, dados_trigger):
+        """
+        Processa os dados do trigger
+        """
+        # ... processa o trigger
+```
+
+#### Acionando Crews a partir de Flows
+
+Ao iniciar uma crew dentro de um flow que foi acionado, passe o payload do trigger conforme ele:
+
+```python
+@start()
+def delegar_para_crew(self, crewai_trigger_payload: dict = None):
+    """
+    Delega processamento para uma crew especializada
+    """
+    crew = MinhaCrewEspecializada()
+
+    # Passa o payload do trigger para a crew
+    resultado = crew.crew().kickoff(
+        inputs={
+            'parametro_personalizado': "valor_personalizado",
+            'crewai_trigger_payload': crewai_trigger_payload
+        },
+    )
+
+    return resultado
+```
+
+## Solução de Problemas
+
+**Trigger não está sendo disparado:**
+- Verifique se o trigger está habilitado
+- Verifique o status de conexão da integração
+
+**Falhas de execução:**
+- Verifique os logs de execução para detalhes do erro
+- Se você está desenvolvendo, certifique-se de que as entradas incluem o parâmetro `crewai_trigger_payload` com o payload correto
+
+Os triggers de automação transformam suas implantações CrewAI em sistemas responsivos orientados por eventos que podem se integrar perfeitamente com seus processos de negócio e ferramentas existentes.

docs/pt-BR/observability/agentops.mdx

@@ -1,126 +0,0 @@
----
-title: Integração com AgentOps
-description: Entendendo e registrando a performance do seu agente com AgentOps.
-icon: paperclip
----
-
-# Introdução
-
-Observabilidade é um aspecto fundamental no desenvolvimento e implantação de agentes de IA conversacional. Ela permite que desenvolvedores compreendam como seus agentes estão performando, 
-como eles estão interagindo com os usuários e como utilizam ferramentas externas e APIs. 
-AgentOps é um produto independente do CrewAI que fornece uma solução completa de observabilidade para agentes.
-
-## AgentOps
-
-[AgentOps](https://agentops.ai/?=crew) oferece replay de sessões, métricas e monitoramento para agentes.
-
-Em um alto nível, o AgentOps oferece a capacidade de monitorar custos, uso de tokens, latência, falhas do agente, estatísticas de sessão e muito mais. 
-Para mais informações, confira o [Repositório do AgentOps](https://github.com/AgentOps-AI/agentops).
-
-### Visão Geral
-
-AgentOps fornece monitoramento para agentes em desenvolvimento e produção. 
-Disponibiliza um dashboard para acompanhamento de performance dos agentes, replay de sessões e relatórios personalizados.
-
-Além disso, o AgentOps traz análises detalhadas das sessões para visualizar interações do agente Crew, chamadas LLM e uso de ferramentas em tempo real. 
-Esse recurso é útil para depuração e entendimento de como os agentes interagem com usuários e entre si.
-
-![Visão geral de uma série selecionada de execuções de sessões do agente](/images/agentops-overview.png)
-![Visão geral das análises detalhadas de sessões para examinar execuções de agentes](/images/agentops-session.png)
-![Visualizando um gráfico de execução passo a passo do replay do agente](/images/agentops-replay.png)
-
-### Funcionalidades
-
-- **Gerenciamento e Rastreamento de Custos de LLM**: Acompanhe gastos com provedores de modelos fundamentais.
-- **Análises de Replay**: Assista gráficos de execução do agente, passo a passo.
-- **Detecção de Pensamento Recursivo**: Identifique quando agentes entram em loops infinitos.
-- **Relatórios Personalizados**: Crie análises customizadas sobre a performance dos agentes.
-- **Dashboard Analítico**: Monitore estatísticas gerais de agentes em desenvolvimento e produção.
-- **Teste de Modelos Públicos**: Teste seus agentes em benchmarks e rankings.
-- **Testes Personalizados**: Execute seus agentes em testes específicos de domínio.
-- **Depuração com Viagem no Tempo**: Reinicie suas sessões a partir de checkpoints.
-- **Conformidade e Segurança**: Crie registros de auditoria e detecte possíveis ameaças como uso de palavrões e vazamento de dados pessoais.
-- **Detecção de Prompt Injection**: Identifique possíveis injeções de código e vazamentos de segredos.
-
-### Utilizando o AgentOps
-
-<Steps>
-   <Step title="Crie uma Chave de API">
-      Crie uma chave de API de usuário aqui: [Create API Key](https://app.agentops.ai/account)
-   </Step>
-   <Step title="Configure seu Ambiente">
-      Adicione sua chave API nas variáveis de ambiente:
-      ```bash
-      AGENTOPS_API_KEY=<YOUR_AGENTOPS_API_KEY>
-      ```
-   </Step>
-   <Step title="Instale o AgentOps">
-      Instale o AgentOps com:
-      ```bash
-      pip install 'crewai[agentops]'
-      ```
-      ou
-      ```bash
-      pip install agentops
-      ```
-   </Step>
-   <Step title="Inicialize o AgentOps">
-      Antes de utilizar o `Crew` no seu script, inclua estas linhas:
-
-      ```python
-      import agentops
-      agentops.init()
-      ```
-
-      Isso irá iniciar uma sessão do AgentOps e também rastrear automaticamente os agentes Crew. Para mais detalhes sobre como adaptar sistemas de agentes mais complexos, 
-      confira a [documentação do AgentOps](https://docs.agentops.ai) ou participe do [Discord](https://discord.gg/j4f3KbeH).
-   </Step>
-</Steps>
-
-### Exemplos de Crew + AgentOps
-
-<CardGroup cols={3}>
-  <Card
-  title="Vaga de Emprego"
-  color="#F3A78B"
-  href="https://github.com/joaomdmoura/crewAI-examples/tree/main/job-posting"
-  icon="briefcase"
-    iconType="solid"
-  >
-    Exemplo de um agente Crew que gera vagas de emprego.
-  </Card>
-  <Card
-    title="Validador de Markdown"
-    color="#F3A78B"
-    href="https://github.com/joaomdmoura/crewAI-examples/tree/main/markdown_validator"
-    icon="markdown"
-      iconType="solid"
-    >
-         Exemplo de um agente Crew que valida arquivos Markdown.
-    </Card>
-    <Card
-    title="Post no Instagram"
-    color="#F3A78B"
-    href="https://github.com/joaomdmoura/crewAI-examples/tree/main/instagram_post"
-    icon="square-instagram"
-      iconType="brands"
-    >
-      Exemplo de um agente Crew que gera posts para Instagram.
-  </Card>
-</CardGroup>
-
-### Mais Informações
-
-Para começar, crie uma [conta AgentOps](https://agentops.ai/?=crew).
-
-Para sugestões de funcionalidades ou relatos de bugs, entre em contato com o time do AgentOps pelo [Repositório do AgentOps](https://github.com/AgentOps-AI/agentops).
-
-#### Links Extras
-
-<a href="https://twitter.com/agentopsai/">🐦 Twitter</a>
-<span>&nbsp;&nbsp;•&nbsp;&nbsp;</span>
-<a href="https://discord.gg/JHPt4C7r">📢 Discord</a>
-<span>&nbsp;&nbsp;•&nbsp;&nbsp;</span>
-<a href="https://app.agentops.ai/?=crew">🖇️ Dashboard AgentOps</a>
-<span>&nbsp;&nbsp;•&nbsp;&nbsp;</span>
-<a href="https://docs.agentops.ai/introduction">📙 Documentação</a>

docs/pt-BR/observability/overview.mdx

@@ -21,9 +21,6 @@ A observabilidade é fundamental para entender como seus agentes CrewAI estão d
 ### Plataformas de Monitoramento e Rastreamento
 
 <CardGroup cols={2}>
-  <Card title="AgentOps" icon="paperclip" href="/pt-BR/observability/agentops">
-    Replays de sessões, métricas e monitoramento para desenvolvimento e produção de agentes.
-  </Card>
 
   <Card title="LangDB" icon="database" href="/pt-BR/observability/langdb">
     Rastreamento ponta a ponta para fluxos de trabalho CrewAI com captura automática de interações de agentes.

pyproject.toml

@@ -48,11 +48,10 @@ Documentation = "https://docs.crewai.com"
 Repository = "https://github.com/crewAIInc/crewAI"
 
 [project.optional-dependencies]
-tools = ["crewai-tools~=0.62.0"]
+tools = ["crewai-tools~=0.69.0"]
 embeddings = [
     "tiktoken~=0.8.0"
 ]
-agentops = ["agentops==0.3.18"]
 pdfplumber = [
     "pdfplumber>=0.11.4",
 ]
@@ -69,12 +68,16 @@ docling = [
 aisuite = [
     "aisuite>=0.1.10",
 ]
+qdrant = [
+    "qdrant-client[fastembed]>=1.14.3",
+]
 
 [tool.uv]
 dev-dependencies = [
-    "ruff>=0.8.2",
-    "mypy>=1.10.0",
-    "pre-commit>=3.6.0",
+    "ruff>=0.12.11",
+    "mypy>=1.17.1",
+    "pre-commit>=4.3.0",
+    "bandit>=1.8.6",
     "pillow>=10.2.0",
     "cairosvg>=2.7.1",
     "pytest>=8.0.0",
@@ -86,19 +89,50 @@ dev-dependencies = [
     "pytest-timeout>=2.3.1",
     "pytest-xdist>=3.6.1",
     "pytest-split>=0.9.0",
+    "types-requests==2.32.*",
+    "types-pyyaml==6.0.*",
+    "types-regex==2024.11.6.*",
+    "types-appdirs==1.4.*",
 ]
 
 [project.scripts]
 crewai = "crewai.cli.cli:crewai"
 
+[tool.ruff]
+exclude = [
+    "src/crewai/cli/templates",
+]
+fix = true
+
+[tool.ruff.lint]
+select = [
+    "B006",
+    "UP006",
+    "UP007",
+    "UP035",
+    "UP037",
+    "UP004",
+    "UP008",
+    "UP010",
+    "UP018",
+    "UP031",
+    "UP032",
+    "I001",
+    "I002",
+]
+
 [tool.mypy]
-ignore_missing_imports = true
-disable_error_code = 'import-untyped'
-exclude = ["cli/templates"]
+strict = true
+exclude = ["src/crewai/cli/templates"]
 
 [tool.bandit]
 exclude_dirs = ["src/crewai/cli/templates"]
 
+[tool.pytest.ini_options]
+markers = [
+    "telemetry: mark test as a telemetry test (don't mock telemetry)",
+]
+
 # PyTorch index configuration, since torch 2.5.0 is not compatible with python 3.13
 [[tool.uv.index]]
 name = "pytorch-nightly"

src/crewai/__init__.py

@@ -1,4 +1,30 @@
 import warnings
+from typing import Any
+
+
+def _suppress_pydantic_deprecation_warnings() -> None:
+    """Suppress Pydantic deprecation warnings using targeted monkey patch."""
+    original_warn = warnings.warn
+
+    def filtered_warn(
+        message: Any,
+        category: type | None = None,
+        stacklevel: int = 1,
+        source: Any = None,
+    ) -> Any:
+        if (
+            category
+            and hasattr(category, "__module__")
+            and category.__module__ == "pydantic.warnings"
+        ):
+            return None
+        return original_warn(message, category, stacklevel + 1, source)
+
+    setattr(warnings, "warn", filtered_warn)
+
+
+_suppress_pydantic_deprecation_warnings()
+
 import threading
 import urllib.request
 
@@ -15,17 +41,10 @@ from crewai.tasks.llm_guardrail import LLMGuardrail
 from crewai.tasks.task_output import TaskOutput
 from crewai.telemetry.telemetry import Telemetry
 
-warnings.filterwarnings(
-    "ignore",
-    message="Pydantic serializer warnings:",
-    category=UserWarning,
-    module="pydantic.main",
-)
-
 _telemetry_submitted = False
 
 
-def _track_install():
+def _track_install() -> None:
     """Track package installation/first-use via Scarf analytics."""
     global _telemetry_submitted
 
@@ -36,7 +55,7 @@ def _track_install():
         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__}')
+        req.add_header("User-Agent", f"CrewAI-Python/{__version__}")
 
         with urllib.request.urlopen(req, timeout=2):  # nosec B310
             _telemetry_submitted = True
@@ -45,7 +64,7 @@ def _track_install():
         pass
 
 
-def _track_install_async():
+def _track_install_async() -> None:
     """Track installation in background thread to avoid blocking imports."""
     if not Telemetry._is_telemetry_disabled():
         thread = threading.Thread(target=_track_install, daemon=True)
@@ -54,7 +73,7 @@ def _track_install_async():
 
 _track_install_async()
 
-__version__ = "0.159.0"
+__version__ = "0.177.0"
 __all__ = [
     "Agent",
     "Crew",

src/crewai/agent.py

@@ -1,7 +1,15 @@
 import shutil
 import subprocess
 import time
-from typing import Any, Callable, Dict, List, Literal, Optional, Sequence, Tuple, Type, Union
+from typing import (
+    Any,
+    Callable,
+    Literal,
+    Optional,
+    Sequence,
+    Type,
+    Union,
+)
 
 from pydantic import Field, InstanceOf, PrivateAttr, model_validator
 
@@ -27,17 +35,17 @@ from crewai.utilities.agent_utils import (
 )
 from crewai.utilities.constants import TRAINED_AGENTS_DATA_FILE, TRAINING_DATA_FILE
 from crewai.utilities.converter import generate_model_description
-from crewai.utilities.events.agent_events import (
+from crewai.events.types.agent_events import (
     AgentExecutionCompletedEvent,
     AgentExecutionErrorEvent,
     AgentExecutionStartedEvent,
 )
-from crewai.utilities.events.crewai_event_bus import crewai_event_bus
-from crewai.utilities.events.memory_events import (
+from crewai.events.event_bus import crewai_event_bus
+from crewai.events.types.memory_events import (
     MemoryRetrievalStartedEvent,
     MemoryRetrievalCompletedEvent,
 )
-from crewai.utilities.events.knowledge_events import (
+from crewai.events.types.knowledge_events import (
     KnowledgeQueryCompletedEvent,
     KnowledgeQueryFailedEvent,
     KnowledgeQueryStartedEvent,
@@ -140,7 +148,7 @@ class Agent(BaseAgent):
         default=None,
         description="Maximum number of reasoning attempts before executing the task. If None, will try until ready.",
     )
-    embedder: Optional[Dict[str, Any]] = Field(
+    embedder: Optional[dict[str, Any]] = Field(
         default=None,
         description="Embedder configuration for the agent.",
     )
@@ -160,9 +168,9 @@ class Agent(BaseAgent):
         default=None,
         description="The Agent's role to be used from your repository.",
     )
-    guardrail: Optional[Union[Callable[[Any], Tuple[bool, Any]], str]] = Field(
+    guardrail: Optional[Union[Callable[[Any], tuple[bool, Any]], str]] = Field(
         default=None,
-        description="Function or string description of a guardrail to validate agent output"
+        description="Function or string description of a guardrail to validate agent output",
     )
     guardrail_max_retries: int = Field(
         default=3, description="Maximum number of retries when guardrail fails"
@@ -197,7 +205,7 @@ class Agent(BaseAgent):
             self.cache_handler = CacheHandler()
         self.set_cache_handler(self.cache_handler)
 
-    def set_knowledge(self, crew_embedder: Optional[Dict[str, Any]] = None):
+    def set_knowledge(self, crew_embedder: Optional[dict[str, Any]] = None):
         try:
             if self.embedder is None and crew_embedder:
                 self.embedder = crew_embedder
@@ -234,7 +242,7 @@ class Agent(BaseAgent):
         self,
         task: Task,
         context: Optional[str] = None,
-        tools: Optional[List[BaseTool]] = None,
+        tools: Optional[list[BaseTool]] = None,
     ) -> str:
         """Execute a task with the agent.
 
