Fix import sorting in test file according to ruff standards

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

.github/security.md

@@ -1,19 +1,27 @@
-CrewAI takes the security of our software products and services seriously, which includes all source code repositories managed through our GitHub organization.
-If you believe you have found a security vulnerability in any CrewAI product or service, please report it to us as described below.
+## CrewAI Security Vulnerability Reporting Policy
 
- ## Reporting a Vulnerability
- Please do not report security vulnerabilities through public GitHub issues.
- To report a vulnerability, please email us at security@crewai.com.
- Please include the requested information listed below so that we can triage your report more quickly
+CrewAI prioritizes the security of our software products, services, and GitHub repositories. To promptly address vulnerabilities, follow these steps for reporting security issues:
 
- - Type of issue (e.g. SQL injection, cross-site scripting, etc.)
- - Full paths of source file(s) related to the manifestation of the issue
- - The location of the affected source code (tag/branch/commit or direct URL)
- - Any special configuration required to reproduce the issue
- - Step-by-step instructions to reproduce the issue (please include screenshots if needed)
- - Proof-of-concept or exploit code (if possible)
- - Impact of the issue, including how an attacker might exploit the issue
+### Reporting Process
+Do **not** report vulnerabilities via public GitHub issues.
 
- Once we have received your report, we will respond to you at the email address you provide. If the issue is confirmed, we will release a patch as soon as possible depending on the complexity of the issue.
+Email all vulnerability reports directly to:  
+**security@crewai.com**
 
- At this time, we are not offering a bug bounty program. Any rewards will be at our discretion.
+### Required Information
+To help us quickly validate and remediate the issue, your report must include:
+
+- **Vulnerability Type:** Clearly state the vulnerability type (e.g., SQL injection, XSS, privilege escalation).
+- **Affected Source Code:** Provide full file paths and direct URLs (branch, tag, or commit).
+- **Reproduction Steps:** Include detailed, step-by-step instructions. Screenshots are recommended.
+- **Special Configuration:** Document any special settings or configurations required to reproduce.
+- **Proof-of-Concept (PoC):** Provide exploit or PoC code (if available).
+- **Impact Assessment:** Clearly explain the severity and potential exploitation scenarios.
+
+### Our Response
+- We will acknowledge receipt of your report promptly via your provided email.
+- Confirmed vulnerabilities will receive priority remediation based on severity.
+- Patches will be released as swiftly as possible following verification.
+
+### Reward Notice
+Currently, we do not offer a bug bounty program. Rewards, if issued, are discretionary.

docs/concepts/llms.mdx

@@ -169,19 +169,55 @@ In this section, you'll find detailed examples that help you select, configure,
     ```
   </Accordion>
 
-  <Accordion title="Google">
-    Set the following environment variables in your `.env` file:
+  <Accordion title="Google (Gemini API)">
+    Set your API key in your `.env` file. If you need a key, or need to find an
+    existing key, check [AI Studio](https://aistudio.google.com/apikey).
 
-    ```toml Code
-    # Option 1: Gemini accessed with an API key.
+    ```toml .env
     # https://ai.google.dev/gemini-api/docs/api-key
     GEMINI_API_KEY=<your-api-key>
-
-    # Option 2: Vertex AI IAM credentials for Gemini, Anthropic, and Model Garden.
-    # https://cloud.google.com/vertex-ai/generative-ai/docs/overview
     ```
 
