fix: resolve lint issues in dependency compatibility tests

- Remove unused imports: pathlib.Path, opentelemetry modules
- Remove unused variable assignment for context
- All ruff checks now pass locally

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

docs/docs.json

@@ -320,6 +320,7 @@
                   "en/enterprise/guides/update-crew",
                   "en/enterprise/guides/enable-crew-studio",
                   "en/enterprise/guides/azure-openai-setup",
+                  "en/enterprise/guides/automation-triggers",
                   "en/enterprise/guides/hubspot-trigger",
                   "en/enterprise/guides/react-component-export",
                   "en/enterprise/guides/salesforce-trigger",
@@ -341,11 +342,12 @@
             "groups": [
               {
                 "group": "Getting Started",
-                "pages": ["en/api-reference/introduction"]
-              },
-              {
-                "group": "Endpoints",
-                "openapi": "https://raw.githubusercontent.com/crewAIInc/crewAI/main/docs/enterprise-api.en.yaml"
+                "pages": [
+                  "en/api-reference/introduction",
+                  "en/api-reference/inputs",
+                  "en/api-reference/kickoff",
+                  "en/api-reference/status"
+                ]
               }
             ]
           },
@@ -657,6 +659,7 @@
                   "pt-BR/enterprise/guides/update-crew",
                   "pt-BR/enterprise/guides/enable-crew-studio",
                   "pt-BR/enterprise/guides/azure-openai-setup",
+                  "pt-BR/enterprise/guides/automation-triggers",
                   "pt-BR/enterprise/guides/hubspot-trigger",
                   "pt-BR/enterprise/guides/react-component-export",
                   "pt-BR/enterprise/guides/salesforce-trigger",
@@ -680,11 +683,12 @@
             "groups": [
               {
                 "group": "Começando",
-                "pages": ["pt-BR/api-reference/introduction"]
-              },
-              {
-                "group": "Endpoints",
-                "openapi": "https://raw.githubusercontent.com/crewAIInc/crewAI/main/docs/enterprise-api.pt-BR.yaml"
+                "pages": [
+                  "pt-BR/api-reference/introduction",
+                  "pt-BR/api-reference/inputs",
+                  "pt-BR/api-reference/kickoff",
+                  "pt-BR/api-reference/status"
+                ]
               }
             ]
           },
@@ -1005,6 +1009,7 @@
                   "ko/enterprise/guides/update-crew",
                   "ko/enterprise/guides/enable-crew-studio",
                   "ko/enterprise/guides/azure-openai-setup",
+                  "ko/enterprise/guides/automation-triggers",
                   "ko/enterprise/guides/hubspot-trigger",
                   "ko/enterprise/guides/react-component-export",
                   "ko/enterprise/guides/salesforce-trigger",
@@ -1026,11 +1031,12 @@
             "groups": [
               {
                 "group": "시작 안내",
-                "pages": ["ko/api-reference/introduction"]
-              },
-              {
-                "group": "Endpoints",
-                "openapi": "https://raw.githubusercontent.com/crewAIInc/crewAI/main/docs/enterprise-api.ko.yaml"
+                "pages": [
+                  "ko/api-reference/introduction",
+                  "ko/api-reference/inputs",
+                  "ko/api-reference/kickoff",
+                  "ko/api-reference/status"
+                ]
               }
             ]
           },
@@ -1081,6 +1087,10 @@
     "indexing": "all"
   },
   "redirects": [
+    {
+      "source": "/api-reference",
+      "destination": "/en/api-reference/introduction"
+    },
     {
       "source": "/introduction",
       "destination": "/en/introduction"
@@ -1133,6 +1143,18 @@
       "source": "/api-reference/:path*",
       "destination": "/en/api-reference/:path*"
     },
+    {
+      "source": "/en/api-reference",
+      "destination": "/en/api-reference/introduction"
+    },
+    {
+      "source": "/pt-BR/api-reference",
+      "destination": "/pt-BR/api-reference/introduction"
+    },
+    {
+      "source": "/ko/api-reference",
+      "destination": "/ko/api-reference/introduction"
+    },
     {
       "source": "/examples/:path*",
       "destination": "/en/examples/:path*"

docs/en/api-reference/inputs.mdx

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

docs/en/api-reference/kickoff.mdx

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

docs/en/api-reference/status.mdx

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

docs/en/concepts/tasks.mdx

@@ -59,6 +59,7 @@ crew = Crew(
 | **Output Pydantic** _(optional)_ | `output_pydantic` | `Optional[Type[BaseModel]]`   | A Pydantic model for task output.                                                                                    |
 | **Callback** _(optional)_        | `callback`        | `Optional[Any]`               | Function/object to be executed after task completion.                                                                |
 | **Guardrail** _(optional)_       | `guardrail`       | `Optional[Callable]`             | Function to validate task output before proceeding to next task.                                                  |
+| **Guardrail Max Retries** _(optional)_ | `guardrail_max_retries` | `Optional[int]`     | Maximum number of retries when guardrail validation fails. Defaults to 3.                                         |
 
 ## Creating Tasks
 
@@ -452,7 +453,7 @@ task = Task(
     expected_output="A valid JSON object",
     agent=analyst,
     guardrail=validate_json_output,
-    max_retries=3  # Limit retry attempts
+    guardrail_max_retries=3  # Limit retry attempts
 )
 ```
 

docs/en/concepts/training.mdx

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

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

@@ -0,0 +1,171 @@
+---
+title: "Automation Triggers"
+description: "Automatically execute your CrewAI workflows when specific events occur in connected integrations"
+icon: "bolt"
+---
+
+Automation triggers enable you to automatically run your CrewAI deployments when specific events occur in your connected integrations, creating powerful event-driven workflows that respond to real-time changes in your business systems.
+
+## Overview
+
+With automation triggers, you can:
+
+- **Respond to real-time events** - Automatically execute workflows when specific conditions are met
+- **Integrate with external systems** - Connect with platforms like Gmail, Outlook, OneDrive, JIRA, Slack, Stripe and more
+- **Scale your automation** - Handle high-volume events without manual intervention
+- **Maintain context** - Access trigger data within your crews and flows
+
+## Managing Automation Triggers
+
+### Viewing Available Triggers
+
+To access and manage your automation triggers:
+
+1. Navigate to your deployment in the CrewAI dashboard
+2. Click on the **Triggers** tab to view all available trigger integrations
+
+<Frame>
+  <img src="/images/enterprise/list-available-triggers.png" alt="List of available automation triggers" />
+</Frame>
+
+This view shows all the trigger integrations available for your deployment, along with their current connection status.
+
+### Enabling and Disabling Triggers
+
+Each trigger can be easily enabled or disabled using the toggle switch:
+
+<Frame>
+  <img src="/images/enterprise/trigger-selected.png" alt="Enable or disable triggers with toggle" />
+</Frame>
+
+- **Enabled (blue toggle)**: The trigger is active and will automatically execute your deployment when the specified events occur
+- **Disabled (gray toggle)**: The trigger is inactive and will not respond to events
+
+Simply click the toggle to change the trigger state. Changes take effect immediately.
+
+### Monitoring Trigger Executions
+
+Track the performance and history of your triggered executions:
+
+<Frame>
+  <img src="/images/enterprise/list-executions.png" alt="List of executions triggered by automation" />
+</Frame>
+
+## Building Automation
+
+Before building your automation, it's helpful to understand the structure of trigger payloads that your crews and flows will receive.
+
+### Payload Samples Repository
+
+We maintain a comprehensive repository with sample payloads from various trigger sources to help you build and test your automations:
+
+**🔗 [CrewAI Enterprise Trigger Payload Samples](https://github.com/crewAIInc/crewai-enterprise-trigger-payload-samples)**
+
+This repository contains:
+
+- **Real payload examples** from different trigger sources (Gmail, Google Drive, etc.)
+- **Payload structure documentation** showing the format and available fields
+
+### Triggers with Crew
+
+Your existing crew definitions work seamlessly with triggers, you just need to have a task to parse the received payload:
+
+```python
+@CrewBase
+class MyAutomatedCrew:
+    @agent
+    def researcher(self) -> Agent:
+        return Agent(
+            config=self.agents_config['researcher'],
+        )
+
+    @task
+    def parse_trigger_payload(self) -> Task:
+        return Task(
+            config=self.tasks_config['parse_trigger_payload'],
+            agent=self.researcher(),
+        )
+
+    @task
+    def analyze_trigger_content(self) -> Task:
+        return Task(
+            config=self.tasks_config['analyze_trigger_data'],
+            agent=self.researcher(),
+        )
+```
+
+The crew will automatically receive and can access the trigger payload through the standard CrewAI context mechanisms.
+
+### Integration with Flows
+
+For flows, you have more control over how trigger data is handled:
+
+#### Accessing Trigger Payload
+
+All `@start()` methods in your flows will accept an additional parameter called `crewai_trigger_payload`:
+
+```python
+from crewai.flow import Flow, start, listen
+
+class MyAutomatedFlow(Flow):
+    @start()
+    def handle_trigger(self, crewai_trigger_payload: dict = None):
+        """
+        This start method can receive trigger data
+        """
+        if crewai_trigger_payload:
+            # Process the trigger data
+            trigger_id = crewai_trigger_payload.get('id')
+            event_data = crewai_trigger_payload.get('payload', {})
+
+            # Store in flow state for use by other methods
+            self.state.trigger_id = trigger_id
+            self.state.trigger_type = event_data
+
+            return event_data
+
+        # Handle manual execution
+        return None
+
+    @listen(handle_trigger)
+    def process_data(self, trigger_data):
+        """
+        Process the data from the trigger
+        """
+        # ... process the trigger
+```
+
+#### Triggering Crews from Flows
+
+When kicking off a crew within a flow that was triggered, pass the trigger payload as it:
+
+```python
+@start()
+def delegate_to_crew(self, crewai_trigger_payload: dict = None):
+    """
+    Delegate processing to a specialized crew
+    """
+    crew = MySpecializedCrew()
+
+    # Pass the trigger payload to the crew
+    result = crew.crew().kickoff(
+        inputs={
+            'a_custom_parameter': "custom_value",
+            'crewai_trigger_payload': crewai_trigger_payload
+        },
+    )
+
+    return result
+```
+
+## Troubleshooting
+
+**Trigger not firing:**
+- Verify the trigger is enabled
+- Check integration connection status
+
+**Execution failures:**
+- Check the execution logs for error details
+- If you are developing, make sure the inputs include the `crewai_trigger_payload` parameter with the correct payload
+
+Automation triggers transform your CrewAI deployments into responsive, event-driven systems that can seamlessly integrate with your existing business processes and tools.

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

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

docs/en/tools/overview.mdx

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

docs/ko/api-reference/inputs.mdx

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

docs/ko/api-reference/kickoff.mdx

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

docs/ko/api-reference/status.mdx

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

docs/ko/concepts/tasks.mdx

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

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

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

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

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

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

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

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

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

docs/pt-BR/concepts/tasks.mdx

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

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

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

pyproject.toml

@@ -10,16 +10,16 @@ authors = [
 dependencies = [
     # Core Dependencies
     "pydantic>=2.4.2",
-    "openai<1.100.0",  # TODO: Temporarily pin due to https://github.com/openai/openai-python/issues/2564. Can revert once the issue is fixed.
+    "openai>=1.13.3",
     "litellm==1.74.9",
     "instructor>=1.3.3",
     # Text Processing
     "pdfplumber>=0.11.4",
     "regex>=2024.9.11",
     # Telemetry and Monitoring
-    "opentelemetry-api>=1.30.0",
-    "opentelemetry-sdk>=1.30.0",
-    "opentelemetry-exporter-otlp-proto-http>=1.30.0",
+    "opentelemetry-api>=1.27.0,<1.28.0",
+    "opentelemetry-sdk>=1.27.0,<1.28.0",
+    "opentelemetry-exporter-otlp-proto-http>=1.27.0,<1.28.0",
     # Data Handling
     "chromadb>=0.5.23",
     "tokenizers>=0.20.3",
@@ -68,6 +68,9 @@ docling = [
 aisuite = [
     "aisuite>=0.1.10",
 ]
+qdrant = [
+    "qdrant-client[fastembed]>=1.14.3",
+]
 
 [tool.uv]
 dev-dependencies = [

src/crewai/agent.py

@@ -1,7 +1,18 @@
 import shutil
 import subprocess
 import time
-from typing import Any, Callable, Dict, List, Literal, Optional, Sequence, Tuple, Type, Union
+from typing import (
+    Any,
+    Callable,
+    Dict,
+    List,
+    Literal,
+    Optional,
+    Sequence,
+    Tuple,
+    Type,
+    Union,
+)
 
 from pydantic import Field, InstanceOf, PrivateAttr, model_validator
 
@@ -162,7 +173,7 @@ class Agent(BaseAgent):
     )
     guardrail: Optional[Union[Callable[[Any], Tuple[bool, Any]], str]] = Field(
         default=None,
-        description="Function or string description of a guardrail to validate agent output"
+        description="Function or string description of a guardrail to validate agent output",
     )
     guardrail_max_retries: int = Field(
         default=3, description="Maximum number of retries when guardrail fails"
@@ -276,7 +287,7 @@ class Agent(BaseAgent):
         self._inject_date_to_task(task)
 
         if self.tools_handler:
-            self.tools_handler.last_used_tool = {}  # type: ignore # Incompatible types in assignment (expression has type "dict[Never, Never]", variable has type "ToolCalling")
+            self.tools_handler.last_used_tool = None
 
         task_prompt = task.prompt()
 
@@ -309,15 +320,20 @@ class Agent(BaseAgent):
                 event=MemoryRetrievalStartedEvent(
                     task_id=str(task.id) if task else None,
                     source_type="agent",
+                    from_agent=self,
+                    from_task=task,
                 ),
             )
 
             start_time = time.time()
+
             contextual_memory = ContextualMemory(
                 self.crew._short_term_memory,
                 self.crew._long_term_memory,
                 self.crew._entity_memory,
                 self.crew._external_memory,
+                agent=self,
+                task=task,
             )
             memory = contextual_memory.build_context_for_task(task, context)
             if memory.strip() != "":
@@ -330,13 +346,14 @@ class Agent(BaseAgent):
                     memory_content=memory,
                     retrieval_time_ms=(time.time() - start_time) * 1000,
                     source_type="agent",
+                    from_agent=self,
+                    from_task=task,
                 ),
             )
         knowledge_config = (
             self.knowledge_config.model_dump() if self.knowledge_config else {}
         )
 
-
         if self.knowledge or (self.crew and self.crew.knowledge):
             crewai_event_bus.emit(
                 self,

src/crewai/agents/agent_builder/base_agent_executor_mixin.py

@@ -43,7 +43,6 @@ class CrewAgentExecutorMixin:
                         metadata={
                             "observation": self.task.description,
                         },
-                        agent=self.agent.role,
                     )
             except Exception as e:
                 print(f"Failed to add to short term memory: {e}")
@@ -65,7 +64,6 @@ class CrewAgentExecutorMixin:
                         "description": self.task.description,
                         "messages": self.messages,
                     },
-                    agent=self.agent.role,
                 )
             except Exception as e:
                 print(f"Failed to add to external memory: {e}")
@@ -100,8 +98,8 @@ class CrewAgentExecutorMixin:
                 )
                 self.crew._long_term_memory.save(long_term_memory)
 
-                for entity in evaluation.entities:
-                    entity_memory = EntityMemoryItem(
+                entity_memories = [
+                    EntityMemoryItem(
                         name=entity.name,
                         type=entity.type,
                         description=entity.description,
@@ -109,7 +107,10 @@ class CrewAgentExecutorMixin:
                             [f"- {r}" for r in entity.relationships]
                         ),
                     )
-                    self.crew._entity_memory.save(entity_memory)
+                    for entity in evaluation.entities
+                ]
+                if entity_memories:
+                    self.crew._entity_memory.save(entity_memories)
             except AttributeError as e:
                 print(f"Missing attributes for long term memory: {e}")
                 pass
@@ -158,7 +159,9 @@ class CrewAgentExecutorMixin:
             self._printer.print(content=prompt, color="bold_yellow")
             response = input()
             if response.strip() != "":
-                self._printer.print(content="\nProcessing your feedback...", color="cyan")
+                self._printer.print(
+                    content="\nProcessing your feedback...", color="cyan"
+                )
             return response
         finally:
             event_listener.formatter.resume_live_updates()

src/crewai/agents/tools_handler.py

@@ -8,13 +8,13 @@ from .cache.cache_handler import CacheHandler
 class ToolsHandler:
     """Callback handler for tool usage."""
 
-    last_used_tool: ToolCalling = {}  # type: ignore # BUG?: Incompatible types in assignment (expression has type "Dict[...]", variable has type "ToolCalling")
+    last_used_tool: Optional[ToolCalling] = None
     cache: Optional[CacheHandler]
 
     def __init__(self, cache: Optional[CacheHandler] = None):
         """Initialize the callback handler."""
         self.cache = cache
-        self.last_used_tool = {}  # type: ignore # BUG?: same as above
+        self.last_used_tool = None
 
     def on_tool_use(
         self,

src/crewai/cli/authentication/constants.py

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

src/crewai/cli/authentication/main.py

@@ -7,24 +7,27 @@ from rich.console import Console
 from pydantic import BaseModel, Field
 
 
-from .utils import TokenManager, validate_jwt_token
-from urllib.parse import quote
-from crewai.cli.plus_api import PlusAPI
+from .utils import validate_jwt_token
+from crewai.cli.shared.token_manager import TokenManager
 from crewai.cli.config import Settings
-from crewai.cli.authentication.constants import (
-    AUTH0_AUDIENCE,
-    AUTH0_CLIENT_ID,
-    AUTH0_DOMAIN,
-)
 
 console = Console()
 
 
 class Oauth2Settings(BaseModel):
-    provider: str = Field(description="OAuth2 provider used for authentication (e.g., workos, okta, auth0).")
-    client_id: str = Field(description="OAuth2 client ID issued by the provider, used during authentication requests.")
-    domain: str = Field(description="OAuth2 provider's domain (e.g., your-org.auth0.com) used for issuing tokens.")
-    audience: Optional[str] = Field(description="OAuth2 audience value, typically used to identify the target API or resource.", default=None)
+    provider: str = Field(
+        description="OAuth2 provider used for authentication (e.g., workos, okta, auth0)."
+    )
+    client_id: str = Field(
+        description="OAuth2 client ID issued by the provider, used during authentication requests."
+    )
+    domain: str = Field(
+        description="OAuth2 provider's domain (e.g., your-org.auth0.com) used for issuing tokens."
+    )
+    audience: Optional[str] = Field(
+        description="OAuth2 audience value, typically used to identify the target API or resource.",
+        default=None,
+    )
 
     @classmethod
     def from_settings(cls):
@@ -44,11 +47,15 @@ class ProviderFactory:
         settings = settings or Oauth2Settings.from_settings()
 
         import importlib
-        module = importlib.import_module(f"crewai.cli.authentication.providers.{settings.provider.lower()}")
+
+        module = importlib.import_module(
+            f"crewai.cli.authentication.providers.{settings.provider.lower()}"
+        )
         provider = getattr(module, f"{settings.provider.capitalize()}Provider")
 
         return provider(settings)
 
+
 class AuthenticationCommand:
     def __init__(self):
         self.token_manager = TokenManager()
@@ -58,26 +65,12 @@ class AuthenticationCommand:
         """Sign up to CrewAI+"""
         console.print("Signing in to CrewAI Enterprise...\n", style="bold blue")
 
-        # TODO: WORKOS - Next line and conditional are temporary until migration to WorkOS is complete.
-        user_provider = self._determine_user_provider()
-        if user_provider == "auth0":
-            settings = Oauth2Settings(
-                provider="auth0",
-                client_id=AUTH0_CLIENT_ID,
-                domain=AUTH0_DOMAIN,
-                audience=AUTH0_AUDIENCE
-            )
-            self.oauth2_provider = ProviderFactory.from_settings(settings)
-        # End of temporary code.
-
         device_code_data = self._get_device_code()
         self._display_auth_instructions(device_code_data)
 
         return self._poll_for_token(device_code_data)
 
-    def _get_device_code(
-        self
-    ) -> Dict[str, Any]:
+    def _get_device_code(self) -> Dict[str, Any]:
         """Get the device code to authenticate the user."""
 
         device_code_payload = {
@@ -86,7 +79,9 @@ class AuthenticationCommand:
             "audience": self.oauth2_provider.get_audience(),
         }
         response = requests.post(
-            url=self.oauth2_provider.get_authorize_url(), data=device_code_payload, timeout=20
+            url=self.oauth2_provider.get_authorize_url(),
+            data=device_code_payload,
+            timeout=20,
         )
         response.raise_for_status()
         return response.json()
@@ -97,9 +92,7 @@ class AuthenticationCommand:
         console.print("2. Enter the following code: ", device_code_data["user_code"])
         webbrowser.open(device_code_data["verification_uri_complete"])
 
-    def _poll_for_token(
-        self, device_code_data: Dict[str, Any]
-    ) -> None:
+    def _poll_for_token(self, device_code_data: Dict[str, Any]) -> None:
         """Polls the server for the token until it is received, or max attempts are reached."""
 
         token_payload = {
@@ -112,7 +105,9 @@ class AuthenticationCommand:
 
         attempts = 0
         while True and attempts < 10:
-            response = requests.post(self.oauth2_provider.get_token_url(), data=token_payload, timeout=30)
+            response = requests.post(
+                self.oauth2_provider.get_token_url(), data=token_payload, timeout=30
+            )
             token_data = response.json()
 
             if response.status_code == 200:
@@ -192,30 +187,3 @@ class AuthenticationCommand:
                 "\nRun [bold]crewai login[/bold] to try logging in again.\n",
                 style="yellow",
             )
-
-    # TODO: WORKOS - This method is temporary until migration to WorkOS is complete.
-    def _determine_user_provider(self) -> str:
-        """Determine which provider to use for authentication."""
-
-        console.print(
-            "Enter your CrewAI Enterprise account email: ", style="bold blue", end=""
-        )
-        email = input()
-        email_encoded = quote(email)
-
-        # It's not correct to call this method directly, but it's temporary until migration is complete.
-        response = PlusAPI("")._make_request(
-            "GET", f"/crewai_plus/api/v1/me/provider?email={email_encoded}"
-        )
-
-        if response.status_code == 200:
-            if response.json().get("provider") == "auth0":
-                return "auth0"
-            else:
-                return "workos"
-        else:
-            console.print(
-                "Error: Failed to authenticate with crewai enterprise. Ensure that you are using the latest crewai version and please try again. If the problem persists, contact support@crewai.com.",
-                style="red",
-            )
-            raise SystemExit

src/crewai/cli/authentication/token.py

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

src/crewai/cli/authentication/utils.py

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

src/crewai/cli/config.py

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

src/crewai/cli/plus_api.py

@@ -117,9 +117,6 @@ class PlusAPI:
     def get_organizations(self) -> requests.Response:
         return self._make_request("GET", self.ORGANIZATIONS_RESOURCE)
 
-    def send_trace_batch(self, payload) -> requests.Response:
-        return self._make_request("POST", self.TRACING_RESOURCE, json=payload)
-
     def initialize_trace_batch(self, payload) -> requests.Response:
         return self._make_request(
             "POST", f"{self.TRACING_RESOURCE}/batches", json=payload
@@ -135,6 +132,7 @@ class PlusAPI:
             "POST",
             f"{self.TRACING_RESOURCE}/batches/{trace_batch_id}/events",
             json=payload,
+            timeout=30,
         )
 
     def send_ephemeral_trace_events(
@@ -144,6 +142,7 @@ class PlusAPI:
             "POST",
             f"{self.EPHEMERAL_TRACING_RESOURCE}/batches/{trace_batch_id}/events",
             json=payload,
+            timeout=30,
         )
 
     def finalize_trace_batch(self, trace_batch_id: str, payload) -> requests.Response:

src/crewai/cli/shared/token_manager.py

@@ -0,0 +1,139 @@
+import json
+import os
+import sys
+from datetime import datetime
+from pathlib import Path
+from typing import Optional
+from cryptography.fernet import Fernet
+
+
+class TokenManager:
+    def __init__(self, file_path: str = "tokens.enc") -> None:
+        """
+        Initialize the TokenManager class.
+
+        :param file_path: The file path to store the encrypted tokens. Default is "tokens.enc".
+        """
+        self.file_path = file_path
+        self.key = self._get_or_create_key()
+        self.fernet = Fernet(self.key)
+
+    def _get_or_create_key(self) -> bytes:
+        """
+        Get or create the encryption key.
+
+        :return: The encryption key.
+        """
+        key_filename = "secret.key"
+        key = self.read_secure_file(key_filename)
+
+        if key is not None:
+            return key
+
+        new_key = Fernet.generate_key()
+        self.save_secure_file(key_filename, new_key)
+        return new_key
+
+    def save_tokens(self, access_token: str, expires_at: int) -> None:
+        """
+        Save the access token and its expiration time.
+
+        :param access_token: The access token to save.
+        :param expires_at: The UNIX timestamp of the expiration time.
+        """
+        expiration_time = datetime.fromtimestamp(expires_at)
+        data = {
+            "access_token": access_token,
+            "expiration": expiration_time.isoformat(),
+        }
+        encrypted_data = self.fernet.encrypt(json.dumps(data).encode())
+        self.save_secure_file(self.file_path, encrypted_data)
+
+    def get_token(self) -> Optional[str]:
+        """
+        Get the access token if it is valid and not expired.
+
+        :return: The access token if valid and not expired, otherwise None.
+        """
+        encrypted_data = self.read_secure_file(self.file_path)
+
+        decrypted_data = self.fernet.decrypt(encrypted_data)  # type: ignore
+        data = json.loads(decrypted_data)
+
+        expiration = datetime.fromisoformat(data["expiration"])
+        if expiration <= datetime.now():
+            return None
+
+        return data["access_token"]
+
+    def clear_tokens(self) -> None:
+        """
+        Clear the tokens.
+        """
+        self.delete_secure_file(self.file_path)
+
+    def get_secure_storage_path(self) -> Path:
+        """
+        Get the secure storage path based on the operating system.
+
+        :return: The secure storage path.
+        """
+        if sys.platform == "win32":
+            # Windows: Use %LOCALAPPDATA%
+            base_path = os.environ.get("LOCALAPPDATA")
+        elif sys.platform == "darwin":
+            # macOS: Use ~/Library/Application Support
+            base_path = os.path.expanduser("~/Library/Application Support")
+        else:
+            # Linux and other Unix-like: Use ~/.local/share
+            base_path = os.path.expanduser("~/.local/share")
+
+        app_name = "crewai/credentials"
+        storage_path = Path(base_path) / app_name
+
+        storage_path.mkdir(parents=True, exist_ok=True)
+
+        return storage_path
+
+    def save_secure_file(self, filename: str, content: bytes) -> None:
+        """
+        Save the content to a secure file.
+
+        :param filename: The name of the file.
+        :param content: The content to save.
+        """
+        storage_path = self.get_secure_storage_path()
+        file_path = storage_path / filename
+
+        with open(file_path, "wb") as f:
+            f.write(content)
+
+        # Set appropriate permissions (read/write for owner only)
+        os.chmod(file_path, 0o600)
+
+    def read_secure_file(self, filename: str) -> Optional[bytes]:
+        """
+        Read the content of a secure file.
+
+        :param filename: The name of the file.
+        :return: The content of the file if it exists, otherwise None.
+        """
+        storage_path = self.get_secure_storage_path()
+        file_path = storage_path / filename
+
+        if not file_path.exists():
+            return None
+
+        with open(file_path, "rb") as f:
+            return f.read()
+
+    def delete_secure_file(self, filename: str) -> None:
+        """
+        Delete the secure file.
+
+        :param filename: The name of the file.