@@ -276,7 +284,7 @@ class Agent(BaseAgent):
         self._inject_date_to_task(task)
 
         if self.tools_handler:
-            self.tools_handler.last_used_tool = {}  # type: ignore # Incompatible types in assignment (expression has type "dict[Never, Never]", variable has type "ToolCalling")
+            self.tools_handler.last_used_tool = None
 
         task_prompt = task.prompt()
 
@@ -309,15 +317,20 @@ class Agent(BaseAgent):
                 event=MemoryRetrievalStartedEvent(
                     task_id=str(task.id) if task else None,
                     source_type="agent",
+                    from_agent=self,
+                    from_task=task,
                 ),
             )
 
             start_time = time.time()
+
             contextual_memory = ContextualMemory(
                 self.crew._short_term_memory,
                 self.crew._long_term_memory,
                 self.crew._entity_memory,
                 self.crew._external_memory,
+                agent=self,
+                task=task,
             )
             memory = contextual_memory.build_context_for_task(task, context)
             if memory.strip() != "":
@@ -330,13 +343,14 @@ class Agent(BaseAgent):
                     memory_content=memory,
                     retrieval_time_ms=(time.time() - start_time) * 1000,
                     source_type="agent",
+                    from_agent=self,
+                    from_task=task,
                 ),
             )
         knowledge_config = (
             self.knowledge_config.model_dump() if self.knowledge_config else {}
         )
 
-
         if self.knowledge or (self.crew and self.crew.knowledge):
             crewai_event_bus.emit(
                 self,
@@ -537,14 +551,14 @@ class Agent(BaseAgent):
         )["output"]
 
     def create_agent_executor(
-        self, tools: Optional[List[BaseTool]] = None, task=None
+        self, tools: Optional[list[BaseTool]] = None, task=None
     ) -> None:
         """Create an agent executor for the agent.
 
         Returns:
             An instance of the CrewAgentExecutor class.
         """
-        raw_tools: List[BaseTool] = tools or self.tools or []
+        raw_tools: list[BaseTool] = tools or self.tools or []
         parsed_tools = parse_tools(raw_tools)
 
         prompt = Prompts(
@@ -586,7 +600,7 @@ class Agent(BaseAgent):
             callbacks=[TokenCalcHandler(self._token_process)],
         )
 
-    def get_delegation_tools(self, agents: List[BaseAgent]):
+    def get_delegation_tools(self, agents: list[BaseAgent]):
         agent_tools = AgentTools(agents=agents)
         tools = agent_tools.tools()
         return tools
@@ -637,7 +651,7 @@ class Agent(BaseAgent):
                 )
         return task_prompt
 
-    def _render_text_description(self, tools: List[Any]) -> str:
+    def _render_text_description(self, tools: list[Any]) -> str:
         """Render the tool name and description in plain text.
 
         Output will be in the format of:
@@ -779,7 +793,7 @@ class Agent(BaseAgent):
 
     def kickoff(
         self,
-        messages: Union[str, List[Dict[str, str]]],
+        messages: Union[str, list[dict[str, str]]],
         response_format: Optional[Type[Any]] = None,
     ) -> LiteAgentOutput:
         """
@@ -819,7 +833,7 @@ class Agent(BaseAgent):
 
     async def kickoff_async(
         self,
-        messages: Union[str, List[Dict[str, str]]],
+        messages: Union[str, list[dict[str, str]]],
         response_format: Optional[Type[Any]] = None,
     ) -> LiteAgentOutput:
         """

src/crewai/agents/__init__.py

@@ -1,5 +1,5 @@
-from .cache.cache_handler import CacheHandler
-from .parser import CrewAgentParser
-from .tools_handler import ToolsHandler
+from crewai.agents.cache.cache_handler import CacheHandler
+from crewai.agents.parser import parse, AgentAction, AgentFinish, OutputParserException
+from crewai.agents.tools_handler import ToolsHandler
 
-__all__ = ["CacheHandler", "CrewAgentParser", "ToolsHandler"]
+__all__ = ["CacheHandler", "parse", "AgentAction", "AgentFinish", "OutputParserException", "ToolsHandler"]

src/crewai/agents/agent_adapters/base_agent_adapter.py

@@ -1,5 +1,5 @@
 from abc import ABC, abstractmethod
-from typing import Any, Dict, List, Optional
+from typing import Any, Optional
 
 from pydantic import PrivateAttr
 
@@ -16,16 +16,16 @@ class BaseAgentAdapter(BaseAgent, ABC):
     """
 
     adapted_structured_output: bool = False
-    _agent_config: Optional[Dict[str, Any]] = PrivateAttr(default=None)
+    _agent_config: Optional[dict[str, Any]] = PrivateAttr(default=None)
 
     model_config = {"arbitrary_types_allowed": True}
 
-    def __init__(self, agent_config: Optional[Dict[str, Any]] = None, **kwargs: Any):
+    def __init__(self, agent_config: Optional[dict[str, Any]] = None, **kwargs: Any):
         super().__init__(adapted_agent=True, **kwargs)
         self._agent_config = agent_config
 
     @abstractmethod
-    def configure_tools(self, tools: Optional[List[BaseTool]] = None) -> None:
+    def configure_tools(self, tools: Optional[list[BaseTool]] = None) -> None:
         """Configure and adapt tools for the specific agent implementation.
 
         Args:

src/crewai/agents/agent_adapters/base_tool_adapter.py

@@ -1,5 +1,5 @@
 from abc import ABC, abstractmethod
-from typing import Any, List, Optional
+from typing import Any, Optional
 
 from crewai.tools.base_tool import BaseTool
 
@@ -12,15 +12,15 @@ class BaseToolAdapter(ABC):
     different frameworks and platforms.
     """
 
-    original_tools: List[BaseTool]
-    converted_tools: List[Any]
+    original_tools: list[BaseTool]
+    converted_tools: list[Any]
 
-    def __init__(self, tools: Optional[List[BaseTool]] = None):
+    def __init__(self, tools: Optional[list[BaseTool]] = None):
         self.original_tools = tools or []
         self.converted_tools = []
 
     @abstractmethod
-    def configure_tools(self, tools: List[BaseTool]) -> None:
+    def configure_tools(self, tools: list[BaseTool]) -> None:
         """Configure and convert tools for the specific implementation.
 
         Args:
@@ -28,7 +28,7 @@ class BaseToolAdapter(ABC):
         """
         pass
 
-    def tools(self) -> List[Any]:
+    def tools(self) -> list[Any]:
         """Return all converted tools."""
         return self.converted_tools
 

src/crewai/agents/agent_adapters/langgraph/langgraph_adapter.py

@@ -1,4 +1,4 @@
-from typing import Any, AsyncIterable, Dict, List, Optional
+from typing import Any, Optional
 
 from pydantic import Field, PrivateAttr
 
@@ -14,15 +14,14 @@ from crewai.tools.agent_tools.agent_tools import AgentTools
 from crewai.tools.base_tool import BaseTool
 from crewai.utilities import Logger
 from crewai.utilities.converter import Converter
-from crewai.utilities.events import crewai_event_bus
-from crewai.utilities.events.agent_events import (
+from crewai.events.event_bus import crewai_event_bus
+from crewai.events.types.agent_events import (
     AgentExecutionCompletedEvent,
     AgentExecutionErrorEvent,
     AgentExecutionStartedEvent,
 )
 
 try:
-    from langchain_core.messages import ToolMessage
     from langgraph.checkpoint.memory import MemorySaver
     from langgraph.prebuilt import create_react_agent
 
@@ -52,10 +51,10 @@ class LangGraphAgentAdapter(BaseAgentAdapter):
         role: str,
         goal: str,
         backstory: str,
-        tools: Optional[List[BaseTool]] = None,
+        tools: Optional[list[BaseTool]] = None,
         llm: Any = None,
         max_iterations: int = 10,
-        agent_config: Optional[Dict[str, Any]] = None,
+        agent_config: Optional[dict[str, Any]] = None,
         **kwargs,
     ):
         """Initialize the LangGraph agent adapter."""
@@ -82,7 +81,7 @@ class LangGraphAgentAdapter(BaseAgentAdapter):
         try:
             self._memory = MemorySaver()
 
-            converted_tools: List[Any] = self._tool_adapter.tools()
+            converted_tools: list[Any] = self._tool_adapter.tools()
             if self._agent_config:
                 self._graph = create_react_agent(
                     model=self.llm,
@@ -125,7 +124,7 @@ class LangGraphAgentAdapter(BaseAgentAdapter):
         self,
         task: Any,
         context: Optional[str] = None,
-        tools: Optional[List[BaseTool]] = None,
+        tools: Optional[list[BaseTool]] = None,
     ) -> str:
         """Execute a task using the LangGraph workflow."""
         self.create_agent_executor(tools)
@@ -198,11 +197,11 @@ class LangGraphAgentAdapter(BaseAgentAdapter):
             )
             raise
 
-    def create_agent_executor(self, tools: Optional[List[BaseTool]] = None) -> None:
+    def create_agent_executor(self, tools: Optional[list[BaseTool]] = None) -> None:
         """Configure the LangGraph agent for execution."""
         self.configure_tools(tools)
 
-    def configure_tools(self, tools: Optional[List[BaseTool]] = None) -> None:
+    def configure_tools(self, tools: Optional[list[BaseTool]] = None) -> None:
         """Configure tools for the LangGraph agent."""
         if tools:
             all_tools = list(self.tools or []) + list(tools or [])
@@ -210,7 +209,7 @@ class LangGraphAgentAdapter(BaseAgentAdapter):
             available_tools = self._tool_adapter.tools()
             self._graph.tools = available_tools
 
-    def get_delegation_tools(self, agents: List[BaseAgent]) -> List[BaseTool]:
+    def get_delegation_tools(self, agents: list[BaseAgent]) -> list[BaseTool]:
         """Implement delegation tools support for LangGraph."""
         agent_tools = AgentTools(agents=agents)
         return agent_tools.tools()

src/crewai/agents/agent_adapters/langgraph/langgraph_tool_adapter.py

@@ -1,5 +1,5 @@
 import inspect
-from typing import Any, List, Optional
+from typing import Any, Optional
 
 from crewai.agents.agent_adapters.base_tool_adapter import BaseToolAdapter
 from crewai.tools.base_tool import BaseTool
@@ -8,11 +8,11 @@ from crewai.tools.base_tool import BaseTool
 class LangGraphToolAdapter(BaseToolAdapter):
     """Adapts CrewAI tools to LangGraph agent tool compatible format"""
 
-    def __init__(self, tools: Optional[List[BaseTool]] = None):
+    def __init__(self, tools: Optional[list[BaseTool]] = None):
         self.original_tools = tools or []
         self.converted_tools = []
 
-    def configure_tools(self, tools: List[BaseTool]) -> None:
+    def configure_tools(self, tools: list[BaseTool]) -> None:
         """
         Configure and convert CrewAI tools to LangGraph-compatible format.
         LangGraph expects tools in langchain_core.tools format.
@@ -57,5 +57,5 @@ class LangGraphToolAdapter(BaseToolAdapter):
 
         self.converted_tools = converted_tools
 
-    def tools(self) -> List[Any]:
+    def tools(self) -> list[Any]:
         return self.converted_tools or []

src/crewai/agents/agent_adapters/openai_agents/openai_adapter.py

@@ -1,4 +1,4 @@
-from typing import Any, List, Optional
+from typing import Any, Optional
 
 from pydantic import Field, PrivateAttr
 
@@ -10,8 +10,8 @@ from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.tools import BaseTool
 from crewai.tools.agent_tools.agent_tools import AgentTools
 from crewai.utilities import Logger
-from crewai.utilities.events import crewai_event_bus
-from crewai.utilities.events.agent_events import (
+from crewai.events.event_bus import crewai_event_bus
+from crewai.events.types.agent_events import (
     AgentExecutionCompletedEvent,
     AgentExecutionErrorEvent,
     AgentExecutionStartedEvent,
@@ -44,7 +44,7 @@ class OpenAIAgentAdapter(BaseAgentAdapter):
     def __init__(
         self,
         model: str = "gpt-4o-mini",
-        tools: Optional[List[BaseTool]] = None,
+        tools: Optional[list[BaseTool]] = None,
         agent_config: Optional[dict] = None,
         **kwargs,
     ):
@@ -85,7 +85,7 @@ class OpenAIAgentAdapter(BaseAgentAdapter):
         self,
         task: Any,
         context: Optional[str] = None,
-        tools: Optional[List[BaseTool]] = None,
+        tools: Optional[list[BaseTool]] = None,
     ) -> str:
         """Execute a task using the OpenAI Assistant"""
         self._converter_adapter.configure_structured_output(task)
@@ -131,7 +131,7 @@ class OpenAIAgentAdapter(BaseAgentAdapter):
             )
             raise
 
-    def create_agent_executor(self, tools: Optional[List[BaseTool]] = None) -> None:
+    def create_agent_executor(self, tools: Optional[list[BaseTool]] = None) -> None:
         """
         Configure the OpenAI agent for execution.
         While OpenAI handles execution differently through Runner,
@@ -152,7 +152,7 @@ class OpenAIAgentAdapter(BaseAgentAdapter):
 
         self.agent_executor = Runner
 
-    def configure_tools(self, tools: Optional[List[BaseTool]] = None) -> None:
+    def configure_tools(self, tools: Optional[list[BaseTool]] = None) -> None:
         """Configure tools for the OpenAI Assistant"""
         if tools:
             self._tool_adapter.configure_tools(tools)
@@ -163,7 +163,7 @@ class OpenAIAgentAdapter(BaseAgentAdapter):
         """Process OpenAI Assistant execution result converting any structured output to a string"""
         return self._converter_adapter.post_process_result(result.final_output)
 
