fix: extract Bedrock Converse API tool arguments from 'input' key (#4972)

The dict branch in _parse_native_tool_call used a truthy default '{}' for
func_info.get('arguments', '{}'), which prevented the or-chain from ever
reaching tool_call.get('input'). Bedrock returns tool calls as
{name, input, toolUseId} dicts (no 'function' key), so every tool call
received an empty dict instead of its actual arguments.

Fix: remove the default from get('arguments') so it returns None (falsy)
when the key is absent, allowing the fallback to 'input'.

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

conftest.py

@@ -43,6 +43,35 @@ def _patched_make_vcr_request(httpx_request: Any, **kwargs: Any) -> Any:
 httpx_stubs._make_vcr_request = _patched_make_vcr_request
 
 
+# Patch the response-side of VCR to fix httpx.ResponseNotRead errors.
+# VCR's _from_serialized_response mocks httpx.Response.read(), which prevents
+# the response's internal _content attribute from being properly initialized.
+# When OpenAI's client (using with_raw_response) accesses response.content,
+# httpx raises ResponseNotRead because read() was never actually called.
+# This patch ensures _content is explicitly set after response creation.
+_original_from_serialized_response = getattr(
+    httpx_stubs, "_from_serialized_response", None
+)
+
+if _original_from_serialized_response is not None:
+
+    def _patched_from_serialized_response(
+        request: Any, serialized_response: Any, history: Any = None
+    ) -> Any:
+        """Patched version that ensures response._content is properly set."""
+        response = _original_from_serialized_response(request, serialized_response, history)
+        # Explicitly set _content to avoid ResponseNotRead errors
+        # The content was passed to the constructor but the mocked read() prevents
+        # proper initialization of the internal state
+        body_content = serialized_response.get("body", {}).get("string", b"")
+        if isinstance(body_content, str):
+            body_content = body_content.encode("utf-8")
+        response._content = body_content
+        return response
+
+    httpx_stubs._from_serialized_response = _patched_from_serialized_response
+
+
 @pytest.fixture(autouse=True, scope="function")
 def cleanup_event_handlers() -> Generator[None, Any, None]:
     """Clean up event bus handlers after each test to prevent test pollution."""

docs/en/changelog.mdx

@@ -4,6 +4,47 @@ description: "Product updates, improvements, and bug fixes for CrewAI"
 icon: "clock"
 mode: "wide"
 ---
+<Update label="Mar 18, 2026">
+  ## v1.11.0
+
+  [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.11.0)
+
+  ## What's Changed
+
+  ### Documentation
+  - Update changelog and version for v1.11.0rc2
+
+  ## Contributors
+
+  @greysonlalonde
+
+</Update>
+
+<Update label="Mar 17, 2026">
+  ## v1.11.0rc2
+
+  [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.11.0rc2)
+
+  ## What's Changed
+
+  ### Bug Fixes
+  - Enhance LLM response handling and serialization.
+  - Upgrade vulnerable transitive dependencies (authlib, PyJWT, snowflake-connector-python).
+  - Replace `os.system` with `subprocess.run` in unsafe mode pip install.
+
+  ### Documentation
+  - Update Exa Search Tool page with improved naming, description, and configuration options.
+  - Add Custom MCP Servers in How-To Guide.
+  - Update OTEL collectors documentation.
+  - Update MCP documentation.
+  - Update changelog and version for v1.11.0rc1.
+
+  ## Contributors
+
+  @10ishq, @greysonlalonde, @joaomdmoura, @lucasgomide, @mattatcha, @theCyberTech, @vinibrsl
+
+</Update>
+
 <Update label="Mar 15, 2026">
   ## v1.11.0rc1
 

docs/en/enterprise/guides/capture_telemetry_logs.mdx

@@ -1,30 +1,39 @@
 ---
-title: "Open Telemetry Logs"
-description: "Understand how to capture telemetry logs from your CrewAI AMP deployments"
+title: "OpenTelemetry Export"
+description: "Export traces and logs from your CrewAI AMP deployments to your own OpenTelemetry collector"
 icon: "magnifying-glass-chart"
 mode: "wide"
 ---
 
-CrewAI AMP provides a powerful way to capture telemetry logs from your deployments. This allows you to monitor the performance of your agents and workflows, and to debug issues that may arise.
+CrewAI AMP can export OpenTelemetry **traces** and **logs** from your deployments directly to your own collector. This lets you monitor agent performance, track LLM calls, and debug issues using your existing observability stack.
+
+Telemetry data follows the [OpenTelemetry GenAI semantic conventions](https://opentelemetry.io/docs/specs/semconv/gen-ai/) plus additional CrewAI-specific attributes.
 
 ## Prerequisites
 
 <CardGroup cols={2}>
-  <Card title="ENTERPRISE OTEL SETUP enabled" icon="users">
-    Your organization should have ENTERPRISE OTEL SETUP enabled
+  <Card title="CrewAI AMP account" icon="users">
+    Your organization must have an active CrewAI AMP account.
   </Card>
-  <Card title="OTEL collector setup" icon="server">
-    Your organization should have an OTEL collector setup or a provider like
-    Datadog log intake setup
+  <Card title="OpenTelemetry collector" icon="server">
+    You need an OpenTelemetry-compatible collector endpoint (e.g., your own OTel Collector, Datadog, Grafana, or any OTLP-compatible backend).
   </Card>
 </CardGroup>
 
-## How to capture telemetry logs
+## Setting up a collector
 
-1. Go to settings/organization tab
-2. Configure your OTEL collector setup
-3. Save
+1. In CrewAI AMP, go to **Settings** > **OpenTelemetry Collectors**.
+2. Click **Add Collector**.
+3. Select an integration type — **OpenTelemetry Traces** or **OpenTelemetry Logs**.
+4. Configure the connection:
+   - **Endpoint** — Your collector's OTLP endpoint (e.g., `https://otel-collector.example.com:4317`).
+   - **Service Name** — A name to identify this service in your observability platform.
+   - **Custom Headers** *(optional)* — Add authentication or routing headers as key-value pairs.
+   - **Certificate** *(optional)* — Provide a TLS certificate if your collector requires one.
+5. Click **Save**.
 
-Example to setup OTEL log collection capture to Datadog.
+<Frame>![OpenTelemetry Collector Configuration](/images/crewai-otel-collector-config.png)</Frame>
 
-<Frame>![Capture Telemetry Logs](/images/crewai-otel-export.png)</Frame>
+<Tip>
+  You can add multiple collectors — for example, one for traces and another for logs, or send to different backends for different purposes.
+</Tip>

docs/en/enterprise/guides/custom-mcp-server.mdx

@@ -0,0 +1,136 @@
+---
+title: "Custom MCP Servers"
+description: "Connect your own MCP servers to CrewAI AMP with public access, API key authentication, or OAuth 2.0"
+icon: "plug"
+mode: "wide"
+---
+
+CrewAI AMP supports connecting to any MCP server that implements the [Model Context Protocol](https://modelcontextprotocol.io/). You can bring public servers that require no authentication, servers protected by an API key or bearer token, and servers that use OAuth 2.0 for secure delegated access.
+
+## Prerequisites
+
+<CardGroup cols={2}>
+  <Card title="CrewAI AMP Account" icon="user">
+    You need an active [CrewAI AMP](https://app.crewai.com) account.
+  </Card>
+  <Card title="MCP Server URL" icon="link">
+    The URL of the MCP server you want to connect. The server must be accessible from the internet and support Streamable HTTP transport.
+  </Card>
+</CardGroup>
+
+## Adding a Custom MCP Server
+
+<Steps>
+    <Step title="Open Tools & Integrations">
+        Navigate to **Tools & Integrations** in the left sidebar of CrewAI AMP, then select the **Connections** tab.
+    </Step>
+
+    <Step title="Start adding a Custom MCP Server">
+        Click the **Add Custom MCP Server** button. A dialog will appear with the configuration form.
+    </Step>
+
+    <Step title="Fill in the basic information">
+        - **Name** (required): A descriptive name for your MCP server (e.g., "My Internal Tools Server").
+        - **Description**: An optional summary of what this MCP server provides.
+        - **Server URL** (required): The full URL to your MCP server endpoint (e.g., `https://my-server.example.com/mcp`).
+    </Step>
+
+    <Step title="Choose an authentication method">
+        Select one of the three available authentication methods based on how your MCP server is secured. See the sections below for details on each method.
+    </Step>
+
+    <Step title="Add custom headers (optional)">
+        If your MCP server requires additional headers on every request (e.g., tenant identifiers or routing headers), click **+ Add Header** and provide the header name and value. You can add multiple custom headers.
+    </Step>
+
+    <Step title="Create the connection">
+        Click **Create MCP Server** to save the connection. Your custom MCP server will now appear in the Connections list and its tools will be available for use in your crews.
+    </Step>
+</Steps>
+
+## Authentication Methods
+
+### No Authentication
+
+Choose this option when your MCP server is publicly accessible and does not require any credentials. This is common for open-source or internal servers running behind a VPN.
+
+### Authentication Token
+
+Use this method when your MCP server is protected by an API key or bearer token.
+
+<Frame>
+    <img src="/images/enterprise/custom-mcp-auth-token.png" alt="Custom MCP Server with Authentication Token" />
+</Frame>
+
+| Field | Required | Description |
+|-------|----------|-------------|
+| **Header Name** | Yes | The name of the HTTP header that carries the token (e.g., `X-API-Key`, `Authorization`). |
+| **Value** | Yes | Your API key or bearer token. |
+| **Add to** | No | Where to attach the credential — **Header** (default) or **Query parameter**. |
+
+<Tip>
+If your server expects a `Bearer` token in the `Authorization` header, set the Header Name to `Authorization` and the Value to `Bearer <your-token>`.
+</Tip>
+
+### OAuth 2.0
+
+Use this method for MCP servers that require OAuth 2.0 authorization. CrewAI will handle the full OAuth flow, including token refresh.
+
+<Frame>
+    <img src="/images/enterprise/custom-mcp-oauth.png" alt="Custom MCP Server with OAuth 2.0" />
+</Frame>
+
+| Field | Required | Description |
+|-------|----------|-------------|
+| **Redirect URI** | — | Pre-filled and read-only. Copy this URI and register it as an authorized redirect URI in your OAuth provider. |
+| **Authorization Endpoint** | Yes | The URL where users are sent to authorize access (e.g., `https://auth.example.com/oauth/authorize`). |
+| **Token Endpoint** | Yes | The URL used to exchange the authorization code for an access token (e.g., `https://auth.example.com/oauth/token`). |
+| **Client ID** | Yes | The OAuth client ID issued by your provider. |
+| **Client Secret** | No | The OAuth client secret. Not required for public clients using PKCE. |
+| **Scopes** | No | Space-separated list of scopes to request (e.g., `read write`). |
+| **Token Auth Method** | No | How the client credentials are sent when exchanging tokens — **Standard (POST body)** or **Basic Auth (header)**. Defaults to Standard. |
+| **PKCE Supported** | No | Enable if your OAuth provider supports Proof Key for Code Exchange. Recommended for improved security. |
+
+<Info>
+**Discover OAuth Config**: If your OAuth provider supports OpenID Connect Discovery, click the **Discover OAuth Config** link to auto-populate the authorization and token endpoints from the provider's `/.well-known/openid-configuration` URL.
+</Info>
+
+#### Setting Up OAuth 2.0 Step by Step
+
+<Steps>
+    <Step title="Register the redirect URI">
+        Copy the **Redirect URI** shown in the form and add it as an authorized redirect URI in your OAuth provider's application settings.
+    </Step>
+
+    <Step title="Enter endpoints and credentials">
+        Fill in the **Authorization Endpoint**, **Token Endpoint**, **Client ID**, and optionally the **Client Secret** and **Scopes**.
+    </Step>
+
+    <Step title="Configure token exchange method">
+        Select the appropriate **Token Auth Method**. Most providers use the default **Standard (POST body)**. Some older providers require **Basic Auth (header)**.
+    </Step>
+
+    <Step title="Enable PKCE (recommended)">
+        Check **PKCE Supported** if your provider supports it. PKCE adds an extra layer of security to the authorization code flow and is recommended for all new integrations.
+    </Step>
+
+    <Step title="Create and authorize">
+        Click **Create MCP Server**. You will be redirected to your OAuth provider to authorize access. Once authorized, CrewAI will store the tokens and automatically refresh them as needed.
+    </Step>
+</Steps>
+
+## Using Your Custom MCP Server
+
+Once connected, your custom MCP server's tools appear alongside built-in connections on the **Tools & Integrations** page. You can:
+
+- **Assign tools to agents** in your crews just like any other CrewAI tool.
+- **Manage visibility** to control which team members can use the server.
+- **Edit or remove** the connection at any time from the Connections list.
+
+<Warning>
+If your MCP server becomes unreachable or the credentials expire, tool calls using that server will fail. Make sure the server URL is stable and credentials are kept up to date.
+</Warning>
+
+<Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
+  Contact our support team for assistance with custom MCP server configuration or troubleshooting.
+</Card>

