return message list for liteagent kickoff response

- Updated the `crewai-tools` dependency in `pyproject.toml` and `uv.lock` to version 0.76.0.
- Updated the `crewai` version in `__init__.py` to 0.203.0.
- Updated the dependency versions in the crew, flow, and tool templates to reflect the new `crewai` version.

.github/codeql/codeql-config.yml

@@ -0,0 +1,21 @@
+name: "CodeQL Config"
+
+paths-ignore:
+  # Ignore template files - these are boilerplate code that shouldn't be analyzed
+  - "src/crewai/cli/templates/**"
+  # Ignore test cassettes - these are test fixtures/recordings
+  - "tests/cassettes/**"
+  # Ignore cache and build artifacts
+  - ".cache/**"
+  # Ignore documentation build artifacts
+  - "docs/.cache/**"
+  
+paths:
+  # Include all Python source code
+  - "src/**"
+  # Include tests (but exclude cassettes)
+  - "tests/**"
+
+# Configure specific queries or packs if needed
+# queries:
+#   - uses: security-and-quality

.github/workflows/build-uv-cache.yml

@@ -7,6 +7,8 @@ on:
     paths:
       - "uv.lock"
       - "pyproject.toml"
+  schedule:
+    - cron: "0 0 */5 * *"  # Run every 5 days at midnight UTC to prevent cache expiration
   workflow_dispatch:
 
 permissions:

.github/workflows/codeql.yml

@@ -73,6 +73,7 @@ jobs:
       with:
         languages: ${{ matrix.language }}
         build-mode: ${{ matrix.build-mode }}
+        config-file: ./.github/codeql/codeql-config.yml
         # If you wish to specify custom queries, you can do so here or in a config file.
         # By default, queries listed here will override any specified in a config file.
         # Prefix the list here with "+" to use these queries and those in the config file.

docs/docs.json

@@ -397,6 +397,7 @@
                   "en/enterprise/guides/kickoff-crew",
                   "en/enterprise/guides/update-crew",
                   "en/enterprise/guides/enable-crew-studio",
+                  "en/enterprise/guides/capture_telemetry_logs",
                   "en/enterprise/guides/azure-openai-setup",
                   "en/enterprise/guides/tool-repository",
                   "en/enterprise/guides/react-component-export",
@@ -421,6 +422,7 @@
                   "en/api-reference/introduction",
                   "en/api-reference/inputs",
                   "en/api-reference/kickoff",
+                  "en/api-reference/resume",
                   "en/api-reference/status"
                 ]
               }
@@ -827,6 +829,7 @@
                   "pt-BR/api-reference/introduction",
                   "pt-BR/api-reference/inputs",
                   "pt-BR/api-reference/kickoff",
+                  "pt-BR/api-reference/resume",
                   "pt-BR/api-reference/status"
                 ]
               }
@@ -1239,6 +1242,7 @@
                   "ko/api-reference/introduction",
                   "ko/api-reference/inputs",
                   "ko/api-reference/kickoff",
+                  "ko/api-reference/resume",
                   "ko/api-reference/status"
                 ]
               }

docs/en/api-reference/resume.mdx

@@ -0,0 +1,6 @@
+---
+title: "POST /resume"
+description: "Resume crew execution with human feedback"
+openapi: "/enterprise-api.en.yaml POST /resume"
+mode: "wide"
+---

docs/en/enterprise/guides/capture_telemetry_logs.mdx

@@ -0,0 +1,35 @@
+---
+title: "Open Telemetry Logs"
+description: "Understand how to capture telemetry logs from your CrewAI AMP deployments"
+icon: "magnifying-glass-chart"
+mode: "wide"
+---
+
+CrewAI AMP provides a powerful way to capture telemetry logs from your deployments. This allows you to monitor the performance of your agents and workflows, and to debug issues that may arise.
+
+## Prerequisites
+
+<CardGroup cols={2}>
+  <Card title="ENTERPRISE OTEL SETUP enabled" icon="users">
+    Your organization should have ENTERPRISE OTEL SETUP enabled
+  </Card>
+  <Card title="OTEL collector setup" icon="server">
+    Your organization should have an OTEL collector setup or a provider like Datadog log intake setup
+  </Card>
+</CardGroup>
+
+
+## How to capture telemetry logs
+
+1. Go to settings/organization tab
+2. Configure your OTEL collector setup
+3. Save
+
+
+
+Example to setup OTEL log collection capture to Datadog.
+
+
+<Frame>
+  ![Capture Telemetry Logs](/images/crewai-otel-export.png)
+</Frame>

docs/en/enterprise/guides/human-in-the-loop.mdx

@@ -40,6 +40,28 @@ Human-In-The-Loop (HITL) is a powerful approach that combines artificial intelli
         <Frame>
             <img src="/images/enterprise/crew-resume-endpoint.png" alt="Crew Resume Endpoint" />
         </Frame>
+
+        <Warning>
+            **Critical: Webhook URLs Must Be Provided Again**:
+            You **must** provide the same webhook URLs (`taskWebhookUrl`, `stepWebhookUrl`, `crewWebhookUrl`) in the resume call that you used in the kickoff call. Webhook configurations are **NOT** automatically carried over from kickoff - they must be explicitly included in the resume request to continue receiving notifications for task completion, agent steps, and crew completion.
+        </Warning>
+
+        Example resume call with webhooks:
+        ```bash
+        curl -X POST {BASE_URL}/resume \
+          -H "Authorization: Bearer YOUR_API_TOKEN" \
+          -H "Content-Type: application/json" \
+          -d '{
+            "execution_id": "abcd1234-5678-90ef-ghij-klmnopqrstuv",
+            "task_id": "research_task",
+            "human_feedback": "Great work! Please add more details.",
+            "is_approve": true,
+            "taskWebhookUrl": "https://your-server.com/webhooks/task",
+            "stepWebhookUrl": "https://your-server.com/webhooks/step",
+            "crewWebhookUrl": "https://your-server.com/webhooks/crew"
+          }'
+        ```
+
         <Warning>
             **Feedback Impact on Task Execution**:
             It's crucial to exercise care when providing feedback, as the entire feedback content will be incorporated as additional context for further task executions.
@@ -76,4 +98,4 @@ HITL workflows are particularly valuable for:
 - Complex decision-making scenarios
 - Sensitive or high-stakes operations
 - Creative tasks requiring human judgment
-- Compliance and regulatory reviews 
+- Compliance and regulatory reviews

docs/en/learn/human-in-the-loop.mdx

@@ -79,6 +79,28 @@ Human-in-the-Loop (HITL) is a powerful approach that combines artificial intelli
         <Frame>
             <img src="/images/enterprise/crew-resume-endpoint.png" alt="Crew Resume Endpoint" />
         </Frame>
+
+        <Warning>
+            **Critical: Webhook URLs Must Be Provided Again**:
+            You **must** provide the same webhook URLs (`taskWebhookUrl`, `stepWebhookUrl`, `crewWebhookUrl`) in the resume call that you used in the kickoff call. Webhook configurations are **NOT** automatically carried over from kickoff - they must be explicitly included in the resume request to continue receiving notifications for task completion, agent steps, and crew completion.
+        </Warning>
+
+        Example resume call with webhooks:
+        ```bash
+        curl -X POST {BASE_URL}/resume \
+          -H "Authorization: Bearer YOUR_API_TOKEN" \
+          -H "Content-Type: application/json" \
+          -d '{
+            "execution_id": "abcd1234-5678-90ef-ghij-klmnopqrstuv",
+            "task_id": "research_task",
+            "human_feedback": "Great work! Please add more details.",
+            "is_approve": true,
+            "taskWebhookUrl": "https://your-server.com/webhooks/task",
+            "stepWebhookUrl": "https://your-server.com/webhooks/step",
+            "crewWebhookUrl": "https://your-server.com/webhooks/crew"
+          }'
+        ```
+
         <Warning>
             **Feedback Impact on Task Execution**:
             It's crucial to exercise care when providing feedback, as the entire feedback content will be incorporated as additional context for further task executions.