-    def get_delegation_tools(self, agents: List[BaseAgent]) -> List[BaseTool]:
+    def get_delegation_tools(self, agents: list[BaseAgent]) -> list[BaseTool]:
         """Implement delegation tools support"""
         agent_tools = AgentTools(agents=agents)
         tools = agent_tools.tools()

src/crewai/agents/agent_adapters/openai_agents/openai_agent_tool_adapter.py

@@ -1,5 +1,5 @@
 import inspect
-from typing import Any, List, Optional
+from typing import Any, Optional
 
 from agents import FunctionTool, Tool
 
@@ -10,10 +10,10 @@ from crewai.tools import BaseTool
 class OpenAIAgentToolAdapter(BaseToolAdapter):
     """Adapter for OpenAI Assistant tools"""
 
-    def __init__(self, tools: Optional[List[BaseTool]] = None):
+    def __init__(self, tools: Optional[list[BaseTool]] = None):
         self.original_tools = tools or []
 
-    def configure_tools(self, tools: List[BaseTool]) -> None:
+    def configure_tools(self, tools: list[BaseTool]) -> None:
         """Configure tools for the OpenAI Assistant"""
         if self.original_tools:
             all_tools = tools + self.original_tools
@@ -23,8 +23,8 @@ class OpenAIAgentToolAdapter(BaseToolAdapter):
             self.converted_tools = self._convert_tools_to_openai_format(all_tools)
 
     def _convert_tools_to_openai_format(
-        self, tools: Optional[List[BaseTool]]
-    ) -> List[Tool]:
+        self, tools: Optional[list[BaseTool]]
+    ) -> list[Tool]:
         """Convert CrewAI tools to OpenAI Assistant tool format"""
         if not tools:
             return []

src/crewai/agents/agent_builder/base_agent.py

@@ -2,7 +2,7 @@ import uuid
 from abc import ABC, abstractmethod
 from copy import copy as shallow_copy
 from hashlib import md5
-from typing import Any, Callable, Dict, List, Optional, TypeVar
+from typing import Any, Callable, Optional, TypeVar
 
 from pydantic import (
     UUID4,
@@ -40,11 +40,11 @@ class BaseAgent(ABC, BaseModel):
         goal (str): Objective of the agent.
         backstory (str): Backstory of the agent.
         cache (bool): Whether the agent should use a cache for tool usage.
-        config (Optional[Dict[str, Any]]): Configuration for the agent.
+        config (Optional[dict[str, Any]]): Configuration for the agent.
         verbose (bool): Verbose mode for the Agent Execution.
         max_rpm (Optional[int]): Maximum number of requests per minute for the agent execution.
         allow_delegation (bool): Allow delegation of tasks to agents.
-        tools (Optional[List[Any]]): Tools at the agent's disposal.
+        tools (Optional[list[Any]]): Tools at the agent's disposal.
         max_iter (int): Maximum iterations for an agent to execute a task.
         agent_executor (InstanceOf): An instance of the CrewAgentExecutor class.
         llm (Any): Language model that will run the agent.
@@ -59,15 +59,15 @@ class BaseAgent(ABC, BaseModel):
 
 
     Methods:
-        execute_task(task: Any, context: Optional[str] = None, tools: Optional[List[BaseTool]] = None) -> str:
+        execute_task(task: Any, context: Optional[str] = None, tools: Optional[list[BaseTool]] = None) -> str:
             Abstract method to execute a task.
         create_agent_executor(tools=None) -> None:
             Abstract method to create an agent executor.
-        get_delegation_tools(agents: List["BaseAgent"]):
+        get_delegation_tools(agents: list["BaseAgent"]):
             Abstract method to set the agents task tools for handling delegation and question asking to other agents in crew.
         get_output_converter(llm, model, instructions):
             Abstract method to get the converter class for the agent to create json/pydantic outputs.
-        interpolate_inputs(inputs: Dict[str, Any]) -> None:
+        interpolate_inputs(inputs: dict[str, Any]) -> None:
             Interpolate inputs into the agent description and backstory.
         set_cache_handler(cache_handler: CacheHandler) -> None:
             Set the cache handler for the agent.
@@ -91,7 +91,7 @@ class BaseAgent(ABC, BaseModel):
     role: str = Field(description="Role of the agent")
     goal: str = Field(description="Objective of the agent")
     backstory: str = Field(description="Backstory of the agent")
-    config: Optional[Dict[str, Any]] = Field(
+    config: Optional[dict[str, Any]] = Field(
         description="Configuration for the agent", default=None, exclude=True
     )
     cache: bool = Field(
@@ -108,7 +108,7 @@ class BaseAgent(ABC, BaseModel):
         default=False,
         description="Enable agent to delegate and ask questions among each other.",
     )
-    tools: Optional[List[BaseTool]] = Field(
+    tools: Optional[list[BaseTool]] = Field(
         default_factory=list, description="Tools at agents' disposal"
     )
     max_iter: int = Field(
@@ -129,7 +129,7 @@ class BaseAgent(ABC, BaseModel):
         default_factory=ToolsHandler,
         description="An instance of the ToolsHandler class.",
     )
-    tools_results: List[Dict[str, Any]] = Field(
+    tools_results: list[dict[str, Any]] = Field(
         default=[], description="Results of the tools used by the agent."
     )
     max_tokens: Optional[int] = Field(
@@ -138,7 +138,7 @@ class BaseAgent(ABC, BaseModel):
     knowledge: Optional[Knowledge] = Field(
         default=None, description="Knowledge for the agent."
     )
-    knowledge_sources: Optional[List[BaseKnowledgeSource]] = Field(
+    knowledge_sources: Optional[list[BaseKnowledgeSource]] = Field(
         default=None,
         description="Knowledge sources for the agent.",
     )
@@ -150,7 +150,7 @@ class BaseAgent(ABC, BaseModel):
         default_factory=SecurityConfig,
         description="Security configuration for the agent, including fingerprinting.",
     )
-    callbacks: List[Callable] = Field(
+    callbacks: list[Callable] = Field(
         default=[], description="Callbacks to be used for the agent"
     )
     adapted_agent: bool = Field(
@@ -168,7 +168,7 @@ class BaseAgent(ABC, BaseModel):
 
     @field_validator("tools")
     @classmethod
-    def validate_tools(cls, tools: List[Any]) -> List[BaseTool]:
+    def validate_tools(cls, tools: list[Any]) -> list[BaseTool]:
         """Validate and process the tools provided to the agent.
 
         This method ensures that each tool is either an instance of BaseTool
@@ -253,7 +253,7 @@ class BaseAgent(ABC, BaseModel):
         self,
         task: Any,
         context: Optional[str] = None,
-        tools: Optional[List[BaseTool]] = None,
+        tools: Optional[list[BaseTool]] = None,
     ) -> str:
         pass
 
@@ -262,7 +262,7 @@ class BaseAgent(ABC, BaseModel):
         pass
 
     @abstractmethod
-    def get_delegation_tools(self, agents: List["BaseAgent"]) -> List[BaseTool]:
+    def get_delegation_tools(self, agents: list["BaseAgent"]) -> list[BaseTool]:
         """Set the task tools that init BaseAgenTools class."""
         pass
 
@@ -320,7 +320,7 @@ class BaseAgent(ABC, BaseModel):
 
         return copied_agent
 
-    def interpolate_inputs(self, inputs: Dict[str, Any]) -> None:
+    def interpolate_inputs(self, inputs: dict[str, Any]) -> None:
         """Interpolate inputs into the agent description and backstory."""
         if self._original_role is None:
             self._original_role = self.role
@@ -362,5 +362,5 @@ class BaseAgent(ABC, BaseModel):
             self._rpm_controller = rpm_controller
             self.create_agent_executor()
 
-    def set_knowledge(self, crew_embedder: Optional[Dict[str, Any]] = None):
+    def set_knowledge(self, crew_embedder: Optional[dict[str, Any]] = None):
         pass

src/crewai/agents/agent_builder/base_agent_executor_mixin.py

@@ -1,5 +1,5 @@
 import time
-from typing import TYPE_CHECKING, Dict, List
+from typing import TYPE_CHECKING
 
 from crewai.memory.entity.entity_memory_item import EntityMemoryItem
 from crewai.memory.long_term.long_term_memory_item import LongTermMemoryItem
@@ -7,7 +7,7 @@ from crewai.utilities import I18N
 from crewai.utilities.converter import ConverterError
 from crewai.utilities.evaluators.task_evaluator import TaskEvaluator
 from crewai.utilities.printer import Printer
-from crewai.utilities.events.event_listener import event_listener
+from crewai.events.event_listener import event_listener
 
 if TYPE_CHECKING:
     from crewai.agents.agent_builder.base_agent import BaseAgent
@@ -21,7 +21,7 @@ class CrewAgentExecutorMixin:
     task: "Task"
     iterations: int
     max_iter: int
-    messages: List[Dict[str, str]]
+    messages: list[dict[str, str]]
     _i18n: I18N
     _printer: Printer = Printer()
 
@@ -43,7 +43,6 @@ class CrewAgentExecutorMixin:
                         metadata={
                             "observation": self.task.description,
                         },
-                        agent=self.agent.role,
                     )
             except Exception as e:
                 print(f"Failed to add to short term memory: {e}")
@@ -65,7 +64,6 @@ class CrewAgentExecutorMixin:
                         "description": self.task.description,
                         "messages": self.messages,
                     },
-                    agent=self.agent.role,
                 )
             except Exception as e:
                 print(f"Failed to add to external memory: {e}")
@@ -100,8 +98,8 @@ class CrewAgentExecutorMixin:
                 )
                 self.crew._long_term_memory.save(long_term_memory)
 
-                for entity in evaluation.entities:
-                    entity_memory = EntityMemoryItem(
+                entity_memories = [
+                    EntityMemoryItem(
                         name=entity.name,
                         type=entity.type,
                         description=entity.description,
@@ -109,7 +107,10 @@ class CrewAgentExecutorMixin:
                             [f"- {r}" for r in entity.relationships]
                         ),
                     )
-                    self.crew._entity_memory.save(entity_memory)
+                    for entity in evaluation.entities
+                ]
+                if entity_memories:
+                    self.crew._entity_memory.save(entity_memories)
             except AttributeError as e:
                 print(f"Missing attributes for long term memory: {e}")
                 pass
@@ -158,7 +159,9 @@ class CrewAgentExecutorMixin:
             self._printer.print(content=prompt, color="bold_yellow")
             response = input()
             if response.strip() != "":
-                self._printer.print(content="\nProcessing your feedback...", color="cyan")
+                self._printer.print(
+                    content="\nProcessing your feedback...", color="cyan"
+                )
             return response
         finally:
             event_listener.formatter.resume_live_updates()

src/crewai/agents/cache/cache_handler.py

@@ -1,4 +1,4 @@
-from typing import Any, Dict, Optional
+from typing import Any, Optional
 
 from pydantic import BaseModel, PrivateAttr
 
@@ -6,7 +6,7 @@ from pydantic import BaseModel, PrivateAttr
 class CacheHandler(BaseModel):
     """Callback handler for tool usage."""
 
-    _cache: Dict[str, Any] = PrivateAttr(default_factory=dict)
+    _cache: dict[str, Any] = PrivateAttr(default_factory=dict)
 
     def add(self, tool, input, output):
         self._cache[f"{tool}-{input}"] = output

src/crewai/agents/constants.py

@@ -0,0 +1,27 @@
+"""Constants for agent-related modules."""
+
+import re
+from typing import Final
+
+# crewai.agents.parser constants
+
+FINAL_ANSWER_ACTION: Final[str] = "Final Answer:"
+MISSING_ACTION_AFTER_THOUGHT_ERROR_MESSAGE: Final[str] = (
+    "I did it wrong. Invalid Format: I missed the 'Action:' after 'Thought:'. I will do right next, and don't use a tool I have already used.\n"
+)
+MISSING_ACTION_INPUT_AFTER_ACTION_ERROR_MESSAGE: Final[str] = (
+    "I did it wrong. Invalid Format: I missed the 'Action Input:' after 'Action:'. I will do right next, and don't use a tool I have already used.\n"
+)
+FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE: Final[str] = (
+    "I did it wrong. Tried to both perform Action and give a Final Answer at the same time, I must do one or the other"
+)
+UNABLE_TO_REPAIR_JSON_RESULTS: Final[list[str]] = ['""', "{}"]
+ACTION_INPUT_REGEX: Final[re.Pattern[str]] = re.compile(
+    r"Action\s*\d*\s*:\s*(.*?)\s*Action\s*\d*\s*Input\s*\d*\s*:\s*(.*)", re.DOTALL
+)
+ACTION_REGEX: Final[re.Pattern[str]] = re.compile(
+    r"Action\s*\d*\s*:\s*(.*?)", re.DOTALL
+)
+ACTION_INPUT_ONLY_REGEX: Final[re.Pattern[str]] = re.compile(
+    r"\s*Action\s*\d*\s*Input\s*\d*\s*:\s*(.*)", re.DOTALL
+)

src/crewai/agents/crew_agent_executor.py

@@ -1,4 +1,4 @@
-from typing import Any, Callable, Dict, List, Optional, Union
+from typing import Any, Callable, Optional, Union
 
 from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.agents.agent_builder.base_agent_executor_mixin import CrewAgentExecutorMixin
@@ -30,11 +30,11 @@ from crewai.utilities.constants import MAX_LLM_RETRY, TRAINING_DATA_FILE
 from crewai.utilities.logger import Logger
 from crewai.utilities.tool_utils import execute_tool_and_check_finality
 from crewai.utilities.training_handler import CrewTrainingHandler
-from crewai.utilities.events.agent_events import (
+from crewai.events.types.logging_events import (
     AgentLogsStartedEvent,
     AgentLogsExecutionEvent,
 )
-from crewai.utilities.events.crewai_event_bus import crewai_event_bus
+from crewai.events.event_bus import crewai_event_bus
 
 
 class CrewAgentExecutor(CrewAgentExecutorMixin):
@@ -48,17 +48,17 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         agent: BaseAgent,
         prompt: dict[str, str],
         max_iter: int,
-        tools: List[CrewStructuredTool],
+        tools: list[CrewStructuredTool],
         tools_names: str,
-        stop_words: List[str],
+        stop_words: list[str],
         tools_description: str,
         tools_handler: ToolsHandler,
         step_callback: Any = None,
-        original_tools: List[Any] = [],
+        original_tools: list[Any] | None = None,
         function_calling_llm: Any = None,
         respect_context_window: bool = False,
         request_within_rpm_limit: Optional[Callable[[], bool]] = None,
-        callbacks: List[Any] = [],
+        callbacks: list[Any] | None = None,
     ):
         self._i18n: I18N = I18N()
         self.llm: BaseLLM = llm
@@ -70,10 +70,10 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         self.tools_names = tools_names
         self.stop = stop_words
         self.max_iter = max_iter
-        self.callbacks = callbacks
+        self.callbacks = callbacks or []
         self._printer: Printer = Printer()
         self.tools_handler = tools_handler
-        self.original_tools = original_tools
+        self.original_tools = original_tools or []
         self.step_callback = step_callback
         self.use_stop_words = self.llm.supports_stop_words()
         self.tools_description = tools_description
@@ -81,10 +81,10 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         self.respect_context_window = respect_context_window
         self.request_within_rpm_limit = request_within_rpm_limit
         self.ask_for_human_input = False
-        self.messages: List[Dict[str, str]] = []
+        self.messages: list[dict[str, str]] = []
         self.iterations = 0
         self.log_error_after = 3
-        self.tool_name_to_tool_map: Dict[str, Union[CrewStructuredTool, BaseTool]] = {
+        self.tool_name_to_tool_map: dict[str, Union[CrewStructuredTool, BaseTool]] = {
             tool.name: tool for tool in self.tools
         }
         existing_stop = self.llm.stop or []
@@ -96,7 +96,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
             )
         )
 