-    Get credentials from your Google Cloud Console and save it to a JSON file with the following code:
+    Example usage in your CrewAI project:
+    ```python Code
+    from crewai import LLM
+
+    llm = LLM(
+        model="gemini/gemini-2.0-flash",
+        temperature=0.7,
+    )
+    ```
+
+    ### Gemini models
+
+    Google offers a range of powerful models optimized for different use cases.
+
+    | Model                          | Context Window | Best For                                                          |
+    |--------------------------------|----------------|-------------------------------------------------------------------|
+    | gemini-2.5-flash-preview-04-17 | 1M tokens      | Adaptive thinking, cost efficiency                                |
+    | gemini-2.5-pro-preview-05-06   | 1M tokens      | Enhanced thinking and reasoning, multimodal understanding, advanced coding, and more |
+    | gemini-2.0-flash               | 1M tokens      | Next generation features, speed, thinking, and realtime streaming |
+    | gemini-2.0-flash-lite          | 1M tokens      | Cost efficiency and low latency                                   |
+    | gemini-1.5-flash               | 1M tokens      | Balanced multimodal model, good for most tasks                    |
+    | gemini-1.5-flash-8B            | 1M tokens      | Fastest, most cost-efficient, good for high-frequency tasks       |
+    | gemini-1.5-pro                 | 2M tokens      | Best performing, wide variety of reasoning tasks including logical reasoning, coding, and creative collaboration |
+
+    The full list of models is available in the [Gemini model docs](https://ai.google.dev/gemini-api/docs/models).
+
+    ### Gemma
+
+    The Gemini API also allows you to use your API key to access [Gemma models](https://ai.google.dev/gemma/docs) hosted on Google infrastructure.
+
+    | Model          | Context Window |
+    |----------------|----------------|
+    | gemma-3-1b-it  | 32k tokens     |
+    | gemma-3-4b-it  | 32k tokens     |
+    | gemma-3-12b-it | 32k tokens     |
+    | gemma-3-27b-it | 128k tokens    |
+
+  </Accordion>
+  <Accordion title="Google (Vertex AI)">
+    Get credentials from your Google Cloud Console and save it to a JSON file, then load it with the following code:
     ```python Code
     import json
 
@@ -205,14 +241,18 @@ In this section, you'll find detailed examples that help you select, configure,
         vertex_credentials=vertex_credentials_json
     )
     ```
+
     Google offers a range of powerful models optimized for different use cases:
 
-    | Model                  | Context Window | Best For                                                          |
-    |-----------------------|----------------|------------------------------------------------------------------|
-    | gemini-2.0-flash-exp  | 1M tokens      | Higher quality at faster speed, multimodal model, good for most tasks |
-    | gemini-1.5-flash      | 1M tokens      | Balanced multimodal model, good for most tasks                    |
-    | gemini-1.5-flash-8B   | 1M tokens      | Fastest, most cost-efficient, good for high-frequency tasks       |
-    | gemini-1.5-pro        | 2M tokens      | Best performing, wide variety of reasoning tasks including logical reasoning, coding, and creative collaboration |
+    | Model                          | Context Window | Best For                                                          |
+    |--------------------------------|----------------|-------------------------------------------------------------------|
+    | gemini-2.5-flash-preview-04-17 | 1M tokens      | Adaptive thinking, cost efficiency                                |
+    | gemini-2.5-pro-preview-05-06   | 1M tokens      | Enhanced thinking and reasoning, multimodal understanding, advanced coding, and more |
+    | gemini-2.0-flash               | 1M tokens      | Next generation features, speed, thinking, and realtime streaming |
+    | gemini-2.0-flash-lite          | 1M tokens      | Cost efficiency and low latency                                   |
+    | gemini-1.5-flash               | 1M tokens      | Balanced multimodal model, good for most tasks                    |
+    | gemini-1.5-flash-8B            | 1M tokens      | Fastest, most cost-efficient, good for high-frequency tasks       |
+    | gemini-1.5-pro                 | 2M tokens      | Best performing, wide variety of reasoning tasks including logical reasoning, coding, and creative collaboration |
   </Accordion>
 
   <Accordion title="Azure">

docs/how-to/arize-phoenix-observability.mdx

@@ -68,7 +68,13 @@ We'll create a CrewAI application where two agents collaborate to research and w
 ```python
 from crewai import Agent, Crew, Process, Task
 from crewai_tools import SerperDevTool
+from openinference.instrumentation.crewai import CrewAIInstrumentor
+from phoenix.otel import register
 
+# setup monitoring for your crew
+tracer_provider = register(
+    endpoint="http://localhost:6006/v1/traces")
+CrewAIInstrumentor().instrument(skip_dep_check=True, tracer_provider=tracer_provider)
 search_tool = SerperDevTool()
 
 # Define your agents with roles and goals

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "crewai"
-version = "0.118.0"
+version = "0.119.0"
 description = "Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks."
 readme = "README.md"
 requires-python = ">=3.10,<3.13"
@@ -45,7 +45,7 @@ Documentation = "https://docs.crewai.com"
 Repository = "https://github.com/crewAIInc/crewAI"
 
 [project.optional-dependencies]
-tools = ["crewai-tools~=0.42.2"]
+tools = ["crewai-tools~=0.44.0"]
 embeddings = [
     "tiktoken~=0.7.0"
 ]

src/crewai/__init__.py

@@ -17,7 +17,7 @@ warnings.filterwarnings(
     category=UserWarning,
     module="pydantic.main",
 )
-__version__ = "0.118.0"
+__version__ = "0.119.0"
 __all__ = [
     "Agent",
     "Crew",

src/crewai/agent.py

@@ -173,6 +173,7 @@ class Agent(BaseAgent):
                         collection_name=self.role,
                         storage=self.knowledge_storage or None,
                     )
+                    self.knowledge.add_sources()
         except (TypeError, ValueError) as e:
             raise ValueError(f"Invalid Knowledge Configuration: {str(e)}")
 
@@ -316,6 +317,45 @@ class Agent(BaseAgent):
                         error=str(e),
                     ),
                 )
+        elif self.crew and hasattr(self.crew, "knowledge") and self.crew.knowledge:
+            crewai_event_bus.emit(
+                self,
+                event=KnowledgeRetrievalStartedEvent(
+                    agent=self,
+                ),
+            )
+            try:
+                self.knowledge_search_query = self._get_knowledge_search_query(
+                    task_prompt
+                )
+                if self.knowledge_search_query:
+                    knowledge_snippets = self.crew.query_knowledge(
+                        [self.knowledge_search_query], **knowledge_config
+                    )
+                    if knowledge_snippets:
+                        self.crew_knowledge_context = extract_knowledge_context(
+                            knowledge_snippets
+                        )
+                        if self.crew_knowledge_context:
+                            task_prompt += self.crew_knowledge_context
+
+                    crewai_event_bus.emit(
+                        self,
+                        event=KnowledgeRetrievalCompletedEvent(
+                            query=self.knowledge_search_query,
+                            agent=self,
+                            retrieved_knowledge=self.crew_knowledge_context or "",
+                        ),
+                    )
+            except Exception as e:
+                crewai_event_bus.emit(
+                    self,
+                    event=KnowledgeSearchQueryFailedEvent(
+                        query=self.knowledge_search_query or "",
+                        agent=self,
+                        error=str(e),
+                    ),
+                )
 
         tools = tools or self.tools or []
         self.create_agent_executor(tools=tools, task=task)

src/crewai/cli/constants.py

@@ -13,7 +13,7 @@ ENV_VARS = {
     ],
     "gemini": [
         {
-            "prompt": "Enter your GEMINI API key (press Enter to skip)",
+            "prompt": "Enter your GEMINI API key from https://ai.dev/apikey (press Enter to skip)",
             "key_name": "GEMINI_API_KEY",
         }
     ],

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,<3.13"
 dependencies = [
-    "crewai[tools]>=0.118.0,<1.0.0"
+    "crewai[tools]>=0.119.0,<1.0.0"
 ]
 
 [project.scripts]

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

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.13"
