chore: merge main into release/v1.0.0

Resolved merge conflicts:
- agent.py: kept main's docker validation, restored get_platform_tools method, fixed KnowledgeRetrievalStartedEvent position
- agent_utils.py: merged both LLM and LiteAgent imports
- test_cache_hitting.yaml: accepted main's test recordings
- Removed 4 deprecated test cassettes from old tests/ directory

Key changes from main:
- Enhanced knowledge event handling with from_agent/from_task parameters
- Updated LLMMessage typing in kickoff methods
- Added guardrail parameter to async kickoff
- Restored runtime validation in guardrail.py

.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

@@ -421,6 +421,7 @@
                   "en/api-reference/introduction",
                   "en/api-reference/inputs",
                   "en/api-reference/kickoff",
+                  "en/api-reference/resume",
                   "en/api-reference/status"
                 ]
               }
@@ -827,6 +828,7 @@
                   "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 +1241,7 @@
                   "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/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

lib/crewai/src/crewai/agent.py

@@ -5,7 +5,6 @@ from collections.abc import Callable, Sequence
 from typing import (
     Any,
     Literal,
-    cast,
 )
 
 from pydantic import Field, InstanceOf, PrivateAttr, model_validator
@@ -54,6 +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):
@@ -348,12 +348,13 @@ class Agent(BaseAgent):
             crewai_event_bus.emit(
                 self,
                 event=KnowledgeRetrievalStartedEvent(
-                    agent=self,
+                    from_task=task,
+                    from_agent=self,
                 ),
             )
             try:
                 self.knowledge_search_query = self._get_knowledge_search_query(
-                    task_prompt
+                    task_prompt, task
                 )
                 if self.knowledge_search_query:
                     # Quering agent specific knowledge
@@ -383,7 +384,8 @@ 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 "")
                                 + (
@@ -401,8 +403,9 @@ class Agent(BaseAgent):
                     self,
                     event=KnowledgeSearchQueryFailedEvent(
                         query=self.knowledge_search_query or "",
-                        agent=self,
                         error=str(e),
+                        from_task=task,
+                        from_agent=self,
                     ),
                 )
 
@@ -577,7 +580,7 @@ class Agent(BaseAgent):
             agent=self,
             crew=self.crew,
             tools=parsed_tools,
-            prompt=cast(dict[str, str], prompt),
+            prompt=prompt,  # type: ignore[arg-type]
             original_tools=raw_tools,
             stop_words=stop_words,
             max_iter=self.max_iter,
@@ -744,13 +747,14 @@ 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(
@@ -765,8 +769,9 @@ 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
@@ -785,7 +790,8 @@ class Agent(BaseAgent):
                 self,
                 event=KnowledgeQueryCompletedEvent(
                     query=query,
-                    agent=self,
+                    from_task=task,
+                    from_agent=self,
                 ),
             )
             return rewritten_query
@@ -793,15 +799,16 @@ 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:
         """
@@ -841,7 +848,7 @@ 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:
         """
@@ -871,6 +878,7 @@ class Agent(BaseAgent):
             response_format=response_format,
             i18n=self.i18n,
             original_agent=self,
+            guardrail=self.guardrail,
         )
 
         return await lite_agent.kickoff_async(messages)

lib/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()

lib/crewai/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

lib/crewai/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

lib/crewai/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,
             )

lib/crewai/src/crewai/lite_agent.py

@@ -3,6 +3,7 @@ from collections.abc import Callable
 import inspect
 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):
@@ -180,7 +182,7 @@ 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 +221,6 @@ 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 +277,7 @@ 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.
 
@@ -371,6 +372,7 @@ class LiteAgent(FlowTrackable, BaseModel):
                 guardrail=self._guardrail,
                 retry_count=self._guardrail_retry_count,
                 event_source=self,
+                from_agent=self,
             )
 
             if not guardrail_result.success:
@@ -420,9 +422,7 @@ 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.
 
@@ -467,9 +467,7 @@ 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}]
@@ -477,7 +475,9 @@ 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)
@@ -589,6 +589,8 @@ 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)))

lib/crewai/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:

lib/crewai/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.llm import LLM
     from crewai.task import Task
 
@@ -224,7 +225,7 @@ 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.
 

lib/crewai/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],

lib/crewai/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.
 