-    def invoke(self, inputs: Dict[str, str]) -> Dict[str, Any]:
+    def invoke(self, inputs: dict[str, str]) -> dict[str, Any]:
         if "system" in self.prompt:
             system_prompt = self._format_prompt(self.prompt.get("system", ""), inputs)
             user_prompt = self._format_prompt(self.prompt.get("user", ""), inputs)
@@ -122,7 +122,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
             handle_unknown_error(self._printer, e)
             raise
 
-
         if self.ask_for_human_input:
             formatted_answer = self._handle_human_feedback(formatted_answer)
 
@@ -156,7 +155,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                     messages=self.messages,
                     callbacks=self.callbacks,
                     printer=self._printer,
-                    from_task=self.task
+                    from_task=self.task,
                 )
                 formatted_answer = process_llm_response(answer, self.use_stop_words)
 
@@ -372,7 +371,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         training_data[agent_id] = agent_training_data
         training_handler.save(training_data)
 
-    def _format_prompt(self, prompt: str, inputs: Dict[str, str]) -> str:
+    def _format_prompt(self, prompt: str, inputs: dict[str, str]) -> str:
         prompt = prompt.replace("{input}", inputs["input"])
         prompt = prompt.replace("{tool_names}", inputs["tool_names"])
         prompt = prompt.replace("{tools}", inputs["tools"])

src/crewai/agents/parser.py

@@ -1,50 +1,67 @@
-import re
-from typing import Any, Optional, Union
+"""Agent output parsing module for ReAct-style LLM responses.
+
+This module provides parsing functionality for agent outputs that follow
+the ReAct (Reasoning and Acting) format, converting them into structured
+AgentAction or AgentFinish objects.
+"""
+
+from dataclasses import dataclass
 
 from json_repair import repair_json
 
+from crewai.agents.constants import (
+    ACTION_INPUT_REGEX,
+    ACTION_REGEX,
+    ACTION_INPUT_ONLY_REGEX,
+    FINAL_ANSWER_ACTION,
+    MISSING_ACTION_AFTER_THOUGHT_ERROR_MESSAGE,
+    MISSING_ACTION_INPUT_AFTER_ACTION_ERROR_MESSAGE,
+    UNABLE_TO_REPAIR_JSON_RESULTS,
+)
 from crewai.utilities import I18N
 
-FINAL_ANSWER_ACTION = "Final Answer:"
-MISSING_ACTION_AFTER_THOUGHT_ERROR_MESSAGE = "I did it wrong. Invalid Format: I missed the 'Action:' after 'Thought:'. I will do right next, and don't use a tool I have already used.\n"
-MISSING_ACTION_INPUT_AFTER_ACTION_ERROR_MESSAGE = "I did it wrong. Invalid Format: I missed the 'Action Input:' after 'Action:'. I will do right next, and don't use a tool I have already used.\n"
-FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE = "I did it wrong. Tried to both perform Action and give a Final Answer at the same time, I must do one or the other"
+_I18N = I18N()
 
 
+@dataclass
 class AgentAction:
+    """Represents an action to be taken by an agent."""
+
     thought: str
     tool: str
     tool_input: str
     text: str
-    result: str
-
-    def __init__(self, thought: str, tool: str, tool_input: str, text: str):
-        self.thought = thought
-        self.tool = tool
-        self.tool_input = tool_input
-        self.text = text
+    result: str | None = None
 
 
+@dataclass
 class AgentFinish:
+    """Represents the final answer from an agent."""
+
     thought: str
     output: str
     text: str
 
-    def __init__(self, thought: str, output: str, text: str):
-        self.thought = thought
-        self.output = output
-        self.text = text
-
 
 class OutputParserException(Exception):
-    error: str
+    """Exception raised when output parsing fails.
 
-    def __init__(self, error: str):
+    Attributes:
+        error: The error message.
+    """
+
+    def __init__(self, error: str) -> None:
+        """Initialize OutputParserException.
+
+        Args:
+            error: The error message.
+        """
         self.error = error
+        super().__init__(error)
 
 
-class CrewAgentParser:
-    """Parses ReAct-style LLM calls that have a single tool input.
+def parse(text: str) -> AgentAction | AgentFinish:
+    """Parse agent output text into AgentAction or AgentFinish.
 
     Expects output to be in one of two formats.
 
@@ -62,108 +79,117 @@ class CrewAgentParser:
 
     Thought: agent thought here
     Final Answer: The temperature is 100 degrees
+
+    Args:
+        text: The agent output text to parse.
+
+    Returns:
+        AgentAction or AgentFinish based on the content.
+
+    Raises:
+        OutputParserException: If the text format is invalid.
     """
+    thought = _extract_thought(text)
+    includes_answer = FINAL_ANSWER_ACTION in text
+    action_match = ACTION_INPUT_REGEX.search(text)
 
-    _i18n: I18N = I18N()
-    agent: Any = None
+    if includes_answer:
+        final_answer = text.split(FINAL_ANSWER_ACTION)[-1].strip()
+        # Check whether the final answer ends with triple backticks.
+        if final_answer.endswith("```"):
+            # Count occurrences of triple backticks in the final answer.
+            count = final_answer.count("```")
+            # If count is odd then it's an unmatched trailing set; remove it.
+            if count % 2 != 0:
+                final_answer = final_answer[:-3].rstrip()
+        return AgentFinish(thought=thought, output=final_answer, text=text)
 
-    def __init__(self, agent: Optional[Any] = None):
-        self.agent = agent
+    elif action_match:
+        action = action_match.group(1)
+        clean_action = _clean_action(action)
 
-    @staticmethod
-    def parse_text(text: str) -> Union[AgentAction, AgentFinish]:
-        """
-        Static method to parse text into an AgentAction or AgentFinish without needing to instantiate the class.
+        action_input = action_match.group(2).strip()
 
-        Args:
-            text: The text to parse.
+        tool_input = action_input.strip(" ").strip('"')
+        safe_tool_input = _safe_repair_json(tool_input)
 
-        Returns:
-            Either an AgentAction or AgentFinish based on the parsed content.
-        """
-        parser = CrewAgentParser()
-        return parser.parse(text)
-
-    def parse(self, text: str) -> Union[AgentAction, AgentFinish]:
-        thought = self._extract_thought(text)
-        includes_answer = FINAL_ANSWER_ACTION in text
-        regex = (
-            r"Action\s*\d*\s*:[\s]*(.*?)[\s]*Action\s*\d*\s*Input\s*\d*\s*:[\s]*(.*)"
+        return AgentAction(
+            thought=thought, tool=clean_action, tool_input=safe_tool_input, text=text
         )
-        action_match = re.search(regex, text, re.DOTALL)
-        if includes_answer:
-            final_answer = text.split(FINAL_ANSWER_ACTION)[-1].strip()
-            # Check whether the final answer ends with triple backticks.
-            if final_answer.endswith("```"):
-                # Count occurrences of triple backticks in the final answer.
-                count = final_answer.count("```")
-                # If count is odd then it's an unmatched trailing set; remove it.
-                if count % 2 != 0:
-                    final_answer = final_answer[:-3].rstrip()
-            return AgentFinish(thought, final_answer, text)
 
-        elif action_match:
-            action = action_match.group(1)
-            clean_action = self._clean_action(action)
+    if not ACTION_REGEX.search(text):
+        raise OutputParserException(
+            f"{MISSING_ACTION_AFTER_THOUGHT_ERROR_MESSAGE}\n{_I18N.slice('final_answer_format')}",
+        )
+    elif not ACTION_INPUT_ONLY_REGEX.search(text):
+        raise OutputParserException(
+            MISSING_ACTION_INPUT_AFTER_ACTION_ERROR_MESSAGE,
+        )
+    else:
+        err_format = _I18N.slice("format_without_tools")
+        error = f"{err_format}"
+        raise OutputParserException(
+            error,
+        )
 
-            action_input = action_match.group(2).strip()
 
-            tool_input = action_input.strip(" ").strip('"')
-            safe_tool_input = self._safe_repair_json(tool_input)
+def _extract_thought(text: str) -> str:
+    """Extract the thought portion from the text.
 
-            return AgentAction(thought, clean_action, safe_tool_input, text)
+    Args:
+        text: The full agent output text.
 
-        if not re.search(r"Action\s*\d*\s*:[\s]*(.*?)", text, re.DOTALL):
-            raise OutputParserException(
-                f"{MISSING_ACTION_AFTER_THOUGHT_ERROR_MESSAGE}\n{self._i18n.slice('final_answer_format')}",
-            )
-        elif not re.search(
-            r"[\s]*Action\s*\d*\s*Input\s*\d*\s*:[\s]*(.*)", text, re.DOTALL
-        ):
-            raise OutputParserException(
-                MISSING_ACTION_INPUT_AFTER_ACTION_ERROR_MESSAGE,
-            )
-        else:
-            format = self._i18n.slice("format_without_tools")
-            error = f"{format}"
-            raise OutputParserException(
-                error,
-            )
+    Returns:
+        The extracted thought string.
+    """
+    thought_index = text.find("\nAction")
+    if thought_index == -1:
+        thought_index = text.find("\nFinal Answer")
+    if thought_index == -1:
+        return ""
+    thought = text[:thought_index].strip()
+    # Remove any triple backticks from the thought string
+    thought = thought.replace("```", "").strip()
+    return thought
 
-    def _extract_thought(self, text: str) -> str:
-        thought_index = text.find("\nAction")
-        if thought_index == -1:
-            thought_index = text.find("\nFinal Answer")
-        if thought_index == -1:
-            return ""
-        thought = text[:thought_index].strip()
-        # Remove any triple backticks from the thought string
-        thought = thought.replace("```", "").strip()
-        return thought
 
-    def _clean_action(self, text: str) -> str:
-        """Clean action string by removing non-essential formatting characters."""
-        return text.strip().strip("*").strip()
+def _clean_action(text: str) -> str:
+    """Clean action string by removing non-essential formatting characters.
 
-    def _safe_repair_json(self, tool_input: str) -> str:
-        UNABLE_TO_REPAIR_JSON_RESULTS = ['""', "{}"]
+    Args:
+        text: The action text to clean.
 
-        # Skip repair if the input starts and ends with square brackets
-        # Explanation: The JSON parser has issues handling inputs that are enclosed in square brackets ('[]').
-        # These are typically valid JSON arrays or strings that do not require repair. Attempting to repair such inputs
-        # might lead to unintended alterations, such as wrapping the entire input in additional layers or modifying
-        # the structure in a way that changes its meaning. By skipping the repair for inputs that start and end with
-        # square brackets, we preserve the integrity of these valid JSON structures and avoid unnecessary modifications.
-        if tool_input.startswith("[") and tool_input.endswith("]"):
-            return tool_input
+    Returns:
+        The cleaned action string.
+    """
+    return text.strip().strip("*").strip()
 
-        # Before repair, handle common LLM issues:
-        # 1. Replace """ with " to avoid JSON parser errors
 
-        tool_input = tool_input.replace('"""', '"')
+def _safe_repair_json(tool_input: str) -> str:
+    """Safely repair JSON input.
 
-        result = repair_json(tool_input)
-        if result in UNABLE_TO_REPAIR_JSON_RESULTS:
-            return tool_input
+    Args:
+        tool_input: The tool input string to repair.
 
-        return str(result)
+    Returns:
+        The repaired JSON string or original if repair fails.
+    """
+    # Skip repair if the input starts and ends with square brackets
+    # Explanation: The JSON parser has issues handling inputs that are enclosed in square brackets ('[]').
+    # These are typically valid JSON arrays or strings that do not require repair. Attempting to repair such inputs
+    # might lead to unintended alterations, such as wrapping the entire input in additional layers or modifying
+    # the structure in a way that changes its meaning. By skipping the repair for inputs that start and end with
+    # square brackets, we preserve the integrity of these valid JSON structures and avoid unnecessary modifications.
+    if tool_input.startswith("[") and tool_input.endswith("]"):
+        return tool_input
+
+    # Before repair, handle common LLM issues:
+    # 1. Replace """ with " to avoid JSON parser errors
+
+    tool_input = tool_input.replace('"""', '"')
+
+    result = repair_json(tool_input)
+    if result in UNABLE_TO_REPAIR_JSON_RESULTS:
+        return tool_input
+
+    return str(result)

src/crewai/agents/tools_handler.py

@@ -1,29 +1,41 @@
-from typing import Any, Optional, Union
+"""Tools handler for managing tool execution and caching."""
 
-from ..tools.cache_tools.cache_tools import CacheTools
-from ..tools.tool_calling import InstructorToolCalling, ToolCalling
-from .cache.cache_handler import CacheHandler
+from crewai.tools.cache_tools.cache_tools import CacheTools
+from crewai.tools.tool_calling import InstructorToolCalling, ToolCalling
+from crewai.agents.cache.cache_handler import CacheHandler
 
 
 class ToolsHandler:
-    """Callback handler for tool usage."""
+    """Callback handler for tool usage.
 