docs/en/guides/tools/publish-custom-tools.mdx

@@ -0,0 +1,244 @@
+---
+title: Publish Custom Tools
+description: How to build, package, and publish your own CrewAI-compatible tools to PyPI so any CrewAI user can install and use them.
+icon: box-open
+mode: "wide"
+---
+
+## Overview
+
+CrewAI's tool system is designed to be extended. If you've built a tool that could benefit others, you can package it as a standalone Python library, publish it to PyPI, and make it available to any CrewAI user — no PR to the CrewAI repo required.
+
+This guide walks through the full process: implementing the tools contract, structuring your package, and publishing to PyPI.
+
+<Note type="info" title="Not looking to publish?">
+If you just need a custom tool for your own project, see the [Create Custom Tools](/en/learn/create-custom-tools) guide instead.
+</Note>
+
+## The Tools Contract
+
+Every CrewAI tool must satisfy one of two interfaces:
+
+### Option 1: Subclass `BaseTool`
+
+Subclass `crewai.tools.BaseTool` and implement the `_run` method. Define `name`, `description`, and optionally an `args_schema` for input validation.
+
+```python
+from crewai.tools import BaseTool
+from pydantic import BaseModel, Field
+
+
+class GeolocateInput(BaseModel):
+    """Input schema for GeolocateTool."""
+    address: str = Field(..., description="The street address to geolocate.")
+
+
+class GeolocateTool(BaseTool):
+    name: str = "Geolocate"
+    description: str = "Converts a street address into latitude/longitude coordinates."
+    args_schema: type[BaseModel] = GeolocateInput
+
+    def _run(self, address: str) -> str:
+        # Your implementation here
+        return f"40.7128, -74.0060"
+```
+
+### Option 2: Use the `@tool` Decorator
+
+For simpler tools, the `@tool` decorator turns a function into a CrewAI tool. The function **must** have a docstring (used as the tool description) and type annotations.
+
+```python
+from crewai.tools import tool
+
+
+@tool("Geolocate")
+def geolocate(address: str) -> str:
+    """Converts a street address into latitude/longitude coordinates."""
+    return "40.7128, -74.0060"
+```
+
+### Key Requirements
+
+Regardless of which approach you use, your tool must:
+
+- Have a **`name`** — a short, descriptive identifier.
+- Have a **`description`** — tells the agent when and how to use the tool. This directly affects how well agents use your tool, so be clear and specific.
+- Implement **`_run`** (BaseTool) or provide a **function body** (@tool) — the synchronous execution logic.
+- Use **type annotations** on all parameters and return values.
+- Return a **string** result (or something that can be meaningfully converted to one).
+
+### Optional: Async Support
+
+If your tool performs I/O-bound work, implement `_arun` for async execution:
+
+```python
+class GeolocateTool(BaseTool):
+    name: str = "Geolocate"
+    description: str = "Converts a street address into latitude/longitude coordinates."
+
+    def _run(self, address: str) -> str:
+        # Sync implementation
+        ...
+
+    async def _arun(self, address: str) -> str:
+        # Async implementation
+        ...
+```
+
+### Optional: Input Validation with `args_schema`
+
+Define a Pydantic model as your `args_schema` to get automatic input validation and clear error messages. If you don't provide one, CrewAI will infer it from your `_run` method's signature.
+
+```python
+from pydantic import BaseModel, Field
+
+
+class TranslateInput(BaseModel):
+    """Input schema for TranslateTool."""
+    text: str = Field(..., description="The text to translate.")
+    target_language: str = Field(
+        default="en",
+        description="ISO 639-1 language code for the target language.",
+    )
+```
+
+Explicit schemas are recommended for published tools — they produce better agent behavior and clearer documentation for your users.
+
+### Optional: Environment Variables
+
+If your tool requires API keys or other configuration, declare them with `env_vars` so users know what to set:
+
+```python
+from crewai.tools import BaseTool, EnvVar
+
+
+class GeolocateTool(BaseTool):
+    name: str = "Geolocate"
+    description: str = "Converts a street address into latitude/longitude coordinates."
+    env_vars: list[EnvVar] = [
+        EnvVar(
+            name="GEOCODING_API_KEY",
+            description="API key for the geocoding service.",
+            required=True,
+        ),
+    ]
+
+    def _run(self, address: str) -> str:
+        ...
+```
+
+## Package Structure
+
+Structure your project as a standard Python package. Here's a recommended layout:
+
+```
+crewai-geolocate/
+├── pyproject.toml
+├── LICENSE
+├── README.md
+└── src/
+    └── crewai_geolocate/
+        ├── __init__.py
+        └── tools.py
+```
+
+### `pyproject.toml`
+
+```toml
+[project]
+name = "crewai-geolocate"
+version = "0.1.0"
+description = "A CrewAI tool for geolocating street addresses."
+requires-python = ">=3.10"
+dependencies = [
+    "crewai",
+]
+
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+```
+
+Declare `crewai` as a dependency so users get a compatible version automatically.
+
+### `__init__.py`
+
+Re-export your tool classes so users can import them directly:
+
+```python
+from crewai_geolocate.tools import GeolocateTool
+
+__all__ = ["GeolocateTool"]
+```
+
+### Naming Conventions
+
+- **Package name**: Use the prefix `crewai-` (e.g., `crewai-geolocate`). This makes your tool discoverable when users search PyPI.
+- **Module name**: Use underscores (e.g., `crewai_geolocate`).
+- **Tool class name**: Use PascalCase ending in `Tool` (e.g., `GeolocateTool`).
+
+## Testing Your Tool
+
+Before publishing, verify your tool works within a crew:
+
+```python
+from crewai import Agent, Crew, Task
+from crewai_geolocate import GeolocateTool
+
+agent = Agent(
+    role="Location Analyst",
+    goal="Find coordinates for given addresses.",
+    backstory="An expert in geospatial data.",
+    tools=[GeolocateTool()],
+)
+
+task = Task(
+    description="Find the coordinates of 1600 Pennsylvania Avenue, Washington, DC.",
+    expected_output="The latitude and longitude of the address.",
+    agent=agent,
+)
+
+crew = Crew(agents=[agent], tasks=[task])
+result = crew.kickoff()
+print(result)
+```
+
+## Publishing to PyPI
+
+Once your tool is tested and ready:
+
+```bash
+# Build the package
+uv build
+
+# Publish to PyPI
+uv publish
+```
+
+If this is your first time publishing, you'll need a [PyPI account](https://pypi.org/account/register/) and an [API token](https://pypi.org/help/#apitoken).
+
+### After Publishing
+
+Users can install your tool with:
+
+```bash
+pip install crewai-geolocate
+```
+
+Or with uv:
+
+```bash
+uv add crewai-geolocate
+```
+
+Then use it in their crews:
+
+```python
+from crewai_geolocate import GeolocateTool
+
+agent = Agent(
+    role="Location Analyst",
+    tools=[GeolocateTool()],
+    # ...
+)
+```

docs/en/learn/create-custom-tools.mdx

@@ -11,6 +11,10 @@ This guide provides detailed instructions on creating custom tools for the CrewA
 incorporating the latest functionalities such as tool delegation, error handling, and dynamic tool calling. It also highlights the importance of collaboration tools,
 enabling agents to perform a wide range of actions.
 
+<Tip>
+  **Want to publish your tool for the community?** If you're building a tool that others could benefit from, check out the [Publish Custom Tools](/en/guides/tools/publish-custom-tools) guide to learn how to package and distribute your tool on PyPI.
+</Tip>
+
 ### Subclassing `BaseTool`
 
 To create a personalized tool, inherit from `BaseTool` and define the necessary attributes, including the `args_schema` for input validation, and the `_run` method.

docs/en/tools/search-research/exasearchtool.mdx

@@ -1,53 +1,110 @@
 ---
-title: EXA Search Web Loader
-description: The `EXASearchTool` is designed to perform a semantic search for a specified query from a text's content across the internet.
-icon: globe-pointer
+title: "Exa Search Tool"
+description: "Search the web using the Exa Search API to find the most relevant results for any query, with options for full page content, highlights, and summaries."
+icon: "magnifying-glass"
 mode: "wide"
 ---
 
-# `EXASearchTool`
-
-## Description
-
-The EXASearchTool is designed to perform a semantic search for a specified query from a text's content across the internet. 
-It utilizes the [exa.ai](https://exa.ai/) API to fetch and display the most relevant search results based on the query provided by the user.
+The `EXASearchTool` lets CrewAI agents search the web using the [Exa](https://exa.ai/) search API. It returns the most relevant results for any query, with options for full page content and AI-generated summaries.
 
 ## Installation
 
-To incorporate this tool into your project, follow the installation instructions below:
+Install the CrewAI tools package:
 
 ```shell
 pip install 'crewai[tools]'
 ```
 
-## Example
+## Environment Variables
 
-The following example demonstrates how to initialize the tool and execute a search with a given query:
+Set your Exa API key as an environment variable:
 
