Merge branch 'main' into gl/fix/cache-handler-types-and-imports

- Make task parameter required in CrewAgentExecutor.__init__
- Update Agent.create_agent_executor to require task parameter
- Handle cases where crew can be None (standalone agent usage)
- Update base class signatures to match
- Remove unnecessary create_agent_executor calls during setup
- Add missing type annotations in base_agent_executor_mixin
- Fix all type errors in base_agent.py using Self return type
- Add assert for agent_executor before use
- Fix crew access checks to handle None case

.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/ directory only (excluding tests/)
+          grep -E "^src/" 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/:"
+            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/"
+            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 ${{ steps.changed-files.outputs.files }}
+
+      - name: No files to check
+        if: steps.changed-files.outputs.has_changes == 'false'
+        run: echo "No Python files in src/ were modified - skipping type checks"
+
+  # Summary job to provide single status for branch protection
+  type-checker:
+    name: type-checker
+    runs-on: ubuntu-latest
+    needs: type-checker-matrix
+    if: always()
+    steps:
+      - name: Check matrix results
+        run: |
+          if [ "${{ 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

.pre-commit-config.yaml

@@ -1,7 +1,16 @@
 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: ["--strict", "--exclude", "src/crewai/cli/templates"]
+        files: ^src/
+        exclude: ^tests/

.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

@@ -320,6 +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",
@@ -658,6 +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",
@@ -1007,6 +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",

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):
@@ -279,7 +279,7 @@ Additional fields vary by event type. For example, `CrewKickoffCompletedEvent` i
 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/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/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/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/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):
@@ -279,7 +279,7 @@ CrewAI는 여러분이 청취할 수 있는 다양한 이벤트를 제공합니
 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/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):
@@ -268,7 +268,7 @@ Campos adicionais variam pelo tipo de evento. Por exemplo, `CrewKickoffCompleted
 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.

pyproject.toml

@@ -48,7 +48,7 @@ Documentation = "https://docs.crewai.com"
 Repository = "https://github.com/crewAIInc/crewAI"
 
 [project.optional-dependencies]
-tools = ["crewai-tools~=0.62.1"]
+tools = ["crewai-tools~=0.69.0"]
 embeddings = [
     "tiktoken~=0.8.0"
 ]
@@ -68,12 +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",
@@ -85,15 +89,40 @@ 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"]
+exclude = ["src/crewai/cli/templates", "tests"]
 
 [tool.bandit]
 exclude_dirs = ["src/crewai/cli/templates"]

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__ = "0.165.1"
+__version__ = "0.177.0"
 __all__ = [
     "Agent",
     "Crew",

src/crewai/agent.py

@@ -1,18 +1,50 @@
 import shutil
 import subprocess
 import time
-from typing import Any, Callable, Dict, List, Literal, Optional, Sequence, Tuple, Type, Union
+from collections.abc import Callable, Sequence
+from typing import (
+    Any,
+    Literal,
+    Optional,
+)
 
-from pydantic import Field, InstanceOf, PrivateAttr, model_validator
+from pydantic import (
+    BeforeValidator,
+    Field,
+    InstanceOf,
+    PrivateAttr,
+    computed_field,
+    field_validator,
+    model_validator,
+)
+from typing_extensions import Self
 
-from crewai.agents import CacheHandler
 from crewai.agents.agent_builder.base_agent import BaseAgent
+from crewai.agents.cache.cache_handler import CacheHandler
 from crewai.agents.crew_agent_executor import CrewAgentExecutor
+from crewai.events.event_bus import crewai_event_bus
+from crewai.events.types.agent_events import (
+    AgentExecutionCompletedEvent,
+    AgentExecutionErrorEvent,
+    AgentExecutionStartedEvent,
+)
+from crewai.events.types.knowledge_events import (
+    KnowledgeQueryCompletedEvent,
+    KnowledgeQueryFailedEvent,
+    KnowledgeQueryStartedEvent,
+    KnowledgeRetrievalCompletedEvent,
+    KnowledgeRetrievalStartedEvent,
+    KnowledgeSearchQueryFailedEvent,
+)
+from crewai.events.types.memory_events import (
+    MemoryRetrievalCompletedEvent,
+    MemoryRetrievalStartedEvent,
+)
 from crewai.knowledge.knowledge import Knowledge
 from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
 from crewai.knowledge.utils.knowledge_utils import extract_knowledge_context
 from crewai.lite_agent import LiteAgent, LiteAgentOutput
-from crewai.llm import BaseLLM
+from crewai.llms.base_llm import BaseLLM
 from crewai.memory.contextual.contextual_memory import ContextualMemory
 from crewai.security import Fingerprint
 from crewai.task import Task
@@ -27,25 +59,7 @@ 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 (
-    AgentExecutionCompletedEvent,
-    AgentExecutionErrorEvent,
-    AgentExecutionStartedEvent,
-)
-from crewai.utilities.events.crewai_event_bus import crewai_event_bus
-from crewai.utilities.events.memory_events import (
-    MemoryRetrievalStartedEvent,
-    MemoryRetrievalCompletedEvent,
-)
-from crewai.utilities.events.knowledge_events import (
-    KnowledgeQueryCompletedEvent,
-    KnowledgeQueryFailedEvent,
-    KnowledgeQueryStartedEvent,
-    KnowledgeRetrievalCompletedEvent,
-    KnowledgeRetrievalStartedEvent,
-    KnowledgeSearchQueryFailedEvent,
-)
-from crewai.utilities.llm_utils import create_llm
+from crewai.utilities.llm_utils import create_default_llm, create_llm
 from crewai.utilities.token_counter_callback import TokenCalcHandler
 from crewai.utilities.training_handler import CrewTrainingHandler
 
@@ -76,6 +90,8 @@ class Agent(BaseAgent):
     """
 
     _times_executed: int = PrivateAttr(default=0)
+    _llm: BaseLLM = PrivateAttr()
+    _function_calling_llm: BaseLLM | None = PrivateAttr(default=None)
     max_execution_time: Optional[int] = Field(
         default=None,
         description="Maximum execution time for an agent to execute a task",
@@ -90,10 +106,11 @@ class Agent(BaseAgent):
         default=True,
         description="Use system prompt for the agent.",
     )
-    llm: Union[str, InstanceOf[BaseLLM], Any] = Field(
-        description="Language model that will run the agent.", default=None
+    llm: str | InstanceOf[BaseLLM] | None = Field(
+        description="Language model that will run the agent.",
+        default_factory=create_default_llm,
     )
-    function_calling_llm: Optional[Union[str, InstanceOf[BaseLLM], Any]] = Field(
+    function_calling_llm: str | InstanceOf[BaseLLM] | None = Field(
         description="Language model that will run the agent.", default=None
     )
     system_template: Optional[str] = Field(
@@ -140,7 +157,7 @@ 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.",
     )
@@ -160,29 +177,45 @@ 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[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"
     )
 
     @model_validator(mode="before")
-    def validate_from_repository(cls, v):
+    @classmethod
+    def validate_from_repository(cls, v: Any) -> Any:
         if v is not None and (from_repository := v.get("from_repository")):
             return load_agent_from_repository(from_repository) | v
         return v
 
+    @field_validator("function_calling_llm", mode="after")
+    @classmethod
+    def validate_function_calling_llm(cls, v: Any) -> BaseLLM | None:
+        if not v or isinstance(v, BaseLLM):
+            return v
+        return create_llm(v)
+
     @model_validator(mode="after")
-    def post_init_setup(self):
+    def post_init_setup(self) -> Self:
         self.agent_ops_agent_name = self.role
 
-        self.llm = create_llm(self.llm)
-        if self.function_calling_llm and not isinstance(
-            self.function_calling_llm, BaseLLM
-        ):
-            self.function_calling_llm = create_llm(self.function_calling_llm)
+        # Validate and set the private LLM attributes
+        if isinstance(self.llm, BaseLLM):
+            self._llm = self.llm
+        elif self.llm is None:
+            self._llm = create_default_llm()
+        else:
+            self._llm = create_llm(self.llm)
+
+        if self.function_calling_llm:
+            if isinstance(self.function_calling_llm, BaseLLM):
+                self._function_calling_llm = self.function_calling_llm
+            else:
+                self._function_calling_llm = create_llm(self.function_calling_llm)
 
         if not self.agent_executor:
             self._setup_agent_executor()
@@ -192,12 +225,12 @@ class Agent(BaseAgent):
 
         return self
 
-    def _setup_agent_executor(self):
+    def _setup_agent_executor(self) -> None:
         if not self.cache_handler:
             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) -> None:
         try:
             if self.embedder is None and crew_embedder:
                 self.embedder = crew_embedder
@@ -234,8 +267,8 @@ class Agent(BaseAgent):
         self,
         task: Task,
         context: Optional[str] = None,
-        tools: Optional[List[BaseTool]] = None,
-    ) -> str:
+        tools: Optional[list[BaseTool]] = None,
+    ) -> Any:
         """Execute a task with the agent.
 
         Args:
@@ -276,7 +309,7 @@ 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 +342,20 @@ 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 +368,14 @@ 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,
@@ -400,7 +439,7 @@ class Agent(BaseAgent):
                 )
 
         tools = tools or self.tools or []
-        self.create_agent_executor(tools=tools, task=task)
+        self.create_agent_executor(task=task, tools=tools)
 
         if self.crew and self.crew._train:
             task_prompt = self._training_handler(task_prompt=task_prompt)
@@ -475,7 +514,7 @@ class Agent(BaseAgent):
         # If there was any tool in self.tools_results that had result_as_answer
         # set to True, return the results of the last tool that had
         # result_as_answer set to True
-        for tool_result in self.tools_results:  # type: ignore # Item "None" of "list[Any] | None" has no attribute "__iter__" (not iterable)
+        for tool_result in self.tools_results:
             if tool_result.get("result_as_answer", False):
                 result = tool_result["result"]
         crewai_event_bus.emit(
@@ -484,7 +523,7 @@ class Agent(BaseAgent):
         )
         return result
 
-    def _execute_with_timeout(self, task_prompt: str, task: Task, timeout: int) -> str:
+    def _execute_with_timeout(self, task_prompt: str, task: Task, timeout: int) -> Any:
         """Execute a task with a timeout.
 
         Args:
@@ -517,7 +556,7 @@ class Agent(BaseAgent):
                 future.cancel()
                 raise RuntimeError(f"Task execution failed: {str(e)}")
 
-    def _execute_without_timeout(self, task_prompt: str, task: Task) -> str:
+    def _execute_without_timeout(self, task_prompt: str, task: Task) -> Any:
         """Execute a task without a timeout.
 
         Args:
@@ -527,6 +566,9 @@ class Agent(BaseAgent):
         Returns:
             The output of the agent.
         """
+        assert self.agent_executor is not None, (
+            "Agent executor must be created before execution"
+        )
         return self.agent_executor.invoke(
             {
                 "input": task_prompt,
@@ -537,14 +579,15 @@ class Agent(BaseAgent):
         )["output"]
 
     def create_agent_executor(
-        self, tools: Optional[List[BaseTool]] = None, task=None
+        self, task: Task, tools: Optional[list[BaseTool]] = None
     ) -> None:
         """Create an agent executor for the agent.
 
-        Returns:
-            An instance of the CrewAgentExecutor class.
+        Args:
+            task: Task to execute.
+            tools: Optional list of tools to use.
         """
-        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(
@@ -565,7 +608,7 @@ class Agent(BaseAgent):
             )
 
         self.agent_executor = CrewAgentExecutor(
-            llm=self.llm,
+            llm=self._llm,
             task=task,
             agent=self,
             crew=self.crew,
@@ -578,15 +621,15 @@ class Agent(BaseAgent):
             tools_names=get_tool_names(parsed_tools),
             tools_description=render_text_description_and_args(parsed_tools),
             step_callback=self.step_callback,
-            function_calling_llm=self.function_calling_llm,
+            function_calling_llm=self._function_calling_llm,
             respect_context_window=self.respect_context_window,
             request_within_rpm_limit=(
                 self._rpm_controller.check_or_wait if self._rpm_controller else None
             ),
-            callbacks=[TokenCalcHandler(self._token_process)],
+            litellm_callbacks=[TokenCalcHandler(self._token_process)],
         )
 
-    def get_delegation_tools(self, agents: List[BaseAgent]):
+    def get_delegation_tools(self, agents: list[BaseAgent]) -> list[BaseTool]:
         agent_tools = AgentTools(agents=agents)
         tools = agent_tools.tools()
         return tools
@@ -596,7 +639,7 @@ class Agent(BaseAgent):
 
         return [AddImageTool()]
 
-    def get_code_execution_tools(self):
+    def get_code_execution_tools(self) -> list[BaseTool]:
         try:
             from crewai_tools import CodeInterpreterTool  # type: ignore
 
@@ -607,8 +650,11 @@ class Agent(BaseAgent):
             self._logger.log(
                 "info", "Coding tools not available. Install crewai_tools. "
             )
+            return []
 
-    def get_output_converter(self, llm, text, model, instructions):
+    def get_output_converter(
+        self, llm: BaseLLM, text: str, model: str, instructions: str
+    ) -> Converter:
         return Converter(llm=llm, text=text, model=model, instructions=instructions)
 
     def _training_handler(self, task_prompt: str) -> str:
@@ -637,7 +683,7 @@ 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:
@@ -656,7 +702,7 @@ class Agent(BaseAgent):
 
         return description
 
-    def _inject_date_to_task(self, task):
+    def _inject_date_to_task(self, task: Task) -> None:
         """Inject the current date into the task description if inject_date is enabled."""
         if self.inject_date:
             from datetime import datetime
@@ -706,7 +752,7 @@ class Agent(BaseAgent):
                 f"Docker is not running. Please start Docker to use code execution with agent: {self.role}"
             )
 
-    def __repr__(self):
+    def __repr__(self) -> str:
         return f"Agent(role={self.role}, goal={self.goal}, backstory={self.backstory})"
 
     @property
@@ -719,7 +765,7 @@ class Agent(BaseAgent):
         """
         return self.security_config.fingerprint
 
-    def set_fingerprint(self, fingerprint: Fingerprint):
+    def set_fingerprint(self, fingerprint: Fingerprint) -> None:
         self.security_config.fingerprint = fingerprint
 
     def _get_knowledge_search_query(self, task_prompt: str) -> str | None:
@@ -735,22 +781,8 @@ class Agent(BaseAgent):
             task_prompt=task_prompt
         )
         rewriter_prompt = self.i18n.slice("knowledge_search_query_system_prompt")
-        if not isinstance(self.llm, BaseLLM):
-            self._logger.log(
-                "warning",
-                f"Knowledge search query failed: LLM for agent '{self.role}' is not an instance of BaseLLM",
-            )
-            crewai_event_bus.emit(
-                self,
-                event=KnowledgeQueryFailedEvent(
-                    agent=self,
-                    error="LLM is not compatible with knowledge search queries",
-                ),
-            )
-            return None
-
         try:
-            rewritten_query = self.llm.call(
+            rewritten_query = self._llm.call(
                 [
                     {
                         "role": "system",
@@ -779,8 +811,8 @@ class Agent(BaseAgent):
 
     def kickoff(
         self,
-        messages: Union[str, List[Dict[str, str]]],
-        response_format: Optional[Type[Any]] = None,
+        messages: str | list[dict[str, str]],
+        response_format: Optional[type[Any]] = None,
     ) -> LiteAgentOutput:
         """
         Execute the agent with the given messages using a LiteAgent instance.
@@ -802,7 +834,7 @@ class Agent(BaseAgent):
             role=self.role,
             goal=self.goal,
             backstory=self.backstory,
-            llm=self.llm,
+            llm=self._llm,
             tools=self.tools or [],
             max_iterations=self.max_iter,
             max_execution_time=self.max_execution_time,
@@ -819,8 +851,8 @@ class Agent(BaseAgent):
 
     async def kickoff_async(
         self,
-        messages: Union[str, List[Dict[str, str]]],
-        response_format: Optional[Type[Any]] = None,
+        messages: str | list[dict[str, str]],
+        response_format: Optional[type[Any]] = None,
     ) -> LiteAgentOutput:
         """
         Execute the agent asynchronously with the given messages using a LiteAgent instance.
@@ -840,7 +872,7 @@ class Agent(BaseAgent):
             role=self.role,
             goal=self.goal,
             backstory=self.backstory,
-            llm=self.llm,
+            llm=self._llm,
             tools=self.tools or [],
             max_iterations=self.max_iter,
             max_execution_time=self.max_execution_time,

src/crewai/agents/__init__.py

@@ -1,5 +1,10 @@
-from .cache.cache_handler import CacheHandler
-from .parser import CrewAgentParser
-from .tools_handler import ToolsHandler
+from crewai.agents.parser import parse, AgentAction, AgentFinish, OutputParserException
+from crewai.agents.tools_handler import ToolsHandler
 
-__all__ = ["CacheHandler", "CrewAgentParser", "ToolsHandler"]
+__all__ = [
+    "parse",
+    "AgentAction",
+    "AgentFinish",
+    "OutputParserException",
+    "ToolsHandler",
+]

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
 
@@ -10,21 +10,22 @@ from crewai.agents.agent_adapters.langgraph.structured_output_converter import (
     LangGraphConverterAdapter,
 )
 from crewai.agents.agent_builder.base_agent import BaseAgent
-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,
 )
+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
 
 try:
-    from langchain_core.messages import ToolMessage
-    from langgraph.checkpoint.memory import MemorySaver
-    from langgraph.prebuilt import create_react_agent
+    from langgraph.checkpoint.memory import (  # type: ignore
+        MemorySaver,
+    )
+    from langgraph.prebuilt import create_react_agent  # type: ignore
 
     LANGGRAPH_AVAILABLE = True
 except ImportError:
@@ -52,11 +53,11 @@ 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,
-        **kwargs,
+        agent_config: Optional[dict[str, Any]] = None,
+        **kwargs: Any,
     ):
         """Initialize the LangGraph agent adapter."""
         if not LANGGRAPH_AVAILABLE:
@@ -82,7 +83,7 @@ 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,
@@ -112,7 +113,7 @@ class LangGraphAgentAdapter(BaseAgentAdapter):
         """Build a system prompt for the LangGraph agent."""
         base_prompt = f"""
             You are {self.role}.
-        
+
             Your goal is: {self.goal}
 
             Your backstory: {self.backstory}
@@ -125,10 +126,10 @@ 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)
+        self.create_agent_executor(task, tools)
 
         self.configure_structured_output(task)
 
@@ -198,11 +199,13 @@ class LangGraphAgentAdapter(BaseAgentAdapter):
             )
             raise
 
-    def create_agent_executor(self, tools: Optional[List[BaseTool]] = None) -> None:
+    def create_agent_executor(
+        self, task: Any = None, 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 +213,7 @@ 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()
@@ -221,6 +224,6 @@ class LangGraphAgentAdapter(BaseAgentAdapter):
         """Convert output format if needed."""
         return Converter(llm=llm, text=text, model=model, instructions=instructions)
 
-    def configure_structured_output(self, task) -> None:
+    def configure_structured_output(self, task: Any) -> None:
         """Configure the structured output for LangGraph."""
         self._converter_adapter.configure_structured_output(task)

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

@@ -1,4 +1,5 @@
 import json
+from typing import Any
 
 from crewai.agents.agent_adapters.base_converter_adapter import BaseConverterAdapter
 from crewai.utilities.converter import generate_model_description
@@ -7,14 +8,15 @@ from crewai.utilities.converter import generate_model_description
 class LangGraphConverterAdapter(BaseConverterAdapter):
     """Adapter for handling structured output conversion in LangGraph agents"""
 
-    def __init__(self, agent_adapter):
+    def __init__(self, agent_adapter: Any) -> None:
         """Initialize the converter adapter with a reference to the agent adapter"""
+        super().__init__(agent_adapter)  # type: ignore
         self.agent_adapter = agent_adapter
-        self._output_format = None
-        self._schema = None
-        self._system_prompt_appendix = None
+        self._output_format: str | None = None
+        self._schema: str | None = None
+        self._system_prompt_appendix: str | None = None
 
-    def configure_structured_output(self, task) -> None:
+    def configure_structured_output(self, task: Any) -> None:
         """Configure the structured output for LangGraph."""
         if not (task.output_json or task.output_pydantic):
             self._output_format = None
@@ -41,7 +43,7 @@ Important: Your final answer MUST be provided in the following structured format
 
 {self._schema}
 
-DO NOT include any markdown code blocks, backticks, or other formatting around your response. 
+DO NOT include any markdown code blocks, backticks, or other formatting around your response.
 The output should be raw JSON that exactly matches the specified schema.
 """
 

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
 
@@ -7,19 +7,19 @@ from crewai.agents.agent_adapters.openai_agents.structured_output_converter impo
     OpenAIConverterAdapter,
 )
 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,
 )
+from crewai.tools import BaseTool
+from crewai.tools.agent_tools.agent_tools import AgentTools
+from crewai.utilities import Logger
 
 try:
-    from agents import Agent as OpenAIAgent  # type: ignore
-    from agents import Runner, enable_verbose_stdout_logging  # type: ignore
+    from agents import Agent as OpenAIAgent  # type: ignore[import-not-found]
+    from agents import Runner, enable_verbose_stdout_logging
 
     from .openai_agent_tool_adapter import OpenAIAgentToolAdapter
 
@@ -40,13 +40,14 @@ class OpenAIAgentAdapter(BaseAgentAdapter):
     step_callback: Any = Field(default=None)
     _tool_adapter: "OpenAIAgentToolAdapter" = PrivateAttr()
     _converter_adapter: OpenAIConverterAdapter = PrivateAttr()
+    agent_executor: Any = Field(default=None)
 
     def __init__(
         self,
         model: str = "gpt-4o-mini",
-        tools: Optional[List[BaseTool]] = None,
-        agent_config: Optional[dict] = None,
-        **kwargs,
+        tools: Optional[list[BaseTool]] = None,
+        agent_config: Optional[dict[str, Any]] = None,
+        **kwargs: Any,
     ):
         if not OPENAI_AVAILABLE:
             raise ImportError(
@@ -72,7 +73,7 @@ class OpenAIAgentAdapter(BaseAgentAdapter):
         """Build a system prompt for the OpenAI agent."""
         base_prompt = f"""
             You are {self.role}.
-        
+
             Your goal is: {self.goal}
 
             Your backstory: {self.backstory}
@@ -85,11 +86,11 @@ class OpenAIAgentAdapter(BaseAgentAdapter):
         self,
         task: Any,
         context: Optional[str] = None,
-        tools: Optional[List[BaseTool]] = None,
-    ) -> str:
+        tools: Optional[list[BaseTool]] = None,
+    ) -> Any:
         """Execute a task using the OpenAI Assistant"""
         self._converter_adapter.configure_structured_output(task)
-        self.create_agent_executor(tools)
+        self.create_agent_executor(task, tools)
 
         if self.verbose:
             enable_verbose_stdout_logging()
@@ -109,6 +110,7 @@ class OpenAIAgentAdapter(BaseAgentAdapter):
                     task=task,
                 ),
             )
+            assert hasattr(self, "agent_executor"), "agent_executor not initialized"
             result = self.agent_executor.run_sync(self._openai_agent, task_prompt)
             final_answer = self.handle_execution_result(result)
             crewai_event_bus.emit(
@@ -131,7 +133,9 @@ class OpenAIAgentAdapter(BaseAgentAdapter):
             )
             raise
 
-    def create_agent_executor(self, tools: Optional[List[BaseTool]] = None) -> None:
+    def create_agent_executor(
+        self, task: Any = None, tools: Optional[list[BaseTool]] = None
+    ) -> None:
         """
         Configure the OpenAI agent for execution.
         While OpenAI handles execution differently through Runner,
@@ -152,24 +156,24 @@ 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)
             if self._tool_adapter.converted_tools:
                 self._openai_agent.tools = self._tool_adapter.converted_tools
 
-    def handle_execution_result(self, result: Any) -> str:
+    def handle_execution_result(self, result: Any) -> Any:
         """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()
         return tools
 
-    def configure_structured_output(self, task) -> None:
+    def configure_structured_output(self, task: Any) -> None:
         """Configure the structured output for the specific agent implementation.
 
         Args:

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

@@ -1,5 +1,6 @@
 import json
 import re
+from typing import Any
 
 from crewai.agents.agent_adapters.base_converter_adapter import BaseConverterAdapter
 from crewai.utilities.converter import generate_model_description
@@ -19,14 +20,15 @@ class OpenAIConverterAdapter(BaseConverterAdapter):
         _output_model: The Pydantic model for the output
     """
 
-    def __init__(self, agent_adapter):
+    def __init__(self, agent_adapter: Any) -> None:
         """Initialize the converter adapter with a reference to the agent adapter"""
+        super().__init__(agent_adapter)  # type: ignore
         self.agent_adapter = agent_adapter
-        self._output_format = None
-        self._schema = None
-        self._output_model = None
+        self._output_format: str | None = None
+        self._schema: str | None = None
+        self._output_model: Any = None
 
-    def configure_structured_output(self, task) -> None:
+    def configure_structured_output(self, task: Any) -> None:
         """
         Configure the structured output for OpenAI agent based on task requirements.
 