-    last_used_tool: ToolCalling = {}  # type: ignore # BUG?: Incompatible types in assignment (expression has type "Dict[...]", variable has type "ToolCalling")
-    cache: Optional[CacheHandler]
+    Attributes:
+        last_used_tool: The most recently used tool calling instance.
+        cache: Optional cache handler for storing tool outputs.
+    """
 
-    def __init__(self, cache: Optional[CacheHandler] = None):
-        """Initialize the callback handler."""
-        self.cache = cache
-        self.last_used_tool = {}  # type: ignore # BUG?: same as above
+    def __init__(self, cache: CacheHandler | None = None) -> None:
+        """Initialize the callback handler.
+
+        Args:
+            cache: Optional cache handler for storing tool outputs.
+        """
+        self.cache: CacheHandler | None = cache
+        self.last_used_tool: ToolCalling | InstructorToolCalling | None = None
 
     def on_tool_use(
         self,
-        calling: Union[ToolCalling, InstructorToolCalling],
+        calling: ToolCalling | InstructorToolCalling,
         output: str,
         should_cache: bool = True,
-    ) -> Any:
-        """Run when tool ends running."""
-        self.last_used_tool = calling  # type: ignore # BUG?: Incompatible types in assignment (expression has type "Union[ToolCalling, InstructorToolCalling]", variable has type "ToolCalling")
+    ) -> None:
+        """Run when tool ends running.
+
+        Args:
+            calling: The tool calling instance.
+            output: The output from the tool execution.
+            should_cache: Whether to cache the tool output.
+        """
+        self.last_used_tool = calling
         if self.cache and should_cache and calling.tool_name != CacheTools().name:
             self.cache.add(
                 tool=calling.tool_name,

src/crewai/cli/authentication/constants.py

@@ -1,6 +1 @@
 ALGORITHMS = ["RS256"]
-
-#TODO: The AUTH0 constants should be removed after WorkOS migration is completed
-AUTH0_DOMAIN = "crewai.us.auth0.com"
-AUTH0_CLIENT_ID = "DEVC5Fw6NlRoSzmDCcOhVq85EfLBjKa8"
-AUTH0_AUDIENCE = "https://crewai.us.auth0.com/api/v2/"

src/crewai/cli/authentication/main.py

@@ -1,30 +1,33 @@
 import time
 import webbrowser
-from typing import Any, Dict, Optional
+from typing import Any, Optional
 
 import requests
 from rich.console import Console
 from pydantic import BaseModel, Field
 
 
-from .utils import TokenManager, validate_jwt_token
-from urllib.parse import quote
-from crewai.cli.plus_api import PlusAPI
+from .utils import validate_jwt_token
+from crewai.cli.shared.token_manager import TokenManager
 from crewai.cli.config import Settings
-from crewai.cli.authentication.constants import (
-    AUTH0_AUDIENCE,
-    AUTH0_CLIENT_ID,
-    AUTH0_DOMAIN,
-)
 
 console = Console()
 
 
 class Oauth2Settings(BaseModel):
-    provider: str = Field(description="OAuth2 provider used for authentication (e.g., workos, okta, auth0).")
-    client_id: str = Field(description="OAuth2 client ID issued by the provider, used during authentication requests.")
-    domain: str = Field(description="OAuth2 provider's domain (e.g., your-org.auth0.com) used for issuing tokens.")
-    audience: Optional[str] = Field(description="OAuth2 audience value, typically used to identify the target API or resource.", default=None)
+    provider: str = Field(
+        description="OAuth2 provider used for authentication (e.g., workos, okta, auth0)."
+    )
+    client_id: str = Field(
+        description="OAuth2 client ID issued by the provider, used during authentication requests."
+    )
+    domain: str = Field(
+        description="OAuth2 provider's domain (e.g., your-org.auth0.com) used for issuing tokens."
+    )
+    audience: Optional[str] = Field(
+        description="OAuth2 audience value, typically used to identify the target API or resource.",
+        default=None,
+    )
 
     @classmethod
     def from_settings(cls):
@@ -44,11 +47,15 @@ class ProviderFactory:
         settings = settings or Oauth2Settings.from_settings()
 
         import importlib
-        module = importlib.import_module(f"crewai.cli.authentication.providers.{settings.provider.lower()}")
+
+        module = importlib.import_module(
+            f"crewai.cli.authentication.providers.{settings.provider.lower()}"
+        )
         provider = getattr(module, f"{settings.provider.capitalize()}Provider")
 
         return provider(settings)
 
+
 class AuthenticationCommand:
     def __init__(self):
         self.token_manager = TokenManager()
@@ -58,26 +65,12 @@ class AuthenticationCommand:
         """Sign up to CrewAI+"""
         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":
-            settings = Oauth2Settings(
-                provider="auth0",
-                client_id=AUTH0_CLIENT_ID,
-                domain=AUTH0_DOMAIN,
-                audience=AUTH0_AUDIENCE
-            )
-            self.oauth2_provider = ProviderFactory.from_settings(settings)
-        # End of temporary code.
-
         device_code_data = self._get_device_code()
         self._display_auth_instructions(device_code_data)
 
         return self._poll_for_token(device_code_data)
 
-    def _get_device_code(
-        self
-    ) -> Dict[str, Any]:
+    def _get_device_code(self) -> dict[str, Any]:
         """Get the device code to authenticate the user."""
 
         device_code_payload = {
@@ -86,20 +79,20 @@ class AuthenticationCommand:
             "audience": self.oauth2_provider.get_audience(),
         }
         response = requests.post(
-            url=self.oauth2_provider.get_authorize_url(), data=device_code_payload, timeout=20
+            url=self.oauth2_provider.get_authorize_url(),
+            data=device_code_payload,
+            timeout=20,
         )
         response.raise_for_status()
         return response.json()
 
-    def _display_auth_instructions(self, device_code_data: Dict[str, str]) -> None:
+    def _display_auth_instructions(self, device_code_data: dict[str, str]) -> None:
         """Display the authentication instructions to the user."""
         console.print("1. Navigate to: ", device_code_data["verification_uri_complete"])
         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:
+    def _poll_for_token(self, device_code_data: dict[str, Any]) -> None:
         """Polls the server for the token until it is received, or max attempts are reached."""
 
         token_payload = {
@@ -112,7 +105,9 @@ class AuthenticationCommand:
 
         attempts = 0
         while True and attempts < 10:
-            response = requests.post(self.oauth2_provider.get_token_url(), data=token_payload, timeout=30)
+            response = requests.post(
+                self.oauth2_provider.get_token_url(), data=token_payload, timeout=30
+            )
             token_data = response.json()
 
             if response.status_code == 200:
@@ -140,7 +135,7 @@ class AuthenticationCommand:
             "Timeout: Failed to get the token. Please try again.", style="bold red"
         )
 
-    def _validate_and_save_token(self, token_data: Dict[str, Any]) -> None:
+    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"]
@@ -192,30 +187,3 @@ class AuthenticationCommand:
                 "\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/token.py

@@ -1,4 +1,4 @@
-from .utils import TokenManager
+from crewai.cli.shared.token_manager import TokenManager
 
 
 class AuthError(Exception):

src/crewai/cli/authentication/utils.py

@@ -1,12 +1,5 @@
-import json
-import os
-import sys
-from datetime import datetime
-from pathlib import Path
-from typing import Optional
 import jwt
 from jwt import PyJWKClient
-from cryptography.fernet import Fernet
 
 
 def validate_jwt_token(
@@ -67,118 +60,3 @@ def validate_jwt_token(
         raise Exception(f"JWKS or key processing error: {str(e)}")
     except jwt.InvalidTokenError as e:
         raise Exception(f"Invalid token: {str(e)}")
-
-
-class TokenManager:
-    def __init__(self, file_path: str = "tokens.enc") -> None:
-        """
-        Initialize the TokenManager class.
-
-        :param file_path: The file path to store the encrypted tokens. Default is "tokens.enc".
-        """
-        self.file_path = file_path
-        self.key = self._get_or_create_key()
-        self.fernet = Fernet(self.key)
-
-    def _get_or_create_key(self) -> bytes:
-        """
-        Get or create the encryption key.
-
-        :return: The encryption key.
-        """
-        key_filename = "secret.key"
-        key = self.read_secure_file(key_filename)
-
-        if key is not None:
-            return key
-
-        new_key = Fernet.generate_key()
-        self.save_secure_file(key_filename, new_key)
-        return new_key
-
-    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_at: The UNIX timestamp of the expiration time.
-        """
-        expiration_time = datetime.fromtimestamp(expires_at)
-        data = {
-            "access_token": access_token,
-            "expiration": expiration_time.isoformat(),
-        }
-        encrypted_data = self.fernet.encrypt(json.dumps(data).encode())
-        self.save_secure_file(self.file_path, encrypted_data)
-
-    def get_token(self) -> Optional[str]:
-        """
-        Get the access token if it is valid and not expired.
-
-        :return: The access token if valid and not expired, otherwise None.
-        """
-        encrypted_data = self.read_secure_file(self.file_path)
-
-        decrypted_data = self.fernet.decrypt(encrypted_data)  # type: ignore
-        data = json.loads(decrypted_data)
-
-        expiration = datetime.fromisoformat(data["expiration"])
-        if expiration <= datetime.now():
-            return None
-
-        return data["access_token"]
-
-    def get_secure_storage_path(self) -> Path:
-        """
-        Get the secure storage path based on the operating system.
-
-        :return: The secure storage path.
-        """
-        if sys.platform == "win32":
-            # Windows: Use %LOCALAPPDATA%
-            base_path = os.environ.get("LOCALAPPDATA")
-        elif sys.platform == "darwin":
-            # macOS: Use ~/Library/Application Support
-            base_path = os.path.expanduser("~/Library/Application Support")
-        else:
-            # Linux and other Unix-like: Use ~/.local/share
-            base_path = os.path.expanduser("~/.local/share")
-
-        app_name = "crewai/credentials"
-        storage_path = Path(base_path) / app_name
-
-        storage_path.mkdir(parents=True, exist_ok=True)
-
-        return storage_path
-
-    def save_secure_file(self, filename: str, content: bytes) -> None:
-        """
-        Save the content to a secure file.
-
-        :param filename: The name of the file.
-        :param content: The content to save.
-        """
-        storage_path = self.get_secure_storage_path()
-        file_path = storage_path / filename
-
-        with open(file_path, "wb") as f:
-            f.write(content)
-
-        # Set appropriate permissions (read/write for owner only)
-        os.chmod(file_path, 0o600)
-
-    def read_secure_file(self, filename: str) -> Optional[bytes]:
-        """
-        Read the content of a secure file.
-
-        :param filename: The name of the file.
-        :return: The content of the file if it exists, otherwise None.
-        """
-        storage_path = self.get_secure_storage_path()
-        file_path = storage_path / filename
-
-        if not file_path.exists():
-            return None
-
-        with open(file_path, "rb") as f:
-            return f.read()

src/crewai/cli/config.py

@@ -11,6 +11,7 @@ from crewai.cli.constants import (
     CREWAI_ENTERPRISE_DEFAULT_OAUTH2_CLIENT_ID,
     CREWAI_ENTERPRISE_DEFAULT_OAUTH2_DOMAIN,
 )
+from crewai.cli.shared.token_manager import TokenManager
 
 DEFAULT_CONFIG_PATH = Path.home() / ".config" / "crewai" / "settings.json"
 
@@ -53,6 +54,7 @@ HIDDEN_SETTINGS_KEYS = [
     "tool_repository_password",
 ]
 
+
 class Settings(BaseModel):
     enterprise_base_url: Optional[str] = Field(
         default=DEFAULT_CLI_SETTINGS["enterprise_base_url"],
@@ -74,12 +76,12 @@ class Settings(BaseModel):
 
     oauth2_provider: str = Field(
         description="OAuth2 provider used for authentication (e.g., workos, okta, auth0).",
-        default=DEFAULT_CLI_SETTINGS["oauth2_provider"]
+        default=DEFAULT_CLI_SETTINGS["oauth2_provider"],
     )
 
     oauth2_audience: Optional[str] = Field(
         description="OAuth2 audience value, typically used to identify the target API or resource.",
-        default=DEFAULT_CLI_SETTINGS["oauth2_audience"]
+        default=DEFAULT_CLI_SETTINGS["oauth2_audience"],
     )
 
     oauth2_client_id: str = Field(
@@ -89,7 +91,7 @@ class Settings(BaseModel):
 
     oauth2_domain: str = Field(
         description="OAuth2 provider's domain (e.g., your-org.auth0.com) used for issuing tokens.",
-        default=DEFAULT_CLI_SETTINGS["oauth2_domain"]
+        default=DEFAULT_CLI_SETTINGS["oauth2_domain"],
     )
 
     def __init__(self, config_path: Path = DEFAULT_CONFIG_PATH, **data):
@@ -116,6 +118,7 @@ class Settings(BaseModel):
         """Reset all settings to default values"""
         self._reset_user_settings()
         self._reset_cli_settings()
+        self._clear_auth_tokens()
         self.dump()
 
     def dump(self) -> None:
@@ -139,3 +142,7 @@ class Settings(BaseModel):
         """Reset all CLI settings to default values"""
         for key in CLI_SETTINGS_KEYS:
             setattr(self, key, DEFAULT_CLI_SETTINGS.get(key))
+
+    def _clear_auth_tokens(self) -> None:
+        """Clear all authentication tokens"""
+        TokenManager().clear_tokens()

src/crewai/cli/crew_chat.py

@@ -5,7 +5,7 @@ import sys
 import threading
 import time
 from pathlib import Path
-from typing import Any, Dict, List, Optional, Set, Tuple
+from typing import Any, Optional
 
 import click
 import tomli
@@ -157,7 +157,7 @@ def build_system_message(crew_chat_inputs: ChatInputs) -> str:
     )
 
 
-def create_tool_function(crew: Crew, messages: List[Dict[str, str]]) -> Any:
+def create_tool_function(crew: Crew, messages: list[dict[str, str]]) -> Any:
     """Creates a wrapper function for running the crew tool with messages."""
 
     def run_crew_tool_with_messages(**kwargs):
@@ -221,9 +221,9 @@ def get_user_input() -> str:
 def handle_user_input(
     user_input: str,
     chat_llm: LLM,
-    messages: List[Dict[str, str]],
-    crew_tool_schema: Dict[str, Any],
-    available_functions: Dict[str, Any],
+    messages: list[dict[str, str]],
+    crew_tool_schema: dict[str, Any],
+    available_functions: dict[str, Any],
 ) -> None:
     if user_input.strip().lower() == "exit":
         click.echo("Exiting chat. Goodbye!")
@@ -281,13 +281,13 @@ def generate_crew_tool_schema(crew_inputs: ChatInputs) -> dict:
     }
 
 
-def run_crew_tool(crew: Crew, messages: List[Dict[str, str]], **kwargs):
+def run_crew_tool(crew: Crew, messages: list[dict[str, str]], **kwargs):
     """
     Runs the crew using crew.kickoff(inputs=kwargs) and returns the output.
 