-```python Code
-from crewai_tools import EXASearchTool
-
-# Initialize the tool for internet searching capabilities
-tool = EXASearchTool()
+```bash
+export EXA_API_KEY='your_exa_api_key'
 ```
 
-## Steps to Get Started
+Get an API key from the [Exa dashboard](https://dashboard.exa.ai/api-keys).
 
-To effectively use the EXASearchTool, follow these steps:
+## Example Usage
 
-<Steps>
-    <Step title="Package Installation">
-        Confirm that the `crewai[tools]` package is installed in your Python environment.
-    </Step>
-    <Step title="API Key Acquisition">
-        Acquire a [exa.ai](https://exa.ai/) API key by registering for a free account at [exa.ai](https://exa.ai/).
-    </Step>
-    <Step title="Environment Configuration">
-        Store your obtained API key in an environment variable named `EXA_API_KEY` to facilitate its use by the tool.
-    </Step>
-</Steps>
+Here's how to use the `EXASearchTool` within a CrewAI agent:
 
-## Conclusion
+```python
+import os
+from crewai import Agent, Task, Crew
+from crewai_tools import EXASearchTool
 
-By integrating the `EXASearchTool` into Python projects, users gain the ability to conduct real-time, relevant searches across the internet directly from their applications. 
-By adhering to the setup and usage guidelines provided, incorporating this tool into projects is streamlined and straightforward.
+# Initialize the tool
+exa_tool = EXASearchTool()
+
+# Create an agent that uses the tool
+researcher = Agent(
+    role='Research Analyst',
+    goal='Find the latest information on any topic',
+    backstory='An expert researcher who finds the most relevant and up-to-date information.',
+    tools=[exa_tool],
+    verbose=True
+)
+
+# Create a task for the agent
+research_task = Task(
+    description='Find the top 3 recent breakthroughs in quantum computing.',
+    expected_output='A summary of the top 3 breakthroughs with source URLs.',
+    agent=researcher
+)
+
+# Form the crew and kick it off
+crew = Crew(
+    agents=[researcher],
+    tasks=[research_task],
+    verbose=True
+)
+
+result = crew.kickoff()
+print(result)
+```
+
+## Configuration Options
+
+The `EXASearchTool` accepts the following parameters during initialization:
+
+- `type` (str, optional): The search type to use. Defaults to `"auto"`. Options: `"auto"`, `"instant"`, `"fast"`, `"deep"`.
+- `content` (bool, optional): Whether to include full page content in results. Defaults to `False`.
+- `summary` (bool, optional): Whether to include AI-generated summaries of each result. Requires `content=True`. Defaults to `False`.
+- `api_key` (str, optional): Your Exa API key. Falls back to the `EXA_API_KEY` environment variable if not provided.
+- `base_url` (str, optional): Custom API server URL. Falls back to the `EXA_BASE_URL` environment variable if not provided.
+
+When calling the tool (or when an agent invokes it), the following search parameters are available:
+
+- `search_query` (str): **Required**. The search query string.
+- `start_published_date` (str, optional): Filter results published after this date (ISO 8601 format, e.g. `"2024-01-01"`).
+- `end_published_date` (str, optional): Filter results published before this date (ISO 8601 format).
+- `include_domains` (list[str], optional): A list of domains to restrict the search to.
+
+## Advanced Usage
+
+You can configure the tool with custom parameters for richer results:
+
+```python
+# Get full page content with AI summaries
+exa_tool = EXASearchTool(
+    content=True,
+    summary=True,
+    type="deep"
+)
+
+# Use it in an agent
+agent = Agent(
+    role="Deep Researcher",
+    goal="Conduct thorough research with full content and summaries",
+    tools=[exa_tool]
+)
+```
+
+## Features
+
+- **Semantic Search**: Find results based on meaning, not just keywords
+- **Full Content Retrieval**: Get the full text of web pages alongside search results
+- **AI Summaries**: Get concise, AI-generated summaries of each result
+- **Date Filtering**: Limit results to specific time periods with published date filters
+- **Domain Filtering**: Restrict searches to specific domains

docs/ko/changelog.mdx

@@ -4,6 +4,47 @@ description: "CrewAI의 제품 업데이트, 개선 사항 및 버그 수정"
 icon: "clock"
 mode: "wide"
 ---
+<Update label="2026년 3월 18일">
+  ## v1.11.0
+
+  [GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.11.0)
+
+  ## 변경 사항
+
+  ### 문서
+  - v1.11.0rc2에 대한 변경 로그 및 버전 업데이트
+
+  ## 기여자
+
+  @greysonlalonde
+
+</Update>
+
+<Update label="2026년 3월 17일">
+  ## v1.11.0rc2
+
+  [GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.11.0rc2)
+
+  ## 변경 사항
+
+  ### 버그 수정
+  - LLM 응답 처리 및 직렬화 개선.
+  - 취약한 전이 종속성(authlib, PyJWT, snowflake-connector-python) 업그레이드.
+  - 안전하지 않은 모드에서 pip 설치 시 `os.system`을 `subprocess.run`으로 교체.
+
+  ### 문서
+  - 개선된 이름, 설명 및 구성 옵션으로 Exa 검색 도구 페이지 업데이트.
+  - 사용 방법 가이드에 사용자 지정 MCP 서버 추가.
+  - OTEL 수집기 문서 업데이트.
+  - MCP 문서 업데이트.
+  - v1.11.0rc1에 대한 변경 로그 및 버전 업데이트.
+
+  ## 기여자
+
+  @10ishq, @greysonlalonde, @joaomdmoura, @lucasgomide, @mattatcha, @theCyberTech, @vinibrsl
+
+</Update>
+
 <Update label="2026년 3월 15일">
   ## v1.11.0rc1
 

docs/ko/enterprise/guides/capture_telemetry_logs.mdx

@@ -0,0 +1,39 @@
+---
+title: "OpenTelemetry 내보내기"
+description: "CrewAI AMP 배포에서 자체 OpenTelemetry 수집기로 트레이스와 로그를 내보내기"
+icon: "magnifying-glass-chart"
+mode: "wide"
+---
+
+CrewAI AMP는 배포에서 OpenTelemetry **트레이스**와 **로그**를 자체 수집기로 직접 내보낼 수 있습니다. 이를 통해 기존 관측 가능성 스택을 사용하여 에이전트 성능을 모니터링하고, LLM 호출을 추적하고, 문제를 디버깅할 수 있습니다.
+
+텔레메트리 데이터는 [OpenTelemetry GenAI 시맨틱 규칙](https://opentelemetry.io/docs/specs/semconv/gen-ai/)과 추가적인 CrewAI 전용 속성을 따릅니다.
+
+## 사전 요구 사항
+
+<CardGroup cols={2}>
+  <Card title="CrewAI AMP 계정" icon="users">
+    조직에 활성 CrewAI AMP 계정이 있어야 합니다.
+  </Card>
+  <Card title="OpenTelemetry 수집기" icon="server">
+    OpenTelemetry 호환 수집기 엔드포인트가 필요합니다 (예: 자체 OTel Collector, Datadog, Grafana 또는 OTLP 호환 백엔드).
+  </Card>
+</CardGroup>
+
+## 수집기 설정
+
+1. CrewAI AMP에서 **Settings** > **OpenTelemetry Collectors**로 이동합니다.
+2. **Add Collector**를 클릭합니다.
+3. 통합 유형을 선택합니다 — **OpenTelemetry Traces** 또는 **OpenTelemetry Logs**.
+4. 연결을 구성합니다:
+   - **Endpoint** — 수집기의 OTLP 엔드포인트 (예: `https://otel-collector.example.com:4317`).
+   - **Service Name** — 관측 가능성 플랫폼에서 이 서비스를 식별하기 위한 이름.
+   - **Custom Headers** *(선택 사항)* — 인증 또는 라우팅 헤더를 키-값 쌍으로 추가합니다.
+   - **Certificate** *(선택 사항)* — 수집기에서 TLS 인증서가 필요한 경우 제공합니다.
+5. **Save**를 클릭합니다.
+
+<Frame>![OpenTelemetry 수집기 구성](/images/crewai-otel-collector-config.png)</Frame>
+
+<Tip>
+  여러 수집기를 추가할 수 있습니다 — 예를 들어, 트레이스용 하나와 로그용 하나를 추가하거나, 다른 목적을 위해 다른 백엔드로 전송할 수 있습니다.
+</Tip>

docs/ko/enterprise/guides/custom-mcp-server.mdx

@@ -0,0 +1,136 @@
+---
+title: "커스텀 MCP 서버"
+description: "공개 액세스, API 키 인증 또는 OAuth 2.0을 사용하여 자체 MCP 서버를 CrewAI AMP에 연결하세요"
+icon: "plug"
+mode: "wide"
+---
+
+CrewAI AMP는 [Model Context Protocol](https://modelcontextprotocol.io/)을 구현하는 모든 MCP 서버에 연결할 수 있습니다. 인증이 필요 없는 공개 서버, API 키 또는 Bearer 토큰으로 보호되는 서버, OAuth 2.0을 사용하는 서버를 연결할 수 있습니다.
+
+## 사전 요구사항
+
+<CardGroup cols={2}>
+  <Card title="CrewAI AMP 계정" icon="user">
+    활성화된 [CrewAI AMP](https://app.crewai.com) 계정이 필요합니다.
+  </Card>
+  <Card title="MCP 서버 URL" icon="link">
+    연결하려는 MCP 서버의 URL입니다. 서버는 인터넷에서 접근 가능해야 하며 Streamable HTTP 전송을 지원해야 합니다.
+  </Card>
+</CardGroup>
+
+## 커스텀 MCP 서버 추가하기
+
+<Steps>
+    <Step title="Tools & Integrations 열기">
+        CrewAI AMP 왼쪽 사이드바에서 **Tools & Integrations**로 이동한 후 **Connections** 탭을 선택합니다.
+    </Step>
+
+    <Step title="커스텀 MCP 서버 추가 시작">
+        **Add Custom MCP Server** 버튼을 클릭합니다. 구성 양식이 포함된 대화 상자가 나타납니다.
+    </Step>
+
+    <Step title="기본 정보 입력">
+        - **Name** (필수): MCP 서버의 설명적 이름 (예: "내부 도구 서버").
+        - **Description**: 이 MCP 서버가 제공하는 기능에 대한 선택적 요약.
+        - **Server URL** (필수): MCP 서버 엔드포인트의 전체 URL (예: `https://my-server.example.com/mcp`).
+    </Step>
+
+    <Step title="인증 방법 선택">
+        MCP 서버의 보안 방식에 따라 세 가지 인증 방법 중 하나를 선택합니다. 각 방법에 대한 자세한 내용은 아래 섹션을 참조하세요.
+    </Step>
+
+    <Step title="커스텀 헤더 추가 (선택사항)">
+        MCP 서버가 모든 요청에 추가 헤더를 요구하는 경우 (예: 테넌트 식별자 또는 라우팅 헤더), **+ Add Header**를 클릭하고 헤더 이름과 값을 입력합니다. 여러 커스텀 헤더를 추가할 수 있습니다.
+    </Step>
+
+    <Step title="연결 생성">
+        **Create MCP Server**를 클릭하여 연결을 저장합니다. 커스텀 MCP 서버가 Connections 목록에 나타나고 해당 도구를 crew에서 사용할 수 있게 됩니다.
+    </Step>
+</Steps>
+
+## 인증 방법
+
+### 인증 없음
+
+MCP 서버가 공개적으로 접근 가능하고 자격 증명이 필요 없을 때 이 옵션을 선택합니다. 오픈 소스 서버나 VPN 뒤에서 실행되는 내부 서버에 일반적입니다.
+
+### 인증 토큰
+
+MCP 서버가 API 키 또는 Bearer 토큰으로 보호되는 경우 이 방법을 사용합니다.
+
+<Frame>
+    <img src="/images/enterprise/custom-mcp-auth-token.png" alt="인증 토큰을 사용하는 커스텀 MCP 서버" />
+</Frame>
+
+| 필드 | 필수 | 설명 |
+|------|------|------|
+| **Header Name** | 예 | 토큰을 전달하는 HTTP 헤더 이름 (예: `X-API-Key`, `Authorization`). |
+| **Value** | 예 | API 키 또는 Bearer 토큰. |
+| **Add to** | 아니오 | 자격 증명을 첨부할 위치 — **Header** (기본값) 또는 **Query parameter**. |
+
+<Tip>
+서버가 `Authorization` 헤더에 `Bearer` 토큰을 예상하는 경우, Header Name을 `Authorization`으로, Value를 `Bearer <토큰>`으로 설정하세요.
+</Tip>
+
+### OAuth 2.0
+
+OAuth 2.0 인증이 필요한 MCP 서버에 이 방법을 사용합니다. CrewAI가 토큰 갱신을 포함한 전체 OAuth 흐름을 처리합니다.
+
+<Frame>
+    <img src="/images/enterprise/custom-mcp-oauth.png" alt="OAuth 2.0을 사용하는 커스텀 MCP 서버" />
+</Frame>
+
+| 필드 | 필수 | 설명 |
+|------|------|------|
+| **Redirect URI** | — | 자동으로 채워지며 읽기 전용입니다. 이 URI를 복사하여 OAuth 제공자에 승인된 리디렉션 URI로 등록하세요. |
+| **Authorization Endpoint** | 예 | 사용자가 접근을 승인하기 위해 이동하는 URL (예: `https://auth.example.com/oauth/authorize`). |
+| **Token Endpoint** | 예 | 인증 코드를 액세스 토큰으로 교환하는 데 사용되는 URL (예: `https://auth.example.com/oauth/token`). |
+| **Client ID** | 예 | OAuth 제공자가 발급한 클라이언트 ID. |
+| **Client Secret** | 아니오 | OAuth 클라이언트 시크릿. PKCE를 사용하는 공개 클라이언트에는 필요하지 않습니다. |
+| **Scopes** | 아니오 | 요청할 스코프의 공백으로 구분된 목록 (예: `read write`). |
+| **Token Auth Method** | 아니오 | 토큰 교환 시 클라이언트 자격 증명을 보내는 방법 — **Standard (POST body)** 또는 **Basic Auth (header)**. 기본값은 Standard입니다. |
+| **PKCE Supported** | 아니오 | OAuth 제공자가 Proof Key for Code Exchange를 지원하는 경우 활성화합니다. 보안 강화를 위해 권장됩니다. |
+
+<Info>
+**Discover OAuth Config**: OAuth 제공자가 OpenID Connect Discovery를 지원하는 경우, **Discover OAuth Config** 링크를 클릭하여 제공자의 `/.well-known/openid-configuration` URL에서 인증 및 토큰 엔드포인트를 자동으로 채울 수 있습니다.
+</Info>
+
+#### OAuth 2.0 단계별 설정
+
+<Steps>
+    <Step title="리디렉션 URI 등록">
+        양식에 표시된 **Redirect URI**를 복사하여 OAuth 제공자의 애플리케이션 설정에서 승인된 리디렉션 URI로 추가합니다.
+    </Step>
+
+    <Step title="엔드포인트 및 자격 증명 입력">
+        **Authorization Endpoint**, **Token Endpoint**, **Client ID**를 입력하고, 선택적으로 **Client Secret**과 **Scopes**를 입력합니다.
+    </Step>
+
+    <Step title="토큰 교환 방법 구성">
+        적절한 **Token Auth Method**를 선택합니다. 대부분의 제공자는 기본값인 **Standard (POST body)**를 사용합니다. 일부 오래된 제공자는 **Basic Auth (header)**를 요구합니다.
+    </Step>
+
+    <Step title="PKCE 활성화 (권장)">
+        제공자가 지원하는 경우 **PKCE Supported**를 체크합니다. PKCE는 인증 코드 흐름에 추가 보안 계층을 제공하며 모든 새 통합에 권장됩니다.
+    </Step>
+
+    <Step title="생성 및 인증">
+        **Create MCP Server**를 클릭합니다. OAuth 제공자로 리디렉션되어 접근을 인증합니다. 인증 완료 후 CrewAI가 토큰을 저장하고 필요에 따라 자동으로 갱신합니다.
+    </Step>
+</Steps>
+
+## 커스텀 MCP 서버 사용하기
+
+연결이 완료되면 커스텀 MCP 서버의 도구가 **Tools & Integrations** 페이지에서 기본 제공 연결과 함께 표시됩니다. 다음을 수행할 수 있습니다:
+
+- 다른 CrewAI 도구와 마찬가지로 crew의 **에이전트에 도구를 할당**합니다.
+- **가시성을 관리**하여 어떤 팀원이 서버를 사용할 수 있는지 제어합니다.
+- Connections 목록에서 언제든지 연결을 **편집하거나 제거**합니다.
+
+<Warning>
+MCP 서버에 접근할 수 없거나 자격 증명이 만료되면 해당 서버를 사용하는 도구 호출이 실패합니다. 서버 URL이 안정적이고 자격 증명이 최신 상태인지 확인하세요.
+</Warning>
+
+<Card title="도움이 필요하신가요?" icon="headset" href="mailto:support@crewai.com">
+  커스텀 MCP 서버 구성 또는 문제 해결에 대한 도움이 필요하면 지원팀에 문의하세요.
+</Card>

docs/ko/guides/coding-tools/agents-md.mdx

@@ -0,0 +1,61 @@
+---
+title: 코딩 도구
+description: AGENTS.md를 사용하여 CrewAI 프로젝트 전반에서 코딩 에이전트와 IDE를 안내합니다.
+icon: terminal
+mode: "wide"
+---
+
+## AGENTS.md를 사용하는 이유
+
+`AGENTS.md`는 가벼운 저장소 로컬 지침 파일로, 코딩 에이전트에게 일관되고 프로젝트별 안내를 제공합니다. 프로젝트 루트에 배치하고 어시스턴트가 작업하는 방식(컨벤션, 명령어, 아키텍처 노트, 가드레일)에 대한 신뢰할 수 있는 소스로 활용하세요.
+
+## CLI로 프로젝트 생성
+
+CrewAI CLI를 사용하여 프로젝트를 스캐폴딩하면, `AGENTS.md`가 루트에 자동으로 추가됩니다.
+
+```bash
+# Crew
+crewai create crew my_crew
+
+# Flow
+crewai create flow my_flow
+
+# Tool repository
+crewai tool create my_tool
+```
+
+## 도구 설정: 어시스턴트에 AGENTS.md 연결
+
+### Codex
+
+Codex는 저장소에 배치된 `AGENTS.md` 파일로 안내할 수 있습니다. 컨벤션, 명령어, 워크플로우 기대치 등 지속적인 프로젝트 컨텍스트를 제공하는 데 사용하세요.