@@ -111,7 +115,12 @@ 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 +133,8 @@ def process_guardrail(
             result=guardrail_result.result,
             error=guardrail_result.error,
             retry_count=retry_count,
+            from_agent=from_agent,
+            from_task=from_task,
         ),
     )
 

lib/crewai/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:

lib/crewai/tests/agents/test_agent.py

@@ -2319,10 +2319,10 @@ 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(
             [

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@@ -1021,17 +1021,17 @@ interactions:
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+      not a summary.\n\nThis is the context you''re working with:\nWe need to evaluate
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+      delegate'', ''type'': ''str''}, ''context'': {''description'': ''The context
+      for the task'', ''type'': ''str''}, ''coworker'': {''description'': ''The role/name
+      of the coworker to delegate to'', ''type'': ''str''}}\nTool Description: Delegate
+      a specific task to one of the following coworkers: Scorer\nThe input to this
+      tool should be the coworker, the task you want them to do, and ALL necessary
+      context to execute the task, they know nothing about the task, so share absolutely
+      everything you know, don''t reference things but instead explain them.\nTool
+      Name: Ask question to coworker\nTool Arguments: {''question'': {''description'':
+      ''The question to ask'', ''type'': ''str''}, ''context'': {''description'':
+      ''The context for the question'', ''type'': ''str''}, ''coworker'': {''description'':
+      ''The role/name of the coworker to ask'', ''type'': ''str''}}\nTool Description:
+      Ask a specific question to one of the following coworkers: Scorer\nThe input
+      to this tool should be the coworker, the question you have for them, and ALL
+      necessary context to ask the question properly, they know nothing about the
+      question, so share absolutely everything you know, don''t reference things but
+      instead explain them.\n\nIMPORTANT: Use the following format in your response:\n\n```\nThought:
+      you should always think about what to do\nAction: the action to take, only one
+      name of [Delegate work to coworker, Ask question to coworker], just the name,
+      exactly as it''s written.\nAction Input: the input to the action, just a simple
+      JSON object, enclosed in curly braces, using \" to wrap keys and values.\nObservation:
+      the result of the action\n```\n\nOnce all necessary information is gathered,
+      return the following format:\n\n```\nThought: I now know the final answer\nFinal
+      Answer: the final answer to the original input question\n```"}, {"role": "user",
+      "content": "\nCurrent Task: Give me an integer score between 1-5 for the following
+      title: ''The impact of AI in the future of work''\n\nThis is the expected criteria
+      for your final answer: The score of the title.\nyou MUST return the actual complete
+      content as the final answer, not a summary.\nEnsure your final answer contains
+      only the content in the following format: {\n  \"score\": int\n}\n\nEnsure the
+      final output does not include any code block markers like ```json or ```python.\n\nBegin!
+      This is VERY important to you, use the tools available and give your best Final
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+      "To provide the score for the title \"The impact of AI in the future of work,\"
+      I need to ask Scorer to evaluate this based on the criteria they use. \n\nAction:
+      Ask question to coworker\nAction Input: {\"question\": \"Can you provide an
+      integer score between 1-5 for the title ''The impact of AI in the future of
+      work''?\", \"context\": \"We need to evaluate this title based on your criteria
+      for scoring titles. The context for the scoring revolves entirely around the
+      perceived impact of AI on the future of work.\", \"coworker\": \"Scorer\"}\nObservation:
+      4 - The title ''The impact of AI in the future of work'' is clear and directly
+      addresses a timely and significant topic that is highly relevant in today''s
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+      "task_name": "Give me an integer score between 1-5 for the following title:
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+      "agent_id": "2fee0a63-ccf0-408a-9368-1b95eb6d628e", "agent_role": "Crew Manager",
+      "from_task": null, "from_agent": null, "messages": [{"role": "system", "content":
+      "You are Crew Manager. You are a seasoned manager with a knack for getting the
+      best out of your team.\nYou are also known for your ability to delegate work
+      to the right people, and to ask the right questions to get the best out of your
+      team.\nEven though you don''t perform tasks by yourself, you have a lot of experience
+      in the field, which allows you to properly evaluate the work of your team members.\nYour
+      personal goal is: Manage the team to complete the task in the best way possible.\nYou