docs/enterprise-api.base.yaml

@@ -276,6 +276,134 @@ paths:
         '500':
           $ref: '#/components/responses/ServerError'
 
+  /resume:
+    post:
+      summary: Resume Crew Execution with Human Feedback
+      description: |
+        **📋 Reference Example Only** - *This shows the request format. To test with your actual crew, copy the cURL example and replace the URL + token with your real values.*
+
+        Resume a paused crew execution with human feedback for Human-in-the-Loop (HITL) workflows.
+        When a task with `human_input=True` completes, the crew execution pauses and waits for human feedback.
+
+        **IMPORTANT**: You must provide the same webhook URLs (`taskWebhookUrl`, `stepWebhookUrl`, `crewWebhookUrl`)
+        that were used in the original kickoff call. Webhook configurations are NOT automatically carried over -
+        they must be explicitly provided in the resume request to continue receiving notifications.
+      operationId: resumeCrewExecution
+      requestBody:
+        required: true
+        content:
+          application/json:
+            schema:
+              type: object
+              required:
+                - execution_id
+                - task_id
+                - human_feedback
+                - is_approve
+              properties:
+                execution_id:
+                  type: string
+                  format: uuid
+                  description: The unique identifier for the crew execution (from kickoff)
+                  example: "abcd1234-5678-90ef-ghij-klmnopqrstuv"
+                task_id:
+                  type: string
+                  description: The ID of the task that requires human feedback
+                  example: "research_task"
+                human_feedback:
+                  type: string
+                  description: Your feedback on the task output. This will be incorporated as additional context for subsequent task executions.
+                  example: "Great research! Please add more details about recent developments in the field."
+                is_approve:
+                  type: boolean
+                  description: "Whether you approve the task output: true = positive feedback (continue), false = negative feedback (retry task)"
+                  example: true
+                taskWebhookUrl:
+                  type: string
+                  format: uri
+                  description: Callback URL executed after each task completion. MUST be provided to continue receiving task notifications.
+                  example: "https://your-server.com/webhooks/task"
+                stepWebhookUrl:
+                  type: string
+                  format: uri
+                  description: Callback URL executed after each agent thought/action. MUST be provided to continue receiving step notifications.
+                  example: "https://your-server.com/webhooks/step"
+                crewWebhookUrl:
+                  type: string
+                  format: uri
+                  description: Callback URL executed when the crew execution completes. MUST be provided to receive completion notification.
+                  example: "https://your-server.com/webhooks/crew"
+            examples:
+              approve_and_continue:
+                summary: Approve task and continue execution
+                value:
+                  execution_id: "abcd1234-5678-90ef-ghij-klmnopqrstuv"
+                  task_id: "research_task"
+                  human_feedback: "Excellent research! Proceed to the next task."
+                  is_approve: true
+                  taskWebhookUrl: "https://api.example.com/webhooks/task"
+                  stepWebhookUrl: "https://api.example.com/webhooks/step"
+                  crewWebhookUrl: "https://api.example.com/webhooks/crew"
+              request_revision:
+                summary: Request task revision with feedback
+                value:
+                  execution_id: "abcd1234-5678-90ef-ghij-klmnopqrstuv"
+                  task_id: "analysis_task"
+                  human_feedback: "Please include more quantitative data and cite your sources."
+                  is_approve: false
+                  taskWebhookUrl: "https://api.example.com/webhooks/task"
+                  crewWebhookUrl: "https://api.example.com/webhooks/crew"
+      responses:
+        '200':
+          description: Execution resumed successfully
+          content:
+            application/json:
+              schema:
+                type: object
+                properties:
+                  status:
+                    type: string
+                    enum: ["resumed", "retrying", "completed"]
+                    description: Status of the resumed execution
+                    example: "resumed"
+                  message:
+                    type: string
+                    description: Human-readable message about the resume operation
+                    example: "Execution resumed successfully"
+              examples:
+                resumed:
+                  summary: Execution resumed with positive feedback
+                  value:
+                    status: "resumed"
+                    message: "Execution resumed successfully"
+                retrying:
+                  summary: Task will be retried with negative feedback
+                  value:
+                    status: "retrying"
+                    message: "Task will be retried with your feedback"
+        '400':
+          description: Invalid request body or execution not in pending state
+          content:
+            application/json:
+              schema:
+                $ref: '#/components/schemas/Error'
+              example:
+                error: "Invalid Request"
+                message: "Execution is not in pending human input state"
+        '401':
+          $ref: '#/components/responses/UnauthorizedError'
+        '404':
+          description: Execution ID or Task ID not found
+          content:
+            application/json:
+              schema:
+                $ref: '#/components/schemas/Error'
+              example:
+                error: "Not Found"
+                message: "Execution ID not found"
+        '500':
+          $ref: '#/components/responses/ServerError'
+
 components:
   securitySchemes:
     BearerAuth:

docs/enterprise-api.en.yaml

@@ -276,6 +276,134 @@ paths:
         '500':
           $ref: '#/components/responses/ServerError'
 