+
+### Claude Code
+
+Claude Code는 프로젝트 메모리를 `CLAUDE.md`에 저장합니다. `/init`으로 부트스트랩하고 `/memory`로 편집할 수 있습니다. Claude Code는 `CLAUDE.md` 내에서 임포트도 지원하므로, `@AGENTS.md`와 같은 한 줄을 추가하여 공유 지침을 중복 없이 가져올 수 있습니다.
+
+간단하게 다음과 같이 사용할 수 있습니다:
+
+```bash
+mv AGENTS.md CLAUDE.md
+```
+
+### Gemini CLI와 Google Antigravity
+
+Gemini CLI와 Antigravity는 저장소 루트 및 상위 디렉토리에서 프로젝트 컨텍스트 파일(기본값: `GEMINI.md`)을 로드합니다. Gemini CLI 설정에서 `context.fileName`을 설정하여 `AGENTS.md`를 대신(또는 추가로) 읽도록 구성할 수 있습니다. 예를 들어, `AGENTS.md`만 설정하거나 각 도구의 형식을 유지하고 싶다면 `AGENTS.md`와 `GEMINI.md`를 모두 포함할 수 있습니다.
+
+간단하게 다음과 같이 사용할 수 있습니다:
+
+```bash
+mv AGENTS.md GEMINI.md
+```
+
+### Cursor
+
+Cursor는 `AGENTS.md`를 프로젝트 지침 파일로 지원합니다. 프로젝트 루트에 배치하여 Cursor의 코딩 어시스턴트에 안내를 제공하세요.
+
+### Windsurf
+
+Claude Code는 Windsurf와의 공식 통합을 제공합니다. Windsurf 내에서 Claude Code를 사용하는 경우, 위의 Claude Code 안내를 따르고 `CLAUDE.md`에서 `AGENTS.md`를 임포트하세요.
+
+Windsurf의 네이티브 어시스턴트를 사용하는 경우, 프로젝트 규칙 또는 지침 기능(사용 가능한 경우)을 구성하여 `AGENTS.md`에서 읽거나 내용을 직접 붙여넣으세요.

docs/ko/guides/tools/publish-custom-tools.mdx

@@ -0,0 +1,244 @@
+---
+title: 커스텀 도구 배포하기
+description: PyPI에 게시할 수 있는 CrewAI 호환 도구를 빌드, 패키징, 배포하는 방법을 안내합니다.
+icon: box-open
+mode: "wide"
+---
+
+## 개요
+
+CrewAI의 도구 시스템은 확장 가능하도록 설계되었습니다. 다른 사용자에게도 유용한 도구를 만들었다면, 독립적인 Python 라이브러리로 패키징하여 PyPI에 게시하고 모든 CrewAI 사용자가 사용할 수 있도록 할 수 있습니다. CrewAI 저장소에 PR을 보낼 필요가 없습니다.
+
+이 가이드에서는 도구 계약 구현, 패키지 구조화, PyPI 게시까지의 전체 과정을 안내합니다.
+
+<Note type="info" title="배포할 계획이 없으신가요?">
+프로젝트 내에서만 사용할 커스텀 도구가 필요하다면 [커스텀 도구 생성](/ko/learn/create-custom-tools) 가이드를 참고하세요.
+</Note>
+
+## 도구 계약
+
+모든 CrewAI 도구는 다음 두 가지 인터페이스 중 하나를 충족해야 합니다:
+
+### 옵션 1: `BaseTool` 서브클래싱
+
+`crewai.tools.BaseTool`을 서브클래싱하고 `_run` 메서드를 구현합니다. `name`, `description`, 그리고 선택적으로 입력 검증을 위한 `args_schema`를 정의합니다.
+
+```python
+from crewai.tools import BaseTool
+from pydantic import BaseModel, Field
+
+
+class GeolocateInput(BaseModel):
+    """GeolocateTool의 입력 스키마."""
+    address: str = Field(..., description="지오코딩할 도로명 주소.")
+
+
+class GeolocateTool(BaseTool):
+    name: str = "Geolocate"
+    description: str = "도로명 주소를 위도/경도 좌표로 변환합니다."
+    args_schema: type[BaseModel] = GeolocateInput
+
+    def _run(self, address: str) -> str:
+        # 구현 로직
+        return f"40.7128, -74.0060"
+```
+
+### 옵션 2: `@tool` 데코레이터 사용
+
+간단한 도구의 경우, `@tool` 데코레이터로 함수를 CrewAI 도구로 변환할 수 있습니다. 함수에는 반드시 독스트링(도구 설명으로 사용됨)과 타입 어노테이션이 있어야 합니다.
+
+```python
+from crewai.tools import tool
+
+
+@tool("Geolocate")
+def geolocate(address: str) -> str:
+    """도로명 주소를 위도/경도 좌표로 변환합니다."""
+    return "40.7128, -74.0060"
+```
+
+### 핵심 요구사항
+
+어떤 방식을 사용하든, 도구는 다음을 충족해야 합니다:
+
+- **`name`** — 짧고 설명적인 식별자.
+- **`description`** — 에이전트에게 도구를 언제, 어떻게 사용할지 알려줍니다. 에이전트가 도구를 얼마나 잘 활용하는지에 직접적으로 영향을 미치므로 명확하고 구체적으로 작성하세요.
+- **`_run`** (BaseTool) 또는 **함수 본문** (@tool) 구현 — 동기 실행 로직.
+- 모든 매개변수와 반환 값에 **타입 어노테이션** 사용.
+- **문자열** 결과를 반환 (또는 의미 있게 문자열로 변환 가능한 값).
+
+### 선택사항: 비동기 지원
+
+I/O 바운드 작업을 수행하는 도구의 경우 비동기 실행을 위해 `_arun`을 구현합니다:
+
+```python
+class GeolocateTool(BaseTool):
+    name: str = "Geolocate"
+    description: str = "도로명 주소를 위도/경도 좌표로 변환합니다."
+
+    def _run(self, address: str) -> str:
+        # 동기 구현
+        ...
+
+    async def _arun(self, address: str) -> str:
+        # 비동기 구현
+        ...
+```
+
+### 선택사항: `args_schema`를 통한 입력 검증
+
+Pydantic 모델을 `args_schema`로 정의하면 자동 입력 검증과 명확한 에러 메시지를 받을 수 있습니다. 제공하지 않으면 CrewAI가 `_run` 메서드의 시그니처에서 추론합니다.
+
+```python
+from pydantic import BaseModel, Field
+
+
+class TranslateInput(BaseModel):
+    """TranslateTool의 입력 스키마."""
+    text: str = Field(..., description="번역할 텍스트.")
+    target_language: str = Field(
+        default="en",
+        description="대상 언어의 ISO 639-1 언어 코드.",
+    )
+```
+
+배포용 도구에는 명시적 스키마를 권장합니다 — 에이전트 동작이 개선되고 사용자에게 더 명확한 문서를 제공합니다.
+
+### 선택사항: 환경 변수
+
+도구에 API 키나 기타 설정이 필요한 경우, `env_vars`로 선언하여 사용자가 무엇을 설정해야 하는지 알 수 있도록 합니다:
+
+```python
+from crewai.tools import BaseTool, EnvVar
+
+
+class GeolocateTool(BaseTool):
+    name: str = "Geolocate"
+    description: str = "도로명 주소를 위도/경도 좌표로 변환합니다."
+    env_vars: list[EnvVar] = [
+        EnvVar(
+            name="GEOCODING_API_KEY",
+            description="지오코딩 서비스 API 키.",
+            required=True,
+        ),
+    ]
+
+    def _run(self, address: str) -> str:
+        ...
+```
+
+## 패키지 구조
+
+프로젝트를 표준 Python 패키지로 구성합니다. 권장 레이아웃:
+
+```
+crewai-geolocate/
+├── pyproject.toml
+├── LICENSE
+├── README.md
+└── src/
+    └── crewai_geolocate/
+        ├── __init__.py
+        └── tools.py
+```
+
+### `pyproject.toml`
+
+```toml
+[project]
+name = "crewai-geolocate"
+version = "0.1.0"
+description = "도로명 주소를 지오코딩하는 CrewAI 도구."
+requires-python = ">=3.10"
+dependencies = [
+    "crewai",
+]
+
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+```
+
+사용자가 자동으로 호환 버전을 받을 수 있도록 `crewai`를 의존성으로 선언합니다.
+
+### `__init__.py`
+
+사용자가 직접 import할 수 있도록 도구 클래스를 re-export합니다:
+
+```python
+from crewai_geolocate.tools import GeolocateTool
+
+__all__ = ["GeolocateTool"]
+```
+
+### 명명 규칙
+
+- **패키지 이름**: `crewai-` 접두사를 사용합니다 (예: `crewai-geolocate`). PyPI에서 검색할 때 도구를 쉽게 찾을 수 있습니다.
+- **모듈 이름**: 밑줄을 사용합니다 (예: `crewai_geolocate`).
+- **도구 클래스 이름**: `Tool`로 끝나는 PascalCase를 사용합니다 (예: `GeolocateTool`).
+
+## 도구 테스트
+
+게시 전에 도구가 크루 내에서 작동하는지 확인합니다:
+
+```python
+from crewai import Agent, Crew, Task
+from crewai_geolocate import GeolocateTool
+
+agent = Agent(
+    role="Location Analyst",
+    goal="주어진 주소의 좌표를 찾습니다.",
+    backstory="지리공간 데이터 전문가.",
+    tools=[GeolocateTool()],
+)
+
+task = Task(
+    description="1600 Pennsylvania Avenue, Washington, DC의 좌표를 찾으세요.",
+    expected_output="해당 주소의 위도와 경도.",
+    agent=agent,
+)
+
+crew = Crew(agents=[agent], tasks=[task])
+result = crew.kickoff()
+print(result)
+```
+
+## PyPI에 게시하기
+
+도구 테스트를 완료하고 준비가 되면:
+
+```bash
+# 패키지 빌드
+uv build
+
+# PyPI에 게시
+uv publish
+```
+
+처음 게시하는 경우 [PyPI 계정](https://pypi.org/account/register/)과 [API 토큰](https://pypi.org/help/#apitoken)이 필요합니다.
+
+### 게시 후
+
+사용자는 다음과 같이 도구를 설치할 수 있습니다:
+
+```bash
+pip install crewai-geolocate
+```
+
+또는 uv를 사용하여:
+
+```bash
+uv add crewai-geolocate
+```
+
+그런 다음 크루에서 사용합니다:
+
+```python
+from crewai_geolocate import GeolocateTool
+
+agent = Agent(
+    role="Location Analyst",
+    tools=[GeolocateTool()],
+    # ...
+)
+```

docs/ko/learn/create-custom-tools.mdx

@@ -9,6 +9,10 @@ mode: "wide"
 
 이 가이드는 CrewAI 프레임워크를 위한 커스텀 툴을 생성하는 방법과 최신 기능(툴 위임, 오류 처리, 동적 툴 호출 등)을 통합하여 이러한 툴을 효율적으로 관리하고 활용하는 방법에 대해 자세히 안내합니다. 또한 협업 툴의 중요성을 강조하며, 에이전트가 다양한 작업을 수행할 수 있도록 지원합니다.
 
+<Tip>
+  **커뮤니티에 도구를 배포하고 싶으신가요?** 다른 사용자에게도 유용한 도구를 만들고 있다면, [커스텀 도구 배포하기](/ko/guides/tools/publish-custom-tools) 가이드에서 도구를 패키징하고 PyPI에 배포하는 방법을 알아보세요.
+</Tip>
+
 ### `BaseTool` 서브클래싱
 
 개인화된 툴을 생성하려면 `BaseTool`을 상속받고, 입력 검증을 위한 `args_schema`와 `_run` 메서드를 포함한 필요한 속성들을 정의해야 합니다.

docs/pt-BR/changelog.mdx

@@ -4,6 +4,47 @@ description: "Atualizações de produto, melhorias e correções do CrewAI"
 icon: "clock"
 mode: "wide"
 ---
+<Update label="18 mar 2026">
+  ## v1.11.0
+
+  [Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.11.0)
+
+  ## O que Mudou
+
+  ### Documentação
+  - Atualizar changelog e versão para v1.11.0rc2
+
+  ## Contribuidores
+
+  @greysonlalonde
+
+</Update>
+
+<Update label="17 mar 2026">
+  ## v1.11.0rc2
+
+  [Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.11.0rc2)
+
+  ## O que Mudou
+
+  ### Correções de Bugs
+  - Aprimorar o manuseio e a serialização das respostas do LLM.
+  - Atualizar dependências transitivas vulneráveis (authlib, PyJWT, snowflake-connector-python).
+  - Substituir `os.system` por `subprocess.run` na instalação do pip em modo inseguro.
+
+  ### Documentação
+  - Atualizar a página da Ferramenta de Pesquisa Exa com nomes, descrições e opções de configuração aprimoradas.
+  - Adicionar Servidores MCP Personalizados no Guia de Como Fazer.
+  - Atualizar a documentação dos coletores OTEL.
+  - Atualizar a documentação do MCP.
+  - Atualizar o changelog e a versão para v1.11.0rc1.
+
+  ## Contributors
+
+  @10ishq, @greysonlalonde, @joaomdmoura, @lucasgomide, @mattatcha, @theCyberTech, @vinibrsl
+
+</Update>
+
 <Update label="15 mar 2026">
   ## v1.11.0rc1
 

docs/pt-BR/enterprise/guides/capture_telemetry_logs.mdx

@@ -0,0 +1,39 @@
+---
+title: "Exportação OpenTelemetry"
+description: "Exporte traces e logs das suas implantações CrewAI AMP para seu próprio coletor OpenTelemetry"
+icon: "magnifying-glass-chart"
+mode: "wide"
+---
+
+O CrewAI AMP pode exportar **traces** e **logs** do OpenTelemetry das suas implantações diretamente para seu próprio coletor. Isso permite que você monitore o desempenho dos agentes, rastreie chamadas de LLM e depure problemas usando sua stack de observabilidade existente.
+
+Os dados de telemetria seguem as [convenções semânticas GenAI do OpenTelemetry](https://opentelemetry.io/docs/specs/semconv/gen-ai/) além de atributos adicionais específicos do CrewAI.
+
+## Pré-requisitos
+
+<CardGroup cols={2}>
+  <Card title="Conta CrewAI AMP" icon="users">
+    Sua organização deve ter uma conta CrewAI AMP ativa.
+  </Card>
+  <Card title="Coletor OpenTelemetry" icon="server">
+    Você precisa de um endpoint de coletor compatível com OpenTelemetry (por exemplo, seu próprio OTel Collector, Datadog, Grafana ou qualquer backend compatível com OTLP).
+  </Card>
+</CardGroup>
+
+## Configurando um coletor
+
+1. No CrewAI AMP, vá para **Settings** > **OpenTelemetry Collectors**.
+2. Clique em **Add Collector**.
+3. Selecione um tipo de integração — **OpenTelemetry Traces** ou **OpenTelemetry Logs**.
+4. Configure a conexão:
+   - **Endpoint** — O endpoint OTLP do seu coletor (por exemplo, `https://otel-collector.example.com:4317`).
+   - **Service Name** — Um nome para identificar este serviço na sua plataforma de observabilidade.
+   - **Custom Headers** *(opcional)* — Adicione headers de autenticação ou roteamento como pares chave-valor.
+   - **Certificate** *(opcional)* — Forneça um certificado TLS se o seu coletor exigir um.
+5. Clique em **Save**.
+
+<Frame>![Configuração do Coletor OpenTelemetry](/images/crewai-otel-collector-config.png)</Frame>
+
+<Tip>
+  Você pode adicionar múltiplos coletores — por exemplo, um para traces e outro para logs, ou enviar para diferentes backends para diferentes propósitos.
+</Tip>

docs/pt-BR/enterprise/guides/custom-mcp-server.mdx

@@ -0,0 +1,136 @@
+---
+title: "Servidores MCP Personalizados"
+description: "Conecte seus próprios servidores MCP ao CrewAI AMP com acesso público, autenticação por token ou OAuth 2.0"
+icon: "plug"
+mode: "wide"
+---
+
+O CrewAI AMP suporta a conexão com qualquer servidor MCP que implemente o [Model Context Protocol](https://modelcontextprotocol.io/). Você pode conectar servidores públicos que não exigem autenticação, servidores protegidos por chave de API ou token bearer, e servidores que utilizam OAuth 2.0 para acesso delegado seguro.
+
+## Pré-requisitos
+
+<CardGroup cols={2}>
+  <Card title="Conta CrewAI AMP" icon="user">
+    Você precisa de uma conta ativa no [CrewAI AMP](https://app.crewai.com).
+  </Card>
+  <Card title="URL do Servidor MCP" icon="link">
+    A URL do servidor MCP que você deseja conectar. O servidor deve ser acessível pela internet e suportar transporte Streamable HTTP.
+  </Card>
+</CardGroup>
+
+## Adicionando um Servidor MCP Personalizado
+
+<Steps>
+    <Step title="Acesse Tools & Integrations">
+        Navegue até **Tools & Integrations** no menu lateral esquerdo do CrewAI AMP e selecione a aba **Connections**.
+    </Step>
+
+    <Step title="Inicie a adição de um Servidor MCP Personalizado">
+        Clique no botão **Add Custom MCP Server**. Um diálogo aparecerá com o formulário de configuração.
+    </Step>
+
+    <Step title="Preencha as informações básicas">
+        - **Name** (obrigatório): Um nome descritivo para seu servidor MCP (ex.: "Meu Servidor de Ferramentas Internas").
+        - **Description**: Um resumo opcional do que este servidor MCP fornece.
+        - **Server URL** (obrigatório): A URL completa do endpoint do seu servidor MCP (ex.: `https://my-server.example.com/mcp`).
+    </Step>
+
+    <Step title="Escolha um método de autenticação">
+        Selecione um dos três métodos de autenticação disponíveis com base em como seu servidor MCP está protegido. Veja as seções abaixo para detalhes sobre cada método.