+      ONLY have access to the following tools, and should NEVER make up tools that
+      are not listed here:\n\nTool Name: Delegate work to coworker\nTool Arguments:
+      {''task'': {''description'': ''The task to delegate'', ''type'': ''str''}, ''context'':
+      {''description'': ''The context for the task'', ''type'': ''str''}, ''coworker'':
+      {''description'': ''The role/name of the coworker to delegate to'', ''type'':
+      ''str''}}\nTool Description: Delegate a specific task to one of the following
+      coworkers: Scorer\nThe input to this tool should be the coworker, the task you
+      want them to do, and ALL necessary context to execute the task, they know nothing
+      about the task, so share absolutely everything you know, don''t reference things
+      but instead explain them.\nTool Name: Ask question to coworker\nTool Arguments:
+      {''question'': {''description'': ''The question to ask'', ''type'': ''str''},
+      ''context'': {''description'': ''The context for the question'', ''type'': ''str''},
+      ''coworker'': {''description'': ''The role/name of the coworker to ask'', ''type'':
+      ''str''}}\nTool Description: Ask a specific question to one of the following
+      coworkers: Scorer\nThe input to this tool should be the coworker, the question
+      you have for them, and ALL necessary context to ask the question properly, they
+      know nothing about the question, so share absolutely everything you know, don''t
+      reference things but instead explain them.\n\nIMPORTANT: Use the following format
+      in your response:\n\n```\nThought: you should always think about what to do\nAction:
+      the action to take, only one name of [Delegate work to coworker, Ask question
+      to coworker], just the name, exactly as it''s written.\nAction Input: the input
+      to the action, just a simple JSON object, enclosed in curly braces, using \"
+      to wrap keys and values.\nObservation: the result of the action\n```\n\nOnce
+      all necessary information is gathered, return the following format:\n\n```\nThought:
+      I now know the final answer\nFinal Answer: the final answer to the original
+      input question\n```"}, {"role": "user", "content": "\nCurrent Task: Give me
+      an integer score between 1-5 for the following title: ''The impact of AI in
+      the future of work''\n\nThis is the expected criteria for your final answer:
+      The score of the title.\nyou MUST return the actual complete content as the
+      final answer, not a summary.\nEnsure your final answer contains only the content
+      in the following format: {\n  \"score\": int\n}\n\nEnsure the final output does
+      not include any code block markers like ```json or ```python.\n\nBegin! This
+      is VERY important to you, use the tools available and give your best Final Answer,
+      your job depends on it!\n\nThought:"}, {"role": "assistant", "content": "To
+      provide the score for the title \"The impact of AI in the future of work,\"
+      I need to ask Scorer to evaluate this based on the criteria they use. \n\nAction:
+      Ask question to coworker\nAction Input: {\"question\": \"Can you provide an
+      integer score between 1-5 for the title ''The impact of AI in the future of
+      work''?\", \"context\": \"We need to evaluate this title based on your criteria
+      for scoring titles. The context for the scoring revolves entirely around the
+      perceived impact of AI on the future of work.\", \"coworker\": \"Scorer\"}\nObservation:
+      4 - The title ''The impact of AI in the future of work'' is clear and directly
+      addresses a timely and significant topic that is highly relevant in today''s
+      context. However, it could be made even more specific to immediately hook the
+      reader or audience."}], "response": "Thought: I have received the score from
+      Scorer.\n\nFinal Answer: 4 - The title ''The impact of AI in the future of work''
+      is clear and directly addresses a timely and significant topic that is highly
+      relevant in today''s context. However, it could be made even more specific to
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+      "timestamp": "2025-10-08T18:18:08.567937+00:00", "type": "agent_execution_completed",
+      "event_data": {"agent_role": "Crew Manager", "agent_goal": "Manage the team
+      to complete the task in the best way possible.", "agent_backstory": "You are
+      a seasoned manager with a knack for getting the best out of your team.\nYou
+      are also known for your ability to delegate work to the right people, and to
+      ask the right questions to get the best out of your team.\nEven though you don''t
+      perform tasks by yourself, you have a lot of experience in the field, which
+      allows you to properly evaluate the work of your team members."}}, {"event_id":
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+      "type": "task_completed", "event_data": {"task_description": "Give me an integer
+      score between 1-5 for the following title: ''The impact of AI in the future
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