+  /resume:
+    post:
+      summary: Resume Crew Execution with Human Feedback
+      description: |
+        **📋 Reference Example Only** - *This shows the request format. To test with your actual crew, copy the cURL example and replace the URL + token with your real values.*
+
+        Resume a paused crew execution with human feedback for Human-in-the-Loop (HITL) workflows.
+        When a task with `human_input=True` completes, the crew execution pauses and waits for human feedback.
+
+        **IMPORTANT**: You must provide the same webhook URLs (`taskWebhookUrl`, `stepWebhookUrl`, `crewWebhookUrl`)
+        that were used in the original kickoff call. Webhook configurations are NOT automatically carried over -
+        they must be explicitly provided in the resume request to continue receiving notifications.
+      operationId: resumeCrewExecution
+      requestBody:
+        required: true
+        content:
+          application/json:
+            schema:
+              type: object
+              required:
+                - execution_id
+                - task_id
+                - human_feedback
+                - is_approve
+              properties:
+                execution_id:
+                  type: string
+                  format: uuid
+                  description: The unique identifier for the crew execution (from kickoff)
+                  example: "abcd1234-5678-90ef-ghij-klmnopqrstuv"
+                task_id:
+                  type: string
+                  description: The ID of the task that requires human feedback
+                  example: "research_task"
+                human_feedback:
+                  type: string
+                  description: Your feedback on the task output. This will be incorporated as additional context for subsequent task executions.
+                  example: "Great research! Please add more details about recent developments in the field."
+                is_approve:
+                  type: boolean
+                  description: "Whether you approve the task output: true = positive feedback (continue), false = negative feedback (retry task)"
+                  example: true
+                taskWebhookUrl:
+                  type: string
+                  format: uri
+                  description: Callback URL executed after each task completion. MUST be provided to continue receiving task notifications.
+                  example: "https://your-server.com/webhooks/task"
+                stepWebhookUrl:
+                  type: string
+                  format: uri
+                  description: Callback URL executed after each agent thought/action. MUST be provided to continue receiving step notifications.
+                  example: "https://your-server.com/webhooks/step"
+                crewWebhookUrl:
+                  type: string
+                  format: uri
+                  description: Callback URL executed when the crew execution completes. MUST be provided to receive completion notification.
+                  example: "https://your-server.com/webhooks/crew"
+            examples:
+              approve_and_continue:
+                summary: Approve task and continue execution
+                value:
+                  execution_id: "abcd1234-5678-90ef-ghij-klmnopqrstuv"
+                  task_id: "research_task"
+                  human_feedback: "Excellent research! Proceed to the next task."
+                  is_approve: true
+                  taskWebhookUrl: "https://api.example.com/webhooks/task"
+                  stepWebhookUrl: "https://api.example.com/webhooks/step"
+                  crewWebhookUrl: "https://api.example.com/webhooks/crew"
+              request_revision:
+                summary: Request task revision with feedback
+                value:
+                  execution_id: "abcd1234-5678-90ef-ghij-klmnopqrstuv"
+                  task_id: "analysis_task"
+                  human_feedback: "Please include more quantitative data and cite your sources."
+                  is_approve: false
+                  taskWebhookUrl: "https://api.example.com/webhooks/task"
+                  crewWebhookUrl: "https://api.example.com/webhooks/crew"
+      responses:
+        '200':
+          description: Execution resumed successfully
+          content:
+            application/json:
+              schema:
+                type: object
+                properties:
+                  status:
+                    type: string
+                    enum: ["resumed", "retrying", "completed"]
+                    description: Status of the resumed execution
+                    example: "resumed"
+                  message:
+                    type: string
+                    description: Human-readable message about the resume operation
+                    example: "Execution resumed successfully"
+              examples:
+                resumed:
+                  summary: Execution resumed with positive feedback
+                  value:
+                    status: "resumed"
+                    message: "Execution resumed successfully"
+                retrying:
+                  summary: Task will be retried with negative feedback
+                  value:
+                    status: "retrying"
+                    message: "Task will be retried with your feedback"
+        '400':
+          description: Invalid request body or execution not in pending state
+          content:
+            application/json:
+              schema:
+                $ref: '#/components/schemas/Error'
+              example:
+                error: "Invalid Request"
+                message: "Execution is not in pending human input state"
+        '401':
+          $ref: '#/components/responses/UnauthorizedError'
+        '404':
+          description: Execution ID or Task ID not found
+          content:
+            application/json:
+              schema:
+                $ref: '#/components/schemas/Error'
+              example:
+                error: "Not Found"
+                message: "Execution ID not found"
+        '500':
+          $ref: '#/components/responses/ServerError'
+
 components:
   securitySchemes:
     BearerAuth:

docs/enterprise-api.ko.yaml

@@ -120,6 +120,134 @@ paths:
         '500':
           $ref: '#/components/responses/ServerError'
 
+  /resume:
+    post:
+      summary: Resume Crew Execution with Human Feedback
+      description: |
+        **📋 Reference Example Only** - *This shows the request format. To test with your actual crew, copy the cURL example and replace the URL + token with your real values.*
+
+        Resume a paused crew execution with human feedback for Human-in-the-Loop (HITL) workflows.
+        When a task with `human_input=True` completes, the crew execution pauses and waits for human feedback.
+
+        **IMPORTANT**: You must provide the same webhook URLs (`taskWebhookUrl`, `stepWebhookUrl`, `crewWebhookUrl`)
+        that were used in the original kickoff call. Webhook configurations are NOT automatically carried over -
+        they must be explicitly provided in the resume request to continue receiving notifications.
+      operationId: resumeCrewExecution
+      requestBody:
+        required: true
+        content:
+          application/json:
+            schema:
+              type: object
+              required:
+                - execution_id
+                - task_id
+                - human_feedback
+                - is_approve
+              properties:
+                execution_id:
+                  type: string
+                  format: uuid
+                  description: The unique identifier for the crew execution (from kickoff)
+                  example: "abcd1234-5678-90ef-ghij-klmnopqrstuv"
+                task_id:
+                  type: string
+                  description: The ID of the task that requires human feedback
+                  example: "research_task"
+                human_feedback:
+                  type: string
+                  description: Your feedback on the task output. This will be incorporated as additional context for subsequent task executions.
+                  example: "Great research! Please add more details about recent developments in the field."
+                is_approve:
+                  type: boolean
+                  description: "Whether you approve the task output: true = positive feedback (continue), false = negative feedback (retry task)"
+                  example: true
+                taskWebhookUrl:
+                  type: string
+                  format: uri
+                  description: Callback URL executed after each task completion. MUST be provided to continue receiving task notifications.
+                  example: "https://your-server.com/webhooks/task"
+                stepWebhookUrl:
+                  type: string
+                  format: uri
+                  description: Callback URL executed after each agent thought/action. MUST be provided to continue receiving step notifications.
+                  example: "https://your-server.com/webhooks/step"
+                crewWebhookUrl:
+                  type: string
+                  format: uri
+                  description: Callback URL executed when the crew execution completes. MUST be provided to receive completion notification.
+                  example: "https://your-server.com/webhooks/crew"
+            examples:
+              approve_and_continue:
+                summary: Approve task and continue execution
+                value:
+                  execution_id: "abcd1234-5678-90ef-ghij-klmnopqrstuv"
+                  task_id: "research_task"
+                  human_feedback: "Excellent research! Proceed to the next task."
+                  is_approve: true
+                  taskWebhookUrl: "https://api.example.com/webhooks/task"
+                  stepWebhookUrl: "https://api.example.com/webhooks/step"
+                  crewWebhookUrl: "https://api.example.com/webhooks/crew"
+              request_revision:
+                summary: Request task revision with feedback
+                value:
+                  execution_id: "abcd1234-5678-90ef-ghij-klmnopqrstuv"
+                  task_id: "analysis_task"
+                  human_feedback: "Please include more quantitative data and cite your sources."
+                  is_approve: false
+                  taskWebhookUrl: "https://api.example.com/webhooks/task"
+                  crewWebhookUrl: "https://api.example.com/webhooks/crew"
+      responses:
+        '200':
+          description: Execution resumed successfully
+          content:
+            application/json:
+              schema:
+                type: object
+                properties:
+                  status:
+                    type: string
+                    enum: ["resumed", "retrying", "completed"]
+                    description: Status of the resumed execution
+                    example: "resumed"
+                  message:
+                    type: string
+                    description: Human-readable message about the resume operation
+                    example: "Execution resumed successfully"
+              examples:
+                resumed:
+                  summary: Execution resumed with positive feedback
+                  value:
+                    status: "resumed"
+                    message: "Execution resumed successfully"
+                retrying:
+                  summary: Task will be retried with negative feedback
+                  value:
+                    status: "retrying"
+                    message: "Task will be retried with your feedback"
+        '400':
+          description: Invalid request body or execution not in pending state
+          content:
+            application/json:
+              schema:
+                $ref: '#/components/schemas/Error'
+              example:
+                error: "Invalid Request"
+                message: "Execution is not in pending human input state"
+        '401':
+          $ref: '#/components/responses/UnauthorizedError'
+        '404':
+          description: Execution ID or Task ID not found
+          content:
+            application/json:
+              schema:
+                $ref: '#/components/schemas/Error'
+              example:
+                error: "Not Found"
+                message: "Execution ID not found"
+        '500':
+          $ref: '#/components/responses/ServerError'
+
 components:
   securitySchemes:
     BearerAuth:

docs/enterprise-api.pt-BR.yaml

@@ -156,6 +156,134 @@ paths:
         '500':
           $ref: '#/components/responses/ServerError'
 