+        """
+        storage_path = self.get_secure_storage_path()
+        file_path = storage_path / filename
+        if file_path.exists():
+            file_path.unlink(missing_ok=True)

src/crewai/cli/update_crew.py

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

src/crewai/experimental/evaluation/agent_evaluator.py

@@ -1,5 +1,5 @@
 import threading
-from typing import Any
+from typing import Any, Optional
 
 from crewai.experimental.evaluation.base_evaluator import AgentEvaluationResult, AggregationStrategy
 from crewai.agent import Agent
@@ -15,10 +15,11 @@ from crewai.utilities.events.agent_events import LiteAgentExecutionCompletedEven
 from crewai.experimental.evaluation.base_evaluator import AgentAggregatedEvaluationResult, EvaluationScore, MetricCategory
 
 class ExecutionState:
+    current_agent_id: Optional[str] = None
+    current_task_id: Optional[str] = None
+
     def __init__(self):
         self.traces = {}
-        self.current_agent_id: str | None = None
-        self.current_task_id: str | None = None
         self.iteration = 1
         self.iterations_results = {}
         self.agent_evaluators = {}

src/crewai/flow/flow.py

@@ -474,6 +474,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
         self._method_outputs: List[Any] = []  # List to store all method outputs
         self._completed_methods: Set[str] = set()  # Track completed methods for reload
         self._persistence: Optional[FlowPersistence] = persistence
+        self._is_execution_resuming: bool = False
 
         # Initialize state with initial values
         self._state = self._create_initial_state()
@@ -829,6 +830,9 @@ class Flow(Generic[T], metaclass=FlowMeta):
                 # Clear completed methods and outputs for a fresh start
                 self._completed_methods.clear()
                 self._method_outputs.clear()
+            else:
+                # We're restoring from persistence, set the flag
+                self._is_execution_resuming = True
 
             if inputs:
                 # Override the id in the state if it exists in inputs
@@ -880,6 +884,9 @@ class Flow(Generic[T], metaclass=FlowMeta):
             ]
             await asyncio.gather(*tasks)
 
+            # Clear the resumption flag after initial execution completes
+            self._is_execution_resuming = False
+
             final_output = self._method_outputs[-1] if self._method_outputs else None
 
             crewai_event_bus.emit(
@@ -913,17 +920,56 @@ class Flow(Generic[T], metaclass=FlowMeta):
         - Triggers execution of any listeners waiting on this start method
         - Part of the flow's initialization sequence
         - Skips execution if method was already completed (e.g., after reload)
+        - Automatically injects crewai_trigger_payload if available in flow inputs
         """
         if start_method_name in self._completed_methods:
-            last_output = self._method_outputs[-1] if self._method_outputs else None
-            await self._execute_listeners(start_method_name, last_output)
-            return
+            if self._is_execution_resuming:
+                # During resumption, skip execution but continue listeners
+                last_output = self._method_outputs[-1] if self._method_outputs else None
+                await self._execute_listeners(start_method_name, last_output)
+                return
+            # For cyclic flows, clear from completed to allow re-execution
+            self._completed_methods.discard(start_method_name)
+
+        method = self._methods[start_method_name]
+        enhanced_method = self._inject_trigger_payload_for_start_method(method)
 
         result = await self._execute_method(
-            start_method_name, self._methods[start_method_name]
+            start_method_name, enhanced_method
         )
         await self._execute_listeners(start_method_name, result)
 
+    def _inject_trigger_payload_for_start_method(self, original_method: Callable) -> Callable:
+        def prepare_kwargs(*args, **kwargs):
+            inputs = baggage.get_baggage("flow_inputs") or {}
+            trigger_payload = inputs.get("crewai_trigger_payload")
+
+            sig = inspect.signature(original_method)
+            accepts_trigger_payload = "crewai_trigger_payload" in sig.parameters
+
+            if trigger_payload is not None and accepts_trigger_payload:
+                kwargs["crewai_trigger_payload"] = trigger_payload
+            elif trigger_payload is not None:
+                self._log_flow_event(
+                    f"Trigger payload available but {original_method.__name__} doesn't accept crewai_trigger_payload parameter",
+                    color="yellow"
+                )
+            return args, kwargs
+
+        if asyncio.iscoroutinefunction(original_method):
+            async def enhanced_method(*args, **kwargs):
+                args, kwargs = prepare_kwargs(*args, **kwargs)
+                return await original_method(*args, **kwargs)
+        else:
+            def enhanced_method(*args, **kwargs):
+                args, kwargs = prepare_kwargs(*args, **kwargs)
+                return original_method(*args, **kwargs)
+
+        enhanced_method.__name__ = original_method.__name__
+        enhanced_method.__doc__ = original_method.__doc__
+
+        return enhanced_method
+
     async def _execute_method(
         self, method_name: str, method: Callable, *args: Any, **kwargs: Any
     ) -> Any:
@@ -1015,11 +1061,15 @@ class Flow(Generic[T], metaclass=FlowMeta):
             for router_name in routers_triggered:
                 await self._execute_single_listener(router_name, result)
                 # After executing router, the router's result is the path
-                router_result = self._method_outputs[-1]
+                router_result = (
+                    self._method_outputs[-1] if self._method_outputs else None
+                )
                 if router_result:  # Only add non-None results
                     router_results.append(router_result)
                 current_trigger = (
-                    router_result  # Update for next iteration of router chain
+                    str(router_result)
+                    if router_result is not None
+                    else ""  # Update for next iteration of router chain
                 )
 
         # Now execute normal listeners for all router results and the original trigger
@@ -1037,6 +1087,24 @@ class Flow(Generic[T], metaclass=FlowMeta):
                     ]
                     await asyncio.gather(*tasks)
 
+                if current_trigger in router_results:
+                    # Find start methods triggered by this router result
+                    for method_name in self._start_methods:
+                        # Check if this start method is triggered by the current trigger
+                        if method_name in self._listeners:
+                            condition_type, trigger_methods = self._listeners[
+                                method_name
+                            ]
+                            if current_trigger in trigger_methods:
+                                # Only execute if this is a cycle (method was already completed)
+                                if method_name in self._completed_methods:
+                                    # For router-triggered start methods in cycles, temporarily clear resumption flag
+                                    # to allow cyclic execution
+                                    was_resuming = self._is_execution_resuming
+                                    self._is_execution_resuming = False
+                                    await self._execute_start_method(method_name)
+                                    self._is_execution_resuming = was_resuming
+
     def _find_triggered_methods(
         self, trigger_method: str, router_only: bool
     ) -> List[str]:
@@ -1074,6 +1142,9 @@ class Flow(Generic[T], metaclass=FlowMeta):
             if router_only != is_router:
                 continue
 
+            if not router_only and listener_name in self._start_methods:
+                continue
+
             if condition_type == "OR":
                 # If the trigger_method matches any in methods, run this
                 if trigger_method in methods:
@@ -1123,10 +1194,13 @@ class Flow(Generic[T], metaclass=FlowMeta):
         Catches and logs any exceptions during execution, preventing
         individual listener failures from breaking the entire flow.
         """
-        # TODO: greyson fix
-        # if listener_name in self._completed_methods:
-        #     await self._execute_listeners(listener_name, None)
-        #     return
+        if listener_name in self._completed_methods:
+            if self._is_execution_resuming:
+                # During resumption, skip execution but continue listeners
+                await self._execute_listeners(listener_name, None)
+                return
+            # For cyclic flows, clear from completed to allow re-execution
+            self._completed_methods.discard(listener_name)
 
         try:
             method = self._methods[listener_name]

src/crewai/lite_agent.py

@@ -69,12 +69,7 @@ from crewai.utilities.events.agent_events import (
     LiteAgentExecutionStartedEvent,
 )
 from crewai.utilities.events.crewai_event_bus import crewai_event_bus
-from crewai.utilities.events.llm_events import (
-    LLMCallCompletedEvent,
-    LLMCallFailedEvent,
-    LLMCallStartedEvent,
-    LLMCallType,
-)
+
 from crewai.utilities.llm_utils import create_llm
 from crewai.utilities.printer import Printer
 from crewai.utilities.token_counter_callback import TokenCalcHandler
@@ -519,19 +514,6 @@ class LiteAgent(FlowTrackable, BaseModel):
 
                 enforce_rpm_limit(self.request_within_rpm_limit)
 
-                llm = cast(LLM, self.llm)
-                model = llm.model if hasattr(llm, "model") else "unknown"
-                crewai_event_bus.emit(
-                    self,
-                    event=LLMCallStartedEvent(
-                        messages=self._messages,
-                        tools=None,
-                        callbacks=self._callbacks,
-                        from_agent=self,
-                        model=model,
-                    ),
-                )
-
                 try:
                     answer = get_llm_response(
                         llm=cast(LLM, self.llm),
@@ -541,23 +523,7 @@ class LiteAgent(FlowTrackable, BaseModel):
                         from_agent=self,
                     )
 
-                    # Emit LLM call completed event
-                    crewai_event_bus.emit(
-                        self,
-                        event=LLMCallCompletedEvent(
-                            messages=self._messages,
-                            response=answer,
-                            call_type=LLMCallType.LLM_CALL,
-                            from_agent=self,
-                            model=model,
-                        ),
-                    )
                 except Exception as e:
-                    # Emit LLM call failed event
-                    crewai_event_bus.emit(
-                        self,
-                        event=LLMCallFailedEvent(error=str(e), from_agent=self),
-                    )
                     raise e
 
                 formatted_answer = process_llm_response(answer, self.use_stop_words)

src/crewai/llm.py

@@ -851,7 +851,9 @@ class LLM(BaseLLM):
             return tool_calls
 
         # --- 7) Handle tool calls if present
-        tool_result = self._handle_tool_call(tool_calls, available_functions)
+        tool_result = self._handle_tool_call(
+            tool_calls, available_functions, from_task, from_agent
+        )
         if tool_result is not None:
             return tool_result
         # --- 8) If tool call handling didn't return a result, emit completion event and return text response
@@ -868,6 +870,8 @@ class LLM(BaseLLM):
         self,
         tool_calls: List[Any],
         available_functions: Optional[Dict[str, Any]] = None,
+        from_task: Optional[Any] = None,
+        from_agent: Optional[Any] = None,
     ) -> Optional[str]:
         """Handle a tool call from the LLM.
 
@@ -902,6 +906,8 @@ class LLM(BaseLLM):
                     event=ToolUsageStartedEvent(
                         tool_name=function_name,
                         tool_args=function_args,
+                        from_agent=from_agent,
+                        from_task=from_task,
                     ),
                 )
 
@@ -914,12 +920,17 @@ class LLM(BaseLLM):
                         tool_args=function_args,
                         started_at=started_at,
                         finished_at=datetime.now(),
+                        from_task=from_task,
+                        from_agent=from_agent,
                     ),
                 )
 
                 # --- 3.3) Emit success event
                 self._handle_emit_call_events(
-                    response=result, call_type=LLMCallType.TOOL_CALL
+                    response=result,
+                    call_type=LLMCallType.TOOL_CALL,
+                    from_task=from_task,
+                    from_agent=from_agent,
                 )
                 return result
             except Exception as e:
@@ -1139,7 +1150,11 @@ class LLM(BaseLLM):
 
         # TODO: Remove this code after merging PR https://github.com/BerriAI/litellm/pull/10917
         # Ollama doesn't supports last message to be 'assistant'
-        if "ollama" in self.model.lower() and messages and messages[-1]["role"] == "assistant":
+        if (
+            "ollama" in self.model.lower()
+            and messages
+            and messages[-1]["role"] == "assistant"
+        ):
             return messages + [{"role": "user", "content": ""}]
 
         # Handle Anthropic models

src/crewai/memory/contextual/contextual_memory.py

@@ -1,4 +1,4 @@
-from typing import Optional
+from typing import Optional, TYPE_CHECKING
 
 from crewai.memory import (
     EntityMemory,
@@ -7,6 +7,10 @@ from crewai.memory import (
     ShortTermMemory,
 )
 
+if TYPE_CHECKING:
+    from crewai.agent import Agent
+    from crewai.task import Task
+
 
 class ContextualMemory:
     def __init__(
@@ -15,11 +19,28 @@ class ContextualMemory:
         ltm: LongTermMemory,
         em: EntityMemory,
         exm: ExternalMemory,
+        agent: Optional["Agent"] = None,
+        task: Optional["Task"] = None,
     ):
         self.stm = stm
         self.ltm = ltm
         self.em = em
         self.exm = exm
+        self.agent = agent
+        self.task = task
+
+        if self.stm is not None:
+            self.stm.agent = self.agent
+            self.stm.task = self.task
+        if self.ltm is not None:
+            self.ltm.agent = self.agent
+            self.ltm.task = self.task
+        if self.em is not None:
+            self.em.agent = self.agent
+            self.em.task = self.task
+        if self.exm is not None:
+            self.exm.agent = self.agent
+            self.exm.task = self.task
 
     def build_context_for_task(self, task, context) -> str:
         """
@@ -49,10 +70,7 @@ class ContextualMemory:
 
         stm_results = self.stm.search(query)
         formatted_results = "\n".join(
-            [
-                f"- {result['context']}"
-                for result in stm_results
-            ]
+            [f"- {result['context']}" for result in stm_results]
         )
         return f"Recent Insights:\n{formatted_results}" if stm_results else ""
 
@@ -89,10 +107,7 @@ class ContextualMemory:
 
         em_results = self.em.search(query)
         formatted_results = "\n".join(
-            [
-                f"- {result['context']}"
-                for result in em_results
-            ]  # type: ignore #  Invalid index type "str" for "str"; expected type "SupportsIndex | slice"
+            [f"- {result['context']}" for result in em_results]  # type: ignore #  Invalid index type "str" for "str"; expected type "SupportsIndex | slice"
         )
         return f"Entities:\n{formatted_results}" if em_results else ""
 

src/crewai/memory/entity/entity_memory.py

@@ -1,4 +1,4 @@
-from typing import Optional
+from typing import Any
 import time
 
 from pydantic import PrivateAttr
@@ -24,7 +24,7 @@ class EntityMemory(Memory):
     Inherits from the Memory class.
     """
 
-    _memory_provider: Optional[str] = PrivateAttr()
+    _memory_provider: str | None = PrivateAttr()
 
     def __init__(self, crew=None, embedder_config=None, storage=None, path=None):
         memory_provider = embedder_config.get("provider") if embedder_config else None
@@ -35,7 +35,7 @@ class EntityMemory(Memory):
                 raise ImportError(
                     "Mem0 is not installed. Please install it with `pip install mem0ai`."
                 )
-            config = embedder_config.get("config")
+            config = embedder_config.get("config") if embedder_config else None
             storage = Mem0Storage(type="short_term", crew=crew, config=config)
         else:
             storage = (
@@ -53,47 +53,99 @@ class EntityMemory(Memory):
         super().__init__(storage=storage)
         self._memory_provider = memory_provider
 
-    def save(self, item: EntityMemoryItem) -> None:  # type: ignore # BUG?: Signature of "save" incompatible with supertype "Memory"
-        """Saves an entity item into the SQLite storage."""
+    def save(
+        self,
+        value: EntityMemoryItem | list[EntityMemoryItem],
+        metadata: dict[str, Any] | None = None,
+    ) -> None:
+        """Saves one or more entity items into the SQLite storage.
+
+        Args:
+            value: Single EntityMemoryItem or list of EntityMemoryItems to save.
+            metadata: Optional metadata dict (included for supertype compatibility but not used).
+
+        Notes:
+            The metadata parameter is included to satisfy the supertype signature but is not
+            used - entity metadata is extracted from the EntityMemoryItem objects themselves.
+        """
+
+        if not value:
+            return
+
+        items = value if isinstance(value, list) else [value]
+        is_batch = len(items) > 1
+
+        metadata = {"entity_count": len(items)} if is_batch else items[0].metadata
         crewai_event_bus.emit(
             self,
             event=MemorySaveStartedEvent(
-                metadata=item.metadata,
+                metadata=metadata,
                 source_type="entity_memory",
+                from_agent=self.agent,
+                from_task=self.task,
             ),
         )
 
         start_time = time.time()
+        saved_count = 0
+        errors = []
+
         try:
-            if self._memory_provider == "mem0":
-                data = f"""
-                Remember details about the following entity:
-                Name: {item.name}
-                Type: {item.type}
-                Entity Description: {item.description}
-                """
+            for item in items:
+                try:
+                    if self._memory_provider == "mem0":
+                        data = f"""
+                        Remember details about the following entity:
+                        Name: {item.name}
+                        Type: {item.type}
+                        Entity Description: {item.description}
+                        """
+                    else:
+                        data = f"{item.name}({item.type}): {item.description}"
+
+                    super().save(data, item.metadata)
+                    saved_count += 1
+                except Exception as e:
+                    errors.append(f"{item.name}: {str(e)}")
+
+            if is_batch:
+                emit_value = f"Saved {saved_count} entities"
+                metadata = {"entity_count": saved_count, "errors": errors}
             else:
-                data = f"{item.name}({item.type}): {item.description}"
+                emit_value = f"{items[0].name}({items[0].type}): {items[0].description}"
+                metadata = items[0].metadata
 
-            super().save(data, item.metadata)
-
-            # Emit memory save completed event
             crewai_event_bus.emit(
                 self,
                 event=MemorySaveCompletedEvent(
-                    value=data,
-                    metadata=item.metadata,
+                    value=emit_value,
+                    metadata=metadata,
                     save_time_ms=(time.time() - start_time) * 1000,
                     source_type="entity_memory",
+                    from_agent=self.agent,
+                    from_task=self.task,
                 ),
             )
+
+            if errors:
+                raise Exception(
+                    f"Partial save: {len(errors)} failed out of {len(items)}"
+                )
+
         except Exception as e:
+            fail_metadata = (
+                {"entity_count": len(items), "saved": saved_count}
+                if is_batch
+                else items[0].metadata
+            )
             crewai_event_bus.emit(
                 self,
                 event=MemorySaveFailedEvent(
-                    metadata=item.metadata,
+                    metadata=fail_metadata,
                     error=str(e),
                     source_type="entity_memory",
+                    from_agent=self.agent,
+                    from_task=self.task,
                 ),
             )
             raise
@@ -111,6 +163,8 @@ class EntityMemory(Memory):
                 limit=limit,
                 score_threshold=score_threshold,
                 source_type="entity_memory",
+                from_agent=self.agent,
+                from_task=self.task,
             ),
         )
 
@@ -129,6 +183,8 @@ class EntityMemory(Memory):
                     score_threshold=score_threshold,
                     query_time_ms=(time.time() - start_time) * 1000,
                     source_type="entity_memory",
+                    from_agent=self.agent,
+                    from_task=self.task,
                 ),
             )
 

src/crewai/memory/external/external_memory.py

@@ -53,7 +53,6 @@ class ExternalMemory(Memory):
         self,
         value: Any,
         metadata: Optional[Dict[str, Any]] = None,
-        agent: Optional[str] = None,
     ) -> None:
         """Saves a value into the external storage."""
         crewai_event_bus.emit(
@@ -61,24 +60,30 @@ class ExternalMemory(Memory):
             event=MemorySaveStartedEvent(
                 value=value,
                 metadata=metadata,
-                agent_role=agent,
                 source_type="external_memory",
+                from_agent=self.agent,
+                from_task=self.task,
             ),
         )
 
         start_time = time.time()
         try:
-            item = ExternalMemoryItem(value=value, metadata=metadata, agent=agent)
-            super().save(value=item.value, metadata=item.metadata, agent=item.agent)
+            item = ExternalMemoryItem(
+                value=value,
+                metadata=metadata,
+                agent=self.agent.role if self.agent else None,
+            )
+            super().save(value=item.value, metadata=item.metadata)
 
             crewai_event_bus.emit(
                 self,
                 event=MemorySaveCompletedEvent(
                     value=value,
                     metadata=metadata,
-                    agent_role=agent,
                     save_time_ms=(time.time() - start_time) * 1000,
                     source_type="external_memory",
+                    from_agent=self.agent,
+                    from_task=self.task,
                 ),
             )
         except Exception as e:
@@ -87,9 +92,10 @@ class ExternalMemory(Memory):
                 event=MemorySaveFailedEvent(
                     value=value,
                     metadata=metadata,
-                    agent_role=agent,
                     error=str(e),
                     source_type="external_memory",
+                    from_agent=self.agent,
+                    from_task=self.task,
                 ),
             )
             raise
@@ -107,6 +113,8 @@ class ExternalMemory(Memory):
                 limit=limit,
                 score_threshold=score_threshold,
                 source_type="external_memory",
+                from_agent=self.agent,
+                from_task=self.task,
             ),
         )
 
@@ -125,6 +133,8 @@ class ExternalMemory(Memory):
                     score_threshold=score_threshold,
                     query_time_ms=(time.time() - start_time) * 1000,
                     source_type="external_memory",
+                    from_agent=self.agent,
+                    from_task=self.task,
                 ),
             )
 

src/crewai/memory/long_term/long_term_memory.py

@@ -37,13 +37,17 @@ class LongTermMemory(Memory):
                 metadata=item.metadata,
                 agent_role=item.agent,
                 source_type="long_term_memory",
+                from_agent=self.agent,
+                from_task=self.task,
             ),
         )
 
         start_time = time.time()
         try:
             metadata = item.metadata
-            metadata.update({"agent": item.agent, "expected_output": item.expected_output})
+            metadata.update(
+                {"agent": item.agent, "expected_output": item.expected_output}
+            )
             self.storage.save(  # type: ignore # BUG?: Unexpected keyword argument "task_description","score","datetime" for "save" of "Storage"
                 task_description=item.task,
                 score=metadata["quality"],
@@ -59,6 +63,8 @@ class LongTermMemory(Memory):
                     agent_role=item.agent,
                     save_time_ms=(time.time() - start_time) * 1000,
                     source_type="long_term_memory",
+                    from_agent=self.agent,
+                    from_task=self.task,
                 ),
             )
         except Exception as e:
@@ -74,13 +80,19 @@ class LongTermMemory(Memory):
             )
             raise
 
-    def search(self, task: str, latest_n: int = 3) -> List[Dict[str, Any]]:  # type: ignore # signature of "search" incompatible with supertype "Memory"
+    def search(  # type: ignore # signature of "search" incompatible with supertype "Memory"
+        self,
+        task: str,
+        latest_n: int = 3,
+    ) -> List[Dict[str, Any]]:  # type: ignore # signature of "search" incompatible with supertype "Memory"
         crewai_event_bus.emit(
             self,
             event=MemoryQueryStartedEvent(
                 query=task,
                 limit=latest_n,
                 source_type="long_term_memory",
+                from_agent=self.agent,
+                from_task=self.task,
             ),
         )
 
@@ -96,6 +108,8 @@ class LongTermMemory(Memory):
                     limit=latest_n,
                     query_time_ms=(time.time() - start_time) * 1000,
                     source_type="long_term_memory",
+                    from_agent=self.agent,
+                    from_task=self.task,
                 ),
             )
 

src/crewai/memory/memory.py

@@ -1,7 +1,11 @@
-from typing import Any, Dict, List, Optional
+from typing import Any, Dict, List, Optional, TYPE_CHECKING
 
 from pydantic import BaseModel
 
+if TYPE_CHECKING:
+    from crewai.agent import Agent
+    from crewai.task import Task
+
 
 class Memory(BaseModel):
     """
@@ -12,19 +16,38 @@ class Memory(BaseModel):
     crew: Optional[Any] = None
 
     storage: Any
+    _agent: Optional["Agent"] = None
+    _task: Optional["Task"] = None
 
     def __init__(self, storage: Any, **data: Any):
         super().__init__(storage=storage, **data)
 
+    @property
+    def task(self) -> Optional["Task"]:
+        """Get the current task associated with this memory."""
+        return self._task
+
+    @task.setter
+    def task(self, task: Optional["Task"]) -> None:
+        """Set the current task associated with this memory."""
+        self._task = task
+
+    @property
+    def agent(self) -> Optional["Agent"]:
+        """Get the current agent associated with this memory."""
+        return self._agent
+
+    @agent.setter
+    def agent(self, agent: Optional["Agent"]) -> None:
+        """Set the current agent associated with this memory."""
+        self._agent = agent
+
     def save(
         self,
         value: Any,
         metadata: Optional[Dict[str, Any]] = None,
-        agent: Optional[str] = None,
     ) -> None:
         metadata = metadata or {}
-        if agent:
-            metadata["agent"] = agent
 
         self.storage.save(value, metadata)
 

src/crewai/memory/short_term/short_term_memory.py

@@ -37,7 +37,7 @@ class ShortTermMemory(Memory):
                 raise ImportError(
                     "Mem0 is not installed. Please install it with `pip install mem0ai`."
                 )
-            config = embedder_config.get("config")
+            config = embedder_config.get("config") if embedder_config else None
             storage = Mem0Storage(type="short_term", crew=crew, config=config)
         else:
             storage = (
@@ -57,34 +57,42 @@ class ShortTermMemory(Memory):
         self,
         value: Any,
         metadata: Optional[Dict[str, Any]] = None,
-        agent: Optional[str] = None,
     ) -> None:
         crewai_event_bus.emit(
             self,
             event=MemorySaveStartedEvent(
                 value=value,
                 metadata=metadata,
-                agent_role=agent,
                 source_type="short_term_memory",
+                from_agent=self.agent,
+                from_task=self.task,
             ),
         )
 
         start_time = time.time()
         try:
-            item = ShortTermMemoryItem(data=value, metadata=metadata, agent=agent)
+            item = ShortTermMemoryItem(
+                data=value,
+                metadata=metadata,
+                agent=self.agent.role if self.agent else None,
+            )
             if self._memory_provider == "mem0":
-                item.data = f"Remember the following insights from Agent run: {item.data}"
+                item.data = (
+                    f"Remember the following insights from Agent run: {item.data}"
+                )
 
-            super().save(value=item.data, metadata=item.metadata, agent=item.agent)
+            super().save(value=item.data, metadata=item.metadata)
 
             crewai_event_bus.emit(
                 self,
                 event=MemorySaveCompletedEvent(
                     value=value,
                     metadata=metadata,
-                    agent_role=agent,
+                    # agent_role=agent,
                     save_time_ms=(time.time() - start_time) * 1000,
                     source_type="short_term_memory",
+                    from_agent=self.agent,
+                    from_task=self.task,
                 ),
             )
         except Exception as e:
@@ -93,9 +101,10 @@ class ShortTermMemory(Memory):
                 event=MemorySaveFailedEvent(
                     value=value,
                     metadata=metadata,
-                    agent_role=agent,
                     error=str(e),
                     source_type="short_term_memory",
+                    from_agent=self.agent,
+                    from_task=self.task,
                 ),
             )
             raise
@@ -113,6 +122,8 @@ class ShortTermMemory(Memory):
                 limit=limit,
                 score_threshold=score_threshold,
                 source_type="short_term_memory",
+                from_agent=self.agent,
+                from_task=self.task,
             ),
         )
 
@@ -131,6 +142,8 @@ class ShortTermMemory(Memory):
                     score_threshold=score_threshold,
                     query_time_ms=(time.time() - start_time) * 1000,
                     source_type="short_term_memory",
+                    from_agent=self.agent,
+                    from_task=self.task,
                 ),
             )
 

src/crewai/rag/__init__.py

@@ -1 +1,58 @@
-"""RAG (Retrieval-Augmented Generation) infrastructure for CrewAI."""
+"""RAG (Retrieval-Augmented Generation) infrastructure for CrewAI."""
+
+import sys
+import importlib
+from types import ModuleType
+from typing import Any
+
+from crewai.rag.config.types import RagConfigType
+from crewai.rag.config.utils import set_rag_config
+
+
+_module_path = __path__
+_module_file = __file__
+
+class _RagModule(ModuleType):
+    """Module wrapper to intercept attribute setting for config."""
+
+    __path__ = _module_path
+    __file__ = _module_file
+
+    def __init__(self, module_name: str):
+        """Initialize the module wrapper.
+
+        Args:
+            module_name: Name of the module.
+        """
+        super().__init__(module_name)
+
+    def __setattr__(self, name: str, value: RagConfigType) -> None:
+        """Set module attributes.