     Args:
         crew (Crew): The crew instance to run.
-        messages (List[Dict[str, str]]): The chat messages up to this point.
+        messages (list[dict[str, str]]): The chat messages up to this point.
         **kwargs: The inputs collected from the user.
 
     Returns:
@@ -314,12 +314,12 @@ def run_crew_tool(crew: Crew, messages: List[Dict[str, str]], **kwargs):
         sys.exit(1)
 
 
-def load_crew_and_name() -> Tuple[Crew, str]:
+def load_crew_and_name() -> tuple[Crew, str]:
     """
     Loads the crew by importing the crew class from the user's project.
 
     Returns:
-        Tuple[Crew, str]: A tuple containing the Crew instance and the name of the crew.
+        tuple[Crew, str]: A tuple containing the Crew instance and the name of the crew.
     """
     # Get the current working directory
     cwd = Path.cwd()
@@ -395,7 +395,7 @@ def generate_crew_chat_inputs(crew: Crew, crew_name: str, chat_llm) -> ChatInput
     )
 
 
-def fetch_required_inputs(crew: Crew) -> Set[str]:
+def fetch_required_inputs(crew: Crew) -> set[str]:
     """
     Extracts placeholders from the crew's tasks and agents.
 
@@ -403,10 +403,10 @@ def fetch_required_inputs(crew: Crew) -> Set[str]:
         crew (Crew): The crew object.
 
     Returns:
-        Set[str]: A set of placeholder names.
+        set[str]: A set of placeholder names.
     """
     placeholder_pattern = re.compile(r"\{(.+?)\}")
-    required_inputs: Set[str] = set()
+    required_inputs: set[str] = set()
 
     # Scan tasks
     for task in crew.tasks:

src/crewai/cli/deploy/main.py

@@ -1,4 +1,4 @@
-from typing import Any, Dict, List, Optional
+from typing import Any, Optional
 
 from rich.console import Console
 
@@ -32,12 +32,12 @@ class DeployCommand(BaseCommand, PlusAPIMixin):
             style="bold red",
         )
 
-    def _display_deployment_info(self, json_response: Dict[str, Any]) -> None:
+    def _display_deployment_info(self, json_response: dict[str, Any]) -> None:
         """
         Display deployment information.
 
         Args:
-            json_response (Dict[str, Any]): The deployment information to display.
+            json_response (dict[str, Any]): The deployment information to display.
         """
         console.print("Deploying the crew...\n", style="bold blue")
         for key, value in json_response.items():
@@ -47,12 +47,12 @@ class DeployCommand(BaseCommand, PlusAPIMixin):
         console.print(" or")
         console.print(f"crewai deploy status --uuid \"{json_response['uuid']}\"")
 
-    def _display_logs(self, log_messages: List[Dict[str, Any]]) -> None:
+    def _display_logs(self, log_messages: list[dict[str, Any]]) -> None:
         """
         Display log messages.
 
         Args:
-            log_messages (List[Dict[str, Any]]): The log messages to display.
+            log_messages (list[dict[str, Any]]): The log messages to display.
         """
         for log_message in log_messages:
             console.print(
@@ -110,13 +110,13 @@ class DeployCommand(BaseCommand, PlusAPIMixin):
         self._display_creation_success(response.json())
 
     def _confirm_input(
-        self, env_vars: Dict[str, str], remote_repo_url: str, confirm: bool
+        self, env_vars: dict[str, str], remote_repo_url: str, confirm: bool
     ) -> None:
         """
         Confirm input parameters with the user.
 
         Args:
-            env_vars (Dict[str, str]): Environment variables.
+            env_vars (dict[str, str]): Environment variables.
             remote_repo_url (str): Remote repository URL.
             confirm (bool): Whether to confirm input.
         """
@@ -128,18 +128,18 @@ class DeployCommand(BaseCommand, PlusAPIMixin):
 
     def _create_payload(
         self,
-        env_vars: Dict[str, str],
+        env_vars: dict[str, str],
         remote_repo_url: str,
-    ) -> Dict[str, Any]:
+    ) -> dict[str, Any]:
         """
         Create the payload for crew creation.
 
         Args:
             remote_repo_url (str): Remote repository URL.
-            env_vars (Dict[str, str]): Environment variables.
+            env_vars (dict[str, str]): Environment variables.
 
         Returns:
-            Dict[str, Any]: The payload for crew creation.
+            dict[str, Any]: The payload for crew creation.
         """
         return {
             "deploy": {
@@ -149,12 +149,12 @@ class DeployCommand(BaseCommand, PlusAPIMixin):
             }
         }
 
-    def _display_creation_success(self, json_response: Dict[str, Any]) -> None:
+    def _display_creation_success(self, json_response: dict[str, Any]) -> None:
         """
         Display success message after crew creation.
 
         Args:
-            json_response (Dict[str, Any]): The response containing crew information.
+            json_response (dict[str, Any]): The response containing crew information.
         """
         console.print("Deployment created successfully!\n", style="bold green")
         console.print(
@@ -179,12 +179,12 @@ class DeployCommand(BaseCommand, PlusAPIMixin):
         else:
             self._display_no_crews_message()
 
-    def _display_crews(self, crews_data: List[Dict[str, Any]]) -> None:
+    def _display_crews(self, crews_data: list[dict[str, Any]]) -> None:
         """
         Display the list of crews.
 
         Args:
-            crews_data (List[Dict[str, Any]]): List of crew data to display.
+            crews_data (list[dict[str, Any]]): List of crew data to display.
         """
         for crew_data in crews_data:
             console.print(
@@ -217,12 +217,12 @@ class DeployCommand(BaseCommand, PlusAPIMixin):
         self._validate_response(response)
         self._display_crew_status(response.json())
 
-    def _display_crew_status(self, status_data: Dict[str, str]) -> None:
+    def _display_crew_status(self, status_data: dict[str, str]) -> None:
         """
         Display the status of a crew.
 
         Args:
-            status_data (Dict[str, str]): The status data to display.
+            status_data (dict[str, str]): The status data to display.
         """
         console.print(f"Name:\t {status_data['name']}")
         console.print(f"Status:\t {status_data['status']}")

src/crewai/cli/enterprise/main.py

@@ -1,5 +1,5 @@
 import requests
-from typing import Dict, Any
+from typing import Any
 from rich.console import Console
 from requests.exceptions import RequestException, JSONDecodeError
 
@@ -32,7 +32,7 @@ class EnterpriseConfigureCommand(BaseCommand):
             console.print(f"❌ Failed to configure Enterprise settings: {str(e)}", style="bold red")
             raise SystemExit(1)
 
-    def _fetch_oauth_config(self, enterprise_url: str) -> Dict[str, Any]:
+    def _fetch_oauth_config(self, enterprise_url: str) -> dict[str, Any]:
         oauth_endpoint = f"{enterprise_url}/auth/parameters"
 
         try:
@@ -64,7 +64,7 @@ class EnterpriseConfigureCommand(BaseCommand):
         except Exception as e:
             raise ValueError(f"Error fetching OAuth2 configuration: {str(e)}")
 
-    def _update_oauth_settings(self, enterprise_url: str, oauth_config: Dict[str, Any]) -> None:
+    def _update_oauth_settings(self, enterprise_url: str, oauth_config: dict[str, Any]) -> None:
         try:
             config_mapping = {
                 'enterprise_base_url': enterprise_url,

src/crewai/cli/plus_api.py

@@ -1,4 +1,4 @@
-from typing import List, Optional
+from typing import Optional
 from urllib.parse import urljoin
 
 import requests
@@ -58,7 +58,7 @@ class PlusAPI:
         version: str,
         description: Optional[str],
         encoded_file: str,
-        available_exports: Optional[List[str]] = None,
+        available_exports: Optional[list[str]] = None,
     ):
         params = {
             "handle": handle,
@@ -117,17 +117,19 @@ class PlusAPI:
     def get_organizations(self) -> requests.Response:
         return self._make_request("GET", self.ORGANIZATIONS_RESOURCE)
 
-    def send_trace_batch(self, payload) -> requests.Response:
-        return self._make_request("POST", self.TRACING_RESOURCE, json=payload)
-
     def initialize_trace_batch(self, payload) -> requests.Response:
         return self._make_request(
-            "POST", f"{self.TRACING_RESOURCE}/batches", json=payload
+            "POST",
+            f"{self.TRACING_RESOURCE}/batches",
+            json=payload,
+            timeout=30,
         )
 
     def initialize_ephemeral_trace_batch(self, payload) -> requests.Response:
         return self._make_request(
-            "POST", f"{self.EPHEMERAL_TRACING_RESOURCE}/batches", json=payload
+            "POST",
+            f"{self.EPHEMERAL_TRACING_RESOURCE}/batches",
+            json=payload,
         )
 
     def send_trace_events(self, trace_batch_id: str, payload) -> requests.Response:
@@ -135,6 +137,7 @@ class PlusAPI:
             "POST",
             f"{self.TRACING_RESOURCE}/batches/{trace_batch_id}/events",
             json=payload,
+            timeout=30,
         )
 
     def send_ephemeral_trace_events(
@@ -144,6 +147,7 @@ class PlusAPI:
             "POST",
             f"{self.EPHEMERAL_TRACING_RESOURCE}/batches/{trace_batch_id}/events",
             json=payload,
+            timeout=30,
         )
 
     def finalize_trace_batch(self, trace_batch_id: str, payload) -> requests.Response:
@@ -151,6 +155,7 @@ class PlusAPI:
             "PATCH",
             f"{self.TRACING_RESOURCE}/batches/{trace_batch_id}/finalize",
             json=payload,
+            timeout=30,
         )
 
     def finalize_ephemeral_trace_batch(
@@ -160,4 +165,5 @@ class PlusAPI:
             "PATCH",
             f"{self.EPHEMERAL_TRACING_RESOURCE}/batches/{trace_batch_id}/finalize",
             json=payload,
+            timeout=30,
         )

src/crewai/cli/run_crew.py

@@ -1,6 +1,6 @@
 import subprocess
 from enum import Enum
-from typing import List, Optional
+from typing import Optional
 
 import click
 from packaging import version

src/crewai/cli/settings/main.py

@@ -10,8 +10,9 @@ console = Console()
 class SettingsCommand(BaseCommand):
     """A class to handle CLI configuration commands."""
 
-    def __init__(self, settings_kwargs: dict[str, Any] = {}):
+    def __init__(self, settings_kwargs: dict[str, Any] | None = None):
         super().__init__()
+        settings_kwargs = settings_kwargs or {}
         self.settings = Settings(**settings_kwargs)
 
     def list(self) -> None:

src/crewai/cli/shared/token_manager.py

@@ -0,0 +1,141 @@
+import json
+import os
+import sys
+from datetime import datetime
+from pathlib import Path
+from typing import Optional
+from cryptography.fernet import Fernet
+
+
+class TokenManager:
+    def __init__(self, file_path: str = "tokens.enc") -> None:
+        """
+        Initialize the TokenManager class.
+
+        :param file_path: The file path to store the encrypted tokens. Default is "tokens.enc".
+        """
+        self.file_path = file_path
+        self.key = self._get_or_create_key()
+        self.fernet = Fernet(self.key)
+
+    def _get_or_create_key(self) -> bytes:
+        """
+        Get or create the encryption key.
+
+        :return: The encryption key.
+        """
+        key_filename = "secret.key"
+        key = self.read_secure_file(key_filename)
+
+        if key is not None:
+            return key
+
+        new_key = Fernet.generate_key()
+        self.save_secure_file(key_filename, new_key)
+        return new_key
+
+    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_at: The UNIX timestamp of the expiration time.
+        """
+        expiration_time = datetime.fromtimestamp(expires_at)
+        data = {
+            "access_token": access_token,
+            "expiration": expiration_time.isoformat(),
+        }
+        encrypted_data = self.fernet.encrypt(json.dumps(data).encode())
+        self.save_secure_file(self.file_path, encrypted_data)
+
+    def get_token(self) -> Optional[str]:
+        """
+        Get the access token if it is valid and not expired.
+
+        :return: The access token if valid and not expired, otherwise None.
+        """
+        encrypted_data = self.read_secure_file(self.file_path)
+        if encrypted_data is None:
+            return None
+
+        decrypted_data = self.fernet.decrypt(encrypted_data)  # type: ignore
+        data = json.loads(decrypted_data)
+
+        expiration = datetime.fromisoformat(data["expiration"])
+        if expiration <= datetime.now():
+            return None
+
+        return data["access_token"]
+
+    def clear_tokens(self) -> None:
+        """
+        Clear the tokens.
+        """
+        self.delete_secure_file(self.file_path)
+
+    def get_secure_storage_path(self) -> Path:
+        """
+        Get the secure storage path based on the operating system.
+
+        :return: The secure storage path.
+        """
+        if sys.platform == "win32":
+            # Windows: Use %LOCALAPPDATA%
+            base_path = os.environ.get("LOCALAPPDATA")
+        elif sys.platform == "darwin":
+            # macOS: Use ~/Library/Application Support
+            base_path = os.path.expanduser("~/Library/Application Support")
+        else:
+            # Linux and other Unix-like: Use ~/.local/share
+            base_path = os.path.expanduser("~/.local/share")
+
+        app_name = "crewai/credentials"
+        storage_path = Path(base_path) / app_name
+
+        storage_path.mkdir(parents=True, exist_ok=True)
+
+        return storage_path
+
+    def save_secure_file(self, filename: str, content: bytes) -> None:
+        """
+        Save the content to a secure file.
+
+        :param filename: The name of the file.
+        :param content: The content to save.
+        """
+        storage_path = self.get_secure_storage_path()
+        file_path = storage_path / filename
+
+        with open(file_path, "wb") as f:
+            f.write(content)
+
+        # Set appropriate permissions (read/write for owner only)
+        os.chmod(file_path, 0o600)
+
+    def read_secure_file(self, filename: str) -> Optional[bytes]:
+        """
+        Read the content of a secure file.
+
+        :param filename: The name of the file.
+        :return: The content of the file if it exists, otherwise None.
+        """
+        storage_path = self.get_secure_storage_path()
+        file_path = storage_path / filename
+
+        if not file_path.exists():
+            return None
+
+        with open(file_path, "rb") as f:
+            return f.read()
+
+    def delete_secure_file(self, filename: str) -> None:
+        """
+        Delete the secure file.
+
+        :param filename: The name of the file.
+        """
+        storage_path = self.get_secure_storage_path()
+        file_path = storage_path / filename
+        if file_path.exists():
+            file_path.unlink(missing_ok=True)

src/crewai/cli/templates/crew/crew.py

@@ -1,7 +1,10 @@
 from crewai import Agent, Crew, Process, Task
 from crewai.project import CrewBase, agent, crew, task
-from crewai.agents.agent_builder.base_agent import BaseAgent
-from typing import List
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from crewai.agents.agent_builder.base_agent import BaseAgent
+    from crewai import Task
 # If you want to run a snippet of code before or after the crew starts,
 # you can use the @before_kickoff and @after_kickoff decorators
 # https://docs.crewai.com/concepts/crews#example-crew-class-with-decorators
@@ -10,8 +13,8 @@ from typing import List
 class {{crew_name}}():
     """{{crew_name}} crew"""
 