+  /resume:
+    post:
+      summary: Resume Crew Execution with Human Feedback
+      description: |
+        **📋 Reference Example Only** - *This shows the request format. To test with your actual crew, copy the cURL example and replace the URL + token with your real values.*
+
+        Resume a paused crew execution with human feedback for Human-in-the-Loop (HITL) workflows.
+        When a task with `human_input=True` completes, the crew execution pauses and waits for human feedback.
+
+        **IMPORTANT**: You must provide the same webhook URLs (`taskWebhookUrl`, `stepWebhookUrl`, `crewWebhookUrl`)
+        that were used in the original kickoff call. Webhook configurations are NOT automatically carried over -
+        they must be explicitly provided in the resume request to continue receiving notifications.
+      operationId: resumeCrewExecution
+      requestBody:
+        required: true
+        content:
+          application/json:
+            schema:
+              type: object
+              required:
+                - execution_id
+                - task_id
+                - human_feedback
+                - is_approve
+              properties:
+                execution_id:
+                  type: string
+                  format: uuid
+                  description: The unique identifier for the crew execution (from kickoff)
+                  example: "abcd1234-5678-90ef-ghij-klmnopqrstuv"
+                task_id:
+                  type: string
+                  description: The ID of the task that requires human feedback
+                  example: "research_task"
+                human_feedback:
+                  type: string
+                  description: Your feedback on the task output. This will be incorporated as additional context for subsequent task executions.
+                  example: "Great research! Please add more details about recent developments in the field."
+                is_approve:
+                  type: boolean
+                  description: "Whether you approve the task output: true = positive feedback (continue), false = negative feedback (retry task)"
+                  example: true
+                taskWebhookUrl:
+                  type: string
+                  format: uri
+                  description: Callback URL executed after each task completion. MUST be provided to continue receiving task notifications.
+                  example: "https://your-server.com/webhooks/task"
+                stepWebhookUrl:
+                  type: string
+                  format: uri
+                  description: Callback URL executed after each agent thought/action. MUST be provided to continue receiving step notifications.
+                  example: "https://your-server.com/webhooks/step"
+                crewWebhookUrl:
+                  type: string
+                  format: uri
+                  description: Callback URL executed when the crew execution completes. MUST be provided to receive completion notification.
+                  example: "https://your-server.com/webhooks/crew"
+            examples:
+              approve_and_continue:
+                summary: Approve task and continue execution
+                value:
+                  execution_id: "abcd1234-5678-90ef-ghij-klmnopqrstuv"
+                  task_id: "research_task"
+                  human_feedback: "Excellent research! Proceed to the next task."
+                  is_approve: true
+                  taskWebhookUrl: "https://api.example.com/webhooks/task"
+                  stepWebhookUrl: "https://api.example.com/webhooks/step"
+                  crewWebhookUrl: "https://api.example.com/webhooks/crew"
+              request_revision:
+                summary: Request task revision with feedback
+                value:
+                  execution_id: "abcd1234-5678-90ef-ghij-klmnopqrstuv"
+                  task_id: "analysis_task"
+                  human_feedback: "Please include more quantitative data and cite your sources."
+                  is_approve: false
+                  taskWebhookUrl: "https://api.example.com/webhooks/task"
+                  crewWebhookUrl: "https://api.example.com/webhooks/crew"
+      responses:
+        '200':
+          description: Execution resumed successfully
+          content:
+            application/json:
+              schema:
+                type: object
+                properties:
+                  status:
+                    type: string
+                    enum: ["resumed", "retrying", "completed"]
+                    description: Status of the resumed execution
+                    example: "resumed"
+                  message:
+                    type: string
+                    description: Human-readable message about the resume operation
+                    example: "Execution resumed successfully"
+              examples:
+                resumed:
+                  summary: Execution resumed with positive feedback
+                  value:
+                    status: "resumed"
+                    message: "Execution resumed successfully"
+                retrying:
+                  summary: Task will be retried with negative feedback
+                  value:
+                    status: "retrying"
+                    message: "Task will be retried with your feedback"
+        '400':
+          description: Invalid request body or execution not in pending state
+          content:
+            application/json:
+              schema:
+                $ref: '#/components/schemas/Error'
+              example:
+                error: "Invalid Request"
+                message: "Execution is not in pending human input state"
+        '401':
+          $ref: '#/components/responses/UnauthorizedError'
+        '404':
+          description: Execution ID or Task ID not found
+          content:
+            application/json:
+              schema:
+                $ref: '#/components/schemas/Error'
+              example:
+                error: "Not Found"
+                message: "Execution ID not found"
+        '500':
+          $ref: '#/components/responses/ServerError'
+
 components:
   securitySchemes:
     BearerAuth:

docs/ko/api-reference/resume.mdx

@@ -0,0 +1,6 @@
+---
+title: "POST /resume"
+description: "인간 피드백으로 crew 실행 재개"
+openapi: "/enterprise-api.ko.yaml POST /resume"
+mode: "wide"
+---

docs/ko/enterprise/guides/human-in-the-loop.mdx

@@ -40,6 +40,28 @@ mode: "wide"
         <Frame>
             <img src="/images/enterprise/crew-resume-endpoint.png" alt="Crew Resume Endpoint" />
         </Frame>
+
+        <Warning>
+            **중요: Webhook URL을 다시 제공해야 합니다**:
+            kickoff 호출에서 사용한 것과 동일한 webhook URL(`taskWebhookUrl`, `stepWebhookUrl`, `crewWebhookUrl`)을 resume 호출에서 **반드시** 제공해야 합니다. Webhook 설정은 kickoff에서 자동으로 전달되지 **않으므로**, 작업 완료, 에이전트 단계, crew 완료에 대한 알림을 계속 받으려면 resume 요청에 명시적으로 포함해야 합니다.
+        </Warning>
+
+        Webhook을 포함한 resume 호출 예시:
+        ```bash
+        curl -X POST {BASE_URL}/resume \
+          -H "Authorization: Bearer YOUR_API_TOKEN" \
+          -H "Content-Type: application/json" \
+          -d '{
+            "execution_id": "abcd1234-5678-90ef-ghij-klmnopqrstuv",
+            "task_id": "research_task",
+            "human_feedback": "훌륭한 작업입니다! 더 자세한 내용을 추가해주세요.",
+            "is_approve": true,
+            "taskWebhookUrl": "https://your-server.com/webhooks/task",
+            "stepWebhookUrl": "https://your-server.com/webhooks/step",
+            "crewWebhookUrl": "https://your-server.com/webhooks/crew"
+          }'
+        ```
+
         <Warning>
             **피드백이 작업 실행에 미치는 영향**:
             피드백 전체 내용이 이후 작업 실행을 위한 추가 컨텍스트로 통합되므로 피드백 제공 시 신중함이 매우 중요합니다.
@@ -76,4 +98,4 @@ HITL 워크플로우는 특히 다음과 같은 경우에 유용합니다:
 - 복잡한 의사 결정 시나리오
 - 민감하거나 위험도가 높은 작업
 - 인간의 판단이 필요한 창의적 작업
-- 준수 및 규제 검토
+- 준수 및 규제 검토