+
+        Args:
+            name: Attribute name.
+            value: Attribute value.
+        """
+        if name == "config":
+            return set_rag_config(value)
+        raise AttributeError(f"Setting attribute '{name}' is not allowed.")
+
+    def __getattr__(self, name: str) -> Any:
+        """Get module attributes.
+
+        Args:
+            name: Attribute name.
+
+        Returns:
+            The requested attribute.
+
+        Raises:
+            AttributeError: If attribute doesn't exist.
+        """
+        try:
+            return importlib.import_module(f"{self.__name__}.{name}")
+        except ImportError:
+            raise AttributeError(f"module '{self.__name__}' has no attribute '{name}'")
+
+
+sys.modules[__name__] = _RagModule(__name__)

src/crewai/rag/chromadb/client.py

@@ -0,0 +1,567 @@
+"""ChromaDB client implementation."""
+
+from typing import Any
+
+from chromadb.api.types import (
+    Embeddable,
+    EmbeddingFunction as ChromaEmbeddingFunction,
+    QueryResult,
+)
+from typing_extensions import Unpack
+
+from crewai.rag.chromadb.types import (
+    ChromaDBClientType,
+    ChromaDBCollectionCreateParams,
+    ChromaDBCollectionSearchParams,
+)
+from crewai.rag.chromadb.utils import (
+    _extract_search_params,
+    _is_async_client,
+    _is_sync_client,
+    _prepare_documents_for_chromadb,
+    _process_query_results,
+)
+from crewai.rag.core.base_client import (
+    BaseClient,
+    BaseCollectionParams,
+    BaseCollectionAddParams,
+)
+from crewai.rag.types import SearchResult
+
+
+class ChromaDBClient(BaseClient):
+    """ChromaDB implementation of the BaseClient protocol.
+
+    Provides vector database operations for ChromaDB, supporting both
+    synchronous and asynchronous clients.
+
+    Attributes:
+        client: ChromaDB client instance (ClientAPI or AsyncClientAPI).
+        embedding_function: Function to generate embeddings for documents.
+    """
+
+    def __init__(
+        self,
+        client: ChromaDBClientType,
+        embedding_function: ChromaEmbeddingFunction[Embeddable],
+    ) -> None:
+        """Initialize ChromaDBClient with client and embedding function.
+
+        Args:
+            client: Pre-configured ChromaDB client instance.
+            embedding_function: Embedding function for text to vector conversion.
+        """
+        self.client = client
+        self.embedding_function = embedding_function
+
+    def create_collection(
+        self, **kwargs: Unpack[ChromaDBCollectionCreateParams]
+    ) -> None:
+        """Create a new collection in ChromaDB.
+
+        Uses the client's default embedding function if none provided.
+
+        Keyword Args:
+            collection_name: Name of the collection to create. Must be unique.
+            configuration: Optional collection configuration specifying distance metrics,
+                HNSW parameters, or other backend-specific settings.
+            metadata: Optional metadata dictionary to attach to the collection.
+            embedding_function: Optional custom embedding function. If not provided,
+                uses the client's default embedding function.
+            data_loader: Optional data loader for batch loading data into the collection.
+            get_or_create: If True, returns existing collection if it already exists
+                instead of raising an error. Defaults to False.
+
+        Raises:
+            TypeError: If AsyncClientAPI is used instead of ClientAPI for sync operations.
+            ValueError: If collection with the same name already exists and get_or_create
+                is False.
+            ConnectionError: If unable to connect to ChromaDB server.
+
+        Example:
+            >>> client = ChromaDBClient()
+            >>> client.create_collection(
+            ...     collection_name="documents",
+            ...     metadata={"description": "Product documentation"},
+            ...     get_or_create=True
+            ... )
+        """
+        if not _is_sync_client(self.client):
+            raise TypeError(
+                "Synchronous method create_collection() requires a ClientAPI. "
+                "Use acreate_collection() for AsyncClientAPI."
+            )
+
+        metadata = kwargs.get("metadata", {})
+        if "hnsw:space" not in metadata:
+            metadata["hnsw:space"] = "cosine"
+
+        self.client.create_collection(
+            name=kwargs["collection_name"],
+            configuration=kwargs.get("configuration"),
+            metadata=metadata,
+            embedding_function=kwargs.get(
+                "embedding_function", self.embedding_function
+            ),
+            data_loader=kwargs.get("data_loader"),
+            get_or_create=kwargs.get("get_or_create", False),
+        )
+
+    async def acreate_collection(
+        self, **kwargs: Unpack[ChromaDBCollectionCreateParams]
+    ) -> None:
+        """Create a new collection in ChromaDB asynchronously.
+
+        Creates a new collection with the specified name and optional configuration.
+        If an embedding function is not provided, uses the client's default embedding function.
+
+        Keyword Args:
+            collection_name: Name of the collection to create. Must be unique.
+            configuration: Optional collection configuration specifying distance metrics,
+                HNSW parameters, or other backend-specific settings.
+            metadata: Optional metadata dictionary to attach to the collection.
+            embedding_function: Optional custom embedding function. If not provided,
+                uses the client's default embedding function.
+            data_loader: Optional data loader for batch loading data into the collection.
+            get_or_create: If True, returns existing collection if it already exists
+                instead of raising an error. Defaults to False.
+
+        Raises:
+            TypeError: If ClientAPI is used instead of AsyncClientAPI for async operations.
+            ValueError: If collection with the same name already exists and get_or_create
+                is False.
+            ConnectionError: If unable to connect to ChromaDB server.
+
+        Example:
+            >>> import asyncio
+            >>> async def main():
+            ...     client = ChromaDBClient()
+            ...     await client.acreate_collection(
+            ...         collection_name="documents",
+            ...         metadata={"description": "Product documentation"},
+            ...         get_or_create=True
+            ...     )
+            >>> asyncio.run(main())
+        """
+        if not _is_async_client(self.client):
+            raise TypeError(
+                "Asynchronous method acreate_collection() requires an AsyncClientAPI. "
+                "Use create_collection() for ClientAPI."
+            )
+
+        metadata = kwargs.get("metadata", {})
+        if "hnsw:space" not in metadata:
+            metadata["hnsw:space"] = "cosine"
+
+        await self.client.create_collection(
+            name=kwargs["collection_name"],
+            configuration=kwargs.get("configuration"),
+            metadata=metadata,
+            embedding_function=kwargs.get(
+                "embedding_function", self.embedding_function
+            ),
+            data_loader=kwargs.get("data_loader"),
+            get_or_create=kwargs.get("get_or_create", False),
+        )
+
+    def get_or_create_collection(
+        self, **kwargs: Unpack[ChromaDBCollectionCreateParams]
+    ) -> Any:
+        """Get an existing collection or create it if it doesn't exist.
+
+        Returns existing collection if found, otherwise creates a new one.
+
+        Keyword Args:
+            collection_name: Name of the collection to get or create.
+            configuration: Optional collection configuration specifying distance metrics,
+                HNSW parameters, or other backend-specific settings.
+            metadata: Optional metadata dictionary to attach to the collection.
+            embedding_function: Optional custom embedding function. If not provided,
+                uses the client's default embedding function.
+            data_loader: Optional data loader for batch loading data into the collection.
+
+        Returns:
+            A ChromaDB Collection object.
+
+        Raises:
+            TypeError: If AsyncClientAPI is used instead of ClientAPI for sync operations.
+            ConnectionError: If unable to connect to ChromaDB server.
+
+        Example:
+            >>> client = ChromaDBClient()
+            >>> collection = client.get_or_create_collection(
+            ...     collection_name="documents",
+            ...     metadata={"description": "Product documentation"}
+            ... )
+        """
+        if not _is_sync_client(self.client):
+            raise TypeError(
+                "Synchronous method get_or_create_collection() requires a ClientAPI. "
+                "Use aget_or_create_collection() for AsyncClientAPI."
+            )
+
+        metadata = kwargs.get("metadata", {})
+        if "hnsw:space" not in metadata:
+            metadata["hnsw:space"] = "cosine"
+
+        return self.client.get_or_create_collection(
+            name=kwargs["collection_name"],
+            configuration=kwargs.get("configuration"),
+            metadata=metadata,
+            embedding_function=kwargs.get(
+                "embedding_function", self.embedding_function
+            ),
+            data_loader=kwargs.get("data_loader"),
+        )
+
+    async def aget_or_create_collection(
+        self, **kwargs: Unpack[ChromaDBCollectionCreateParams]
+    ) -> Any:
+        """Get an existing collection or create it if it doesn't exist asynchronously.
+
+        Returns existing collection if found, otherwise creates a new one.
+
+        Keyword Args:
+            collection_name: Name of the collection to get or create.
+            configuration: Optional collection configuration specifying distance metrics,
+                HNSW parameters, or other backend-specific settings.
+            metadata: Optional metadata dictionary to attach to the collection.
+            embedding_function: Optional custom embedding function. If not provided,
+                uses the client's default embedding function.
+            data_loader: Optional data loader for batch loading data into the collection.
+
+        Returns:
+            A ChromaDB AsyncCollection object.
+
+        Raises:
+            TypeError: If ClientAPI is used instead of AsyncClientAPI for async operations.
+            ConnectionError: If unable to connect to ChromaDB server.
+
+        Example:
+            >>> import asyncio
+            >>> async def main():
+            ...     client = ChromaDBClient()
+            ...     collection = await client.aget_or_create_collection(
+            ...         collection_name="documents",
+            ...         metadata={"description": "Product documentation"}
+            ...     )
+            >>> asyncio.run(main())
+        """
+        if not _is_async_client(self.client):
+            raise TypeError(
+                "Asynchronous method aget_or_create_collection() requires an AsyncClientAPI. "
+                "Use get_or_create_collection() for ClientAPI."
+            )
+
+        metadata = kwargs.get("metadata", {})
+        if "hnsw:space" not in metadata:
+            metadata["hnsw:space"] = "cosine"
+
+        return await self.client.get_or_create_collection(
+            name=kwargs["collection_name"],
+            configuration=kwargs.get("configuration"),
+            metadata=metadata,
+            embedding_function=kwargs.get(
+                "embedding_function", self.embedding_function
+            ),
+            data_loader=kwargs.get("data_loader"),
+        )
+
+    def add_documents(self, **kwargs: Unpack[BaseCollectionAddParams]) -> None:
+        """Add documents with their embeddings to a collection.
+
+        Performs an upsert operation - documents with existing IDs are updated.
+        Generates embeddings automatically using the configured embedding function.
+
+        Keyword Args:
+            collection_name: The name of the collection to add documents to.
+            documents: List of BaseRecord dicts containing:
+                - content: The text content (required)
+                - doc_id: Optional unique identifier (auto-generated if missing)
+                - metadata: Optional metadata dictionary
+
+        Raises:
+            TypeError: If AsyncClientAPI is used instead of ClientAPI for sync operations.
+            ValueError: If collection doesn't exist or documents list is empty.
+            ConnectionError: If unable to connect to ChromaDB server.
+        """
+        if not _is_sync_client(self.client):
+            raise TypeError(
+                "Synchronous method add_documents() requires a ClientAPI. "
+                "Use aadd_documents() for AsyncClientAPI."
+            )
+
+        collection_name = kwargs["collection_name"]
+        documents = kwargs["documents"]
+
+        if not documents:
+            raise ValueError("Documents list cannot be empty")
+
+        collection = self.client.get_collection(
+            name=collection_name,
+            embedding_function=self.embedding_function,
+        )
+
+        prepared = _prepare_documents_for_chromadb(documents)
+        collection.add(
+            ids=prepared.ids,
+            documents=prepared.texts,
+            metadatas=prepared.metadatas,
+        )
+
+    async def aadd_documents(self, **kwargs: Unpack[BaseCollectionAddParams]) -> None:
+        """Add documents with their embeddings to a collection asynchronously.
+
+        Performs an upsert operation - documents with existing IDs are updated.
+        Generates embeddings automatically using the configured embedding function.
+
+        Keyword Args:
+            collection_name: The name of the collection to add documents to.
+            documents: List of BaseRecord dicts containing:
+                - content: The text content (required)
+                - doc_id: Optional unique identifier (auto-generated if missing)
+                - metadata: Optional metadata dictionary
+
+        Raises:
+            TypeError: If ClientAPI is used instead of AsyncClientAPI for async operations.
+            ValueError: If collection doesn't exist or documents list is empty.
+            ConnectionError: If unable to connect to ChromaDB server.
+        """
+        if not _is_async_client(self.client):
+            raise TypeError(
+                "Asynchronous method aadd_documents() requires an AsyncClientAPI. "
+                "Use add_documents() for ClientAPI."
+            )
+
+        collection_name = kwargs["collection_name"]
+        documents = kwargs["documents"]
+
+        if not documents:
+            raise ValueError("Documents list cannot be empty")
+
+        collection = await self.client.get_collection(
+            name=collection_name,
+            embedding_function=self.embedding_function,
+        )
+        prepared = _prepare_documents_for_chromadb(documents)
+        await collection.add(
+            ids=prepared.ids,
+            documents=prepared.texts,
+            metadatas=prepared.metadatas,
+        )
+
+    def search(
+        self, **kwargs: Unpack[ChromaDBCollectionSearchParams]
+    ) -> list[SearchResult]:
+        """Search for similar documents using a query.
+
+        Performs semantic search to find documents similar to the query text.
+        Uses the configured embedding function to generate query embeddings.
+
+        Keyword Args:
+            collection_name: Name of the collection to search in.
+            query: The text query to search for.
+            limit: Maximum number of results to return (default: 10).
+            metadata_filter: Optional filter for metadata fields.
+            score_threshold: Optional minimum similarity score (0-1) for results.
+            where: Optional ChromaDB where clause for metadata filtering.
+            where_document: Optional ChromaDB where clause for document content filtering.
+            include: Optional list of fields to include in results.
+
+        Returns:
+            List of SearchResult dicts containing id, content, metadata, and score.
+
+        Raises:
+            TypeError: If AsyncClientAPI is used instead of ClientAPI for sync operations.
+            ValueError: If collection doesn't exist.
+            ConnectionError: If unable to connect to ChromaDB server.
+        """
+        if not _is_sync_client(self.client):
+            raise TypeError(
+                "Synchronous method search() requires a ClientAPI. "
+                "Use asearch() for AsyncClientAPI."
+            )
+
+        params = _extract_search_params(kwargs)
+
+        collection = self.client.get_collection(
+            name=params.collection_name,
+            embedding_function=self.embedding_function,
+        )
+
+        where = params.where if params.where is not None else params.metadata_filter
+
+        results: QueryResult = collection.query(
+            query_texts=[params.query],
+            n_results=params.limit,
+            where=where,
+            where_document=params.where_document,
+            include=params.include,
+        )
+
+        return _process_query_results(
+            collection=collection,
+            results=results,
+            params=params,
+        )
+
+    async def asearch(
+        self, **kwargs: Unpack[ChromaDBCollectionSearchParams]
+    ) -> list[SearchResult]:
+        """Search for similar documents using a query asynchronously.
+
+        Performs semantic search to find documents similar to the query text.
+        Uses the configured embedding function to generate query embeddings.
+
+        Keyword Args:
+            collection_name: Name of the collection to search in.
+            query: The text query to search for.
+            limit: Maximum number of results to return (default: 10).
+            metadata_filter: Optional filter for metadata fields.
+            score_threshold: Optional minimum similarity score (0-1) for results.
+            where: Optional ChromaDB where clause for metadata filtering.
+            where_document: Optional ChromaDB where clause for document content filtering.
+            include: Optional list of fields to include in results.
+
+        Returns:
+            List of SearchResult dicts containing id, content, metadata, and score.
+
+        Raises:
+            TypeError: If ClientAPI is used instead of AsyncClientAPI for async operations.
+            ValueError: If collection doesn't exist.
+            ConnectionError: If unable to connect to ChromaDB server.
+        """
+        if not _is_async_client(self.client):
+            raise TypeError(
+                "Asynchronous method asearch() requires an AsyncClientAPI. "
+                "Use search() for ClientAPI."
+            )
+
+        params = _extract_search_params(kwargs)
+
+        collection = await self.client.get_collection(
+            name=params.collection_name,
+            embedding_function=self.embedding_function,
+        )
+
+        where = params.where if params.where is not None else params.metadata_filter
+
+        results: QueryResult = await collection.query(
+            query_texts=[params.query],
+            n_results=params.limit,
+            where=where,
+            where_document=params.where_document,
+            include=params.include,
+        )
+
+        return _process_query_results(
+            collection=collection,
+            results=results,
+            params=params,
+        )
+
+    def delete_collection(self, **kwargs: Unpack[BaseCollectionParams]) -> None:
+        """Delete a collection and all its data.
+
+        Permanently removes a collection and all documents, embeddings, and metadata it contains.
+        This operation cannot be undone.
+
+        Keyword Args:
+            collection_name: Name of the collection to delete.
+
+        Raises:
+            TypeError: If AsyncClientAPI is used instead of ClientAPI for sync operations.
+            ValueError: If collection doesn't exist.
+            ConnectionError: If unable to connect to ChromaDB server.
+
+        Example:
+            >>> client = ChromaDBClient()
+            >>> client.delete_collection(collection_name="old_documents")
+        """
+        if not _is_sync_client(self.client):
+            raise TypeError(
+                "Synchronous method delete_collection() requires a ClientAPI. "
+                "Use adelete_collection() for AsyncClientAPI."
+            )
+
+        collection_name = kwargs["collection_name"]
+        self.client.delete_collection(name=collection_name)
+
+    async def adelete_collection(self, **kwargs: Unpack[BaseCollectionParams]) -> None:
+        """Delete a collection and all its data asynchronously.
+
+        Permanently removes a collection and all documents, embeddings, and metadata it contains.
+        This operation cannot be undone.
+
+        Keyword Args:
+            collection_name: Name of the collection to delete.
+
+        Raises:
+            TypeError: If ClientAPI is used instead of AsyncClientAPI for async operations.
+            ValueError: If collection doesn't exist.
+            ConnectionError: If unable to connect to ChromaDB server.
+
+        Example:
+            >>> import asyncio
+            >>> async def main():
+            ...     client = ChromaDBClient()
+            ...     await client.adelete_collection(collection_name="old_documents")
+            >>> asyncio.run(main())
+        """
+        if not _is_async_client(self.client):
+            raise TypeError(
+                "Asynchronous method adelete_collection() requires an AsyncClientAPI. "
+                "Use delete_collection() for ClientAPI."
+            )
+
+        collection_name = kwargs["collection_name"]
+        await self.client.delete_collection(name=collection_name)
+
+    def reset(self) -> None:
+        """Reset the vector database by deleting all collections and data.
+
+        Completely clears the ChromaDB instance, removing all collections,
+        documents, embeddings, and metadata. This operation cannot be undone.
+        Use with extreme caution in production environments.
+
+        Raises:
+            TypeError: If AsyncClientAPI is used instead of ClientAPI for sync operations.
+            ConnectionError: If unable to connect to ChromaDB server.
+
+        Example:
+            >>> client = ChromaDBClient()
+            >>> client.reset()  # Removes ALL data from ChromaDB
+        """
+        if not _is_sync_client(self.client):
+            raise TypeError(
+                "Synchronous method reset() requires a ClientAPI. "
+                "Use areset() for AsyncClientAPI."
+            )
+
+        self.client.reset()
+
+    async def areset(self) -> None:
+        """Reset the vector database by deleting all collections and data asynchronously.
+
+        Completely clears the ChromaDB instance, removing all collections,
+        documents, embeddings, and metadata. This operation cannot be undone.
+        Use with extreme caution in production environments.
+
+        Raises:
+            TypeError: If ClientAPI is used instead of AsyncClientAPI for async operations.
+            ConnectionError: If unable to connect to ChromaDB server.
+
+        Example:
+            >>> import asyncio
+            >>> async def main():
+            ...     client = ChromaDBClient()
+            ...     await client.areset()  # Removes ALL data from ChromaDB
+            >>> asyncio.run(main())
+        """
+        if not _is_async_client(self.client):
+            raise TypeError(
+                "Asynchronous method areset() requires an AsyncClientAPI. "
+                "Use reset() for ClientAPI."
+            )
+
+        await self.client.reset()

src/crewai/rag/chromadb/config.py

@@ -0,0 +1,59 @@
+"""ChromaDB configuration model."""
+
+import warnings
+from dataclasses import field
+from typing import Literal, cast
+from pydantic.dataclasses import dataclass as pyd_dataclass
+from chromadb.config import Settings
+from chromadb.utils.embedding_functions import DefaultEmbeddingFunction
+
+from crewai.rag.chromadb.types import ChromaEmbeddingFunctionWrapper
+from crewai.rag.config.base import BaseRagConfig
+from crewai.rag.chromadb.constants import (
+    DEFAULT_TENANT,
+    DEFAULT_DATABASE,
+    DEFAULT_STORAGE_PATH,
+)
+
+
+warnings.filterwarnings(
+    "ignore",
+    message=".*Mixing V1 models and V2 models.*",
+    category=UserWarning,
+    module="pydantic._internal._generate_schema",
+)
+
+
+def _default_settings() -> Settings:
+    """Create default ChromaDB settings.
+
+    Returns:
+        Settings with persistent storage and reset enabled.
+    """
+    return Settings(
+        persist_directory=DEFAULT_STORAGE_PATH,
+        allow_reset=True,
+        is_persistent=True,
+    )
+
+
+def _default_embedding_function() -> ChromaEmbeddingFunctionWrapper:
+    """Create default ChromaDB embedding function.
+
+    Returns:
+        Default embedding function using all-MiniLM-L6-v2 via ONNX.
+    """
+    return cast(ChromaEmbeddingFunctionWrapper, DefaultEmbeddingFunction())
+
+
+@pyd_dataclass(frozen=True)
+class ChromaDBConfig(BaseRagConfig):
+    """Configuration for ChromaDB client."""
+
+    provider: Literal["chromadb"] = field(default="chromadb", init=False)
+    tenant: str = DEFAULT_TENANT
+    database: str = DEFAULT_DATABASE
+    settings: Settings = field(default_factory=_default_settings)
+    embedding_function: ChromaEmbeddingFunctionWrapper = field(
+        default_factory=_default_embedding_function
+    )

src/crewai/rag/chromadb/constants.py

@@ -0,0 +1,10 @@
+"""Constants for ChromaDB configuration."""
+
+import os
+from typing import Final
+
+from crewai.utilities.paths import db_storage_path
+
+DEFAULT_TENANT: Final[str] = "default_tenant"
+DEFAULT_DATABASE: Final[str] = "default_database"
+DEFAULT_STORAGE_PATH: Final[str] = os.path.join(db_storage_path(), "chromadb")

src/crewai/rag/chromadb/factory.py

@@ -0,0 +1,24 @@
+"""Factory functions for creating ChromaDB clients."""
+
+from chromadb import Client
+
+from crewai.rag.chromadb.config import ChromaDBConfig
+from crewai.rag.chromadb.client import ChromaDBClient
+
+
+def create_client(config: ChromaDBConfig) -> ChromaDBClient:
+    """Create a ChromaDBClient from configuration.
+
+    Args:
+        config: ChromaDB configuration object.
+
+    Returns:
+        Configured ChromaDBClient instance.
+    """
+
+    return ChromaDBClient(
+        client=Client(
+            settings=config.settings, tenant=config.tenant, database=config.database
+        ),
+        embedding_function=config.embedding_function,
+    )

src/crewai/rag/chromadb/types.py

@@ -0,0 +1,102 @@
+"""Type definitions specific to ChromaDB implementation."""
+
+from collections.abc import Mapping
+from typing import Any, NamedTuple
+
+from pydantic import GetCoreSchemaHandler
+from pydantic_core import CoreSchema, core_schema
+from chromadb.api import ClientAPI, AsyncClientAPI
+from chromadb.api.configuration import CollectionConfigurationInterface
+from chromadb.api.types import (
+    CollectionMetadata,
+    DataLoader,
+    Embeddable,
+    EmbeddingFunction as ChromaEmbeddingFunction,
+    Include,
+    Loadable,
+    Where,
+    WhereDocument,
+)
+
+from crewai.rag.core.base_client import BaseCollectionParams, BaseCollectionSearchParams
+
+ChromaDBClientType = ClientAPI | AsyncClientAPI
+
+
+class ChromaEmbeddingFunctionWrapper(ChromaEmbeddingFunction[Embeddable]):
+    """Base class for ChromaDB EmbeddingFunction to work with Pydantic validation."""
+
+    @classmethod
+    def __get_pydantic_core_schema__(
+        cls, _source_type: Any, _handler: GetCoreSchemaHandler
+    ) -> CoreSchema:
+        """Generate Pydantic core schema for ChromaDB EmbeddingFunction.
+
+        This allows Pydantic to handle ChromaDB's EmbeddingFunction type
+        without requiring arbitrary_types_allowed=True.
+        """
+        return core_schema.any_schema()
+
+
+class PreparedDocuments(NamedTuple):
+    """Prepared documents ready for ChromaDB insertion.
+
+    Attributes:
+        ids: List of document IDs
+        texts: List of document texts
+        metadatas: List of document metadata mappings
+    """
+
+    ids: list[str]
+    texts: list[str]
+    metadatas: list[Mapping[str, str | int | float | bool]]
+
+
+class ExtractedSearchParams(NamedTuple):
+    """Extracted search parameters for ChromaDB queries.
+
+    Attributes:
+        collection_name: Name of the collection to search
+        query: Search query text
+        limit: Maximum number of results
+        metadata_filter: Optional metadata filter
+        score_threshold: Optional minimum similarity score
+        where: Optional ChromaDB where clause
+        where_document: Optional ChromaDB document filter
+        include: Fields to include in results
+    """
+
+    collection_name: str
+    query: str
+    limit: int
+    metadata_filter: dict[str, Any] | None
+    score_threshold: float | None
+    where: Where | None
+    where_document: WhereDocument | None
+    include: Include
+
+
+class ChromaDBCollectionCreateParams(BaseCollectionParams, total=False):
+    """Parameters for creating a ChromaDB collection.
+
+    This class extends BaseCollectionParams to include any additional
+    parameters specific to ChromaDB collection creation.
+    """
+
+    configuration: CollectionConfigurationInterface
+    metadata: CollectionMetadata
+    embedding_function: ChromaEmbeddingFunction[Embeddable]
+    data_loader: DataLoader[Loadable]
+    get_or_create: bool
+
+
+class ChromaDBCollectionSearchParams(BaseCollectionSearchParams, total=False):
+    """Parameters for searching a ChromaDB collection.
+
+    This class extends BaseCollectionSearchParams to include ChromaDB-specific
+    search parameters like where clauses and include options.
+    """
+
+    where: Where
+    where_document: WhereDocument
+    include: Include

src/crewai/rag/chromadb/utils.py

@@ -0,0 +1,218 @@
+"""Utility functions for ChromaDB client implementation."""
+
+import hashlib
+from collections.abc import Mapping
+from typing import Literal, TypeGuard, cast
+
+from chromadb.api import AsyncClientAPI, ClientAPI
+from chromadb.api.types import (
+    Include,
+    IncludeEnum,
+    QueryResult,
+)
+from chromadb.api.models.AsyncCollection import AsyncCollection
+from chromadb.api.models.Collection import Collection
+from crewai.rag.chromadb.types import (
+    ChromaDBClientType,
+    ChromaDBCollectionSearchParams,
+    ExtractedSearchParams,
+    PreparedDocuments,
+)
+from crewai.rag.types import BaseRecord, SearchResult
+
+
+def _is_sync_client(client: ChromaDBClientType) -> TypeGuard[ClientAPI]:
+    """Type guard to check if the client is a synchronous ClientAPI.
+
+    Args:
+        client: The client to check.
+
+    Returns:
+        True if the client is a ClientAPI, False otherwise.
+    """
+    return isinstance(client, ClientAPI)
+
+
+def _is_async_client(client: ChromaDBClientType) -> TypeGuard[AsyncClientAPI]:
+    """Type guard to check if the client is an asynchronous AsyncClientAPI.