@@ -75,7 +77,7 @@ class OpenAIConverterAdapter(BaseConverterAdapter):
 
         return f"{base_prompt}\n\n{output_schema}"
 
-    def post_process_result(self, result: str) -> str:
+    def post_process_result(self, result: str) -> Any:
         """
         Post-process the result to ensure it matches the expected format.
 

src/crewai/agents/agent_builder/base_agent.py

@@ -1,8 +1,9 @@
 import uuid
 from abc import ABC, abstractmethod
+from collections.abc import Callable
 from copy import copy as shallow_copy
 from hashlib import md5
-from typing import Any, Callable, Dict, List, Optional, TypeVar
+from typing import Any, Optional, TypeVar
 
 from pydantic import (
     UUID4,
@@ -14,6 +15,7 @@ from pydantic import (
     model_validator,
 )
 from pydantic_core import PydanticCustomError
+from typing_extensions import Self
 
 from crewai.agents.agent_builder.utilities.base_token_process import TokenProcess
 from crewai.agents.cache.cache_handler import CacheHandler
@@ -61,7 +63,7 @@ class BaseAgent(ABC, BaseModel):
     Methods:
         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:
+        create_agent_executor(task, tools=None) -> None:
             Abstract method to create an agent executor.
         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.
@@ -79,7 +81,7 @@ class BaseAgent(ABC, BaseModel):
             Set private attributes.
     """
 
-    __hash__ = object.__hash__  # type: ignore
+    __hash__ = object.__hash__
     _logger: Logger = PrivateAttr(default_factory=lambda: Logger(verbose=False))
     _rpm_controller: Optional[RPMController] = PrivateAttr(default=None)
     _request_within_rpm_limit: Any = PrivateAttr(default=None)
@@ -91,7 +93,7 @@ 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(
@@ -108,14 +110,14 @@ 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(
         default=25, description="Maximum iterations for an agent to execute a task"
     )
-    agent_executor: InstanceOf = Field(
-        default=None, description="An instance of the CrewAgentExecutor class."
+    agent_executor: Optional[Any] = Field(
+        default=None, description="An instance of the agent executor class."
     )
     llm: Any = Field(
         default=None, description="Language model that will run the agent."
@@ -129,7 +131,7 @@ 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 +140,7 @@ 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 +152,7 @@ class BaseAgent(ABC, BaseModel):
         default_factory=SecurityConfig,
         description="Security configuration for the agent, including fingerprinting.",
     )
-    callbacks: List[Callable] = Field(
+    callbacks: list[Callable[..., Any]] = Field(
         default=[], description="Callbacks to be used for the agent"
     )
     adapted_agent: bool = Field(
@@ -163,12 +165,12 @@ class BaseAgent(ABC, BaseModel):
 
     @model_validator(mode="before")
     @classmethod
-    def process_model_config(cls, values):
+    def process_model_config(cls, values: Any) -> Any:
         return process_config(values, cls)
 
     @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
@@ -196,7 +198,7 @@ class BaseAgent(ABC, BaseModel):
         return processed_tools
 
     @model_validator(mode="after")
-    def validate_and_set_attributes(self):
+    def validate_and_set_attributes(self) -> Self:
         # Validate required fields
         for field in ["role", "goal", "backstory"]:
             if getattr(self, field) is None:
@@ -228,7 +230,7 @@ class BaseAgent(ABC, BaseModel):
             )
 
     @model_validator(mode="after")
-    def set_private_attrs(self):
+    def set_private_attrs(self) -> Self:
         """Set private attributes."""
         self._logger = Logger(verbose=self.verbose)
         if self.max_rpm and not self._rpm_controller:
@@ -240,7 +242,7 @@ class BaseAgent(ABC, BaseModel):
         return self
 
     @property
-    def key(self):
+    def key(self) -> str:
         source = [
             self._original_role or self.role,
             self._original_goal or self.goal,
@@ -253,16 +255,18 @@ class BaseAgent(ABC, BaseModel):
         self,
         task: Any,
         context: Optional[str] = None,
-        tools: Optional[List[BaseTool]] = None,
+        tools: Optional[list[BaseTool]] = None,
     ) -> str:
         pass
 
     @abstractmethod
-    def create_agent_executor(self, tools=None) -> None:
+    def create_agent_executor(
+        self, task: Any, tools: Optional[list[BaseTool]] = None
+    ) -> None:
         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 +324,7 @@ 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
@@ -350,7 +354,7 @@ class BaseAgent(ABC, BaseModel):
         if self.cache:
             self.cache_handler = cache_handler
             self.tools_handler.cache = cache_handler
-        self.create_agent_executor()
+        # Executor will be created when a task is executed
 
     def set_rpm_controller(self, rpm_controller: RPMController) -> None:
         """Set the rpm controller for the agent.
@@ -360,7 +364,7 @@ class BaseAgent(ABC, BaseModel):
         """
         if not self._rpm_controller:
             self._rpm_controller = rpm_controller
-            self.create_agent_executor()
+        # Executor will be created when a task is executed
 
-    def set_knowledge(self, crew_embedder: Optional[Dict[str, Any]] = None):
+    def set_knowledge(self, crew_embedder: Optional[dict[str, Any]] = None) -> None:
         pass

src/crewai/agents/agent_builder/base_agent_executor_mixin.py

@@ -1,31 +1,32 @@
 import time
-from typing import TYPE_CHECKING, Dict, List
+from typing import TYPE_CHECKING
 
+from crewai.events.event_listener import event_listener
 from crewai.memory.entity.entity_memory_item import EntityMemoryItem
 from crewai.memory.long_term.long_term_memory_item import LongTermMemoryItem
 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
 
 if TYPE_CHECKING:
     from crewai.agents.agent_builder.base_agent import BaseAgent
+    from crewai.agents.parser import AgentFinish
     from crewai.crew import Crew
     from crewai.task import Task
 
 
 class CrewAgentExecutorMixin:
-    crew: "Crew"
+    crew: "Crew | None"
     agent: "BaseAgent"
     task: "Task"
     iterations: int
     max_iter: int
-    messages: List[Dict[str, str]]
+    messages: list[dict[str, str]]
     _i18n: I18N
     _printer: Printer = Printer()
 
-    def _create_short_term_memory(self, output) -> None:
+    def _create_short_term_memory(self, output: "AgentFinish") -> None:
         """Create and save a short-term memory item if conditions are met."""
         if (
             self.crew
@@ -35,7 +36,8 @@ class CrewAgentExecutorMixin:
         ):
             try:
                 if (
-                    hasattr(self.crew, "_short_term_memory")
+                    self.crew
+                    and hasattr(self.crew, "_short_term_memory")
                     and self.crew._short_term_memory
                 ):
                     self.crew._short_term_memory.save(
@@ -43,13 +45,12 @@ 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}")
                 pass
 
-    def _create_external_memory(self, output) -> None:
+    def _create_external_memory(self, output: "AgentFinish") -> None:
         """Create and save a external-term memory item if conditions are met."""
         if (
             self.crew
@@ -65,13 +66,12 @@ 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}")
                 pass
 
-    def _create_long_term_memory(self, output) -> None:
+    def _create_long_term_memory(self, output: "AgentFinish") -> None:
         """Create and save long-term and entity memory items based on evaluation."""
         if (
             self.crew
@@ -100,8 +100,8 @@ 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 +109,10 @@ 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 +161,9 @@ 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/__init__.py

@@ -1,3 +1,5 @@
-from .cache_handler import CacheHandler
+"""Internal caching utilities for agent tool execution.
 
-__all__ = ["CacheHandler"]
+This package provides caching mechanisms for storing and retrieving
+tool execution results to avoid redundant operations.
+"""

src/crewai/agents/cache/cache_handler.py

@@ -1,15 +1,50 @@
-from typing import Any, Dict, Optional
+"""Cache handler for storing and retrieving tool execution results.
+
+This module provides a caching mechanism for tool outputs in the CrewAI framework,
+allowing agents to reuse previous tool execution results when the same tool is
+called with identical arguments.
+
+Classes:
+    CacheHandler: Manages the caching of tool execution results using an in-memory
+        dictionary with serialized tool arguments as keys.
+"""
+
+import json
+from typing import Any
 
 from pydantic import BaseModel, PrivateAttr
 
 
 class CacheHandler(BaseModel):
-    """Callback handler for tool usage."""
+    """Callback handler for tool usage.
 
-    _cache: Dict[str, Any] = PrivateAttr(default_factory=dict)
 
-    def add(self, tool, input, output):
-        self._cache[f"{tool}-{input}"] = output
+    Notes:
+        TODO: Make thread-safe, currently not thread-safe.
+    """
 
-    def read(self, tool, input) -> Optional[str]:
-        return self._cache.get(f"{tool}-{input}")
+    _cache: dict[str, Any] = PrivateAttr(default_factory=dict)
+
+    def add(self, tool: str, input_data: dict[str, Any] | None, output: str) -> None:
+        """Add a tool execution result to the cache.
+
+        Args:
+            tool: The name of the tool.
+            input_data: The input arguments for the tool.
+            output: The output from the tool execution.
+        """
+        cache_key = json.dumps(input_data, sort_keys=True) if input_data else ""
+        self._cache[f"{tool}-{cache_key}"] = output
+
+    def read(self, tool: str, input_data: dict[str, Any] | None) -> str | None:
+        """Read a tool execution result from the cache.
+
+        Args:
+            tool: The name of the tool.
+            input_data: The input arguments for the tool.
+
+        Returns:
+            The cached output if found, None otherwise.
+        """
+        cache_key = json.dumps(input_data, sort_keys=True) if input_data else ""
+        return self._cache.get(f"{tool}-{cache_key}")

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 +1,17 @@
-from typing import Any, Callable, Dict, List, Optional, Union
+"""Agent executor for crew AI agents.
+
+Handles agent execution flow including LLM interactions, tool execution,
+and memory management.
+"""
+
+from __future__ import annotations
+
+from collections.abc import Callable
+from typing import TYPE_CHECKING, Any
+
+if TYPE_CHECKING:
+    from crewai.crew import Crew
+    from crewai.task import Task
 
 from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.agents.agent_builder.base_agent_executor_mixin import CrewAgentExecutorMixin
@@ -8,7 +21,12 @@ from crewai.agents.parser import (
     OutputParserException,
 )
 from crewai.agents.tools_handler import ToolsHandler
-from crewai.llm import BaseLLM
+from crewai.events.event_bus import crewai_event_bus
+from crewai.events.types.logging_events import (
+    AgentLogsExecutionEvent,
+    AgentLogsStartedEvent,
+)
+from crewai.llms.base_llm import BaseLLM
 from crewai.tools.base_tool import BaseTool
 from crewai.tools.structured_tool import CrewStructuredTool
 from crewai.tools.tool_types import ToolResult
@@ -26,54 +44,73 @@ from crewai.utilities.agent_utils import (
     is_context_length_exceeded,
     process_llm_response,
 )
-from crewai.utilities.constants import MAX_LLM_RETRY, TRAINING_DATA_FILE
-from crewai.utilities.logger import Logger
+from crewai.utilities.constants import TRAINING_DATA_FILE
 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 (
-    AgentLogsStartedEvent,
-    AgentLogsExecutionEvent,
-)
-from crewai.utilities.events.crewai_event_bus import crewai_event_bus
 
 
 class CrewAgentExecutor(CrewAgentExecutorMixin):
-    _logger: Logger = Logger()
+    """Executor for crew agents.
+
+    Manages the execution lifecycle of an agent including prompt formatting,
+    LLM interactions, tool execution, and feedback handling.
+    """
 
     def __init__(
         self,
-        llm: Any,
-        task: Any,
-        crew: Any,
+        llm: BaseLLM,
+        task: Task,
+        crew: Crew | None,
         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] = [],
-        function_calling_llm: Any = None,
+        step_callback: Callable[[AgentAction | AgentFinish], None] | None = None,
+        original_tools: list[BaseTool] | None = None,
+        function_calling_llm: BaseLLM | None = None,
         respect_context_window: bool = False,
-        request_within_rpm_limit: Optional[Callable[[], bool]] = None,
-        callbacks: List[Any] = [],
-    ):
+        request_within_rpm_limit: Callable[[], bool] | None = None,
+        litellm_callbacks: list[Any] | None = None,
+    ) -> None:
+        """Initialize executor.
+
+        Args:
+            llm: Language model instance.
+            task: Task to execute.
+            crew: Optional Crew instance.
+            agent: Agent to execute.
+            prompt: Prompt templates.
+            max_iter: Maximum iterations.
+            tools: Available tools.
+            tools_names: Tool names string.
+            stop_words: Stop word list.
+            tools_description: Tool descriptions.
+            tools_handler: Tool handler instance.
+            step_callback: Optional step callback.
+            original_tools: Original tool list.
+            function_calling_llm: Optional function calling LLM.
+            respect_context_window: Respect context limits.
+            request_within_rpm_limit: RPM limit check function.
+            litellm_callbacks: Optional litellm callbacks list.
+        """
         self._i18n: I18N = I18N()
-        self.llm: BaseLLM = llm
+        self.llm = llm
         self.task = task
         self.agent = agent
-        self.crew = crew
+        self.crew: Crew | None = crew
         self.prompt = prompt
         self.tools = tools
         self.tools_names = tools_names
         self.stop = stop_words
         self.max_iter = max_iter
-        self.callbacks = callbacks
+        self.litellm_callbacks = litellm_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
@@ -81,12 +118,9 @@ 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]] = {
-            tool.name: tool for tool in self.tools
-        }
         existing_stop = self.llm.stop or []
         self.llm.stop = list(
             set(
@@ -96,7 +130,19 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
             )
         )
 
-    def invoke(self, inputs: Dict[str, str]) -> Dict[str, Any]:
+    def invoke(self, inputs: dict[str, str]) -> dict[str, str]:
+        """Execute the agent with given inputs.
+
+        Args:
+            inputs: Input dictionary containing prompt variables.
+
+        Returns:
+            Dictionary with agent output.
+
+        Raises:
+            AssertionError: If agent fails to reach final answer.
+            Exception: If unknown error occurs during execution.
+        """
         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 +168,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
             handle_unknown_error(self._printer, e)
             raise
 
-
         if self.ask_for_human_input:
             formatted_answer = self._handle_human_feedback(formatted_answer)
 
@@ -132,9 +177,13 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         return {"output": formatted_answer.output}
 
     def _invoke_loop(self) -> AgentFinish:
-        """
-        Main loop to invoke the agent's thought process until it reaches a conclusion
-        or the maximum number of iterations is reached.
+        """Execute agent loop until completion.
+
+        Returns:
+            Final answer from the agent.
+
+        Raises:
+            Exception: If litellm error or unknown error occurs.
         """
         formatted_answer = None
         while not isinstance(formatted_answer, AgentFinish):
@@ -146,7 +195,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                         i18n=self._i18n,
                         messages=self.messages,
                         llm=self.llm,
-                        callbacks=self.callbacks,
+                        callbacks=self.litellm_callbacks,
                     )
 
                 enforce_rpm_limit(self.request_within_rpm_limit)
@@ -154,19 +203,17 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                 answer = get_llm_response(
                     llm=self.llm,
                     messages=self.messages,
-                    callbacks=self.callbacks,
+                    callbacks=self.litellm_callbacks,
                     printer=self._printer,
-                    from_task=self.task
+                    from_task=self.task,
                 )
                 formatted_answer = process_llm_response(answer, self.use_stop_words)
 
                 if isinstance(formatted_answer, AgentAction):
                     # Extract agent fingerprint if available
                     fingerprint_context = {}
-                    if (
-                        self.agent
-                        and hasattr(self.agent, "security_config")
-                        and hasattr(self.agent.security_config, "fingerprint")
+                    if hasattr(self.agent, "security_config") and hasattr(
+                        self.agent.security_config, "fingerprint"
                     ):
                         fingerprint_context = {
                             "agent_fingerprint": str(
@@ -179,8 +226,8 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                         fingerprint_context=fingerprint_context,
                         tools=self.tools,
                         i18n=self._i18n,
-                        agent_key=self.agent.key if self.agent else None,
-                        agent_role=self.agent.role if self.agent else None,
+                        agent_key=self.agent.key,
+                        agent_role=self.agent.role,
                         tools_handler=self.tools_handler,
                         task=self.task,
                         agent=self.agent,
@@ -191,7 +238,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                     )
 
                 self._invoke_step_callback(formatted_answer)
-                self._append_message(formatted_answer.text, role="assistant")
+                self._append_message(formatted_answer.text)
 
             except OutputParserException as e:
                 formatted_answer = handle_output_parser_exception(
@@ -212,7 +259,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                         printer=self._printer,
                         messages=self.messages,
                         llm=self.llm,
-                        callbacks=self.callbacks,
+                        callbacks=self.litellm_callbacks,
                         i18n=self._i18n,
                     )
                     continue
@@ -232,8 +279,16 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
 
     def _handle_agent_action(
         self, formatted_answer: AgentAction, tool_result: ToolResult
-    ) -> Union[AgentAction, AgentFinish]:
-        """Handle the AgentAction, execute tools, and process the results."""
+    ) -> AgentAction | AgentFinish:
+        """Process agent action and tool execution.
+
+        Args:
+            formatted_answer: Agent's action to execute.
+            tool_result: Result from tool execution.
+
+        Returns:
+            Updated action or final answer.
+        """
         # Special case for add_image_tool
         add_image_tool = self._i18n.tools("add_image")
         if (
@@ -252,88 +307,65 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
             show_logs=self._show_logs,
         )
 
-    def _invoke_step_callback(self, formatted_answer) -> None:
-        """Invoke the step callback if it exists."""
+    def _invoke_step_callback(
+        self, formatted_answer: AgentAction | AgentFinish
+    ) -> None:
+        """Invoke step callback.
+
+        Args:
+            formatted_answer: Current agent response.
+        """
         if self.step_callback:
             self.step_callback(formatted_answer)
 
     def _append_message(self, text: str, role: str = "assistant") -> None:
-        """Append a message to the message list with the given role."""
+        """Add message to conversation history.
+
+        Args:
+            text: Message content.
+            role: Message role (default: assistant).
+        """
         self.messages.append(format_message_for_llm(text, role=role))
 
-    def _show_start_logs(self):
-        """Show logs for the start of agent execution."""
-        if self.agent is None:
-            raise ValueError("Agent cannot be None")
-
+    def _show_start_logs(self) -> None:
+        """Emit agent start event."""
         crewai_event_bus.emit(
             self.agent,
             AgentLogsStartedEvent(
                 agent_role=self.agent.role,
-                task_description=(
-                    getattr(self.task, "description") if self.task else "Not Found"
-                ),
+                task_description=self.task.description,
                 verbose=self.agent.verbose
-                or (hasattr(self, "crew") and getattr(self.crew, "verbose", False)),
+                or (self.crew.verbose if self.crew else False),
             ),
         )
 
-    def _show_logs(self, formatted_answer: Union[AgentAction, AgentFinish]):
-        """Show logs for the agent's execution."""
-        if self.agent is None:
-            raise ValueError("Agent cannot be None")
+    def _show_logs(self, formatted_answer: AgentAction | AgentFinish) -> None:
+        """Emit agent execution event.
 
+        Args:
+            formatted_answer: Agent's response to log.
+        """
         crewai_event_bus.emit(
             self.agent,
             AgentLogsExecutionEvent(
                 agent_role=self.agent.role,
                 formatted_answer=formatted_answer,
                 verbose=self.agent.verbose
-                or (hasattr(self, "crew") and getattr(self.crew, "verbose", False)),
+                or (self.crew.verbose if self.crew else False),
             ),
         )
 
-    def _summarize_messages(self) -> None:
-        messages_groups = []
-        for message in self.messages:
-            content = message["content"]
-            cut_size = self.llm.get_context_window_size()
-            for i in range(0, len(content), cut_size):
-                messages_groups.append({"content": content[i : i + cut_size]})
-
-        summarized_contents = []
-        for group in messages_groups:
-            summary = self.llm.call(
-                [
-                    format_message_for_llm(
-                        self._i18n.slice("summarizer_system_message"), role="system"
-                    ),
-                    format_message_for_llm(
-                        self._i18n.slice("summarize_instruction").format(
-                            group=group["content"]
-                        ),
-                    ),
-                ],
-                callbacks=self.callbacks,
-            )
-            summarized_contents.append({"content": str(summary)})
-
-        merged_summary = " ".join(content["content"] for content in summarized_contents)
-
-        self.messages = [
-            format_message_for_llm(
-                self._i18n.slice("summary").format(merged_summary=merged_summary)
-            )
-        ]
-
     def _handle_crew_training_output(
-        self, result: AgentFinish, human_feedback: Optional[str] = None
+        self, result: AgentFinish, human_feedback: str | None = None
     ) -> None:
-        """Handle the process of saving training data."""
-        agent_id = str(self.agent.id)  # type: ignore
-        train_iteration = (
-            getattr(self.crew, "_train_iteration", None) if self.crew else None
-        )
+        """Save training data.
+
+        Args:
+            result: Agent's final output.
+            human_feedback: Optional feedback from human.
+        """
+        agent_id = str(self.agent.id)
+        train_iteration = getattr(self.crew, "_train_iteration", None)
 
         if train_iteration is None or not isinstance(train_iteration, int):
             self._printer.print(
@@ -372,20 +404,30 @@ 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:
+    @staticmethod
+    def _format_prompt(prompt: str, inputs: dict[str, str]) -> str:
+        """Format prompt with input values.
+
+        Args:
+            prompt: Template string.
+            inputs: Values to substitute.
+
+        Returns:
+            Formatted prompt.
+        """
         prompt = prompt.replace("{input}", inputs["input"])
         prompt = prompt.replace("{tool_names}", inputs["tool_names"])
         prompt = prompt.replace("{tools}", inputs["tools"])
         return prompt
 
     def _handle_human_feedback(self, formatted_answer: AgentFinish) -> AgentFinish:
-        """Handle human feedback with different flows for training vs regular use.
+        """Process human feedback.
 
         Args:
-            formatted_answer: The initial AgentFinish result to get feedback on
+            formatted_answer: Initial agent result.
 
         Returns:
-            AgentFinish: The final answer after processing feedback
+            Final answer after feedback.
         """
         human_feedback = self._ask_human_input(formatted_answer.output)
 
@@ -395,13 +437,25 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         return self._handle_regular_feedback(formatted_answer, human_feedback)
 
     def _is_training_mode(self) -> bool:
-        """Check if crew is in training mode."""
+        """Check if training mode is active.
+
+        Returns:
+            True if in training mode.
+        """
         return bool(self.crew and self.crew._train)
 
     def _handle_training_feedback(
         self, initial_answer: AgentFinish, feedback: str
     ) -> AgentFinish:
-        """Process feedback for training scenarios with single iteration."""
+        """Process training feedback.
+
+        Args:
+            initial_answer: Initial agent output.
+            feedback: Training feedback.
+
+        Returns:
+            Improved answer.
+        """
         self._handle_crew_training_output(initial_answer, feedback)
         self.messages.append(
             format_message_for_llm(
@@ -416,7 +470,15 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
     def _handle_regular_feedback(
         self, current_answer: AgentFinish, initial_feedback: str
     ) -> AgentFinish:
-        """Process feedback for regular use with potential multiple iterations."""
+        """Process regular feedback iteratively.
+
+        Args:
+            current_answer: Current agent output.
+            initial_feedback: Initial user feedback.
+
+        Returns:
+            Final answer after iterations.
+        """
         feedback = initial_feedback
         answer = current_answer
 
@@ -431,30 +493,17 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         return answer
 
     def _process_feedback_iteration(self, feedback: str) -> AgentFinish:
-        """Process a single feedback iteration."""
+        """Process single feedback iteration.
+
+        Args:
+            feedback: User feedback.
+
+        Returns:
+            Updated agent response.
+        """
         self.messages.append(
             format_message_for_llm(
                 self._i18n.slice("feedback_instructions").format(feedback=feedback)
             )
         )
         return self._invoke_loop()
-
-    def _log_feedback_error(self, retry_count: int, error: Exception) -> None:
-        """Log feedback processing errors."""
-        self._printer.print(
-            content=(
-                f"Error processing feedback: {error}. "
-                f"Retrying... ({retry_count + 1}/{MAX_LLM_RETRY})"
-            ),
-            color="red",
-        )
-
-    def _log_max_retries_exceeded(self) -> None:
-        """Log when max retries for feedback processing are exceeded."""
-        self._printer.print(
-            content=(
-                f"Failed to process feedback after {MAX_LLM_RETRY} attempts. "
-                "Ending feedback loop."
-            ),
-            color="red",
-        )

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.
 
-from json_repair import repair_json
+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 crewai.utilities import I18N
+from dataclasses import dataclass
 
-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"
+from json_repair import repair_json  # type: ignore[import-untyped]
+
+from crewai.agents.constants import (
+    ACTION_INPUT_ONLY_REGEX,
+    ACTION_INPUT_REGEX,
+    ACTION_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.i18n import I18N
+
+_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

@@ -1,32 +1,44 @@
-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.agents.cache.cache_handler import CacheHandler
+from crewai.tools.cache_tools.cache_tools import CacheTools
+from crewai.tools.tool_calling import InstructorToolCalling, ToolCalling
 
 
 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,
-                input=calling.arguments,
+                input_data=calling.arguments,
                 output=output,
             )

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

@@ -7,24 +7,27 @@ 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 = {
@@ -86,7 +79,9 @@ 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()
@@ -97,9 +92,7 @@ class AuthenticationCommand:
         console.print("2. Enter the following code: ", device_code_data["user_code"])
         webbrowser.open(device_code_data["verification_uri_complete"])
 
-    def _poll_for_token(
-        self, device_code_data: Dict[str, Any]
-    ) -> None:
+    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:
@@ -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/plus_api.py

@@ -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/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/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]>=0.165.1,<1.0.0"
+    "crewai[tools]>=0.177.0,<1.0.0"
 ]
 
 [project.scripts]

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

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.14"
 dependencies = [
-    "crewai[tools]>=0.165.1,<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]>=0.165.1"
+    "crewai[tools]>=0.177.0"
 ]
 
 [tool.crewai]

src/crewai/crew.py

@@ -3,26 +3,18 @@ import json
 import re
 import uuid
 import warnings
+from collections.abc import Callable, Mapping, Set
 from concurrent.futures import Future
 from copy import copy as shallow_copy
 from hashlib import md5
 from typing import (
     Any,
-    Callable,
-    Dict,
-    List,
     Optional,
-    Set,
-    Tuple,
-    Union,
     cast,
 )
 
 from opentelemetry import baggage
 from opentelemetry.context import attach, detach
-
-from crewai.utilities.crew.models import CrewContext
-
 from pydantic import (
     UUID4,
     BaseModel,
@@ -34,15 +26,36 @@ from pydantic import (
     model_validator,
 )
 from pydantic_core import PydanticCustomError
+from typing_extensions import Self
 
 from crewai.agent import Agent
 from crewai.agents.agent_builder.base_agent import BaseAgent
-from crewai.agents.cache import CacheHandler
+from crewai.agents.cache.cache_handler import CacheHandler
 from crewai.crews.crew_output import CrewOutput
+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.events.listeners.tracing.utils import (
+    is_tracing_enabled,
+)
+from crewai.events.types.crew_events import (
+    CrewKickoffCompletedEvent,
+    CrewKickoffFailedEvent,
+    CrewKickoffStartedEvent,
+    CrewTestCompletedEvent,
+    CrewTestFailedEvent,
+    CrewTestStartedEvent,
+    CrewTrainCompletedEvent,
+    CrewTrainFailedEvent,
+    CrewTrainStartedEvent,
+)
 from crewai.flow.flow_trackable import FlowTrackable
 from crewai.knowledge.knowledge import Knowledge
 from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
-from crewai.llm import LLM, BaseLLM
+from crewai.llm import LLM
+from crewai.llms.base_llm import BaseLLM
 from crewai.memory.entity.entity_memory import EntityMemory
 from crewai.memory.external.external_memory import ExternalMemory
 from crewai.memory.long_term.long_term_memory import LongTermMemory
@@ -57,29 +70,9 @@ from crewai.tools.base_tool import BaseTool, Tool
 from crewai.types.usage_metrics import UsageMetrics
 from crewai.utilities import I18N, FileHandler, Logger, RPMController
 from crewai.utilities.constants import NOT_SPECIFIED, TRAINING_DATA_FILE
+from crewai.utilities.crew.models import CrewContext
 from crewai.utilities.evaluators.crew_evaluator_handler import CrewEvaluator
 from crewai.utilities.evaluators.task_evaluator import TaskEvaluator
-from crewai.utilities.events.crew_events import (
-    CrewKickoffCompletedEvent,
-    CrewKickoffFailedEvent,
-    CrewKickoffStartedEvent,
-    CrewTestCompletedEvent,
-    CrewTestFailedEvent,
-    CrewTestStartedEvent,
-    CrewTrainCompletedEvent,
-    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 (
-    TraceCollectionListener,
-)
-
-
-from crewai.utilities.events.listeners.tracing.utils import (
-    is_tracing_enabled,
-)
 from crewai.utilities.formatter import (
     aggregate_raw_outputs_from_task_outputs,
     aggregate_raw_outputs_from_tasks,
@@ -116,9 +109,12 @@ class Crew(FlowTrackable, BaseModel):
         planning: Plan the crew execution and add the plan to the crew.
         chat_llm: The language model used for orchestrating chat interactions with the crew.
         security_config: Security configuration for the crew, including fingerprinting.
+
+    Notes:
+        TODO: Improve the embedder type from dict[str, Any] to a more specific TypedDict or dataclass.
     """
 
-    __hash__ = object.__hash__  # type: ignore
+    __hash__ = object.__hash__
     _execution_span: Any = PrivateAttr()
     _rpm_controller: RPMController = PrivateAttr()
     _logger: Logger = PrivateAttr()
@@ -130,7 +126,7 @@ 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",
     )
@@ -140,8 +136,8 @@ class Crew(FlowTrackable, BaseModel):
 
     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(
@@ -164,7 +160,7 @@ class Crew(FlowTrackable, BaseModel):
         default=None,
         description="An Instance of the ExternalMemory to be used by the Crew",
     )
-    embedder: Optional[dict] = Field(
+    embedder: Optional[dict[str, Any]] = Field(
         default=None,
         description="Configuration for the embedder to be used for the crew.",
     )
@@ -172,16 +168,16 @@ class Crew(FlowTrackable, BaseModel):
         default=None,
         description="Metrics for the LLM usage during all tasks execution.",
     )
-    manager_llm: Optional[Union[str, InstanceOf[BaseLLM], Any]] = Field(
+    manager_llm: Optional[str | InstanceOf[BaseLLM] | Any] = Field(
         description="Language model that will run the agent.", default=None
     )
     manager_agent: Optional[BaseAgent] = Field(
         description="Custom agent that will be used as manager.", default=None
     )
-    function_calling_llm: Optional[Union[str, InstanceOf[LLM], Any]] = Field(
+    function_calling_llm: Optional[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[Json[dict[str, Any]] | 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(
@@ -192,13 +188,13 @@ 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.",
     )
@@ -210,7 +206,7 @@ class Crew(FlowTrackable, BaseModel):
         default=None,
         description="Path to the prompt json file to be used for the crew.",
     )
-    output_log_file: Optional[Union[bool, str]] = Field(
+    output_log_file: Optional[bool | str] = Field(
         default=None,
         description="Path to the log file to be saved",
     )
@@ -218,23 +214,23 @@ class Crew(FlowTrackable, BaseModel):
         default=False,
         description="Plan the crew execution and add the plan to the crew.",
     )
-    planning_llm: Optional[Union[str, InstanceOf[BaseLLM], Any]] = Field(
+    planning_llm: Optional[str | InstanceOf[BaseLLM] | Any] = Field(
         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.",
     )
-    chat_llm: Optional[Union[str, InstanceOf[BaseLLM], Any]] = Field(
+    chat_llm: Optional[str | InstanceOf[BaseLLM] | Any] = Field(
         default=None,
         description="LLM used to handle chatting with the crew.",
     )
@@ -267,8 +263,8 @@ 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: Json[dict[str, Any]] | dict[str, Any]
+    ) -> Json[dict[str, Any]] | dict[str, Any]:
         """Validates that the config is a valid type.
         Args:
             v: The config to be validated.
@@ -277,10 +273,10 @@ class Crew(FlowTrackable, BaseModel):
         """
 
         # TODO: Improve typing
-        return json.loads(v) if isinstance(v, Json) else v  # type: ignore
+        return json.loads(v) if isinstance(v, str) else v
 
     @model_validator(mode="after")
-    def set_private_attrs(self) -> "Crew":
+    def set_private_attrs(self) -> Self:
         """Set private attributes."""
 
         self._cache_handler = CacheHandler()
@@ -300,7 +296,7 @@ class Crew(FlowTrackable, BaseModel):
 
         return self
 
-    def _initialize_default_memories(self):
+    def _initialize_default_memories(self) -> None:
         self._long_term_memory = self._long_term_memory or LongTermMemory()
         self._short_term_memory = self._short_term_memory or ShortTermMemory(
             crew=self,
@@ -311,7 +307,7 @@ class Crew(FlowTrackable, BaseModel):
         )
 
     @model_validator(mode="after")
-    def create_crew_memory(self) -> "Crew":
+    def create_crew_memory(self) -> Self:
         """Initialize private memory attributes."""
         self._external_memory = (
             # External memory doesn’t support a default value since it was designed to be managed entirely externally
@@ -328,7 +324,7 @@ class Crew(FlowTrackable, BaseModel):
         return self
 
     @model_validator(mode="after")
-    def create_crew_knowledge(self) -> "Crew":
+    def create_crew_knowledge(self) -> Self:
         """Create the knowledge for the crew."""
         if self.knowledge_sources:
             try:
@@ -349,7 +345,7 @@ class Crew(FlowTrackable, BaseModel):
         return self
 
     @model_validator(mode="after")
-    def check_manager_llm(self):
+    def check_manager_llm(self) -> Self:
         """Validates that the language model is set when using hierarchical process."""
         if self.process == Process.hierarchical:
             if not self.manager_llm and not self.manager_agent:
@@ -371,7 +367,7 @@ class Crew(FlowTrackable, BaseModel):
         return self
 
     @model_validator(mode="after")
-    def check_config(self):
+    def check_config(self) -> Self:
         """Validates that the crew is properly configured with agents and tasks."""
         if not self.config and not self.tasks and not self.agents:
             raise PydanticCustomError(
@@ -392,20 +388,20 @@ class Crew(FlowTrackable, BaseModel):
         return self
 
     @model_validator(mode="after")
-    def validate_tasks(self):
+    def validate_tasks(self) -> Self:
         if self.process == Process.sequential:
             for task in self.tasks:
                 if task.agent is None:
                     raise PydanticCustomError(
                         "missing_agent_in_task",
-                        f"Sequential process error: Agent is missing in the task with the following description: {task.description}",  # type: ignore # Argument of type "str" cannot be assigned to parameter "message_template" of type "LiteralString"
+                        f"Sequential process error: Agent is missing in the task with the following description: {task.description}",
                         {},
                     )
 
         return self
 
     @model_validator(mode="after")
-    def validate_end_with_at_most_one_async_task(self):
+    def validate_end_with_at_most_one_async_task(self) -> Self:
         """Validates that the crew ends with at most one asynchronous task."""
         final_async_task_count = 0
 
@@ -426,7 +422,7 @@ class Crew(FlowTrackable, BaseModel):
         return self
 
     @model_validator(mode="after")
-    def validate_must_have_non_conditional_task(self) -> "Crew":
+    def validate_must_have_non_conditional_task(self) -> Self:
         """Ensure that a crew has at least one non-conditional task."""
         if not self.tasks:
             return self
@@ -442,7 +438,7 @@ class Crew(FlowTrackable, BaseModel):
         return self
 
     @model_validator(mode="after")
-    def validate_first_task(self) -> "Crew":
+    def validate_first_task(self) -> Self:
         """Ensure the first task is not a ConditionalTask."""
         if self.tasks and isinstance(self.tasks[0], ConditionalTask):
             raise PydanticCustomError(
@@ -453,19 +449,21 @@ class Crew(FlowTrackable, BaseModel):
         return self
 
     @model_validator(mode="after")
-    def validate_async_tasks_not_async(self) -> "Crew":
+    def validate_async_tasks_not_async(self) -> Self:
         """Ensure that ConditionalTask is not async."""
         for task in self.tasks:
             if task.async_execution and isinstance(task, ConditionalTask):
                 raise PydanticCustomError(
                     "invalid_async_conditional_task",
-                    f"Conditional Task: {task.description} , cannot be executed asynchronously.",  # type: ignore # Argument of type "str" cannot be assigned to parameter "message_template" of type "LiteralString"
+                    f"Conditional Task: {task.description} , cannot be executed asynchronously.",
                     {},
                 )
         return self
 
     @model_validator(mode="after")
-    def validate_async_task_cannot_include_sequential_async_tasks_in_context(self):
+    def validate_async_task_cannot_include_sequential_async_tasks_in_context(
+        self,
+    ) -> Self:
         """
         Validates that if a task is set to be executed asynchronously,
         it cannot include other asynchronous tasks in its context unless
@@ -485,7 +483,7 @@ class Crew(FlowTrackable, BaseModel):
         return self
 
     @model_validator(mode="after")
-    def validate_context_no_future_tasks(self):
+    def validate_context_no_future_tasks(self) -> Self:
         """Validates that a task's context does not include future tasks."""
         task_indices = {id(task): i for i, task in enumerate(self.tasks)}
 
@@ -502,7 +500,7 @@ 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()
@@ -517,7 +515,7 @@ class Crew(FlowTrackable, BaseModel):
         """
         return self.security_config.fingerprint
 
-    def _setup_from_config(self):
+    def _setup_from_config(self) -> None:
         assert self.config is not None, "Config should not be None."
 
         """Initializes agents and tasks from the provided config."""
@@ -530,7 +528,7 @@ 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:
@@ -559,9 +557,10 @@ 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,
@@ -610,7 +609,7 @@ class Crew(FlowTrackable, BaseModel):
 
     def kickoff(
         self,
-        inputs: Optional[Dict[str, Any]] = None,
+        inputs: Optional[dict[str, Any]] = None,
     ) -> CrewOutput:
         ctx = baggage.set_baggage(
             "crew_context", CrewContext(id=str(self.id), key=self.key)
@@ -642,8 +641,7 @@ class Crew(FlowTrackable, BaseModel):
 
             for agent in self.agents:
                 agent.i18n = i18n
-                # type: ignore[attr-defined] # Argument 1 to "_interpolate_inputs" of "Crew" has incompatible type "dict[str, Any] | None"; expected "dict[str, Any]"
-                agent.crew = self  # type: ignore[attr-defined]
+                agent.crew = self
                 agent.set_knowledge(crew_embedder=self.embedder)
                 # TODO: Create an AgentFunctionCalling protocol for future refactoring
                 if not agent.function_calling_llm:  # type: ignore # "BaseAgent" has no attribute "function_calling_llm"
@@ -652,7 +650,7 @@ class Crew(FlowTrackable, BaseModel):
                 if not agent.step_callback:  # type: ignore # "BaseAgent" has no attribute "step_callback"
                     agent.step_callback = self.step_callback  # type: ignore # "BaseAgent" has no attribute "step_callback"
 
-                agent.create_agent_executor()
+                # Agent executor will be created when tasks are executed
 
             if self.planning:
                 self._handle_crew_planning()
@@ -681,9 +679,9 @@ 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()
@@ -702,14 +700,19 @@ 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[str, Any]]
+    ) -> list[CrewOutput]:
         crew_copies = [self.copy() for _ in inputs]
 
-        async def run_crew(crew, input_data):
+        async def run_crew(crew: Self, input_data: dict[str, Any]) -> CrewOutput:
             return await crew.kickoff_async(inputs=input_data)
 
         tasks = [
@@ -728,7 +731,7 @@ class Crew(FlowTrackable, BaseModel):
         self._task_output_handler.reset()
         return results
 
-    def _handle_crew_planning(self):
+    def _handle_crew_planning(self) -> None:
         """Handles the Crew planning."""
         self._logger.log("info", "Planning the crew execution")
         result = CrewPlanner(
@@ -744,7 +747,7 @@ class Crew(FlowTrackable, BaseModel):
         output: TaskOutput,
         task_index: int,
         was_replayed: bool = False,
-    ):
+    ) -> None:
         if self._inputs:
             inputs = self._inputs
         else:
@@ -776,7 +779,7 @@ class Crew(FlowTrackable, BaseModel):
         self._create_manager_agent()
         return self._execute_tasks(self.tasks)
 
-    def _create_manager_agent(self):
+    def _create_manager_agent(self) -> None:
         i18n = I18N(prompt_file=self.prompt_file)
         if self.manager_agent is not None:
             self.manager_agent.allow_delegation = True
@@ -788,7 +791,12 @@ class Crew(FlowTrackable, BaseModel):
                 manager.tools = []
                 raise Exception("Manager agent should not have tools")
         else:
-            self.manager_llm = create_llm(self.manager_llm)
+            if self.manager_llm is None:
+                from crewai.utilities.llm_utils import create_default_llm
+
+                self.manager_llm = create_default_llm()
+            else:
+                self.manager_llm = create_llm(self.manager_llm)
             manager = Agent(
                 role=i18n.retrieve("hierarchical_manager_agent", "role"),
                 goal=i18n.retrieve("hierarchical_manager_agent", "goal"),
@@ -803,7 +811,7 @@ class Crew(FlowTrackable, BaseModel):
 
     def _execute_tasks(
         self,
-        tasks: List[Task],
+        tasks: list[Task],
         start_index: Optional[int] = 0,
         was_replayed: bool = False,
     ) -> CrewOutput:
@@ -817,8 +825,8 @@ class Crew(FlowTrackable, BaseModel):
             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):
@@ -843,7 +851,7 @@ class Crew(FlowTrackable, BaseModel):
             tools_for_task = self._prepare_tools(
                 agent_to_use,
                 task,
-                cast(Union[List[Tool], List[BaseTool]], tools_for_task),
+                cast(list[Tool] | list[BaseTool], tools_for_task),
             )
 
             self._log_task_start(task, agent_to_use.role)
@@ -863,7 +871,7 @@ class Crew(FlowTrackable, BaseModel):
                 future = task.execute_async(
                     agent=agent_to_use,
                     context=context,
-                    tools=cast(List[BaseTool], tools_for_task),
+                    tools=tools_for_task,
                 )
                 futures.append((task, future, task_index))
             else:
@@ -875,7 +883,7 @@ class Crew(FlowTrackable, BaseModel):
                 task_output = task.execute_sync(
                     agent=agent_to_use,
                     context=context,
-                    tools=cast(List[BaseTool], tools_for_task),
+                    tools=tools_for_task,
                 )
                 task_outputs.append(task_output)
                 self._process_task_result(task, task_output)
@@ -889,8 +897,8 @@ 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]:
@@ -913,8 +921,8 @@ 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: list[Tool] | list[BaseTool]
+    ) -> list[BaseTool]:
         # Add delegation tools if agent allows delegation
         if hasattr(agent, "allow_delegation") and getattr(
             agent, "allow_delegation", False
@@ -944,7 +952,7 @@ 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 cast(list[BaseTool], tools)
 
     def _get_agent_to_use(self, task: Task) -> Optional[BaseAgent]:
         if self.process == Process.hierarchical:
@@ -953,12 +961,12 @@ 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: list[Tool] | list[BaseTool],
+        new_tools: 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}
@@ -969,41 +977,41 @@ class Crew(FlowTrackable, BaseModel):
         # Add all new tools
         tools.extend(new_tools)
 
-        return cast(List[BaseTool], tools)
+        return tools
 
     def _inject_delegation_tools(
         self,
-        tools: Union[List[Tool], List[BaseTool]],
+        tools: 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, 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: 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: 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: 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:
@@ -1011,17 +1019,17 @@ 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"):
+    def _log_task_start(self, task: Task, role: str = "None") -> None:
         if self.output_log_file:
             self._file_handler.log(
                 task_name=task.name, task=task.description, agent=role, status="started"
             )
 
     def _update_manager_tools(
-        self, task: Task, tools: Union[List[Tool], List[BaseTool]]
-    ) -> List[BaseTool]:
+        self, task: Task, tools: list[Tool] | list[BaseTool]
+    ) -> list[BaseTool]:
         if self.manager_agent:
             if task.agent:
                 tools = self._inject_delegation_tools(tools, task.agent, [task.agent])
@@ -1029,9 +1037,9 @@ 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 ""
 
@@ -1053,7 +1061,7 @@ 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.")
 
@@ -1084,10 +1092,10 @@ 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:
             task_output = future.result()
             task_outputs.append(task_output)
@@ -1098,7 +1106,7 @@ 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(
             (
@@ -1110,7 +1118,7 @@ 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:
@@ -1151,15 +1159,15 @@ 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
+    ) -> 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
@@ -1168,7 +1176,7 @@ 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:
@@ -1184,7 +1192,18 @@ class Crew(FlowTrackable, BaseModel):
 
         return required_inputs
 
-    def copy(self):
+    def copy(
+        self,
+        *,
+        include: Optional[
+            Set[int] | Set[str] | Mapping[int, Any] | Mapping[str, Any]
+        ] = None,
+        exclude: Optional[
+            Set[int] | Set[str] | Mapping[int, Any] | Mapping[str, Any]
+        ] = None,
+        update: Optional[dict[str, Any]] = None,
+        deep: bool = True,
+    ) -> "Crew":
         """
         Creates a deep copy of the Crew instance.
 
@@ -1215,7 +1234,7 @@ class Crew(FlowTrackable, BaseModel):
         manager_agent = self.manager_agent.copy() if self.manager_agent else None
         manager_llm = shallow_copy(self.manager_llm) if self.manager_llm else None
 
-        task_mapping = {}
+        task_mapping: dict[str, Task] = {}
 
         cloned_tasks = []
         existing_knowledge_sources = shallow_copy(self.knowledge_sources)
@@ -1270,16 +1289,10 @@ 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(
-                # type: ignore # "interpolate_inputs" of "Task" does not return a value (it only ever returns None)
-                inputs
-            )
-            for task in self.tasks
-        ]
-        # type: ignore # "interpolate_inputs" of "Agent" does not return a value (it only ever returns None)
+        for task in self.tasks:
+            task.interpolate_inputs_and_add_conversation_history(inputs)
         for agent in self.agents:
             agent.interpolate_inputs(inputs)
 
@@ -1303,8 +1316,8 @@ class Crew(FlowTrackable, BaseModel):
     def test(
         self,
         n_iterations: int,
-        eval_llm: Union[str, InstanceOf[BaseLLM]],
-        inputs: Optional[Dict[str, Any]] = None,
+        eval_llm: str | InstanceOf[BaseLLM],
+        inputs: Optional[dict[str, Any]] = None,
     ) -> None:
         """Test and evaluate the Crew with the given inputs for n iterations concurrently using concurrent.futures."""
         try:
@@ -1345,7 +1358,7 @@ class Crew(FlowTrackable, BaseModel):
             )
             raise
 
-    def __repr__(self):
+    def __repr__(self) -> str:
         return f"Crew(id={self.id}, process={self.process}, number_of_agents={len(self.agents)}, number_of_tasks={len(self.tasks)})"
 
     def reset_memories(self, command_type: str) -> None:
@@ -1397,7 +1410,9 @@ class Crew(FlowTrackable, BaseModel):
             if (system := config.get("system")) is not None:
                 name = config.get("name")
                 try:
-                    reset_fn: Callable = cast(Callable, config.get("reset"))
+                    reset_fn: Callable[..., None] = cast(
+                        Callable[..., None], config.get("reset")
+                    )
                     reset_fn(system)
                     self._logger.log(
                         "info",
@@ -1426,7 +1441,9 @@ class Crew(FlowTrackable, BaseModel):
             raise RuntimeError(f"{name} memory system is not initialized")
 
         try:
-            reset_fn: Callable = cast(Callable, config.get("reset"))
+            reset_fn: Callable[..., None] = cast(
+                Callable[..., None], config.get("reset")
+            )
             reset_fn(system)
             self._logger.log(
                 "info",
@@ -1437,18 +1454,18 @@ class Crew(FlowTrackable, BaseModel):
                 f"[Crew ({self.name if self.name else self.id})] Failed to reset {name} memory: {str(e)}"
             ) from e
 
-    def _get_memory_systems(self):
+    def _get_memory_systems(self) -> dict[str, dict[str, Any]]:
         """Get all available memory systems with their configuration.
 
         Returns:
             Dict containing all memory systems with their reset functions and display names.
         """
 
-        def default_reset(memory):
-            return memory.reset()
+        def default_reset(memory: Any) -> None:
+            memory.reset()
 
-        def knowledge_reset(memory):
-            return self.reset_knowledge(memory)
+        def knowledge_reset(memory: Any) -> None:
+            self.reset_knowledge(memory)
 
         # Get knowledge for agents
         agent_knowledges = [
@@ -1502,12 +1519,12 @@ 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):
+    def _set_allow_crewai_trigger_context_for_first_task(self) -> None:
         crewai_trigger_payload = self._inputs and self._inputs.get(
             "crewai_trigger_payload"
         )

src/crewai/events/__init__.py

@@ -0,0 +1,56 @@
+"""CrewAI events system for monitoring and extending agent behavior.
+
+This module provides the event infrastructure that allows users to:
+- Monitor agent, task, and crew execution
+- Track memory operations and performance
+- Build custom logging and analytics
+- Extend CrewAI with custom event handlers
+"""
+
+from crewai.events.base_event_listener import BaseEventListener
+from crewai.events.event_bus import crewai_event_bus
+
+from crewai.events.types.memory_events import (
+    MemoryQueryCompletedEvent,
+    MemorySaveCompletedEvent,
+    MemorySaveStartedEvent,
+    MemoryQueryStartedEvent,
+    MemoryRetrievalCompletedEvent,
+    MemorySaveFailedEvent,
+    MemoryQueryFailedEvent,
+)
+
+from crewai.events.types.knowledge_events import (
+    KnowledgeRetrievalStartedEvent,
+    KnowledgeRetrievalCompletedEvent,
+)
+
+from crewai.events.types.crew_events import (
+    CrewKickoffStartedEvent,
+    CrewKickoffCompletedEvent,
+)
+from crewai.events.types.agent_events import (
+    AgentExecutionCompletedEvent,
+)
+
+from crewai.events.types.llm_events import (
+    LLMStreamChunkEvent,
+)
+
+__all__ = [
+    "BaseEventListener",
+    "crewai_event_bus",
+    "MemoryQueryCompletedEvent",
+    "MemorySaveCompletedEvent",
+    "MemorySaveStartedEvent",
+    "MemoryQueryStartedEvent",
+    "MemoryRetrievalCompletedEvent",
+    "MemorySaveFailedEvent",
+    "MemoryQueryFailedEvent",
+    "KnowledgeRetrievalStartedEvent",
+    "KnowledgeRetrievalCompletedEvent",
+    "CrewKickoffStartedEvent",
+    "CrewKickoffCompletedEvent",
+    "AgentExecutionCompletedEvent",
+    "LLMStreamChunkEvent",
+]

src/crewai/events/base_event_listener.py

@@ -0,0 +1,15 @@
+from abc import ABC, abstractmethod
+
+from crewai.events.event_bus import CrewAIEventsBus, crewai_event_bus
+
+
+class BaseEventListener(ABC):
+    verbose: bool = False
+
+    def __init__(self) -> None:
+        super().__init__()
+        self.setup_listeners(crewai_event_bus)
+
+    @abstractmethod
+    def setup_listeners(self, crewai_event_bus: CrewAIEventsBus) -> None:
+        pass

src/crewai/events/base_events.py

@@ -11,7 +11,9 @@ class BaseEvent(BaseModel):
     timestamp: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
     type: str
     source_fingerprint: Optional[str] = None  # UUID string of the source entity
-    source_type: Optional[str] = None  # "agent", "task", "crew", "memory", "entity_memory", "short_term_memory", "long_term_memory", "external_memory"
+    source_type: Optional[str] = (
+        None  # "agent", "task", "crew", "memory", "entity_memory", "short_term_memory", "long_term_memory", "external_memory"
+    )
     fingerprint_metadata: Optional[Dict[str, Any]] = None  # Any relevant metadata
 
     def to_json(self, exclude: set[str] | None = None):
@@ -25,3 +27,20 @@ class BaseEvent(BaseModel):
             dict: A JSON-serializable dictionary.
         """
         return to_serializable(self, exclude=exclude)
+
+    def _set_task_params(self, data: Dict[str, Any]):
+        if "from_task" in data and (task := data["from_task"]):
+            self.task_id = task.id
+            self.task_name = task.name or task.description
+            self.from_task = None
+
+    def _set_agent_params(self, data: Dict[str, Any]):
+        task = data.get("from_task", None)
+        agent = task.agent if task else data.get("from_agent", None)
+
+        if not agent:
+            return
+
+        self.agent_id = agent.id
+        self.agent_role = agent.role
+        self.from_agent = None

src/crewai/events/event_bus.py

@@ -0,0 +1,115 @@
+from __future__ import annotations
+
+import threading
+from collections.abc import Callable, Iterator
+from contextlib import contextmanager
+from typing import Any, ParamSpec, TypeVar, cast
+
+from blinker import Signal
+from typing_extensions import Self
+
+from crewai.events.base_events import BaseEvent
+
+EventT = TypeVar("EventT", bound=BaseEvent)
+P = ParamSpec("P")
+
+
+class CrewAIEventsBus:
+    """
+    A singleton event bus that uses blinker signals for event handling.
+    Allows both internal (Flow/Crew) and external event handling.
+    """
+
+    _instance = None
+    _lock = threading.Lock()
+
+    def __new__(cls) -> Self:
+        if cls._instance is None:
+            with cls._lock:
+                if cls._instance is None:  # prevent race condition
+                    cls._instance = super().__new__(cls)
+                    cls._instance._initialize()
+        return cls._instance
+
+    def _initialize(self) -> None:
+        """Initialize the event bus internal state"""
+        self._signal = Signal("crewai_event_bus")
+        self._handlers: dict[type[BaseEvent], list[Callable[[Any, Any], None]]] = {}
+
+    def on(
+        self, event_type: type[EventT]
+    ) -> Callable[[Callable[[Any, EventT], None]], Callable[[Any, EventT], None]]:
+        """
+        Decorator to register an event handler for a specific event type.
+
+        Usage:
+            @crewai_event_bus.on(AgentExecutionCompletedEvent)
+            def on_agent_execution_completed(
+                source: Any, event: AgentExecutionCompletedEvent
+            ):
+                print(f"👍 Agent '{event.agent}' completed task")
+                print(f"   Output: {event.output}")
+        """
+
+        def decorator(
+            handler: Callable[[Any, EventT], None],
+        ) -> Callable[[Any, EventT], None]:
+            if event_type not in self._handlers:
+                self._handlers[event_type] = []
+            self._handlers[event_type].append(cast(Callable[[Any, Any], None], handler))
+            return handler
+
+        return decorator
+
+    def emit(self, source: Any, event: BaseEvent) -> None:
+        """
+        Emit an event to all registered handlers
+
+        Args:
+            source: The object emitting the event
+            event: The event instance to emit
+        """
+        for event_type, handlers in self._handlers.items():
+            if isinstance(event, event_type):
+                for handler in handlers:
+                    try:
+                        handler(source, event)
+                    except Exception as e:
+                        print(
+                            f"[EventBus Error] Handler '{handler.__name__}' failed for event '{event_type.__name__}': {e}"
+                        )
+
+        self._signal.send(source, event=event)
+
+    def register_handler(
+        self, event_type: type[BaseEvent], handler: Callable[[Any, Any], None]
+    ) -> None:
+        """Register an event handler for a specific event type"""
+        if event_type not in self._handlers:
+            self._handlers[event_type] = []
+        self._handlers[event_type].append(handler)
+
+    @contextmanager
+    def scoped_handlers(self) -> Iterator[None]:
+        """
+        Context manager for temporary event handling scope.
+        Useful for testing or temporary event handling.
+
+        Usage:
+            with crewai_event_bus.scoped_handlers():
+                @crewai_event_bus.on(CrewKickoffStarted)
+                def temp_handler(source, event):
+                    print("Temporary handler")
+                # Do stuff...
+            # Handlers are cleared after the context
+        """
+        previous_handlers = self._handlers.copy()
+        self._handlers.clear()
+        try:
+            yield
+        finally:
+            self._handlers = previous_handlers
+
+
+# Global instance
+crewai_event_bus = CrewAIEventsBus()

src/crewai/events/event_listener.py

@@ -1,43 +1,21 @@
+from __future__ import annotations
+
 from io import StringIO
-from typing import Any, Dict
+from typing import Any
 
 from pydantic import Field, PrivateAttr
-from crewai.llm import LLM
-from crewai.task import Task
-from crewai.telemetry.telemetry import Telemetry
-from crewai.utilities import Logger
-from crewai.utilities.constants import EMITTER_COLOR
-from crewai.utilities.events.base_event_listener import BaseEventListener
-from crewai.utilities.events.knowledge_events import (
-    KnowledgeQueryCompletedEvent,
-    KnowledgeQueryFailedEvent,
-    KnowledgeQueryStartedEvent,
-    KnowledgeRetrievalCompletedEvent,
-    KnowledgeRetrievalStartedEvent,
-    KnowledgeSearchQueryFailedEvent,
-)
-from crewai.utilities.events.llm_events import (
-    LLMCallCompletedEvent,
-    LLMCallFailedEvent,
-    LLMCallStartedEvent,
-    LLMStreamChunkEvent,
-)
-from crewai.utilities.events.llm_guardrail_events import (
-    LLMGuardrailStartedEvent,
-    LLMGuardrailCompletedEvent,
-)
-from crewai.utilities.events.utils.console_formatter import ConsoleFormatter
+from typing_extensions import Self
 
-from .agent_events import (
+from crewai.events.base_event_listener import BaseEventListener
+from crewai.events.event_bus import CrewAIEventsBus
+from crewai.events.types.agent_events import (
     AgentExecutionCompletedEvent,
     AgentExecutionStartedEvent,
-    AgentLogsStartedEvent,
-    AgentLogsExecutionEvent,
     LiteAgentExecutionCompletedEvent,
     LiteAgentExecutionErrorEvent,
     LiteAgentExecutionStartedEvent,
 )
-from .crew_events import (
+from crewai.events.types.crew_events import (
     CrewKickoffCompletedEvent,
     CrewKickoffFailedEvent,
     CrewKickoffStartedEvent,
@@ -49,7 +27,37 @@ from .crew_events import (
     CrewTrainFailedEvent,
     CrewTrainStartedEvent,
 )
-from .flow_events import (
+from crewai.events.types.knowledge_events import (
+    KnowledgeQueryCompletedEvent,
+    KnowledgeQueryFailedEvent,
+    KnowledgeQueryStartedEvent,
+    KnowledgeRetrievalCompletedEvent,
+    KnowledgeRetrievalStartedEvent,
+    KnowledgeSearchQueryFailedEvent,
+)
+from crewai.events.types.llm_events import (
+    LLMCallCompletedEvent,
+    LLMCallFailedEvent,
+    LLMCallStartedEvent,
+    LLMStreamChunkEvent,
+)
+from crewai.events.types.llm_guardrail_events import (
+    LLMGuardrailCompletedEvent,
+    LLMGuardrailStartedEvent,
+)
+from crewai.events.types.logging_events import (
+    AgentLogsExecutionEvent,
+    AgentLogsStartedEvent,
+)
+from crewai.events.utils.console_formatter import ConsoleFormatter
+from crewai.llm import LLM
+from crewai.task import Task
+from crewai.telemetry.telemetry import Telemetry
+from crewai.utilities import Logger
+from crewai.utilities.constants import EMITTER_COLOR
+
+from .listeners.memory_listener import MemoryListener
+from .types.flow_events import (
     FlowCreatedEvent,
     FlowFinishedEvent,
     FlowStartedEvent,
@@ -57,38 +65,37 @@ from .flow_events import (
     MethodExecutionFinishedEvent,
     MethodExecutionStartedEvent,
 )
-from .task_events import TaskCompletedEvent, TaskFailedEvent, TaskStartedEvent
-from .tool_usage_events import (
+from .types.reasoning_events import (
+    AgentReasoningCompletedEvent,
+    AgentReasoningFailedEvent,
+    AgentReasoningStartedEvent,
+)
+from .types.task_events import TaskCompletedEvent, TaskFailedEvent, TaskStartedEvent
+from .types.tool_usage_events import (
     ToolUsageErrorEvent,
     ToolUsageFinishedEvent,
     ToolUsageStartedEvent,
 )
-from .reasoning_events import (
-    AgentReasoningStartedEvent,
-    AgentReasoningCompletedEvent,
-    AgentReasoningFailedEvent,
-)
-
-from .listeners.memory_listener import MemoryListener
 
 
 class EventListener(BaseEventListener):
     _instance = None
+    _initialized: bool = False
     _telemetry: Telemetry = PrivateAttr(default_factory=lambda: Telemetry())
     logger = Logger(verbose=True, default_color=EMITTER_COLOR)
-    execution_spans: Dict[Task, Any] = Field(default_factory=dict)
+    execution_spans: dict[Task, Any] = Field(default_factory=dict)
     next_chunk = 0
     text_stream = StringIO()
     knowledge_retrieval_in_progress = False
     knowledge_query_in_progress = False
 
-    def __new__(cls):
+    def __new__(cls) -> Self:
         if cls._instance is None:
             cls._instance = super().__new__(cls)
             cls._instance._initialized = False
         return cls._instance
 
-    def __init__(self):
+    def __init__(self) -> None:
         if not hasattr(self, "_initialized") or not self._initialized:
             super().__init__()
             self._telemetry = Telemetry()
@@ -101,14 +108,14 @@ class EventListener(BaseEventListener):
 
     # ----------- CREW EVENTS -----------
 
-    def setup_listeners(self, crewai_event_bus):
+    def setup_listeners(self, crewai_event_bus: CrewAIEventsBus) -> None:
         @crewai_event_bus.on(CrewKickoffStartedEvent)
-        def on_crew_started(source, event: CrewKickoffStartedEvent):
+        def on_crew_started(source: Any, event: CrewKickoffStartedEvent) -> None:
             self.formatter.create_crew_tree(event.crew_name or "Crew", source.id)
             self._telemetry.crew_execution_span(source, event.inputs)
 
         @crewai_event_bus.on(CrewKickoffCompletedEvent)
-        def on_crew_completed(source, event: CrewKickoffCompletedEvent):
+        def on_crew_completed(source: Any, event: CrewKickoffCompletedEvent) -> None:
             # Handle telemetry
             final_string_output = event.output.raw
             self._telemetry.end_crew(source, final_string_output)
@@ -122,7 +129,7 @@ class EventListener(BaseEventListener):
             )
 
         @crewai_event_bus.on(CrewKickoffFailedEvent)
-        def on_crew_failed(source, event: CrewKickoffFailedEvent):
+        def on_crew_failed(source: Any, event: CrewKickoffFailedEvent) -> None:
             self.formatter.update_crew_tree(
                 self.formatter.current_crew_tree,
                 event.crew_name or "Crew",
@@ -131,23 +138,25 @@ class EventListener(BaseEventListener):
             )
 
         @crewai_event_bus.on(CrewTrainStartedEvent)
-        def on_crew_train_started(source, event: CrewTrainStartedEvent):
+        def on_crew_train_started(source: Any, event: CrewTrainStartedEvent) -> None:
             self.formatter.handle_crew_train_started(
                 event.crew_name or "Crew", str(event.timestamp)
             )
 
         @crewai_event_bus.on(CrewTrainCompletedEvent)
-        def on_crew_train_completed(source, event: CrewTrainCompletedEvent):
+        def on_crew_train_completed(
+            source: Any, event: CrewTrainCompletedEvent
+        ) -> None:
             self.formatter.handle_crew_train_completed(
                 event.crew_name or "Crew", str(event.timestamp)
             )
 
         @crewai_event_bus.on(CrewTrainFailedEvent)
-        def on_crew_train_failed(source, event: CrewTrainFailedEvent):
+        def on_crew_train_failed(source: Any, event: CrewTrainFailedEvent) -> None:
             self.formatter.handle_crew_train_failed(event.crew_name or "Crew")
 
         @crewai_event_bus.on(CrewTestResultEvent)
-        def on_crew_test_result(source, event: CrewTestResultEvent):
+        def on_crew_test_result(source: Any, event: CrewTestResultEvent) -> None:
             self._telemetry.individual_test_result_span(
                 source.crew,
                 event.quality,
@@ -158,17 +167,17 @@ class EventListener(BaseEventListener):
         # ----------- TASK EVENTS -----------
 
         @crewai_event_bus.on(TaskStartedEvent)
-        def on_task_started(source, event: TaskStartedEvent):
+        def on_task_started(source: Any, event: TaskStartedEvent) -> None:
             span = self._telemetry.task_started(crew=source.agent.crew, task=source)
             self.execution_spans[source] = span
             # Pass both task ID and task name (if set)
-            task_name = source.name if hasattr(source, 'name') and source.name else None
+            task_name = source.name if hasattr(source, "name") and source.name else None
             self.formatter.create_task_branch(
                 self.formatter.current_crew_tree, source.id, task_name
             )
 
         @crewai_event_bus.on(TaskCompletedEvent)
-        def on_task_completed(source, event: TaskCompletedEvent):
+        def on_task_completed(source: Any, event: TaskCompletedEvent) -> None:
             # Handle telemetry
             span = self.execution_spans.get(source)
             if span:
@@ -176,17 +185,17 @@ class EventListener(BaseEventListener):
             self.execution_spans[source] = None
 
             # Pass task name if it exists
-            task_name = source.name if hasattr(source, 'name') and source.name else None
+            task_name = source.name if hasattr(source, "name") and source.name else None
             self.formatter.update_task_status(
                 self.formatter.current_crew_tree,
                 source.id,
                 source.agent.role,
                 "completed",
-                task_name
+                task_name,
             )
 
         @crewai_event_bus.on(TaskFailedEvent)
-        def on_task_failed(source, event: TaskFailedEvent):
+        def on_task_failed(source: Any, event: TaskFailedEvent) -> None:
             span = self.execution_spans.get(source)
             if span:
                 if source.agent and source.agent.crew:
@@ -194,19 +203,21 @@ class EventListener(BaseEventListener):
                 self.execution_spans[source] = None
 
             # Pass task name if it exists
-            task_name = source.name if hasattr(source, 'name') and source.name else None
+            task_name = source.name if hasattr(source, "name") and source.name else None
             self.formatter.update_task_status(
                 self.formatter.current_crew_tree,
                 source.id,
                 source.agent.role,
                 "failed",
-                task_name
+                task_name,
             )
 
         # ----------- AGENT EVENTS -----------
 
         @crewai_event_bus.on(AgentExecutionStartedEvent)
-        def on_agent_execution_started(source, event: AgentExecutionStartedEvent):
+        def on_agent_execution_started(
+            source: Any, event: AgentExecutionStartedEvent
+        ) -> None:
             self.formatter.create_agent_branch(
                 self.formatter.current_task_branch,
                 event.agent.role,
@@ -214,7 +225,9 @@ class EventListener(BaseEventListener):
             )
 
         @crewai_event_bus.on(AgentExecutionCompletedEvent)
-        def on_agent_execution_completed(source, event: AgentExecutionCompletedEvent):
+        def on_agent_execution_completed(
+            source: Any, event: AgentExecutionCompletedEvent
+        ) -> None:
             self.formatter.update_agent_status(
                 self.formatter.current_agent_branch,
                 event.agent.role,
@@ -225,8 +238,8 @@ class EventListener(BaseEventListener):
 
         @crewai_event_bus.on(LiteAgentExecutionStartedEvent)
         def on_lite_agent_execution_started(
-            source, event: LiteAgentExecutionStartedEvent
-        ):
+            source: Any, event: LiteAgentExecutionStartedEvent
+        ) -> None:
             """Handle LiteAgent execution started event."""
             self.formatter.handle_lite_agent_execution(
                 event.agent_info["role"], status="started", **event.agent_info
@@ -234,15 +247,17 @@ class EventListener(BaseEventListener):
 
         @crewai_event_bus.on(LiteAgentExecutionCompletedEvent)
         def on_lite_agent_execution_completed(
-            source, event: LiteAgentExecutionCompletedEvent
-        ):
+            source: Any, event: LiteAgentExecutionCompletedEvent
+        ) -> None:
             """Handle LiteAgent execution completed event."""
             self.formatter.handle_lite_agent_execution(
                 event.agent_info["role"], status="completed", **event.agent_info
             )
 
         @crewai_event_bus.on(LiteAgentExecutionErrorEvent)
-        def on_lite_agent_execution_error(source, event: LiteAgentExecutionErrorEvent):
+        def on_lite_agent_execution_error(
+            source: Any, event: LiteAgentExecutionErrorEvent
+        ) -> None:
             """Handle LiteAgent execution error event."""
             self.formatter.handle_lite_agent_execution(
                 event.agent_info["role"],
@@ -254,25 +269,27 @@ class EventListener(BaseEventListener):
         # ----------- FLOW EVENTS -----------
 
         @crewai_event_bus.on(FlowCreatedEvent)
-        def on_flow_created(source, event: FlowCreatedEvent):
+        def on_flow_created(source: Any, event: FlowCreatedEvent) -> None:
             self._telemetry.flow_creation_span(event.flow_name)
             self.formatter.create_flow_tree(event.flow_name, str(source.flow_id))
 
         @crewai_event_bus.on(FlowStartedEvent)
-        def on_flow_started(source, event: FlowStartedEvent):
+        def on_flow_started(source: Any, event: FlowStartedEvent) -> None:
             self._telemetry.flow_execution_span(
                 event.flow_name, list(source._methods.keys())
             )
             self.formatter.start_flow(event.flow_name, str(source.flow_id))
 
         @crewai_event_bus.on(FlowFinishedEvent)
-        def on_flow_finished(source, event: FlowFinishedEvent):
+        def on_flow_finished(source: Any, event: FlowFinishedEvent) -> None:
             self.formatter.update_flow_status(
                 self.formatter.current_flow_tree, event.flow_name, source.flow_id
             )
 
         @crewai_event_bus.on(MethodExecutionStartedEvent)
-        def on_method_execution_started(source, event: MethodExecutionStartedEvent):
+        def on_method_execution_started(
+            source: Any, event: MethodExecutionStartedEvent
+        ) -> None:
             self.formatter.update_method_status(
                 self.formatter.current_method_branch,
                 self.formatter.current_flow_tree,
@@ -281,7 +298,9 @@ class EventListener(BaseEventListener):
             )
 
         @crewai_event_bus.on(MethodExecutionFinishedEvent)
-        def on_method_execution_finished(source, event: MethodExecutionFinishedEvent):
+        def on_method_execution_finished(
+            source: Any, event: MethodExecutionFinishedEvent
+        ) -> None:
             self.formatter.update_method_status(
                 self.formatter.current_method_branch,
                 self.formatter.current_flow_tree,
@@ -290,7 +309,9 @@ class EventListener(BaseEventListener):
             )
 
         @crewai_event_bus.on(MethodExecutionFailedEvent)
-        def on_method_execution_failed(source, event: MethodExecutionFailedEvent):
+        def on_method_execution_failed(
+            source: Any, event: MethodExecutionFailedEvent
+        ) -> None:
             self.formatter.update_method_status(
                 self.formatter.current_method_branch,
                 self.formatter.current_flow_tree,
@@ -301,7 +322,7 @@ class EventListener(BaseEventListener):
         # ----------- TOOL USAGE EVENTS -----------
 
         @crewai_event_bus.on(ToolUsageStartedEvent)
-        def on_tool_usage_started(source, event: ToolUsageStartedEvent):
+        def on_tool_usage_started(source: Any, event: ToolUsageStartedEvent) -> None:
             if isinstance(source, LLM):
                 self.formatter.handle_llm_tool_usage_started(
                     event.tool_name,
@@ -315,7 +336,7 @@ class EventListener(BaseEventListener):
                 )
 
         @crewai_event_bus.on(ToolUsageFinishedEvent)
-        def on_tool_usage_finished(source, event: ToolUsageFinishedEvent):
+        def on_tool_usage_finished(source: Any, event: ToolUsageFinishedEvent) -> None:
             if isinstance(source, LLM):
                 self.formatter.handle_llm_tool_usage_finished(
                     event.tool_name,
@@ -328,7 +349,7 @@ class EventListener(BaseEventListener):
                 )
 
         @crewai_event_bus.on(ToolUsageErrorEvent)
-        def on_tool_usage_error(source, event: ToolUsageErrorEvent):
+        def on_tool_usage_error(source: Any, event: ToolUsageErrorEvent) -> None:
             if isinstance(source, LLM):
                 self.formatter.handle_llm_tool_usage_error(
                     event.tool_name,
@@ -345,7 +366,7 @@ class EventListener(BaseEventListener):
         # ----------- LLM EVENTS -----------
 
         @crewai_event_bus.on(LLMCallStartedEvent)
-        def on_llm_call_started(source, event: LLMCallStartedEvent):
+        def on_llm_call_started(source: Any, event: LLMCallStartedEvent) -> None:
             # Capture the returned tool branch and update the current_tool_branch reference
             thinking_branch = self.formatter.handle_llm_call_started(
                 self.formatter.current_agent_branch,
@@ -356,7 +377,7 @@ class EventListener(BaseEventListener):
                 self.formatter.current_tool_branch = thinking_branch
 
         @crewai_event_bus.on(LLMCallCompletedEvent)
-        def on_llm_call_completed(source, event: LLMCallCompletedEvent):
+        def on_llm_call_completed(source: Any, event: LLMCallCompletedEvent) -> None:
             self.formatter.handle_llm_call_completed(
                 self.formatter.current_tool_branch,
                 self.formatter.current_agent_branch,
@@ -364,7 +385,7 @@ class EventListener(BaseEventListener):
             )
 
         @crewai_event_bus.on(LLMCallFailedEvent)
-        def on_llm_call_failed(source, event: LLMCallFailedEvent):
+        def on_llm_call_failed(source: Any, event: LLMCallFailedEvent) -> None:
             self.formatter.handle_llm_call_failed(
                 self.formatter.current_tool_branch,
                 event.error,
@@ -372,7 +393,7 @@ class EventListener(BaseEventListener):
             )
 
         @crewai_event_bus.on(LLMStreamChunkEvent)
-        def on_llm_stream_chunk(source, event: LLMStreamChunkEvent):
+        def on_llm_stream_chunk(source: Any, event: LLMStreamChunkEvent) -> None:
             self.text_stream.write(event.chunk)
 
             self.text_stream.seek(self.next_chunk)
@@ -385,7 +406,9 @@ class EventListener(BaseEventListener):
         # ----------- LLM GUARDRAIL EVENTS -----------
 
         @crewai_event_bus.on(LLMGuardrailStartedEvent)
-        def on_llm_guardrail_started(source, event: LLMGuardrailStartedEvent):
+        def on_llm_guardrail_started(
+            source: Any, event: LLMGuardrailStartedEvent
+        ) -> None:
             guardrail_str = str(event.guardrail)
             guardrail_name = (
                 guardrail_str[:50] + "..." if len(guardrail_str) > 50 else guardrail_str
@@ -394,13 +417,15 @@ class EventListener(BaseEventListener):
             self.formatter.handle_guardrail_started(guardrail_name, event.retry_count)
 
         @crewai_event_bus.on(LLMGuardrailCompletedEvent)
-        def on_llm_guardrail_completed(source, event: LLMGuardrailCompletedEvent):
+        def on_llm_guardrail_completed(
+            source: Any, event: LLMGuardrailCompletedEvent
+        ) -> None:
             self.formatter.handle_guardrail_completed(
                 event.success, event.error, event.retry_count
             )
 
         @crewai_event_bus.on(CrewTestStartedEvent)
-        def on_crew_test_started(source, event: CrewTestStartedEvent):
+        def on_crew_test_started(source: Any, event: CrewTestStartedEvent) -> None:
             cloned_crew = source.copy()
             self._telemetry.test_execution_span(
                 cloned_crew,
@@ -414,20 +439,20 @@ class EventListener(BaseEventListener):
             )
 
         @crewai_event_bus.on(CrewTestCompletedEvent)
-        def on_crew_test_completed(source, event: CrewTestCompletedEvent):
+        def on_crew_test_completed(source: Any, event: CrewTestCompletedEvent) -> None:
             self.formatter.handle_crew_test_completed(
                 self.formatter.current_flow_tree,
                 event.crew_name or "Crew",
             )
 
         @crewai_event_bus.on(CrewTestFailedEvent)
-        def on_crew_test_failed(source, event: CrewTestFailedEvent):
+        def on_crew_test_failed(source: Any, event: CrewTestFailedEvent) -> None:
             self.formatter.handle_crew_test_failed(event.crew_name or "Crew")
 
         @crewai_event_bus.on(KnowledgeRetrievalStartedEvent)
         def on_knowledge_retrieval_started(
-            source, event: KnowledgeRetrievalStartedEvent
-        ):
+            source: Any, event: KnowledgeRetrievalStartedEvent
+        ) -> None:
             if self.knowledge_retrieval_in_progress:
                 return
 
@@ -440,8 +465,8 @@ class EventListener(BaseEventListener):
 
         @crewai_event_bus.on(KnowledgeRetrievalCompletedEvent)
         def on_knowledge_retrieval_completed(
-            source, event: KnowledgeRetrievalCompletedEvent
-        ):
+            source: Any, event: KnowledgeRetrievalCompletedEvent
+        ) -> None:
             if not self.knowledge_retrieval_in_progress:
                 return
 
@@ -453,11 +478,15 @@ class EventListener(BaseEventListener):
             )
 
         @crewai_event_bus.on(KnowledgeQueryStartedEvent)
-        def on_knowledge_query_started(source, event: KnowledgeQueryStartedEvent):
+        def on_knowledge_query_started(
+            source: Any, event: KnowledgeQueryStartedEvent
+        ) -> None:
             pass
 
         @crewai_event_bus.on(KnowledgeQueryFailedEvent)
-        def on_knowledge_query_failed(source, event: KnowledgeQueryFailedEvent):
+        def on_knowledge_query_failed(
+            source: Any, event: KnowledgeQueryFailedEvent
+        ) -> None:
             self.formatter.handle_knowledge_query_failed(
                 self.formatter.current_agent_branch,
                 event.error,
@@ -465,13 +494,15 @@ class EventListener(BaseEventListener):
             )
 
         @crewai_event_bus.on(KnowledgeQueryCompletedEvent)
-        def on_knowledge_query_completed(source, event: KnowledgeQueryCompletedEvent):
+        def on_knowledge_query_completed(
+            source: Any, event: KnowledgeQueryCompletedEvent
+        ) -> None:
             pass
 
         @crewai_event_bus.on(KnowledgeSearchQueryFailedEvent)
         def on_knowledge_search_query_failed(
-            source, event: KnowledgeSearchQueryFailedEvent
-        ):
+            source: Any, event: KnowledgeSearchQueryFailedEvent
+        ) -> None:
             self.formatter.handle_knowledge_search_query_failed(
                 self.formatter.current_agent_branch,
                 event.error,
@@ -481,7 +512,9 @@ class EventListener(BaseEventListener):
         # ----------- REASONING EVENTS -----------
 
         @crewai_event_bus.on(AgentReasoningStartedEvent)
-        def on_agent_reasoning_started(source, event: AgentReasoningStartedEvent):
+        def on_agent_reasoning_started(
+            source: Any, event: AgentReasoningStartedEvent
+        ) -> None:
             self.formatter.handle_reasoning_started(
                 self.formatter.current_agent_branch,
                 event.attempt,
@@ -489,7 +522,9 @@ class EventListener(BaseEventListener):
             )
 
         @crewai_event_bus.on(AgentReasoningCompletedEvent)
-        def on_agent_reasoning_completed(source, event: AgentReasoningCompletedEvent):
+        def on_agent_reasoning_completed(
+            source: Any, event: AgentReasoningCompletedEvent
+        ) -> None:
             self.formatter.handle_reasoning_completed(
                 event.plan,
                 event.ready,
@@ -497,7 +532,9 @@ class EventListener(BaseEventListener):
             )
 
         @crewai_event_bus.on(AgentReasoningFailedEvent)
-        def on_agent_reasoning_failed(source, event: AgentReasoningFailedEvent):
+        def on_agent_reasoning_failed(
+            source: Any, event: AgentReasoningFailedEvent
+        ) -> None:
             self.formatter.handle_reasoning_failed(
                 event.error,
                 self.formatter.current_crew_tree,
@@ -506,7 +543,7 @@ class EventListener(BaseEventListener):
         # ----------- AGENT LOGGING EVENTS -----------
 
         @crewai_event_bus.on(AgentLogsStartedEvent)
-        def on_agent_logs_started(source, event: AgentLogsStartedEvent):
+        def on_agent_logs_started(source: Any, event: AgentLogsStartedEvent) -> None:
             self.formatter.handle_agent_logs_started(
                 event.agent_role,
                 event.task_description,
@@ -514,7 +551,9 @@ class EventListener(BaseEventListener):
             )
 
         @crewai_event_bus.on(AgentLogsExecutionEvent)
-        def on_agent_logs_execution(source, event: AgentLogsExecutionEvent):
+        def on_agent_logs_execution(
+            source: Any, event: AgentLogsExecutionEvent
+        ) -> None:
             self.formatter.handle_agent_logs_execution(
                 event.agent_role,
                 event.formatted_answer,

src/crewai/events/event_types.py

@@ -1,12 +1,12 @@
 from typing import Union
 
-from .agent_events import (
+from crewai.events.types.agent_events import (
     AgentExecutionCompletedEvent,
     AgentExecutionErrorEvent,
     AgentExecutionStartedEvent,
     LiteAgentExecutionCompletedEvent,
 )
-from .crew_events import (
+from .types.crew_events import (
     CrewKickoffCompletedEvent,
     CrewKickoffFailedEvent,
     CrewKickoffStartedEvent,
@@ -17,39 +17,39 @@ from .crew_events import (
     CrewTrainFailedEvent,
     CrewTrainStartedEvent,
 )
-from .flow_events import (
+from .types.flow_events import (
     FlowFinishedEvent,
     FlowStartedEvent,
     MethodExecutionFailedEvent,
     MethodExecutionFinishedEvent,
     MethodExecutionStartedEvent,
 )
-from .llm_events import (
+from .types.llm_events import (
     LLMCallCompletedEvent,
     LLMCallFailedEvent,
     LLMCallStartedEvent,
     LLMStreamChunkEvent,
 )
-from .llm_guardrail_events import (
+from .types.llm_guardrail_events import (
     LLMGuardrailCompletedEvent,
     LLMGuardrailStartedEvent,
 )
-from .task_events import (
+from .types.task_events import (
     TaskCompletedEvent,
     TaskFailedEvent,
     TaskStartedEvent,
 )
-from .tool_usage_events import (
+from .types.tool_usage_events import (
     ToolUsageErrorEvent,
     ToolUsageFinishedEvent,
     ToolUsageStartedEvent,
 )
-from .reasoning_events import (
+from .types.reasoning_events import (
     AgentReasoningStartedEvent,
     AgentReasoningCompletedEvent,
     AgentReasoningFailedEvent,
 )
-from .knowledge_events import (
+from .types.knowledge_events import (
     KnowledgeRetrievalStartedEvent,
     KnowledgeRetrievalCompletedEvent,
     KnowledgeQueryStartedEvent,
@@ -58,7 +58,7 @@ from .knowledge_events import (
     KnowledgeSearchQueryFailedEvent,
 )
 
-from .memory_events import (
+from .types.memory_events import (
     MemorySaveStartedEvent,
     MemorySaveCompletedEvent,
     MemorySaveFailedEvent,

src/crewai/events/listeners/__init__.py

@@ -0,0 +1,5 @@
+"""Event listener implementations for CrewAI.
+
+This module contains various event listener implementations
+for handling memory, tracing, and other event-driven functionality.
+"""

src/crewai/events/listeners/memory_listener.py

@@ -1,27 +1,30 @@
-from crewai.utilities.events.base_event_listener import BaseEventListener
-from crewai.utilities.events.memory_events import (
+from typing import Any
+
+from crewai.events.base_event_listener import BaseEventListener
+from crewai.events.event_bus import CrewAIEventsBus
+from crewai.events.types.memory_events import (
+    MemoryQueryCompletedEvent,
+    MemoryQueryFailedEvent,
     MemoryRetrievalCompletedEvent,
     MemoryRetrievalStartedEvent,
-    MemoryQueryFailedEvent,
-    MemoryQueryCompletedEvent,
-    MemorySaveStartedEvent,
     MemorySaveCompletedEvent,
     MemorySaveFailedEvent,
+    MemorySaveStartedEvent,
 )
 
-class MemoryListener(BaseEventListener):
 
-    def __init__(self, formatter):
+class MemoryListener(BaseEventListener):
+    def __init__(self, formatter: Any) -> None:
         super().__init__()
         self.formatter = formatter
         self.memory_retrieval_in_progress = False
         self.memory_save_in_progress = False
 
-    def setup_listeners(self, crewai_event_bus):
+    def setup_listeners(self, crewai_event_bus: CrewAIEventsBus) -> None:
         @crewai_event_bus.on(MemoryRetrievalStartedEvent)
         def on_memory_retrieval_started(
-            source, event: MemoryRetrievalStartedEvent
-        ):
+            source: Any, event: MemoryRetrievalStartedEvent
+        ) -> None:
             if self.memory_retrieval_in_progress:
                 return
 
@@ -34,8 +37,8 @@ class MemoryListener(BaseEventListener):
 
         @crewai_event_bus.on(MemoryRetrievalCompletedEvent)
         def on_memory_retrieval_completed(
-            source, event: MemoryRetrievalCompletedEvent
-        ):
+            source: Any, event: MemoryRetrievalCompletedEvent
+        ) -> None:
             if not self.memory_retrieval_in_progress:
                 return
 
@@ -44,11 +47,13 @@ class MemoryListener(BaseEventListener):
                 self.formatter.current_agent_branch,
                 self.formatter.current_crew_tree,
                 event.memory_content,
-                event.retrieval_time_ms
+                event.retrieval_time_ms,
             )
 
         @crewai_event_bus.on(MemoryQueryCompletedEvent)
-        def on_memory_query_completed(source, event: MemoryQueryCompletedEvent):
+        def on_memory_query_completed(
+            source: Any, event: MemoryQueryCompletedEvent
+        ) -> None:
             if not self.memory_retrieval_in_progress:
                 return
 
@@ -60,7 +65,7 @@ class MemoryListener(BaseEventListener):
             )
 
         @crewai_event_bus.on(MemoryQueryFailedEvent)
-        def on_memory_query_failed(source, event: MemoryQueryFailedEvent):
+        def on_memory_query_failed(source: Any, event: MemoryQueryFailedEvent) -> None:
             if not self.memory_retrieval_in_progress:
                 return
 
@@ -72,7 +77,7 @@ class MemoryListener(BaseEventListener):
             )
 
         @crewai_event_bus.on(MemorySaveStartedEvent)
-        def on_memory_save_started(source, event: MemorySaveStartedEvent):
+        def on_memory_save_started(source: Any, event: MemorySaveStartedEvent) -> None:
             if self.memory_save_in_progress:
                 return
 
@@ -84,7 +89,9 @@ class MemoryListener(BaseEventListener):
             )
 
         @crewai_event_bus.on(MemorySaveCompletedEvent)
-        def on_memory_save_completed(source, event: MemorySaveCompletedEvent):
+        def on_memory_save_completed(
+            source: Any, event: MemorySaveCompletedEvent
+        ) -> None:
             if not self.memory_save_in_progress:
                 return
 
@@ -98,7 +105,7 @@ class MemoryListener(BaseEventListener):
             )
 
         @crewai_event_bus.on(MemorySaveFailedEvent)
-        def on_memory_save_failed(source, event: MemorySaveFailedEvent):
+        def on_memory_save_failed(source: Any, event: MemorySaveFailedEvent) -> None:
             if not self.memory_save_in_progress:
                 return
 
@@ -107,4 +114,4 @@ class MemoryListener(BaseEventListener):
                 event.error,
                 event.source_type,
                 self.formatter.current_crew_tree,
-            )
+            )

src/crewai/events/listeners/tracing/trace_batch_manager.py

@@ -11,7 +11,7 @@ from crewai.cli.plus_api import PlusAPI
 from rich.console import Console
 from rich.panel import Panel
 
-from crewai.utilities.events.listeners.tracing.types import TraceEvent
+from crewai.events.listeners.tracing.types import TraceEvent
 from logging import getLogger
 
 logger = getLogger(__name__)
@@ -41,18 +41,21 @@ class TraceBatchManager:
     """Single responsibility: Manage batches and event buffering"""
 
     is_current_batch_ephemeral: bool = False
+    trace_batch_id: Optional[str] = None
+    current_batch: Optional[TraceBatch] = None
+    event_buffer: List[TraceEvent] = []
+    execution_start_times: Dict[str, datetime] = {}
+    batch_owner_type: Optional[str] = None
+    batch_owner_id: Optional[str] = None
 
     def __init__(self):
         try:
-            self.plus_api = PlusAPI(api_key=get_auth_token())
+            self.plus_api = PlusAPI(
+                api_key=get_auth_token(),
+            )
         except AuthError:
             self.plus_api = PlusAPI(api_key="")
 
-        self.trace_batch_id: Optional[str] = None  # Backend ID
-        self.current_batch: Optional[TraceBatch] = None
-        self.event_buffer: List[TraceEvent] = []
-        self.execution_start_times: Dict[str, datetime] = {}
-
     def initialize_batch(
         self,
         user_context: Dict[str, str],
@@ -113,7 +116,13 @@ class TraceBatchManager:
                 else self.plus_api.initialize_trace_batch(payload)
             )
 
-            if response.status_code == 201 or response.status_code == 200:
+            if response is None:
+                logger.warning(
+                    "Trace batch initialization failed gracefully. Continuing without tracing."
+                )
+                return
+
+            if response.status_code in [201, 200]:
                 response_data = response.json()
                 self.trace_batch_id = (
                     response_data["trace_id"]
@@ -128,21 +137,23 @@ class TraceBatchManager:
                 )
                 console.print(panel)
             else:
-                logger.error(
-                    f"❌ Failed to initialize trace batch: {response.status_code} - {response.text}"
+                logger.warning(
+                    f"Trace batch initialization returned status {response.status_code}. Continuing without tracing."
                 )
 
         except Exception as e:
-            logger.error(f"❌ Error initializing trace batch: {str(e)}")
+            logger.warning(
+                f"Error initializing trace batch: {str(e)}. Continuing without tracing."
+            )
 
     def add_event(self, trace_event: TraceEvent):
         """Add event to buffer"""
         self.event_buffer.append(trace_event)
 
-    def _send_events_to_backend(self):
-        """Send buffered events to backend"""
+    def _send_events_to_backend(self) -> int:
+        """Send buffered events to backend with graceful failure handling"""
         if not self.plus_api or not self.trace_batch_id or not self.event_buffer:
-            return
+            return 500
 
         try:
             payload = {
@@ -154,38 +165,48 @@ class TraceBatchManager:
                 },
             }
 
-            if not self.trace_batch_id:
-                raise Exception("❌ Trace batch ID not found")
-
             response = (
                 self.plus_api.send_ephemeral_trace_events(self.trace_batch_id, payload)
                 if self.is_current_batch_ephemeral
                 else self.plus_api.send_trace_events(self.trace_batch_id, payload)
             )
 
-            if response.status_code == 200 or response.status_code == 201:
+            if response is None:
+                logger.warning("Failed to send trace events. Events will be lost.")
+                return 500
+
+            if response.status_code in [200, 201]:
                 self.event_buffer.clear()
+                return 200
             else:
-                logger.error(
-                    f"❌ Failed to send events: {response.status_code} - {response.text}"
+                logger.warning(
+                    f"Failed to send events: {response.status_code}. Events will be lost."
                 )
+                return 500
 
         except Exception as e:
-            logger.error(f"❌ Error sending events to backend: {str(e)}")
+            logger.warning(
+                f"Error sending events to backend: {str(e)}. Events will be lost."
+            )
+            return 500
 
     def finalize_batch(self) -> Optional[TraceBatch]:
         """Finalize batch and return it for sending"""
         if not self.current_batch:
             return None
 
+        self.current_batch.events = self.event_buffer.copy()
         if self.event_buffer:
-            self._send_events_to_backend()
+            events_sent_to_backend_status = self._send_events_to_backend()
+            if events_sent_to_backend_status == 500:
+                return None
         self._finalize_backend_batch()
 
-        self.current_batch.events = self.event_buffer.copy()
-
         finalized_batch = self.current_batch
 
+        self.batch_owner_type = None
+        self.batch_owner_id = None
+
         self.current_batch = None
         self.event_buffer.clear()
         self.trace_batch_id = None

src/crewai/events/listeners/tracing/trace_listener.py

@@ -1,73 +1,70 @@
 import os
 import uuid
+from typing import Any, Optional
 
-from typing import Dict, Any, Optional
+from typing_extensions import Self
 
-from crewai.utilities.events.base_event_listener import BaseEventListener
-from crewai.utilities.events.agent_events import (
+from crewai.cli.authentication.token import AuthError, get_auth_token
+from crewai.cli.version import get_crewai_version
+from crewai.events.base_event_listener import BaseEventListener
+from crewai.events.event_bus import CrewAIEventsBus
+from crewai.events.listeners.tracing.trace_batch_manager import TraceBatchManager
+from crewai.events.listeners.tracing.types import TraceEvent
+from crewai.events.types.agent_events import (
     AgentExecutionCompletedEvent,
+    AgentExecutionErrorEvent,
     AgentExecutionStartedEvent,
-    LiteAgentExecutionStartedEvent,
     LiteAgentExecutionCompletedEvent,
     LiteAgentExecutionErrorEvent,
-    AgentExecutionErrorEvent,
+    LiteAgentExecutionStartedEvent,
 )
-from crewai.utilities.events.listeners.tracing.types import TraceEvent
-from crewai.utilities.events.reasoning_events import (
-    AgentReasoningStartedEvent,
-    AgentReasoningCompletedEvent,
-    AgentReasoningFailedEvent,
-)
-from crewai.utilities.events.crew_events import (
+from crewai.events.types.crew_events import (
     CrewKickoffCompletedEvent,
     CrewKickoffFailedEvent,
     CrewKickoffStartedEvent,
 )
-from crewai.utilities.events.task_events import (
-    TaskCompletedEvent,
-    TaskFailedEvent,
-    TaskStartedEvent,
+from crewai.events.types.flow_events import (
+    FlowCreatedEvent,
+    FlowFinishedEvent,
+    FlowPlotEvent,
+    FlowStartedEvent,
+    MethodExecutionFailedEvent,
+    MethodExecutionFinishedEvent,
+    MethodExecutionStartedEvent,
 )
-from crewai.utilities.events.tool_usage_events import (
-    ToolUsageErrorEvent,
-    ToolUsageFinishedEvent,
-    ToolUsageStartedEvent,
-)
-from crewai.utilities.events.llm_events import (
+from crewai.events.types.llm_events import (
     LLMCallCompletedEvent,
     LLMCallFailedEvent,
     LLMCallStartedEvent,
 )
-
-from crewai.utilities.events.flow_events import (
-    FlowCreatedEvent,
-    FlowStartedEvent,
-    FlowFinishedEvent,
-    MethodExecutionStartedEvent,
-    MethodExecutionFinishedEvent,
-    MethodExecutionFailedEvent,
-    FlowPlotEvent,
-)
-from crewai.utilities.events.llm_guardrail_events import (
-    LLMGuardrailStartedEvent,
+from crewai.events.types.llm_guardrail_events import (
     LLMGuardrailCompletedEvent,
+    LLMGuardrailStartedEvent,
 )
-from crewai.utilities.serialization import to_serializable
-
-
-from .trace_batch_manager import TraceBatchManager
-
-from crewai.utilities.events.memory_events import (
-    MemoryQueryStartedEvent,
+from crewai.events.types.memory_events import (
     MemoryQueryCompletedEvent,
     MemoryQueryFailedEvent,
-    MemorySaveStartedEvent,
+    MemoryQueryStartedEvent,
     MemorySaveCompletedEvent,
     MemorySaveFailedEvent,
+    MemorySaveStartedEvent,
 )
-
-from crewai.cli.authentication.token import AuthError, get_auth_token
-from crewai.cli.version import get_crewai_version
+from crewai.events.types.reasoning_events import (
+    AgentReasoningCompletedEvent,
+    AgentReasoningFailedEvent,
+    AgentReasoningStartedEvent,
+)
+from crewai.events.types.task_events import (
+    TaskCompletedEvent,
+    TaskFailedEvent,
+    TaskStartedEvent,
+)
+from crewai.events.types.tool_usage_events import (
+    ToolUsageErrorEvent,
+    ToolUsageFinishedEvent,
+    ToolUsageStartedEvent,
+)
+from crewai.utilities.serialization import to_serializable
 
 
 class TraceCollectionListener(BaseEventListener):
@@ -75,12 +72,20 @@ class TraceCollectionListener(BaseEventListener):
     Trace collection listener that orchestrates trace collection
     """
 
-    complex_events = ["task_started", "llm_call_started", "llm_call_completed"]
+    complex_events = [
+        "task_started",
+        "task_completed",
+        "llm_call_started",
+        "llm_call_completed",
+        "agent_execution_started",
+        "agent_execution_completed",
+    ]
 
     _instance = None
     _initialized = False
+    _listeners_setup = False
 
-    def __new__(cls, batch_manager=None):
+    def __new__(cls, batch_manager: Optional[Any] = None) -> Self:
         if cls._instance is None:
             cls._instance = super().__new__(cls)
         return cls._instance
@@ -93,10 +98,11 @@ class TraceCollectionListener(BaseEventListener):
             return
 
         super().__init__()
-        self.batch_manager = batch_manager or TraceBatchManager()
+        self.batch_manager = batch_manager or TraceBatchManager()  # type: ignore
         self._initialized = True
 
-    def _check_authenticated(self) -> bool:
+    @staticmethod
+    def _check_authenticated() -> bool:
         """Check if tracing should be enabled"""
         try:
             res = bool(get_auth_token())
@@ -104,7 +110,8 @@ class TraceCollectionListener(BaseEventListener):
         except AuthError:
             return False
 
-    def _get_user_context(self) -> Dict[str, str]:
+    @staticmethod
+    def _get_user_context() -> dict[str, str]:
         """Extract user context for tracing"""
         return {
             "user_id": os.getenv("CREWAI_USER_ID", "anonymous"),
@@ -113,174 +120,181 @@ class TraceCollectionListener(BaseEventListener):
             "trace_id": str(uuid.uuid4()),
         }
 
-    def setup_listeners(self, crewai_event_bus):
+    def setup_listeners(self, crewai_event_bus: CrewAIEventsBus) -> None:
         """Setup event listeners - delegates to specific handlers"""
 
+        if self._listeners_setup:
+            return
+
         self._register_flow_event_handlers(crewai_event_bus)
         self._register_context_event_handlers(crewai_event_bus)
         self._register_action_event_handlers(crewai_event_bus)
 
-    def _register_flow_event_handlers(self, event_bus):
+        self._listeners_setup = True
+
+    def _register_flow_event_handlers(self, event_bus: CrewAIEventsBus) -> None:
         """Register handlers for flow events"""
 
         @event_bus.on(FlowCreatedEvent)
-        def on_flow_created(source, event):
+        def on_flow_created(source: Any, event: Any) -> None:
             pass
 
         @event_bus.on(FlowStartedEvent)
-        def on_flow_started(source, event):
+        def on_flow_started(source: Any, event: Any) -> None:
             if not self.batch_manager.is_batch_initialized():
                 self._initialize_flow_batch(source, event)
             self._handle_trace_event("flow_started", source, event)
 
         @event_bus.on(MethodExecutionStartedEvent)
-        def on_method_started(source, event):
+        def on_method_started(source: Any, event: Any) -> None:
             self._handle_trace_event("method_execution_started", source, event)
 
         @event_bus.on(MethodExecutionFinishedEvent)
-        def on_method_finished(source, event):
+        def on_method_finished(source: Any, event: Any) -> None:
             self._handle_trace_event("method_execution_finished", source, event)
 
         @event_bus.on(MethodExecutionFailedEvent)
-        def on_method_failed(source, event):
+        def on_method_failed(source: Any, event: Any) -> None:
             self._handle_trace_event("method_execution_failed", source, event)
 
         @event_bus.on(FlowFinishedEvent)
-        def on_flow_finished(source, event):
+        def on_flow_finished(source: Any, event: Any) -> None:
             self._handle_trace_event("flow_finished", source, event)
-            self.batch_manager.finalize_batch()
+            if self.batch_manager.batch_owner_type == "flow":
+                self.batch_manager.finalize_batch()
 
         @event_bus.on(FlowPlotEvent)
-        def on_flow_plot(source, event):
+        def on_flow_plot(source: Any, event: Any) -> None:
             self._handle_action_event("flow_plot", source, event)
 
-    def _register_context_event_handlers(self, event_bus):
+    def _register_context_event_handlers(self, event_bus: CrewAIEventsBus) -> None:
         """Register handlers for context events (start/end)"""
 
         @event_bus.on(CrewKickoffStartedEvent)
-        def on_crew_started(source, event):
+        def on_crew_started(source: Any, event: Any) -> None:
             if not self.batch_manager.is_batch_initialized():
                 self._initialize_crew_batch(source, event)
             self._handle_trace_event("crew_kickoff_started", source, event)
 
         @event_bus.on(CrewKickoffCompletedEvent)
-        def on_crew_completed(source, event):
+        def on_crew_completed(source: Any, event: Any) -> None:
             self._handle_trace_event("crew_kickoff_completed", source, event)
-            self.batch_manager.finalize_batch()
+            if self.batch_manager.batch_owner_type == "crew":
+                self.batch_manager.finalize_batch()
 
         @event_bus.on(CrewKickoffFailedEvent)
-        def on_crew_failed(source, event):
+        def on_crew_failed(source: Any, event: Any) -> None:
             self._handle_trace_event("crew_kickoff_failed", source, event)
             self.batch_manager.finalize_batch()
 
         @event_bus.on(TaskStartedEvent)
-        def on_task_started(source, event):
+        def on_task_started(source: Any, event: Any) -> None:
             self._handle_trace_event("task_started", source, event)
 
         @event_bus.on(TaskCompletedEvent)
-        def on_task_completed(source, event):
+        def on_task_completed(source: Any, event: Any) -> None:
             self._handle_trace_event("task_completed", source, event)
 
         @event_bus.on(TaskFailedEvent)
-        def on_task_failed(source, event):
+        def on_task_failed(source: Any, event: Any) -> None:
             self._handle_trace_event("task_failed", source, event)
 
         @event_bus.on(AgentExecutionStartedEvent)
-        def on_agent_started(source, event):
+        def on_agent_started(source: Any, event: Any) -> None:
             self._handle_trace_event("agent_execution_started", source, event)
 
         @event_bus.on(AgentExecutionCompletedEvent)
-        def on_agent_completed(source, event):
+        def on_agent_completed(source: Any, event: Any) -> None:
             self._handle_trace_event("agent_execution_completed", source, event)
 
         @event_bus.on(LiteAgentExecutionStartedEvent)
-        def on_lite_agent_started(source, event):
+        def on_lite_agent_started(source: Any, event: Any) -> None:
             self._handle_trace_event("lite_agent_execution_started", source, event)
 
         @event_bus.on(LiteAgentExecutionCompletedEvent)
-        def on_lite_agent_completed(source, event):
+        def on_lite_agent_completed(source: Any, event: Any) -> None:
             self._handle_trace_event("lite_agent_execution_completed", source, event)
 
         @event_bus.on(LiteAgentExecutionErrorEvent)
-        def on_lite_agent_error(source, event):
+        def on_lite_agent_error(source: Any, event: Any) -> None:
             self._handle_trace_event("lite_agent_execution_error", source, event)
 
         @event_bus.on(AgentExecutionErrorEvent)
-        def on_agent_error(source, event):
+        def on_agent_error(source: Any, event: Any) -> None:
             self._handle_trace_event("agent_execution_error", source, event)
 
         @event_bus.on(LLMGuardrailStartedEvent)
-        def on_guardrail_started(source, event):
+        def on_guardrail_started(source: Any, event: Any) -> None:
             self._handle_trace_event("llm_guardrail_started", source, event)
 
         @event_bus.on(LLMGuardrailCompletedEvent)
-        def on_guardrail_completed(source, event):
+        def on_guardrail_completed(source: Any, event: Any) -> None:
             self._handle_trace_event("llm_guardrail_completed", source, event)
 
-    def _register_action_event_handlers(self, event_bus):
-        """Register handlers for action events (LLM calls, tool usage, memory)"""
+    def _register_action_event_handlers(self, event_bus: CrewAIEventsBus) -> None:
+        """Register handlers for action events (LLM calls, tool usage)"""
 
         @event_bus.on(LLMCallStartedEvent)
-        def on_llm_call_started(source, event):
+        def on_llm_call_started(source: Any, event: Any) -> None:
             self._handle_action_event("llm_call_started", source, event)
 
         @event_bus.on(LLMCallCompletedEvent)
-        def on_llm_call_completed(source, event):
+        def on_llm_call_completed(source: Any, event: Any) -> None:
             self._handle_action_event("llm_call_completed", source, event)
 
         @event_bus.on(LLMCallFailedEvent)
-        def on_llm_call_failed(source, event):
+        def on_llm_call_failed(source: Any, event: Any) -> None:
             self._handle_action_event("llm_call_failed", source, event)
 
         @event_bus.on(ToolUsageStartedEvent)
-        def on_tool_started(source, event):
+        def on_tool_started(source: Any, event: Any) -> None:
             self._handle_action_event("tool_usage_started", source, event)
 
         @event_bus.on(ToolUsageFinishedEvent)
-        def on_tool_finished(source, event):
+        def on_tool_finished(source: Any, event: Any) -> None:
             self._handle_action_event("tool_usage_finished", source, event)
 
         @event_bus.on(ToolUsageErrorEvent)
-        def on_tool_error(source, event):
+        def on_tool_error(source: Any, event: Any) -> None:
             self._handle_action_event("tool_usage_error", source, event)
 
         @event_bus.on(MemoryQueryStartedEvent)
-        def on_memory_query_started(source, event):
+        def on_memory_query_started(source: Any, event: Any) -> None:
             self._handle_action_event("memory_query_started", source, event)
 
         @event_bus.on(MemoryQueryCompletedEvent)
-        def on_memory_query_completed(source, event):
+        def on_memory_query_completed(source: Any, event: Any) -> None:
             self._handle_action_event("memory_query_completed", source, event)
 
         @event_bus.on(MemoryQueryFailedEvent)
-        def on_memory_query_failed(source, event):
+        def on_memory_query_failed(source: Any, event: Any) -> None:
             self._handle_action_event("memory_query_failed", source, event)
 
         @event_bus.on(MemorySaveStartedEvent)
-        def on_memory_save_started(source, event):
+        def on_memory_save_started(source: Any, event: Any) -> None:
             self._handle_action_event("memory_save_started", source, event)
 
         @event_bus.on(MemorySaveCompletedEvent)
-        def on_memory_save_completed(source, event):
+        def on_memory_save_completed(source: Any, event: Any) -> None:
             self._handle_action_event("memory_save_completed", source, event)
 
         @event_bus.on(MemorySaveFailedEvent)
-        def on_memory_save_failed(source, event):
+        def on_memory_save_failed(source: Any, event: Any) -> None:
             self._handle_action_event("memory_save_failed", source, event)
 
         @event_bus.on(AgentReasoningStartedEvent)
-        def on_agent_reasoning_started(source, event):
+        def on_agent_reasoning_started(source: Any, event: Any) -> None:
             self._handle_action_event("agent_reasoning_started", source, event)
 
         @event_bus.on(AgentReasoningCompletedEvent)
-        def on_agent_reasoning_completed(source, event):
+        def on_agent_reasoning_completed(source: Any, event: Any) -> None:
             self._handle_action_event("agent_reasoning_completed", source, event)
 
         @event_bus.on(AgentReasoningFailedEvent)
-        def on_agent_reasoning_failed(source, event):
+        def on_agent_reasoning_failed(source: Any, event: Any) -> None:
             self._handle_action_event("agent_reasoning_failed", source, event)
 
-    def _initialize_crew_batch(self, source: Any, event: Any):
+    def _initialize_crew_batch(self, source: Any, event: Any) -> None:
         """Initialize trace batch"""
         user_context = self._get_user_context()
         execution_metadata = {
@@ -289,9 +303,12 @@ class TraceCollectionListener(BaseEventListener):
             "crewai_version": get_crewai_version(),
         }
 
+        self.batch_manager.batch_owner_type = "crew"
+        self.batch_manager.batch_owner_id = getattr(source, "id", str(uuid.uuid4()))
+
         self._initialize_batch(user_context, execution_metadata)
 
-    def _initialize_flow_batch(self, source: Any, event: Any):
+    def _initialize_flow_batch(self, source: Any, event: Any) -> None:
         """Initialize trace batch for Flow execution"""
         user_context = self._get_user_context()
         execution_metadata = {
@@ -301,29 +318,30 @@ class TraceCollectionListener(BaseEventListener):
             "execution_type": "flow",
         }
 
+        self.batch_manager.batch_owner_type = "flow"
+        self.batch_manager.batch_owner_id = getattr(source, "id", str(uuid.uuid4()))
+
         self._initialize_batch(user_context, execution_metadata)
 
     def _initialize_batch(
-        self, user_context: Dict[str, str], execution_metadata: Dict[str, Any]
-    ):
+        self, user_context: dict[str, str], execution_metadata: dict[str, Any]
+    ) -> None:
         """Initialize trace batch if ephemeral"""
         if not self._check_authenticated():
             self.batch_manager.initialize_batch(
                 user_context, execution_metadata, use_ephemeral=True
             )
         else:
-            self.batch_manager.initialize_batch(
-                user_context, execution_metadata, use_ephemeral=False
-            )
+            self.batch_manager.initialize_batch(user_context, execution_metadata)
 
-    def _handle_trace_event(self, event_type: str, source: Any, event: Any):
+    def _handle_trace_event(self, event_type: str, source: Any, event: Any) -> None:
         """Generic handler for context end events"""
 
         trace_event = self._create_trace_event(event_type, source, event)
 
         self.batch_manager.add_event(trace_event)
 
-    def _handle_action_event(self, event_type: str, source: Any, event: Any):
+    def _handle_action_event(self, event_type: str, source: Any, event: Any) -> None:
         """Generic handler for action events (LLM calls, tool usage)"""
 
         if not self.batch_manager.is_batch_initialized():
@@ -350,7 +368,7 @@ class TraceCollectionListener(BaseEventListener):
 
     def _build_event_data(
         self, event_type: str, event: Any, source: Any
-    ) -> Dict[str, Any]:
+    ) -> dict[str, Any]:
         """Build event data"""
         if event_type not in self.complex_events:
             return self._safe_serialize_to_dict(event)
@@ -358,12 +376,44 @@ class TraceCollectionListener(BaseEventListener):
             return {
                 "task_description": event.task.description,
                 "expected_output": event.task.expected_output,
-                "task_name": event.task.name,
+                "task_name": event.task.name or event.task.description,
                 "context": event.context,
-                "agent": source.agent.role,
+                "agent_role": source.agent.role,
+                "task_id": str(event.task.id),
+            }
+        elif event_type == "task_completed":
+            return {
+                "task_description": event.task.description if event.task else None,
+                "task_name": event.task.name or event.task.description
+                if event.task
+                else None,
+                "task_id": str(event.task.id) if event.task else None,
+                "output_raw": event.output.raw if event.output else None,
+                "output_format": str(event.output.output_format)
+                if event.output
+                else None,
+                "agent_role": event.output.agent if event.output else None,
+            }
+        elif event_type == "agent_execution_started":
+            return {
+                "agent_role": event.agent.role,
+                "agent_goal": event.agent.goal,
+                "agent_backstory": event.agent.backstory,
+            }
+        elif event_type == "agent_execution_completed":
+            return {
+                "agent_role": event.agent.role,
+                "agent_goal": event.agent.goal,
+                "agent_backstory": event.agent.backstory,
             }
         elif event_type == "llm_call_started":
-            return self._safe_serialize_to_dict(event)
+            event_data = self._safe_serialize_to_dict(event)
+            event_data["task_name"] = (
+                event.task_name or event.task_description
+                if hasattr(event, "task_name") and event.task_name
+                else None
+            )
+            return event_data
         elif event_type == "llm_call_completed":
             return self._safe_serialize_to_dict(event)
         else:
@@ -373,11 +423,19 @@ class TraceCollectionListener(BaseEventListener):
                 "source": source,
             }
 
-    # TODO: move to utils
+    @staticmethod
     def _safe_serialize_to_dict(
-        self, obj, exclude: set[str] | None = None
-    ) -> Dict[str, Any]:
-        """Safely serialize an object to a dictionary for event data."""
+        obj: Any, exclude: set[str] | None = None
+    ) -> dict[str, Any]:
+        """Safely serialize an object to a dictionary for event data.
+
+        Args:
+            obj: The object to serialize.
+            exclude: Optional set of attribute names to exclude from serialization.
+
+        Notes:
+            - TODO: refactor to utilities function.
+        """
         try:
             serialized = to_serializable(obj, exclude)
             if isinstance(serialized, dict):
@@ -387,9 +445,20 @@ class TraceCollectionListener(BaseEventListener):
         except Exception as e:
             return {"serialization_error": str(e), "object_type": type(obj).__name__}
 
-    # TODO: move to utils
-    def _truncate_messages(self, messages, max_content_length=500, max_messages=5):
-        """Truncate message content and limit number of messages"""
+    @staticmethod
+    def _truncate_messages(
+        messages: Any, max_content_length: int = 500, max_messages: int = 5
+    ) -> Any:
+        """Truncate message content and limit number of messages
+
+        Args:
+            messages: List of message dicts with 'content' keys.
+            max_content_length: Max length of each message content.
+            max_messages: Max number of messages to retain.
+
+        Notes:
+            - TODO: refactor to utilities function.
+        """
         if not messages or not isinstance(messages, list):
             return messages
 

src/crewai/events/types/__init__.py

@@ -0,0 +1,5 @@
+"""Event type definitions for CrewAI.
+
+This module contains all event types used throughout the CrewAI system
+for monitoring and extending agent, crew, task, and tool execution.
+"""

src/crewai/events/types/agent_events.py

@@ -1,13 +1,15 @@
-from typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence, Union
+"""Agent-related events moved to break circular dependencies."""
+
+from __future__ import annotations
+
+from typing import Any, Dict, List, Optional, Sequence, Union
+
+from pydantic import model_validator
 
 from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.tools.base_tool import BaseTool
 from crewai.tools.structured_tool import CrewStructuredTool
-
-from .base_events import BaseEvent
-
-if TYPE_CHECKING:
-    from crewai.agents.agent_builder.base_agent import BaseAgent
+from crewai.events.base_events import BaseEvent
 
 
 class AgentExecutionStartedEvent(BaseEvent):
@@ -21,9 +23,9 @@ class AgentExecutionStartedEvent(BaseEvent):
 
     model_config = {"arbitrary_types_allowed": True}
 
-    def __init__(self, **data):
-        super().__init__(**data)
-        # Set fingerprint data from the agent
+    @model_validator(mode="after")
+    def set_fingerprint_data(self):
+        """Set fingerprint data from the agent if available."""
         if hasattr(self.agent, "fingerprint") and self.agent.fingerprint:
             self.source_fingerprint = self.agent.fingerprint.uuid_str
             self.source_type = "agent"
@@ -32,6 +34,7 @@ class AgentExecutionStartedEvent(BaseEvent):
                 and self.agent.fingerprint.metadata
             ):
                 self.fingerprint_metadata = self.agent.fingerprint.metadata
+        return self
 
 
 class AgentExecutionCompletedEvent(BaseEvent):
@@ -42,9 +45,11 @@ class AgentExecutionCompletedEvent(BaseEvent):
     output: str
     type: str = "agent_execution_completed"
 
-    def __init__(self, **data):
-        super().__init__(**data)
-        # Set fingerprint data from the agent
+    model_config = {"arbitrary_types_allowed": True}
+
+    @model_validator(mode="after")
+    def set_fingerprint_data(self):
+        """Set fingerprint data from the agent if available."""
         if hasattr(self.agent, "fingerprint") and self.agent.fingerprint:
             self.source_fingerprint = self.agent.fingerprint.uuid_str
             self.source_type = "agent"
@@ -53,6 +58,7 @@ class AgentExecutionCompletedEvent(BaseEvent):
                 and self.agent.fingerprint.metadata
             ):
                 self.fingerprint_metadata = self.agent.fingerprint.metadata
+        return self
 
 
 class AgentExecutionErrorEvent(BaseEvent):
@@ -63,9 +69,11 @@ class AgentExecutionErrorEvent(BaseEvent):
     error: str
     type: str = "agent_execution_error"
 
-    def __init__(self, **data):
-        super().__init__(**data)
-        # Set fingerprint data from the agent
+    model_config = {"arbitrary_types_allowed": True}
+
+    @model_validator(mode="after")
+    def set_fingerprint_data(self):
+        """Set fingerprint data from the agent if available."""
         if hasattr(self.agent, "fingerprint") and self.agent.fingerprint:
             self.source_fingerprint = self.agent.fingerprint.uuid_str
             self.source_type = "agent"
@@ -74,6 +82,7 @@ class AgentExecutionErrorEvent(BaseEvent):
                 and self.agent.fingerprint.metadata
             ):
                 self.fingerprint_metadata = self.agent.fingerprint.metadata
+        return self
 
 
 # New event classes for LiteAgent
@@ -104,26 +113,6 @@ class LiteAgentExecutionErrorEvent(BaseEvent):
     type: str = "lite_agent_execution_error"
 
 
-# New logging events
-class AgentLogsStartedEvent(BaseEvent):
-    """Event emitted when agent logs should be shown at start"""
-
-    agent_role: str
-    task_description: Optional[str] = None
-    verbose: bool = False
-    type: str = "agent_logs_started"
-
-
-class AgentLogsExecutionEvent(BaseEvent):
-    """Event emitted when agent logs should be shown during execution"""
-
-    agent_role: str
-    formatted_answer: Any
-    verbose: bool = False
-    type: str = "agent_logs_execution"
-
-    model_config = {"arbitrary_types_allowed": True}
-
 # Agent Eval events
 class AgentEvaluationStartedEvent(BaseEvent):
     agent_id: str
@@ -132,6 +121,7 @@ class AgentEvaluationStartedEvent(BaseEvent):
     iteration: int
     type: str = "agent_evaluation_started"
 
+
 class AgentEvaluationCompletedEvent(BaseEvent):
     agent_id: str
     agent_role: str
@@ -141,6 +131,7 @@ class AgentEvaluationCompletedEvent(BaseEvent):
     score: Any
     type: str = "agent_evaluation_completed"
 
+
 class AgentEvaluationFailedEvent(BaseEvent):
     agent_id: str
     agent_role: str

src/crewai/events/types/crew_events.py

@@ -1,6 +1,6 @@
 from typing import TYPE_CHECKING, Any, Dict, Optional, Union
 
-from crewai.utilities.events.base_events import BaseEvent
+from crewai.events.base_events import BaseEvent
 
 if TYPE_CHECKING:
     from crewai.crew import Crew

src/crewai/events/types/flow_events.py

@@ -2,7 +2,7 @@ from typing import Any, Dict, Optional, Union
 
 from pydantic import BaseModel, ConfigDict
 
-from .base_events import BaseEvent
+from crewai.events.base_events import BaseEvent
 
 
 class FlowEvent(BaseEvent):

src/crewai/events/types/knowledge_events.py

@@ -1,10 +1,6 @@
-from typing import TYPE_CHECKING, Any
+from crewai.events.base_events import BaseEvent
 
 from crewai.agents.agent_builder.base_agent import BaseAgent
-from crewai.utilities.events.base_events import BaseEvent
-
-if TYPE_CHECKING:
-    from crewai.agents.agent_builder.base_agent import BaseAgent
 
 
 class KnowledgeRetrievalStartedEvent(BaseEvent):
@@ -20,7 +16,7 @@ class KnowledgeRetrievalCompletedEvent(BaseEvent):
     query: str
     type: str = "knowledge_search_query_completed"
     agent: BaseAgent
-    retrieved_knowledge: Any
+    retrieved_knowledge: str
 
 
 class KnowledgeQueryStartedEvent(BaseEvent):

src/crewai/events/types/llm_events.py

@@ -3,7 +3,7 @@ from typing import Any, Dict, List, Optional, Union
 
 from pydantic import BaseModel
 
-from crewai.utilities.events.base_events import BaseEvent
+from crewai.events.base_events import BaseEvent
 
 
 class LLMEventBase(BaseEvent):
@@ -13,26 +13,14 @@ class LLMEventBase(BaseEvent):
     agent_id: Optional[str] = None
     agent_role: Optional[str] = None
 
+    from_task: Optional[Any] = None
+    from_agent: Optional[Any] = None
+
     def __init__(self, **data):
         super().__init__(**data)
         self._set_agent_params(data)
         self._set_task_params(data)
 
-    def _set_agent_params(self, data: Dict[str, Any]):
-        task = data.get("from_task", None)
-        agent = task.agent if task else data.get("from_agent", None)
-
-        if not agent:
-            return
-
-        self.agent_id = agent.id
-        self.agent_role = agent.role
-
-    def _set_task_params(self, data: Dict[str, Any]):
-        if "from_task" in data and (task := data["from_task"]):
-            self.task_id = task.id
-            self.task_name = task.name
-
 
 class LLMCallType(Enum):
     """Type of LLM call being made"""

src/crewai/events/types/llm_guardrail_events.py

@@ -1,7 +1,7 @@
 from inspect import getsource
 from typing import Any, Callable, Optional, Union
 
-from crewai.utilities.events.base_events import BaseEvent
+from crewai.events.base_events import BaseEvent
 
 
 class LLMGuardrailStartedEvent(BaseEvent):

src/crewai/events/types/logging_events.py

@@ -0,0 +1,25 @@
+"""Agent logging events that don't reference BaseAgent to avoid circular imports."""
+
+from typing import Any, Optional
+
+from crewai.events.base_events import BaseEvent
+
+
+class AgentLogsStartedEvent(BaseEvent):
+    """Event emitted when agent logs should be shown at start"""
+
+    agent_role: str
+    task_description: Optional[str] = None
+    verbose: bool = False
+    type: str = "agent_logs_started"
+
+
+class AgentLogsExecutionEvent(BaseEvent):
+    """Event emitted when agent logs should be shown during execution"""
+
+    agent_role: str
+    formatted_answer: Any
+    verbose: bool = False
+    type: str = "agent_logs_execution"
+
+    model_config = {"arbitrary_types_allowed": True}

src/crewai/events/types/memory_events.py

@@ -1,9 +1,26 @@
 from typing import Any, Dict, Optional
 
-from crewai.utilities.events.base_events import BaseEvent
+from crewai.events.base_events import BaseEvent
 
 
-class MemoryQueryStartedEvent(BaseEvent):
+class MemoryBaseEvent(BaseEvent):
+    """Base event for memory operations"""
+
+    type: str
+    task_id: Optional[str] = None
+    task_name: Optional[str] = None
+    from_task: Optional[Any] = None
+    from_agent: Optional[Any] = None
+    agent_role: Optional[str] = None
+    agent_id: Optional[str] = None
+
+    def __init__(self, **data):
+        super().__init__(**data)
+        self._set_agent_params(data)
+        self._set_task_params(data)
+
+
+class MemoryQueryStartedEvent(MemoryBaseEvent):
     """Event emitted when a memory query is started"""
 
     type: str = "memory_query_started"
@@ -12,7 +29,7 @@ class MemoryQueryStartedEvent(BaseEvent):
     score_threshold: Optional[float] = None
 
 
-class MemoryQueryCompletedEvent(BaseEvent):
+class MemoryQueryCompletedEvent(MemoryBaseEvent):
     """Event emitted when a memory query is completed successfully"""
 
     type: str = "memory_query_completed"
@@ -23,7 +40,7 @@ class MemoryQueryCompletedEvent(BaseEvent):
     query_time_ms: float
 
 
-class MemoryQueryFailedEvent(BaseEvent):
+class MemoryQueryFailedEvent(MemoryBaseEvent):
     """Event emitted when a memory query fails"""
 
     type: str = "memory_query_failed"
@@ -33,7 +50,7 @@ class MemoryQueryFailedEvent(BaseEvent):
     error: str
 
 
-class MemorySaveStartedEvent(BaseEvent):
+class MemorySaveStartedEvent(MemoryBaseEvent):
     """Event emitted when a memory save operation is started"""
 
     type: str = "memory_save_started"
@@ -42,7 +59,7 @@ class MemorySaveStartedEvent(BaseEvent):
     agent_role: Optional[str] = None
 
 
-class MemorySaveCompletedEvent(BaseEvent):
+class MemorySaveCompletedEvent(MemoryBaseEvent):
     """Event emitted when a memory save operation is completed successfully"""
 
     type: str = "memory_save_completed"
@@ -52,7 +69,7 @@ class MemorySaveCompletedEvent(BaseEvent):
     save_time_ms: float
 
 
-class MemorySaveFailedEvent(BaseEvent):
+class MemorySaveFailedEvent(MemoryBaseEvent):
     """Event emitted when a memory save operation fails"""
 
     type: str = "memory_save_failed"
@@ -62,14 +79,14 @@ class MemorySaveFailedEvent(BaseEvent):
     error: str
 
 
-class MemoryRetrievalStartedEvent(BaseEvent):
+class MemoryRetrievalStartedEvent(MemoryBaseEvent):
     """Event emitted when memory retrieval for a task prompt starts"""
 
     type: str = "memory_retrieval_started"
     task_id: Optional[str] = None
 
 
-class MemoryRetrievalCompletedEvent(BaseEvent):
+class MemoryRetrievalCompletedEvent(MemoryBaseEvent):
     """Event emitted when memory retrieval for a task prompt completes successfully"""
 
     type: str = "memory_retrieval_completed"

src/crewai/events/types/reasoning_events.py

@@ -0,0 +1,47 @@
+from crewai.events.base_events import BaseEvent
+from typing import Any, Optional
+
+
+class ReasoningEvent(BaseEvent):
+    """Base event for reasoning events."""
+
+    type: str
+    attempt: int = 1
+    agent_role: str
+    task_id: str
+    task_name: Optional[str] = None
+    from_task: Optional[Any] = None
+    agent_id: Optional[str] = None
+    from_agent: Optional[Any] = None
+
+    def __init__(self, **data):
+        super().__init__(**data)
+        self._set_task_params(data)
+        self._set_agent_params(data)
+
+
+class AgentReasoningStartedEvent(ReasoningEvent):
+    """Event emitted when an agent starts reasoning about a task."""
+
+    type: str = "agent_reasoning_started"
+    agent_role: str
+    task_id: str
+
+
+class AgentReasoningCompletedEvent(ReasoningEvent):
+    """Event emitted when an agent finishes its reasoning process."""
+
+    type: str = "agent_reasoning_completed"
+    agent_role: str
+    task_id: str
+    plan: str
+    ready: bool
+
+
+class AgentReasoningFailedEvent(ReasoningEvent):
+    """Event emitted when the reasoning process fails."""
+
+    type: str = "agent_reasoning_failed"
+    agent_role: str
+    task_id: str
+    error: str

src/crewai/events/types/task_events.py

@@ -1,7 +1,7 @@
 from typing import Any, Optional
 
+from crewai.events.base_events import BaseEvent
 from crewai.tasks.task_output import TaskOutput
-from crewai.utilities.events.base_events import BaseEvent
 
 
 class TaskStartedEvent(BaseEvent):
@@ -11,14 +11,15 @@ class TaskStartedEvent(BaseEvent):
     context: Optional[str]
     task: Optional[Any] = None
 