docs/ko/learn/human-in-the-loop.mdx

@@ -40,6 +40,28 @@ mode: "wide"
         <Frame>
             <img src="/images/enterprise/crew-resume-endpoint.png" alt="Crew Resume Endpoint" />
         </Frame>
+
+        <Warning>
+            **중요: Webhook URL을 다시 제공해야 합니다**:
+            kickoff 호출에서 사용한 것과 동일한 webhook URL(`taskWebhookUrl`, `stepWebhookUrl`, `crewWebhookUrl`)을 resume 호출에서 **반드시** 제공해야 합니다. Webhook 설정은 kickoff에서 자동으로 전달되지 **않으므로**, 작업 완료, 에이전트 단계, crew 완료에 대한 알림을 계속 받으려면 resume 요청에 명시적으로 포함해야 합니다.
+        </Warning>
+
+        Webhook을 포함한 resume 호출 예시:
+        ```bash
+        curl -X POST {BASE_URL}/resume \
+          -H "Authorization: Bearer YOUR_API_TOKEN" \
+          -H "Content-Type: application/json" \
+          -d '{
+            "execution_id": "abcd1234-5678-90ef-ghij-klmnopqrstuv",
+            "task_id": "research_task",
+            "human_feedback": "훌륭한 작업입니다! 더 자세한 내용을 추가해주세요.",
+            "is_approve": true,
+            "taskWebhookUrl": "https://your-server.com/webhooks/task",
+            "stepWebhookUrl": "https://your-server.com/webhooks/step",
+            "crewWebhookUrl": "https://your-server.com/webhooks/crew"
+          }'
+        ```
+
         <Warning>
             **피드백이 작업 실행에 미치는 영향**:
             피드백의 전체 내용이 추가 컨텍스트로서 이후 작업 실행에 통합되므로, 피드백 제공 시 신중을 기하는 것이 매우 중요합니다.
@@ -76,4 +98,4 @@ HITL 워크플로우는 다음과 같은 경우에 특히 유용합니다:
 - 복잡한 의사결정 시나리오
 - 민감하거나 고위험 작업
 - 인간의 판단이 필요한 창의적 과제
-- 컴플라이언스 및 규제 검토
+- 컴플라이언스 및 규제 검토

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

@@ -0,0 +1,6 @@
+---
+title: "POST /resume"
+description: "Retomar execução do crew com feedback humano"
+openapi: "/enterprise-api.pt-BR.yaml POST /resume"
+mode: "wide"
+---

docs/pt-BR/enterprise/guides/human-in-the-loop.mdx

@@ -40,6 +40,28 @@ Human-In-The-Loop (HITL) é uma abordagem poderosa que combina inteligência art
         <Frame>
             <img src="/images/enterprise/crew-resume-endpoint.png" alt="Crew Resume Endpoint" />
         </Frame>
+
+        <Warning>
+            **Crítico: URLs de Webhook Devem Ser Fornecidas Novamente**:
+            Você **deve** fornecer as mesmas URLs de webhook (`taskWebhookUrl`, `stepWebhookUrl`, `crewWebhookUrl`) na chamada de resume que você usou na chamada de kickoff. As configurações de webhook **NÃO** são automaticamente transferidas do kickoff - elas devem ser explicitamente incluídas na solicitação de resume para continuar recebendo notificações de conclusão de tarefa, etapas do agente e conclusão do crew.
+        </Warning>
+
+        Exemplo de chamada resume com webhooks:
+        ```bash
+        curl -X POST {BASE_URL}/resume \
+          -H "Authorization: Bearer YOUR_API_TOKEN" \
+          -H "Content-Type: application/json" \
+          -d '{
+            "execution_id": "abcd1234-5678-90ef-ghij-klmnopqrstuv",
+            "task_id": "research_task",
+            "human_feedback": "Ótimo trabalho! Por favor, adicione mais detalhes.",
+            "is_approve": true,
+            "taskWebhookUrl": "https://your-server.com/webhooks/task",
+            "stepWebhookUrl": "https://your-server.com/webhooks/step",
+            "crewWebhookUrl": "https://your-server.com/webhooks/crew"
+          }'
+        ```
+
         <Warning>
             **Impacto do Feedback na Execução da Tarefa**:
             É crucial ter cuidado ao fornecer o feedback, pois todo o conteúdo do feedback será incorporado como contexto adicional para as próximas execuções da tarefa.
@@ -76,4 +98,4 @@ Workflows HITL são particularmente valiosos para:
 - Cenários de tomada de decisão complexa
 - Operações sensíveis ou de alto risco
 - Tarefas criativas que exigem julgamento humano
-- Revisões de conformidade e regulatórias
+- Revisões de conformidade e regulatórias

docs/pt-BR/learn/human-in-the-loop.mdx

@@ -40,6 +40,28 @@ Human-in-the-Loop (HITL) é uma abordagem poderosa que combina a inteligência a
         <Frame>
             <img src="/images/enterprise/crew-resume-endpoint.png" alt="Endpoint de Retomada Crew" />
         </Frame>
+
+        <Warning>
+            **Crítico: URLs de Webhook Devem Ser Fornecidas Novamente**:
+            Você **deve** fornecer as mesmas URLs de webhook (`taskWebhookUrl`, `stepWebhookUrl`, `crewWebhookUrl`) na chamada de resume que você usou na chamada de kickoff. As configurações de webhook **NÃO** são automaticamente transferidas do kickoff - elas devem ser explicitamente incluídas na solicitação de resume para continuar recebendo notificações de conclusão de tarefa, etapas do agente e conclusão do crew.
+        </Warning>
+
+        Exemplo de chamada resume com webhooks:
+        ```bash
+        curl -X POST {BASE_URL}/resume \
+          -H "Authorization: Bearer YOUR_API_TOKEN" \
+          -H "Content-Type: application/json" \
+          -d '{
+            "execution_id": "abcd1234-5678-90ef-ghij-klmnopqrstuv",
+            "task_id": "research_task",
+            "human_feedback": "Ótimo trabalho! Por favor, adicione mais detalhes.",
+            "is_approve": true,
+            "taskWebhookUrl": "https://your-server.com/webhooks/task",
+            "stepWebhookUrl": "https://your-server.com/webhooks/step",
+            "crewWebhookUrl": "https://your-server.com/webhooks/crew"
+          }'
+        ```
+
         <Warning>
             **Impacto do Feedback na Execução da Tarefa**:
             É fundamental ter cuidado ao fornecer feedback, pois todo o conteúdo do feedback será incorporado como contexto adicional para execuções futuras da tarefa.
@@ -76,4 +98,4 @@ Workflows HITL são particularmente valiosos para:
 - Cenários de tomada de decisão complexa
 - Operações sensíveis ou de alto risco
 - Tarefas criativas que requerem julgamento humano
-- Revisões de conformidade e regulamentação
+- Revisões de conformidade e regulamentação

pyproject.toml

@@ -49,7 +49,7 @@ Repository = "https://github.com/crewAIInc/crewAI"
 
 [project.optional-dependencies]
 tools = [
-    "crewai-tools>=0.74.0",
+    "crewai-tools>=0.76.0",
 ]
 embeddings = [
     "tiktoken~=0.8.0"

src/crewai/__init__.py

@@ -40,7 +40,7 @@ def _suppress_pydantic_deprecation_warnings() -> None:
 
 _suppress_pydantic_deprecation_warnings()
 
-__version__ = "0.201.1"
+__version__ = "0.203.0"
 _telemetry_submitted = False
 
 

src/crewai/agent.py

@@ -53,6 +53,7 @@ from crewai.utilities.converter import generate_model_description
 from crewai.utilities.llm_utils import create_llm
 from crewai.utilities.token_counter_callback import TokenCalcHandler
 from crewai.utilities.training_handler import CrewTrainingHandler
+from crewai.utilities.types import LLMMessage
 
 
 class Agent(BaseAgent):
@@ -174,7 +175,7 @@ class Agent(BaseAgent):
     )
 