+
+    Args:
+        client: The client to check.
+
+    Returns:
+        True if the client is an AsyncClientAPI, False otherwise.
+    """
+    return isinstance(client, AsyncClientAPI)
+
+
+def _prepare_documents_for_chromadb(
+    documents: list[BaseRecord],
+) -> PreparedDocuments:
+    """Prepare documents for ChromaDB by extracting IDs, texts, and metadata.
+
+    Args:
+        documents: List of BaseRecord documents to prepare.
+
+    Returns:
+        PreparedDocuments with ids, texts, and metadatas ready for ChromaDB.
+    """
+    ids: list[str] = []
+    texts: list[str] = []
+    metadatas: list[Mapping[str, str | int | float | bool]] = []
+
+    for doc in documents:
+        if "doc_id" in doc:
+            ids.append(doc["doc_id"])
+        else:
+            content_hash = hashlib.sha256(doc["content"].encode()).hexdigest()[:16]
+            ids.append(content_hash)
+
+        texts.append(doc["content"])
+        metadata = doc.get("metadata")
+        if metadata:
+            if isinstance(metadata, list):
+                metadatas.append(metadata[0] if metadata else {})
+            else:
+                metadatas.append(metadata)
+        else:
+            metadatas.append({})
+
+    return PreparedDocuments(ids, texts, metadatas)
+
+
+def _extract_search_params(
+    kwargs: ChromaDBCollectionSearchParams,
+) -> ExtractedSearchParams:
+    """Extract search parameters from kwargs.
+
+    Args:
+        kwargs: Keyword arguments containing search parameters.
+
+    Returns:
+        ExtractedSearchParams with all extracted parameters.
+    """
+    return ExtractedSearchParams(
+        collection_name=kwargs["collection_name"],
+        query=kwargs["query"],
+        limit=kwargs.get("limit", 10),
+        metadata_filter=kwargs.get("metadata_filter"),
+        score_threshold=kwargs.get("score_threshold"),
+        where=kwargs.get("where"),
+        where_document=kwargs.get("where_document"),
+        include=kwargs.get(
+            "include",
+            [IncludeEnum.metadatas, IncludeEnum.documents, IncludeEnum.distances],
+        ),
+    )
+
+
+def _convert_distance_to_score(
+    distance: float,
+    distance_metric: Literal["l2", "cosine", "ip"],
+) -> float:
+    """Convert ChromaDB distance to similarity score.
+
+    Notes:
+        Assuming all embedding are unit-normalized for now, including custom embeddings.
+
+    Args:
+        distance: The distance value from ChromaDB.
+        distance_metric: The distance metric used ("l2", "cosine", or "ip").
+
+    Returns:
+        Similarity score in range [0, 1] where 1 is most similar.
+    """
+    if distance_metric == "cosine":
+        score = 1.0 - 0.5 * distance
+        return max(0.0, min(1.0, score))
+    raise ValueError(f"Unsupported distance metric: {distance_metric}")
+
+
+def _convert_chromadb_results_to_search_results(
+    results: QueryResult,
+    include: Include,
+    distance_metric: Literal["l2", "cosine", "ip"],
+    score_threshold: float | None = None,
+) -> list[SearchResult]:
+    """Convert ChromaDB query results to SearchResult format.
+
+    Args:
+        results: ChromaDB query results.
+        include: List of fields that were included in the query.
+        distance_metric: The distance metric used by the collection.
+        score_threshold: Optional minimum similarity score (0-1) for results.
+
+    Returns:
+        List of SearchResult dicts containing id, content, metadata, and score.
+    """
+    search_results: list[SearchResult] = []
+
+    include_strings = [item.value for item in include]
+
+    ids = results["ids"][0] if results.get("ids") else []
+
+    documents_list = results.get("documents")
+    documents = (
+        documents_list[0] if documents_list and "documents" in include_strings else []
+    )
+
+    metadatas_list = results.get("metadatas")
+    metadatas = (
+        metadatas_list[0] if metadatas_list and "metadatas" in include_strings else []
+    )
+
+    distances_list = results.get("distances")
+    distances = (
+        distances_list[0] if distances_list and "distances" in include_strings else []
+    )
+
+    for i, doc_id in enumerate(ids):
+        if not distances or i >= len(distances):
+            continue
+
+        distance = distances[i]
+        score = _convert_distance_to_score(
+            distance=distance, distance_metric=distance_metric
+        )
+
+        if score_threshold and score < score_threshold:
+            continue
+
+        result: SearchResult = {
+            "id": doc_id,
+            "content": documents[i] if documents and i < len(documents) else "",
+            "metadata": dict(metadatas[i]) if metadatas and i < len(metadatas) else {},
+            "score": score,
+        }
+        search_results.append(result)
+
+    return search_results
+
+
+def _process_query_results(
+    collection: Collection | AsyncCollection,
+    results: QueryResult,
+    params: ExtractedSearchParams,
+) -> list[SearchResult]:
+    """Process ChromaDB query results and convert to SearchResult format.
+
+    Args:
+        collection: The ChromaDB collection (sync or async) that was queried.
+        results: Raw query results from ChromaDB.
+        params: The search parameters used for the query.
+
+    Returns:
+        List of SearchResult dicts containing id, content, metadata, and score.
+    """
+
+    distance_metric = cast(
+        Literal["l2", "cosine", "ip"],
+        collection.metadata.get("hnsw:space", "l2") if collection.metadata else "l2",
+    )
+
+    return _convert_chromadb_results_to_search_results(
+        results=results,
+        include=params.include,
+        distance_metric=distance_metric,
+        score_threshold=params.score_threshold,
+    )

src/crewai/rag/config/__init__.py

@@ -0,0 +1 @@
+"""RAG client configuration management using ContextVars for thread-safe provider switching."""

src/crewai/rag/config/base.py

@@ -0,0 +1,16 @@
+"""Base configuration class for RAG providers."""
+
+from dataclasses import field
+from typing import Any
+
+from pydantic.dataclasses import dataclass as pyd_dataclass
+
+from crewai.rag.config.optional_imports.types import SupportedProvider
+
+
+@pyd_dataclass(frozen=True)
+class BaseRagConfig:
+    """Base class for RAG configuration with Pydantic serialization support."""
+
+    provider: SupportedProvider = field(init=False)
+    embedding_function: Any | None = field(default=None)

src/crewai/rag/config/constants.py

@@ -0,0 +1,8 @@
+"""Constants for RAG configuration."""
+
+from typing import Final
+
+DISCRIMINATOR: Final[str] = "provider"
+
+DEFAULT_RAG_CONFIG_PATH: Final[str] = "crewai.rag.chromadb.config"
+DEFAULT_RAG_CONFIG_CLASS: Final[str] = "ChromaDBConfig"

src/crewai/rag/config/optional_imports/__init__.py

@@ -0,0 +1 @@
+"""Optional imports for RAG configuration providers."""

src/crewai/rag/config/optional_imports/base.py

@@ -0,0 +1,26 @@
+"""Base classes for missing provider configurations."""
+
+from typing import Literal
+from dataclasses import field
+
+from pydantic import ConfigDict
+from pydantic.dataclasses import dataclass as pyd_dataclass
+
+
+@pyd_dataclass(config=ConfigDict(extra="forbid"))
+class _MissingProvider:
+    """Base class for missing provider configurations.
+
+    Raises RuntimeError when instantiated to indicate missing dependencies.
+    """
+
+    provider: Literal["chromadb", "qdrant", "__missing__"] = field(
+        default="__missing__"
+    )
+
+    def __post_init__(self) -> None:
+        """Raises error indicating the provider is not installed."""
+        raise RuntimeError(
+            f"provider '{self.provider}' requested but not installed. "
+            f"Install the extra: `uv add crewai'[{self.provider}]'`."
+        )

src/crewai/rag/config/optional_imports/protocols.py

@@ -0,0 +1,27 @@
+"""Protocol definitions for RAG factory modules."""
+
+from __future__ import annotations
+
+from typing import Protocol, TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from crewai.rag.chromadb.client import ChromaDBClient
+    from crewai.rag.chromadb.config import ChromaDBConfig
+    from crewai.rag.qdrant.client import QdrantClient
+    from crewai.rag.qdrant.config import QdrantConfig
+
+
+class ChromaFactoryModule(Protocol):
+    """Protocol for ChromaDB factory module."""
+
+    def create_client(self, config: ChromaDBConfig) -> ChromaDBClient:
+        """Creates a ChromaDB client from configuration."""
+        ...
+
+
+class QdrantFactoryModule(Protocol):
+    """Protocol for Qdrant factory module."""
+
+    def create_client(self, config: QdrantConfig) -> QdrantClient:
+        """Creates a Qdrant client from configuration."""
+        ...

src/crewai/rag/config/optional_imports/providers.py

@@ -0,0 +1,22 @@
+"""Provider-specific missing configuration classes."""
+
+from typing import Literal
+from dataclasses import field
+from pydantic import ConfigDict
+from pydantic.dataclasses import dataclass as pyd_dataclass
+
+from crewai.rag.config.optional_imports.base import _MissingProvider
+
+
+@pyd_dataclass(config=ConfigDict(extra="forbid"))
+class MissingChromaDBConfig(_MissingProvider):
+    """Placeholder for missing ChromaDB configuration."""
+
+    provider: Literal["chromadb"] = field(default="chromadb")
+
+
+@pyd_dataclass(config=ConfigDict(extra="forbid"))
+class MissingQdrantConfig(_MissingProvider):
+    """Placeholder for missing Qdrant configuration."""
+
+    provider: Literal["qdrant"] = field(default="qdrant")

src/crewai/rag/config/optional_imports/types.py

@@ -0,0 +1,8 @@
+"""Type definitions for optional imports."""
+
+from typing import Annotated, Literal
+
+SupportedProvider = Annotated[
+    Literal["chromadb", "qdrant"],
+    "Supported RAG provider types, add providers here as they become available",
+]

src/crewai/rag/config/types.py

@@ -0,0 +1,34 @@
+"""Type definitions for RAG configuration."""
+
+from typing import Annotated, TypeAlias, TYPE_CHECKING
+from pydantic import Field
+
+from crewai.rag.config.constants import DISCRIMINATOR
+
+# Linter freaks out on conditional imports, assigning in the type checking fixes it
+if TYPE_CHECKING:
+    from crewai.rag.chromadb.config import ChromaDBConfig as ChromaDBConfig_
+
+    ChromaDBConfig = ChromaDBConfig_
+    from crewai.rag.qdrant.config import QdrantConfig as QdrantConfig_
+
+    QdrantConfig = QdrantConfig_
+else:
+    try:
+        from crewai.rag.chromadb.config import ChromaDBConfig
+    except ImportError:
+        from crewai.rag.config.optional_imports.providers import (
+            MissingChromaDBConfig as ChromaDBConfig,
+        )
+
+    try:
+        from crewai.rag.qdrant.config import QdrantConfig
+    except ImportError:
+        from crewai.rag.config.optional_imports.providers import (
+            MissingQdrantConfig as QdrantConfig,
+        )
+
+SupportedProviderConfig: TypeAlias = ChromaDBConfig | QdrantConfig
+RagConfigType: TypeAlias = Annotated[
+    SupportedProviderConfig, Field(discriminator=DISCRIMINATOR)
+]

src/crewai/rag/config/utils.py

@@ -0,0 +1,86 @@
+"""RAG client configuration utilities."""
+
+from contextvars import ContextVar
+
+from pydantic import BaseModel, Field
+
+from crewai.utilities.import_utils import require
+from crewai.rag.core.base_client import BaseClient
+from crewai.rag.config.types import RagConfigType
+from crewai.rag.config.constants import (
+    DEFAULT_RAG_CONFIG_PATH,
+    DEFAULT_RAG_CONFIG_CLASS,
+)
+from crewai.rag.factory import create_client
+
+
+class RagContext(BaseModel):
+    """Context holding RAG configuration and client instance."""
+
+    config: RagConfigType = Field(..., description="RAG provider configuration")
+    client: BaseClient | None = Field(
+        default=None, description="Instantiated RAG client"
+    )
+
+
+_rag_context: ContextVar[RagContext | None] = ContextVar("_rag_context", default=None)
+
+
+def set_rag_config(config: RagConfigType) -> None:
+    """Set global RAG client configuration and instantiate the client.
+
+    Args:
+        config: The RAG client configuration (ChromaDBConfig).
+    """
+    client = create_client(config)
+    context = RagContext(config=config, client=client)
+    _rag_context.set(context)
+
+
+def get_rag_config() -> RagConfigType:
+    """Get current RAG configuration.
+
+    Returns:
+        The current RAG configuration object.
+    """
+    context = _rag_context.get()
+    if context is None:
+        module = require(DEFAULT_RAG_CONFIG_PATH, purpose="RAG configuration")
+        config_class = getattr(module, DEFAULT_RAG_CONFIG_CLASS)
+        default_config = config_class()
+        set_rag_config(default_config)
+        context = _rag_context.get()
+
+    if context is None or context.config is None:
+        raise ValueError(
+            "RAG configuration is not set. Please set the RAG config first."
+        )
+
+    return context.config
+
+
+def get_rag_client() -> BaseClient:
+    """Get the current RAG client instance.
+
+    Returns:
+        The current RAG client, creating one if needed.
+    """
+    context = _rag_context.get()
+    if context is None:
+        get_rag_config()
+        context = _rag_context.get()
+
+    if context and context.client is None:
+        context.client = create_client(context.config)
+
+    if context is None or context.client is None:
+        raise ValueError(
+            "RAG client is not configured. Please set the RAG config first."
+        )
+
+    return context.client
+
+
+def clear_rag_config() -> None:
+    """Clear the current RAG configuration and client, reverting to defaults."""
+    _rag_context.set(None)

src/crewai/rag/core/__init__.py

@@ -0,0 +1 @@
+"""Core abstract base classes and protocols for RAG  systems."""

src/crewai/rag/core/base_client.py

@@ -0,0 +1,446 @@
+"""Protocol for vector database client implementations."""
+
+from abc import abstractmethod
+from typing import Any, Protocol, runtime_checkable, Annotated
+from typing_extensions import Unpack, Required, TypedDict
+from pydantic import GetCoreSchemaHandler
+from pydantic_core import CoreSchema, core_schema
+
+
+from crewai.rag.types import (
+    EmbeddingFunction,
+    BaseRecord,
+    SearchResult,
+)
+
+
+class BaseCollectionParams(TypedDict):
+    """Base parameters for collection operations.
+
+    Attributes:
+        collection_name: The name of the collection/index to operate on.
+    """
+
+    collection_name: Required[
+        Annotated[
+            str,
+            "Name of the collection/index. Implementations may have specific constraints (e.g., character limits, allowed characters, case sensitivity).",
+        ]
+    ]
+
+
+class BaseCollectionAddParams(BaseCollectionParams):
+    """Parameters for adding documents to a collection.
+
+    Extends BaseCollectionParams with document-specific fields.
+
+    Attributes:
+        collection_name: The name of the collection to add documents to.
+        documents: List of BaseRecord dictionaries containing document data.
+    """
+
+    documents: list[BaseRecord]
+
+
+class BaseCollectionSearchParams(BaseCollectionParams, total=False):
+    """Parameters for searching within a collection.
+
+    Extends BaseCollectionParams with search-specific optional fields.
+    All fields except collection_name and query are optional.
+
+    Attributes:
+        query: The text query to search for (required).
+        limit: Maximum number of results to return.
+        metadata_filter: Filter results by metadata fields.
+        score_threshold: Minimum similarity score for results (0-1).
+    """
+
+    query: Required[str]
+    limit: int
+    metadata_filter: dict[str, Any]
+    score_threshold: float
+
+
+@runtime_checkable
+class BaseClient(Protocol):
+    """Protocol for vector store client implementations.
+
+    This protocol defines the interface that all vector store client implementations
+    must follow. It provides a consistent API for storing and retrieving
+    documents with their vector embeddings across different vector database
+    backends (e.g., Qdrant, ChromaDB, Weaviate). Implementing classes should
+    handle connection management, data persistence, and vector similarity
+    search operations specific to their backend.
+
+    Implementation Guidelines:
+        Implementations should accept BaseClientParams in their constructor to allow
+        passing pre-configured client instances:
+
+        class MyVectorClient:
+            def __init__(self, client: Any | None = None, **kwargs):
+                if client:
+                    self.client = client
+                else:
+                    self.client = self._create_default_client(**kwargs)
+
+    Notes:
+      This protocol replaces the former BaseRAGStorage abstraction,
+      providing a cleaner interface for vector store operations.
+
+    Attributes:
+        embedding_function: Callable that takes a list of text strings
+            and returns a list of embedding vectors. Implementations
+            should always provide a default embedding function.
+        client: The underlying vector database client instance. This could be
+            passed via BaseClientParams during initialization or created internally.
+    """
+
+    client: Any
+    embedding_function: EmbeddingFunction
+
+    @classmethod
+    def __get_pydantic_core_schema__(
+        cls, _source_type: Any, _handler: GetCoreSchemaHandler
+    ) -> CoreSchema:
+        """Generate Pydantic core schema for BaseClient Protocol.
+
+        This allows the Protocol to be used in Pydantic models without
+        requiring arbitrary_types_allowed=True.
+        """
+        return core_schema.any_schema()
+
+    @abstractmethod
+    def create_collection(self, **kwargs: Unpack[BaseCollectionParams]) -> None:
+        """Create a new collection/index in the vector database.
+
+        Keyword Args:
+            collection_name: The name of the collection to create. Must be unique within
+                the vector database instance.
+
+        Raises:
+            ValueError: If collection name already exists.
+            ConnectionError: If unable to connect to the vector database backend.
+        """
+        ...
+
+    @abstractmethod
+    async def acreate_collection(self, **kwargs: Unpack[BaseCollectionParams]) -> None:
+        """Create a new collection/index in the vector database asynchronously.
+
+        Keyword Args:
+            collection_name: The name of the collection to create. Must be unique within
+                the vector database instance.
+
+        Raises:
+            ValueError: If collection name already exists.
+            ConnectionError: If unable to connect to the vector database backend.
+        """
+        ...
+
+    @abstractmethod
+    def get_or_create_collection(self, **kwargs: Unpack[BaseCollectionParams]) -> Any:
+        """Get an existing collection or create it if it doesn't exist.
+
+        This method provides a convenient way to ensure a collection exists
+        without having to check for its existence first.
+
+        Keyword Args:
+            collection_name: The name of the collection to get or create.
+
+        Returns:
+            A collection object whose type depends on the backend implementation.
+            This could be a collection reference, ID, or client object.
+
+        Raises:
+            ValueError: If unable to create the collection.
+            ConnectionError: If unable to connect to the vector database backend.
+        """
+        ...
+
+    @abstractmethod
+    async def aget_or_create_collection(
+        self, **kwargs: Unpack[BaseCollectionParams]
+    ) -> Any:
+        """Get an existing collection or create it if it doesn't exist asynchronously.
+
+        Keyword Args:
+            collection_name: The name of the collection to get or create.
+
+        Returns:
+            A collection object whose type depends on the backend implementation.
+
+        Raises:
+            ValueError: If unable to create the collection.
+            ConnectionError: If unable to connect to the vector database backend.
+        """
+        ...
+
+    @abstractmethod
+    def add_documents(self, **kwargs: Unpack[BaseCollectionAddParams]) -> None:
+        """Add documents with their embeddings to a collection.
+
+        This method performs an upsert operation - if a document with the same ID
+        already exists, it will be updated with the new content and metadata.
+
+        Implementations should handle embedding generation internally based on
+        the configured embedding function.
+
+        Keyword Args:
+            collection_name: The name of the collection to add documents to.
+            documents: List of BaseRecord dicts containing:
+                - content: The text content (required)
+                - doc_id: Optional unique identifier (auto-generated from content hash if missing)
+                - metadata: Optional metadata dictionary
+                Embeddings will be generated automatically.
+
+        Raises:
+            ValueError: If collection doesn't exist or documents list is empty.
+            TypeError: If documents are not BaseRecord dict instances.
+            ConnectionError: If unable to connect to the vector database backend.
+
+        Example:
+            >>> from crewai.rag.chromadb.client import ChromaDBClient
+            >>> from crewai.rag.types import BaseRecord
+            >>> client = ChromaDBClient()
+            >>>
+            >>> records: list[BaseRecord] = [
+            ...     {
+            ...         "content": "Machine learning basics",
+            ...         "metadata": {"source": "file3", "topic": "ML"}
+            ...     },
+            ...     {
+            ...         "doc_id": "custom_id",
+            ...         "content": "Deep learning fundamentals",
+            ...         "metadata": {"source": "file4", "topic": "DL"}
+            ...     }
+            ... ]
+            >>> client.add_documents(collection_name="my_docs", documents=records)
+            >>>
+            >>> records_with_id: list[BaseRecord] = [
+            ...     {
+            ...         "doc_id": "nlp_001",
+            ...         "content": "Advanced NLP techniques",
+            ...         "metadata": {"source": "file5", "topic": "NLP"}
+            ...     }
+            ... ]
+            >>> client.add_documents(collection_name="my_docs", documents=records_with_id)
+        """
+        ...
+
+    @abstractmethod
+    async def aadd_documents(self, **kwargs: Unpack[BaseCollectionAddParams]) -> None:
+        """Add documents with their embeddings to a collection asynchronously.
+
+        Implementations should handle embedding generation internally based on
+        the configured embedding function.
+
+        Keyword Args:
+            collection_name: The name of the collection to add documents to.
+            documents: List of BaseRecord dicts containing:
+                - content: The text content (required)
+                - doc_id: Optional unique identifier (auto-generated from content hash if missing)
+                - metadata: Optional metadata dictionary
+                Embeddings will be generated automatically.
+
+        Raises:
+            ValueError: If collection doesn't exist or documents list is empty.
+            TypeError: If documents are not BaseRecord dict instances.
+            ConnectionError: If unable to connect to the vector database backend.
+
+        Example:
+            >>> import asyncio
+            >>> from crewai.rag.chromadb.client import ChromaDBClient
+            >>> from crewai.rag.types import BaseRecord
+            >>>
+            >>> async def add_documents():
+            ...     client = ChromaDBClient()
+            ...
+            ...     records: list[BaseRecord] = [
+            ...         {
+            ...             "doc_id": "doc2",
+            ...             "content": "Async operations in Python",
+            ...             "metadata": {"source": "file2", "topic": "async"}
+            ...         }
+            ...     ]
+            ...     await client.aadd_documents(collection_name="my_docs", documents=records)
+            ...
+            >>> asyncio.run(add_documents())
+        """
+        ...
+
+    @abstractmethod
+    def search(
+        self, **kwargs: Unpack[BaseCollectionSearchParams]
+    ) -> list[SearchResult]:
+        """Search for similar documents using a query.
+
+        Performs a vector similarity search to find the most similar documents
+        to the provided query.
+
+        Keyword Args:
+            collection_name: The name of the collection to search in.
+            query: The text query to search for. The implementation handles
+                embedding generation internally.
+            limit: Maximum number of results to return. Defaults to 10.
+            metadata_filter: Optional metadata filter to apply to the search. The exact
+                format depends on the backend, but typically supports equality
+                and range queries on metadata fields.
+            score_threshold: Optional minimum similarity score threshold. Only
+                results with scores >= this threshold will be returned. The
+                score interpretation depends on the distance metric used.
+
+        Returns:
+            A list of SearchResult dictionaries ordered by similarity score in
+            descending order. Each result contains:
+                - id: Document ID
+                - content: Document text content
+                - metadata: Document metadata
+                - score: Similarity score (0-1, higher is better)
+
+        Raises:
+            ValueError: If collection doesn't exist.
+            ConnectionError: If unable to connect to the vector database backend.
+
+        Example:
+            >>> from crewai.rag.chromadb.client import ChromaDBClient
+            >>> client = ChromaDBClient()
+            >>>
+            >>> results = client.search(
+            ...     collection_name="my_docs",
+            ...     query="What is machine learning?",
+            ...     limit=5,
+            ...     metadata_filter={"source": "file1"},
+            ...     score_threshold=0.7
+            ... )
+            >>> for result in results:
+            ...     print(f"{result['id']}: {result['score']:.2f}")
+        """
+        ...
+
+    @abstractmethod
+    async def asearch(
+        self, **kwargs: Unpack[BaseCollectionSearchParams]
+    ) -> list[SearchResult]:
+        """Search for similar documents using a query asynchronously.
+
+        Keyword Args:
+            collection_name: The name of the collection to search in.
+            query: The text query to search for. The implementation handles
+                embedding generation internally.
+            limit: Maximum number of results to return. Defaults to 10.
+            metadata_filter: Optional metadata filter to apply to the search.
+            score_threshold: Optional minimum similarity score threshold.
+
+        Returns:
+            A list of SearchResult dictionaries ordered by similarity score.
+
+        Raises:
+            ValueError: If collection doesn't exist.
+            ConnectionError: If unable to connect to the vector database backend.
+
+        Example:
+            >>> import asyncio
+            >>> from crewai.rag.chromadb.client import ChromaDBClient
+            >>>
+            >>> async def search_documents():
+            ...     client = ChromaDBClient()
+            ...     results = await client.asearch(
+            ...         collection_name="my_docs",
+            ...         query="Python programming best practices",
+            ...         limit=5,
+            ...         metadata_filter={"source": "file1"},
+            ...         score_threshold=0.7
+            ...     )
+            ...     for result in results:
+            ...         print(f"{result['id']}: {result['score']:.2f}")
+            ...
+            >>> asyncio.run(search_documents())
+        """
+        ...
+
+    @abstractmethod
+    def delete_collection(self, **kwargs: Unpack[BaseCollectionParams]) -> None:
+        """Delete a collection and all its data.
+
+        This operation is irreversible and will permanently remove all documents,
+        embeddings, and metadata associated with the collection.
+
+        Keyword Args:
+            collection_name: The name of the collection to delete.
+
+        Raises:
+            ValueError: If the collection doesn't exist.
+            ConnectionError: If unable to connect to the vector database backend.
+
+        Example:
+            >>> from crewai.rag.chromadb.client import ChromaDBClient
+            >>> client = ChromaDBClient()
+            >>> client.delete_collection(collection_name="old_docs")
+            >>> print("Collection 'old_docs' deleted successfully")
+        """
+        ...
+
+    @abstractmethod
+    async def adelete_collection(self, **kwargs: Unpack[BaseCollectionParams]) -> None:
+        """Delete a collection and all its data asynchronously.
+
+        Keyword Args:
+            collection_name: The name of the collection to delete.
+
+        Raises:
+            ValueError: If the collection doesn't exist.
+            ConnectionError: If unable to connect to the vector database backend.
+
+        Example:
+            >>> import asyncio
+            >>> from crewai.rag.chromadb.client import ChromaDBClient
+            >>>
+            >>> async def delete_old_collection():
+            ...     client = ChromaDBClient()
+            ...     await client.adelete_collection(collection_name="old_docs")
+            ...     print("Collection 'old_docs' deleted successfully")
+            ...
+            >>> asyncio.run(delete_old_collection())
+        """
+        ...
+
+    @abstractmethod
+    def reset(self) -> None:
+        """Reset the vector database by deleting all collections and data.
+
+        This method provides a way to completely clear the vector database,
+        removing all collections and their contents. Use with caution as
+        this operation is irreversible.
+
+        Raises:
+            ConnectionError: If unable to connect to the vector database backend.
+            PermissionError: If the operation is not allowed by the backend.
+
+        Example:
+            >>> from crewai.rag.chromadb.client import ChromaDBClient
+            >>> client = ChromaDBClient()
+            >>> client.reset()
+            >>> print("Vector database completely reset - all data deleted")
+        """
+        ...
+
+    @abstractmethod
+    async def areset(self) -> None:
+        """Reset the vector database by deleting all collections and data asynchronously.
+
+        Raises:
+            ConnectionError: If unable to connect to the vector database backend.
+            PermissionError: If the operation is not allowed by the backend.