-    agents: List[BaseAgent]
-    tasks: List[Task]
+    agents: list["BaseAgent"]
+    tasks: list["Task"]
 
     # Learn more about YAML configuration files here:
     # Agents: https://docs.crewai.com/concepts/agents#yaml-configuration-recommended

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]>=0.159.0,<1.0.0"
+    "crewai[tools]>=0.177.0,<1.0.0"
 ]
 
 [project.scripts]

src/crewai/cli/templates/flow/crews/poem_crew/poem_crew.py

@@ -1,7 +1,6 @@
 from crewai import Agent, Crew, Process, Task
 from crewai.project import CrewBase, agent, crew, task
 from crewai.agents.agent_builder.base_agent import BaseAgent
-from typing import List
 
 # If you want to run a snippet of code before or after the crew starts,
 # you can use the @before_kickoff and @after_kickoff decorators
@@ -12,8 +11,8 @@ from typing import List
 class PoemCrew:
     """Poem Crew"""
 
-    agents: List[BaseAgent]
-    tasks: List[Task]
+    agents: list[BaseAgent]
+    tasks: list[Task]
 
     # Learn more about YAML configuration files here:
     # Agents: https://docs.crewai.com/concepts/agents#yaml-configuration-recommended

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]>=0.159.0,<1.0.0",
+    "crewai[tools]>=0.177.0,<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]>=0.159.0"
+    "crewai[tools]>=0.177.0"
 ]
 
 [tool.crewai]

src/crewai/cli/update_crew.py

@@ -44,8 +44,9 @@ def migrate_pyproject(input_file, output_file):
         ]
         new_pyproject["project"]["requires-python"] = poetry_data.get("python")
     else:
-        # If it's already in the new format, just copy the project section
+        # If it's already in the new format, just copy the project and tool sections
         new_pyproject["project"] = pyproject_data.get("project", {})
+        new_pyproject["tool"] = pyproject_data.get("tool", {})
 
     # Migrate or copy dependencies
     if "dependencies" in new_pyproject["project"]:

src/crewai/cli/utils.py

@@ -5,7 +5,7 @@ import sys
 from functools import reduce
 from inspect import getmro, isclass, isfunction, ismethod
 from pathlib import Path
-from typing import Any, Dict, List, get_type_hints
+from typing import Any, get_type_hints
 
 import click
 import tomli
@@ -77,7 +77,7 @@ def get_project_description(
 
 
 def _get_project_attribute(
-    pyproject_path: str, keys: List[str], require: bool
+    pyproject_path: str, keys: list[str], require: bool
 ) -> Any | None:
     """Get an attribute from the pyproject.toml file."""
     attribute = None
@@ -117,7 +117,7 @@ def _get_project_attribute(
     return attribute
 
 
-def _get_nested_value(data: Dict[str, Any], keys: List[str]) -> Any:
+def _get_nested_value(data: dict[str, Any], keys: list[str]) -> Any:
     return reduce(dict.__getitem__, keys, data)
 
 

src/crewai/crew.py

@@ -1,6 +1,7 @@
 import asyncio
 import json
 import re
+import threading
 import uuid
 import warnings
 from concurrent.futures import Future
@@ -9,11 +10,7 @@ from hashlib import md5
 from typing import (
     Any,
     Callable,
-    Dict,
-    List,
     Optional,
-    Set,
-    Tuple,
     Union,
     cast,
 )
@@ -59,7 +56,8 @@ from crewai.utilities import I18N, FileHandler, Logger, RPMController
 from crewai.utilities.constants import NOT_SPECIFIED, TRAINING_DATA_FILE
 from crewai.utilities.evaluators.crew_evaluator_handler import CrewEvaluator
 from crewai.utilities.evaluators.task_evaluator import TaskEvaluator
-from crewai.utilities.events.crew_events import (
+from crewai.events.types.crew_events import (
+    CrewKickoffCancelledEvent,
     CrewKickoffCompletedEvent,
     CrewKickoffFailedEvent,
     CrewKickoffStartedEvent,
@@ -70,16 +68,15 @@ from crewai.utilities.events.crew_events import (
     CrewTrainFailedEvent,
     CrewTrainStartedEvent,
 )
-from crewai.utilities.events.crewai_event_bus import crewai_event_bus
-from crewai.utilities.events.event_listener import EventListener
-from crewai.utilities.events.listeners.tracing.trace_listener import (
+from crewai.events.event_bus import crewai_event_bus
+from crewai.events.event_listener import EventListener
+from crewai.events.listeners.tracing.trace_listener import (
     TraceCollectionListener,
 )
 
 
-from crewai.utilities.events.listeners.tracing.utils import (
+from crewai.events.listeners.tracing.utils import (
     is_tracing_enabled,
-    on_first_execution_tracing_confirmation,
 )
 from crewai.utilities.formatter import (
     aggregate_raw_outputs_from_task_outputs,
@@ -131,18 +128,19 @@ class Crew(FlowTrackable, BaseModel):
     _external_memory: Optional[InstanceOf[ExternalMemory]] = PrivateAttr()
     _train: Optional[bool] = PrivateAttr(default=False)
     _train_iteration: Optional[int] = PrivateAttr()
-    _inputs: Optional[Dict[str, Any]] = PrivateAttr(default=None)
+    _inputs: Optional[dict[str, Any]] = PrivateAttr(default=None)
     _logging_color: str = PrivateAttr(
         default="bold_purple",
     )
     _task_output_handler: TaskOutputStorageHandler = PrivateAttr(
         default_factory=TaskOutputStorageHandler
     )
+    _cancellation_event: threading.Event = PrivateAttr(default_factory=threading.Event)
 
     name: Optional[str] = Field(default="crew")
     cache: bool = Field(default=True)
-    tasks: List[Task] = Field(default_factory=list)
-    agents: List[BaseAgent] = Field(default_factory=list)
+    tasks: list[Task] = Field(default_factory=list)
+    agents: list[BaseAgent] = Field(default_factory=list)
     process: Process = Field(default=Process.sequential)
     verbose: bool = Field(default=False)
     memory: bool = Field(
@@ -182,7 +180,7 @@ class Crew(FlowTrackable, BaseModel):
     function_calling_llm: Optional[Union[str, InstanceOf[LLM], Any]] = Field(
         description="Language model that will run the agent.", default=None
     )
-    config: Optional[Union[Json, Dict[str, Any]]] = Field(default=None)
+    config: Optional[Union[Json, dict[str, Any]]] = Field(default=None)
     id: UUID4 = Field(default_factory=uuid.uuid4, frozen=True)
     share_crew: Optional[bool] = Field(default=False)
     step_callback: Optional[Any] = Field(
@@ -193,13 +191,13 @@ class Crew(FlowTrackable, BaseModel):
         default=None,
         description="Callback to be executed after each task for all agents execution.",
     )
-    before_kickoff_callbacks: List[
-        Callable[[Optional[Dict[str, Any]]], Optional[Dict[str, Any]]]
+    before_kickoff_callbacks: list[
+        Callable[[Optional[dict[str, Any]]], Optional[dict[str, Any]]]
     ] = Field(
         default_factory=list,
         description="List of callbacks to be executed before crew kickoff. It may be used to adjust inputs before the crew is executed.",
     )
-    after_kickoff_callbacks: List[Callable[[CrewOutput], CrewOutput]] = Field(
+    after_kickoff_callbacks: list[Callable[[CrewOutput], CrewOutput]] = Field(
         default_factory=list,
         description="List of callbacks to be executed after crew kickoff. It may be used to adjust the output of the crew.",
     )
@@ -223,15 +221,15 @@ class Crew(FlowTrackable, BaseModel):
         default=None,
         description="Language model that will run the AgentPlanner if planning is True.",
     )
-    task_execution_output_json_files: Optional[List[str]] = Field(
+    task_execution_output_json_files: Optional[list[str]] = Field(
         default=None,
         description="List of file paths for task execution JSON files.",
     )
-    execution_logs: List[Dict[str, Any]] = Field(
+    execution_logs: list[dict[str, Any]] = Field(
         default=[],
         description="List of execution logs for tasks",
     )
-    knowledge_sources: Optional[List[BaseKnowledgeSource]] = Field(
+    knowledge_sources: Optional[list[BaseKnowledgeSource]] = Field(
         default=None,
         description="Knowledge sources for the crew. Add knowledge sources to the knowledge object.",
     )
@@ -268,8 +266,8 @@ class Crew(FlowTrackable, BaseModel):
     @field_validator("config", mode="before")
     @classmethod
     def check_config_type(
-        cls, v: Union[Json, Dict[str, Any]]
-    ) -> Union[Json, Dict[str, Any]]:
+        cls, v: Union[Json, dict[str, Any]]
+    ) -> Union[Json, dict[str, Any]]:
         """Validates that the config is a valid type.
         Args:
             v: The config to be validated.
@@ -286,8 +284,6 @@ class Crew(FlowTrackable, BaseModel):
 
         self._cache_handler = CacheHandler()
         event_listener = EventListener()
-        if on_first_execution_tracing_confirmation():
-            self.tracing = True
 
         if is_tracing_enabled() or self.tracing:
             trace_listener = TraceCollectionListener()
@@ -505,7 +501,7 @@ class Crew(FlowTrackable, BaseModel):
 
     @property
     def key(self) -> str:
-        source: List[str] = [agent.key for agent in self.agents] + [
+        source: list[str] = [agent.key for agent in self.agents] + [
             task.key for task in self.tasks
         ]
         return md5("|".join(source).encode(), usedforsecurity=False).hexdigest()
@@ -533,7 +529,7 @@ class Crew(FlowTrackable, BaseModel):
         self.agents = [Agent(**agent) for agent in self.config["agents"]]
         self.tasks = [self._create_task(task) for task in self.config["tasks"]]
 
-    def _create_task(self, task_config: Dict[str, Any]) -> Task:
+    def _create_task(self, task_config: dict[str, Any]) -> Task:
         """Creates a task instance from its configuration.
 
         Args:
@@ -562,9 +558,10 @@ class Crew(FlowTrackable, BaseModel):
         CrewTrainingHandler(filename).initialize_file()
 
     def train(
-        self, n_iterations: int, filename: str, inputs: Optional[Dict[str, Any]] = {}
+        self, n_iterations: int, filename: str, inputs: Optional[dict[str, Any]] = None
     ) -> None:
         """Trains the crew for a given number of iterations."""
+        inputs = inputs or {}
         try:
             crewai_event_bus.emit(
                 self,
@@ -613,8 +610,10 @@ class Crew(FlowTrackable, BaseModel):
 
     def kickoff(
         self,
-        inputs: Optional[Dict[str, Any]] = None,
+        inputs: Optional[dict[str, Any]] = None,
     ) -> CrewOutput:
+        self._reset_cancellation()
+        
         ctx = baggage.set_baggage(
             "crew_context", CrewContext(id=str(self.id), key=self.key)
         )
@@ -639,6 +638,7 @@ class Crew(FlowTrackable, BaseModel):
                 self._inputs = inputs
                 self._interpolate_inputs(inputs)
             self._set_tasks_callbacks()
+            self._set_allow_crewai_trigger_context_for_first_task()
 
             i18n = I18N(prompt_file=self.prompt_file)
 
@@ -683,9 +683,9 @@ class Crew(FlowTrackable, BaseModel):
         finally:
             detach(token)
 
-    def kickoff_for_each(self, inputs: List[Dict[str, Any]]) -> List[CrewOutput]:
+    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."""
-        results: List[CrewOutput] = []
+        results: list[CrewOutput] = []
 
         # Initialize the parent crew's usage metrics
         total_usage_metrics = UsageMetrics()
@@ -704,11 +704,14 @@ class Crew(FlowTrackable, BaseModel):
         self._task_output_handler.reset()
         return results
 
-    async def kickoff_async(self, inputs: Optional[Dict[str, Any]] = {}) -> CrewOutput:
+    async def kickoff_async(
+        self, inputs: Optional[dict[str, Any]] = None
+    ) -> CrewOutput:
         """Asynchronous kickoff method to start the crew execution."""
+        inputs = inputs or {}
         return await asyncio.to_thread(self.kickoff, inputs)
 
-    async def kickoff_for_each_async(self, inputs: List[Dict]) -> List[CrewOutput]:
+    async def kickoff_for_each_async(self, inputs: list[dict]) -> list[CrewOutput]:
         crew_copies = [self.copy() for _ in inputs]
 
         async def run_crew(crew, input_data):
@@ -805,25 +808,37 @@ class Crew(FlowTrackable, BaseModel):
 
     def _execute_tasks(
         self,
-        tasks: List[Task],
+        tasks: list[Task],
         start_index: Optional[int] = 0,
         was_replayed: bool = False,
     ) -> CrewOutput:
         """Executes tasks sequentially and returns the final output.
 
         Args:
-            tasks (List[Task]): List of tasks to execute
+            tasks (list[Task]): List of tasks to execute
             manager (Optional[BaseAgent], optional): Manager agent to use for delegation. Defaults to None.
 