     @model_validator(mode="before")
-    def validate_from_repository(cls, v): # noqa: N805
+    def validate_from_repository(cls, v):  # noqa: N805
         if v is not None and (from_repository := v.get("from_repository")):
             return load_agent_from_repository(from_repository) | v
         return v
@@ -347,15 +348,16 @@ class Agent(BaseAgent):
         )
 
         if self.knowledge or (self.crew and self.crew.knowledge):
-            crewai_event_bus.emit(
-                self,
-                event=KnowledgeRetrievalStartedEvent(
-                    agent=self,
-                ),
-            )
             try:
                 self.knowledge_search_query = self._get_knowledge_search_query(
-                    task_prompt
+                    task_prompt, task
+                )
+                crewai_event_bus.emit(
+                    self,
+                    event=KnowledgeRetrievalStartedEvent(
+                        from_task=task,
+                        from_agent=self,
+                    ),
                 )
                 if self.knowledge_search_query:
                     # Quering agent specific knowledge
@@ -385,7 +387,8 @@ class Agent(BaseAgent):
                         self,
                         event=KnowledgeRetrievalCompletedEvent(
                             query=self.knowledge_search_query,
-                            agent=self,
+                            from_task=task,
+                            from_agent=self,
                             retrieved_knowledge=(
                                 (self.agent_knowledge_context or "")
                                 + (
@@ -403,8 +406,9 @@ class Agent(BaseAgent):
                     self,
                     event=KnowledgeSearchQueryFailedEvent(
                         query=self.knowledge_search_query or "",
-                        agent=self,
                         error=str(e),
+                        from_task=task,
+                        from_agent=self,
                     ),
                 )
 
@@ -702,7 +706,7 @@ class Agent(BaseAgent):
 
         try:
             subprocess.run(
-                ["/usr/bin/docker", "info"],
+                ["docker", "info"],  # noqa: S607
                 check=True,
                 stdout=subprocess.PIPE,
                 stderr=subprocess.PIPE,
@@ -728,13 +732,14 @@ class Agent(BaseAgent):
     def set_fingerprint(self, fingerprint: Fingerprint):
         self.security_config.fingerprint = fingerprint
 
-    def _get_knowledge_search_query(self, task_prompt: str) -> str | None:
+    def _get_knowledge_search_query(self, task_prompt: str, task: Task) -> str | None:
         """Generate a search query for the knowledge base based on the task description."""
         crewai_event_bus.emit(
             self,
             event=KnowledgeQueryStartedEvent(
                 task_prompt=task_prompt,
-                agent=self,
+                from_task=task,
+                from_agent=self,
             ),
         )
         query = self.i18n.slice("knowledge_search_query").format(
@@ -749,8 +754,9 @@ class Agent(BaseAgent):
             crewai_event_bus.emit(
                 self,
                 event=KnowledgeQueryFailedEvent(
-                    agent=self,
                     error="LLM is not compatible with knowledge search queries",
+                    from_task=task,
+                    from_agent=self,
                 ),
             )
             return None
@@ -769,7 +775,8 @@ class Agent(BaseAgent):
                 self,
                 event=KnowledgeQueryCompletedEvent(
                     query=query,
-                    agent=self,
+                    from_task=task,
+                    from_agent=self,
                 ),
             )
             return rewritten_query
@@ -777,15 +784,16 @@ class Agent(BaseAgent):
             crewai_event_bus.emit(
                 self,
                 event=KnowledgeQueryFailedEvent(
-                    agent=self,
                     error=str(e),
+                    from_task=task,
+                    from_agent=self,
                 ),
             )
             return None
 
     def kickoff(
         self,
-        messages: str | list[dict[str, str]],
+        messages: str | list[LLMMessage],
         response_format: type[Any] | None = None,
     ) -> LiteAgentOutput:
         """
@@ -825,7 +833,7 @@ class Agent(BaseAgent):
 
     async def kickoff_async(
         self,
-        messages: str | list[dict[str, str]],
+        messages: str | list[LLMMessage],
         response_format: type[Any] | None = None,
     ) -> LiteAgentOutput:
         """
@@ -855,6 +863,7 @@ class Agent(BaseAgent):
             response_format=response_format,
             i18n=self.i18n,
             original_agent=self,
+            guardrail=self.guardrail,
         )
 
         return await lite_agent.kickoff_async(messages)

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]>=0.201.1,<1.0.0"
+    "crewai[tools]>=0.203.0,<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]>=0.201.1,<1.0.0",
+    "crewai[tools]>=0.203.0,<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]>=0.201.1"
+    "crewai[tools]>=0.203.0"
 ]
 
 [tool.crewai]

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

@@ -32,6 +32,13 @@ from crewai.events.types.flow_events import (
     MethodExecutionFinishedEvent,
     MethodExecutionStartedEvent,
 )
+from crewai.events.types.knowledge_events import (
+    KnowledgeQueryCompletedEvent,
+    KnowledgeQueryFailedEvent,
+    KnowledgeQueryStartedEvent,
+    KnowledgeRetrievalCompletedEvent,
+    KnowledgeRetrievalStartedEvent,
+)
 from crewai.events.types.llm_events import (
     LLMCallCompletedEvent,
     LLMCallFailedEvent,
@@ -310,6 +317,26 @@ class TraceCollectionListener(BaseEventListener):
         def on_agent_reasoning_failed(source, event):
             self._handle_action_event("agent_reasoning_failed", source, event)
 
+        @event_bus.on(KnowledgeRetrievalStartedEvent)
+        def on_knowledge_retrieval_started(source, event):
+            self._handle_action_event("knowledge_retrieval_started", source, event)
+
+        @event_bus.on(KnowledgeRetrievalCompletedEvent)
+        def on_knowledge_retrieval_completed(source, event):
+            self._handle_action_event("knowledge_retrieval_completed", source, event)
+
+        @event_bus.on(KnowledgeQueryStartedEvent)
+        def on_knowledge_query_started(source, event):
+            self._handle_action_event("knowledge_query_started", source, event)
+
+        @event_bus.on(KnowledgeQueryCompletedEvent)
+        def on_knowledge_query_completed(source, event):
+            self._handle_action_event("knowledge_query_completed", source, event)
+
+        @event_bus.on(KnowledgeQueryFailedEvent)
+        def on_knowledge_query_failed(source, event):
+            self._handle_action_event("knowledge_query_failed", source, event)
+
     def _initialize_crew_batch(self, source: Any, event: Any):
         """Initialize trace batch"""
         user_context = self._get_user_context()

src/crewai/events/types/knowledge_events.py

@@ -1,51 +1,60 @@
-from crewai.agents.agent_builder.base_agent import BaseAgent
+from typing import Any
+
 from crewai.events.base_events import BaseEvent
 
 
-class KnowledgeRetrievalStartedEvent(BaseEvent):
+class KnowledgeEventBase(BaseEvent):
+    task_id: str | None = None
+    task_name: str | None = None
+    from_task: Any | None = None
+    from_agent: Any | None = None
+    agent_role: str | None = None
+    agent_id: str | None = None
+
+    def __init__(self, **data):
+        super().__init__(**data)
+        self._set_agent_params(data)
+        self._set_task_params(data)
+
+
+class KnowledgeRetrievalStartedEvent(KnowledgeEventBase):
     """Event emitted when a knowledge retrieval is started."""
 
     type: str = "knowledge_search_query_started"
-    agent: BaseAgent
 
 
-class KnowledgeRetrievalCompletedEvent(BaseEvent):
+class KnowledgeRetrievalCompletedEvent(KnowledgeEventBase):
     """Event emitted when a knowledge retrieval is completed."""
 
     query: str
     type: str = "knowledge_search_query_completed"
-    agent: BaseAgent
     retrieved_knowledge: str
 
 
-class KnowledgeQueryStartedEvent(BaseEvent):
+class KnowledgeQueryStartedEvent(KnowledgeEventBase):
     """Event emitted when a knowledge query is started."""
 
     task_prompt: str
     type: str = "knowledge_query_started"
-    agent: BaseAgent
 
 
-class KnowledgeQueryFailedEvent(BaseEvent):
+class KnowledgeQueryFailedEvent(KnowledgeEventBase):
     """Event emitted when a knowledge query fails."""
 