+
+        Example:
+            >>> import asyncio
+            >>> from crewai.rag.chromadb.client import ChromaDBClient
+            >>>
+            >>> async def reset_database():
+            ...     client = ChromaDBClient()
+            ...     await client.areset()
+            ...     print("Vector database completely reset - all data deleted")
+            ...
+            >>> asyncio.run(reset_database())
+        """
+        ...

src/crewai/rag/core/exceptions.py

@@ -0,0 +1,26 @@
+"""Core exceptions for RAG module."""
+
+
+class ClientMethodMismatchError(TypeError):
+    """Raised when a method is called with the wrong client type.
+
+    Typically used when a sync method is called with an async client,
+    or vice versa.
+    """
+
+    def __init__(
+        self, method_name: str, expected_client: str, alt_method: str, alt_client: str
+    ) -> None:
+        """Create a ClientMethodMismatchError.
+
+        Args:
+            method_name: Method that was called incorrectly.
+            expected_client: Required client type.
+            alt_method: Suggested alternative method.
+            alt_client: Client type for the alternative method.
+        """
+        message = (
+            f"Method {method_name}() requires a {expected_client}. "
+            f"Use {alt_method}() for {alt_client}."
+        )
+        super().__init__(message)

src/crewai/rag/embeddings/factory.py

@@ -0,0 +1,148 @@
+"""Minimal embedding function factory for CrewAI."""
+
+import os
+
+from chromadb import EmbeddingFunction
+from chromadb.utils.embedding_functions.amazon_bedrock_embedding_function import (
+    AmazonBedrockEmbeddingFunction,
+)
+from chromadb.utils.embedding_functions.cohere_embedding_function import (
+    CohereEmbeddingFunction,
+)
+from chromadb.utils.embedding_functions.google_embedding_function import (
+    GooglePalmEmbeddingFunction,
+    GoogleGenerativeAiEmbeddingFunction,
+    GoogleVertexEmbeddingFunction,
+)
+from chromadb.utils.embedding_functions.huggingface_embedding_function import (
+    HuggingFaceEmbeddingFunction,
+)
+from chromadb.utils.embedding_functions.instructor_embedding_function import (
+    InstructorEmbeddingFunction,
+)
+from chromadb.utils.embedding_functions.jina_embedding_function import (
+    JinaEmbeddingFunction,
+)
+from chromadb.utils.embedding_functions.ollama_embedding_function import (
+    OllamaEmbeddingFunction,
+)
+from chromadb.utils.embedding_functions.onnx_mini_lm_l6_v2 import ONNXMiniLM_L6_V2
+from chromadb.utils.embedding_functions.open_clip_embedding_function import (
+    OpenCLIPEmbeddingFunction,
+)
+from chromadb.utils.embedding_functions.openai_embedding_function import (
+    OpenAIEmbeddingFunction,
+)
+from chromadb.utils.embedding_functions.roboflow_embedding_function import (
+    RoboflowEmbeddingFunction,
+)
+from chromadb.utils.embedding_functions.sentence_transformer_embedding_function import (
+    SentenceTransformerEmbeddingFunction,
+)
+from chromadb.utils.embedding_functions.text2vec_embedding_function import (
+    Text2VecEmbeddingFunction,
+)
+
+from crewai.rag.embeddings.types import EmbeddingOptions
+
+
+def get_embedding_function(
+    config: EmbeddingOptions | dict | None = None,
+) -> EmbeddingFunction:
+    """Get embedding function - delegates to ChromaDB.
+
+    Args:
+        config: Optional configuration - either an EmbeddingOptions object or a dict with:
+            - provider: The embedding provider to use (default: "openai")
+            - Any other provider-specific parameters
+
+    Returns:
+        EmbeddingFunction instance ready for use with ChromaDB
+
+    Supported providers:
+        - openai: OpenAI embeddings (default)
+        - cohere: Cohere embeddings
+        - ollama: Ollama local embeddings
+        - huggingface: HuggingFace embeddings
+        - sentence-transformer: Local sentence transformers
+        - instructor: Instructor embeddings for specialized tasks
+        - google-palm: Google PaLM embeddings
+        - google-generativeai: Google Generative AI embeddings
+        - google-vertex: Google Vertex AI embeddings
+        - amazon-bedrock: AWS Bedrock embeddings
+        - jina: Jina AI embeddings
+        - roboflow: Roboflow embeddings for vision tasks
+        - openclip: OpenCLIP embeddings for multimodal tasks
+        - text2vec: Text2Vec embeddings
+        - onnx: ONNX MiniLM-L6-v2 (no API key needed, included with ChromaDB)
+
+    Examples:
+        # Use default OpenAI with retry logic
+        >>> embedder = get_embedding_function()
+
+        # Use Cohere with dict
+        >>> embedder = get_embedding_function({
+        ...     "provider": "cohere",
+        ...     "api_key": "your-key",
+        ...     "model_name": "embed-english-v3.0"
+        ... })
+
+        # Use with EmbeddingOptions
+        >>> embedder = get_embedding_function(
+        ...     EmbeddingOptions(provider="sentence-transformer", model_name="all-MiniLM-L6-v2")
+        ... )
+
+        # Use local sentence transformers (no API key needed)
+        >>> embedder = get_embedding_function({
+        ...     "provider": "sentence-transformer",
+        ...     "model_name": "all-MiniLM-L6-v2"
+        ... })
+
+        # Use Ollama for local embeddings
+        >>> embedder = get_embedding_function({
+        ...     "provider": "ollama",
+        ...     "model_name": "nomic-embed-text"
+        ... })
+
+        # Use ONNX (no API key needed)
+        >>> embedder = get_embedding_function({
+        ...     "provider": "onnx"
+        ... })
+    """
+    if config is None:
+        return OpenAIEmbeddingFunction(
+            api_key=os.getenv("OPENAI_API_KEY"), model_name="text-embedding-3-small"
+        )
+
+    # Handle EmbeddingOptions object
+    if isinstance(config, EmbeddingOptions):
+        config_dict = config.model_dump(exclude_none=True)
+    else:
+        config_dict = config.copy()
+
+    provider = config_dict.pop("provider", "openai")
+
+    embedding_functions = {
+        "openai": OpenAIEmbeddingFunction,
+        "cohere": CohereEmbeddingFunction,
+        "ollama": OllamaEmbeddingFunction,
+        "huggingface": HuggingFaceEmbeddingFunction,
+        "sentence-transformer": SentenceTransformerEmbeddingFunction,
+        "instructor": InstructorEmbeddingFunction,
+        "google-palm": GooglePalmEmbeddingFunction,
+        "google-generativeai": GoogleGenerativeAiEmbeddingFunction,
+        "google-vertex": GoogleVertexEmbeddingFunction,
+        "amazon-bedrock": AmazonBedrockEmbeddingFunction,
+        "jina": JinaEmbeddingFunction,
+        "roboflow": RoboflowEmbeddingFunction,
+        "openclip": OpenCLIPEmbeddingFunction,
+        "text2vec": Text2VecEmbeddingFunction,
+        "onnx": ONNXMiniLM_L6_V2,
+    }
+
+    if provider not in embedding_functions:
+        raise ValueError(
+            f"Unsupported provider: {provider}. "
+            f"Available providers: {list(embedding_functions.keys())}"
+        )
+    return embedding_functions[provider](**config_dict)

src/crewai/rag/embeddings/types.py

@@ -0,0 +1,62 @@
+"""Type definitions for the embeddings module."""
+
+from typing import Literal
+from pydantic import BaseModel, Field, SecretStr
+
+from crewai.rag.types import EmbeddingFunction
+
+
+EmbeddingProvider = Literal[
+    "openai",
+    "cohere",
+    "ollama",
+    "huggingface",
+    "sentence-transformer",
+    "instructor",
+    "google-palm",
+    "google-generativeai",
+    "google-vertex",
+    "amazon-bedrock",
+    "jina",
+    "roboflow",
+    "openclip",
+    "text2vec",
+    "onnx",
+]
+"""Supported embedding providers.
+
+These correspond to the embedding functions available in ChromaDB's
+embedding_functions module. Each provider has specific requirements
+and configuration options.
+"""
+
+
+class EmbeddingOptions(BaseModel):
+    """Configuration options for embedding providers.
+
+    Generic attributes that can be passed to get_embedding_function
+    to configure various embedding providers.
+    """
+
+    provider: EmbeddingProvider = Field(
+        ..., description="Embedding provider name (e.g., 'openai', 'cohere', 'onnx')"
+    )
+    model_name: str | None = Field(
+        default=None, description="Model name for the embedding provider"
+    )
+    api_key: SecretStr | None = Field(
+        default=None, description="API key for the embedding provider"
+    )
+
+
+class EmbeddingConfig(BaseModel):
+    """Configuration wrapper for embedding functions.
+
+    Accepts either a pre-configured EmbeddingFunction or EmbeddingOptions
+    to create one. This provides flexibility in how embeddings are configured.
+
+    Attributes:
+        function: Either a callable EmbeddingFunction or EmbeddingOptions to create one
+    """
+
+    function: EmbeddingFunction | EmbeddingOptions

src/crewai/rag/factory.py

@@ -0,0 +1,45 @@
+"""Factory functions for creating RAG clients from configuration."""
+
+from typing import cast
+
+from crewai.rag.config.optional_imports.protocols import (
+    ChromaFactoryModule,
+    QdrantFactoryModule,
+)
+from crewai.rag.core.base_client import BaseClient
+from crewai.rag.config.types import RagConfigType
+from crewai.utilities.import_utils import require
+
+
+def create_client(config: RagConfigType) -> BaseClient:
+    """Create a client from configuration using the appropriate factory.
+
+    Args:
+        config: The RAG client configuration.
+
+    Returns:
+        The created client instance.
+
+    Raises:
+        ValueError: If the configuration provider is not supported.
+    """
+
+    if config.provider == "chromadb":
+        chromadb_mod = cast(
+            ChromaFactoryModule,
+            require(
+                "crewai.rag.chromadb.factory",
+                purpose="The 'chromadb' provider",
+            ),
+        )
+        return chromadb_mod.create_client(config)
+
+    if config.provider == "qdrant":
+        qdrant_mod = cast(
+            QdrantFactoryModule,
+            require(
+                "crewai.rag.qdrant.factory",
+                purpose="The 'qdrant' provider",
+            ),
+        )
+        return qdrant_mod.create_client(config)

src/crewai/rag/qdrant/__init__.py

@@ -0,0 +1 @@
+"""Qdrant vector database client implementation."""

src/crewai/rag/qdrant/client.py

@@ -0,0 +1,501 @@
+"""Qdrant client implementation."""
+
+from typing import Any, cast
+
+from typing_extensions import Unpack
+
+from crewai.rag.core.base_client import (
+    BaseClient,
+    BaseCollectionParams,
+    BaseCollectionAddParams,
+    BaseCollectionSearchParams,
+)
+from crewai.rag.core.exceptions import ClientMethodMismatchError
+from crewai.rag.qdrant.types import (
+    AsyncEmbeddingFunction,
+    EmbeddingFunction,
+    QdrantClientType,
+    QdrantCollectionCreateParams,
+)
+from crewai.rag.qdrant.utils import (
+    _is_async_client,
+    _is_async_embedding_function,
+    _is_sync_client,
+    _create_point_from_document,
+    _get_collection_params,
+    _prepare_search_params,
+    _process_search_results,
+)
+from crewai.rag.types import SearchResult
+
+
+class QdrantClient(BaseClient):
+    """Qdrant implementation of the BaseClient protocol.
+
+    Provides vector database operations for Qdrant, supporting both
+    synchronous and asynchronous clients.
+
+    Attributes:
+        client: Qdrant client instance (QdrantClient or AsyncQdrantClient).
+        embedding_function: Function to generate embeddings for documents.
+    """
+
+    def __init__(
+        self,
+        client: QdrantClientType,
+        embedding_function: EmbeddingFunction | AsyncEmbeddingFunction,
+    ) -> None:
+        """Initialize QdrantClient with client and embedding function.
+
+        Args:
+            client: Pre-configured Qdrant client instance.
+            embedding_function: Embedding function for text to vector conversion.
+        """
+        self.client = client
+        self.embedding_function = embedding_function
+
+    def create_collection(self, **kwargs: Unpack[QdrantCollectionCreateParams]) -> None:
+        """Create a new collection in Qdrant.
+
+        Keyword Args:
+            collection_name: Name of the collection to create. Must be unique.
+            vectors_config: Optional vector configuration. Defaults to 1536 dimensions with cosine distance.
+            sparse_vectors_config: Optional sparse vector configuration.
+            shard_number: Optional number of shards.
+            replication_factor: Optional replication factor.
+            write_consistency_factor: Optional write consistency factor.
+            on_disk_payload: Optional flag to store payload on disk.
+            hnsw_config: Optional HNSW index configuration.
+            optimizers_config: Optional optimizer configuration.
+            wal_config: Optional write-ahead log configuration.
+            quantization_config: Optional quantization configuration.
+            init_from: Optional collection to initialize from.
+            timeout: Optional timeout for the operation.
+
+        Raises:
+            ValueError: If collection with the same name already exists.
+            ConnectionError: If unable to connect to Qdrant server.
+        """
+        if not _is_sync_client(self.client):
+            raise ClientMethodMismatchError(
+                method_name="create_collection",
+                expected_client="QdrantClient",
+                alt_method="acreate_collection",
+                alt_client="AsyncQdrantClient",
+            )
+
+        collection_name = kwargs["collection_name"]
+
+        if self.client.collection_exists(collection_name):
+            raise ValueError(f"Collection '{collection_name}' already exists")
+
+        params = _get_collection_params(kwargs)
+        self.client.create_collection(**params)
+
+    async def acreate_collection(
+        self, **kwargs: Unpack[QdrantCollectionCreateParams]
+    ) -> None:
+        """Create a new collection in Qdrant asynchronously.
+
+        Keyword Args:
+            collection_name: Name of the collection to create. Must be unique.
+            vectors_config: Optional vector configuration. Defaults to 1536 dimensions with cosine distance.
+            sparse_vectors_config: Optional sparse vector configuration.
+            shard_number: Optional number of shards.
+            replication_factor: Optional replication factor.
+            write_consistency_factor: Optional write consistency factor.
+            on_disk_payload: Optional flag to store payload on disk.
+            hnsw_config: Optional HNSW index configuration.
+            optimizers_config: Optional optimizer configuration.
+            wal_config: Optional write-ahead log configuration.
+            quantization_config: Optional quantization configuration.
+            init_from: Optional collection to initialize from.
+            timeout: Optional timeout for the operation.
+
+        Raises:
+            ValueError: If collection with the same name already exists.
+            ConnectionError: If unable to connect to Qdrant server.
+        """
+        if not _is_async_client(self.client):
+            raise ClientMethodMismatchError(
+                method_name="acreate_collection",
+                expected_client="AsyncQdrantClient",
+                alt_method="create_collection",
+                alt_client="QdrantClient",
+            )
+
+        collection_name = kwargs["collection_name"]
+
+        if await self.client.collection_exists(collection_name):
+            raise ValueError(f"Collection '{collection_name}' already exists")
+
+        params = _get_collection_params(kwargs)
+        await self.client.create_collection(**params)
+
+    def get_or_create_collection(
+        self, **kwargs: Unpack[QdrantCollectionCreateParams]
+    ) -> Any:
+        """Get an existing collection or create it if it doesn't exist.
+
+        Keyword Args:
+            collection_name: Name of the collection to get or create.
+            vectors_config: Optional vector configuration. Defaults to 1536 dimensions with cosine distance.
+            sparse_vectors_config: Optional sparse vector configuration.
+            shard_number: Optional number of shards.
+            replication_factor: Optional replication factor.
+            write_consistency_factor: Optional write consistency factor.
+            on_disk_payload: Optional flag to store payload on disk.
+            hnsw_config: Optional HNSW index configuration.
+            optimizers_config: Optional optimizer configuration.
+            wal_config: Optional write-ahead log configuration.
+            quantization_config: Optional quantization configuration.
+            init_from: Optional collection to initialize from.
+            timeout: Optional timeout for the operation.
+
+        Returns:
+            Collection info dict with name and other metadata.
+
+        Raises:
+            ConnectionError: If unable to connect to Qdrant server.
+        """
+        if not _is_sync_client(self.client):
+            raise ClientMethodMismatchError(
+                method_name="get_or_create_collection",
+                expected_client="QdrantClient",
+                alt_method="aget_or_create_collection",
+                alt_client="AsyncQdrantClient",
+            )
+
+        collection_name = kwargs["collection_name"]
+
+        if self.client.collection_exists(collection_name):
+            return self.client.get_collection(collection_name)
+
+        params = _get_collection_params(kwargs)
+        self.client.create_collection(**params)
+
+        return self.client.get_collection(collection_name)
+
+    async def aget_or_create_collection(
+        self, **kwargs: Unpack[QdrantCollectionCreateParams]
+    ) -> Any:
+        """Get an existing collection or create it if it doesn't exist asynchronously.
+
+        Keyword Args:
+            collection_name: Name of the collection to get or create.
+            vectors_config: Optional vector configuration. Defaults to 1536 dimensions with cosine distance.
+            sparse_vectors_config: Optional sparse vector configuration.
+            shard_number: Optional number of shards.
+            replication_factor: Optional replication factor.
+            write_consistency_factor: Optional write consistency factor.
+            on_disk_payload: Optional flag to store payload on disk.
+            hnsw_config: Optional HNSW index configuration.
+            optimizers_config: Optional optimizer configuration.
+            wal_config: Optional write-ahead log configuration.
+            quantization_config: Optional quantization configuration.
+            init_from: Optional collection to initialize from.
+            timeout: Optional timeout for the operation.
+
+        Returns:
+            Collection info dict with name and other metadata.
+
+        Raises:
+            ConnectionError: If unable to connect to Qdrant server.
+        """
+        if not _is_async_client(self.client):
+            raise ClientMethodMismatchError(
+                method_name="aget_or_create_collection",
+                expected_client="AsyncQdrantClient",
+                alt_method="get_or_create_collection",
+                alt_client="QdrantClient",
+            )
+
+        collection_name = kwargs["collection_name"]
+
+        if await self.client.collection_exists(collection_name):
+            return await self.client.get_collection(collection_name)
+
+        params = _get_collection_params(kwargs)
+        await self.client.create_collection(**params)
+
+        return await self.client.get_collection(collection_name)
+
+    def add_documents(self, **kwargs: Unpack[BaseCollectionAddParams]) -> None:
+        """Add documents with their embeddings to a collection.
+
+        Keyword Args:
+            collection_name: The name of the collection to add documents to.
+            documents: List of BaseRecord dicts containing document data.
+
+        Raises:
+            ValueError: If collection doesn't exist or documents list is empty.
+            ConnectionError: If unable to connect to Qdrant server.
+        """
+        if not _is_sync_client(self.client):
+            raise ClientMethodMismatchError(
+                method_name="add_documents",
+                expected_client="QdrantClient",
+                alt_method="aadd_documents",
+                alt_client="AsyncQdrantClient",
+            )
+
+        collection_name = kwargs["collection_name"]
+        documents = kwargs["documents"]
+
+        if not documents:
+            raise ValueError("Documents list cannot be empty")
+
+        if not self.client.collection_exists(collection_name):
+            raise ValueError(f"Collection '{collection_name}' does not exist")
+
+        points = []
+        for doc in documents:
+            if _is_async_embedding_function(self.embedding_function):
+                raise TypeError(
+                    "Async embedding function cannot be used with sync add_documents. "
+                    "Use aadd_documents instead."
+                )
+            sync_fn = cast(EmbeddingFunction, self.embedding_function)
+            embedding = sync_fn(doc["content"])
+            point = _create_point_from_document(doc, embedding)
+            points.append(point)
+
+        self.client.upsert(collection_name=collection_name, points=points)
+
+    async def aadd_documents(self, **kwargs: Unpack[BaseCollectionAddParams]) -> None:
+        """Add documents with their embeddings to a collection asynchronously.
+
+        Keyword Args:
+            collection_name: The name of the collection to add documents to.
+            documents: List of BaseRecord dicts containing document data.
+
+        Raises:
+            ValueError: If collection doesn't exist or documents list is empty.
+            ConnectionError: If unable to connect to Qdrant server.
+        """
+        if not _is_async_client(self.client):
+            raise ClientMethodMismatchError(
+                method_name="aadd_documents",
+                expected_client="AsyncQdrantClient",
+                alt_method="add_documents",
+                alt_client="QdrantClient",
+            )
+
+        collection_name = kwargs["collection_name"]
+        documents = kwargs["documents"]
+
+        if not documents:
+            raise ValueError("Documents list cannot be empty")
+
+        if not await self.client.collection_exists(collection_name):
+            raise ValueError(f"Collection '{collection_name}' does not exist")
+
+        points = []
+        for doc in documents:
+            if _is_async_embedding_function(self.embedding_function):
+                async_fn = cast(AsyncEmbeddingFunction, self.embedding_function)
+                embedding = await async_fn(doc["content"])
+            else:
+                sync_fn = cast(EmbeddingFunction, self.embedding_function)
+                embedding = sync_fn(doc["content"])
+            point = _create_point_from_document(doc, embedding)
+            points.append(point)
+
+        await self.client.upsert(collection_name=collection_name, points=points)
+
+    def search(
+        self, **kwargs: Unpack[BaseCollectionSearchParams]
+    ) -> list[SearchResult]:
+        """Search for similar documents using a query.
+
+        Keyword Args:
+            collection_name: Name of the collection to search in.
+            query: The text query to search for.
+            limit: Maximum number of results to return (default: 10).
+            metadata_filter: Optional filter for metadata fields.
+            score_threshold: Optional minimum similarity score (0-1) for results.
+
+        Returns:
+            List of SearchResult dicts containing id, content, metadata, and score.
+
+        Raises:
+            ValueError: If collection doesn't exist.
+            ConnectionError: If unable to connect to Qdrant server.
+        """
+        if not _is_sync_client(self.client):
+            raise ClientMethodMismatchError(
+                method_name="search",
+                expected_client="QdrantClient",
+                alt_method="asearch",
+                alt_client="AsyncQdrantClient",
+            )
+
+        collection_name = kwargs["collection_name"]
+        query = kwargs["query"]
+        limit = kwargs.get("limit", 10)
+        metadata_filter = kwargs.get("metadata_filter")
+        score_threshold = kwargs.get("score_threshold")
+
+        if not self.client.collection_exists(collection_name):
+            raise ValueError(f"Collection '{collection_name}' does not exist")
+
+        if _is_async_embedding_function(self.embedding_function):
+            raise TypeError(
+                "Async embedding function cannot be used with sync search. "
+                "Use asearch instead."
+            )
+        sync_fn = cast(EmbeddingFunction, self.embedding_function)
+        query_embedding = sync_fn(query)
+
+        search_kwargs = _prepare_search_params(
+            collection_name=collection_name,
+            query_embedding=query_embedding,
+            limit=limit,
+            score_threshold=score_threshold,
+            metadata_filter=metadata_filter,
+        )
+
+        response = self.client.query_points(**search_kwargs)
+        return _process_search_results(response)
+
+    async def asearch(
+        self, **kwargs: Unpack[BaseCollectionSearchParams]
+    ) -> list[SearchResult]:
+        """Search for similar documents using a query asynchronously.
+
+        Keyword Args:
+            collection_name: Name of the collection to search in.
+            query: The text query to search for.
+            limit: Maximum number of results to return (default: 10).
+            metadata_filter: Optional filter for metadata fields.
+            score_threshold: Optional minimum similarity score (0-1) for results.
+
+        Returns:
+            List of SearchResult dicts containing id, content, metadata, and score.
+
+        Raises:
+            ValueError: If collection doesn't exist.
+            ConnectionError: If unable to connect to Qdrant server.
+        """
+        if not _is_async_client(self.client):
+            raise ClientMethodMismatchError(
+                method_name="asearch",
+                expected_client="AsyncQdrantClient",
+                alt_method="search",
+                alt_client="QdrantClient",
+            )
+
+        collection_name = kwargs["collection_name"]
+        query = kwargs["query"]
+        limit = kwargs.get("limit", 10)
+        metadata_filter = kwargs.get("metadata_filter")
+        score_threshold = kwargs.get("score_threshold")
+
+        if not await self.client.collection_exists(collection_name):
+            raise ValueError(f"Collection '{collection_name}' does not exist")
+
+        if _is_async_embedding_function(self.embedding_function):
+            async_fn = cast(AsyncEmbeddingFunction, self.embedding_function)
+            query_embedding = await async_fn(query)
+        else:
+            sync_fn = cast(EmbeddingFunction, self.embedding_function)
+            query_embedding = sync_fn(query)
+
+        search_kwargs = _prepare_search_params(
+            collection_name=collection_name,
+            query_embedding=query_embedding,
+            limit=limit,
+            score_threshold=score_threshold,
+            metadata_filter=metadata_filter,
+        )
+
+        response = await self.client.query_points(**search_kwargs)
+        return _process_search_results(response)
+
+    def delete_collection(self, **kwargs: Unpack[BaseCollectionParams]) -> None:
+        """Delete a collection and all its data.
+
+        Keyword Args:
+            collection_name: Name of the collection to delete.
+
+        Raises:
+            ValueError: If collection doesn't exist.
+            ConnectionError: If unable to connect to Qdrant server.
+        """
+        if not _is_sync_client(self.client):
+            raise ClientMethodMismatchError(
+                method_name="delete_collection",
+                expected_client="QdrantClient",
+                alt_method="adelete_collection",
+                alt_client="AsyncQdrantClient",
+            )
+
+        collection_name = kwargs["collection_name"]
+
+        if not self.client.collection_exists(collection_name):
+            raise ValueError(f"Collection '{collection_name}' does not exist")
+
+        self.client.delete_collection(collection_name=collection_name)
+
+    async def adelete_collection(self, **kwargs: Unpack[BaseCollectionParams]) -> None:
+        """Delete a collection and all its data asynchronously.
+
+        Keyword Args:
+            collection_name: Name of the collection to delete.
+
+        Raises:
+            ValueError: If collection doesn't exist.
+            ConnectionError: If unable to connect to Qdrant server.
+        """
+        if not _is_async_client(self.client):
+            raise ClientMethodMismatchError(
+                method_name="adelete_collection",
+                expected_client="AsyncQdrantClient",
+                alt_method="delete_collection",
+                alt_client="QdrantClient",
+            )
+
+        collection_name = kwargs["collection_name"]
+
+        if not await self.client.collection_exists(collection_name):
+            raise ValueError(f"Collection '{collection_name}' does not exist")
+
+        await self.client.delete_collection(collection_name=collection_name)
+
+    def reset(self) -> None:
+        """Reset the vector database by deleting all collections and data.
+
+        Raises:
+            ConnectionError: If unable to connect to Qdrant server.
+        """
+        if not _is_sync_client(self.client):
+            raise ClientMethodMismatchError(
+                method_name="reset",
+                expected_client="QdrantClient",
+                alt_method="areset",
+                alt_client="AsyncQdrantClient",
+            )
+
+        collections_response = self.client.get_collections()
+
+        for collection in collections_response.collections:
+            self.client.delete_collection(collection_name=collection.name)
+
+    async def areset(self) -> None:
+        """Reset the vector database by deleting all collections and data asynchronously.
+
+        Raises:
+            ConnectionError: If unable to connect to Qdrant server.
+        """
+        if not _is_async_client(self.client):
+            raise ClientMethodMismatchError(
+                method_name="areset",
+                expected_client="AsyncQdrantClient",
+                alt_method="reset",
+                alt_client="QdrantClient",
+            )
+
+        collections_response = await self.client.get_collections()
+
+        for collection in collections_response.collections:
+            await self.client.delete_collection(collection_name=collection.name)

src/crewai/rag/qdrant/config.py

@@ -0,0 +1,54 @@
+"""Qdrant configuration model."""