         Returns:
             CrewOutput: Final output of the crew
         """
 
-        task_outputs: List[TaskOutput] = []
-        futures: List[Tuple[Task, Future[TaskOutput], int]] = []
+        task_outputs: list[TaskOutput] = []
+        futures: list[tuple[Task, Future[TaskOutput], int]] = []
         last_sync_output: Optional[TaskOutput] = None
 
         for task_index, task in enumerate(tasks):
+            if self.is_cancelled():
+                self._logger.log("info", f"Crew execution cancelled after {task_index} tasks", color="yellow")
+                crewai_event_bus.emit(
+                    self,
+                    CrewKickoffCancelledEvent(
+                        crew_name=self.name,
+                        completed_tasks=task_index,
+                        total_tasks=len(tasks),
+                    ),
+                )
+                return self._create_crew_output(task_outputs)
+            
             if start_index is not None and task_index < start_index:
                 if task.output:
                     if task.async_execution:
@@ -845,7 +860,7 @@ class Crew(FlowTrackable, BaseModel):
             tools_for_task = self._prepare_tools(
                 agent_to_use,
                 task,
-                cast(Union[List[Tool], List[BaseTool]], tools_for_task),
+                cast(Union[list[Tool], list[BaseTool]], tools_for_task),
             )
 
             self._log_task_start(task, agent_to_use.role)
@@ -865,7 +880,7 @@ class Crew(FlowTrackable, BaseModel):
                 future = task.execute_async(
                     agent=agent_to_use,
                     context=context,
-                    tools=cast(List[BaseTool], tools_for_task),
+                    tools=cast(list[BaseTool], tools_for_task),
                 )
                 futures.append((task, future, task_index))
             else:
@@ -877,7 +892,7 @@ class Crew(FlowTrackable, BaseModel):
                 task_output = task.execute_sync(
                     agent=agent_to_use,
                     context=context,
-                    tools=cast(List[BaseTool], tools_for_task),
+                    tools=cast(list[BaseTool], tools_for_task),
                 )
                 task_outputs.append(task_output)
                 self._process_task_result(task, task_output)
@@ -891,8 +906,8 @@ class Crew(FlowTrackable, BaseModel):
     def _handle_conditional_task(
         self,
         task: ConditionalTask,
-        task_outputs: List[TaskOutput],
-        futures: List[Tuple[Task, Future[TaskOutput], int]],
+        task_outputs: list[TaskOutput],
+        futures: list[tuple[Task, Future[TaskOutput], int]],
         task_index: int,
         was_replayed: bool,
     ) -> Optional[TaskOutput]:
@@ -915,8 +930,8 @@ class Crew(FlowTrackable, BaseModel):
         return None
 
     def _prepare_tools(
-        self, agent: BaseAgent, task: Task, tools: Union[List[Tool], List[BaseTool]]
-    ) -> List[BaseTool]:
+        self, agent: BaseAgent, task: Task, tools: Union[list[Tool], list[BaseTool]]
+    ) -> list[BaseTool]:
         # Add delegation tools if agent allows delegation
         if hasattr(agent, "allow_delegation") and getattr(
             agent, "allow_delegation", False
@@ -945,8 +960,8 @@ class Crew(FlowTrackable, BaseModel):
         ):
             tools = self._add_multimodal_tools(agent, tools)
 
-        # Return a List[BaseTool] which is compatible with both Task.execute_sync and Task.execute_async
-        return cast(List[BaseTool], tools)
+        # Return a list[BaseTool] which is compatible with both Task.execute_sync and Task.execute_async
+        return cast(list[BaseTool], tools)
 
     def _get_agent_to_use(self, task: Task) -> Optional[BaseAgent]:
         if self.process == Process.hierarchical:
@@ -955,12 +970,12 @@ class Crew(FlowTrackable, BaseModel):
 
     def _merge_tools(
         self,
-        existing_tools: Union[List[Tool], List[BaseTool]],
-        new_tools: Union[List[Tool], List[BaseTool]],
-    ) -> List[BaseTool]:
+        existing_tools: Union[list[Tool], list[BaseTool]],
+        new_tools: Union[list[Tool], list[BaseTool]],
+    ) -> list[BaseTool]:
         """Merge new tools into existing tools list, avoiding duplicates by tool name."""
         if not new_tools:
-            return cast(List[BaseTool], existing_tools)
+            return cast(list[BaseTool], existing_tools)
 
         # Create mapping of tool names to new tools
         new_tool_map = {tool.name: tool for tool in new_tools}
@@ -971,41 +986,41 @@ class Crew(FlowTrackable, BaseModel):
         # Add all new tools
         tools.extend(new_tools)
 
-        return cast(List[BaseTool], tools)
+        return cast(list[BaseTool], tools)
 
     def _inject_delegation_tools(
         self,
-        tools: Union[List[Tool], List[BaseTool]],
+        tools: Union[list[Tool], list[BaseTool]],
         task_agent: BaseAgent,
-        agents: List[BaseAgent],
-    ) -> List[BaseTool]:
+        agents: list[BaseAgent],
+    ) -> list[BaseTool]:
         if hasattr(task_agent, "get_delegation_tools"):
             delegation_tools = task_agent.get_delegation_tools(agents)
             # Cast delegation_tools to the expected type for _merge_tools
-            return self._merge_tools(tools, cast(List[BaseTool], delegation_tools))
-        return cast(List[BaseTool], tools)
+            return self._merge_tools(tools, cast(list[BaseTool], delegation_tools))
+        return cast(list[BaseTool], tools)
 
     def _add_multimodal_tools(
-        self, agent: BaseAgent, tools: Union[List[Tool], List[BaseTool]]
-    ) -> List[BaseTool]:
+        self, agent: BaseAgent, tools: Union[list[Tool], list[BaseTool]]
+    ) -> list[BaseTool]:
         if hasattr(agent, "get_multimodal_tools"):
             multimodal_tools = agent.get_multimodal_tools()
             # Cast multimodal_tools to the expected type for _merge_tools
-            return self._merge_tools(tools, cast(List[BaseTool], multimodal_tools))
-        return cast(List[BaseTool], tools)
+            return self._merge_tools(tools, cast(list[BaseTool], multimodal_tools))
+        return cast(list[BaseTool], tools)
 
     def _add_code_execution_tools(
-        self, agent: BaseAgent, tools: Union[List[Tool], List[BaseTool]]
-    ) -> List[BaseTool]:
+        self, agent: BaseAgent, tools: Union[list[Tool], list[BaseTool]]
+    ) -> list[BaseTool]:
         if hasattr(agent, "get_code_execution_tools"):
             code_tools = agent.get_code_execution_tools()
             # Cast code_tools to the expected type for _merge_tools
-            return self._merge_tools(tools, cast(List[BaseTool], code_tools))
-        return cast(List[BaseTool], tools)
+            return self._merge_tools(tools, cast(list[BaseTool], code_tools))
+        return cast(list[BaseTool], tools)
 
     def _add_delegation_tools(
-        self, task: Task, tools: Union[List[Tool], List[BaseTool]]
-    ) -> List[BaseTool]:
+        self, task: Task, tools: Union[list[Tool], list[BaseTool]]
+    ) -> list[BaseTool]:
         agents_for_delegation = [agent for agent in self.agents if agent != task.agent]
         if len(self.agents) > 1 and len(agents_for_delegation) > 0 and task.agent:
             if not tools:
@@ -1013,7 +1028,7 @@ class Crew(FlowTrackable, BaseModel):
             tools = self._inject_delegation_tools(
                 tools, task.agent, agents_for_delegation
             )
-        return cast(List[BaseTool], tools)
+        return cast(list[BaseTool], tools)
 
     def _log_task_start(self, task: Task, role: str = "None"):
         if self.output_log_file:
@@ -1022,8 +1037,8 @@ class Crew(FlowTrackable, BaseModel):
             )
 
     def _update_manager_tools(
-        self, task: Task, tools: Union[List[Tool], List[BaseTool]]
-    ) -> List[BaseTool]:
+        self, task: Task, tools: Union[list[Tool], list[BaseTool]]
+    ) -> list[BaseTool]:
         if self.manager_agent:
             if task.agent:
                 tools = self._inject_delegation_tools(tools, task.agent, [task.agent])
@@ -1031,9 +1046,9 @@ class Crew(FlowTrackable, BaseModel):
                 tools = self._inject_delegation_tools(
                     tools, self.manager_agent, self.agents
                 )
-        return cast(List[BaseTool], tools)
+        return cast(list[BaseTool], tools)
 
-    def _get_context(self, task: Task, task_outputs: List[TaskOutput]) -> str:
+    def _get_context(self, task: Task, task_outputs: list[TaskOutput]) -> str:
         if not task.context:
             return ""
 
@@ -1055,7 +1070,7 @@ class Crew(FlowTrackable, BaseModel):
                 output=output.raw,
             )
 
-    def _create_crew_output(self, task_outputs: List[TaskOutput]) -> CrewOutput:
+    def _create_crew_output(self, task_outputs: list[TaskOutput]) -> CrewOutput:
         if not task_outputs:
             raise ValueError("No task outputs available to create crew output.")
 
@@ -1086,11 +1101,15 @@ class Crew(FlowTrackable, BaseModel):
 
     def _process_async_tasks(
         self,
-        futures: List[Tuple[Task, Future[TaskOutput], int]],
+        futures: list[tuple[Task, Future[TaskOutput], int]],
         was_replayed: bool = False,
-    ) -> List[TaskOutput]:
-        task_outputs: List[TaskOutput] = []
+    ) -> list[TaskOutput]:
+        task_outputs: list[TaskOutput] = []
         for future_task, future, task_index in futures:
+            if self.is_cancelled():
+                future.cancel()
+                continue
+            
             task_output = future.result()
             task_outputs.append(task_output)
             self._process_task_result(future_task, task_output)
@@ -1100,7 +1119,7 @@ class Crew(FlowTrackable, BaseModel):
         return task_outputs
 
     def _find_task_index(
-        self, task_id: str, stored_outputs: List[Any]
+        self, task_id: str, stored_outputs: list[Any]
     ) -> Optional[int]:
         return next(
             (
@@ -1112,7 +1131,7 @@ class Crew(FlowTrackable, BaseModel):
         )
 
     def replay(
-        self, task_id: str, inputs: Optional[Dict[str, Any]] = None
+        self, task_id: str, inputs: Optional[dict[str, Any]] = None
     ) -> CrewOutput:
         stored_outputs = self._task_output_handler.load()
         if not stored_outputs:
@@ -1153,15 +1172,15 @@ class Crew(FlowTrackable, BaseModel):
         return result
 
     def query_knowledge(
-        self, query: List[str], results_limit: int = 3, score_threshold: float = 0.35
-    ) -> Union[List[Dict[str, Any]], None]:
+        self, query: list[str], results_limit: int = 3, score_threshold: float = 0.35
+    ) -> Union[list[dict[str, Any]], None]:
         if self.knowledge:
             return self.knowledge.query(
                 query, results_limit=results_limit, score_threshold=score_threshold
             )
         return None
 
-    def fetch_inputs(self) -> Set[str]:
+    def fetch_inputs(self) -> set[str]:
         """
         Gathers placeholders (e.g., {something}) referenced in tasks or agents.
         Scans each task's 'description' + 'expected_output', and each agent's
@@ -1170,7 +1189,7 @@ class Crew(FlowTrackable, BaseModel):
         Returns a set of all discovered placeholder names.
         """
         placeholder_pattern = re.compile(r"\{(.+?)\}")
-        required_inputs: Set[str] = set()
+        required_inputs: set[str] = set()
 
         # Scan tasks for inputs
         for task in self.tasks:
@@ -1272,7 +1291,7 @@ class Crew(FlowTrackable, BaseModel):
             if not task.callback:
                 task.callback = self.task_callback
 
-    def _interpolate_inputs(self, inputs: Dict[str, Any]) -> None:
+    def _interpolate_inputs(self, inputs: dict[str, Any]) -> None:
         """Interpolates the inputs in the tasks and agents."""
         [
             task.interpolate_inputs_and_add_conversation_history(
@@ -1306,7 +1325,7 @@ class Crew(FlowTrackable, BaseModel):
         self,
         n_iterations: int,
         eval_llm: Union[str, InstanceOf[BaseLLM]],
-        inputs: Optional[Dict[str, Any]] = None,
+        inputs: Optional[dict[str, Any]] = None,
     ) -> None:
         """Test and evaluate the Crew with the given inputs for n iterations concurrently using concurrent.futures."""
         try:
@@ -1504,7 +1523,35 @@ class Crew(FlowTrackable, BaseModel):
             },
         }
 
-    def reset_knowledge(self, knowledges: List[Knowledge]) -> None:
+    def reset_knowledge(self, knowledges: list[Knowledge]) -> None:
         """Reset crew and agent knowledge storage."""
         for ks in knowledges:
             ks.reset()
+
+    def _set_allow_crewai_trigger_context_for_first_task(self):
+        crewai_trigger_payload = self._inputs and self._inputs.get(
+            "crewai_trigger_payload"
+        )
+        able_to_inject = (
+            self.tasks and self.tasks[0].allow_crewai_trigger_context is None
+        )
+
+        if (
+            self.process == Process.sequential
+            and crewai_trigger_payload
+            and able_to_inject
+        ):
+            self.tasks[0].allow_crewai_trigger_context = True
+
+    def cancel(self) -> None:
+        """Cancel the crew execution. This will stop the crew after the current task completes."""
+        self._cancellation_event.set()
+        self._logger.log("info", "Crew cancellation requested", color="yellow")
+
+    def is_cancelled(self) -> bool:
+        """Check if the crew execution has been cancelled."""
+        return self._cancellation_event.is_set()
+
+    def _reset_cancellation(self) -> None:
+        """Reset the cancellation state for reuse of the crew instance."""
+        self._cancellation_event.clear()

src/crewai/crews/crew_output.py

@@ -1,5 +1,5 @@
 import json
-from typing import Any, Dict, Optional
+from typing import Any, Optional
 
 from pydantic import BaseModel, Field
 
@@ -15,7 +15,7 @@ class CrewOutput(BaseModel):
     pydantic: Optional[BaseModel] = Field(
         description="Pydantic output of Crew", default=None
     )
-    json_dict: Optional[Dict[str, Any]] = Field(
+    json_dict: Optional[dict[str, Any]] = Field(
         description="JSON dict output of Crew", default=None
     )
     tasks_output: list[TaskOutput] = Field(
@@ -32,7 +32,7 @@ class CrewOutput(BaseModel):
 
         return json.dumps(self.json_dict)
 
-    def to_dict(self) -> Dict[str, Any]:
+    def to_dict(self) -> dict[str, Any]:
         """Convert json_output and pydantic_output to a dictionary."""
         output_dict = {}
         if self.json_dict: