fix: remove unused pytest import to resolve lint failure

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

.gitignore

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

docs/docs.json

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

docs/en/api-reference/inputs.mdx

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

docs/en/api-reference/kickoff.mdx

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

docs/en/api-reference/status.mdx

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

docs/en/concepts/event-listener.mdx

@@ -177,14 +177,7 @@ class MyCustomCrew:
     # Your crew implementation...
 ```
 
-This is exactly how CrewAI's built-in `agentops_listener` is registered. In the CrewAI codebase, you'll find:
-
-```python
-# src/crewai/utilities/events/third_party/__init__.py
-from .agentops_listener import agentops_listener
-```
-
-This ensures the `agentops_listener` is loaded when the `crewai.utilities.events` package is imported.
+This is how third-party event listeners are registered in the CrewAI codebase.
 
 ## Available Event Types
 
@@ -280,77 +273,6 @@ The structure of the event object depends on the event type, but all events inhe
 
 Additional fields vary by event type. For example, `CrewKickoffCompletedEvent` includes `crew_name` and `output` fields.
 
-## Real-World Example: Integration with AgentOps
-
-CrewAI includes an example of a third-party integration with [AgentOps](https://github.com/AgentOps-AI/agentops), a monitoring and observability platform for AI agents. Here's how it's implemented:
-
-```python
-from typing import Optional
-
-from crewai.utilities.events import (
-    CrewKickoffCompletedEvent,
-    ToolUsageErrorEvent,
-    ToolUsageStartedEvent,
-)
-from crewai.utilities.events.base_event_listener import BaseEventListener
-from crewai.utilities.events.crew_events import CrewKickoffStartedEvent
-from crewai.utilities.events.task_events import TaskEvaluationEvent
-
-try:
-    import agentops
-    AGENTOPS_INSTALLED = True
-except ImportError:
-    AGENTOPS_INSTALLED = False
-
-class AgentOpsListener(BaseEventListener):
-    tool_event: Optional["agentops.ToolEvent"] = None
-    session: Optional["agentops.Session"] = None
-
-    def __init__(self):
-        super().__init__()
-
-    def setup_listeners(self, crewai_event_bus):
-        if not AGENTOPS_INSTALLED:
-            return
-
-        @crewai_event_bus.on(CrewKickoffStartedEvent)
-        def on_crew_kickoff_started(source, event: CrewKickoffStartedEvent):
-            self.session = agentops.init()
-            for agent in source.agents:
-                if self.session:
-                    self.session.create_agent(
-                        name=agent.role,
-                        agent_id=str(agent.id),
-                    )
-
-        @crewai_event_bus.on(CrewKickoffCompletedEvent)
-        def on_crew_kickoff_completed(source, event: CrewKickoffCompletedEvent):
-            if self.session:
-                self.session.end_session(
-                    end_state="Success",
-                    end_state_reason="Finished Execution",
-                )
-
-        @crewai_event_bus.on(ToolUsageStartedEvent)
-        def on_tool_usage_started(source, event: ToolUsageStartedEvent):
-            self.tool_event = agentops.ToolEvent(name=event.tool_name)
-            if self.session:
-                self.session.record(self.tool_event)
-
-        @crewai_event_bus.on(ToolUsageErrorEvent)
-        def on_tool_usage_error(source, event: ToolUsageErrorEvent):
-            agentops.ErrorEvent(exception=event.error, trigger_event=self.tool_event)
-```
-
-This listener initializes an AgentOps session when a Crew starts, registers agents with AgentOps, tracks tool usage, and ends the session when the Crew completes.
-
-The AgentOps listener is registered in CrewAI's event system through the import in `src/crewai/utilities/events/third_party/__init__.py`:
-
-```python
-from .agentops_listener import agentops_listener
-```
-
-This ensures the `agentops_listener` is loaded when the `crewai.utilities.events` package is imported.
 
 ## Advanced Usage: Scoped Handlers
 

docs/en/concepts/memory.mdx

@@ -539,16 +539,71 @@ crew = Crew(
 )
 ```
 
+### Mem0 Provider
+
+Short-Term Memory and Entity Memory both supports a tight integration with both Mem0 OSS and Mem0 Client as a provider. Here is how you can use Mem0 as a provider.
+
+```python
+from crewai.memory.short_term.short_term_memory import ShortTermMemory
+from crewai.memory.entity_entity_memory import EntityMemory
+
+mem0_oss_embedder_config = {
+        "provider": "mem0",
+        "config": {
+            "user_id": "john",
+            "local_mem0_config": {
+                "vector_store": {"provider": "qdrant","config": {"host": "localhost", "port": 6333}},
+                "llm": {"provider": "openai","config": {"api_key": "your-api-key", "model": "gpt-4"}},
+                "embedder": {"provider": "openai","config": {"api_key": "your-api-key", "model": "text-embedding-3-small"}}
+            },
+            "infer": True # Optional defaults to True
+        },
+    }
+
+
+mem0_client_embedder_config = {
+        "provider": "mem0",
+        "config": {
+            "user_id": "john",
+            "org_id": "my_org_id",        # Optional
+            "project_id": "my_project_id", # Optional
+            "api_key": "custom-api-key"    # Optional - overrides env var
+            "run_id": "my_run_id",        # Optional - for short-term memory
+            "includes": "include1",       # Optional 
+            "excludes": "exclude1",       # Optional
+            "infer": True                 # Optional defaults to True
+            "custom_categories": new_categories  # Optional - custom categories for user memory
+        },
+    }
+
+
+short_term_memory_mem0_oss = ShortTermMemory(embedder_config=mem0_oss_embedder_config) # Short Term Memory with Mem0 OSS
+short_term_memory_mem0_client = ShortTermMemory(embedder_config=mem0_client_embedder_config) # Short Term Memory with Mem0 Client
+entity_memory_mem0_oss = EntityMemory(embedder_config=mem0_oss_embedder_config) # Entity Memory with Mem0 OSS
+entity_memory_mem0_client = EntityMemory(embedder_config=mem0_client_embedder_config) # Short Term Memory with Mem0 Client
+
+crew = Crew(
+    memory=True,
+    short_term_memory=short_term_memory_mem0_oss, # or short_term_memory_mem0_client
+    entity_memory=entity_memory_mem0_oss # or entity_memory_mem0_client
+)
+```
+
 ### Choosing the Right Embedding Provider
 
-| Provider | Best For | Pros | Cons |
-|:---------|:----------|:------|:------|
-| **OpenAI** | General use, reliability | High quality, well-tested | Cost, requires API key |
-| **Ollama** | Privacy, cost savings | Free, local, private | Requires local setup |
-| **Google AI** | Google ecosystem | Good performance | Requires Google account |
-| **Azure OpenAI** | Enterprise, compliance | Enterprise features | Complex setup |
-| **Cohere** | Multilingual content | Great language support | Specialized use case |
-| **VoyageAI** | Retrieval tasks | Optimized for search | Newer provider |
+When selecting an embedding provider, consider factors like performance, privacy, cost, and integration needs.  
+Below is a comparison to help you decide:
+
+| Provider       | Best For                       | Pros                              | Cons                      |
+| -------------- | ------------------------------ | --------------------------------- | ------------------------- |
+| **OpenAI**     | General use, high reliability  | High quality, widely tested       | Paid service, API key required |
+| **Ollama**     | Privacy-focused, cost savings  | Free, runs locally, fully private | Requires local installation/setup |
+| **Google AI**  | Integration in Google ecosystem| Strong performance, good support  | Google account required   |
+| **Azure OpenAI** | Enterprise & compliance needs| Enterprise-grade features, security | More complex setup process |
+| **Cohere**     | Multilingual content handling  | Excellent language support        | More niche use cases      |
+| **VoyageAI**   | Information retrieval & search | Optimized for retrieval tasks     | Relatively new provider   |
+| **Mem0**       | Per-user personalization       | Search-optimized embeddings       | Paid service, API key required |
+
 
 ### Environment Variable Configuration
 

docs/en/concepts/training.mdx

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

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

@@ -35,6 +35,22 @@ crewai tool install <tool-name>
 
 This installs the tool and adds it to `pyproject.toml`.
 
+You can use the tool by importing it and adding it to your agents:
+
+```python
+from your_tool.tool import YourTool
+
+custom_tool = YourTool()
+
+researcher = Agent(
+    role='Market Research Analyst',
+    goal='Provide up-to-date market analysis of the AI industry',
+    backstory='An expert analyst with a keen eye for market trends.',
+    tools=[custom_tool],
+    verbose=True
+)
+```
+
 ## Creating and Publishing Tools
 
 To create a new tool project:

docs/en/enterprise/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/observability/agentops.mdx

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

docs/en/observability/overview.mdx

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

docs/en/tools/overview.mdx

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

docs/ko/api-reference/inputs.mdx

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

docs/ko/api-reference/kickoff.mdx

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

docs/ko/api-reference/status.mdx

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

docs/ko/concepts/event-listener.mdx

@@ -177,14 +177,7 @@ class MyCustomCrew:
     # Your crew implementation...
 ```
 
-이것이 바로 CrewAI의 내장 `agentops_listener`가 등록되는 방식과 동일합니다. CrewAI 코드베이스에서는 다음과 같이 되어 있습니다:
-
-```python
-# src/crewai/utilities/events/third_party/__init__.py
-from .agentops_listener import agentops_listener
-```
-
-이렇게 하면 `crewai.utilities.events` 패키지가 임포트될 때 `agentops_listener`가 자동으로 로드됩니다.
+이것이 CrewAI 코드베이스에서 서드파티 이벤트 리스너가 등록되는 방식입니다.
 
 ## 사용 가능한 이벤트 유형
 
@@ -280,77 +273,6 @@ CrewAI는 여러분이 청취할 수 있는 다양한 이벤트를 제공합니
 
 추가 필드는 이벤트 타입에 따라 다릅니다. 예를 들어, `CrewKickoffCompletedEvent`에는 `crew_name`과 `output` 필드가 포함됩니다.
 
-## 실제 예시: AgentOps와의 통합
-
-CrewAI는 AI 에이전트를 위한 모니터링 및 관찰 플랫폼인 [AgentOps](https://github.com/AgentOps-AI/agentops)와의 서드파티 통합 예시를 포함하고 있습니다. 구현 방식은 다음과 같습니다:
-
-```python
-from typing import Optional
-
-from crewai.utilities.events import (
-    CrewKickoffCompletedEvent,
-    ToolUsageErrorEvent,
-    ToolUsageStartedEvent,
-)
-from crewai.utilities.events.base_event_listener import BaseEventListener
-from crewai.utilities.events.crew_events import CrewKickoffStartedEvent
-from crewai.utilities.events.task_events import TaskEvaluationEvent
-
-try:
-    import agentops
-    AGENTOPS_INSTALLED = True
-except ImportError:
-    AGENTOPS_INSTALLED = False
-
-class AgentOpsListener(BaseEventListener):
-    tool_event: Optional["agentops.ToolEvent"] = None
-    session: Optional["agentops.Session"] = None
-
-    def __init__(self):
-        super().__init__()
-
-    def setup_listeners(self, crewai_event_bus):
-        if not AGENTOPS_INSTALLED:
-            return
-
-        @crewai_event_bus.on(CrewKickoffStartedEvent)
-        def on_crew_kickoff_started(source, event: CrewKickoffStartedEvent):
-            self.session = agentops.init()
-            for agent in source.agents:
-                if self.session:
-                    self.session.create_agent(
-                        name=agent.role,
-                        agent_id=str(agent.id),
-                    )
-
-        @crewai_event_bus.on(CrewKickoffCompletedEvent)
-        def on_crew_kickoff_completed(source, event: CrewKickoffCompletedEvent):
-            if self.session:
-                self.session.end_session(
-                    end_state="Success",
-                    end_state_reason="Finished Execution",
-                )
-
-        @crewai_event_bus.on(ToolUsageStartedEvent)
-        def on_tool_usage_started(source, event: ToolUsageStartedEvent):
-            self.tool_event = agentops.ToolEvent(name=event.tool_name)
-            if self.session:
-                self.session.record(self.tool_event)
-
-        @crewai_event_bus.on(ToolUsageErrorEvent)
-        def on_tool_usage_error(source, event: ToolUsageErrorEvent):
-            agentops.ErrorEvent(exception=event.error, trigger_event=self.tool_event)
-```
-
-이 listener는 crew가 시작될 때 AgentOps 세션을 초기화하고, agent를 AgentOps에 등록하며, 도구 사용을 추적하고, crew가 완료되면 세션을 종료합니다.
-
-AgentOps listener는 `src/crewai/utilities/events/third_party/__init__.py` 파일의 import를 통해 CrewAI 이벤트 시스템에 등록됩니다:
-
-```python
-from .agentops_listener import agentops_listener
-```
-
-이렇게 하면 `crewai.utilities.events` 패키지가 import될 때 `agentops_listener`가 로드되는 것이 보장됩니다.
 
 ## 고급 사용법: Scoped Handlers
 

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

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

docs/ko/introduction.mdx

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

docs/ko/observability/agentops.mdx

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

docs/ko/observability/overview.mdx

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

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

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

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

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

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

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

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

@@ -177,14 +177,7 @@ class MyCustomCrew:
     # Sua implementação do crew...
 ```
 