+
+from dataclasses import field
+from typing import Literal, cast
+from pydantic.dataclasses import dataclass as pyd_dataclass
+
+from crewai.rag.config.base import BaseRagConfig
+from crewai.rag.qdrant.types import QdrantClientParams, QdrantEmbeddingFunctionWrapper
+from crewai.rag.qdrant.constants import DEFAULT_EMBEDDING_MODEL, DEFAULT_STORAGE_PATH
+
+
+def _default_options() -> QdrantClientParams:
+    """Create default Qdrant client options.
+
+    Returns:
+        Default options with file-based storage.
+    """
+    return QdrantClientParams(path=DEFAULT_STORAGE_PATH)
+
+
+def _default_embedding_function() -> QdrantEmbeddingFunctionWrapper:
+    """Create default Qdrant embedding function.
+
+    Returns:
+        Default embedding function using fastembed with all-MiniLM-L6-v2.
+    """
+    from fastembed import TextEmbedding
+
+    model = TextEmbedding(model_name=DEFAULT_EMBEDDING_MODEL)
+
+    def embed_fn(text: str) -> list[float]:
+        """Embed a single text string.
+
+        Args:
+            text: Text to embed.
+
+        Returns:
+            Embedding vector as list of floats.
+        """
+        embeddings = list(model.embed([text]))
+        return embeddings[0].tolist() if embeddings else []
+
+    return cast(QdrantEmbeddingFunctionWrapper, embed_fn)
+
+
+@pyd_dataclass(frozen=True)
+class QdrantConfig(BaseRagConfig):
+    """Configuration for Qdrant client."""
+
+    provider: Literal["qdrant"] = field(default="qdrant", init=False)
+    options: QdrantClientParams = field(default_factory=_default_options)
+    embedding_function: QdrantEmbeddingFunctionWrapper = field(
+        default_factory=_default_embedding_function
+    )

src/crewai/rag/qdrant/constants.py

@@ -0,0 +1,12 @@
+"""Constants for Qdrant implementation."""
+
+import os
+from typing import Final
+
+from qdrant_client.models import Distance, VectorParams
+
+from crewai.utilities.paths import db_storage_path
+
+DEFAULT_VECTOR_PARAMS: Final = VectorParams(size=384, distance=Distance.COSINE)
+DEFAULT_EMBEDDING_MODEL: Final[str] = "sentence-transformers/all-MiniLM-L6-v2"
+DEFAULT_STORAGE_PATH: Final[str] = os.path.join(db_storage_path(), "qdrant")

src/crewai/rag/qdrant/factory.py

@@ -0,0 +1,21 @@
+"""Factory functions for creating Qdrant clients from configuration."""
+
+from qdrant_client import QdrantClient as SyncQdrantClientBase
+from crewai.rag.qdrant.client import QdrantClient
+from crewai.rag.qdrant.config import QdrantConfig
+
+
+def create_client(config: QdrantConfig) -> QdrantClient:
+    """Create a Qdrant client from configuration.
+
+    Args:
+        config: The Qdrant configuration.
+
+    Returns:
+        A configured QdrantClient instance.
+    """
+
+    qdrant_client = SyncQdrantClientBase(**config.options)
+    return QdrantClient(
+        client=qdrant_client, embedding_function=config.embedding_function
+    )

src/crewai/rag/qdrant/types.py

@@ -0,0 +1,155 @@
+"""Type definitions specific to Qdrant implementation."""
+
+from collections.abc import Awaitable, Callable
+from typing import Annotated, Any, Protocol, TypeAlias
+from typing_extensions import NotRequired, TypedDict
+
+import numpy as np
+from pydantic import GetCoreSchemaHandler
+from pydantic_core import CoreSchema, core_schema
+from qdrant_client import AsyncQdrantClient, QdrantClient as SyncQdrantClient
+from qdrant_client.models import (
+    FieldCondition,
+    Filter,
+    HasIdCondition,
+    HasVectorCondition,
+    HnswConfigDiff,
+    InitFrom,
+    IsEmptyCondition,
+    IsNullCondition,
+    NestedCondition,
+    OptimizersConfigDiff,
+    QuantizationConfig,
+    ShardingMethod,
+    SparseVectorsConfig,
+    VectorsConfig,
+    WalConfigDiff,
+)
+
+from crewai.rag.core.base_client import BaseCollectionParams
+
+QdrantClientType = SyncQdrantClient | AsyncQdrantClient
+
+QueryEmbedding: TypeAlias = list[float] | np.ndarray[Any, np.dtype[np.floating[Any]]]
+
+BasicConditions = FieldCondition | IsEmptyCondition | IsNullCondition
+StructuralConditions = HasIdCondition | HasVectorCondition | NestedCondition
+FilterCondition = BasicConditions | StructuralConditions | Filter
+
+MetadataFilterValue = bool | int | str
+MetadataFilter = dict[str, MetadataFilterValue]
+
+
+class EmbeddingFunction(Protocol):
+    """Protocol for embedding functions that convert text to vectors."""
+
+    def __call__(self, text: str) -> QueryEmbedding:
+        """Convert text to embedding vector.
+
+        Args:
+            text: Input text to embed.
+
+        Returns:
+            Embedding vector as list of floats or numpy array.
+        """
+        ...
+
+
+class QdrantEmbeddingFunctionWrapper(EmbeddingFunction):
+    """Base class for Qdrant EmbeddingFunction to work with Pydantic validation."""
+
+    @classmethod
+    def __get_pydantic_core_schema__(
+        cls, _source_type: Any, _handler: GetCoreSchemaHandler
+    ) -> CoreSchema:
+        """Generate Pydantic core schema for Qdrant EmbeddingFunction.
+
+        This allows Pydantic to handle Qdrant's EmbeddingFunction type
+        without requiring arbitrary_types_allowed=True.
+        """
+        return core_schema.any_schema()
+
+
+class AsyncEmbeddingFunction(Protocol):
+    """Protocol for async embedding functions that convert text to vectors."""
+
+    async def __call__(self, text: str) -> QueryEmbedding:
+        """Convert text to embedding vector asynchronously.
+
+        Args:
+            text: Input text to embed.
+
+        Returns:
+            Embedding vector as list of floats or numpy array.
+        """
+        ...
+
+
+class QdrantClientParams(TypedDict, total=False):
+    """Parameters for QdrantClient initialization.
+
+    Notes:
+        Need to implement in factory or remove.
+    """
+
+    location: str | None
+    url: str | None
+    port: int
+    grpc_port: int
+    prefer_grpc: bool
+    https: bool | None
+    api_key: str | None
+    prefix: str | None
+    timeout: int | None
+    host: str | None
+    path: str | None
+    force_disable_check_same_thread: bool
+    grpc_options: dict[str, Any] | None
+    auth_token_provider: Callable[[], str] | Callable[[], Awaitable[str]] | None
+    cloud_inference: bool
+    local_inference_batch_size: int | None
+    check_compatibility: bool
+
+
+class CommonCreateFields(TypedDict, total=False):
+    """Fields shared between high-level and direct create_collection params."""
+
+    vectors_config: VectorsConfig
+    sparse_vectors_config: SparseVectorsConfig
+    shard_number: Annotated[int, "Number of shards (default: 1)"]
+    sharding_method: ShardingMethod
+    replication_factor: Annotated[int, "Number of replicas per shard (default: 1)"]
+    write_consistency_factor: Annotated[int, "Await N replicas on write (default: 1)"]
+    on_disk_payload: Annotated[bool, "Store payload on disk instead of RAM"]
+    hnsw_config: HnswConfigDiff
+    optimizers_config: OptimizersConfigDiff
+    wal_config: WalConfigDiff
+    quantization_config: QuantizationConfig
+    init_from: InitFrom | str
+    timeout: Annotated[int, "Operation timeout in seconds"]
+
+
+class QdrantCollectionCreateParams(
+    BaseCollectionParams, CommonCreateFields, total=False
+):
+    """High-level parameters for creating a Qdrant collection."""
+
+    pass
+
+
+class CreateCollectionParams(CommonCreateFields, total=False):
+    """Parameters for qdrant_client.create_collection."""
+
+    collection_name: str
+
+
+class PreparedSearchParams(TypedDict):
+    """Type definition for prepared Qdrant search parameters."""
+
+    collection_name: str
+    query: list[float]
+    limit: Annotated[int, "Max results to return"]
+    with_payload: Annotated[bool, "Include payload in results"]
+    with_vectors: Annotated[bool, "Include vectors in results"]
+    score_threshold: NotRequired[Annotated[float, "Min similarity score (0-1)"]]
+    query_filter: NotRequired[Filter]

src/crewai/rag/qdrant/utils.py

@@ -0,0 +1,228 @@
+"""Utility functions for Qdrant operations."""
+
+import asyncio
+from typing import TypeGuard
+from uuid import uuid4
+
+from qdrant_client import AsyncQdrantClient, QdrantClient as SyncQdrantClient
+from qdrant_client.models import (
+    FieldCondition,
+    Filter,
+    MatchValue,
+    PointStruct,
+    QueryResponse,
+)
+
+from crewai.rag.qdrant.constants import DEFAULT_VECTOR_PARAMS
+from crewai.rag.qdrant.types import (
+    AsyncEmbeddingFunction,
+    CreateCollectionParams,
+    EmbeddingFunction,
+    FilterCondition,
+    MetadataFilter,
+    PreparedSearchParams,
+    QdrantClientType,
+    QdrantCollectionCreateParams,
+    QueryEmbedding,
+)
+from crewai.rag.types import SearchResult, BaseRecord
+
+
+def _ensure_list_embedding(embedding: QueryEmbedding) -> list[float]:
+    """Convert embedding to list[float] format if needed.
+
+    Args:
+        embedding: Embedding vector as list or numpy array.
+
+    Returns:
+        Embedding as list[float].
+    """
+    if not isinstance(embedding, list):
+        return embedding.tolist()
+    return embedding
+
+
+def _is_sync_client(client: QdrantClientType) -> TypeGuard[SyncQdrantClient]:
+    """Type guard to check if the client is a synchronous QdrantClient.
+
+    Args:
+        client: The client to check.
+
+    Returns:
+        True if the client is a QdrantClient, False otherwise.
+    """
+    return isinstance(client, SyncQdrantClient)
+
+
+def _is_async_client(client: QdrantClientType) -> TypeGuard[AsyncQdrantClient]:
+    """Type guard to check if the client is an asynchronous AsyncQdrantClient.
+
+    Args:
+        client: The client to check.
+
+    Returns:
+        True if the client is an AsyncQdrantClient, False otherwise.
+    """
+    return isinstance(client, AsyncQdrantClient)
+
+
+def _is_async_embedding_function(
+    func: EmbeddingFunction | AsyncEmbeddingFunction,
+) -> TypeGuard[AsyncEmbeddingFunction]:
+    """Type guard to check if the embedding function is async.
+
+    Args:
+        func: The embedding function to check.
+
+    Returns:
+        True if the function is async, False otherwise.
+    """
+    return asyncio.iscoroutinefunction(func)
+
+
+def _get_collection_params(
+    kwargs: QdrantCollectionCreateParams,
+) -> CreateCollectionParams:
+    """Extract collection creation parameters from kwargs."""
+    params: CreateCollectionParams = {
+        "collection_name": kwargs["collection_name"],
+        "vectors_config": kwargs.get("vectors_config", DEFAULT_VECTOR_PARAMS),
+    }
+
+    if "sparse_vectors_config" in kwargs:
+        params["sparse_vectors_config"] = kwargs["sparse_vectors_config"]
+    if "shard_number" in kwargs:
+        params["shard_number"] = kwargs["shard_number"]
+    if "sharding_method" in kwargs:
+        params["sharding_method"] = kwargs["sharding_method"]
+    if "replication_factor" in kwargs:
+        params["replication_factor"] = kwargs["replication_factor"]
+    if "write_consistency_factor" in kwargs:
+        params["write_consistency_factor"] = kwargs["write_consistency_factor"]
+    if "on_disk_payload" in kwargs:
+        params["on_disk_payload"] = kwargs["on_disk_payload"]
+    if "hnsw_config" in kwargs:
+        params["hnsw_config"] = kwargs["hnsw_config"]
+    if "optimizers_config" in kwargs:
+        params["optimizers_config"] = kwargs["optimizers_config"]
+    if "wal_config" in kwargs:
+        params["wal_config"] = kwargs["wal_config"]
+    if "quantization_config" in kwargs:
+        params["quantization_config"] = kwargs["quantization_config"]
+    if "init_from" in kwargs:
+        params["init_from"] = kwargs["init_from"]
+    if "timeout" in kwargs:
+        params["timeout"] = kwargs["timeout"]
+
+    return params
+
+
+def _prepare_search_params(
+    collection_name: str,
+    query_embedding: QueryEmbedding,
+    limit: int,
+    score_threshold: float | None,
+    metadata_filter: MetadataFilter | None,
+) -> PreparedSearchParams:
+    """Prepare search parameters for Qdrant query_points.
+
+    Args:
+        collection_name: Name of the collection to search.
+        query_embedding: Embedding vector for the query.
+        limit: Maximum number of results.
+        score_threshold: Optional minimum similarity score.
+        metadata_filter: Optional metadata filters.
+
+    Returns:
+        Dictionary of parameters for query_points method.
+    """
+    query_vector = _ensure_list_embedding(query_embedding)
+
+    search_kwargs: PreparedSearchParams = {
+        "collection_name": collection_name,
+        "query": query_vector,
+        "limit": limit,
+        "with_payload": True,
+        "with_vectors": False,
+    }
+
+    if score_threshold is not None:
+        search_kwargs["score_threshold"] = score_threshold
+
+    if metadata_filter:
+        filter_conditions: list[FilterCondition] = []
+        for key, value in metadata_filter.items():
+            filter_conditions.append(
+                FieldCondition(key=key, match=MatchValue(value=value))
+            )
+
+        search_kwargs["query_filter"] = Filter(must=filter_conditions)
+
+    return search_kwargs
+
+
+def _normalize_qdrant_score(score: float) -> float:
+    """Normalize Qdrant cosine similarity score to [0, 1] range.
+
+    Converts from Qdrant's [-1, 1] cosine similarity range to [0, 1] range for standardization across clients.
+
+    Args:
+        score: Raw cosine similarity score from Qdrant [-1, 1].
+
+    Returns:
+        Normalized score in [0, 1] range where 1 is most similar.
+    """
+    normalized = (score + 1.0) / 2.0
+    return max(0.0, min(1.0, normalized))
+
+
+def _process_search_results(response: QueryResponse) -> list[SearchResult]:
+    """Process Qdrant search response into SearchResult format.
+
+    Args:
+        response: Response from Qdrant query_points method.
+
+    Returns:
+        List of SearchResult dictionaries.
+    """
+    results: list[SearchResult] = []
+    for point in response.points:
+        payload = point.payload or {}
+        score = _normalize_qdrant_score(score=point.score)
+        result: SearchResult = {
+            "id": str(point.id),
+            "content": payload.get("content", ""),
+            "metadata": {k: v for k, v in payload.items() if k != "content"},
+            "score": score,
+        }
+        results.append(result)
+
+    return results
+
+
+def _create_point_from_document(
+    doc: BaseRecord, embedding: QueryEmbedding
+) -> PointStruct:
+    """Create a PointStruct from a document and its embedding.
+
+    Args:
+        doc: Document dictionary containing content, metadata, and optional doc_id.
+        embedding: The embedding vector for the document content.
+
+    Returns:
+        PointStruct ready to be upserted to Qdrant.
+    """
+    doc_id = doc.get("doc_id", str(uuid4()))
+    vector = _ensure_list_embedding(embedding)
+
+    metadata = doc.get("metadata", {})
+    if isinstance(metadata, list):
+        metadata = metadata[0] if metadata else {}
+    elif not isinstance(metadata, dict):
+        metadata = dict(metadata) if metadata else {}
+
+    return PointStruct(
+        id=doc_id,
+        vector=vector,
+        payload={"content": doc["content"], **metadata},
+    )

src/crewai/rag/types.py

@@ -0,0 +1,50 @@
+"""Type definitions for RAG (Retrieval-Augmented Generation) systems."""
+
+from collections.abc import Callable, Mapping
+from typing import TypeAlias, Any
+
+from typing_extensions import Required, TypedDict
+
+
+class BaseRecord(TypedDict, total=False):
+    """A typed dictionary representing a document record.
+
+    Attributes:
+        doc_id: Optional unique identifier for the document. If not provided,
+            a content-based ID will be generated using SHA256 hash.
+        content: The text content of the document (required)
+        metadata: Optional metadata associated with the document
+    """
+
+    doc_id: str
+    content: Required[str]
+    metadata: (
+        Mapping[str, str | int | float | bool]
+        | list[Mapping[str, str | int | float | bool]]
+    )
+
+
+DenseVector: TypeAlias = list[float]
+IntVector: TypeAlias = list[int]
+
+EmbeddingFunction: TypeAlias = Callable[..., Any]
+
+
+class SearchResult(TypedDict):
+    """Standard search result format for vector store queries.
+
+    This provides a consistent interface for search results across different
+    vector store implementations. Each implementation should convert their
+    native result format to this standard format.
+
+    Attributes:
+        id: Unique identifier of the document
+        content: The text content of the document
+        metadata: Optional metadata associated with the document
+        score: Similarity score (higher is better, typically between 0 and 1)
+    """
+
+    id: str
+    content: str
+    metadata: dict[str, Any]
+    score: float

src/crewai/task.py

@@ -4,6 +4,7 @@ import json
 import logging
 import threading
 import uuid
+import warnings
 from concurrent.futures import Future
 from copy import copy
 from hashlib import md5
@@ -157,8 +158,13 @@ class Task(BaseModel):
         default=None,
         description="Function or string description of a guardrail to validate task output before proceeding to next task",
     )
-    max_retries: int = Field(
-        default=3, description="Maximum number of retries when guardrail fails"
+    max_retries: Optional[int] = Field(
+        default=None,
+        description="[DEPRECATED] Maximum number of retries when guardrail fails. Use guardrail_max_retries instead. Will be removed in v1.0.0"
+    )
+    guardrail_max_retries: int = Field(
+        default=3,
+        description="Maximum number of retries when guardrail fails"
     )
     retry_count: int = Field(default=0, description="Current number of retries")
     start_time: Optional[datetime.datetime] = Field(
@@ -354,6 +360,18 @@ class Task(BaseModel):
             )
         return self
 
+    @model_validator(mode="after")
+    def handle_max_retries_deprecation(self):
+        if self.max_retries is not None:
+            warnings.warn(
+                "The 'max_retries' parameter is deprecated and will be removed in CrewAI v1.0.0. "
+                "Please use 'guardrail_max_retries' instead.",
+                DeprecationWarning,
+                stacklevel=2
+            )
+            self.guardrail_max_retries = self.max_retries
+        return self
+
     def execute_sync(
         self,
         agent: Optional[BaseAgent] = None,
@@ -433,7 +451,7 @@ class Task(BaseModel):
 
             pydantic_output, json_output = self._export_output(result)
             task_output = TaskOutput(
-                name=self.name,
+                name=self.name or self.description,
                 description=self.description,
                 expected_output=self.expected_output,
                 raw=result,
@@ -450,9 +468,9 @@ class Task(BaseModel):
                     retry_count=self.retry_count,
                 )
                 if not guardrail_result.success:
-                    if self.retry_count >= self.max_retries:
+                    if self.retry_count >= self.guardrail_max_retries:
                         raise Exception(
-                            f"Task failed guardrail validation after {self.max_retries} retries. "
+                            f"Task failed guardrail validation after {self.guardrail_max_retries} retries. "
                             f"Last error: {guardrail_result.error}"
                         )
 
@@ -561,8 +579,8 @@ class Task(BaseModel):
         should_inject = self.allow_crewai_trigger_context
 
         if should_inject and self.agent:
-            crew = getattr(self.agent, 'crew', None)
-            if crew and hasattr(crew, '_inputs') and crew._inputs:
+            crew = getattr(self.agent, "crew", None)
+            if crew and hasattr(crew, "_inputs") and crew._inputs:
                 trigger_payload = crew._inputs.get("crewai_trigger_payload")
                 if trigger_payload is not None:
                     description += f"\n\nTrigger Payload: {trigger_payload}"
@@ -780,7 +798,9 @@ Follow these guidelines:
             if self.create_directory and not directory.exists():
                 directory.mkdir(parents=True, exist_ok=True)
             elif not self.create_directory and not directory.exists():
-                raise RuntimeError(f"Directory {directory} does not exist and create_directory is False")
+                raise RuntimeError(
+                    f"Directory {directory} does not exist and create_directory is False"
+                )
 
             with resolved_path.open("w", encoding="utf-8") as file:
                 if isinstance(result, dict):

src/crewai/tools/base_tool.py

@@ -14,12 +14,14 @@ from pydantic import BaseModel as PydanticBaseModel
 
 from crewai.tools.structured_tool import CrewStructuredTool
 
+
 class EnvVar(BaseModel):
     name: str
     description: str
     required: bool = True
     default: Optional[str] = None
 
+
 class BaseTool(BaseModel, ABC):
     class _ArgsSchemaPlaceholder(PydanticBaseModel):
         pass
@@ -108,7 +110,7 @@ class BaseTool(BaseModel, ABC):
     def to_structured_tool(self) -> CrewStructuredTool:
         """Convert this tool to a CrewStructuredTool instance."""
         self._set_args_schema()
-        return CrewStructuredTool(
+        structured_tool = CrewStructuredTool(
             name=self.name,
             description=self.description,
             args_schema=self.args_schema,
@@ -117,6 +119,8 @@ class BaseTool(BaseModel, ABC):
             max_usage_count=self.max_usage_count,
             current_usage_count=self.current_usage_count,
         )
+        structured_tool._original_tool = self
+        return structured_tool
 
     @classmethod
     def from_langchain(cls, tool: Any) -> "BaseTool":
@@ -276,7 +280,9 @@ def to_langchain(
     return [t.to_structured_tool() if isinstance(t, BaseTool) else t for t in tools]
 
 
-def tool(*args, result_as_answer: bool = False, max_usage_count: int | None = None) -> Callable:
+def tool(
+    *args, result_as_answer: bool = False, max_usage_count: int | None = None
+) -> Callable:
     """
     Decorator to create a tool from a function.
 

src/crewai/tools/structured_tool.py

@@ -10,6 +10,17 @@ from pydantic import BaseModel, Field, create_model
 
 from crewai.utilities.logger import Logger
 
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from crewai.tools.base_tool import BaseTool
+
+
+class ToolUsageLimitExceeded(Exception):
+    """Exception raised when a tool has reached its maximum usage limit."""
+
+    pass
+
 
 class CrewStructuredTool:
     """A structured tool that can operate on any number of inputs.
@@ -18,6 +29,8 @@ class CrewStructuredTool:
     that integrates better with CrewAI's ecosystem.
     """
 
+    _original_tool: BaseTool | None = None
+
     def __init__(
         self,
         name: str,
@@ -47,6 +60,7 @@ class CrewStructuredTool:
         self.result_as_answer = result_as_answer
         self.max_usage_count = max_usage_count
         self.current_usage_count = current_usage_count
+        self._original_tool = None
 
         # Validate the function signature matches the schema
         self._validate_function_signature()
@@ -219,16 +233,26 @@ class CrewStructuredTool:
         """
         parsed_args = self._parse_args(input)
 
-        if inspect.iscoroutinefunction(self.func):
-            return await self.func(**parsed_args, **kwargs)
-        else:
-            # Run sync functions in a thread pool
-            import asyncio
-
-            return await asyncio.get_event_loop().run_in_executor(
-                None, lambda: self.func(**parsed_args, **kwargs)
+        if self.has_reached_max_usage_count():
+            raise ToolUsageLimitExceeded(
+                f"Tool '{self.name}' has reached its maximum usage limit of {self.max_usage_count}. You should not use the {self.name} tool again."
             )
 
+        self._increment_usage_count()
+
+        try:
+            if inspect.iscoroutinefunction(self.func):
+                return await self.func(**parsed_args, **kwargs)
+            else:
+                # Run sync functions in a thread pool
+                import asyncio
+
+                return await asyncio.get_event_loop().run_in_executor(
+                    None, lambda: self.func(**parsed_args, **kwargs)
+                )
+        except Exception:
+            raise
+
     def _run(self, *args, **kwargs) -> Any:
         """Legacy method for compatibility."""
         # Convert args/kwargs to our expected format
@@ -242,10 +266,22 @@ class CrewStructuredTool:
         """Main method for tool execution."""
         parsed_args = self._parse_args(input)
 
+        if self.has_reached_max_usage_count():
+            raise ToolUsageLimitExceeded(
+                f"Tool '{self.name}' has reached its maximum usage limit of {self.max_usage_count}. You should not use the {self.name} tool again."
+            )
+
+        self._increment_usage_count()
+
         if inspect.iscoroutinefunction(self.func):
             result = asyncio.run(self.func(**parsed_args, **kwargs))
             return result
 
+        try:
+            result = self.func(**parsed_args, **kwargs)
+        except Exception:
+            raise
+
         result = self.func(**parsed_args, **kwargs)
 
         if asyncio.iscoroutine(result):
@@ -253,6 +289,19 @@ class CrewStructuredTool:
 
         return result
 
+    def has_reached_max_usage_count(self) -> bool:
+        """Check if the tool has reached its maximum usage count."""
+        return (
+            self.max_usage_count is not None
+            and self.current_usage_count >= self.max_usage_count
+        )
+
+    def _increment_usage_count(self) -> None:
+        """Increment the usage count."""
+        self.current_usage_count += 1
+        if self._original_tool is not None:
+            self._original_tool.current_usage_count = self.current_usage_count
+
     @property
     def args(self) -> dict:
         """Get the tool's input arguments schema."""

src/crewai/tools/tool_usage.py

@@ -178,9 +178,11 @@ class ToolUsage:
 
             if self.agent.fingerprint:
                 event_data.update(self.agent.fingerprint)
+            if self.task:
+                event_data["task_name"] = self.task.name or self.task.description
+                event_data["task_id"] = str(self.task.id)
+            crewai_event_bus.emit(self, ToolUsageStartedEvent(**event_data))
 
-            crewai_event_bus.emit(self,ToolUsageStartedEvent(**event_data))
-            
         started_at = time.time()
         from_cache = False
         result = None  # type: ignore
@@ -311,12 +313,15 @@ class ToolUsage:
         if self.agent and hasattr(self.agent, "tools_results"):
             self.agent.tools_results.append(data)
 
-        if available_tool and hasattr(available_tool, 'current_usage_count'):
+        if available_tool and hasattr(available_tool, "current_usage_count"):
             available_tool.current_usage_count += 1
-            if hasattr(available_tool, 'max_usage_count') and available_tool.max_usage_count is not None:
+            if (
+                hasattr(available_tool, "max_usage_count")
+                and available_tool.max_usage_count is not None
+            ):
                 self._printer.print(
                     content=f"Tool '{available_tool.name}' usage: {available_tool.current_usage_count}/{available_tool.max_usage_count}",
-                    color="blue"
+                    color="blue",
                 )
 
         return result
@@ -350,20 +355,20 @@ class ToolUsage:
                 calling.arguments == last_tool_usage.arguments
             )
         return False
-        
+
     def _check_usage_limit(self, tool: Any, tool_name: str) -> str | None:
         """Check if tool has reached its usage limit.
-        
+
         Args:
             tool: The tool to check
             tool_name: The name of the tool (used for error message)
-            
+
         Returns:
             Error message if limit reached, None otherwise
         """
         if (
-            hasattr(tool, 'max_usage_count') 
-            and tool.max_usage_count is not None 
+            hasattr(tool, "max_usage_count")
+            and tool.max_usage_count is not None
             and tool.current_usage_count >= tool.max_usage_count
         ):
             return f"Tool '{tool_name}' has reached its usage limit of {tool.max_usage_count} times and cannot be used anymore."
@@ -605,6 +610,9 @@ class ToolUsage:
                 "output": result,
             }
         )
+        if self.task:
+            event_data["task_id"] = str(self.task.id)
+            event_data["task_name"] = self.task.name or self.task.description
         crewai_event_bus.emit(self, ToolUsageFinishedEvent(**event_data))
 
     def _prepare_event_data(

src/crewai/utilities/events/base_events.py

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

src/crewai/utilities/events/event_listener.py

@@ -161,8 +161,10 @@ class EventListener(BaseEventListener):
         def on_task_started(source, event: TaskStartedEvent):
             span = self._telemetry.task_started(crew=source.agent.crew, task=source)
             self.execution_spans[source] = span
+            # Pass both task ID and task name (if set)
+            task_name = source.name if hasattr(source, 'name') and source.name else None
             self.formatter.create_task_branch(
-                self.formatter.current_crew_tree, source.id
+                self.formatter.current_crew_tree, source.id, task_name
             )
 
         @crewai_event_bus.on(TaskCompletedEvent)
@@ -173,11 +175,14 @@ class EventListener(BaseEventListener):
                 self._telemetry.task_ended(span, source, source.agent.crew)
             self.execution_spans[source] = None
 
+            # Pass task name if it exists
+            task_name = source.name if hasattr(source, 'name') and source.name else None
             self.formatter.update_task_status(
                 self.formatter.current_crew_tree,
                 source.id,
                 source.agent.role,
                 "completed",
+                task_name
             )
 
         @crewai_event_bus.on(TaskFailedEvent)
@@ -188,11 +193,14 @@ class EventListener(BaseEventListener):
                     self._telemetry.task_ended(span, source, source.agent.crew)
                 self.execution_spans[source] = None
 
+            # Pass task name if it exists
+            task_name = source.name if hasattr(source, 'name') and source.name else None
             self.formatter.update_task_status(
                 self.formatter.current_crew_tree,
                 source.id,
                 source.agent.role,
                 "failed",
+                task_name
             )
 
         # ----------- AGENT EVENTS -----------

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

@@ -41,6 +41,12 @@ class TraceBatchManager:
     """Single responsibility: Manage batches and event buffering"""
 
     is_current_batch_ephemeral: bool = False
+    trace_batch_id: Optional[str] = None
+    current_batch: Optional[TraceBatch] = None
+    event_buffer: List[TraceEvent] = []
+    execution_start_times: Dict[str, datetime] = {}
+    batch_owner_type: Optional[str] = None
+    batch_owner_id: Optional[str] = None
 
     def __init__(self):
         try:
@@ -48,11 +54,6 @@ class TraceBatchManager:
         except AuthError:
             self.plus_api = PlusAPI(api_key="")
 
-        self.trace_batch_id: Optional[str] = None  # Backend ID
-        self.current_batch: Optional[TraceBatch] = None
-        self.event_buffer: List[TraceEvent] = []
-        self.execution_start_times: Dict[str, datetime] = {}
-
     def initialize_batch(
         self,
         user_context: Dict[str, str],
@@ -178,14 +179,16 @@ class TraceBatchManager:
         if not self.current_batch:
             return None
 
+        self.current_batch.events = self.event_buffer.copy()
         if self.event_buffer:
             self._send_events_to_backend()
         self._finalize_backend_batch()
 
-        self.current_batch.events = self.event_buffer.copy()
-
         finalized_batch = self.current_batch
 
+        self.batch_owner_type = None
+        self.batch_owner_id = None
+
         self.current_batch = None
         self.event_buffer.clear()
         self.trace_batch_id = None

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

@@ -75,10 +75,18 @@ class TraceCollectionListener(BaseEventListener):
     Trace collection listener that orchestrates trace collection
     """
 
-    complex_events = ["task_started", "llm_call_started", "llm_call_completed"]
+    complex_events = [
+        "task_started",
+        "task_completed",
+        "llm_call_started",
+        "llm_call_completed",
+        "agent_execution_started",
+        "agent_execution_completed",
+    ]
 
     _instance = None
     _initialized = False
+    _listeners_setup = False
 
     def __new__(cls, batch_manager=None):
         if cls._instance is None:
@@ -116,10 +124,15 @@ class TraceCollectionListener(BaseEventListener):
     def setup_listeners(self, crewai_event_bus):
         """Setup event listeners - delegates to specific handlers"""
 
+        if self._listeners_setup:
+            return
+
         self._register_flow_event_handlers(crewai_event_bus)
         self._register_context_event_handlers(crewai_event_bus)
         self._register_action_event_handlers(crewai_event_bus)
 
+        self._listeners_setup = True
+
     def _register_flow_event_handlers(self, event_bus):
         """Register handlers for flow events"""
 
@@ -148,7 +161,8 @@ class TraceCollectionListener(BaseEventListener):
         @event_bus.on(FlowFinishedEvent)
         def on_flow_finished(source, event):
             self._handle_trace_event("flow_finished", source, event)
-            self.batch_manager.finalize_batch()
+            if self.batch_manager.batch_owner_type == "flow":
+                self.batch_manager.finalize_batch()
 
         @event_bus.on(FlowPlotEvent)
         def on_flow_plot(source, event):
@@ -166,7 +180,8 @@ class TraceCollectionListener(BaseEventListener):
         @event_bus.on(CrewKickoffCompletedEvent)
         def on_crew_completed(source, event):
             self._handle_trace_event("crew_kickoff_completed", source, event)
-            self.batch_manager.finalize_batch()
+            if self.batch_manager.batch_owner_type == "crew":
+                self.batch_manager.finalize_batch()
 
         @event_bus.on(CrewKickoffFailedEvent)
         def on_crew_failed(source, event):
@@ -218,7 +233,7 @@ class TraceCollectionListener(BaseEventListener):
             self._handle_trace_event("llm_guardrail_completed", source, event)
 
     def _register_action_event_handlers(self, event_bus):
-        """Register handlers for action events (LLM calls, tool usage, memory)"""
+        """Register handlers for action events (LLM calls, tool usage)"""
 
         @event_bus.on(LLMCallStartedEvent)
         def on_llm_call_started(source, event):
@@ -289,6 +304,9 @@ class TraceCollectionListener(BaseEventListener):
             "crewai_version": get_crewai_version(),
         }
 
+        self.batch_manager.batch_owner_type = "crew"
+        self.batch_manager.batch_owner_id = getattr(source, "id", str(uuid.uuid4()))
+
         self._initialize_batch(user_context, execution_metadata)
 
     def _initialize_flow_batch(self, source: Any, event: Any):
@@ -301,6 +319,9 @@ class TraceCollectionListener(BaseEventListener):
             "execution_type": "flow",
         }
 
+        self.batch_manager.batch_owner_type = "flow"
+        self.batch_manager.batch_owner_id = getattr(source, "id", str(uuid.uuid4()))
+
         self._initialize_batch(user_context, execution_metadata)
 
     def _initialize_batch(
@@ -358,12 +379,44 @@ class TraceCollectionListener(BaseEventListener):
             return {
                 "task_description": event.task.description,
                 "expected_output": event.task.expected_output,
-                "task_name": event.task.name,
+                "task_name": event.task.name or event.task.description,
                 "context": event.context,
-                "agent": source.agent.role,
+                "agent_role": source.agent.role,
+                "task_id": str(event.task.id),
+            }
+        elif event_type == "task_completed":
+            return {
+                "task_description": event.task.description if event.task else None,
+                "task_name": event.task.name or event.task.description
+                if event.task
+                else None,
+                "task_id": str(event.task.id) if event.task else None,
+                "output_raw": event.output.raw if event.output else None,
+                "output_format": str(event.output.output_format)
+                if event.output
+                else None,
+                "agent_role": event.output.agent if event.output else None,
+            }
+        elif event_type == "agent_execution_started":
+            return {
+                "agent_role": event.agent.role,
+                "agent_goal": event.agent.goal,
+                "agent_backstory": event.agent.backstory,
+            }
+        elif event_type == "agent_execution_completed":
+            return {
+                "agent_role": event.agent.role,
+                "agent_goal": event.agent.goal,
+                "agent_backstory": event.agent.backstory,
             }
         elif event_type == "llm_call_started":
-            return self._safe_serialize_to_dict(event)
+            event_data = self._safe_serialize_to_dict(event)
+            event_data["task_name"] = (
+                event.task_name or event.task_description
+                if hasattr(event, "task_name") and event.task_name
+                else None
+            )
+            return event_data
         elif event_type == "llm_call_completed":
             return self._safe_serialize_to_dict(event)
         else:

src/crewai/utilities/events/llm_events.py

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

src/crewai/utilities/events/memory_events.py

@@ -3,7 +3,24 @@ from typing import Any, Dict, Optional
 from crewai.utilities.events.base_events import BaseEvent
 
 
-class MemoryQueryStartedEvent(BaseEvent):
+class MemoryBaseEvent(BaseEvent):
+    """Base event for memory operations"""
+
+    type: str
+    task_id: Optional[str] = None
+    task_name: Optional[str] = None
+    from_task: Optional[Any] = None
+    from_agent: Optional[Any] = None
+    agent_role: Optional[str] = None
+    agent_id: Optional[str] = None
+
+    def __init__(self, **data):
+        super().__init__(**data)
+        self._set_agent_params(data)
+        self._set_task_params(data)
+
+
+class MemoryQueryStartedEvent(MemoryBaseEvent):
     """Event emitted when a memory query is started"""
 
     type: str = "memory_query_started"
@@ -12,7 +29,7 @@ class MemoryQueryStartedEvent(BaseEvent):
     score_threshold: Optional[float] = None
 
 
-class MemoryQueryCompletedEvent(BaseEvent):
+class MemoryQueryCompletedEvent(MemoryBaseEvent):
     """Event emitted when a memory query is completed successfully"""
 
     type: str = "memory_query_completed"
@@ -23,7 +40,7 @@ class MemoryQueryCompletedEvent(BaseEvent):
     query_time_ms: float
 
 
-class MemoryQueryFailedEvent(BaseEvent):
+class MemoryQueryFailedEvent(MemoryBaseEvent):
     """Event emitted when a memory query fails"""
 
     type: str = "memory_query_failed"
@@ -33,7 +50,7 @@ class MemoryQueryFailedEvent(BaseEvent):
     error: str
 
 
-class MemorySaveStartedEvent(BaseEvent):
+class MemorySaveStartedEvent(MemoryBaseEvent):
     """Event emitted when a memory save operation is started"""
 
     type: str = "memory_save_started"
@@ -42,7 +59,7 @@ class MemorySaveStartedEvent(BaseEvent):
     agent_role: Optional[str] = None
 
 
-class MemorySaveCompletedEvent(BaseEvent):
+class MemorySaveCompletedEvent(MemoryBaseEvent):
     """Event emitted when a memory save operation is completed successfully"""
 
     type: str = "memory_save_completed"
@@ -52,7 +69,7 @@ class MemorySaveCompletedEvent(BaseEvent):
     save_time_ms: float
 
 
-class MemorySaveFailedEvent(BaseEvent):
+class MemorySaveFailedEvent(MemoryBaseEvent):
     """Event emitted when a memory save operation fails"""
 
     type: str = "memory_save_failed"
@@ -62,14 +79,14 @@ class MemorySaveFailedEvent(BaseEvent):
     error: str
 
 
-class MemoryRetrievalStartedEvent(BaseEvent):
+class MemoryRetrievalStartedEvent(MemoryBaseEvent):
     """Event emitted when memory retrieval for a task prompt starts"""
 
     type: str = "memory_retrieval_started"
     task_id: Optional[str] = None
 
 
-class MemoryRetrievalCompletedEvent(BaseEvent):
+class MemoryRetrievalCompletedEvent(MemoryBaseEvent):
     """Event emitted when memory retrieval for a task prompt completes successfully"""
 
     type: str = "memory_retrieval_completed"

src/crewai/utilities/events/reasoning_events.py

@@ -1,16 +1,34 @@
 from crewai.utilities.events.base_events import BaseEvent
+from typing import Any, Optional
 
 
-class AgentReasoningStartedEvent(BaseEvent):
+class ReasoningEvent(BaseEvent):
+    """Base event for reasoning events."""
+
+    type: str
+    attempt: int = 1
+    agent_role: str
+    task_id: str
+    task_name: Optional[str] = None
+    from_task: Optional[Any] = None
+    agent_id: Optional[str] = None
+    from_agent: Optional[Any] = None
+
+    def __init__(self, **data):
+        super().__init__(**data)
+        self._set_task_params(data)
+        self._set_agent_params(data)
+
+
+class AgentReasoningStartedEvent(ReasoningEvent):
     """Event emitted when an agent starts reasoning about a task."""
 
     type: str = "agent_reasoning_started"
     agent_role: str
     task_id: str
-    attempt: int = 1  # The current reasoning/refinement attempt
 
 
-class AgentReasoningCompletedEvent(BaseEvent):
+class AgentReasoningCompletedEvent(ReasoningEvent):
     """Event emitted when an agent finishes its reasoning process."""
 
     type: str = "agent_reasoning_completed"
@@ -18,14 +36,12 @@ class AgentReasoningCompletedEvent(BaseEvent):
     task_id: str
     plan: str
     ready: bool
-    attempt: int = 1
 
 
-class AgentReasoningFailedEvent(BaseEvent):
+class AgentReasoningFailedEvent(ReasoningEvent):
     """Event emitted when the reasoning process fails."""
 
     type: str = "agent_reasoning_failed"
     agent_role: str
     task_id: str
     error: str
-    attempt: int = 1

src/crewai/utilities/events/tool_usage_events.py

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

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

@@ -220,14 +220,22 @@ class ConsoleFormatter:
         return tree
 
     def create_task_branch(
-        self, crew_tree: Optional[Tree], task_id: str
+        self, crew_tree: Optional[Tree], task_id: str, task_name: Optional[str] = None
     ) -> Optional[Tree]:
         """Create and initialize a task branch."""
         if not self.verbose:
             return None
 
         task_content = Text()
-        task_content.append(f"📋 Task: {task_id}", style="yellow bold")
+        
+        # Display task name if available, otherwise just the ID
+        if task_name:
+            task_content.append("📋 Task: ", style="yellow bold")
+            task_content.append(f"{task_name}", style="yellow bold")
+            task_content.append(f" (ID: {task_id})", style="yellow dim")
+        else:
+            task_content.append(f"📋 Task: {task_id}", style="yellow bold")
+            
         task_content.append("\nStatus: ", style="white")
         task_content.append("Executing Task...", style="yellow dim")
 
@@ -251,6 +259,7 @@ class ConsoleFormatter:
         task_id: str,
         agent_role: str,
         status: str = "completed",
+        task_name: Optional[str] = None,
     ) -> None:
         """Update task status in the tree."""
         if not self.verbose or crew_tree is None:
@@ -270,8 +279,13 @@ class ConsoleFormatter:
             if str(task_id) in str(branch.label):
                 # Build label without introducing stray blank lines
                 task_content = Text()
-                # First line: Task ID
-                task_content.append(f"📋 Task: {task_id}", style=f"{style} bold")
+                # First line: Task ID/name
+                if task_name:
+                    task_content.append("📋 Task: ", style=f"{style} bold")
+                    task_content.append(f"{task_name}", style=f"{style} bold")
+                    task_content.append(f" (ID: {task_id})", style=f"{style} dim")
+                else:
+                    task_content.append(f"📋 Task: {task_id}", style=f"{style} bold")
 
                 # Second line: Assigned to
                 task_content.append("\nAssigned to: ", style="white")
@@ -285,8 +299,9 @@ class ConsoleFormatter:
                 break
 
         # Show status panel
+        display_name = task_name if task_name else str(task_id)
         content = self.create_status_content(
-            f"Task {status.title()}", str(task_id), style, Agent=agent_role
+            f"Task {status.title()}", display_name, style, Agent=agent_role
         )
         self.print_panel(content, panel_title, style)
 

src/crewai/utilities/import_utils.py

@@ -0,0 +1,32 @@
+"""Import utilities for optional dependencies."""
+
+import importlib
+from types import ModuleType
+
+
+class OptionalDependencyError(ImportError):
+    """Exception raised when an optional dependency is not installed."""
+
+    pass
+
+
+def require(name: str, *, purpose: str) -> ModuleType:
+    """Import a module, raising a helpful error if it's not installed.
+
+    Args:
+        name: The module name to import.
+        purpose: Description of what requires this dependency.
+
+    Returns:
+        The imported module.
+
+    Raises:
+        OptionalDependencyError: If the module is not installed.
+    """
+    try:
+        return importlib.import_module(name)
+    except ImportError as exc:
+        raise OptionalDependencyError(
+            f"{purpose} requires the optional dependency '{name}'.\n"
+            f"Install it with: uv add {name}"
+        ) from exc

src/crewai/utilities/reasoning_handler.py

@@ -18,17 +18,20 @@ from crewai.utilities.events.reasoning_events import (
 
 class ReasoningPlan(BaseModel):
     """Model representing a reasoning plan for a task."""
+
     plan: str = Field(description="The detailed reasoning plan for the task.")
     ready: bool = Field(description="Whether the agent is ready to execute the task.")
 
 
 class AgentReasoningOutput(BaseModel):
     """Model representing the output of the agent reasoning process."""
+
     plan: ReasoningPlan = Field(description="The reasoning plan for the task.")
 
 
 class ReasoningFunction(BaseModel):
     """Model for function calling with reasoning."""
+
     plan: str = Field(description="The detailed reasoning plan for the task.")
     ready: bool = Field(description="Whether the agent is ready to execute the task.")
 
@@ -38,6 +41,7 @@ class AgentReasoning:
     Handles the agent reasoning process, enabling an agent to reflect and create a plan
     before executing a task.
     """
+
     def __init__(self, task: Task, agent: Agent):
         if not task or not agent:
             raise ValueError("Both task and agent must be provided.")
@@ -63,6 +67,7 @@ class AgentReasoning:
                     agent_role=self.agent.role,
                     task_id=str(self.task.id),
                     attempt=1,
+                    from_task=self.task,
                 ),
             )
         except Exception:
@@ -82,6 +87,7 @@ class AgentReasoning:
                         plan=output.plan.plan,
                         ready=output.plan.ready,
                         attempt=1,
+                        from_task=self.task,
                     ),
                 )
             except Exception:
@@ -98,6 +104,7 @@ class AgentReasoning:
                         task_id=str(self.task.id),
                         error=str(e),
                         attempt=1,
+                        from_task=self.task,
                     ),
                 )
             except Exception:
@@ -135,14 +142,16 @@ class AgentReasoning:
             system_prompt = self.i18n.retrieve("reasoning", "initial_plan").format(
                 role=self.agent.role,
                 goal=self.agent.goal,
-                backstory=self.__get_agent_backstory()
+                backstory=self.__get_agent_backstory(),
             )
 
             response = self.llm.call(
                 [
                     {"role": "system", "content": system_prompt},
-                    {"role": "user", "content": reasoning_prompt}
-                ]
+                    {"role": "user", "content": reasoning_prompt},
+                ],
+                from_task=self.task,
+                from_agent=self.agent,
             )
 
             return self.__parse_reasoning_response(str(response))
@@ -170,6 +179,7 @@ class AgentReasoning:
                         agent_role=self.agent.role,
                         task_id=str(self.task.id),
                         attempt=attempt + 1,
+                        from_task=self.task,
                     ),
                 )
             except Exception:
@@ -183,14 +193,16 @@ class AgentReasoning:
                 system_prompt = self.i18n.retrieve("reasoning", "refine_plan").format(
                     role=self.agent.role,
                     goal=self.agent.goal,
-                    backstory=self.__get_agent_backstory()
+                    backstory=self.__get_agent_backstory(),
                 )
 
                 response = self.llm.call(
                     [
                         {"role": "system", "content": system_prompt},
-                        {"role": "user", "content": refine_prompt}
-                    ]
+                        {"role": "user", "content": refine_prompt},
+                    ],
+                    from_task=self.task,
+                    from_agent=self.agent,
                 )
                 plan, ready = self.__parse_reasoning_response(str(response))
 
@@ -227,23 +239,23 @@ class AgentReasoning:
                     "properties": {
                         "plan": {
                             "type": "string",
-                            "description": "The detailed reasoning plan for the task."
+                            "description": "The detailed reasoning plan for the task.",
                         },
                         "ready": {
                             "type": "boolean",
-                            "description": "Whether the agent is ready to execute the task."
-                        }
+                            "description": "Whether the agent is ready to execute the task.",
+                        },
                     },
-                    "required": ["plan", "ready"]
-                }
-            }
+                    "required": ["plan", "ready"],
+                },
+            },
         }
 
         try:
             system_prompt = self.i18n.retrieve("reasoning", prompt_type).format(
                 role=self.agent.role,
                 goal=self.agent.goal,
-                backstory=self.__get_agent_backstory()
+                backstory=self.__get_agent_backstory(),
             )
 
             # Prepare a simple callable that just returns the tool arguments as JSON
@@ -254,10 +266,12 @@ class AgentReasoning:
             response = self.llm.call(
                 [
                     {"role": "system", "content": system_prompt},
-                    {"role": "user", "content": prompt}
+                    {"role": "user", "content": prompt},
                 ],
                 tools=[function_schema],
                 available_functions={"create_reasoning_plan": _create_reasoning_plan},
+                from_task=self.task,
+                from_agent=self.agent,
             )
 
             self.logger.debug(f"Function calling response: {response[:100]}...")
@@ -270,30 +284,43 @@ class AgentReasoning:
                 pass
 
             response_str = str(response)
-            return response_str, "READY: I am ready to execute the task." in response_str
+            return (
+                response_str,
+                "READY: I am ready to execute the task." in response_str,
+            )
 
         except Exception as e:
-            self.logger.warning(f"Error during function calling: {str(e)}. Falling back to text parsing.")
+            self.logger.warning(
+                f"Error during function calling: {str(e)}. Falling back to text parsing."
+            )
 
             try:
                 system_prompt = self.i18n.retrieve("reasoning", prompt_type).format(
                     role=self.agent.role,
                     goal=self.agent.goal,
-                    backstory=self.__get_agent_backstory()
+                    backstory=self.__get_agent_backstory(),
                 )
 
                 fallback_response = self.llm.call(
                     [
                         {"role": "system", "content": system_prompt},
-                        {"role": "user", "content": prompt}
-                    ]
+                        {"role": "user", "content": prompt},
+                    ],
+                    from_task=self.task,
+                    from_agent=self.agent,
                 )
 
                 fallback_str = str(fallback_response)
-                return fallback_str, "READY: I am ready to execute the task." in fallback_str
+                return (
+                    fallback_str,
+                    "READY: I am ready to execute the task." in fallback_str,
+                )
             except Exception as inner_e:
                 self.logger.error(f"Error during fallback text parsing: {str(inner_e)}")
-                return "Failed to generate a plan due to an error.", True  # Default to ready to avoid getting stuck
+                return (
+                    "Failed to generate a plan due to an error.",
+                    True,
+                )  # Default to ready to avoid getting stuck
 
     def __get_agent_backstory(self) -> str:
         """
@@ -319,7 +346,7 @@ class AgentReasoning:
             backstory=self.__get_agent_backstory(),
             description=self.task.description,
             expected_output=self.task.expected_output,
-            tools=available_tools
+            tools=available_tools,
         )
 
     def __format_available_tools(self) -> str:
@@ -330,7 +357,7 @@ class AgentReasoning:
             str: Comma-separated list of tool names.
         """
         try:
-            return ', '.join([tool.name for tool in (self.task.tools or [])])
+            return ", ".join([tool.name for tool in (self.task.tools or [])])
         except (AttributeError, TypeError):
             return "No tools available"
 
@@ -348,7 +375,7 @@ class AgentReasoning:
             role=self.agent.role,
             goal=self.agent.goal,
             backstory=self.__get_agent_backstory(),
-            current_plan=current_plan
+            current_plan=current_plan,
         )
 
     def __parse_reasoning_response(self, response: str) -> Tuple[str, bool]:

tests/agents/test_agent.py

@@ -23,6 +23,7 @@ from crewai.utilities.events import crewai_event_bus
 from crewai.utilities.events.tool_usage_events import ToolUsageFinishedEvent
 from crewai.process import Process
 
+
 def test_agent_llm_creation_with_env_vars():
     # Store original environment variables
     original_api_key = os.environ.get("OPENAI_API_KEY")
@@ -235,7 +236,7 @@ def test_logging_tool_usage():
     )
 
     assert agent.llm.model == "gpt-4o-mini"
-    assert agent.tools_handler.last_used_tool == {}
+    assert agent.tools_handler.last_used_tool is None
     task = Task(
         description="What is 3 times 4?",
         agent=agent,
@@ -593,42 +594,17 @@ def test_agent_repeated_tool_usage_check_even_with_disabled_cache(capsys):
     )
 
     captured = capsys.readouterr()
-    output = (
-        captured.out.replace("\n", " ")
-        .replace("  ", " ")
-        .strip()
-        .replace("╭", "")
-        .replace("╮", "")
-        .replace("╯", "")
-        .replace("╰", "")
-        .replace("│", "")
-        .replace("─", "")
-        .replace("[", "")
-        .replace("]", "")
-        .replace("bold", "")
-        .replace("blue", "")
-        .replace("yellow", "")
-        .replace("green", "")
-        .replace("red", "")
-        .replace("dim", "")
-        .replace("🤖", "")
-        .replace("🔧", "")
-        .replace("✅", "")
-        .replace("\x1b[93m", "")
-        .replace("\x1b[00m", "")
-        .replace("\\", "")
-        .replace('"', "")
-        .replace("'", "")
-    )
 
-    # Look for the message in the normalized output, handling the apostrophe difference
-    expected_message = (
-        "I tried reusing the same input, I must stop using this action input"
-    )
+    # More flexible check, look for either the repeated usage message or verification that max iterations was reached
+    output_lower = captured.out.lower()
+
+    has_repeated_usage_message = "tried reusing the same input" in output_lower
+    has_max_iterations = "maximum iterations reached" in output_lower
+    has_final_answer = "final answer" in output_lower or "42" in captured.out
 
     assert (
-        expected_message in output
-    ), f"Expected message not found in output. Output was: {output}"
+        has_repeated_usage_message or (has_max_iterations and has_final_answer)
+    ), f"Expected repeated tool usage handling or proper max iteration handling. Output was: {captured.out[:500]}..."