-    def __init__(self, **data):
+    def __init__(self, **data: Any) -> None:
         super().__init__(**data)
         # Set fingerprint data from the task
-        if hasattr(self.task, "fingerprint") and self.task.fingerprint:
+        if self.task and hasattr(self.task, "fingerprint") and self.task.fingerprint:
             self.source_fingerprint = self.task.fingerprint.uuid_str
             self.source_type = "task"
             if (
-                hasattr(self.task.fingerprint, "metadata")
+                self.task
+                and hasattr(self.task.fingerprint, "metadata")
                 and self.task.fingerprint.metadata
             ):
                 self.fingerprint_metadata = self.task.fingerprint.metadata
@@ -31,14 +32,15 @@ class TaskCompletedEvent(BaseEvent):
     type: str = "task_completed"
     task: Optional[Any] = None
 
-    def __init__(self, **data):
+    def __init__(self, **data: Any) -> None:
         super().__init__(**data)
         # Set fingerprint data from the task
-        if hasattr(self.task, "fingerprint") and self.task.fingerprint:
+        if self.task and hasattr(self.task, "fingerprint") and self.task.fingerprint:
             self.source_fingerprint = self.task.fingerprint.uuid_str
             self.source_type = "task"
             if (
-                hasattr(self.task.fingerprint, "metadata")
+                self.task
+                and hasattr(self.task.fingerprint, "metadata")
                 and self.task.fingerprint.metadata
             ):
                 self.fingerprint_metadata = self.task.fingerprint.metadata
@@ -51,14 +53,15 @@ class TaskFailedEvent(BaseEvent):
     type: str = "task_failed"
     task: Optional[Any] = None
 
-    def __init__(self, **data):
+    def __init__(self, **data: Any) -> None:
         super().__init__(**data)
         # Set fingerprint data from the task
-        if hasattr(self.task, "fingerprint") and self.task.fingerprint:
+        if self.task and hasattr(self.task, "fingerprint") and self.task.fingerprint:
             self.source_fingerprint = self.task.fingerprint.uuid_str
             self.source_type = "task"
             if (
-                hasattr(self.task.fingerprint, "metadata")
+                self.task
+                and hasattr(self.task.fingerprint, "metadata")
                 and self.task.fingerprint.metadata
             ):
                 self.fingerprint_metadata = self.task.fingerprint.metadata
@@ -71,14 +74,15 @@ class TaskEvaluationEvent(BaseEvent):
     evaluation_type: str
     task: Optional[Any] = None
 
-    def __init__(self, **data):
+    def __init__(self, **data: Any) -> None:
         super().__init__(**data)
         # Set fingerprint data from the task
-        if hasattr(self.task, "fingerprint") and self.task.fingerprint:
+        if self.task and hasattr(self.task, "fingerprint") and self.task.fingerprint:
             self.source_fingerprint = self.task.fingerprint.uuid_str
             self.source_type = "task"
             if (
-                hasattr(self.task.fingerprint, "metadata")
+                self.task
+                and hasattr(self.task.fingerprint, "metadata")
                 and self.task.fingerprint.metadata
             ):
                 self.fingerprint_metadata = self.task.fingerprint.metadata

src/crewai/events/types/tool_usage_events.py

@@ -1,7 +1,7 @@
 from datetime import datetime
 from typing import Any, Callable, Dict, Optional
 
-from .base_events import BaseEvent
+from crewai.events.base_events import BaseEvent
 
 
 class ToolUsageEvent(BaseEvent):
@@ -9,17 +9,24 @@ class ToolUsageEvent(BaseEvent):
 
     agent_key: Optional[str] = None
     agent_role: Optional[str] = None
+    agent_id: Optional[str] = None
     tool_name: str
     tool_args: Dict[str, Any] | str
     tool_class: Optional[str] = None
     run_attempts: int | None = None
     delegations: int | None = None
     agent: Optional[Any] = None
+    task_name: Optional[str] = None
+    task_id: Optional[str] = None
+    from_task: Optional[Any] = None
+    from_agent: Optional[Any] = None
 
     model_config = {"arbitrary_types_allowed": True}
 
     def __init__(self, **data):
         super().__init__(**data)
+        self._set_agent_params(data)
+        self._set_task_params(data)
         # Set fingerprint data from the agent
         if self.agent and hasattr(self.agent, "fingerprint") and self.agent.fingerprint:
             self.source_fingerprint = self.agent.fingerprint.uuid_str

src/crewai/events/utils/console_formatter.py

@@ -227,7 +227,7 @@ class ConsoleFormatter:
             return None
 
         task_content = Text()
-        
+
         # Display task name if available, otherwise just the ID
         if task_name:
             task_content.append("📋 Task: ", style="yellow bold")
@@ -235,7 +235,7 @@ class ConsoleFormatter:
             task_content.append(f" (ID: {task_id})", style="yellow dim")
         else:
             task_content.append(f"📋 Task: {task_id}", style="yellow bold")
-            
+
         task_content.append("\nStatus: ", style="white")
         task_content.append("Executing Task...", style="yellow dim")
 

src/crewai/experimental/evaluation/agent_evaluator.py

@@ -1,28 +1,42 @@
 import threading
-from typing import Any
+from typing import Any, Optional
 
-from crewai.experimental.evaluation.base_evaluator import AgentEvaluationResult, AggregationStrategy
+from crewai.experimental.evaluation.base_evaluator import (
+    AgentEvaluationResult,
+    AggregationStrategy,
+)
 from crewai.agent import Agent
 from crewai.task import Task
 from crewai.experimental.evaluation.evaluation_display import EvaluationDisplayFormatter
-from crewai.utilities.events.agent_events import AgentEvaluationStartedEvent, AgentEvaluationCompletedEvent, AgentEvaluationFailedEvent
+from crewai.events.types.agent_events import (
+    AgentEvaluationStartedEvent,
+    AgentEvaluationCompletedEvent,
+    AgentEvaluationFailedEvent,
+)
 from crewai.experimental.evaluation import BaseEvaluator, create_evaluation_callbacks
 from collections.abc import Sequence
-from crewai.utilities.events.crewai_event_bus import crewai_event_bus
-from crewai.utilities.events.utils.console_formatter import ConsoleFormatter
-from crewai.utilities.events.task_events import TaskCompletedEvent
-from crewai.utilities.events.agent_events import LiteAgentExecutionCompletedEvent
-from crewai.experimental.evaluation.base_evaluator import AgentAggregatedEvaluationResult, EvaluationScore, MetricCategory
+from crewai.events.event_bus import crewai_event_bus
+from crewai.events.utils.console_formatter import ConsoleFormatter
+from crewai.events.types.task_events import TaskCompletedEvent
+from crewai.events.types.agent_events import LiteAgentExecutionCompletedEvent
+from crewai.experimental.evaluation.base_evaluator import (
+    AgentAggregatedEvaluationResult,
+    EvaluationScore,
+    MetricCategory,
+)
+
 
 class ExecutionState:
+    current_agent_id: Optional[str] = None
+    current_task_id: Optional[str] = None
+
     def __init__(self):
         self.traces = {}
-        self.current_agent_id: str | None = None
-        self.current_task_id: str | None = None
         self.iteration = 1
         self.iterations_results = {}
         self.agent_evaluators = {}
 
+
 class AgentEvaluator:
     def __init__(
         self,
@@ -45,27 +59,45 @@ class AgentEvaluator:
 
     @property
     def _execution_state(self) -> ExecutionState:
-        if not hasattr(self._thread_local, 'execution_state'):
+        if not hasattr(self._thread_local, "execution_state"):
             self._thread_local.execution_state = ExecutionState()
         return self._thread_local.execution_state
 
     def _subscribe_to_events(self) -> None:
         from typing import cast
-        crewai_event_bus.register_handler(TaskCompletedEvent, cast(Any, self._handle_task_completed))
-        crewai_event_bus.register_handler(LiteAgentExecutionCompletedEvent, cast(Any, self._handle_lite_agent_completed))
+
+        crewai_event_bus.register_handler(
+            TaskCompletedEvent, cast(Any, self._handle_task_completed)
+        )
+        crewai_event_bus.register_handler(
+            LiteAgentExecutionCompletedEvent,
+            cast(Any, self._handle_lite_agent_completed),
+        )
 
     def _handle_task_completed(self, source: Any, event: TaskCompletedEvent) -> None:
         assert event.task is not None
         agent = event.task.agent
-        if agent and str(getattr(agent, 'id', 'unknown')) in self._execution_state.agent_evaluators:
-            self.emit_evaluation_started_event(agent_role=agent.role, agent_id=str(agent.id), task_id=str(event.task.id))
+        if (
+            agent
+            and str(getattr(agent, "id", "unknown"))
+            in self._execution_state.agent_evaluators
+        ):
+            self.emit_evaluation_started_event(
+                agent_role=agent.role,
+                agent_id=str(agent.id),
+                task_id=str(event.task.id),
+            )
 
             state = ExecutionState()
             state.current_agent_id = str(agent.id)
             state.current_task_id = str(event.task.id)
 
-            assert state.current_agent_id is not None and state.current_task_id is not None
-            trace = self.callback.get_trace(state.current_agent_id, state.current_task_id)
+            assert (
+                state.current_agent_id is not None and state.current_task_id is not None
+            )
+            trace = self.callback.get_trace(
+                state.current_agent_id, state.current_task_id
+            )
 
             if not trace:
                 return
@@ -75,19 +107,28 @@ class AgentEvaluator:
                 task=event.task,
                 execution_trace=trace,
                 final_output=event.output,
-                state=state
+                state=state,
             )
 
             current_iteration = self._execution_state.iteration
             if current_iteration not in self._execution_state.iterations_results:
                 self._execution_state.iterations_results[current_iteration] = {}
 
-            if agent.role not in self._execution_state.iterations_results[current_iteration]:
-                self._execution_state.iterations_results[current_iteration][agent.role] = []
+            if (
+                agent.role
+                not in self._execution_state.iterations_results[current_iteration]
+            ):
+                self._execution_state.iterations_results[current_iteration][
+                    agent.role
+                ] = []
 
-            self._execution_state.iterations_results[current_iteration][agent.role].append(result)
+            self._execution_state.iterations_results[current_iteration][
+                agent.role
+            ].append(result)
 
-    def _handle_lite_agent_completed(self, source: object, event: LiteAgentExecutionCompletedEvent) -> None:
+    def _handle_lite_agent_completed(
+        self, source: object, event: LiteAgentExecutionCompletedEvent
+    ) -> None:
         agent_info = event.agent_info
         agent_id = str(agent_info["id"])
 
@@ -105,8 +146,12 @@ class AgentEvaluator:
             if not target_agent:
                 return
 
-            assert state.current_agent_id is not None and state.current_task_id is not None
-            trace = self.callback.get_trace(state.current_agent_id, state.current_task_id)
+            assert (
+                state.current_agent_id is not None and state.current_task_id is not None
+            )
+            trace = self.callback.get_trace(
+                state.current_agent_id, state.current_task_id
+            )
 
             if not trace:
                 return
@@ -115,7 +160,7 @@ class AgentEvaluator:
                 agent=target_agent,
                 execution_trace=trace,
                 final_output=event.output,
-                state=state
+                state=state,
             )
 
             current_iteration = self._execution_state.iteration
@@ -123,10 +168,17 @@ class AgentEvaluator:
                 self._execution_state.iterations_results[current_iteration] = {}
 
             agent_role = target_agent.role
-            if agent_role not in self._execution_state.iterations_results[current_iteration]:
-                self._execution_state.iterations_results[current_iteration][agent_role] = []
+            if (
+                agent_role
+                not in self._execution_state.iterations_results[current_iteration]
+            ):
+                self._execution_state.iterations_results[current_iteration][
+                    agent_role
+                ] = []
 
-            self._execution_state.iterations_results[current_iteration][agent_role].append(result)
+            self._execution_state.iterations_results[current_iteration][
+                agent_role
+            ].append(result)
 
     def set_iteration(self, iteration: int) -> None:
         self._execution_state.iteration = iteration
@@ -135,14 +187,26 @@ class AgentEvaluator:
         self._execution_state.iterations_results = {}
 
     def get_evaluation_results(self) -> dict[str, list[AgentEvaluationResult]]:
-        if self._execution_state.iterations_results and self._execution_state.iteration in self._execution_state.iterations_results:
-            return self._execution_state.iterations_results[self._execution_state.iteration]
+        if (
+            self._execution_state.iterations_results
+            and self._execution_state.iteration
+            in self._execution_state.iterations_results
+        ):
+            return self._execution_state.iterations_results[
+                self._execution_state.iteration
+            ]
         return {}
 
     def display_results_with_iterations(self) -> None:
-        self.display_formatter.display_summary_results(self._execution_state.iterations_results)
+        self.display_formatter.display_summary_results(
+            self._execution_state.iterations_results
+        )
 
-    def get_agent_evaluation(self, strategy: AggregationStrategy = AggregationStrategy.SIMPLE_AVERAGE, include_evaluation_feedback: bool = True) -> dict[str, AgentAggregatedEvaluationResult]:
+    def get_agent_evaluation(
+        self,
+        strategy: AggregationStrategy = AggregationStrategy.SIMPLE_AVERAGE,
+        include_evaluation_feedback: bool = True,
+    ) -> dict[str, AgentAggregatedEvaluationResult]:
         agent_results = {}
         with crewai_event_bus.scoped_handlers():
             task_results = self.get_evaluation_results()
@@ -156,13 +220,16 @@ class AgentEvaluator:
                     agent_id=agent_id,
                     agent_role=agent_role,
                     results=results,
-                    strategy=strategy
+                    strategy=strategy,
                 )
 
                 agent_results[agent_role] = aggregated_result
 
-
-            if self._execution_state.iterations_results and self._execution_state.iteration == max(self._execution_state.iterations_results.keys(), default=0):
+            if (
+                self._execution_state.iterations_results
+                and self._execution_state.iteration
+                == max(self._execution_state.iterations_results.keys(), default=0)
+            ):
                 self.display_results_with_iterations()
 
             if include_evaluation_feedback:
@@ -171,7 +238,9 @@ class AgentEvaluator:
         return agent_results
 
     def display_evaluation_with_feedback(self) -> None:
-        self.display_formatter.display_evaluation_with_feedback(self._execution_state.iterations_results)
+        self.display_formatter.display_evaluation_with_feedback(
+            self._execution_state.iterations_results
+        )
 
     def evaluate(
         self,
@@ -183,46 +252,91 @@ class AgentEvaluator:
     ) -> AgentEvaluationResult:
         result = AgentEvaluationResult(
             agent_id=state.current_agent_id or str(agent.id),
-            task_id=state.current_task_id or (str(task.id) if task else "unknown_task")
+            task_id=state.current_task_id or (str(task.id) if task else "unknown_task"),
         )
 
         assert self.evaluators is not None
         task_id = str(task.id) if task else None
         for evaluator in self.evaluators:
             try:
-                self.emit_evaluation_started_event(agent_role=agent.role, agent_id=str(agent.id), task_id=task_id)
+                self.emit_evaluation_started_event(
+                    agent_role=agent.role, agent_id=str(agent.id), task_id=task_id
+                )
                 score = evaluator.evaluate(
                     agent=agent,
                     task=task,
                     execution_trace=execution_trace,
-                    final_output=final_output
+                    final_output=final_output,
                 )
                 result.metrics[evaluator.metric_category] = score
-                self.emit_evaluation_completed_event(agent_role=agent.role, agent_id=str(agent.id), task_id=task_id, metric_category=evaluator.metric_category, score=score)
+                self.emit_evaluation_completed_event(
+                    agent_role=agent.role,
+                    agent_id=str(agent.id),
+                    task_id=task_id,
+                    metric_category=evaluator.metric_category,
+                    score=score,
+                )
             except Exception as e:
-                self.emit_evaluation_failed_event(agent_role=agent.role, agent_id=str(agent.id), task_id=task_id, error=str(e))
-                self.console_formatter.print(f"Error in {evaluator.metric_category.value} evaluator: {str(e)}")
+                self.emit_evaluation_failed_event(
+                    agent_role=agent.role,
+                    agent_id=str(agent.id),
+                    task_id=task_id,
+                    error=str(e),
+                )
+                self.console_formatter.print(
+                    f"Error in {evaluator.metric_category.value} evaluator: {str(e)}"
+                )
 
         return result
 
-    def emit_evaluation_started_event(self, agent_role: str, agent_id: str, task_id: str | None = None):
+    def emit_evaluation_started_event(
+        self, agent_role: str, agent_id: str, task_id: str | None = None
+    ):
         crewai_event_bus.emit(
             self,
-            AgentEvaluationStartedEvent(agent_role=agent_role, agent_id=agent_id, task_id=task_id, iteration=self._execution_state.iteration)
+            AgentEvaluationStartedEvent(
+                agent_role=agent_role,
+                agent_id=agent_id,
+                task_id=task_id,
+                iteration=self._execution_state.iteration,
+            ),
         )
 
-    def emit_evaluation_completed_event(self, agent_role: str, agent_id: str, task_id: str | None = None, metric_category: MetricCategory | None = None, score: EvaluationScore | None = None):
+    def emit_evaluation_completed_event(
+        self,
+        agent_role: str,
+        agent_id: str,
+        task_id: str | None = None,
+        metric_category: MetricCategory | None = None,
+        score: EvaluationScore | None = None,
+    ):
         crewai_event_bus.emit(
             self,
-            AgentEvaluationCompletedEvent(agent_role=agent_role, agent_id=agent_id, task_id=task_id, iteration=self._execution_state.iteration, metric_category=metric_category, score=score)
+            AgentEvaluationCompletedEvent(
+                agent_role=agent_role,
+                agent_id=agent_id,
+                task_id=task_id,
+                iteration=self._execution_state.iteration,
+                metric_category=metric_category,
+                score=score,
+            ),
         )
 
-    def emit_evaluation_failed_event(self, agent_role: str, agent_id: str, error: str, task_id: str | None = None):
+    def emit_evaluation_failed_event(
+        self, agent_role: str, agent_id: str, error: str, task_id: str | None = None
+    ):
         crewai_event_bus.emit(
             self,
-            AgentEvaluationFailedEvent(agent_role=agent_role, agent_id=agent_id, task_id=task_id, iteration=self._execution_state.iteration, error=error)
+            AgentEvaluationFailedEvent(
+                agent_role=agent_role,
+                agent_id=agent_id,
+                task_id=task_id,
+                iteration=self._execution_state.iteration,
+                error=error,
+            ),
         )
 
+
 def create_default_evaluator(agents: list[Agent], llm: None = None):
     from crewai.experimental.evaluation import (
         GoalAlignmentEvaluator,
@@ -230,7 +344,7 @@ def create_default_evaluator(agents: list[Agent], llm: None = None):
         ToolSelectionEvaluator,
         ParameterExtractionEvaluator,
         ToolInvocationEvaluator,
-        ReasoningEfficiencyEvaluator
+        ReasoningEfficiencyEvaluator,
     )
 
     evaluators = [

src/crewai/experimental/evaluation/base_evaluator.py

@@ -1,14 +1,15 @@
 import abc
 import enum
 from enum import Enum
-from typing import Any, Dict, List, Optional
+from typing import Any, Optional
 
 from pydantic import BaseModel, Field
 
 from crewai.agent import Agent
+from crewai.llms.base_llm import BaseLLM
 from crewai.task import Task
-from crewai.llm import BaseLLM
-from crewai.utilities.llm_utils import create_llm
+from crewai.utilities.llm_utils import create_default_llm, create_llm
+
 
 class MetricCategory(enum.Enum):
     GOAL_ALIGNMENT = "goal_alignment"
@@ -18,8 +19,8 @@ class MetricCategory(enum.Enum):
     PARAMETER_EXTRACTION = "parameter_extraction"
     TOOL_INVOCATION = "tool_invocation"
 
-    def title(self):
-        return self.value.replace('_', ' ').title()
+    def title(self) -> str:
+        return self.value.replace("_", " ").title()
 
 
 class EvaluationScore(BaseModel):
@@ -27,15 +28,13 @@ class EvaluationScore(BaseModel):
         default=5.0,
         description="Numeric score from 0-10 where 0 is worst and 10 is best, None if not applicable",
         ge=0.0,
-        le=10.0
+        le=10.0,
     )
     feedback: str = Field(
-        default="",
-        description="Detailed feedback explaining the evaluation score"
+        default="", description="Detailed feedback explaining the evaluation score"
     )
     raw_response: str | None = Field(
-        default=None,
-        description="Raw response from the evaluator (e.g., LLM)"
+        default=None, description="Raw response from the evaluator (e.g., LLM)"
     )
 
     def __str__(self) -> str:
@@ -46,7 +45,9 @@ class EvaluationScore(BaseModel):
 
 class BaseEvaluator(abc.ABC):
     def __init__(self, llm: BaseLLM | None = None):
-        self.llm: BaseLLM | None = create_llm(llm)
+        self.llm: BaseLLM | None = (
+            create_llm(llm) if llm is not None else create_default_llm()
+        )
 
     @property
     @abc.abstractmethod
@@ -57,7 +58,7 @@ class BaseEvaluator(abc.ABC):
     def evaluate(
         self,
         agent: Agent,
-        execution_trace: Dict[str, Any],
+        execution_trace: dict[str, Any],
         final_output: Any,
         task: Task | None = None,
     ) -> EvaluationScore:
@@ -67,9 +68,8 @@ class BaseEvaluator(abc.ABC):
 class AgentEvaluationResult(BaseModel):
     agent_id: str = Field(description="ID of the evaluated agent")
     task_id: str = Field(description="ID of the task that was executed")
-    metrics: Dict[MetricCategory, EvaluationScore] = Field(
-        default_factory=dict,
-        description="Evaluation scores for each metric category"
+    metrics: dict[MetricCategory, EvaluationScore] = Field(
+        default_factory=dict, description="Evaluation scores for each metric category"
     )
 
 
@@ -81,33 +81,23 @@ class AggregationStrategy(Enum):
 
 
 class AgentAggregatedEvaluationResult(BaseModel):
-    agent_id: str = Field(
-        default="",
-        description="ID of the agent"
-    )
-    agent_role: str = Field(
-        default="",
-        description="Role of the agent"
-    )
+    agent_id: str = Field(default="", description="ID of the agent")
+    agent_role: str = Field(default="", description="Role of the agent")
     task_count: int = Field(
-        default=0,
-        description="Number of tasks included in this aggregation"
+        default=0, description="Number of tasks included in this aggregation"
     )
     aggregation_strategy: AggregationStrategy = Field(
         default=AggregationStrategy.SIMPLE_AVERAGE,
-        description="Strategy used for aggregation"
+        description="Strategy used for aggregation",
     )
-    metrics: Dict[MetricCategory, EvaluationScore] = Field(
-        default_factory=dict,
-        description="Aggregated metrics across all tasks"
+    metrics: dict[MetricCategory, EvaluationScore] = Field(
+        default_factory=dict, description="Aggregated metrics across all tasks"
     )
-    task_results: List[str] = Field(
-        default_factory=list,
-        description="IDs of tasks included in this aggregation"
+    task_results: list[str] = Field(
+        default_factory=list, description="IDs of tasks included in this aggregation"
     )
     overall_score: Optional[float] = Field(
-        default=None,
-        description="Overall score for this agent"
+        default=None, description="Overall score for this agent"
     )
 
     def __str__(self) -> str:
@@ -119,7 +109,7 @@ class AgentAggregatedEvaluationResult(BaseModel):
             result += f"\n\n- {category.value.upper()}: {score.score}/10\n"
 
             if score.feedback:
-                detailed_feedback = "\n  ".join(score.feedback.split('\n'))
+                detailed_feedback = "\n  ".join(score.feedback.split("\n"))
                 result += f"  {detailed_feedback}\n"
 
-        return result
+        return result

src/crewai/experimental/evaluation/evaluation_display.py

@@ -3,18 +3,28 @@ from typing import Dict, Any, List
 from rich.table import Table
 from rich.box import HEAVY_EDGE, ROUNDED
 from collections.abc import Sequence
-from crewai.experimental.evaluation.base_evaluator import AgentAggregatedEvaluationResult, AggregationStrategy, AgentEvaluationResult, MetricCategory
+from crewai.experimental.evaluation.base_evaluator import (
+    AgentAggregatedEvaluationResult,
+    AggregationStrategy,
+    AgentEvaluationResult,
+    MetricCategory,
+)
 from crewai.experimental.evaluation import EvaluationScore
-from crewai.utilities.events.utils.console_formatter import ConsoleFormatter
+from crewai.events.utils.console_formatter import ConsoleFormatter
 from crewai.utilities.llm_utils import create_llm
 