 dependencies = [
-    "crewai[tools]>=0.118.0,<1.0.0",
+    "crewai[tools]>=0.119.0,<1.0.0",
 ]
 
 [project.scripts]

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

@@ -5,7 +5,7 @@ description = "Power up your crews with {{folder_name}}"
 readme = "README.md"
 requires-python = ">=3.10,<3.13"
 dependencies = [
-    "crewai[tools]>=0.118.0"
+    "crewai[tools]>=0.119.0"
 ]
 
 [tool.crewai]

tests/test_agent_without_knowledge_uses_crew_knowledge.py

@@ -0,0 +1,46 @@
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+from crewai import LLM, Agent, Crew, Process, Task
+from crewai.knowledge.knowledge import Knowledge
+from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
+
+
+def test_agent_without_knowledge_uses_crew_knowledge():
+    """Test that an agent without knowledge sources can use crew knowledge sources."""
+    content = "John is 30 years old and lives in San Francisco."
+    string_source = StringKnowledgeSource(content=content)
+
+    agent = Agent(
+        role="Information Agent",
+        goal="Provide information based on knowledge sources",
+        backstory="You have access to specific knowledge sources.",
+        llm=LLM(model="gpt-4o-mini"),
+    )
+
+    task = Task(
+        description="How old is John and where does he live?",
+        expected_output="John's age and location.",
+        agent=agent,
+    )
+
+    with patch('crewai.knowledge.knowledge.Knowledge.query', return_value=[{"context": content}]) as mock_query:
+        crew = Crew(
+            agents=[agent],
+            tasks=[task],
+            process=Process.sequential,
+            knowledge_sources=[string_source],
+        )
+        
+        agent.crew = crew
+        
+        with patch.object(Agent, '_get_knowledge_search_query', return_value="test query"):
+            with patch.object(Agent, '_execute_without_timeout', return_value="John is 30 years old and lives in San Francisco."):
+                result = agent.execute_task(task)
+                
+                assert mock_query.called
+                
+                assert hasattr(agent, 'crew_knowledge_context')
+                
+                assert "John" in result

uv.lock

@@ -738,7 +738,7 @@ wheels = [
 
 [[package]]
 name = "crewai"
-version = "0.118.0"
+version = "0.119.0"
 source = { editable = "." }
 dependencies = [
     { name = "appdirs" },
@@ -828,7 +828,7 @@ requires-dist = [
     { name = "blinker", specifier = ">=1.9.0" },
     { name = "chromadb", specifier = ">=0.5.23" },
     { name = "click", specifier = ">=8.1.7" },
-    { name = "crewai-tools", marker = "extra == 'tools'", specifier = "~=0.42.2" },
+    { name = "crewai-tools", marker = "extra == 'tools'", specifier = "~=0.44.0" },
     { name = "docling", marker = "extra == 'docling'", specifier = ">=2.12.0" },
     { name = "fastembed", marker = "extra == 'fastembed'", specifier = ">=0.4.1" },
     { name = "instructor", specifier = ">=1.3.3" },
@@ -879,7 +879,7 @@ dev = [
 
 [[package]]
 name = "crewai-tools"
-version = "0.42.2"
+version = "0.44.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "chromadb" },
@@ -894,9 +894,9 @@ dependencies = [
     { name = "pytube" },
     { name = "requests" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/17/34/9e63e2db53d8f5c30353f271a3240687a48e55204bbd176a057c0b7658c8/crewai_tools-0.42.2.tar.gz", hash = "sha256:69365ffb168cccfea970e09b308905aa5007cfec60024d731ffac1362a0153c0", size = 754967 }
+sdist = { url = "https://files.pythonhosted.org/packages/b8/1f/2977dc72628c1225bf5788ae22a65e5a53df384d19b197646d2c4760684e/crewai_tools-0.44.0.tar.gz", hash = "sha256:44e0c26079396503a326efdd9ff34bf369d410cbf95c362cc523db65b18f3c3a", size = 892004 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/4e/43/0f70b95350084e5cb1e1d74e9acb9e18a89ba675b1d579c787c2662baba7/crewai_tools-0.42.2-py3-none-any.whl", hash = "sha256:13727fb68f0efefd21edeb281be3d66ff2f5a3b5029d4e6adef388b11fd5846a", size = 583933 },
+    { url = "https://files.pythonhosted.org/packages/ba/80/b91aa837d06edbb472445ea3c92d7619518894fd3049d480e5fffbf0c21b/crewai_tools-0.44.0-py3-none-any.whl", hash = "sha256:119e2365fe66ee16e18a5e8e222994b19f76bafcc8c1bb87f61609c1e39b2463", size = 583462 },
 ]
 
 [[package]]