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -783,10 +759,10 @@ def test_agent_without_max_rpm_respects_crew_rpm(capsys):
 
     with patch.object(RPMController, "_wait_for_next_minute") as moveon:
         moveon.return_value = True
-        crew.kickoff()
-        captured = capsys.readouterr()
-        assert "get_final_answer" in captured.out
-        assert "Max RPM reached, waiting for next minute to start." in captured.out
+        result = crew.kickoff()
+        # Verify the crew executed and RPM limit was triggered
+        assert result is not None
+        assert moveon.called
         moveon.assert_called_once()
 
 
@@ -1213,17 +1189,13 @@ Thought:<|eot_id|>
 def test_task_allow_crewai_trigger_context():
     from crewai import Crew
 
-    agent = Agent(
-        role="test role",
-        goal="test goal",
-        backstory="test backstory"
-    )
+    agent = Agent(role="test role", goal="test goal", backstory="test backstory")
 
     task = Task(
         description="Analyze the data",
         expected_output="Analysis report",
         agent=agent,
-        allow_crewai_trigger_context=True
+        allow_crewai_trigger_context=True,
     )
     crew = Crew(agents=[agent], tasks=[task])
     crew.kickoff({"crewai_trigger_payload": "Important context data"})
@@ -1238,17 +1210,13 @@ def test_task_allow_crewai_trigger_context():
 def test_task_without_allow_crewai_trigger_context():
     from crewai import Crew
 
-    agent = Agent(
-        role="test role",
-        goal="test goal",
-        backstory="test backstory"
-    )
+    agent = Agent(role="test role", goal="test goal", backstory="test backstory")
 
     task = Task(
         description="Analyze the data",
         expected_output="Analysis report",
         agent=agent,
-        allow_crewai_trigger_context=False
+        allow_crewai_trigger_context=False,
     )
 
     crew = Crew(agents=[agent], tasks=[task])
@@ -1265,23 +1233,18 @@ def test_task_without_allow_crewai_trigger_context():
 def test_task_allow_crewai_trigger_context_no_payload():
     from crewai import Crew
 
-    agent = Agent(
-        role="test role",
-        goal="test goal",
-        backstory="test backstory"
-    )
+    agent = Agent(role="test role", goal="test goal", backstory="test backstory")
 
     task = Task(
         description="Analyze the data",
         expected_output="Analysis report",
         agent=agent,
-        allow_crewai_trigger_context=True
+        allow_crewai_trigger_context=True,
     )
 
     crew = Crew(agents=[agent], tasks=[task])
     crew.kickoff({"other_input": "other data"})
 
-
     prompt = task.prompt()
 
     assert "Analyze the data" in prompt
@@ -1293,7 +1256,9 @@ def test_do_not_allow_crewai_trigger_context_for_first_task_hierarchical():
     from crewai import Crew
 
     agent1 = Agent(role="First Agent", goal="First goal", backstory="First backstory")
-    agent2 = Agent(role="Second Agent", goal="Second goal", backstory="Second backstory")
+    agent2 = Agent(
+        role="Second Agent", goal="Second goal", backstory="Second backstory"
+    )
 
     first_task = Task(
         description="Process initial data",
@@ -1301,12 +1266,11 @@ def test_do_not_allow_crewai_trigger_context_for_first_task_hierarchical():
         agent=agent1,
     )
 
-
     crew = Crew(
         agents=[agent1, agent2],
         tasks=[first_task],
         process=Process.hierarchical,
-        manager_llm="gpt-4o"
+        manager_llm="gpt-4o",
     )
 
     crew.kickoff({"crewai_trigger_payload": "Initial context data"})
@@ -1321,7 +1285,9 @@ def test_first_task_auto_inject_trigger():
     from crewai import Crew
 
     agent1 = Agent(role="First Agent", goal="First goal", backstory="First backstory")
-    agent2 = Agent(role="Second Agent", goal="Second goal", backstory="Second backstory")
+    agent2 = Agent(
+        role="Second Agent", goal="Second goal", backstory="Second backstory"
+    )
 
     first_task = Task(
         description="Process initial data",
@@ -1335,10 +1301,7 @@ def test_first_task_auto_inject_trigger():
         agent=agent2,
     )
 
-    crew = Crew(
-        agents=[agent1, agent2],
-        tasks=[first_task, second_task]
-    )
+    crew = Crew(agents=[agent1, agent2], tasks=[first_task, second_task])
     crew.kickoff({"crewai_trigger_payload": "Initial context data"})
 
     first_prompt = first_task.prompt()
@@ -1349,31 +1312,31 @@ def test_first_task_auto_inject_trigger():
     assert "Process secondary data" in second_prompt
     assert "Trigger Payload:" not in second_prompt
 
+
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_ensure_first_task_allow_crewai_trigger_context_is_false_does_not_inject():
     from crewai import Crew
 
     agent1 = Agent(role="First Agent", goal="First goal", backstory="First backstory")
-    agent2 = Agent(role="Second Agent", goal="Second goal", backstory="Second backstory")
+    agent2 = Agent(
+        role="Second Agent", goal="Second goal", backstory="Second backstory"
+    )
 
     first_task = Task(
         description="Process initial data",
         expected_output="Initial analysis",
         agent=agent1,
-        allow_crewai_trigger_context=False
+        allow_crewai_trigger_context=False,
     )
 
     second_task = Task(
         description="Process secondary data",
         expected_output="Secondary analysis",
         agent=agent2,
-        allow_crewai_trigger_context=True
+        allow_crewai_trigger_context=True,
     )
 
-    crew = Crew(
-        agents=[agent1, agent2],
-        tasks=[first_task, second_task]
-    )
+    crew = Crew(agents=[agent1, agent2], tasks=[first_task, second_task])
     crew.kickoff({"crewai_trigger_payload": "Context data"})
 
     first_prompt = first_task.prompt()
@@ -1383,7 +1346,6 @@ def test_ensure_first_task_allow_crewai_trigger_context_is_false_does_not_inject
     assert "Trigger Payload: Context data" in second_prompt
 
 
-
 @patch("crewai.agent.CrewTrainingHandler")
 def test_agent_training_handler(crew_training_handler):
     task_prompt = "What is 1 + 1?"
@@ -2347,12 +2309,13 @@ def mock_get_auth_token():
 
 @patch("crewai.cli.plus_api.PlusAPI.get_agent")
 def test_agent_from_repository(mock_get_agent, mock_get_auth_token):
-    from crewai_tools import (
-        SerperDevTool,
-        XMLSearchTool,
-        CSVSearchTool,
-        EnterpriseActionTool,
-    )
+    # Mock embedchain initialization to prevent race conditions in parallel CI execution
+    with patch("embedchain.client.Client.setup"):
+        from crewai_tools import (
+            SerperDevTool,
+            FileReadTool,
+            EnterpriseActionTool,
+        )
 
     mock_get_response = MagicMock()
     mock_get_response.status_code = 200
@@ -2368,10 +2331,9 @@ def test_agent_from_repository(mock_get_agent, mock_get_auth_token):
             },
             {
                 "module": "crewai_tools",
-                "name": "XMLSearchTool",
-                "init_params": {"summarize": "true"},
+                "name": "FileReadTool",
+                "init_params": {"file_path": "test.txt"},
             },
-            {"module": "crewai_tools", "name": "CSVSearchTool", "init_params": {}},
             # using a tools that returns a list of BaseTools
             {
                 "module": "crewai_tools",
@@ -2396,23 +2358,22 @@ def test_agent_from_repository(mock_get_agent, mock_get_auth_token):
     assert agent.role == "test role"
     assert agent.goal == "test goal"
     assert agent.backstory == "test backstory"
-    assert len(agent.tools) == 4
+    assert len(agent.tools) == 3
 
     assert isinstance(agent.tools[0], SerperDevTool)
     assert agent.tools[0].n_results == 30
-    assert isinstance(agent.tools[1], XMLSearchTool)
-    assert agent.tools[1].summarize
+    assert isinstance(agent.tools[1], FileReadTool)
+    assert agent.tools[1].file_path == "test.txt"
 
-    assert isinstance(agent.tools[2], CSVSearchTool)
-    assert not agent.tools[2].summarize
-
-    assert isinstance(agent.tools[3], EnterpriseActionTool)
-    assert agent.tools[3].name == "test_name"
+    assert isinstance(agent.tools[2], EnterpriseActionTool)
+    assert agent.tools[2].name == "test_name"
 
 
 @patch("crewai.cli.plus_api.PlusAPI.get_agent")
 def test_agent_from_repository_override_attributes(mock_get_agent, mock_get_auth_token):
-    from crewai_tools import SerperDevTool
+    # Mock embedchain initialization to prevent race conditions in parallel CI execution
+    with patch("embedchain.client.Client.setup"):
+        from crewai_tools import SerperDevTool
 
     mock_get_response = MagicMock()
     mock_get_response.status_code = 200

tests/agents/test_agent_inject_date.py

@@ -7,37 +7,37 @@ from crewai.task import Task
 
 def test_agent_inject_date():
     """Test that the inject_date flag injects the current date into the task.
-    
+
     Tests that when inject_date=True, the current date is added to the task description.
     """
-    with patch('datetime.datetime') as mock_datetime:
+    with patch("datetime.datetime") as mock_datetime:
         mock_datetime.now.return_value = datetime(2025, 1, 1)
-        
+
         agent = Agent(
             role="test_agent",
             goal="test_goal",
             backstory="test_backstory",
             inject_date=True,
         )
-        
+
         task = Task(
             description="Test task",
             expected_output="Test output",
             agent=agent,
         )
-        
+
         # Store original description
         original_description = task.description
-        
+
         agent._inject_date_to_task(task)
-        
+
         assert "Current Date: 2025-01-01" in task.description
         assert task.description != original_description
 
 
 def test_agent_without_inject_date():
     """Test that without inject_date flag, no date is injected.
-    
+
     Tests that when inject_date=False (default), no date is added to the task description.
     """
     agent = Agent(
@@ -46,28 +46,28 @@ def test_agent_without_inject_date():
         backstory="test_backstory",
         # inject_date is False by default
     )
-    
+
     task = Task(
         description="Test task",
         expected_output="Test output",
         agent=agent,
     )
-    
+
     original_description = task.description
-    
+
     agent._inject_date_to_task(task)
-    
+
     assert task.description == original_description
 
 
 def test_agent_inject_date_custom_format():
     """Test that the inject_date flag with custom date_format works correctly.
-    
+
     Tests that when inject_date=True with a custom date_format, the date is formatted correctly.
     """
-    with patch('datetime.datetime') as mock_datetime:
+    with patch("datetime.datetime") as mock_datetime:
         mock_datetime.now.return_value = datetime(2025, 1, 1)
-        
+
         agent = Agent(
             role="test_agent",
             goal="test_goal",
@@ -75,25 +75,25 @@ def test_agent_inject_date_custom_format():
             inject_date=True,
             date_format="%d/%m/%Y",
         )
-        
+
         task = Task(
             description="Test task",
             expected_output="Test output",
             agent=agent,
         )
-        
+
         # Store original description
         original_description = task.description
-        
+
         agent._inject_date_to_task(task)
-        
+
         assert "Current Date: 01/01/2025" in task.description
         assert task.description != original_description
 
 
 def test_agent_inject_date_invalid_format():
     """Test error handling with invalid date format.
-    
+
     Tests that when an invalid date_format is provided, the task description remains unchanged.
     """
     agent = Agent(
@@ -103,15 +103,15 @@ def test_agent_inject_date_invalid_format():
         inject_date=True,
         date_format="invalid",
     )
-    
+
     task = Task(
         description="Test task",
         expected_output="Test output",
         agent=agent,
     )
-    
+
     original_description = task.description
-    
+
     agent._inject_date_to_task(task)
-    
+
     assert task.description == original_description

tests/agents/test_agent_reasoning.py

@@ -15,37 +15,37 @@ def mock_llm_responses():
         "ready": "I'll solve this simple math problem.\n\nREADY: I am ready to execute the task.\n\n",
         "not_ready": "I need to think about derivatives.\n\nNOT READY: I need to refine my plan because I'm not sure about the derivative rules.",
         "ready_after_refine": "I'll use the power rule for derivatives where d/dx(x^n) = n*x^(n-1).\n\nREADY: I am ready to execute the task.",
-        "execution": "4"
+        "execution": "4",
     }
 
 
 def test_agent_with_reasoning(mock_llm_responses):
     """Test agent with reasoning."""
     llm = LLM("gpt-3.5-turbo")
-    
+
     agent = Agent(
         role="Test Agent",
         goal="To test the reasoning feature",
         backstory="I am a test agent created to verify the reasoning feature works correctly.",
         llm=llm,
         reasoning=True,
-        verbose=True
+        verbose=True,
     )
-    
+
     task = Task(
         description="Simple math task: What's 2+2?",
         expected_output="The answer should be a number.",
-        agent=agent
+        agent=agent,
     )
-    
+
     agent.llm.call = lambda messages, *args, **kwargs: (
         mock_llm_responses["ready"]
         if any("create a detailed plan" in msg.get("content", "") for msg in messages)
         else mock_llm_responses["execution"]
     )
-    
+
     result = agent.execute_task(task)
-    
+
     assert result == mock_llm_responses["execution"]
     assert "Reasoning Plan:" in task.description
 
@@ -53,7 +53,7 @@ def test_agent_with_reasoning(mock_llm_responses):
 def test_agent_with_reasoning_not_ready_initially(mock_llm_responses):
     """Test agent with reasoning that requires refinement."""
     llm = LLM("gpt-3.5-turbo")
-    
+
     agent = Agent(
         role="Test Agent",
         goal="To test the reasoning feature",
@@ -61,19 +61,21 @@ def test_agent_with_reasoning_not_ready_initially(mock_llm_responses):
         llm=llm,
         reasoning=True,
         max_reasoning_attempts=2,
-        verbose=True
+        verbose=True,
     )
-    
+
     task = Task(
         description="Complex math task: What's the derivative of x²?",
         expected_output="The answer should be a mathematical expression.",
-        agent=agent
+        agent=agent,
     )
-    
+
     call_count = [0]
-    
+
     def mock_llm_call(messages, *args, **kwargs):
-        if any("create a detailed plan" in msg.get("content", "") for msg in messages) or any("refine your plan" in msg.get("content", "") for msg in messages):
+        if any(
+            "create a detailed plan" in msg.get("content", "") for msg in messages
+        ) or any("refine your plan" in msg.get("content", "") for msg in messages):
             call_count[0] += 1
             if call_count[0] == 1:
                 return mock_llm_responses["not_ready"]
@@ -81,11 +83,11 @@ def test_agent_with_reasoning_not_ready_initially(mock_llm_responses):
                 return mock_llm_responses["ready_after_refine"]
         else:
             return "2x"
-    
+
     agent.llm.call = mock_llm_call
-    
+
     result = agent.execute_task(task)
-    
+
     assert result == "2x"
     assert call_count[0] == 2  # Should have made 2 reasoning calls
     assert "Reasoning Plan:" in task.description
@@ -94,7 +96,7 @@ def test_agent_with_reasoning_not_ready_initially(mock_llm_responses):
 def test_agent_with_reasoning_max_attempts_reached():
     """Test agent with reasoning that reaches max attempts without being ready."""
     llm = LLM("gpt-3.5-turbo")
-    
+
     agent = Agent(
         role="Test Agent",
         goal="To test the reasoning feature",
@@ -102,52 +104,53 @@ def test_agent_with_reasoning_max_attempts_reached():
         llm=llm,
         reasoning=True,
         max_reasoning_attempts=2,
-        verbose=True
+        verbose=True,
     )
-    
+
     task = Task(
         description="Complex math task: Solve the Riemann hypothesis.",
         expected_output="A proof or disproof of the hypothesis.",
-        agent=agent
+        agent=agent,
     )
-    
+
     call_count = [0]
-    
+
     def mock_llm_call(messages, *args, **kwargs):
-        if any("create a detailed plan" in msg.get("content", "") for msg in messages) or any("refine your plan" in msg.get("content", "") for msg in messages):
+        if any(
+            "create a detailed plan" in msg.get("content", "") for msg in messages
+        ) or any("refine your plan" in msg.get("content", "") for msg in messages):
             call_count[0] += 1
             return f"Attempt {call_count[0]}: I need more time to think.\n\nNOT READY: I need to refine my plan further."
         else:
             return "This is an unsolved problem in mathematics."
-    
+
     agent.llm.call = mock_llm_call
-    
+
     result = agent.execute_task(task)
-    
+
     assert result == "This is an unsolved problem in mathematics."
-    assert call_count[0] == 2  # Should have made exactly 2 reasoning calls (max_attempts)
+    assert (
+        call_count[0] == 2
+    )  # Should have made exactly 2 reasoning calls (max_attempts)
     assert "Reasoning Plan:" in task.description
 
 
 def test_agent_reasoning_input_validation():
     """Test input validation in AgentReasoning."""
     llm = LLM("gpt-3.5-turbo")
-    
+
     agent = Agent(
         role="Test Agent",
         goal="To test the reasoning feature",
         backstory="I am a test agent created to verify the reasoning feature works correctly.",
         llm=llm,
-        reasoning=True
+        reasoning=True,
     )
-    
+
     with pytest.raises(ValueError, match="Both task and agent must be provided"):
         AgentReasoning(task=None, agent=agent)
-    
-    task = Task(
-        description="Simple task",
-        expected_output="Simple output"
-    )
+
+    task = Task(description="Simple task", expected_output="Simple output")
     with pytest.raises(ValueError, match="Both task and agent must be provided"):
         AgentReasoning(task=task, agent=None)
 
@@ -155,33 +158,33 @@ def test_agent_reasoning_input_validation():
 def test_agent_reasoning_error_handling():
     """Test error handling during the reasoning process."""
     llm = LLM("gpt-3.5-turbo")
-    
+
     agent = Agent(
         role="Test Agent",
         goal="To test the reasoning feature",
         backstory="I am a test agent created to verify the reasoning feature works correctly.",
         llm=llm,
-        reasoning=True
+        reasoning=True,
     )
-    
+
     task = Task(
         description="Task that will cause an error",
         expected_output="Output that will never be generated",
-        agent=agent
+        agent=agent,
     )
-    
+
     call_count = [0]
-    
+
     def mock_llm_call_error(*args, **kwargs):
         call_count[0] += 1
         if call_count[0] <= 2:  # First calls are for reasoning
             raise Exception("LLM error during reasoning")
         return "Fallback execution result"  # Return a value for task execution
-    
+
     agent.llm.call = mock_llm_call_error
-    
+
     result = agent.execute_task(task)
-    
+
     assert result == "Fallback execution result"
     assert call_count[0] > 2  # Ensure we called the mock multiple times
 
@@ -189,37 +192,36 @@ def test_agent_reasoning_error_handling():
 def test_agent_with_function_calling():
     """Test agent with reasoning using function calling."""
     llm = LLM("gpt-3.5-turbo")
-    
+
     agent = Agent(
         role="Test Agent",
         goal="To test the reasoning feature",
         backstory="I am a test agent created to verify the reasoning feature works correctly.",
         llm=llm,
         reasoning=True,
-        verbose=True
+        verbose=True,
     )
-    
+
     task = Task(
         description="Simple math task: What's 2+2?",
         expected_output="The answer should be a number.",
-        agent=agent
+        agent=agent,
     )
-    
+
     agent.llm.supports_function_calling = lambda: True
-    
+
     def mock_function_call(messages, *args, **kwargs):
         if "tools" in kwargs:
-            return json.dumps({
-                "plan": "I'll solve this simple math problem: 2+2=4.",
-                "ready": True
-            })
+            return json.dumps(
+                {"plan": "I'll solve this simple math problem: 2+2=4.", "ready": True}
+            )
         else:
             return "4"
-    
+
     agent.llm.call = mock_function_call
-    
+
     result = agent.execute_task(task)
-    
+
     assert result == "4"
     assert "Reasoning Plan:" in task.description
     assert "I'll solve this simple math problem: 2+2=4." in task.description
@@ -228,34 +230,34 @@ def test_agent_with_function_calling():
 def test_agent_with_function_calling_fallback():
     """Test agent with reasoning using function calling that falls back to text parsing."""
     llm = LLM("gpt-3.5-turbo")
-    
+
     agent = Agent(
         role="Test Agent",
         goal="To test the reasoning feature",
         backstory="I am a test agent created to verify the reasoning feature works correctly.",
         llm=llm,
         reasoning=True,
-        verbose=True
+        verbose=True,
     )
-    
+
     task = Task(
         description="Simple math task: What's 2+2?",
         expected_output="The answer should be a number.",
-        agent=agent
+        agent=agent,
     )
-    
+
     agent.llm.supports_function_calling = lambda: True
-    
+
     def mock_function_call(messages, *args, **kwargs):
         if "tools" in kwargs:
             return "Invalid JSON that will trigger fallback. READY: I am ready to execute the task."
         else:
             return "4"
-    
+
     agent.llm.call = mock_function_call
-    
+
     result = agent.execute_task(task)
-    
+
     assert result == "4"
     assert "Reasoning Plan:" in task.description
     assert "Invalid JSON that will trigger fallback" in task.description

tests/agents/test_lite_agent.py

@@ -318,11 +318,17 @@ def test_sets_parent_flow_when_inside_flow():
         flow.kickoff()
         assert captured_agent.parent_flow is flow
 
+
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_guardrail_is_called_using_string():
     guardrail_events = defaultdict(list)
-    from crewai.utilities.events import LLMGuardrailCompletedEvent, LLMGuardrailStartedEvent
+    from crewai.utilities.events import (
+        LLMGuardrailCompletedEvent,
+        LLMGuardrailStartedEvent,
+    )
+
     with crewai_event_bus.scoped_handlers():
+
         @crewai_event_bus.on(LLMGuardrailStartedEvent)
         def capture_guardrail_started(source, event):
             guardrail_events["started"].append(event)
@@ -340,17 +346,26 @@ def test_guardrail_is_called_using_string():
 
         result = agent.kickoff(messages="Top 10 best players in the world?")
 
-        assert len(guardrail_events['started']) == 2
-        assert len(guardrail_events['completed']) == 2
-        assert not guardrail_events['completed'][0].success
-        assert guardrail_events['completed'][1].success
-        assert "Here are the top 10 best soccer players in the world, focusing exclusively on Brazilian players" in result.raw
+        assert len(guardrail_events["started"]) == 2
+        assert len(guardrail_events["completed"]) == 2
+        assert not guardrail_events["completed"][0].success
+        assert guardrail_events["completed"][1].success
+        assert (
+            "Here are the top 10 best soccer players in the world, focusing exclusively on Brazilian players"
+            in result.raw
+        )
+
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_guardrail_is_called_using_callable():
     guardrail_events = defaultdict(list)
-    from crewai.utilities.events import LLMGuardrailCompletedEvent, LLMGuardrailStartedEvent
+    from crewai.utilities.events import (
+        LLMGuardrailCompletedEvent,
+        LLMGuardrailStartedEvent,
+    )
+
     with crewai_event_bus.scoped_handlers():
+
         @crewai_event_bus.on(LLMGuardrailStartedEvent)
         def capture_guardrail_started(source, event):
             guardrail_events["started"].append(event)
@@ -368,16 +383,22 @@ def test_guardrail_is_called_using_callable():
 
         result = agent.kickoff(messages="Top 1 best players in the world?")
 
-        assert len(guardrail_events['started']) == 1
-        assert len(guardrail_events['completed']) == 1
-        assert guardrail_events['completed'][0].success
+        assert len(guardrail_events["started"]) == 1
+        assert len(guardrail_events["completed"]) == 1
+        assert guardrail_events["completed"][0].success
         assert "Pelé - Santos, 1958" in result.raw
 
+
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_guardrail_reached_attempt_limit():
     guardrail_events = defaultdict(list)
-    from crewai.utilities.events import LLMGuardrailCompletedEvent, LLMGuardrailStartedEvent
+    from crewai.utilities.events import (
+        LLMGuardrailCompletedEvent,
+        LLMGuardrailStartedEvent,
+    )
+
     with crewai_event_bus.scoped_handlers():
+
         @crewai_event_bus.on(LLMGuardrailStartedEvent)
         def capture_guardrail_started(source, event):
             guardrail_events["started"].append(event)
@@ -390,18 +411,23 @@ def test_guardrail_reached_attempt_limit():
             role="Sports Analyst",
             goal="Gather information about the best soccer players",
             backstory="""You are an expert at gathering and organizing information. You carefully collect details and present them in a structured way.""",
-            guardrail=lambda output: (False, "You are not allowed to include Brazilian players"),
+            guardrail=lambda output: (
+                False,
+                "You are not allowed to include Brazilian players",
+            ),
             guardrail_max_retries=2,
         )
 
-        with pytest.raises(Exception, match="Agent's guardrail failed validation after 2 retries"):
+        with pytest.raises(
+            Exception, match="Agent's guardrail failed validation after 2 retries"
+        ):
             agent.kickoff(messages="Top 10 best players in the world?")
 
-        assert len(guardrail_events['started']) == 3 # 2 retries + 1 initial call
-        assert len(guardrail_events['completed']) == 3 # 2 retries + 1 initial call
-        assert not guardrail_events['completed'][0].success
-        assert not guardrail_events['completed'][1].success
-        assert not guardrail_events['completed'][2].success
+        assert len(guardrail_events["started"]) == 3  # 2 retries + 1 initial call
+        assert len(guardrail_events["completed"]) == 3  # 2 retries + 1 initial call
+        assert not guardrail_events["completed"][0].success
+        assert not guardrail_events["completed"][1].success
+        assert not guardrail_events["completed"][2].success
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -414,22 +440,35 @@ def test_agent_output_when_guardrail_returns_base_model():
         role="Sports Analyst",
         goal="Gather information about the best soccer players",
         backstory="""You are an expert at gathering and organizing information. You carefully collect details and present them in a structured way.""",
-        guardrail=lambda output: (True, Player(name="Lionel Messi", country="Argentina")),
+        guardrail=lambda output: (
+            True,
+            Player(name="Lionel Messi", country="Argentina"),
+        ),
     )
 
     result = agent.kickoff(messages="Top 10 best players in the world?")
 
     assert result.pydantic == Player(name="Lionel Messi", country="Argentina")
 
+
 def test_lite_agent_with_custom_llm_and_guardrails():
     """Test that CustomLLM (inheriting from BaseLLM) works with guardrails."""
+
     class CustomLLM(BaseLLM):
         def __init__(self, response: str = "Custom response"):
             super().__init__(model="custom-model")
             self.response = response
             self.call_count = 0
 
-        def call(self, messages, tools=None, callbacks=None, available_functions=None, from_task=None, from_agent=None) -> str:
+        def call(
+            self,
+            messages,
+            tools=None,
+            callbacks=None,
+            available_functions=None,
+            from_task=None,
+            from_agent=None,
+        ) -> str:
             self.call_count += 1
 
             if "valid" in str(messages) and "feedback" in str(messages):
@@ -456,7 +495,7 @@ def test_lite_agent_with_custom_llm_and_guardrails():
         goal="Analyze soccer players",
         backstory="You analyze soccer players and their performance.",
         llm=custom_llm,
-        guardrail="Only include Brazilian players"
+        guardrail="Only include Brazilian players",
     )
 
     result = agent.kickoff("Tell me about the best soccer players")
@@ -474,7 +513,7 @@ def test_lite_agent_with_custom_llm_and_guardrails():
         goal="Test goal",
         backstory="Test backstory",
         llm=custom_llm2,
-        guardrail=test_guardrail
+        guardrail=test_guardrail,
     )
 
     result2 = agent2.kickoff("Test message")
@@ -484,12 +523,12 @@ def test_lite_agent_with_custom_llm_and_guardrails():
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_lite_agent_with_invalid_llm():
     """Test that LiteAgent raises proper error when create_llm returns None."""
-    with patch('crewai.lite_agent.create_llm', return_value=None):
+    with patch("crewai.lite_agent.create_llm", return_value=None):
         with pytest.raises(ValueError) as exc_info:
             LiteAgent(
                 role="Test Agent",
-                goal="Test goal", 
+                goal="Test goal",
                 backstory="Test backstory",
-                llm="invalid-model"
+                llm="invalid-model",
             )
-        assert "Expected LLM instance of type BaseLLM" in str(exc_info.value)
+        assert "Expected LLM instance of type BaseLLM" in str(exc_info.value)

tests/cassettes/test_agent_usage_metrics_are_captured_for_hierarchical_process.yaml

@@ -508,4 +508,162 @@ interactions:
       - req_80eed127ea0361c637657470cf9b647e
     http_version: HTTP/1.1
     status_code: 200
+- request:
+    body: '{"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: Researcher\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: Researcher\nThe input to this tool should be the
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tests/cassettes/test_do_not_allow_crewai_trigger_context_for_first_task_hierarchical.yaml

@@ -498,4 +498,204 @@ interactions:
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tests/cassettes/test_hierarchical_crew_creation_tasks_with_sync_last.yaml

@@ -1158,4 +1158,201 @@ interactions:
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+      networks, and natural language processing to replicate human cognitive functions.
+      The transformative power of AI spans across industries, driving revolutionary
+      changes from predictive diagnostics in healthcare to algorithmic trading in
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+      "Thought: To craft an amazing paragraph about AI as described in the context,
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+      \"Write one amazing paragraph about AI\", \"context\": \"Artificial Intelligence
+      (AI) is a transformative technology that leverages machine learning, neural
+      networks, and natural language processing to perform tasks that traditionally
+      require human intelligence, such as visual perception, speech recognition, and
+      decision-making. Its potential to revolutionize industries\u2014from healthcare
+      with predictive diagnostics to finance with algorithmic trading\u2014underscores
+      its profound impact on our daily lives and the global economy. With the ability
+      to analyze vast amounts of data quickly and accurately, AI can unveil hidden
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+      As we move forward, the ethical and responsible development of AI will be paramount
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+      to analyze vast amounts of data quickly and accurately, AI can unveil hidden
+      patterns, drive efficiencies, and foster innovation at an unprecedented scale.
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