+
 class EvaluationDisplayFormatter:
     def __init__(self):
         self.console_formatter = ConsoleFormatter()
 
-    def display_evaluation_with_feedback(self, iterations_results: Dict[int, Dict[str, List[Any]]]):
+    def display_evaluation_with_feedback(
+        self, iterations_results: Dict[int, Dict[str, List[Any]]]
+    ):
         if not iterations_results:
-            self.console_formatter.print("[yellow]No evaluation results to display[/yellow]")
+            self.console_formatter.print(
+                "[yellow]No evaluation results to display[/yellow]"
+            )
             return
 
         all_agent_roles: set[str] = set()
@@ -22,7 +32,9 @@ class EvaluationDisplayFormatter:
             all_agent_roles.update(iter_results.keys())
 
         for agent_role in sorted(all_agent_roles):
-            self.console_formatter.print(f"\n[bold cyan]Agent: {agent_role}[/bold cyan]")
+            self.console_formatter.print(
+                f"\n[bold cyan]Agent: {agent_role}[/bold cyan]"
+            )
 
             for iter_num, results in sorted(iterations_results.items()):
                 if agent_role not in results or not results[agent_role]:
@@ -62,9 +74,7 @@ class EvaluationDisplayFormatter:
 
                         table.add_section()
                         table.add_row(
-                            metric.title(),
-                            score_text,
-                            evaluation_score.feedback or ""
+                            metric.title(), score_text, evaluation_score.feedback or ""
                         )
 
                 if aggregated_result.overall_score is not None:
@@ -82,19 +92,26 @@ class EvaluationDisplayFormatter:
                     table.add_row(
                         "Overall Score",
                         f"[{overall_color}]{overall_score:.1f}[/]",
-                        "Overall agent evaluation score"
+                        "Overall agent evaluation score",
                     )
 
                 self.console_formatter.print(table)
 
-    def display_summary_results(self, iterations_results: Dict[int, Dict[str, List[AgentAggregatedEvaluationResult]]]):
+    def display_summary_results(
+        self,
+        iterations_results: Dict[int, Dict[str, List[AgentAggregatedEvaluationResult]]],
+    ):
         if not iterations_results:
-            self.console_formatter.print("[yellow]No evaluation results to display[/yellow]")
+            self.console_formatter.print(
+                "[yellow]No evaluation results to display[/yellow]"
+            )
             return
 
         self.console_formatter.print("\n")
 
-        table = Table(title="Agent Performance Scores \n (1-10 Higher is better)", box=HEAVY_EDGE)
+        table = Table(
+            title="Agent Performance Scores \n (1-10 Higher is better)", box=HEAVY_EDGE
+        )
 
         table.add_column("Agent/Metric", style="cyan")
 
@@ -123,11 +140,14 @@ class EvaluationDisplayFormatter:
                     agent_id=agent_id,
                     agent_role=agent_role,
                     results=agent_results,
-                    strategy=AggregationStrategy.SIMPLE_AVERAGE
+                    strategy=AggregationStrategy.SIMPLE_AVERAGE,
                 )
 
-                valid_scores = [score.score for score in aggregated_result.metrics.values()
-                               if score.score is not None]
+                valid_scores = [
+                    score.score
+                    for score in aggregated_result.metrics.values()
+                    if score.score is not None
+                ]
                 if valid_scores:
                     avg_score = sum(valid_scores) / len(valid_scores)
                     agent_scores_by_iteration[iter_num] = avg_score
@@ -137,7 +157,9 @@ class EvaluationDisplayFormatter:
             if not agent_scores_by_iteration:
                 continue
 
-            avg_across_iterations = sum(agent_scores_by_iteration.values()) / len(agent_scores_by_iteration)
+            avg_across_iterations = sum(agent_scores_by_iteration.values()) / len(
+                agent_scores_by_iteration
+            )
 
             row = [f"[bold]{agent_role}[/bold]"]
 
@@ -178,9 +200,13 @@ class EvaluationDisplayFormatter:
                 row = [f"  - {metric.title()}"]
 
                 for iter_num in sorted(iterations_results.keys()):
-                    if (iter_num in agent_metrics_by_iteration and
-                            metric in agent_metrics_by_iteration[iter_num]):
-                        metric_score = agent_metrics_by_iteration[iter_num][metric].score
+                    if (
+                        iter_num in agent_metrics_by_iteration
+                        and metric in agent_metrics_by_iteration[iter_num]
+                    ):
+                        metric_score = agent_metrics_by_iteration[iter_num][
+                            metric
+                        ].score
                         if metric_score is not None:
                             metric_scores.append(metric_score)
                             if metric_score >= 8.0:
@@ -225,7 +251,9 @@ class EvaluationDisplayFormatter:
         results: Sequence[AgentEvaluationResult],
         strategy: AggregationStrategy = AggregationStrategy.SIMPLE_AVERAGE,
     ) -> AgentAggregatedEvaluationResult:
-        metrics_by_category: dict[MetricCategory, list[EvaluationScore]] = defaultdict(list)
+        metrics_by_category: dict[MetricCategory, list[EvaluationScore]] = defaultdict(
+            list
+        )
 
         for result in results:
             for metric_name, evaluation_score in result.metrics.items():
@@ -246,19 +274,20 @@ class EvaluationDisplayFormatter:
                         metric=category.title(),
                         feedbacks=feedbacks,
                         scores=[s.score for s in scores],
-                        strategy=strategy
+                        strategy=strategy,
                     )
                 else:
                     feedback_summary = feedbacks[0]
 
             aggregated_metrics[category] = EvaluationScore(
-                score=avg_score,
-                feedback=feedback_summary
+                score=avg_score, feedback=feedback_summary
             )
 
         overall_score = None
         if aggregated_metrics:
-            valid_scores = [m.score for m in aggregated_metrics.values() if m.score is not None]
+            valid_scores = [
+                m.score for m in aggregated_metrics.values() if m.score is not None
+            ]
             if valid_scores:
                 overall_score = sum(valid_scores) / len(valid_scores)
 
@@ -268,7 +297,7 @@ class EvaluationDisplayFormatter:
             metrics=aggregated_metrics,
             overall_score=overall_score,
             task_count=len(results),
-            aggregation_strategy=strategy
+            aggregation_strategy=strategy,
         )
 
     def _summarize_feedbacks(
@@ -277,10 +306,12 @@ class EvaluationDisplayFormatter:
         metric: str,
         feedbacks: List[str],
         scores: List[float | None],
-        strategy: AggregationStrategy
+        strategy: AggregationStrategy,
     ) -> str:
         if len(feedbacks) <= 2 and all(len(fb) < 200 for fb in feedbacks):
-            return "\n\n".join([f"Feedback {i+1}: {fb}" for i, fb in enumerate(feedbacks)])
+            return "\n\n".join(
+                [f"Feedback {i+1}: {fb}" for i, fb in enumerate(feedbacks)]
+            )
 
         try:
             llm = create_llm()
@@ -290,20 +321,26 @@ class EvaluationDisplayFormatter:
                 if len(feedback) > 500:
                     feedback = feedback[:500] + "..."
                 score_text = f"{score:.1f}" if score is not None else "N/A"
-                formatted_feedbacks.append(f"Feedback #{i+1} (Score: {score_text}):\n{feedback}")
+                formatted_feedbacks.append(
+                    f"Feedback #{i+1} (Score: {score_text}):\n{feedback}"
+                )
 
             all_feedbacks = "\n\n" + "\n\n---\n\n".join(formatted_feedbacks)
 
             strategy_guidance = ""
             if strategy == AggregationStrategy.BEST_PERFORMANCE:
-                strategy_guidance = "Focus on the highest-scoring aspects and strengths demonstrated."
+                strategy_guidance = (
+                    "Focus on the highest-scoring aspects and strengths demonstrated."
+                )
             elif strategy == AggregationStrategy.WORST_PERFORMANCE:
                 strategy_guidance = "Focus on areas that need improvement and common issues across tasks."
             else:
                 strategy_guidance = "Provide a balanced analysis of strengths and weaknesses across all tasks."
 
             prompt = [
-                {"role": "system", "content": f"""You are an expert evaluator creating a comprehensive summary of agent performance feedback.
+                {
+                    "role": "system",
+                    "content": f"""You are an expert evaluator creating a comprehensive summary of agent performance feedback.
                 Your job is to synthesize multiple feedback points about the same metric across different tasks.
 
                 Create a concise, insightful summary that captures the key patterns and themes from all feedback.
@@ -315,14 +352,18 @@ class EvaluationDisplayFormatter:
                 3. Highlighting patterns across tasks
                 4. 150-250 words in length
 
-                The summary should be directly usable as final feedback for the agent's performance on this metric."""},
-                {"role": "user", "content": f"""I need a synthesized summary of the following feedback for:
+                The summary should be directly usable as final feedback for the agent's performance on this metric.""",
+                },
+                {
+                    "role": "user",
+                    "content": f"""I need a synthesized summary of the following feedback for:
 
                 Agent Role: {agent_role}
                 Metric: {metric.title()}
 
                 {all_feedbacks}
-                """}
+                """,
+                },
             ]
             assert llm is not None
             response = llm.call(prompt)
@@ -330,4 +371,6 @@ class EvaluationDisplayFormatter:
             return response
 
         except Exception:
-            return "Synthesized from multiple tasks: " + "\n\n".join([f"- {fb[:500]}..." for fb in feedbacks])
+            return "Synthesized from multiple tasks: " + "\n\n".join(
+                [f"- {fb[:500]}..." for fb in feedbacks]
+            )

src/crewai/experimental/evaluation/evaluation_listener.py

@@ -5,25 +5,23 @@ from collections.abc import Sequence
 
 from crewai.agent import Agent
 from crewai.task import Task
-from crewai.utilities.events.base_event_listener import BaseEventListener
-from crewai.utilities.events.crewai_event_bus import CrewAIEventsBus
-from crewai.utilities.events.agent_events import (
+from crewai.events.base_event_listener import BaseEventListener
+from crewai.events.event_bus import CrewAIEventsBus
+from crewai.events.types.agent_events import (
     AgentExecutionStartedEvent,
     AgentExecutionCompletedEvent,
     LiteAgentExecutionStartedEvent,
-    LiteAgentExecutionCompletedEvent
+    LiteAgentExecutionCompletedEvent,
 )
-from crewai.utilities.events.tool_usage_events import (
+from crewai.events.types.tool_usage_events import (
     ToolUsageFinishedEvent,
     ToolUsageErrorEvent,
     ToolExecutionErrorEvent,
     ToolSelectionErrorEvent,
-    ToolValidateInputErrorEvent
-)
-from crewai.utilities.events.llm_events import (
-    LLMCallStartedEvent,
-    LLMCallCompletedEvent
+    ToolValidateInputErrorEvent,
 )
+from crewai.events.types.llm_events import LLMCallStartedEvent, LLMCallCompletedEvent
+
 
 class EvaluationTraceCallback(BaseEventListener):
     """Event listener for collecting execution traces for evaluation.
@@ -68,27 +66,49 @@ class EvaluationTraceCallback(BaseEventListener):
 
         @event_bus.on(ToolUsageFinishedEvent)
         def on_tool_completed(source, event: ToolUsageFinishedEvent):
-            self.on_tool_use(event.tool_name, event.tool_args, event.output, success=True)
+            self.on_tool_use(
+                event.tool_name, event.tool_args, event.output, success=True
+            )
 
         @event_bus.on(ToolUsageErrorEvent)
         def on_tool_usage_error(source, event: ToolUsageErrorEvent):
-            self.on_tool_use(event.tool_name, event.tool_args, event.error,
-                           success=False, error_type="usage_error")
+            self.on_tool_use(
+                event.tool_name,
+                event.tool_args,
+                event.error,
+                success=False,
+                error_type="usage_error",
+            )
 
         @event_bus.on(ToolExecutionErrorEvent)
         def on_tool_execution_error(source, event: ToolExecutionErrorEvent):
-            self.on_tool_use(event.tool_name, event.tool_args, event.error,
-                           success=False, error_type="execution_error")
+            self.on_tool_use(
+                event.tool_name,
+                event.tool_args,
+                event.error,
+                success=False,
+                error_type="execution_error",
+            )
 
         @event_bus.on(ToolSelectionErrorEvent)
         def on_tool_selection_error(source, event: ToolSelectionErrorEvent):
-            self.on_tool_use(event.tool_name, event.tool_args, event.error,
-                           success=False, error_type="selection_error")
+            self.on_tool_use(
+                event.tool_name,
+                event.tool_args,
+                event.error,
+                success=False,
+                error_type="selection_error",
+            )
 
         @event_bus.on(ToolValidateInputErrorEvent)
         def on_tool_validate_input_error(source, event: ToolValidateInputErrorEvent):
-            self.on_tool_use(event.tool_name, event.tool_args, event.error,
-                           success=False, error_type="validation_error")
+            self.on_tool_use(
+                event.tool_name,
+                event.tool_args,
+                event.error,
+                success=False,
+                error_type="validation_error",
+            )
 
         @event_bus.on(LLMCallStartedEvent)
         def on_llm_call_started(source, event: LLMCallStartedEvent):
@@ -99,7 +119,7 @@ class EvaluationTraceCallback(BaseEventListener):
             self.on_llm_call_end(event.messages, event.response)
 
     def on_lite_agent_start(self, agent_info: dict[str, Any]):
-        self.current_agent_id = agent_info['id']
+        self.current_agent_id = agent_info["id"]
         self.current_task_id = "lite_task"
 
         trace_key = f"{self.current_agent_id}_{self.current_task_id}"
@@ -110,7 +130,7 @@ class EvaluationTraceCallback(BaseEventListener):
             tool_uses=[],
             llm_calls=[],
             start_time=datetime.now(),
-            final_output=None
+            final_output=None,
         )
 
     def _init_trace(self, trace_key: str, **kwargs: Any):
@@ -128,7 +148,7 @@ class EvaluationTraceCallback(BaseEventListener):
             tool_uses=[],
             llm_calls=[],
             start_time=datetime.now(),
-            final_output=None
+            final_output=None,
         )
 
     def on_agent_finish(self, agent: Agent, task: Task, output: Any):
@@ -151,8 +171,14 @@ class EvaluationTraceCallback(BaseEventListener):
 
         self._reset_current()
 
-    def on_tool_use(self, tool_name: str, tool_args: dict[str, Any] | str, result: Any,
-                   success: bool = True, error_type: str | None = None):
+    def on_tool_use(
+        self,
+        tool_name: str,
+        tool_args: dict[str, Any] | str,
+        result: Any,
+        success: bool = True,
+        error_type: str | None = None,
+    ):
         if not self.current_agent_id or not self.current_task_id:
             return
 
@@ -163,7 +189,7 @@ class EvaluationTraceCallback(BaseEventListener):
                 "args": tool_args,
                 "result": result,
                 "success": success,
-                "timestamp": datetime.now()
+                "timestamp": datetime.now(),
             }
 
             # Add error information if applicable
@@ -173,7 +199,11 @@ class EvaluationTraceCallback(BaseEventListener):
 
             self.traces[trace_key]["tool_uses"].append(tool_use)
 
-    def on_llm_call_start(self, messages: str | Sequence[dict[str, Any]] | None, tools: Sequence[dict[str, Any]] | None = None):
+    def on_llm_call_start(
+        self,
+        messages: str | Sequence[dict[str, Any]] | None,
+        tools: Sequence[dict[str, Any]] | None = None,
+    ):
         if not self.current_agent_id or not self.current_task_id:
             return
 
@@ -186,10 +216,12 @@ class EvaluationTraceCallback(BaseEventListener):
             "tools": tools,
             "start_time": datetime.now(),
             "response": None,
-            "end_time": None
+            "end_time": None,
         }
 
-    def on_llm_call_end(self, messages: str | list[dict[str, Any]] | None, response: Any):
+    def on_llm_call_end(
+        self, messages: str | list[dict[str, Any]] | None, response: Any
+    ):
         if not self.current_agent_id or not self.current_task_id:
             return
 
@@ -213,7 +245,7 @@ class EvaluationTraceCallback(BaseEventListener):
             "response": response,
             "start_time": start_time,
             "end_time": current_time,
-            "total_tokens": total_tokens
+            "total_tokens": total_tokens,
         }
 
         self.traces[trace_key]["llm_calls"].append(llm_call)
@@ -227,7 +259,7 @@ class EvaluationTraceCallback(BaseEventListener):
 
 
 def create_evaluation_callbacks() -> EvaluationTraceCallback:
-    from crewai.utilities.events.crewai_event_bus import crewai_event_bus
+    from crewai.events.event_bus import crewai_event_bus
 
     callback = EvaluationTraceCallback()
     callback.setup_listeners(crewai_event_bus)

src/crewai/flow/flow.py

@@ -25,8 +25,8 @@ from crewai.flow.flow_visualizer import plot_flow
 from crewai.flow.persistence.base import FlowPersistence
 from crewai.flow.types import FlowExecutionData
 from crewai.flow.utils import get_possible_return_constants
-from crewai.utilities.events.crewai_event_bus import crewai_event_bus
-from crewai.utilities.events.flow_events import (
+from crewai.events.event_bus import crewai_event_bus
+from crewai.events.types.flow_events import (
     FlowCreatedEvent,
     FlowFinishedEvent,
     FlowPlotEvent,
@@ -35,10 +35,10 @@ from crewai.utilities.events.flow_events import (
     MethodExecutionFinishedEvent,
     MethodExecutionStartedEvent,
 )
-from crewai.utilities.events.listeners.tracing.trace_listener import (
+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,
 )
 from crewai.utilities.printer import Printer
@@ -474,6 +474,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
         self._method_outputs: List[Any] = []  # List to store all method outputs
         self._completed_methods: Set[str] = set()  # Track completed methods for reload
         self._persistence: Optional[FlowPersistence] = persistence
+        self._is_execution_resuming: bool = False
 
         # Initialize state with initial values
         self._state = self._create_initial_state()
@@ -829,6 +830,9 @@ class Flow(Generic[T], metaclass=FlowMeta):
                 # Clear completed methods and outputs for a fresh start
                 self._completed_methods.clear()
                 self._method_outputs.clear()
+            else:
+                # We're restoring from persistence, set the flag
+                self._is_execution_resuming = True
 
             if inputs:
                 # Override the id in the state if it exists in inputs
@@ -880,6 +884,9 @@ class Flow(Generic[T], metaclass=FlowMeta):
             ]
             await asyncio.gather(*tasks)
 
+            # Clear the resumption flag after initial execution completes
+            self._is_execution_resuming = False
+
             final_output = self._method_outputs[-1] if self._method_outputs else None
 
             crewai_event_bus.emit(
@@ -916,19 +923,23 @@ class Flow(Generic[T], metaclass=FlowMeta):
         - Automatically injects crewai_trigger_payload if available in flow inputs
         """
         if start_method_name in self._completed_methods:
-            last_output = self._method_outputs[-1] if self._method_outputs else None
-            await self._execute_listeners(start_method_name, last_output)
-            return
+            if self._is_execution_resuming:
+                # During resumption, skip execution but continue listeners
+                last_output = self._method_outputs[-1] if self._method_outputs else None
+                await self._execute_listeners(start_method_name, last_output)
+                return
+            # For cyclic flows, clear from completed to allow re-execution
+            self._completed_methods.discard(start_method_name)
 
         method = self._methods[start_method_name]
         enhanced_method = self._inject_trigger_payload_for_start_method(method)
 
-        result = await self._execute_method(
-            start_method_name, enhanced_method
-        )
+        result = await self._execute_method(start_method_name, enhanced_method)
         await self._execute_listeners(start_method_name, result)
 
-    def _inject_trigger_payload_for_start_method(self, original_method: Callable) -> Callable:
+    def _inject_trigger_payload_for_start_method(
+        self, original_method: Callable
+    ) -> Callable:
         def prepare_kwargs(*args, **kwargs):
             inputs = baggage.get_baggage("flow_inputs") or {}
             trigger_payload = inputs.get("crewai_trigger_payload")
@@ -941,15 +952,17 @@ class Flow(Generic[T], metaclass=FlowMeta):
             elif trigger_payload is not None:
                 self._log_flow_event(
                     f"Trigger payload available but {original_method.__name__} doesn't accept crewai_trigger_payload parameter",
-                    color="yellow"
+                    color="yellow",
                 )
             return args, kwargs
 
         if asyncio.iscoroutinefunction(original_method):
+
             async def enhanced_method(*args, **kwargs):
                 args, kwargs = prepare_kwargs(*args, **kwargs)
                 return await original_method(*args, **kwargs)
         else:
+
             def enhanced_method(*args, **kwargs):
                 args, kwargs = prepare_kwargs(*args, **kwargs)
                 return original_method(*args, **kwargs)
@@ -1050,11 +1063,15 @@ class Flow(Generic[T], metaclass=FlowMeta):
             for router_name in routers_triggered:
                 await self._execute_single_listener(router_name, result)
                 # After executing router, the router's result is the path
-                router_result = self._method_outputs[-1]
+                router_result = (
+                    self._method_outputs[-1] if self._method_outputs else None
+                )
                 if router_result:  # Only add non-None results
                     router_results.append(router_result)
                 current_trigger = (
-                    router_result  # Update for next iteration of router chain
+                    str(router_result)
+                    if router_result is not None
+                    else ""  # Update for next iteration of router chain
                 )
 
         # Now execute normal listeners for all router results and the original trigger
@@ -1072,6 +1089,24 @@ class Flow(Generic[T], metaclass=FlowMeta):
                     ]
                     await asyncio.gather(*tasks)
 
+                if current_trigger in router_results:
+                    # Find start methods triggered by this router result
+                    for method_name in self._start_methods:
+                        # Check if this start method is triggered by the current trigger
+                        if method_name in self._listeners:
+                            condition_type, trigger_methods = self._listeners[
+                                method_name
+                            ]
+                            if current_trigger in trigger_methods:
+                                # Only execute if this is a cycle (method was already completed)
+                                if method_name in self._completed_methods:
+                                    # For router-triggered start methods in cycles, temporarily clear resumption flag
+                                    # to allow cyclic execution
+                                    was_resuming = self._is_execution_resuming
+                                    self._is_execution_resuming = False
+                                    await self._execute_start_method(method_name)
+                                    self._is_execution_resuming = was_resuming
+
     def _find_triggered_methods(
         self, trigger_method: str, router_only: bool
     ) -> List[str]:
@@ -1109,6 +1144,9 @@ class Flow(Generic[T], metaclass=FlowMeta):
             if router_only != is_router:
                 continue
 
+            if not router_only and listener_name in self._start_methods:
+                continue
+
             if condition_type == "OR":
                 # If the trigger_method matches any in methods, run this
                 if trigger_method in methods:
@@ -1158,10 +1196,13 @@ class Flow(Generic[T], metaclass=FlowMeta):
         Catches and logs any exceptions during execution, preventing
         individual listener failures from breaking the entire flow.
         """
-        # TODO: greyson fix
-        # if listener_name in self._completed_methods:
-        #     await self._execute_listeners(listener_name, None)
-        #     return
+        if listener_name in self._completed_methods:
+            if self._is_execution_resuming:
+                # During resumption, skip execution but continue listeners
+                await self._execute_listeners(listener_name, None)
+                return
+            # For cyclic flows, clear from completed to allow re-execution
+            self._completed_methods.discard(listener_name)
 
         try:
             method = self._methods[listener_name]

src/crewai/knowledge/knowledge.py

@@ -1,5 +1,5 @@
 import os
-from typing import Any, Dict, List, Optional
+from typing import Any, Optional
 
 from pydantic import BaseModel, ConfigDict, Field
 
@@ -18,20 +18,20 @@ class Knowledge(BaseModel):
         embedder: Optional[Dict[str, Any]] = None
     """
 
-    sources: List[BaseKnowledgeSource] = Field(default_factory=list)
+    sources: list[BaseKnowledgeSource] = Field(default_factory=list)
     model_config = ConfigDict(arbitrary_types_allowed=True)
     storage: Optional[KnowledgeStorage] = Field(default=None)
-    embedder: Optional[Dict[str, Any]] = None
+    embedder: Optional[dict[str, Any]] = None
     collection_name: Optional[str] = None
 
     def __init__(
         self,
         collection_name: str,
-        sources: List[BaseKnowledgeSource],
-        embedder: Optional[Dict[str, Any]] = None,
+        sources: list[BaseKnowledgeSource],
+        embedder: Optional[dict[str, Any]] = None,
         storage: Optional[KnowledgeStorage] = None,
-        **data,
-    ):
+        **data: Any,
+    ) -> None:
         super().__init__(**data)
         if storage:
             self.storage = storage
@@ -43,8 +43,8 @@ class Knowledge(BaseModel):
         self.storage.initialize_knowledge_storage()
 
     def query(
-        self, query: List[str], results_limit: int = 3, score_threshold: float = 0.35
-    ) -> List[Dict[str, Any]]:
+        self, query: list[str], results_limit: int = 3, score_threshold: float = 0.35
+    ) -> list[dict[str, Any]]:
         """
         Query across all knowledge sources to find the most relevant information.
         Returns the top_k most relevant chunks.
@@ -62,7 +62,7 @@ class Knowledge(BaseModel):
         )
         return results
 
-    def add_sources(self):
+    def add_sources(self) -> None:
         try:
             for source in self.sources:
                 source.storage = self.storage