-É exatamente assim que o `agentops_listener` integrado do CrewAI é registrado. No código-fonte do CrewAI, você encontrará:
-
-```python
-# src/crewai/utilities/events/third_party/__init__.py
-from .agentops_listener import agentops_listener
-```
-
-Isso garante que o `agentops_listener` seja carregado quando o pacote `crewai.utilities.events` for importado.
+É assim que listeners de eventos de terceiros são registrados no código do CrewAI.
 
 ## Tipos de Eventos Disponíveis
 
@@ -269,77 +262,6 @@ A estrutura do objeto de evento depende do tipo do evento, mas todos herdam de `
 
 Campos adicionais variam pelo tipo de evento. Por exemplo, `CrewKickoffCompletedEvent` inclui os campos `crew_name` e `output`.
 
-## Exemplo Real: Integração com AgentOps
-
-O CrewAI inclui um exemplo de integração com [AgentOps](https://github.com/AgentOps-AI/agentops), uma plataforma de monitoramento e observabilidade para agentes de IA. Veja como é implementado:
-
-```python
-from typing import Optional
-
-from crewai.utilities.events import (
-    CrewKickoffCompletedEvent,
-    ToolUsageErrorEvent,
-    ToolUsageStartedEvent,
-)
-from crewai.utilities.events.base_event_listener import BaseEventListener
-from crewai.utilities.events.crew_events import CrewKickoffStartedEvent
-from crewai.utilities.events.task_events import TaskEvaluationEvent
-
-try:
-    import agentops
-    AGENTOPS_INSTALLED = True
-except ImportError:
-    AGENTOPS_INSTALLED = False
-
-class AgentOpsListener(BaseEventListener):
-    tool_event: Optional["agentops.ToolEvent"] = None
-    session: Optional["agentops.Session"] = None
-
-    def __init__(self):
-        super().__init__()
-
-    def setup_listeners(self, crewai_event_bus):
-        if not AGENTOPS_INSTALLED:
-            return
-
-        @crewai_event_bus.on(CrewKickoffStartedEvent)
-        def on_crew_kickoff_started(source, event: CrewKickoffStartedEvent):
-            self.session = agentops.init()
-            for agent in source.agents:
-                if self.session:
-                    self.session.create_agent(
-                        name=agent.role,
-                        agent_id=str(agent.id),
-                    )
-
-        @crewai_event_bus.on(CrewKickoffCompletedEvent)
-        def on_crew_kickoff_completed(source, event: CrewKickoffCompletedEvent):
-            if self.session:
-                self.session.end_session(
-                    end_state="Success",
-                    end_state_reason="Finished Execution",
-                )
-
-        @crewai_event_bus.on(ToolUsageStartedEvent)
-        def on_tool_usage_started(source, event: ToolUsageStartedEvent):
-            self.tool_event = agentops.ToolEvent(name=event.tool_name)
-            if self.session:
-                self.session.record(self.tool_event)
-
-        @crewai_event_bus.on(ToolUsageErrorEvent)
-        def on_tool_usage_error(source, event: ToolUsageErrorEvent):
-            agentops.ErrorEvent(exception=event.error, trigger_event=self.tool_event)
-```
-
-Esse listener inicializa uma sessão do AgentOps quando um Crew inicia, cadastra agentes no AgentOps, rastreia o uso de ferramentas e finaliza a sessão quando o Crew é concluído.
-
-O listener AgentOps é registrado no sistema de eventos do CrewAI via importação em `src/crewai/utilities/events/third_party/__init__.py`:
-
-```python
-from .agentops_listener import agentops_listener
-```
-
-Isso garante que o `agentops_listener` seja carregado quando o pacote `crewai.utilities.events` for importado.
 
 ## Uso Avançado: Handlers Escopados
 

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

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

docs/pt-BR/observability/agentops.mdx

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

docs/pt-BR/observability/overview.mdx

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

pyproject.toml

@@ -48,11 +48,10 @@ Documentation = "https://docs.crewai.com"
 Repository = "https://github.com/crewAIInc/crewAI"
 
 [project.optional-dependencies]
-tools = ["crewai-tools~=0.62.0"]
+tools = ["crewai-tools~=0.62.1"]
 embeddings = [
     "tiktoken~=0.8.0"
 ]
-agentops = ["agentops==0.3.18"]
 pdfplumber = [
     "pdfplumber>=0.11.4",
 ]
@@ -99,6 +98,11 @@ exclude = ["cli/templates"]
 [tool.bandit]
 exclude_dirs = ["src/crewai/cli/templates"]
 
+[tool.pytest.ini_options]
+markers = [
+    "telemetry: mark test as a telemetry test (don't mock telemetry)",
+]
+
 # PyTorch index configuration, since torch 2.5.0 is not compatible with python 3.13
 [[tool.uv.index]]
 name = "pytorch-nightly"

src/crewai/__init__.py

@@ -54,7 +54,7 @@ def _track_install_async():
 
 _track_install_async()
 
-__version__ = "0.159.0"
+__version__ = "0.165.1"
 __all__ = [
     "Agent",
     "Crew",

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()
 
@@ -336,7 +347,6 @@ class Agent(BaseAgent):
             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

@@ -1,5 +1,5 @@
 import time
-from typing import TYPE_CHECKING
+from typing import TYPE_CHECKING, Dict, List
 
 from crewai.memory.entity.entity_memory_item import EntityMemoryItem
 from crewai.memory.long_term.long_term_memory_item import LongTermMemoryItem
@@ -21,6 +21,7 @@ class CrewAgentExecutorMixin:
     task: "Task"
     iterations: int
     max_iter: int
+    messages: List[Dict[str, str]]
     _i18n: I18N
     _printer: Printer = Printer()
 
@@ -62,6 +63,7 @@ class CrewAgentExecutorMixin:
                     value=output.text,
                     metadata={
                         "description": self.task.description,
+                        "messages": self.messages,
                     },
                     agent=self.agent.role,
                 )
@@ -127,7 +129,6 @@ class CrewAgentExecutorMixin:
     def _ask_human_input(self, final_answer: str) -> str:
         """Prompt human input with mode-appropriate messaging."""
         event_listener.formatter.pause_live_updates()
-        
         try:
             self._printer.print(
                 content=f"\033[1m\033[95m ## Final Result:\033[00m \033[92m{final_answer}\033[00m"

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/token.py

@@ -1,9 +1,13 @@
 from .utils import TokenManager
 
 
+class AuthError(Exception):
+    pass
+
+
 def get_auth_token() -> str:
     """Get the authentication token."""
     access_token = TokenManager().get_token()
     if not access_token:
-        raise Exception("No token found, make sure you are logged in")
+        raise AuthError("No token found, make sure you are logged in")
     return access_token

src/crewai/cli/constants.py

@@ -135,14 +135,21 @@ MODELS = {
         "gpt-4.1-nano-2025-04-14",
         "gpt-4o",
         "gpt-4o-mini",
+        "gpt-5",
+        "gpt-5-mini",
+        "gpt-5-nano",
         "o1-mini",
         "o1-preview",
+        "o3-mini",
     ],
     "anthropic": [
         "claude-3-5-sonnet-20240620",
         "claude-3-sonnet-20240229",
         "claude-3-opus-20240229",
         "claude-3-haiku-20240307",
+        "claude-3.7-sonnet-20250219",
+        "claude-4-sonnet-20250301",
+        "claude-4.1-opus-20250315",
     ],
     "gemini": [
         "gemini/gemini-1.5-flash",
@@ -152,6 +159,9 @@ MODELS = {
         "gemini/gemini-2.0-flash-thinking-exp-01-21",
         "gemini/gemini-2.5-flash-preview-04-17",
         "gemini/gemini-2.5-pro-exp-03-25",
+        "gemini/gemini-2.5-flash-lite",
+        "gemini/gemini-2.5-flash",
+        "gemini/gemini-2.5-pro",
         "gemini/gemini-gemma-2-9b-it",
         "gemini/gemini-gemma-2-27b-it",
         "gemini/gemma-3-1b-it",

src/crewai/cli/plus_api.py

@@ -18,6 +18,7 @@ class PlusAPI:
     CREWS_RESOURCE = "/crewai_plus/api/v1/crews"
     AGENTS_RESOURCE = "/crewai_plus/api/v1/agents"
     TRACING_RESOURCE = "/crewai_plus/api/v1/tracing"
+    EPHEMERAL_TRACING_RESOURCE = "/crewai_plus/api/v1/tracing/ephemeral"
 
     def __init__(self, api_key: str) -> None:
         self.api_key = api_key
@@ -124,6 +125,11 @@ class PlusAPI:
             "POST", f"{self.TRACING_RESOURCE}/batches", json=payload
         )
 
+    def initialize_ephemeral_trace_batch(self, payload) -> requests.Response:
+        return self._make_request(
+            "POST", f"{self.EPHEMERAL_TRACING_RESOURCE}/batches", json=payload
+        )
+
     def send_trace_events(self, trace_batch_id: str, payload) -> requests.Response:
         return self._make_request(
             "POST",
@@ -131,9 +137,27 @@ class PlusAPI:
             json=payload,
         )
 
+    def send_ephemeral_trace_events(
+        self, trace_batch_id: str, payload
+    ) -> requests.Response:
+        return self._make_request(
+            "POST",
+            f"{self.EPHEMERAL_TRACING_RESOURCE}/batches/{trace_batch_id}/events",
+            json=payload,
+        )
+
     def finalize_trace_batch(self, trace_batch_id: str, payload) -> requests.Response:
         return self._make_request(
             "PATCH",
             f"{self.TRACING_RESOURCE}/batches/{trace_batch_id}/finalize",
             json=payload,
         )
+
+    def finalize_ephemeral_trace_batch(
+        self, trace_batch_id: str, payload
+    ) -> requests.Response:
+        return self._make_request(
+            "PATCH",
+            f"{self.EPHEMERAL_TRACING_RESOURCE}/batches/{trace_batch_id}/finalize",
+            json=payload,
+        )

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

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.14"
 dependencies = [
-    "crewai[tools]>=0.159.0,<1.0.0"
+    "crewai[tools]>=0.165.1,<1.0.0"
 ]
 
 [project.scripts]

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

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.14"
 dependencies = [
-    "crewai[tools]>=0.159.0,<1.0.0",
+    "crewai[tools]>=0.165.1,<1.0.0",
 ]
 
 [project.scripts]

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

@@ -5,7 +5,7 @@ description = "Power up your crews with {{folder_name}}"
 readme = "README.md"
 requires-python = ">=3.10,<3.14"
 dependencies = [
-    "crewai[tools]>=0.159.0"
+    "crewai[tools]>=0.165.1"
 ]
 
 [tool.crewai]

src/crewai/cli/update_crew.py

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

src/crewai/crew.py

@@ -77,7 +77,9 @@ from crewai.utilities.events.listeners.tracing.trace_listener import (
 )
 
 
-from crewai.utilities.events.listeners.tracing.utils import is_tracing_enabled
+from crewai.utilities.events.listeners.tracing.utils import (
+    is_tracing_enabled,
+)
 from crewai.utilities.formatter import (
     aggregate_raw_outputs_from_task_outputs,
     aggregate_raw_outputs_from_tasks,
@@ -283,8 +285,9 @@ class Crew(FlowTrackable, BaseModel):
 
         self._cache_handler = CacheHandler()
         event_listener = EventListener()
+
         if is_tracing_enabled() or self.tracing:
-            trace_listener = TraceCollectionListener(tracing=self.tracing)
+            trace_listener = TraceCollectionListener()
             trace_listener.setup_listeners(crewai_event_bus)
         event_listener.verbose = self.verbose
         event_listener.formatter.verbose = self.verbose
@@ -633,6 +636,7 @@ class Crew(FlowTrackable, BaseModel):
                 self._inputs = inputs
                 self._interpolate_inputs(inputs)
             self._set_tasks_callbacks()
+            self._set_allow_crewai_trigger_context_for_first_task()
 
             i18n = I18N(prompt_file=self.prompt_file)
 
@@ -1502,3 +1506,18 @@ class Crew(FlowTrackable, BaseModel):
         """Reset crew and agent knowledge storage."""
         for ks in knowledges:
             ks.reset()
+
+    def _set_allow_crewai_trigger_context_for_first_task(self):
+        crewai_trigger_payload = self._inputs and self._inputs.get(
+            "crewai_trigger_payload"
+        )
+        able_to_inject = (
+            self.tasks and self.tasks[0].allow_crewai_trigger_context is None
+        )
+
+        if (
+            self.process == Process.sequential
+            and crewai_trigger_payload
+            and able_to_inject
+        ):
+            self.tasks[0].allow_crewai_trigger_context = True

src/crewai/flow/flow.py

@@ -38,7 +38,9 @@ from crewai.utilities.events.flow_events import (
 from crewai.utilities.events.listeners.tracing.trace_listener import (
     TraceCollectionListener,
 )
-from crewai.utilities.events.listeners.tracing.utils import is_tracing_enabled
+from crewai.utilities.events.listeners.tracing.utils import (
+    is_tracing_enabled,
+)
 from crewai.utilities.printer import Printer
 
 logger = logging.getLogger(__name__)
@@ -472,12 +474,13 @@ 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()
         self.tracing = tracing
-        if is_tracing_enabled() or tracing:
-            trace_listener = TraceCollectionListener(tracing=tracing)
+        if is_tracing_enabled() or self.tracing:
+            trace_listener = TraceCollectionListener()
             trace_listener.setup_listeners(crewai_event_bus)
         # Apply any additional kwargs
         if kwargs:
@@ -827,6 +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
@@ -878,6 +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(
@@ -911,17 +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:
@@ -1013,11 +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
@@ -1035,6 +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]:
@@ -1072,6 +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:
@@ -1121,10 +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/knowledge/storage/knowledge_storage.py

@@ -11,6 +11,7 @@ import chromadb.errors
 from chromadb.api import ClientAPI
 from chromadb.api.types import OneOrMany
 from chromadb.config import Settings
+import warnings
 
 from crewai.knowledge.storage.base_knowledge_storage import BaseKnowledgeStorage
 from crewai.rag.embeddings.configurator import EmbeddingConfigurator
@@ -85,6 +86,14 @@ class KnowledgeStorage(BaseKnowledgeStorage):
                 raise Exception("Collection not initialized")
 
     def initialize_knowledge_storage(self):
+        # Suppress deprecation warnings from chromadb, which are not relevant to us
+        # TODO: Remove this once we upgrade chromadb to at least 1.0.8.
+        warnings.filterwarnings(
+            "ignore",
+            message=r".*'model_fields'.*is deprecated.*",
+            module=r"^chromadb(\.|$)",
+        )
+
         self.app = create_persistent_client(
             path=os.path.join(db_storage_path(), "knowledge"),
             settings=Settings(allow_reset=True),

src/crewai/memory/storage/rag_storage.py

@@ -12,6 +12,7 @@ from crewai.rag.embeddings.configurator import EmbeddingConfigurator
 from crewai.utilities.chromadb import create_persistent_client
 from crewai.utilities.constants import MAX_FILE_NAME_LENGTH
 from crewai.utilities.paths import db_storage_path
+import warnings
 
 
 @contextlib.contextmanager
@@ -62,6 +63,14 @@ class RAGStorage(BaseRAGStorage):
     def _initialize_app(self):
         from chromadb.config import Settings
 
+        # Suppress deprecation warnings from chromadb, which are not relevant to us
+        # TODO: Remove this once we upgrade chromadb to at least 1.0.8.
+        warnings.filterwarnings(
+            "ignore",
+            message=r".*'model_fields'.*is deprecated.*",
+            module=r"^chromadb(\.|$)",
+        )
+
         self._set_embedder_config()
 
         self.app = create_persistent_client(

src/crewai/rag/chromadb/client.py

@@ -0,0 +1,556 @@
+"""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.
+    """
+
+    client: ChromaDBClientType
+    embedding_function: ChromaEmbeddingFunction[Embeddable]
+
+    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/types.py

@@ -0,0 +1,85 @@
+"""Type definitions specific to ChromaDB implementation."""
+
+from collections.abc import Mapping
+from typing import Any, NamedTuple
+
+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 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 +1,220 @@
+"""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/core/__init__.py

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

src/crewai/rag/core/base_client.py

@@ -0,0 +1,433 @@
+"""Protocol for vector database client implementations."""
+
+from abc import abstractmethod
+from typing import Any, Protocol, runtime_checkable, TypedDict, Annotated
+from typing_extensions import Unpack, Required
+
+
+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
+
+    @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/base_provider.py

@@ -0,0 +1,30 @@
+"""Base provider protocol for vector database client creation."""
+
+from abc import ABC
+from typing import Any, Protocol, runtime_checkable, Union
+from pydantic import BaseModel, Field
+
+from crewai.rag.types import EmbeddingFunction
+from crewai.rag.embeddings.types import EmbeddingOptions
+
+
+class BaseProviderOptions(BaseModel, ABC):
+    """Base configuration for all provider options."""
+
+    client_type: str = Field(..., description="Type of client to create")
+    embedding_config: Union[EmbeddingOptions, EmbeddingFunction, None] = Field(
+        default=None,
+        description="Embedding configuration - either options for built-in providers or a custom function",
+    )
+    options: Any = Field(
+        default=None, description="Additional provider-specific options"
+    )
+
+
+@runtime_checkable
+class BaseProvider(Protocol):
+    """Protocol for vector database client providers."""
+
+    def __call__(self, options: BaseProviderOptions) -> Any:
+        """Create and return a configured client instance."""
+        ...

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/types.py

@@ -0,0 +1,50 @@
+"""Type definitions for RAG (Retrieval-Augmented Generation) systems."""
+
+from collections.abc import Callable, Mapping
+from typing import TypeAlias, TypedDict, Any
+
+from typing_extensions import Required
+
+
+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

@@ -72,6 +72,10 @@ class Task(BaseModel):
         output_pydantic: Pydantic model for task output.
         security_config: Security configuration including fingerprinting.
         tools: List of tools/resources limited for task execution.
+        allow_crewai_trigger_context: Optional flag to control crewai_trigger_payload injection.
+                              None (default): Auto-inject for first task only.
+                              True: Always inject trigger payload for this task.
+                              False: Never inject trigger payload, even for first task.
     """
 
     __hash__ = object.__hash__  # type: ignore
@@ -163,6 +167,10 @@ class Task(BaseModel):
     end_time: Optional[datetime.datetime] = Field(
         default=None, description="End time of the task execution"
     )
+    allow_crewai_trigger_context: Optional[bool] = Field(
+        default=None,
+        description="Whether this task should append 'Trigger Payload: {crewai_trigger_payload}' to the task description when crewai_trigger_payload exists in crew inputs.",
+    )
     model_config = {"arbitrary_types_allowed": True}
 
     @field_validator("guardrail")
@@ -548,12 +556,23 @@ class Task(BaseModel):
             str: The formatted prompt string containing the task description,
                  expected output, and optional markdown formatting instructions.
         """
-        tasks_slices = [self.description]
+        description = self.description
+
+        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:
+                trigger_payload = crew._inputs.get("crewai_trigger_payload")
+                if trigger_payload is not None:
+                    description += f"\n\nTrigger Payload: {trigger_payload}"
+
+        tasks_slices = [description]
 
         output = self.i18n.slice("expected_output").format(
             expected_output=self.expected_output
         )
-        tasks_slices = [self.description, output]
+        tasks_slices = [description, output]
 
         if self.markdown:
             markdown_instruction = """Your final answer MUST be formatted in Markdown syntax.

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 +10,11 @@ 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 CrewStructuredTool:
     """A structured tool that can operate on any number of inputs.
@@ -18,6 +23,8 @@ class CrewStructuredTool:
     that integrates better with CrewAI's ecosystem.
     """
 
+    _original_tool: BaseTool | None = None
+
     def __init__(
         self,
         name: str,
@@ -47,6 +54,7 @@ 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()
@@ -219,6 +227,8 @@ class CrewStructuredTool:
         """
         parsed_args = self._parse_args(input)
 
+        self._increment_usage_count()
+
         if inspect.iscoroutinefunction(self.func):
             return await self.func(**parsed_args, **kwargs)
         else:
@@ -242,6 +252,8 @@ class CrewStructuredTool:
         """Main method for tool execution."""
         parsed_args = self._parse_args(input)
 
+        self._increment_usage_count()
+
         if inspect.iscoroutinefunction(self.func):
             result = asyncio.run(self.func(**parsed_args, **kwargs))
             return result
@@ -253,6 +265,12 @@ class CrewStructuredTool:
 
         return result
 
+    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/utilities/chromadb.py

@@ -1,9 +1,10 @@
+import os
 import re
 import portalocker
 from chromadb import PersistentClient
 from hashlib import md5
 from typing import Optional
-
+from crewai.utilities.paths import db_storage_path
 
 MIN_COLLECTION_LENGTH = 3
 MAX_COLLECTION_LENGTH = 63
@@ -27,7 +28,9 @@ def is_ipv4_pattern(name: str) -> bool:
     return bool(IPV4_PATTERN.match(name))
 
 
-def sanitize_collection_name(name: Optional[str], max_collection_length: int = MAX_COLLECTION_LENGTH) -> str:
+def sanitize_collection_name(
+    name: Optional[str], max_collection_length: int = MAX_COLLECTION_LENGTH
+) -> str:
     """
     Sanitize a collection name to meet ChromaDB requirements:
     1. 3-63 characters long
@@ -72,7 +75,8 @@ def create_persistent_client(path: str, **kwargs):
     concurrent creations. Works for both multi-threads and multi-processes
     environments.
     """
-    lockfile = f"chromadb-{md5(path.encode(), usedforsecurity=False).hexdigest()}.lock"
+    lock_id = md5(path.encode(), usedforsecurity=False).hexdigest()
+    lockfile = os.path.join(db_storage_path(), f"chromadb-{lock_id}.lock")
     with portalocker.Lock(lockfile):
         client = PersistentClient(path=path, **kwargs)
 

src/crewai/utilities/events/__init__.py

@@ -67,11 +67,9 @@ from .memory_events import (
 
 # events
 from .event_listener import EventListener
-from .third_party.agentops_listener import agentops_listener
 
 __all__ = [
     "EventListener",
-    "agentops_listener",
     "CrewAIEventsBus",
     "crewai_event_bus",
     "AgentExecutionStartedEvent",
@@ -105,7 +103,6 @@ __all__ = [
     "MemoryRetrievalStartedEvent",
     "MemoryRetrievalCompletedEvent",
     "EventListener",
-    "agentops_listener",
     "CrewKickoffStartedEvent",
     "CrewKickoffCompletedEvent",
     "CrewKickoffFailedEvent",

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

@@ -4,7 +4,7 @@ from typing import Dict, List, Any, Optional
 from dataclasses import dataclass, field
 
 from crewai.utilities.constants import CREWAI_BASE_URL
-from crewai.cli.authentication.token import get_auth_token
+from crewai.cli.authentication.token import AuthError, get_auth_token
 
 from crewai.cli.version import get_crewai_version
 from crewai.cli.plus_api import PlusAPI
@@ -40,30 +40,42 @@ class TraceBatch:
 class TraceBatchManager:
     """Single responsibility: Manage batches and event buffering"""
 
+    is_current_batch_ephemeral: bool = False
+
     def __init__(self):
-        self.plus_api = PlusAPI(api_key=get_auth_token())
+        try:
+            self.plus_api = PlusAPI(api_key=get_auth_token())
+        except AuthError:
+            self.plus_api = PlusAPI(api_key="")
+
         self.trace_batch_id: Optional[str] = None  # Backend ID
         self.current_batch: Optional[TraceBatch] = None
         self.event_buffer: List[TraceEvent] = []
         self.execution_start_times: Dict[str, datetime] = {}
 
     def initialize_batch(
-        self, user_context: Dict[str, str], execution_metadata: Dict[str, Any]
+        self,
+        user_context: Dict[str, str],
+        execution_metadata: Dict[str, Any],
+        use_ephemeral: bool = False,
     ) -> TraceBatch:
         """Initialize a new trace batch"""
         self.current_batch = TraceBatch(
             user_context=user_context, execution_metadata=execution_metadata
         )
         self.event_buffer.clear()
+        self.is_current_batch_ephemeral = use_ephemeral
 
         self.record_start_time("execution")
-
-        self._initialize_backend_batch(user_context, execution_metadata)
+        self._initialize_backend_batch(user_context, execution_metadata, use_ephemeral)
 
         return self.current_batch
 
     def _initialize_backend_batch(
-        self, user_context: Dict[str, str], execution_metadata: Dict[str, Any]
+        self,
+        user_context: Dict[str, str],
+        execution_metadata: Dict[str, Any],
+        use_ephemeral: bool = False,
     ):
         """Send batch initialization to backend"""
 
@@ -74,6 +86,7 @@ class TraceBatchManager:
             payload = {
                 "trace_id": self.current_batch.batch_id,
                 "execution_type": execution_metadata.get("execution_type", "crew"),
+                "user_identifier": execution_metadata.get("user_context", None),
                 "execution_context": {
                     "crew_fingerprint": execution_metadata.get("crew_fingerprint"),
                     "crew_name": execution_metadata.get("crew_name", None),
@@ -91,12 +104,22 @@ class TraceBatchManager:
                     "execution_started_at": datetime.now(timezone.utc).isoformat(),
                 },
             }
+            if use_ephemeral:
+                payload["ephemeral_trace_id"] = self.current_batch.batch_id
 
-            response = self.plus_api.initialize_trace_batch(payload)
+            response = (
+                self.plus_api.initialize_ephemeral_trace_batch(payload)
+                if use_ephemeral
+                else self.plus_api.initialize_trace_batch(payload)
+            )
 
             if response.status_code == 201 or response.status_code == 200:
                 response_data = response.json()
-                self.trace_batch_id = response_data["trace_id"]
+                self.trace_batch_id = (
+                    response_data["trace_id"]
+                    if not use_ephemeral
+                    else response_data["ephemeral_trace_id"]
+                )
                 console = Console()
                 panel = Panel(
                     f"✅ Trace batch initialized with session ID: {self.trace_batch_id}",
@@ -134,7 +157,11 @@ class TraceBatchManager:
             if not self.trace_batch_id:
                 raise Exception("❌ Trace batch ID not found")
 
-            response = self.plus_api.send_trace_events(self.trace_batch_id, payload)
+            response = (
+                self.plus_api.send_ephemeral_trace_events(self.trace_batch_id, payload)
+                if self.is_current_batch_ephemeral
+                else self.plus_api.send_trace_events(self.trace_batch_id, payload)
+            )
 
             if response.status_code == 200 or response.status_code == 201:
                 self.event_buffer.clear()
@@ -153,7 +180,6 @@ class TraceBatchManager:
 
         if self.event_buffer:
             self._send_events_to_backend()
-
         self._finalize_backend_batch()
 
         self.current_batch.events = self.event_buffer.copy()
@@ -163,6 +189,7 @@ class TraceBatchManager:
         self.current_batch = None
         self.event_buffer.clear()
         self.trace_batch_id = None
+        self.is_current_batch_ephemeral = False
 
         self._cleanup_batch_data()
 
@@ -182,12 +209,24 @@ class TraceBatchManager:
                 "final_event_count": total_events,
             }
 
-            response = self.plus_api.finalize_trace_batch(self.trace_batch_id, payload)
+            response = (
+                self.plus_api.finalize_ephemeral_trace_batch(
+                    self.trace_batch_id, payload
+                )
+                if self.is_current_batch_ephemeral
+                else self.plus_api.finalize_trace_batch(self.trace_batch_id, payload)
+            )
 
             if response.status_code == 200:
+                access_code = response.json().get("access_code", None)
                 console = Console()
+                return_link = (
+                    f"{CREWAI_BASE_URL}/crewai_plus/trace_batches/{self.trace_batch_id}"
+                    if not self.is_current_batch_ephemeral and access_code is None
+                    else f"{CREWAI_BASE_URL}/crewai_plus/ephemeral_trace_batches/{self.trace_batch_id}?access_code={access_code}"
+                )
                 panel = Panel(
-                    f"✅ Trace batch finalized with session ID: {self.trace_batch_id}. View here: {CREWAI_BASE_URL}/crewai_plus/trace_batches/{self.trace_batch_id}",
+                    f"✅ Trace batch finalized with session ID: {self.trace_batch_id}. View here: {return_link} {f', Access Code: {access_code}' if access_code else ''}",
                     title="Trace Batch Finalization",
                     border_style="green",
                 )

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

@@ -13,7 +13,6 @@ from crewai.utilities.events.agent_events import (
     AgentExecutionErrorEvent,
 )
 from crewai.utilities.events.listeners.tracing.types import TraceEvent
-from crewai.utilities.events.listeners.tracing.utils import is_tracing_enabled
 from crewai.utilities.events.reasoning_events import (
     AgentReasoningStartedEvent,
     AgentReasoningCompletedEvent,
@@ -67,7 +66,7 @@ from crewai.utilities.events.memory_events import (
     MemorySaveFailedEvent,
 )
 
-from crewai.cli.authentication.token import get_auth_token
+from crewai.cli.authentication.token import AuthError, get_auth_token
 from crewai.cli.version import get_crewai_version
 
 
@@ -76,13 +75,12 @@ class TraceCollectionListener(BaseEventListener):
     Trace collection listener that orchestrates trace collection
     """
 
-    trace_enabled: Optional[bool] = False
     complex_events = ["task_started", "llm_call_started", "llm_call_completed"]
 
     _instance = None
     _initialized = False
 
-    def __new__(cls, batch_manager=None, tracing: Optional[bool] = False):
+    def __new__(cls, batch_manager=None):
         if cls._instance is None:
             cls._instance = super().__new__(cls)
         return cls._instance
@@ -90,25 +88,22 @@ class TraceCollectionListener(BaseEventListener):
     def __init__(
         self,
         batch_manager: Optional[TraceBatchManager] = None,
-        tracing: Optional[bool] = False,
     ):
         if self._initialized:
             return
 
         super().__init__()
         self.batch_manager = batch_manager or TraceBatchManager()
-        self.tracing = tracing or False
-        self.trace_enabled = self._check_trace_enabled()
         self._initialized = True
 
-    def _check_trace_enabled(self) -> bool:
+    def _check_authenticated(self) -> bool:
         """Check if tracing should be enabled"""
-        auth_token = get_auth_token()
-        if not auth_token:
+        try:
+            res = bool(get_auth_token())
+            return res
+        except AuthError:
             return False
 
-        return is_tracing_enabled() or self.tracing
-
     def _get_user_context(self) -> Dict[str, str]:
         """Extract user context for tracing"""
         return {
@@ -120,8 +115,6 @@ class TraceCollectionListener(BaseEventListener):
 
     def setup_listeners(self, crewai_event_bus):
         """Setup event listeners - delegates to specific handlers"""
-        if not self.trace_enabled:
-            return
 
         self._register_flow_event_handlers(crewai_event_bus)
         self._register_context_event_handlers(crewai_event_bus)
@@ -167,7 +160,7 @@ class TraceCollectionListener(BaseEventListener):
         @event_bus.on(CrewKickoffStartedEvent)
         def on_crew_started(source, event):
             if not self.batch_manager.is_batch_initialized():
-                self._initialize_batch(source, event)
+                self._initialize_crew_batch(source, event)
             self._handle_trace_event("crew_kickoff_started", source, event)
 
         @event_bus.on(CrewKickoffCompletedEvent)
@@ -287,7 +280,7 @@ class TraceCollectionListener(BaseEventListener):
         def on_agent_reasoning_failed(source, event):
             self._handle_action_event("agent_reasoning_failed", source, event)
 
-    def _initialize_batch(self, source: Any, event: Any):
+    def _initialize_crew_batch(self, source: Any, event: Any):
         """Initialize trace batch"""
         user_context = self._get_user_context()
         execution_metadata = {
@@ -296,7 +289,7 @@ class TraceCollectionListener(BaseEventListener):
             "crewai_version": get_crewai_version(),
         }
 
-        self.batch_manager.initialize_batch(user_context, execution_metadata)
+        self._initialize_batch(user_context, execution_metadata)
 
     def _initialize_flow_batch(self, source: Any, event: Any):
         """Initialize trace batch for Flow execution"""
@@ -308,7 +301,20 @@ class TraceCollectionListener(BaseEventListener):
             "execution_type": "flow",
         }
 
-        self.batch_manager.initialize_batch(user_context, execution_metadata)
+        self._initialize_batch(user_context, execution_metadata)
+
+    def _initialize_batch(
+        self, user_context: Dict[str, str], execution_metadata: Dict[str, Any]
+    ):
+        """Initialize trace batch if ephemeral"""
+        if not self._check_authenticated():
+            self.batch_manager.initialize_batch(
+                user_context, execution_metadata, use_ephemeral=True
+            )
+        else:
+            self.batch_manager.initialize_batch(
+                user_context, execution_metadata, use_ephemeral=False
+            )
 
     def _handle_trace_event(self, event_type: str, source: Any, event: Any):
         """Generic handler for context end events"""

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

@@ -1,5 +1,153 @@
 import os
+import platform
+import uuid
+import hashlib
+import subprocess
+import getpass
+from pathlib import Path
+from datetime import datetime
+import re
+import json
+
+import click
+
+from crewai.utilities.paths import db_storage_path
 
 
 def is_tracing_enabled() -> bool:
     return os.getenv("CREWAI_TRACING_ENABLED", "false").lower() == "true"
+
+
+def on_first_execution_tracing_confirmation() -> bool:
+    if _is_test_environment():
+        return False
+
+    if is_first_execution():
+        mark_first_execution_done()
+        return click.confirm(
+            "This is the first execution of CrewAI. Do you want to enable tracing?",
+            default=True,
+            show_default=True,
+        )
+    return False
+
+
+def _is_test_environment() -> bool:
+    """Detect if we're running in a test environment."""
+    return os.environ.get("CREWAI_TESTING", "").lower() == "true"
+
+
+def _get_machine_id() -> str:
+    """Stable, privacy-preserving machine fingerprint (cross-platform)."""
+    parts = []
+
+    try:
+        mac = ":".join(
+            ["{:02x}".format((uuid.getnode() >> b) & 0xFF) for b in range(0, 12, 2)][
+                ::-1
+            ]
+        )
+        parts.append(mac)
+    except Exception:
+        pass
+
+    sysname = platform.system()
+    parts.append(sysname)
+
+    try:
+        if sysname == "Darwin":
+            res = subprocess.run(
+                ["system_profiler", "SPHardwareDataType"],
+                capture_output=True,
+                text=True,
+                timeout=2,
+            )
+            m = re.search(r"Hardware UUID:\s*([A-Fa-f0-9\-]+)", res.stdout)
+            if m:
+                parts.append(m.group(1))
+        elif sysname == "Linux":
+            try:
+                parts.append(Path("/etc/machine-id").read_text().strip())
+            except Exception:
+                parts.append(Path("/sys/class/dmi/id/product_uuid").read_text().strip())
+        elif sysname == "Windows":
+            res = subprocess.run(
+                ["wmic", "csproduct", "get", "UUID"],
+                capture_output=True,
+                text=True,
+                timeout=2,
+            )
+            lines = [line.strip() for line in res.stdout.splitlines() if line.strip()]
+            if len(lines) >= 2:
+                parts.append(lines[1])
+    except Exception:
+        pass
+
+    return hashlib.sha256("".join(parts).encode()).hexdigest()
+
+
+def _user_data_file() -> Path:
+    base = Path(db_storage_path())
+    base.mkdir(parents=True, exist_ok=True)
+    return base / ".crewai_user.json"
+
+
+def _load_user_data() -> dict:
+    p = _user_data_file()
+    if p.exists():
+        try:
+            return json.loads(p.read_text())
+        except Exception:
+            pass
+    return {}
+
+
+def _save_user_data(data: dict) -> None:
+    try:
+        p = _user_data_file()
+        p.write_text(json.dumps(data, indent=2))
+    except Exception:
+        pass
+
+
+def get_user_id() -> str:
+    """Stable, anonymized user identifier with caching."""
+    data = _load_user_data()
+
+    if "user_id" in data:
+        return data["user_id"]
+
+    try:
+        username = getpass.getuser()
+    except Exception:
+        username = "unknown"
+
+    seed = f"{username}|{_get_machine_id()}"
+    uid = hashlib.sha256(seed.encode()).hexdigest()
+
+    data["user_id"] = uid
+    _save_user_data(data)
+    return uid
+
+
+def is_first_execution() -> bool:
+    """True if this is the first execution for this user."""
+    data = _load_user_data()
+    return not data.get("first_execution_done", False)
+
+
+def mark_first_execution_done() -> None:
+    """Mark that the first execution has been completed."""
+    data = _load_user_data()
+    if data.get("first_execution_done", False):
+        return
+
+    data.update(
+        {
+            "first_execution_done": True,
+            "first_execution_at": datetime.now().timestamp(),
+            "user_id": get_user_id(),
+            "machine_id": _get_machine_id(),
+        }
+    )
+    _save_user_data(data)

src/crewai/utilities/events/third_party/__init__.py

@@ -1 +0,0 @@
-from .agentops_listener import agentops_listener

src/crewai/utilities/events/third_party/agentops_listener.py

@@ -1,67 +0,0 @@
-from typing import Optional
-
-from crewai.utilities.events import (
-    CrewKickoffCompletedEvent,
-    ToolUsageErrorEvent,
-    ToolUsageStartedEvent,
-)
-from crewai.utilities.events.base_event_listener import BaseEventListener
-from crewai.utilities.events.crew_events import CrewKickoffStartedEvent
-from crewai.utilities.events.task_events import TaskEvaluationEvent
-
-try:
-    import agentops
-
-    AGENTOPS_INSTALLED = True
-except ImportError:
-    AGENTOPS_INSTALLED = False
-
-
-class AgentOpsListener(BaseEventListener):
-    tool_event: Optional["agentops.ToolEvent"] = None
-    session: Optional["agentops.Session"] = None
-
-    def __init__(self):
-        super().__init__()
-
-    def setup_listeners(self, crewai_event_bus):
-        if not AGENTOPS_INSTALLED:
-            return
-
-        @crewai_event_bus.on(CrewKickoffStartedEvent)
-        def on_crew_kickoff_started(source, event: CrewKickoffStartedEvent):
-            self.session = agentops.init()
-            for agent in source.agents:
-                if self.session:
-                    self.session.create_agent(
-                        name=agent.role,
-                        agent_id=str(agent.id),
-                    )
-
-        @crewai_event_bus.on(CrewKickoffCompletedEvent)
-        def on_crew_kickoff_completed(source, event: CrewKickoffCompletedEvent):
-            if self.session:
-                self.session.end_session(
-                    end_state="Success",
-                    end_state_reason="Finished Execution",
-                )
-
-        @crewai_event_bus.on(ToolUsageStartedEvent)
-        def on_tool_usage_started(source, event: ToolUsageStartedEvent):
-            self.tool_event = agentops.ToolEvent(name=event.tool_name)
-            if self.session:
-                self.session.record(self.tool_event)
-
-        @crewai_event_bus.on(ToolUsageErrorEvent)
-        def on_tool_usage_error(source, event: ToolUsageErrorEvent):
-            agentops.ErrorEvent(exception=event.error, trigger_event=self.tool_event)
-
-        @crewai_event_bus.on(TaskEvaluationEvent)
-        def on_task_evaluation(source, event: TaskEvaluationEvent):
-            if self.session:
-                self.session.create_agent(
-                    name="Task Evaluator", agent_id=str(source.original_agent.id)
-                )
-
-
-agentops_listener = AgentOpsListener()

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)
 

tests/agents/test_agent.py

@@ -21,6 +21,7 @@ from crewai.utilities import RPMController
 from crewai.utilities.errors import AgentRepositoryError
 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():
@@ -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()
 
 
@@ -1209,6 +1185,167 @@ Thought:<|eot_id|>
         assert mock_format_prompt.return_value == expected_prompt
 
 
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_task_allow_crewai_trigger_context():
+    from crewai import Crew
+
+    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,
+    )
+    crew = Crew(agents=[agent], tasks=[task])
+    crew.kickoff({"crewai_trigger_payload": "Important context data"})
+
+    prompt = task.prompt()
+
+    assert "Analyze the data" in prompt
+    assert "Trigger Payload: Important context data" in prompt
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_task_without_allow_crewai_trigger_context():
+    from crewai import Crew
+
+    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,
+    )
+
+    crew = Crew(agents=[agent], tasks=[task])
+    crew.kickoff({"crewai_trigger_payload": "Important context data"})
+
+    prompt = task.prompt()
+
+    assert "Analyze the data" in prompt
+    assert "Trigger Payload:" not in prompt
+    assert "Important context data" not in prompt
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_task_allow_crewai_trigger_context_no_payload():
+    from crewai import Crew
+
+    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,
+    )
+
+    crew = Crew(agents=[agent], tasks=[task])
+    crew.kickoff({"other_input": "other data"})
+
+    prompt = task.prompt()
+
+    assert "Analyze the data" in prompt
+    assert "Trigger Payload:" not in prompt
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+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"
+    )
+
+    first_task = Task(
+        description="Process initial data",
+        expected_output="Initial analysis",
+        agent=agent1,
+    )
+
+    crew = Crew(
+        agents=[agent1, agent2],
+        tasks=[first_task],
+        process=Process.hierarchical,
+        manager_llm="gpt-4o",
+    )
+
+    crew.kickoff({"crewai_trigger_payload": "Initial context data"})
+
+    first_prompt = first_task.prompt()
+    assert "Process initial data" in first_prompt
+    assert "Trigger Payload: Initial context data" not in first_prompt
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+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"
+    )
+
+    first_task = Task(
+        description="Process initial data",
+        expected_output="Initial analysis",
+        agent=agent1,
+    )
+
+    second_task = Task(
+        description="Process secondary data",
+        expected_output="Secondary analysis",
+        agent=agent2,
+    )
+
+    crew = Crew(agents=[agent1, agent2], tasks=[first_task, second_task])
+    crew.kickoff({"crewai_trigger_payload": "Initial context data"})
+
+    first_prompt = first_task.prompt()
+    assert "Process initial data" in first_prompt
+    assert "Trigger Payload: Initial context data" in first_prompt
+
+    second_prompt = second_task.prompt()
+    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"
+    )
+
+    first_task = Task(
+        description="Process initial data",
+        expected_output="Initial analysis",
+        agent=agent1,
+        allow_crewai_trigger_context=False,
+    )
+
+    second_task = Task(
+        description="Process secondary data",
+        expected_output="Secondary analysis",
+        agent=agent2,
+        allow_crewai_trigger_context=True,
+    )
+
+    crew = Crew(agents=[agent1, agent2], tasks=[first_task, second_task])
+    crew.kickoff({"crewai_trigger_payload": "Context data"})
+
+    first_prompt = first_task.prompt()
+    assert "Trigger Payload: Context data" not in first_prompt
+
+    second_prompt = second_task.prompt()
+    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?"
@@ -1896,7 +2033,7 @@ def test_agent_with_knowledge_sources_generate_search_query():
         assert "red" in result.raw.lower()
 
 
-@pytest.mark.vcr(record_mode='none', filter_headers=["authorization"])
+@pytest.mark.vcr(record_mode="none", filter_headers=["authorization"])
 def test_agent_with_knowledge_with_no_crewai_knowledge():
     mock_knowledge = MagicMock(spec=Knowledge)
 
@@ -1904,8 +2041,11 @@ def test_agent_with_knowledge_with_no_crewai_knowledge():
         role="Information Agent",
         goal="Provide information based on knowledge sources",
         backstory="You have access to specific knowledge sources.",
-        llm=LLM(model="openrouter/openai/gpt-4o-mini",api_key=os.getenv('OPENROUTER_API_KEY')),
-        knowledge=mock_knowledge
+        llm=LLM(
+            model="openrouter/openai/gpt-4o-mini",
+            api_key=os.getenv("OPENROUTER_API_KEY"),
+        ),
+        knowledge=mock_knowledge,
     )
 
     # Create a task that requires the agent to use the knowledge
@@ -1920,7 +2060,7 @@ def test_agent_with_knowledge_with_no_crewai_knowledge():
     mock_knowledge.query.assert_called_once()
 
 
-@pytest.mark.vcr(record_mode='none', filter_headers=["authorization"])
+@pytest.mark.vcr(record_mode="none", filter_headers=["authorization"])
 def test_agent_with_only_crewai_knowledge():
     mock_knowledge = MagicMock(spec=Knowledge)
 
@@ -1928,33 +2068,10 @@ def test_agent_with_only_crewai_knowledge():
         role="Information Agent",
         goal="Provide information based on knowledge sources",
         backstory="You have access to specific knowledge sources.",
-        llm=LLM(model="openrouter/openai/gpt-4o-mini",api_key=os.getenv('OPENROUTER_API_KEY'))
-    )
-
-    # Create a task that requires the agent to use the knowledge
-    task = Task(
-        description="What is Vidit's favorite color?",
-        expected_output="Vidit's favorclearite color.",
-        agent=agent
-    )
-
-    crew = Crew(agents=[agent], tasks=[task],knowledge=mock_knowledge)
-    crew.kickoff()
-    mock_knowledge.query.assert_called_once()
-
-
-@pytest.mark.vcr(record_mode='none', filter_headers=["authorization"])
-def test_agent_knowledege_with_crewai_knowledge():
-    crew_knowledge = MagicMock(spec=Knowledge)
-    agent_knowledge = MagicMock(spec=Knowledge)
-
-
-    agent = Agent(
-        role="Information Agent",
-        goal="Provide information based on knowledge sources",
-        backstory="You have access to specific knowledge sources.",
-        llm=LLM(model="openrouter/openai/gpt-4o-mini",api_key=os.getenv('OPENROUTER_API_KEY')),
-        knowledge=agent_knowledge
+        llm=LLM(
+            model="openrouter/openai/gpt-4o-mini",
+            api_key=os.getenv("OPENROUTER_API_KEY"),
+        ),
     )
 
     # Create a task that requires the agent to use the knowledge
@@ -1964,7 +2081,35 @@ def test_agent_knowledege_with_crewai_knowledge():
         agent=agent,
     )
 
-    crew = Crew(agents=[agent],tasks=[task],knowledge=crew_knowledge)
+    crew = Crew(agents=[agent], tasks=[task], knowledge=mock_knowledge)
+    crew.kickoff()
+    mock_knowledge.query.assert_called_once()
+
+
+@pytest.mark.vcr(record_mode="none", filter_headers=["authorization"])
+def test_agent_knowledege_with_crewai_knowledge():
+    crew_knowledge = MagicMock(spec=Knowledge)
+    agent_knowledge = MagicMock(spec=Knowledge)
+
+    agent = Agent(
+        role="Information Agent",
+        goal="Provide information based on knowledge sources",
+        backstory="You have access to specific knowledge sources.",
+        llm=LLM(
+            model="openrouter/openai/gpt-4o-mini",
+            api_key=os.getenv("OPENROUTER_API_KEY"),
+        ),
+        knowledge=agent_knowledge,
+    )
+
+    # Create a task that requires the agent to use the knowledge
+    task = Task(
+        description="What is Vidit's favorite color?",
+        expected_output="Vidit's favorclearite color.",
+        agent=agent,
+    )
+
+    crew = Crew(agents=[agent], tasks=[task], knowledge=crew_knowledge)
     crew.kickoff()
     agent_knowledge.query.assert_called_once()
     crew_knowledge.query.assert_called_once()
@@ -2164,7 +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
@@ -2173,12 +2324,22 @@ def test_agent_from_repository(mock_get_agent, mock_get_auth_token):
         "goal": "test goal",
         "backstory": "test backstory",
         "tools": [
-            {"module": "crewai_tools", "name": "SerperDevTool", "init_params": {"n_results": 30}},
-            {"module": "crewai_tools", "name": "XMLSearchTool", "init_params": {"summarize": True}},
-            {"module": "crewai_tools", "name": "CSVSearchTool", "init_params": {}},
-
+            {
+                "module": "crewai_tools",
+                "name": "SerperDevTool",
+                "init_params": {"n_results": "30"},
+            },
+            {
+                "module": "crewai_tools",
+                "name": "FileReadTool",
+                "init_params": {"file_path": "test.txt"},
+            },
             # using a tools that returns a list of BaseTools
-            {"module": "crewai_tools", "name": "CrewaiEnterpriseTools", "init_params": {"actions_list": [], "enterprise_token": "test_key"}},
+            {
+                "module": "crewai_tools",
+                "name": "CrewaiEnterpriseTools",
+                "init_params": {"actions_list": [], "enterprise_token": "test_key"},
+            },
         ],
     }
     mock_get_agent.return_value = mock_get_response
@@ -2197,23 +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
@@ -2221,7 +2381,9 @@ def test_agent_from_repository_override_attributes(mock_get_agent, mock_get_auth
         "role": "test role",
         "goal": "test goal",
         "backstory": "test backstory",
-        "tools": [{"name": "SerperDevTool", "module": "crewai_tools", "init_params": {}}],
+        "tools": [
+            {"name": "SerperDevTool", "module": "crewai_tools", "init_params": {}}
+        ],
     }
     mock_get_agent.return_value = mock_get_response
     agent = Agent(from_repository="test_agent", role="Custom Role")

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/TestTraceListenerSetup.test_trace_listener_ephemeral_batch.yaml

@@ -0,0 +1,126 @@
+interactions:
+- request:
+    body: '{"messages": [{"role": "system", "content": "You are Test Agent. Test backstory\nYour
+      personal goal is: Test goal\nTo give my best complete final answer to the task
+      respond using the exact following format:\n\nThought: I now can give a great
+      answer\nFinal Answer: Your final answer must be the great and the most complete
+      as possible, it must be outcome described.\n\nI MUST use these formats, my job
+      depends on it!"}, {"role": "user", "content": "\nCurrent Task: Say hello to
+      the world\n\nThis is the expected criteria for your final answer: hello world\nyou
+      MUST return the actual complete content as the final answer, not a summary.\n\nBegin!
+      This is VERY important to you, use the tools available and give your best Final
+      Answer, your job depends on it!\n\nThought:"}], "model": "gpt-4o-mini", "stop":
+      ["\nObservation:"]}'
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate, zstd
+      connection:
+      - keep-alive
+      content-length:
+      - '825'
+      content-type:
+      - application/json
+      host:
+      - api.openai.com
+      user-agent:
+      - OpenAI/Python 1.93.0
+      x-stainless-arch:
+      - arm64
+      x-stainless-async:
+      - 'false'
+      x-stainless-lang:
+      - python
+      x-stainless-os:
+      - MacOS
+      x-stainless-package-version:
+      - 1.93.0
+      x-stainless-raw-response:
+      - 'true'
+      x-stainless-read-timeout:
+      - '600.0'
+      x-stainless-retry-count:
+      - '0'
+      x-stainless-runtime:
+      - CPython
+      x-stainless-runtime-version:
+      - 3.12.9
+    method: POST
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tests/cli/authentication/test_utils.py

@@ -108,7 +108,9 @@ class TestValidateToken(unittest.TestCase):
 
 
 class TestTokenManager(unittest.TestCase):
-    def setUp(self):
+    @patch("crewai.cli.authentication.utils.TokenManager._get_or_create_key")
+    def setUp(self, mock_get_key):
+        mock_get_key.return_value = Fernet.generate_key()
         self.token_manager = TokenManager()
 
     @patch("crewai.cli.authentication.utils.TokenManager.read_secure_file")

tests/cli/test_constants.py

@@ -1,5 +1,3 @@
-import pytest
-
 from crewai.cli.constants import ENV_VARS, MODELS, PROVIDERS
 
 
@@ -21,3 +19,44 @@ def test_huggingface_models():
     """Test that Huggingface models are properly configured."""
     assert "huggingface" in MODELS
     assert len(MODELS["huggingface"]) > 0
+
+
+def test_openai_models_include_latest():
+    """Test that OpenAI models include the latest GPT-5 series."""
+    openai_models = MODELS["openai"]
+    assert "gpt-5" in openai_models
+    assert "gpt-5-mini" in openai_models
+    assert "gpt-5-nano" in openai_models
+    assert "gpt-4.1" in openai_models
+    assert "o3-mini" in openai_models
+
+
+def test_anthropic_models_include_latest():
+    """Test that Anthropic models include the latest Claude 4 series."""
+    anthropic_models = MODELS["anthropic"]
+    assert "claude-3.7-sonnet-20250219" in anthropic_models
+    assert "claude-4-sonnet-20250301" in anthropic_models
+    assert "claude-4.1-opus-20250315" in anthropic_models
+
+
+def test_gemini_models_include_latest():
+    """Test that Gemini models include the latest 2.5 series."""
+    gemini_models = MODELS["gemini"]
+    assert "gemini/gemini-2.5-pro" in gemini_models
+    assert "gemini/gemini-2.5-flash" in gemini_models
+    assert "gemini/gemini-2.5-flash-lite" in gemini_models
+
+
+def test_all_providers_have_models():
+    """Test that all providers in PROVIDERS have corresponding models."""
+    for provider in PROVIDERS:
+        if provider in MODELS:
+            assert len(MODELS[provider]) > 0, f"Provider {provider} has no models"
+
+
+def test_model_format_consistency():
+    """Test that model names follow consistent formatting patterns."""
+    for provider, models in MODELS.items():
+        for model in models:
+            assert isinstance(model, str), f"Model {model} in {provider} is not a string"
+            assert len(model.strip()) > 0, f"Empty model name in {provider}"

tests/cli/tools/test_main.py

@@ -4,6 +4,7 @@ import unittest
 import unittest.mock
 from datetime import datetime, timedelta
 from contextlib import contextmanager
+from pathlib import Path
 from unittest import mock
 from unittest.mock import MagicMock, patch
 
@@ -27,12 +28,18 @@ def in_temp_dir():
 
 @pytest.fixture
 def tool_command():
-    TokenManager().save_tokens(
-        "test-token", (datetime.now() + timedelta(seconds=36000)).timestamp()
-    )
-    tool_command = ToolCommand()
-    with patch.object(tool_command, "login"):
-        yield tool_command
+    # Create a temporary directory for each test to avoid token storage conflicts
+    with tempfile.TemporaryDirectory() as temp_dir:
+        # Mock the secure storage path to use the temp directory
+        with patch.object(
+            TokenManager, "get_secure_storage_path", return_value=Path(temp_dir)
+        ):
+            TokenManager().save_tokens(
+                "test-token", (datetime.now() + timedelta(seconds=36000)).timestamp()
+            )
+            tool_command = ToolCommand()
+            with patch.object(tool_command, "login"):
+                yield tool_command
 
 
 @patch("crewai.cli.tools.main.subprocess.run")

tests/conftest.py

@@ -2,6 +2,7 @@
 import os
 import tempfile
 from pathlib import Path
+from unittest.mock import Mock, patch
 
 import pytest
 from dotenv import load_dotenv
@@ -34,14 +35,130 @@ def setup_test_environment():
                 f"Test storage directory {storage_dir} is not writable: {e}"
             )
 
-        # Set environment variable to point to the test storage directory
         os.environ["CREWAI_STORAGE_DIR"] = str(storage_dir)
-
+        os.environ["CREWAI_TESTING"] = "true"
         yield
 
+        os.environ.pop("CREWAI_TESTING", None)
         # Cleanup is handled automatically when tempfile context exits
 
 
+def pytest_configure(config):
+    config.addinivalue_line(
+        "markers", "telemetry: mark test as a telemetry test (don't mock telemetry)"
+    )
+
+
+@pytest.fixture(autouse=True)
+def auto_mock_telemetry(request):
+    if request.node.get_closest_marker("telemetry"):
+        telemetry_env = {
+            key: value
+            for key, value in os.environ.items()
+            if key not in ["CREWAI_DISABLE_TELEMETRY", "OTEL_SDK_DISABLED"]
+        }
+        with patch.dict(os.environ, telemetry_env, clear=True):
+            yield
+        return
+
+    if "telemetry" in str(request.fspath):
+        telemetry_env = {
+            key: value
+            for key, value in os.environ.items()
+            if key not in ["CREWAI_DISABLE_TELEMETRY", "OTEL_SDK_DISABLED"]
+        }
+        with patch.dict(os.environ, telemetry_env, clear=True):
+            yield
+        return
+
+    with patch.dict(
+        os.environ, {"CREWAI_DISABLE_TELEMETRY": "true", "OTEL_SDK_DISABLED": "true"}
+    ):
+        with patch("crewai.telemetry.Telemetry") as mock_telemetry_class:
+            mock_instance = create_mock_telemetry_instance()
+            mock_telemetry_class.return_value = mock_instance
+
+            with (
+                patch(
+                    "crewai.utilities.events.event_listener.Telemetry",
+                    mock_telemetry_class,
+                ),
+                patch("crewai.tools.tool_usage.Telemetry", mock_telemetry_class),
+                patch("crewai.cli.command.Telemetry", mock_telemetry_class),
+                patch("crewai.cli.create_flow.Telemetry", mock_telemetry_class),
+            ):
+                yield mock_instance
+
+
+def create_mock_telemetry_instance():
+    mock_instance = Mock()
+
+    mock_instance.ready = False
+    mock_instance.trace_set = False
+    mock_instance._initialized = True
+
+    mock_instance._is_telemetry_disabled.return_value = True
+    mock_instance._should_execute_telemetry.return_value = False
+
+    telemetry_methods = [
+        "set_tracer",
+        "crew_creation",
+        "task_started",
+        "task_ended",
+        "tool_usage",
+        "tool_repeated_usage",
+        "tool_usage_error",
+        "crew_execution_span",
+        "end_crew",
+        "flow_creation_span",
+        "flow_execution_span",
+        "individual_test_result_span",
+        "test_execution_span",
+        "deploy_signup_error_span",
+        "start_deployment_span",
+        "create_crew_deployment_span",
+        "get_crew_logs_span",
+        "remove_crew_span",
+        "flow_plotting_span",
+        "_add_attribute",
+        "_safe_telemetry_operation",
+    ]
+
+    for method in telemetry_methods:
+        setattr(mock_instance, method, Mock(return_value=None))
+
+    mock_instance.task_started.return_value = None
+
+    return mock_instance
+
+
+@pytest.fixture
+def mock_opentelemetry_components():
+    with (
+        patch("opentelemetry.trace.get_tracer") as mock_get_tracer,
+        patch("opentelemetry.trace.set_tracer_provider") as mock_set_provider,
+        patch("opentelemetry.baggage.set_baggage") as mock_set_baggage,
+        patch("opentelemetry.baggage.get_baggage") as mock_get_baggage,
+        patch("opentelemetry.context.attach") as mock_attach,
+        patch("opentelemetry.context.detach") as mock_detach,
+    ):
+        mock_tracer = Mock()
+        mock_span = Mock()
+        mock_tracer.start_span.return_value = mock_span
+        mock_get_tracer.return_value = mock_tracer
+
+        yield {
+            "get_tracer": mock_get_tracer,
+            "set_tracer_provider": mock_set_provider,
+            "tracer": mock_tracer,
+            "span": mock_span,
+            "set_baggage": mock_set_baggage,
+            "get_baggage": mock_get_baggage,
+            "attach": mock_attach,
+            "detach": mock_detach,
+        }
+
+
 @pytest.fixture(scope="module")
 def vcr_config(request) -> dict:
     return {

tests/experimental/evaluation/metrics/test_base_evaluation_metrics.py

@@ -3,6 +3,7 @@ from unittest.mock import MagicMock
 from crewai.agent import Agent
 from crewai.task import Task
 
+
 class BaseEvaluationMetricsTest:
     @pytest.fixture
     def mock_agent(self):
@@ -24,5 +25,5 @@ class BaseEvaluationMetricsTest:
     def execution_trace(self):
         return {
             "thinking": ["I need to analyze this data carefully"],
-            "actions": ["Gathered information", "Analyzed data"]
-        }
+            "actions": ["Gathered information", "Analyzed data"],
+        }

tests/experimental/evaluation/metrics/test_goal_metrics.py

@@ -1,5 +1,7 @@
 from unittest.mock import patch, MagicMock
-from tests.experimental.evaluation.metrics.base_evaluation_metrics_test import BaseEvaluationMetricsTest
+from tests.experimental.evaluation.metrics.test_base_evaluation_metrics import (
+    BaseEvaluationMetricsTest,
+)
 
 from crewai.experimental.evaluation.base_evaluator import EvaluationScore
 from crewai.experimental.evaluation.metrics.goal_metrics import GoalAlignmentEvaluator
@@ -8,7 +10,9 @@ from crewai.utilities.llm_utils import LLM
 
 class TestGoalAlignmentEvaluator(BaseEvaluationMetricsTest):
     @patch("crewai.utilities.llm_utils.create_llm")
-    def test_evaluate_success(self, mock_create_llm, mock_agent, mock_task, execution_trace):
+    def test_evaluate_success(
+        self, mock_create_llm, mock_agent, mock_task, execution_trace
+    ):
         mock_llm = MagicMock(spec=LLM)
         mock_llm.call.return_value = """
         {
@@ -24,7 +28,7 @@ class TestGoalAlignmentEvaluator(BaseEvaluationMetricsTest):
             agent=mock_agent,
             task=mock_task,
             execution_trace=execution_trace,
-            final_output="This is the final output"
+            final_output="This is the final output",
         )
 
         assert isinstance(result, EvaluationScore)
@@ -40,7 +44,9 @@ class TestGoalAlignmentEvaluator(BaseEvaluationMetricsTest):
         assert mock_task.description in prompt[1]["content"]
 
     @patch("crewai.utilities.llm_utils.create_llm")
-    def test_evaluate_error_handling(self, mock_create_llm, mock_agent, mock_task, execution_trace):
+    def test_evaluate_error_handling(
+        self, mock_create_llm, mock_agent, mock_task, execution_trace
+    ):
         mock_llm = MagicMock(spec=LLM)
         mock_llm.call.return_value = "Invalid JSON response"
         mock_create_llm.return_value = mock_llm
@@ -51,7 +57,7 @@ class TestGoalAlignmentEvaluator(BaseEvaluationMetricsTest):
             agent=mock_agent,
             task=mock_task,
             execution_trace=execution_trace,
-            final_output="This is the final output"
+            final_output="This is the final output",
         )
 
         assert isinstance(result, EvaluationScore)

tests/experimental/evaluation/metrics/test_reasoning_metrics.py

@@ -6,10 +6,13 @@ from crewai.tasks.task_output import TaskOutput
 from crewai.experimental.evaluation.metrics.reasoning_metrics import (
     ReasoningEfficiencyEvaluator,
 )
-from tests.experimental.evaluation.metrics.base_evaluation_metrics_test import BaseEvaluationMetricsTest
+from tests.experimental.evaluation.metrics.test_base_evaluation_metrics import (
+    BaseEvaluationMetricsTest,
+)
 from crewai.utilities.llm_utils import LLM
 from crewai.experimental.evaluation.base_evaluator import EvaluationScore
 
+
 class TestReasoningEfficiencyEvaluator(BaseEvaluationMetricsTest):
     @pytest.fixture
     def mock_output(self):
@@ -23,18 +26,18 @@ class TestReasoningEfficiencyEvaluator(BaseEvaluationMetricsTest):
             {
                 "prompt": "How should I approach this task?",
                 "response": "I'll first research the topic, then compile findings.",
-                "timestamp": 1626987654
+                "timestamp": 1626987654,
             },
             {
                 "prompt": "What resources should I use?",
                 "response": "I'll use relevant academic papers and reliable websites.",
-                "timestamp": 1626987754
+                "timestamp": 1626987754,
             },
             {
                 "prompt": "How should I structure the output?",
                 "response": "I'll organize information clearly with headings and bullet points.",
-                "timestamp": 1626987854
-            }
+                "timestamp": 1626987854,
+            },
         ]
 
     def test_insufficient_llm_calls(self, mock_agent, mock_task, mock_output):
@@ -45,7 +48,7 @@ class TestReasoningEfficiencyEvaluator(BaseEvaluationMetricsTest):
             agent=mock_agent,
             task=mock_task,
             execution_trace=execution_trace,
-            final_output=mock_output
+            final_output=mock_output,
         )
 
         assert isinstance(result, EvaluationScore)
@@ -53,7 +56,9 @@ class TestReasoningEfficiencyEvaluator(BaseEvaluationMetricsTest):
         assert "Insufficient LLM calls" in result.feedback
 
     @patch("crewai.utilities.llm_utils.create_llm")
-    def test_successful_evaluation(self, mock_create_llm, mock_agent, mock_task, mock_output, llm_calls):
+    def test_successful_evaluation(
+        self, mock_create_llm, mock_agent, mock_task, mock_output, llm_calls
+    ):
         mock_llm = MagicMock(spec=LLM)
         mock_llm.call.return_value = """
         {
@@ -83,7 +88,7 @@ class TestReasoningEfficiencyEvaluator(BaseEvaluationMetricsTest):
             agent=mock_agent,
             task=mock_task,
             execution_trace=execution_trace,
-            final_output=mock_output
+            final_output=mock_output,
         )
 
         # Assertions
@@ -97,7 +102,9 @@ class TestReasoningEfficiencyEvaluator(BaseEvaluationMetricsTest):
         mock_llm.call.assert_called_once()
 
     @patch("crewai.utilities.llm_utils.create_llm")
-    def test_parse_error_handling(self, mock_create_llm, mock_agent, mock_task, mock_output, llm_calls):
+    def test_parse_error_handling(
+        self, mock_create_llm, mock_agent, mock_task, mock_output, llm_calls
+    ):
         mock_llm = MagicMock(spec=LLM)
         mock_llm.call.return_value = "Invalid JSON response"
         mock_create_llm.return_value = mock_llm
@@ -114,7 +121,7 @@ class TestReasoningEfficiencyEvaluator(BaseEvaluationMetricsTest):
             agent=mock_agent,
             task=mock_task,
             execution_trace=execution_trace,
-            final_output=mock_output
+            final_output=mock_output,
         )
 
         # Assertions for error handling
@@ -126,11 +133,31 @@ class TestReasoningEfficiencyEvaluator(BaseEvaluationMetricsTest):
     def test_loop_detection(self, mock_create_llm, mock_agent, mock_task, mock_output):
         # Setup LLM calls with a repeating pattern
         repetitive_llm_calls = [
-            {"prompt": "How to solve?", "response": "I'll try method A", "timestamp": 1000},
-            {"prompt": "Let me try method A", "response": "It didn't work", "timestamp": 1100},
-            {"prompt": "How to solve?", "response": "I'll try method A again", "timestamp": 1200},
-            {"prompt": "Let me try method A", "response": "It didn't work", "timestamp": 1300},
-            {"prompt": "How to solve?", "response": "I'll try method A one more time", "timestamp": 1400}
+            {
+                "prompt": "How to solve?",
+                "response": "I'll try method A",
+                "timestamp": 1000,
+            },
+            {
+                "prompt": "Let me try method A",
+                "response": "It didn't work",
+                "timestamp": 1100,
+            },
+            {
+                "prompt": "How to solve?",
+                "response": "I'll try method A again",
+                "timestamp": 1200,
+            },
+            {
+                "prompt": "Let me try method A",
+                "response": "It didn't work",
+                "timestamp": 1300,
+            },
+            {
+                "prompt": "How to solve?",
+                "response": "I'll try method A one more time",
+                "timestamp": 1400,
+            },
         ]
 
         mock_llm = MagicMock(spec=LLM)
@@ -158,7 +185,7 @@ class TestReasoningEfficiencyEvaluator(BaseEvaluationMetricsTest):
             agent=mock_agent,
             task=mock_task,
             execution_trace=execution_trace,
-            final_output=mock_output
+            final_output=mock_output,
         )
 
         assert isinstance(result, EvaluationScore)

tests/experimental/evaluation/metrics/test_semantic_quality_metrics.py

@@ -1,13 +1,20 @@
 from unittest.mock import patch, MagicMock
 
 from crewai.experimental.evaluation.base_evaluator import EvaluationScore
-from crewai.experimental.evaluation.metrics.semantic_quality_metrics import SemanticQualityEvaluator
-from tests.experimental.evaluation.metrics.base_evaluation_metrics_test import BaseEvaluationMetricsTest
+from crewai.experimental.evaluation.metrics.semantic_quality_metrics import (
+    SemanticQualityEvaluator,
+)
+from tests.experimental.evaluation.metrics.test_base_evaluation_metrics import (
+    BaseEvaluationMetricsTest,
+)
 from crewai.utilities.llm_utils import LLM
 
+
 class TestSemanticQualityEvaluator(BaseEvaluationMetricsTest):
     @patch("crewai.utilities.llm_utils.create_llm")
-    def test_evaluate_success(self, mock_create_llm, mock_agent, mock_task, execution_trace):
+    def test_evaluate_success(
+        self, mock_create_llm, mock_agent, mock_task, execution_trace
+    ):
         mock_llm = MagicMock(spec=LLM)
         mock_llm.call.return_value = """
         {
@@ -23,7 +30,7 @@ class TestSemanticQualityEvaluator(BaseEvaluationMetricsTest):
             agent=mock_agent,
             task=mock_task,
             execution_trace=execution_trace,
-            final_output="This is a well-structured analysis of the data."
+            final_output="This is a well-structured analysis of the data.",
         )
 
         assert isinstance(result, EvaluationScore)
@@ -39,7 +46,9 @@ class TestSemanticQualityEvaluator(BaseEvaluationMetricsTest):
         assert mock_task.description in prompt[1]["content"]
 
     @patch("crewai.utilities.llm_utils.create_llm")
-    def test_evaluate_with_empty_output(self, mock_create_llm, mock_agent, mock_task, execution_trace):
+    def test_evaluate_with_empty_output(
+        self, mock_create_llm, mock_agent, mock_task, execution_trace
+    ):
         mock_llm = MagicMock(spec=LLM)
         mock_llm.call.return_value = """
         {
@@ -55,7 +64,7 @@ class TestSemanticQualityEvaluator(BaseEvaluationMetricsTest):
             agent=mock_agent,
             task=mock_task,
             execution_trace=execution_trace,
-            final_output=""
+            final_output="",
         )
 
         assert isinstance(result, EvaluationScore)
@@ -63,7 +72,9 @@ class TestSemanticQualityEvaluator(BaseEvaluationMetricsTest):
         assert "empty or minimal" in result.feedback
 
     @patch("crewai.utilities.llm_utils.create_llm")
-    def test_evaluate_error_handling(self, mock_create_llm, mock_agent, mock_task, execution_trace):
+    def test_evaluate_error_handling(
+        self, mock_create_llm, mock_agent, mock_task, execution_trace
+    ):
         mock_llm = MagicMock(spec=LLM)
         mock_llm.call.return_value = "Invalid JSON response"
         mock_create_llm.return_value = mock_llm
@@ -74,9 +85,9 @@ class TestSemanticQualityEvaluator(BaseEvaluationMetricsTest):
             agent=mock_agent,
             task=mock_task,
             execution_trace=execution_trace,
-            final_output="This is the output."
+            final_output="This is the output.",
         )
 
         assert isinstance(result, EvaluationScore)
         assert result.score is None
-        assert "Failed to parse" in result.feedback
+        assert "Failed to parse" in result.feedback

tests/experimental/evaluation/metrics/test_tools_metrics.py

@@ -3,10 +3,13 @@ from unittest.mock import patch, MagicMock
 from crewai.experimental.evaluation.metrics.tools_metrics import (
     ToolSelectionEvaluator,
     ParameterExtractionEvaluator,
-    ToolInvocationEvaluator
+    ToolInvocationEvaluator,
 )
 from crewai.utilities.llm_utils import LLM
-from tests.experimental.evaluation.metrics.base_evaluation_metrics_test import BaseEvaluationMetricsTest
+from tests.experimental.evaluation.metrics.test_base_evaluation_metrics import (
+    BaseEvaluationMetricsTest,
+)
+
 
 class TestToolSelectionEvaluator(BaseEvaluationMetricsTest):
     def test_no_tools_available(self, mock_task, mock_agent):
@@ -20,7 +23,7 @@ class TestToolSelectionEvaluator(BaseEvaluationMetricsTest):
             agent=mock_agent,
             task=mock_task,
             execution_trace=execution_trace,
-            final_output="Final output"
+            final_output="Final output",
         )
 
         assert result.score is None
@@ -35,7 +38,7 @@ class TestToolSelectionEvaluator(BaseEvaluationMetricsTest):
             agent=mock_agent,
             task=mock_task,
             execution_trace=execution_trace,
-            final_output="Final output"
+            final_output="Final output",
         )
 
         assert result.score is None
@@ -56,8 +59,12 @@ class TestToolSelectionEvaluator(BaseEvaluationMetricsTest):
         # Setup execution trace with tool uses
         execution_trace = {
             "tool_uses": [
-                {"tool": "search_tool", "input": {"query": "test query"}, "output": "search results"},
-                {"tool": "calculator", "input": {"expression": "2+2"}, "output": "4"}
+                {
+                    "tool": "search_tool",
+                    "input": {"query": "test query"},
+                    "output": "search results",
+                },
+                {"tool": "calculator", "input": {"expression": "2+2"}, "output": "4"},
             ]
         }
 
@@ -66,7 +73,7 @@ class TestToolSelectionEvaluator(BaseEvaluationMetricsTest):
             agent=mock_agent,
             task=mock_task,
             execution_trace=execution_trace,
-            final_output="Final output"
+            final_output="Final output",
         )
 
         assert result.score == 8.5
@@ -90,7 +97,7 @@ class TestParameterExtractionEvaluator(BaseEvaluationMetricsTest):
             agent=mock_agent,
             task=mock_task,
             execution_trace=execution_trace,
-            final_output="Final output"
+            final_output="Final output",
         )
 
         assert result.score is None
@@ -117,14 +124,14 @@ class TestParameterExtractionEvaluator(BaseEvaluationMetricsTest):
                     "tool": "search_tool",
                     "input": {"query": "test query"},
                     "output": "search results",
-                    "error": None
+                    "error": None,
                 },
                 {
                     "tool": "calculator",
                     "input": {"expression": "2+2"},
                     "output": "4",
-                    "error": None
-                }
+                    "error": None,
+                },
             ]
         }
 
@@ -133,7 +140,7 @@ class TestParameterExtractionEvaluator(BaseEvaluationMetricsTest):
             agent=mock_agent,
             task=mock_task,
             execution_trace=execution_trace,
-            final_output="Final output"
+            final_output="Final output",
         )
 
         assert result.score == 9.0
@@ -149,7 +156,7 @@ class TestToolInvocationEvaluator(BaseEvaluationMetricsTest):
             agent=mock_agent,
             task=mock_task,
             execution_trace=execution_trace,
-            final_output="Final output"
+            final_output="Final output",
         )
 
         assert result.score is None
@@ -171,8 +178,12 @@ class TestToolInvocationEvaluator(BaseEvaluationMetricsTest):
         # Setup execution trace with tool uses
         execution_trace = {
             "tool_uses": [
-                {"tool": "search_tool", "input": {"query": "test query"}, "output": "search results"},
-                {"tool": "calculator", "input": {"expression": "2+2"}, "output": "4"}
+                {
+                    "tool": "search_tool",
+                    "input": {"query": "test query"},
+                    "output": "search results",
+                },
+                {"tool": "calculator", "input": {"expression": "2+2"}, "output": "4"},
             ]
         }
 
@@ -181,7 +192,7 @@ class TestToolInvocationEvaluator(BaseEvaluationMetricsTest):
             agent=mock_agent,
             task=mock_task,
             execution_trace=execution_trace,
-            final_output="Final output"
+            final_output="Final output",
         )
 
         assert result.score == 8.0
@@ -207,14 +218,14 @@ class TestToolInvocationEvaluator(BaseEvaluationMetricsTest):
                     "tool": "search_tool",
                     "input": {"query": "test query"},
                     "output": "search results",
-                    "error": None
+                    "error": None,
                 },
                 {
                     "tool": "calculator",
                     "input": {"expression": "2+"},
                     "output": None,
-                    "error": "Invalid expression"
-                }
+                    "error": "Invalid expression",
+                },
             ]
         }
 
@@ -223,7 +234,7 @@ class TestToolInvocationEvaluator(BaseEvaluationMetricsTest):
             agent=mock_agent,
             task=mock_task,
             execution_trace=execution_trace,
-            final_output="Final output"
+            final_output="Final output",
         )
 
         assert result.score == 5.5