     type: str = "knowledge_query_failed"
-    agent: BaseAgent
     error: str
 
 
-class KnowledgeQueryCompletedEvent(BaseEvent):
+class KnowledgeQueryCompletedEvent(KnowledgeEventBase):
     """Event emitted when a knowledge query is completed."""
 
     query: str
     type: str = "knowledge_query_completed"
-    agent: BaseAgent
 
 
-class KnowledgeSearchQueryFailedEvent(BaseEvent):
+class KnowledgeSearchQueryFailedEvent(KnowledgeEventBase):
     """Event emitted when a knowledge search query fails."""
 
     query: str
     type: str = "knowledge_search_query_failed"
-    agent: BaseAgent
     error: str

src/crewai/events/types/llm_guardrail_events.py

@@ -5,7 +5,21 @@ from typing import Any
 from crewai.events.base_events import BaseEvent
 
 
-class LLMGuardrailStartedEvent(BaseEvent):
+class LLMGuardrailBaseEvent(BaseEvent):
+    task_id: str | None = None
+    task_name: str | None = None
+    from_task: Any | None = None
+    from_agent: Any | None = None
+    agent_role: str | None = None
+    agent_id: str | None = None
+
+    def __init__(self, **data):
+        super().__init__(**data)
+        self._set_agent_params(data)
+        self._set_task_params(data)
+
+
+class LLMGuardrailStartedEvent(LLMGuardrailBaseEvent):
     """Event emitted when a guardrail task starts
 
     Attributes:
@@ -29,7 +43,7 @@ class LLMGuardrailStartedEvent(BaseEvent):
             self.guardrail = getsource(self.guardrail).strip()
 
 
-class LLMGuardrailCompletedEvent(BaseEvent):
+class LLMGuardrailCompletedEvent(LLMGuardrailBaseEvent):
     """Event emitted when a guardrail task completes
 
     Attributes:
@@ -44,3 +58,16 @@ class LLMGuardrailCompletedEvent(BaseEvent):
     result: Any
     error: str | None = None
     retry_count: int
+
+
+class LLMGuardrailFailedEvent(LLMGuardrailBaseEvent):
+    """Event emitted when a guardrail task fails
+
+    Attributes:
+        error: The error message
+        retry_count: The number of times the guardrail has been retried
+    """
+
+    type: str = "llm_guardrail_failed"
+    error: str
+    retry_count: int

src/crewai/events/utils/console_formatter.py

@@ -1377,7 +1377,7 @@ class ConsoleFormatter:
         if isinstance(formatted_answer, AgentAction):
             thought = re.sub(r"\n+", "\n", formatted_answer.thought)
             formatted_json = json.dumps(
-                formatted_answer.tool_input,
+                json.loads(formatted_answer.tool_input),
                 indent=2,
                 ensure_ascii=False,
             )

src/crewai/lite_agent.py

@@ -4,6 +4,7 @@ import uuid
 from collections.abc import Callable
 from typing import (
     Any,
+    Literal,
     cast,
     get_args,
     get_origin,
@@ -62,6 +63,7 @@ from crewai.utilities.llm_utils import create_llm
 from crewai.utilities.printer import Printer
 from crewai.utilities.token_counter_callback import TokenCalcHandler
 from crewai.utilities.tool_utils import execute_tool_and_check_finality
+from crewai.utilities.types import LLMMessage
 
 
 class LiteAgentOutput(BaseModel):
@@ -77,6 +79,7 @@ class LiteAgentOutput(BaseModel):
     usage_metrics: dict[str, Any] | None = Field(
         description="Token usage metrics for this execution", default=None
     )
+    messages: list[LLMMessage] = Field(description="Messages of the agent", default=[])
 
     def to_dict(self) -> dict[str, Any]:
         """Convert pydantic_output to a dictionary."""
@@ -180,7 +183,7 @@ class LiteAgent(FlowTrackable, BaseModel):
     _token_process: TokenProcess = PrivateAttr(default_factory=TokenProcess)
     _cache_handler: CacheHandler = PrivateAttr(default_factory=CacheHandler)
     _key: str = PrivateAttr(default_factory=lambda: str(uuid.uuid4()))
-    _messages: list[dict[str, str]] = PrivateAttr(default_factory=list)
+    _messages: list[LLMMessage] = PrivateAttr(default_factory=list)
     _iterations: int = PrivateAttr(default=0)
     _printer: Printer = PrivateAttr(default_factory=Printer)
     _guardrail: Callable | None = PrivateAttr(default=None)
@@ -219,7 +222,6 @@ class LiteAgent(FlowTrackable, BaseModel):
                 raise TypeError(
                     f"Guardrail requires LLM instance of type BaseLLM, got {type(self.llm).__name__}"
                 )
-
             self._guardrail = LLMGuardrail(description=self.guardrail, llm=self.llm)
 
         return self
@@ -276,7 +278,7 @@ class LiteAgent(FlowTrackable, BaseModel):
         """Return the original role for compatibility with tool interfaces."""
         return self.role
 
-    def kickoff(self, messages: str | list[dict[str, str]]) -> LiteAgentOutput:
+    def kickoff(self, messages: str | list[LLMMessage]) -> LiteAgentOutput:
         """
         Execute the agent with the given messages.
 
@@ -359,6 +361,7 @@ class LiteAgent(FlowTrackable, BaseModel):
             pydantic=formatted_result,
             agent_role=self.role,
             usage_metrics=usage_metrics.model_dump() if usage_metrics else None,
+            messages=self._messages,
         )
 
         # Process guardrail if set
@@ -368,6 +371,7 @@ class LiteAgent(FlowTrackable, BaseModel):
                 guardrail=self._guardrail,
                 retry_count=self._guardrail_retry_count,
                 event_source=self,
+                from_agent=self,
             )
 
             if not guardrail_result.success:
@@ -414,9 +418,7 @@ class LiteAgent(FlowTrackable, BaseModel):
 
         return output
 
-    async def kickoff_async(
-        self, messages: str | list[dict[str, str]]
-    ) -> LiteAgentOutput:
+    async def kickoff_async(self, messages: str | list[LLMMessage]) -> LiteAgentOutput:
         """
         Execute the agent asynchronously with the given messages.
 
@@ -461,9 +463,7 @@ class LiteAgent(FlowTrackable, BaseModel):
 
         return base_prompt
 
-    def _format_messages(
-        self, messages: str | list[dict[str, str]]
-    ) -> list[dict[str, str]]:
+    def _format_messages(self, messages: str | list[LLMMessage]) -> list[LLMMessage]:
         """Format messages for the LLM."""
         if isinstance(messages, str):
             messages = [{"role": "user", "content": messages}]
@@ -471,7 +471,9 @@ class LiteAgent(FlowTrackable, BaseModel):
         system_prompt = self._get_default_system_prompt()
 
         # Add system message at the beginning
-        formatted_messages = [{"role": "system", "content": system_prompt}]
+        formatted_messages: list[LLMMessage] = [
+            {"role": "system", "content": system_prompt}
+        ]
 
         # Add the rest of the messages
         formatted_messages.extend(messages)
@@ -583,6 +585,8 @@ class LiteAgent(FlowTrackable, BaseModel):
             ),
         )
 
-    def _append_message(self, text: str, role: str = "assistant") -> None:
+    def _append_message(
+        self, text: str, role: Literal["user", "assistant", "system"] = "assistant"
+    ) -> None:
         """Append a message to the message list with the given role."""
-        self._messages.append(format_message_for_llm(text, role=role))
+        self._messages.append(cast(LLMMessage, format_message_for_llm(text, role=role)))

src/crewai/task.py

@@ -462,6 +462,8 @@ class Task(BaseModel):
                     guardrail=self._guardrail,
                     retry_count=self.retry_count,
                     event_source=self,
+                    from_task=self,
+                    from_agent=agent,
                 )
                 if not guardrail_result.success:
                     if self.retry_count >= self.guardrail_max_retries:

src/crewai/utilities/agent_utils.py

@@ -31,6 +31,7 @@ from crewai.utilities.types import LLMMessage
 
 if TYPE_CHECKING:
     from crewai.agent import Agent
+    from crewai.lite_agent import LiteAgent
     from crewai.task import Task
 
 
@@ -222,7 +223,7 @@ def get_llm_response(
     callbacks: list[Callable[..., Any]],
     printer: Printer,
     from_task: Task | None = None,
-    from_agent: Agent | None = None,
+    from_agent: Agent | LiteAgent | None = None,
 ) -> str:
     """Call the LLM and return the response, handling any invalid responses.
 

src/crewai/utilities/converter.py

@@ -14,6 +14,7 @@ from crewai.utilities.pydantic_schema_parser import PydanticSchemaParser
 
 if TYPE_CHECKING:
     from crewai.agent import Agent
+    from crewai.agents.agent_builder.base_agent import BaseAgent
     from crewai.llm import LLM
     from crewai.llms.base_llm import BaseLLM
 
@@ -143,7 +144,7 @@ def convert_to_model(
     result: str,
     output_pydantic: type[BaseModel] | None,
     output_json: type[BaseModel] | None,
-    agent: Agent | None = None,
+    agent: Agent | BaseAgent | None = None,
     converter_cls: type[Converter] | None = None,
 ) -> dict[str, Any] | BaseModel | str:
     """Convert a result string to a Pydantic model or JSON.
@@ -215,7 +216,7 @@ def handle_partial_json(
     result: str,
     model: type[BaseModel],
     is_json_output: bool,
-    agent: Agent | None,
+    agent: Agent | BaseAgent | None,
     converter_cls: type[Converter] | None = None,
 ) -> dict[str, Any] | BaseModel | str:
     """Handle partial JSON in a result string and convert to Pydantic model or dict.
@@ -260,7 +261,7 @@ def convert_with_instructions(
     result: str,
     model: type[BaseModel],
     is_json_output: bool,
-    agent: Agent | None,
+    agent: Agent | BaseAgent | None,
     converter_cls: type[Converter] | None = None,
 ) -> dict | BaseModel | str:
     """Convert a result string to a Pydantic model or JSON using instructions.
@@ -283,7 +284,12 @@ def convert_with_instructions(
     """
     if agent is None:
         raise TypeError("Agent must be provided if converter_cls is not specified.")
-    llm = agent.function_calling_llm or agent.llm
+
+    llm = getattr(agent, "function_calling_llm", None) or agent.llm
+
+    if llm is None:
+        raise ValueError("Agent must have a valid LLM instance for conversion")
+
     instructions = get_conversion_instructions(model=model, llm=llm)
     converter = create_converter(
         agent=agent,
@@ -299,7 +305,7 @@ def convert_with_instructions(
 
     if isinstance(exported_result, ConverterError):
         Printer().print(
-            content=f"{exported_result.message} Using raw output instead.",
+            content=f"Failed to convert result to model: {exported_result}",
             color="red",
         )
         return result
@@ -308,7 +314,7 @@ def convert_with_instructions(
 
 
 def get_conversion_instructions(
-    model: type[BaseModel], llm: BaseLLM | LLM | str
+    model: type[BaseModel], llm: BaseLLM | LLM | str | Any
 ) -> str:
     """Generate conversion instructions based on the model and LLM capabilities.
 
@@ -357,7 +363,7 @@ class CreateConverterKwargs(TypedDict, total=False):
 
 
 def create_converter(
-    agent: Agent | None = None,
+    agent: Agent | BaseAgent | None = None,
     converter_cls: type[Converter] | None = None,
     *args: Any,
     **kwargs: Unpack[CreateConverterKwargs],

src/crewai/utilities/guardrail.py

@@ -7,7 +7,9 @@ from pydantic import BaseModel, Field, field_validator
 from typing_extensions import Self
 
 if TYPE_CHECKING:
-    from crewai.lite_agent import LiteAgentOutput
+    from crewai.agents.agent_builder.base_agent import BaseAgent
+    from crewai.lite_agent import LiteAgent, LiteAgentOutput
+    from crewai.task import Task
     from crewai.tasks.task_output import TaskOutput
 
 
@@ -79,6 +81,8 @@ def process_guardrail(
     guardrail: Callable[[Any], tuple[bool, Any | str]],
     retry_count: int,
     event_source: Any | None = None,
+    from_agent: BaseAgent | LiteAgent | None = None,
+    from_task: Task | None = None,
 ) -> GuardrailResult:
     """Process the guardrail for the agent output.
 
@@ -95,14 +99,6 @@ def process_guardrail(
         TypeError: If output is not a TaskOutput or LiteAgentOutput
         ValueError: If guardrail is None
     """
-    from crewai.lite_agent import LiteAgentOutput
-    from crewai.tasks.task_output import TaskOutput
-
-    if not isinstance(output, (TaskOutput, LiteAgentOutput)):
-        raise TypeError("Output must be a TaskOutput or LiteAgentOutput")
-    if guardrail is None:
-        raise ValueError("Guardrail must not be None")
-
     from crewai.events.event_bus import crewai_event_bus
     from crewai.events.types.llm_guardrail_events import (
         LLMGuardrailCompletedEvent,
@@ -111,7 +107,12 @@ def process_guardrail(
 
     crewai_event_bus.emit(
         event_source,
-        LLMGuardrailStartedEvent(guardrail=guardrail, retry_count=retry_count),
+        LLMGuardrailStartedEvent(
+            guardrail=guardrail,
+            retry_count=retry_count,
+            from_agent=from_agent,
+            from_task=from_task,
+        ),
     )
 
     result = guardrail(output)
@@ -124,6 +125,8 @@ def process_guardrail(
             result=guardrail_result.result,
             error=guardrail_result.error,
             retry_count=retry_count,
+            from_agent=from_agent,
+            from_task=from_task,
         ),
     )
 

src/crewai/utilities/tool_utils.py

@@ -12,6 +12,7 @@ from crewai.utilities.i18n import I18N
 
 if TYPE_CHECKING:
     from crewai.agent import Agent
+    from crewai.agents.agent_builder.base_agent import BaseAgent
     from crewai.llm import LLM
     from crewai.llms.base_llm import BaseLLM
     from crewai.task import Task
@@ -25,7 +26,7 @@ def execute_tool_and_check_finality(
     agent_role: str | None = None,
     tools_handler: ToolsHandler | None = None,
     task: Task | None = None,
-    agent: Agent | None = None,
+    agent: Agent | BaseAgent | None = None,
     function_calling_llm: BaseLLM | LLM | None = None,
     fingerprint_context: dict[str, str] | None = None,
 ) -> ToolResult:

tests/agents/test_agent.py

@@ -2299,10 +2299,10 @@ def test_get_knowledge_search_query():
         crew = Crew(agents=[agent], tasks=[task])
         crew.kickoff()
 
-        mock_get_query.assert_called_once_with(task_prompt)
+        mock_get_query.assert_called_once_with(task_prompt, task)
 
     with patch.object(agent.llm, "call") as mock_llm_call:
-        agent._get_knowledge_search_query(task_prompt)
+        agent._get_knowledge_search_query(task_prompt, task)
 
         mock_llm_call.assert_called_once_with(
             [