+    </Step>
+
+    <Step title="Adicione headers personalizados (opcional)">
+        Se seu servidor MCP requer headers adicionais em cada requisição (ex.: identificadores de tenant ou headers de roteamento), clique em **+ Add Header** e forneça o nome e valor do header. Você pode adicionar múltiplos headers personalizados.
+    </Step>
+
+    <Step title="Crie a conexão">
+        Clique em **Create MCP Server** para salvar a conexão. Seu servidor MCP personalizado aparecerá na lista de Connections e suas ferramentas estarão disponíveis para uso nas suas crews.
+    </Step>
+</Steps>
+
+## Métodos de Autenticação
+
+### Sem Autenticação
+
+Escolha esta opção quando seu servidor MCP é publicamente acessível e não requer nenhuma credencial. Isso é comum para servidores open-source ou servidores internos rodando atrás de uma VPN.
+
+### Token de Autenticação
+
+Use este método quando seu servidor MCP é protegido por uma chave de API ou token bearer.
+
+<Frame>
+    <img src="/images/enterprise/custom-mcp-auth-token.png" alt="Servidor MCP Personalizado com Token de Autenticação" />
+</Frame>
+
+| Campo | Obrigatório | Descrição |
+|-------|-------------|-----------|
+| **Header Name** | Sim | O nome do header HTTP que carrega o token (ex.: `X-API-Key`, `Authorization`). |
+| **Value** | Sim | Sua chave de API ou token bearer. |
+| **Add to** | Não | Onde anexar a credencial — **Header** (padrão) ou **Query parameter**. |
+
+<Tip>
+Se seu servidor espera um token `Bearer` no header `Authorization`, defina o Header Name como `Authorization` e o Value como `Bearer <seu-token>`.
+</Tip>
+
+### OAuth 2.0
+
+Use este método para servidores MCP que requerem autorização OAuth 2.0. O CrewAI gerenciará todo o fluxo OAuth, incluindo a renovação de tokens.
+
+<Frame>
+    <img src="/images/enterprise/custom-mcp-oauth.png" alt="Servidor MCP Personalizado com OAuth 2.0" />
+</Frame>
+
+| Campo | Obrigatório | Descrição |
+|-------|-------------|-----------|
+| **Redirect URI** | — | Preenchido automaticamente e somente leitura. Copie esta URI e registre-a como URI de redirecionamento autorizada no seu provedor OAuth. |
+| **Authorization Endpoint** | Sim | A URL para onde os usuários são enviados para autorizar o acesso (ex.: `https://auth.example.com/oauth/authorize`). |
+| **Token Endpoint** | Sim | A URL usada para trocar o código de autorização por um token de acesso (ex.: `https://auth.example.com/oauth/token`). |
+| **Client ID** | Sim | O Client ID OAuth emitido pelo seu provedor. |
+| **Client Secret** | Não | O Client Secret OAuth. Não é necessário para clientes públicos usando PKCE. |
+| **Scopes** | Não | Lista de escopos separados por espaço a solicitar (ex.: `read write`). |
+| **Token Auth Method** | Não | Como as credenciais do cliente são enviadas ao trocar tokens — **Standard (POST body)** ou **Basic Auth (header)**. Padrão é Standard. |
+| **PKCE Supported** | Não | Ative se seu provedor OAuth suporta Proof Key for Code Exchange. Recomendado para maior segurança. |
+
+<Info>
+**Discover OAuth Config**: Se seu provedor OAuth suporta OpenID Connect Discovery, clique no link **Discover OAuth Config** para preencher automaticamente os endpoints de autorização e token a partir da URL `/.well-known/openid-configuration` do provedor.
+</Info>
+
+#### Configurando OAuth 2.0 Passo a Passo
+
+<Steps>
+    <Step title="Registre a URI de redirecionamento">
+        Copie a **Redirect URI** exibida no formulário e adicione-a como URI de redirecionamento autorizada nas configurações do seu provedor OAuth.
+    </Step>
+
+    <Step title="Insira os endpoints e credenciais">
+        Preencha o **Authorization Endpoint**, **Token Endpoint**, **Client ID** e, opcionalmente, o **Client Secret** e **Scopes**.
+    </Step>
+
+    <Step title="Configure o método de troca de tokens">
+        Selecione o **Token Auth Method** apropriado. A maioria dos provedores usa o padrão **Standard (POST body)**. Alguns provedores mais antigos requerem **Basic Auth (header)**.
+    </Step>
+
+    <Step title="Ative o PKCE (recomendado)">
+        Marque **PKCE Supported** se seu provedor suporta. O PKCE adiciona uma camada extra de segurança ao fluxo de código de autorização e é recomendado para todas as novas integrações.
+    </Step>
+
+    <Step title="Crie e autorize">
+        Clique em **Create MCP Server**. Você será redirecionado ao seu provedor OAuth para autorizar o acesso. Uma vez autorizado, o CrewAI armazenará os tokens e os renovará automaticamente conforme necessário.
+    </Step>
+</Steps>
+
+## Usando Seu Servidor MCP Personalizado
+
+Uma vez conectado, as ferramentas do seu servidor MCP personalizado aparecem junto com as conexões integradas na página **Tools & Integrations**. Você pode:
+
+- **Atribuir ferramentas a agentes** nas suas crews, assim como qualquer outra ferramenta CrewAI.
+- **Gerenciar visibilidade** para controlar quais membros da equipe podem usar o servidor.
+- **Editar ou remover** a conexão a qualquer momento na lista de Connections.
+
+<Warning>
+Se seu servidor MCP ficar inacessível ou as credenciais expirarem, as chamadas de ferramentas usando esse servidor falharão. Certifique-se de que a URL do servidor seja estável e as credenciais estejam atualizadas.
+</Warning>
+
+<Card title="Precisa de Ajuda?" icon="headset" href="mailto:support@crewai.com">
+  Entre em contato com nossa equipe de suporte para assistência com configuração ou resolução de problemas de servidores MCP personalizados.
+</Card>

docs/pt-BR/guides/coding-tools/agents-md.mdx

@@ -0,0 +1,61 @@
+---
+title: Ferramentas de Codificação
+description: Use o AGENTS.md para guiar agentes de codificação e IDEs em seus projetos CrewAI.
+icon: terminal
+mode: "wide"
+---
+
+## Por que AGENTS.md
+
+`AGENTS.md` é um arquivo de instruções leve e local do repositório que fornece aos agentes de codificação orientações consistentes e específicas do projeto. Mantenha-o na raiz do projeto e trate-o como a fonte da verdade para como você deseja que os assistentes trabalhem: convenções, comandos, notas de arquitetura e proteções.
+
+## Criar um Projeto com o CLI
+
+Use o CLI do CrewAI para criar a estrutura de um projeto, e o `AGENTS.md` será automaticamente adicionado na raiz.
+
+```bash
+# Crew
+crewai create crew my_crew
+
+# Flow
+crewai create flow my_flow
+
+# Tool repository
+crewai tool create my_tool
+```
+
+## Configuração de Ferramentas: Direcione Assistentes para o AGENTS.md
+
+### Codex
+
+O Codex pode ser guiado por arquivos `AGENTS.md` colocados no seu repositório. Use-os para fornecer contexto persistente do projeto, como convenções, comandos e expectativas de fluxo de trabalho.
+
+### Claude Code
+
+O Claude Code armazena a memória do projeto em `CLAUDE.md`. Você pode inicializá-lo com `/init` e editá-lo usando `/memory`. O Claude Code também suporta importações dentro do `CLAUDE.md`, então você pode adicionar uma única linha como `@AGENTS.md` para incluir as instruções compartilhadas sem duplicá-las.
+
+Você pode simplesmente usar:
+
+```bash
+mv AGENTS.md CLAUDE.md
+```
+
+### Gemini CLI e Google Antigravity
+
+O Gemini CLI e o Antigravity carregam um arquivo de contexto do projeto (padrão: `GEMINI.md`) da raiz do repositório e diretórios pais. Você pode configurá-lo para ler o `AGENTS.md` em vez disso (ou além) definindo `context.fileName` nas configurações do Gemini CLI. Por exemplo, defina apenas para `AGENTS.md`, ou inclua tanto `AGENTS.md` quanto `GEMINI.md` se quiser manter o formato de cada ferramenta.
+
+Você pode simplesmente usar:
+
+```bash
+mv AGENTS.md GEMINI.md
+```
+
+### Cursor
+
+O Cursor suporta `AGENTS.md` como arquivo de instruções do projeto. Coloque-o na raiz do projeto para fornecer orientação ao assistente de codificação do Cursor.
+
+### Windsurf
+
+O Claude Code fornece uma integração oficial com o Windsurf. Se você usa o Claude Code dentro do Windsurf, siga a orientação do Claude Code acima e importe o `AGENTS.md` a partir do `CLAUDE.md`.
+
+Se você está usando o assistente nativo do Windsurf, configure o recurso de regras ou instruções do projeto (se disponível) para ler o `AGENTS.md` ou cole o conteúdo diretamente.

docs/pt-BR/guides/tools/publish-custom-tools.mdx

@@ -0,0 +1,244 @@
+---
+title: Publicar Ferramentas Personalizadas
+description: Como construir, empacotar e publicar suas próprias ferramentas compatíveis com CrewAI no PyPI para que qualquer usuário do CrewAI possa instalá-las e usá-las.
+icon: box-open
+mode: "wide"
+---
+
+## Visão Geral
+
+O sistema de ferramentas do CrewAI foi projetado para ser extensível. Se você construiu uma ferramenta que pode beneficiar outros, pode empacotá-la como uma biblioteca Python independente, publicá-la no PyPI e disponibilizá-la para qualquer usuário do CrewAI — sem necessidade de PR para o repositório do CrewAI.
+
+Este guia percorre todo o processo: implementação do contrato de ferramentas, estruturação do pacote e publicação no PyPI.
+
+<Note type="info" title="Não pretende publicar?">
+Se você precisa apenas de uma ferramenta personalizada para seu próprio projeto, consulte o guia [Criar Ferramentas Personalizadas](/pt-BR/learn/create-custom-tools).
+</Note>
+
+## O Contrato de Ferramentas
+
+Toda ferramenta CrewAI deve satisfazer uma das duas interfaces:
+
+### Opção 1: Subclassificar `BaseTool`
+
+Subclassifique `crewai.tools.BaseTool` e implemente o método `_run`. Defina `name`, `description` e, opcionalmente, um `args_schema` para validação de entrada.
+
+```python
+from crewai.tools import BaseTool
+from pydantic import BaseModel, Field
+
+
+class GeolocateInput(BaseModel):
+    """Esquema de entrada para GeolocateTool."""
+    address: str = Field(..., description="O endereço para geolocalizar.")
+
+
+class GeolocateTool(BaseTool):
+    name: str = "Geolocate"
+    description: str = "Converte um endereço em coordenadas de latitude/longitude."
+    args_schema: type[BaseModel] = GeolocateInput
+
+    def _run(self, address: str) -> str:
+        # Sua implementação aqui
+        return f"40.7128, -74.0060"
+```
+
+### Opção 2: Usar o Decorador `@tool`
+
+Para ferramentas mais simples, o decorador `@tool` transforma uma função em uma ferramenta CrewAI. A função **deve** ter uma docstring (usada como descrição da ferramenta) e anotações de tipo.
+
+```python
+from crewai.tools import tool
+
+
+@tool("Geolocate")
+def geolocate(address: str) -> str:
+    """Converte um endereço em coordenadas de latitude/longitude."""
+    return "40.7128, -74.0060"
+```
+
+### Requisitos Essenciais
+
+Independentemente da abordagem escolhida, sua ferramenta deve:
+
+- Ter um **`name`** — um identificador curto e descritivo.
+- Ter uma **`description`** — informa ao agente quando e como usar a ferramenta. Isso afeta diretamente a qualidade do uso da ferramenta pelo agente, então seja claro e específico.
+- Implementar **`_run`** (BaseTool) ou fornecer um **corpo de função** (@tool) — a lógica de execução síncrona.
+- Usar **anotações de tipo** em todos os parâmetros e valores de retorno.
+- Retornar um resultado em **string** (ou algo que possa ser convertido de forma significativa).
+
+### Opcional: Suporte Assíncrono
+
+Se sua ferramenta realiza operações de I/O, implemente `_arun` para execução assíncrona:
+
+```python
+class GeolocateTool(BaseTool):
+    name: str = "Geolocate"
+    description: str = "Converte um endereço em coordenadas de latitude/longitude."
+
+    def _run(self, address: str) -> str:
+        # Implementação síncrona
+        ...
+
+    async def _arun(self, address: str) -> str:
+        # Implementação assíncrona
+        ...
+```
+
+### Opcional: Validação de Entrada com `args_schema`
+
+Defina um modelo Pydantic como seu `args_schema` para obter validação automática de entrada e mensagens de erro claras. Se não fornecer um, o CrewAI irá inferi-lo da assinatura do seu método `_run`.
+
+```python
+from pydantic import BaseModel, Field
+
+
+class TranslateInput(BaseModel):
+    """Esquema de entrada para TranslateTool."""
+    text: str = Field(..., description="O texto a ser traduzido.")
+    target_language: str = Field(
+        default="en",
+        description="Código de idioma ISO 639-1 para o idioma de destino.",
+    )
+```
+
+Esquemas explícitos são recomendados para ferramentas publicadas — produzem melhor comportamento do agente e documentação mais clara para seus usuários.
+
+### Opcional: Variáveis de Ambiente
+
+Se sua ferramenta requer chaves de API ou outra configuração, declare-as com `env_vars` para que os usuários saibam o que configurar:
+
+```python
+from crewai.tools import BaseTool, EnvVar
+
+
+class GeolocateTool(BaseTool):
+    name: str = "Geolocate"
+    description: str = "Converte um endereço em coordenadas de latitude/longitude."
+    env_vars: list[EnvVar] = [
+        EnvVar(
+            name="GEOCODING_API_KEY",
+            description="Chave de API para o serviço de geocodificação.",
+            required=True,
+        ),
+    ]
+
+    def _run(self, address: str) -> str:
+        ...
+```
+
+## Estrutura do Pacote
+
+Estruture seu projeto como um pacote Python padrão. Layout recomendado:
+
+```
+crewai-geolocate/
+├── pyproject.toml
+├── LICENSE
+├── README.md
+└── src/
+    └── crewai_geolocate/
+        ├── __init__.py
+        └── tools.py
+```
+
+### `pyproject.toml`
+
+```toml
+[project]
+name = "crewai-geolocate"
+version = "0.1.0"
+description = "Uma ferramenta CrewAI para geolocalizar endereços."
+requires-python = ">=3.10"
+dependencies = [
+    "crewai",
+]
+
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+```
+
+Declare `crewai` como dependência para que os usuários obtenham automaticamente uma versão compatível.
+
+### `__init__.py`
+
+Re-exporte suas classes de ferramenta para que os usuários possam importá-las diretamente:
+
+```python
+from crewai_geolocate.tools import GeolocateTool
+
+__all__ = ["GeolocateTool"]
+```
+
+### Convenções de Nomenclatura
+
+- **Nome do pacote**: Use o prefixo `crewai-` (ex.: `crewai-geolocate`). Isso torna sua ferramenta fácil de encontrar no PyPI.
+- **Nome do módulo**: Use underscores (ex.: `crewai_geolocate`).
+- **Nome da classe da ferramenta**: Use PascalCase terminando em `Tool` (ex.: `GeolocateTool`).
+
+## Testando sua Ferramenta
+
+Antes de publicar, verifique se sua ferramenta funciona dentro de uma crew:
+
+```python
+from crewai import Agent, Crew, Task
+from crewai_geolocate import GeolocateTool
+
+agent = Agent(
+    role="Analista de Localização",
+    goal="Encontrar coordenadas para os endereços fornecidos.",
+    backstory="Um especialista em dados geoespaciais.",
+    tools=[GeolocateTool()],
+)
+
+task = Task(
+    description="Encontre as coordenadas de 1600 Pennsylvania Avenue, Washington, DC.",
+    expected_output="A latitude e longitude do endereço.",
+    agent=agent,
+)
+
+crew = Crew(agents=[agent], tasks=[task])
+result = crew.kickoff()
+print(result)
+```
+
+## Publicando no PyPI
+
+Quando sua ferramenta estiver testada e pronta:
+
+```bash
+# Construir o pacote
+uv build
+
+# Publicar no PyPI
+uv publish
+```
+
+Se é sua primeira vez publicando, você precisará de uma [conta no PyPI](https://pypi.org/account/register/) e um [token de API](https://pypi.org/help/#apitoken).
+
+### Após a Publicação
+
+Os usuários podem instalar sua ferramenta com:
+
+```bash
+pip install crewai-geolocate
+```
+
+Ou com uv:
+
+```bash
+uv add crewai-geolocate
+```
+
+E então usá-la em suas crews:
+
+```python
+from crewai_geolocate import GeolocateTool
+
+agent = Agent(
+    role="Analista de Localização",
+    tools=[GeolocateTool()],
+    # ...
+)
+```

docs/pt-BR/learn/create-custom-tools.mdx

@@ -11,6 +11,10 @@ Este guia traz instruções detalhadas sobre como criar ferramentas personalizad
 incorporando funcionalidades recentes, como delegação de ferramentas, tratamento de erros e chamada dinâmica de ferramentas. Destaca também a importância de ferramentas de colaboração,
 permitindo que agentes executem uma ampla gama de ações.
 
+<Tip>
+  **Quer publicar sua ferramenta para a comunidade?** Se você está construindo uma ferramenta que pode beneficiar outros, confira o guia [Publicar Ferramentas Personalizadas](/pt-BR/guides/tools/publish-custom-tools) para aprender como empacotar e distribuir sua ferramenta no PyPI.
+</Tip>
+
 ### Subclassificando `BaseTool`
 
 Para criar uma ferramenta personalizada, herde de `BaseTool` e defina os atributos necessários, incluindo o `args_schema` para validação de entrada e o método `_run`.

lib/crewai-files/src/crewai_files/__init__.py

@@ -152,4 +152,4 @@ __all__ = [
     "wrap_file_source",
 ]
 
-__version__ = "1.11.0rc1"
+__version__ = "1.11.0"

lib/crewai-tools/pyproject.toml

@@ -11,7 +11,7 @@ dependencies = [
     "pytube~=15.0.0",
     "requests~=2.32.5",
     "docker~=7.1.0",
-    "crewai==1.11.0rc1",
+    "crewai==1.11.0",
     "tiktoken~=0.8.0",
     "beautifulsoup4~=4.13.4",
     "python-docx~=1.2.0",

lib/crewai-tools/src/crewai_tools/__init__.py

@@ -309,4 +309,4 @@ __all__ = [
     "ZapierActionTools",
 ]
 
-__version__ = "1.11.0rc1"
+__version__ = "1.11.0"

lib/crewai-tools/src/crewai_tools/tools/file_writer_tool/file_writer_tool.py

@@ -1,4 +1,5 @@
 import os
+from pathlib import Path
 from typing import Any
 
 from crewai.tools import BaseTool
@@ -30,27 +31,39 @@ class FileWriterTool(BaseTool):
 
     def _run(self, **kwargs: Any) -> str:
         try:
+            directory = kwargs.get("directory") or "./"
+            filename = kwargs["filename"]
+
+            filepath = os.path.join(directory, filename)
+
+            # Prevent path traversal: the resolved path must be strictly inside
+            # the resolved directory. This blocks ../sequences, absolute paths in
+            # filename, and symlink escapes regardless of how directory is set.
+            # is_relative_to() does a proper path-component comparison that is
+            # safe on case-insensitive filesystems and avoids the "// " edge case
+            # that plagues startswith(real_directory + os.sep).
+            # We also reject the case where filepath resolves to the directory
+            # itself, since that is not a valid file target.
+            real_directory = Path(directory).resolve()
+            real_filepath = Path(filepath).resolve()
+            if not real_filepath.is_relative_to(real_directory) or real_filepath == real_directory:
+                return "Error: Invalid file path — the filename must not escape the target directory."
+
             if kwargs.get("directory"):
-                os.makedirs(kwargs["directory"], exist_ok=True)
+                os.makedirs(real_directory, exist_ok=True)
 
-            # Construct the full path
-            filepath = os.path.join(kwargs.get("directory") or "", kwargs["filename"])
-
-            # Convert overwrite to boolean
             kwargs["overwrite"] = strtobool(kwargs["overwrite"])
 
-            # Check if file exists and overwrite is not allowed
-            if os.path.exists(filepath) and not kwargs["overwrite"]:
-                return f"File {filepath} already exists and overwrite option was not passed."
+            if os.path.exists(real_filepath) and not kwargs["overwrite"]:
+                return f"File {real_filepath} already exists and overwrite option was not passed."
 
-            # Write content to the file
             mode = "w" if kwargs["overwrite"] else "x"
-            with open(filepath, mode) as file:
+            with open(real_filepath, mode) as file:
                 file.write(kwargs["content"])
-            return f"Content successfully written to {filepath}"
+            return f"Content successfully written to {real_filepath}"
         except FileExistsError:
             return (
-                f"File {filepath} already exists and overwrite option was not passed."
+                f"File {real_filepath} already exists and overwrite option was not passed."
             )
         except KeyError as e:
             return f"An error occurred while accessing key: {e!s}"

lib/crewai-tools/tests/tools/test_file_writer_tool.py

@@ -135,3 +135,59 @@ def test_file_exists_error_handling(tool, temp_env, overwrite):
 
     assert "already exists and overwrite option was not passed" in result
     assert read_file(path) == "Pre-existing content"
+
+
+# --- Path traversal prevention ---
+
+def test_blocks_traversal_in_filename(tool, temp_env):
+    # Create a sibling "outside" directory so we can assert nothing was written there.
+    outside_dir = tempfile.mkdtemp()
+    outside_file = os.path.join(outside_dir, "outside.txt")
+    try:
+        result = tool._run(
+            filename=f"../{os.path.basename(outside_dir)}/outside.txt",
+            directory=temp_env["temp_dir"],
+            content="should not be written",
+            overwrite=True,
+        )
+        assert "Error" in result
+        assert not os.path.exists(outside_file)
+    finally:
+        shutil.rmtree(outside_dir, ignore_errors=True)
+
+
+def test_blocks_absolute_path_in_filename(tool, temp_env):
+    # Use a temp file outside temp_dir as the absolute target so we don't
+    # depend on /etc/passwd existing or being writable on the host.
+    outside_dir = tempfile.mkdtemp()
+    outside_file = os.path.join(outside_dir, "target.txt")
+    try:
+        result = tool._run(
+            filename=outside_file,
+            directory=temp_env["temp_dir"],
+            content="should not be written",
+            overwrite=True,
+        )
+        assert "Error" in result
+        assert not os.path.exists(outside_file)
+    finally:
+        shutil.rmtree(outside_dir, ignore_errors=True)
+
+
+def test_blocks_symlink_escape(tool, temp_env):
+    # Symlink inside temp_dir pointing to a separate temp "outside" directory.
+    outside_dir = tempfile.mkdtemp()
+    outside_file = os.path.join(outside_dir, "target.txt")
+    link = os.path.join(temp_env["temp_dir"], "escape")
+    os.symlink(outside_dir, link)
+    try:
+        result = tool._run(
+            filename="escape/target.txt",
+            directory=temp_env["temp_dir"],
+            content="should not be written",
+            overwrite=True,
+        )
+        assert "Error" in result
+        assert not os.path.exists(outside_file)
+    finally:
+        shutil.rmtree(outside_dir, ignore_errors=True)

lib/crewai/pyproject.toml

@@ -53,7 +53,7 @@ Repository = "https://github.com/crewAIInc/crewAI"
 
 [project.optional-dependencies]
 tools = [
-    "crewai-tools==1.11.0rc1",
+    "crewai-tools==1.11.0",
 ]
 embeddings = [
     "tiktoken~=0.8.0"

lib/crewai/src/crewai/__init__.py

@@ -42,7 +42,7 @@ def _suppress_pydantic_deprecation_warnings() -> None:
 
 _suppress_pydantic_deprecation_warnings()
 
-__version__ = "1.11.0rc1"
+__version__ = "1.11.0"
 _telemetry_submitted = False
 
 

lib/crewai/src/crewai/agent/core.py

@@ -75,6 +75,7 @@ from crewai.utilities.agent_utils import (
 )
 from crewai.utilities.constants import TRAINED_AGENTS_DATA_FILE, TRAINING_DATA_FILE
 from crewai.utilities.converter import Converter, ConverterError
+from crewai.utilities.env import get_env_context
 from crewai.utilities.guardrail import process_guardrail
 from crewai.utilities.guardrail_types import GuardrailType
 from crewai.utilities.llm_utils import create_llm
@@ -364,6 +365,7 @@ class Agent(BaseAgent):
             ValueError: If the max execution time is not a positive integer.
             RuntimeError: If the agent execution fails for other reasons.
         """
+        get_env_context()
         # Only call handle_reasoning for legacy CrewAgentExecutor
         # For AgentExecutor, planning is handled in AgentExecutor.generate_plan()
         if self.executor_class is not AgentExecutor:

lib/crewai/src/crewai/agents/crew_agent_executor.py

@@ -847,7 +847,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
             func_name = sanitize_tool_name(
                 func_info.get("name", "") or tool_call.get("name", "")
             )
-            func_args = func_info.get("arguments", "{}") or tool_call.get("input", {})
+            func_args = func_info.get("arguments") or tool_call.get("input") or "{}"
             return call_id, func_name, func_args
         return None
 

lib/crewai/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]==1.11.0rc1"
+    "crewai[tools]==1.11.0"
 ]
 
 [project.scripts]

lib/crewai/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]==1.11.0rc1"
+    "crewai[tools]==1.11.0"
 ]
 
 [project.scripts]

lib/crewai/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]==1.11.0rc1"
+    "crewai[tools]==1.11.0"
 ]
 
 [tool.crewai]

lib/crewai/src/crewai/crew.py

@@ -98,6 +98,7 @@ from crewai.types.streaming import CrewStreamingOutput
 from crewai.types.usage_metrics import UsageMetrics
 from crewai.utilities.constants import NOT_SPECIFIED, TRAINING_DATA_FILE
 from crewai.utilities.crew.models import CrewContext
+from crewai.utilities.env import get_env_context
 from crewai.utilities.evaluators.crew_evaluator_handler import CrewEvaluator
 from crewai.utilities.evaluators.task_evaluator import TaskEvaluator
 from crewai.utilities.file_handler import FileHandler
@@ -679,6 +680,7 @@ class Crew(FlowTrackable, BaseModel):
         Returns:
             CrewOutput or CrewStreamingOutput if streaming is enabled.
         """
+        get_env_context()
         if self.stream:
             enable_agent_streaming(self.agents)
             ctx = StreamingContext()

lib/crewai/src/crewai/events/event_listener.py

@@ -34,6 +34,12 @@ from crewai.events.types.crew_events import (
     CrewTrainFailedEvent,
     CrewTrainStartedEvent,
 )
+from crewai.events.types.env_events import (
+    CCEnvEvent,
+    CodexEnvEvent,
+    CursorEnvEvent,
+    DefaultEnvEvent,
+)
 from crewai.events.types.flow_events import (
     FlowCreatedEvent,
     FlowFinishedEvent,
@@ -143,6 +149,23 @@ class EventListener(BaseEventListener):
     # ----------- CREW EVENTS -----------
 
     def setup_listeners(self, crewai_event_bus: CrewAIEventsBus) -> None:
+
+        @crewai_event_bus.on(CCEnvEvent)
+        def on_cc_env(_: Any, event: CCEnvEvent) -> None:
+            self._telemetry.env_context_span(event.type)
+
+        @crewai_event_bus.on(CodexEnvEvent)
+        def on_codex_env(_: Any, event: CodexEnvEvent) -> None:
+            self._telemetry.env_context_span(event.type)
+
+        @crewai_event_bus.on(CursorEnvEvent)
+        def on_cursor_env(_: Any, event: CursorEnvEvent) -> None:
+            self._telemetry.env_context_span(event.type)
+
+        @crewai_event_bus.on(DefaultEnvEvent)
+        def on_default_env(_: Any, event: DefaultEnvEvent) -> None:
+            self._telemetry.env_context_span(event.type)
+
         @crewai_event_bus.on(CrewKickoffStartedEvent)
         def on_crew_started(source: Any, event: CrewKickoffStartedEvent) -> None:
             self.formatter.handle_crew_started(event.crew_name or "Crew", source.id)

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

@@ -58,6 +58,12 @@ from crewai.events.types.crew_events import (
     CrewKickoffFailedEvent,
     CrewKickoffStartedEvent,
 )
+from crewai.events.types.env_events import (
+    CCEnvEvent,
+    CodexEnvEvent,
+    CursorEnvEvent,
+    DefaultEnvEvent,
+)
 from crewai.events.types.flow_events import (
     FlowCreatedEvent,
     FlowFinishedEvent,
@@ -192,6 +198,7 @@ class TraceCollectionListener(BaseEventListener):
         if self._listeners_setup:
             return
 
+        self._register_env_event_handlers(crewai_event_bus)
         self._register_flow_event_handlers(crewai_event_bus)
         self._register_context_event_handlers(crewai_event_bus)
         self._register_action_event_handlers(crewai_event_bus)
@@ -200,6 +207,25 @@ class TraceCollectionListener(BaseEventListener):
 
         self._listeners_setup = True
 
+    def _register_env_event_handlers(self, event_bus: CrewAIEventsBus) -> None:
+        """Register handlers for environment context events."""
+
+        @event_bus.on(CCEnvEvent)
+        def on_cc_env(source: Any, event: CCEnvEvent) -> None:
+            self._handle_action_event("cc_env", source, event)
+
+        @event_bus.on(CodexEnvEvent)
+        def on_codex_env(source: Any, event: CodexEnvEvent) -> None:
+            self._handle_action_event("codex_env", source, event)
+
+        @event_bus.on(CursorEnvEvent)
+        def on_cursor_env(source: Any, event: CursorEnvEvent) -> None:
+            self._handle_action_event("cursor_env", source, event)
+
+        @event_bus.on(DefaultEnvEvent)
+        def on_default_env(source: Any, event: DefaultEnvEvent) -> None:
+            self._handle_action_event("default_env", source, event)
+
     def _register_flow_event_handlers(self, event_bus: CrewAIEventsBus) -> None:
         """Register handlers for flow events."""
 

lib/crewai/src/crewai/events/types/env_events.py

@@ -0,0 +1,36 @@
+from typing import Annotated, Literal
+
+from pydantic import Field, TypeAdapter
+
+from crewai.events.base_events import BaseEvent
+
+
+class CCEnvEvent(BaseEvent):
+    type: Literal["cc_env"] = "cc_env"
+
+
+class CodexEnvEvent(BaseEvent):
+    type: Literal["codex_env"] = "codex_env"
+
+
+class CursorEnvEvent(BaseEvent):
+    type: Literal["cursor_env"] = "cursor_env"
+
+
+class DefaultEnvEvent(BaseEvent):
+    type: Literal["default_env"] = "default_env"
+
+
+EnvContextEvent = Annotated[
+    CCEnvEvent | CodexEnvEvent | CursorEnvEvent | DefaultEnvEvent,
+    Field(discriminator="type"),
+]
+
+env_context_event_adapter: TypeAdapter[EnvContextEvent] = TypeAdapter(EnvContextEvent)
+
+ENV_CONTEXT_EVENT_TYPES: tuple[type[BaseEvent], ...] = (
+    CCEnvEvent,
+    CodexEnvEvent,
+    CursorEnvEvent,
+    DefaultEnvEvent,
+)

lib/crewai/src/crewai/flow/flow.py

@@ -110,6 +110,7 @@ if TYPE_CHECKING:
 
 from crewai.flow.visualization import build_flow_structure, render_interactive
 from crewai.types.streaming import CrewStreamingOutput, FlowStreamingOutput
+from crewai.utilities.env import get_env_context
 from crewai.utilities.streaming import (
     TaskInfo,
     create_async_chunk_generator,
@@ -1770,6 +1771,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
         Returns:
             The final output from the flow or FlowStreamingOutput if streaming.
         """
+        get_env_context()
         if self.stream:
             result_holder: list[Any] = []
             current_task_info: TaskInfo = {
@@ -3086,25 +3088,35 @@ class Flow(Generic[T], metaclass=FlowMeta):
             logger.warning(
                 f"Structured output failed, falling back to simple prompting: {e}"
             )
-            response = llm_instance.call(messages=prompt)
-            response_clean = str(response).strip()
+            try:
+                response = llm_instance.call(
+                    messages=[{"role": "user", "content": prompt}],
+                )
+                response_clean = str(response).strip()
 
-            # Exact match (case-insensitive)
-            for outcome in outcomes:
-                if outcome.lower() == response_clean.lower():
-                    return outcome
+                # Exact match (case-insensitive)
+                for outcome in outcomes:
+                    if outcome.lower() == response_clean.lower():
+                        return outcome
 
-            # Partial match
-            for outcome in outcomes:
-                if outcome.lower() in response_clean.lower():
-                    return outcome
+                # Partial match
+                for outcome in outcomes:
+                    if outcome.lower() in response_clean.lower():
+                        return outcome
 
-            # Fallback to first outcome
-            logger.warning(
-                f"Could not match LLM response '{response_clean}' to outcomes {list(outcomes)}. "
-                f"Falling back to first outcome: {outcomes[0]}"
-            )
-            return outcomes[0]
+                # Fallback to first outcome
+                logger.warning(
+                    f"Could not match LLM response '{response_clean}' to outcomes {list(outcomes)}. "
+                    f"Falling back to first outcome: {outcomes[0]}"
+                )
+                return outcomes[0]
+
+            except Exception as fallback_err:
+                logger.warning(
+                    f"Simple prompting also failed: {fallback_err}. "
+                    f"Falling back to first outcome: {outcomes[0]}"
+                )
+                return outcomes[0]
 
     def _log_flow_event(
         self,

lib/crewai/src/crewai/flow/human_feedback.py

@@ -76,6 +76,24 @@ if TYPE_CHECKING:
 F = TypeVar("F", bound=Callable[..., Any])
 
 
+def _serialize_llm_for_context(llm: Any) -> str | None:
+    """Serialize a BaseLLM object to a model string with provider prefix.
+
+    When persisting the LLM for HITL resume, we need to store enough info
+    to reconstruct a working LLM on the resume worker. Just storing the bare
+    model name (e.g. "gemini-3-flash-preview") causes provider inference to
+    fail — it defaults to OpenAI. Including the provider prefix (e.g.
+    "gemini/gemini-3-flash-preview") allows LLM() to correctly route.
+    """
+    model = getattr(llm, "model", None)
+    if not model:
+        return None
+    provider = getattr(llm, "provider", None)
+    if provider and "/" not in model:
+        return f"{provider}/{model}"
+    return model
+
+
 @dataclass
 class HumanFeedbackResult:
     """Result from a @human_feedback decorated method.
@@ -412,7 +430,7 @@ def human_feedback(
                 emit=list(emit) if emit else None,
                 default_outcome=default_outcome,
                 metadata=metadata or {},
-                llm=llm if isinstance(llm, str) else getattr(llm, "model", None),
+                llm=llm if isinstance(llm, str) else _serialize_llm_for_context(llm),
             )
 
             # Determine effective provider:

lib/crewai/src/crewai/llms/constants.py

@@ -240,6 +240,7 @@ ANTHROPIC_MODELS: list[AnthropicModels] = [
 
 GeminiModels: TypeAlias = Literal[
     "gemini-3-pro-preview",
+    "gemini-3-flash-preview",
     "gemini-2.5-pro",
     "gemini-2.5-pro-preview-03-25",
     "gemini-2.5-pro-preview-05-06",
@@ -294,6 +295,7 @@ GeminiModels: TypeAlias = Literal[
 ]
 GEMINI_MODELS: list[GeminiModels] = [
     "gemini-3-pro-preview",
+    "gemini-3-flash-preview",
     "gemini-2.5-pro",
     "gemini-2.5-pro-preview-03-25",
     "gemini-2.5-pro-preview-05-06",

lib/crewai/src/crewai/telemetry/telemetry.py

@@ -986,6 +986,22 @@ class Telemetry:
 
         self._safe_telemetry_operation(_operation)
 
+    def env_context_span(self, tool: str) -> None:
+        """Records the coding tool environment context."""
+
+        def _operation() -> None:
+            tracer = trace.get_tracer("crewai.telemetry")
+            span = tracer.start_span("Environment Context")
+            self._add_attribute(
+                span,
+                "crewai_version",
+                version("crewai"),
+            )
+            self._add_attribute(span, "tool", tool)
+            close_span(span)
+
+        self._safe_telemetry_operation(_operation)
+
     def human_feedback_span(
         self,
         event_type: str,

lib/crewai/src/crewai/utilities/constants.py

@@ -8,6 +8,21 @@ TRAINED_AGENTS_DATA_FILE: Final[str] = "trained_agents_data.pkl"
 KNOWLEDGE_DIRECTORY: Final[str] = "knowledge"
 MAX_FILE_NAME_LENGTH: Final[int] = 255
 EMITTER_COLOR: Final[PrinterColor] = "bold_blue"
+CC_ENV_VAR: Final[str] = "CLAUDECODE"
+CODEX_ENV_VARS: Final[tuple[str, ...]] = (
+    "CODEX_CI",
+    "CODEX_MANAGED_BY_NPM",
+    "CODEX_SANDBOX",
+    "CODEX_SANDBOX_NETWORK_DISABLED",
+    "CODEX_THREAD_ID",
+)
+CURSOR_ENV_VARS: Final[tuple[str, ...]] = (
+    "CURSOR_AGENT",
+    "CURSOR_EXTENSION_HOST_ROLE",
+    "CURSOR_SANDBOX",
+    "CURSOR_TRACE_ID",
+    "CURSOR_WORKSPACE_LABEL",
+)
 
 
 class _NotSpecified:

lib/crewai/src/crewai/utilities/env.py

@@ -0,0 +1,39 @@
+import contextvars
+import os
+
+from crewai.events.event_bus import crewai_event_bus
+from crewai.events.types.env_events import (
+    CCEnvEvent,
+    CodexEnvEvent,
+    CursorEnvEvent,
+    DefaultEnvEvent,
+)
+from crewai.utilities.constants import CC_ENV_VAR, CODEX_ENV_VARS, CURSOR_ENV_VARS
+
+
+_env_context_emitted: contextvars.ContextVar[bool] = contextvars.ContextVar(
+    "_env_context_emitted", default=False
+)
+
+
+def _is_codex_env() -> bool:
+    return any(os.environ.get(var) for var in CODEX_ENV_VARS)
+
+
+def _is_cursor_env() -> bool:
+    return any(os.environ.get(var) for var in CURSOR_ENV_VARS)
+
+
+def get_env_context() -> None:
+    if _env_context_emitted.get():
+        return
+    _env_context_emitted.set(True)
+
+    if os.environ.get(CC_ENV_VAR):
+        crewai_event_bus.emit(None, CCEnvEvent())
+    elif _is_codex_env():
+        crewai_event_bus.emit(None, CodexEnvEvent())
+    elif _is_cursor_env():
+        crewai_event_bus.emit(None, CursorEnvEvent())
+    else:
+        crewai_event_bus.emit(None, DefaultEnvEvent())

lib/crewai/src/crewai/utilities/lock_store.py

@@ -1,7 +1,7 @@
 """Centralised lock factory.
 
-If ``REDIS_URL`` is set, locks are distributed via ``portalocker.RedisLock``. Otherwise, falls
-back to the standard ``portalocker.Lock``.
+If ``REDIS_URL`` is set and the ``redis`` package is installed, locks are distributed via
+``portalocker.RedisLock``. Otherwise, falls back to the standard ``portalocker.Lock``.
 """
 
 from __future__ import annotations
@@ -30,6 +30,18 @@ _REDIS_URL: str | None = os.environ.get("REDIS_URL")
 _DEFAULT_TIMEOUT: Final[int] = 120
 
 
+def _redis_available() -> bool:
+    """Return True if redis is installed and REDIS_URL is set."""
+    if not _REDIS_URL:
+        return False
+    try:
+        import redis  # noqa: F401
+
+        return True
+    except ImportError:
+        return False
+
+
 @lru_cache(maxsize=1)
 def _redis_connection() -> redis.Redis:
     """Return a cached Redis connection, creating one on first call."""
@@ -51,7 +63,7 @@ def lock(name: str, *, timeout: float = _DEFAULT_TIMEOUT) -> Iterator[None]:
     """
     channel = f"crewai:{md5(name.encode(), usedforsecurity=False).hexdigest()}"
 
-    if _REDIS_URL:
+    if _redis_available():
         with portalocker.RedisLock(
             channel=channel,
             connection=_redis_connection(),

lib/crewai/tests/agents/test_native_tool_calling.py

@@ -1276,3 +1276,160 @@ class TestNativeToolCallingJsonParseError:
 
         assert "Error" in result["result"]
         assert "validation failed" in result["result"].lower() or "missing" in result["result"].lower()
+
+
+# =============================================================================
+# _parse_native_tool_call — Bedrock Converse API dict format (issue #4972)
+# =============================================================================
+
+
+class TestParseNativeToolCallBedrockDict:
+    """Verify that _parse_native_tool_call correctly extracts arguments from
+    Bedrock-style dict tool calls that use ``{"name": ..., "input": {...}, "toolUseId": ...}``
+    instead of OpenAI-style ``{"function": {"name": ..., "arguments": ...}}``.
+
+    Regression tests for https://github.com/crewAIInc/crewAI/issues/4972
+    """
+
+    def _make_executor(self) -> "CrewAgentExecutor":
+        """Create a minimal CrewAgentExecutor for unit-testing parsing."""
+        from crewai.agents.crew_agent_executor import CrewAgentExecutor
+
+        executor = object.__new__(CrewAgentExecutor)
+        return executor
+
+    # --- Bedrock-style dicts (the bug scenario) ---
+
+    def test_bedrock_dict_tool_call_extracts_input_args(self) -> None:
+        """Bedrock Converse API returns {name, input, toolUseId}; args must come from 'input'."""
+        executor = self._make_executor()
+        tool_call = {
+            "name": "search_knowledge",
+            "input": {"search_query": "latest updates"},
+            "toolUseId": "tooluse_abc123",
+        }
+
+        result = executor._parse_native_tool_call(tool_call)
+
+        assert result is not None
+        call_id, func_name, func_args = result
+        assert call_id == "tooluse_abc123"
+        assert func_name == "search_knowledge"
+        assert func_args == {"search_query": "latest updates"}
+
+    def test_bedrock_dict_with_multiple_input_args(self) -> None:
+        """Multiple args in the Bedrock 'input' dict should all be preserved."""
+        executor = self._make_executor()
+        tool_call = {
+            "name": "create_document",
+            "input": {"title": "Report", "content": "body text", "format": "pdf"},
+            "toolUseId": "tooluse_xyz789",
+        }
+
+        result = executor._parse_native_tool_call(tool_call)
+
+        assert result is not None
+        _, _, func_args = result
+        assert func_args == {"title": "Report", "content": "body text", "format": "pdf"}
+
+    def test_bedrock_dict_with_empty_input(self) -> None:
+        """A Bedrock tool call with an empty 'input' dict should fall through to default '{}'."""
+        executor = self._make_executor()
+        tool_call = {
+            "name": "no_args_tool",
+            "input": {},
+            "toolUseId": "tooluse_empty",
+        }
+
+        result = executor._parse_native_tool_call(tool_call)
+
+        assert result is not None
+        _, _, func_args = result
+        # Empty dict is falsy, so the or-chain falls through to the final "{}"
+        assert func_args == "{}"
+
+    # --- OpenAI-style dicts (must still work after the fix) ---
+
+    def test_openai_dict_tool_call_still_works(self) -> None:
+        """OpenAI-style dict tool calls must continue to extract from 'function.arguments'."""
+        executor = self._make_executor()
+        tool_call = {
+            "id": "call_openai_123",
+            "function": {
+                "name": "calculator",
+                "arguments": '{"expression": "15 * 8"}',
+            },
+        }
+
+        result = executor._parse_native_tool_call(tool_call)
+
+        assert result is not None
+        call_id, func_name, func_args = result
+        assert call_id == "call_openai_123"
+        assert func_name == "calculator"
+        assert func_args == '{"expression": "15 * 8"}'
+
+    def test_openai_dict_with_empty_string_arguments(self) -> None:
+        """OpenAI dict with empty string arguments should fall through to '{}'."""
+        executor = self._make_executor()
+        tool_call = {
+            "id": "call_empty",
+            "function": {
+                "name": "ping",
+                "arguments": "",
+            },
+        }
+
+        result = executor._parse_native_tool_call(tool_call)
+
+        assert result is not None
+        _, _, func_args = result
+        # Empty string is falsy, so we fall through to "{}"
+        assert func_args == "{}"
+
+    # --- Dict with neither function nor input ---
+
+    def test_dict_with_only_name_no_function_no_input(self) -> None:
+        """Dict with 'name' but no 'function' and no 'input' keys should default to '{}'."""
+        executor = self._make_executor()
+        tool_call = {
+            "name": "simple_tool",
+        }
+
+        result = executor._parse_native_tool_call(tool_call)
+
+        assert result is not None
+        _, func_name, func_args = result
+        assert func_name == "simple_tool"
+        assert func_args == "{}"
+
+    # --- Bedrock toolUseId used as call_id ---
+
+    def test_bedrock_dict_uses_toolUseId_as_call_id(self) -> None:
+        """Bedrock's 'toolUseId' should be used as the call_id."""
+        executor = self._make_executor()
+        tool_call = {
+            "name": "my_tool",
+            "input": {"query": "test"},
+            "toolUseId": "tooluse_unique_id",
+        }
+
+        result = executor._parse_native_tool_call(tool_call)
+
+        assert result is not None
+        call_id, _, _ = result
+        assert call_id == "tooluse_unique_id"
+
+    def test_bedrock_dict_fallback_call_id(self) -> None:
+        """Without 'id' or 'toolUseId', should generate a fallback call_id."""
+        executor = self._make_executor()
+        tool_call = {
+            "name": "my_tool",
+            "input": {"query": "test"},
+        }
+
+        result = executor._parse_native_tool_call(tool_call)
+
+        assert result is not None
+        call_id, _, _ = result
+        assert call_id.startswith("call_")

lib/crewai/tests/test_async_human_feedback.py

@@ -989,8 +989,10 @@ class TestLLMObjectPreservedInContext:
             persistence = SQLiteFlowPersistence(db_path)
 
             # Create a mock BaseLLM object (not a string)
+            # Simulates LLM(model="gemini-2.0-flash", provider="gemini")
             mock_llm_obj = MagicMock()
-            mock_llm_obj.model = "gemini/gemini-2.0-flash"
+            mock_llm_obj.model = "gemini-2.0-flash"
+            mock_llm_obj.provider = "gemini"
 
             class PausingProvider:
                 def __init__(self, persistence: SQLiteFlowPersistence):
@@ -1086,11 +1088,36 @@ class TestLLMObjectPreservedInContext:
 
     def test_none_llm_when_no_model_attr(self) -> None:
         """Test that llm is None when object has no model attribute."""
-        mock_obj = MagicMock(spec=[])  # No attributes
+        from crewai.flow.human_feedback import _serialize_llm_for_context
 
-        # Simulate what the decorator does
-        llm_value = mock_obj if isinstance(mock_obj, str) else getattr(mock_obj, "model", None)
-        assert llm_value is None
+        mock_obj = MagicMock(spec=[])  # No attributes
+        assert _serialize_llm_for_context(mock_obj) is None
+
+    def test_provider_prefix_added_to_bare_model(self) -> None:
+        """Test that provider prefix is added when model has no slash."""
+        from crewai.flow.human_feedback import _serialize_llm_for_context
+
+        mock_obj = MagicMock()
+        mock_obj.model = "gemini-3-flash-preview"
+        mock_obj.provider = "gemini"
+        assert _serialize_llm_for_context(mock_obj) == "gemini/gemini-3-flash-preview"
+
+    def test_provider_prefix_not_doubled_when_already_present(self) -> None:
+        """Test that provider prefix is not added when model already has a slash."""
+        from crewai.flow.human_feedback import _serialize_llm_for_context
+
+        mock_obj = MagicMock()
+        mock_obj.model = "gemini/gemini-2.0-flash"
+        mock_obj.provider = "gemini"
+        assert _serialize_llm_for_context(mock_obj) == "gemini/gemini-2.0-flash"
+
+    def test_no_provider_attr_falls_back_to_bare_model(self) -> None:
+        """Test that bare model is used when no provider attribute exists."""
+        from crewai.flow.human_feedback import _serialize_llm_for_context
+
+        mock_obj = MagicMock(spec=[])
+        mock_obj.model = "gpt-4o-mini"
+        assert _serialize_llm_for_context(mock_obj) == "gpt-4o-mini"
 
 
 class TestAsyncHumanFeedbackEdgeCases:

lib/crewai/tests/test_human_feedback_decorator.py

@@ -400,6 +400,45 @@ class TestCollapseToOutcome:
 
         assert result == "approved"  # First in list
 
+    def test_both_llm_calls_fail_returns_first_outcome(self):
+        """When both structured and simple prompting fail, return outcomes[0]."""
+        flow = Flow()
+
+        with patch("crewai.llm.LLM") as MockLLM:
+            mock_llm = MagicMock()
+            # Both calls raise — simulates wrong provider / auth failure
+            mock_llm.call.side_effect = RuntimeError("Model not found")
+            MockLLM.return_value = mock_llm
+
+            result = flow._collapse_to_outcome(
+                feedback="looks great, approve it",
+                outcomes=["needs_changes", "approved"],
+                llm="gemini-3-flash-preview",
+            )
+
+        assert result == "needs_changes"  # First in list (safe fallback)
+
+    def test_structured_fails_but_simple_succeeds(self):
+        """When structured output fails but simple prompting works, use that."""
+        flow = Flow()
+
+        with patch("crewai.llm.LLM") as MockLLM:
+            mock_llm = MagicMock()
+            # First call (structured) fails, second call (simple) succeeds
+            mock_llm.call.side_effect = [
+                RuntimeError("Function calling not supported"),
+                "approved",
+            ]
+            MockLLM.return_value = mock_llm
+
+            result = flow._collapse_to_outcome(
+                feedback="looks great",
+                outcomes=["needs_changes", "approved"],
+                llm="gpt-4o-mini",
+            )
+
+        assert result == "approved"
+
 
 # -- HITL Learning tests --
 

lib/crewai/tests/utilities/test_lock_store.py

@@ -0,0 +1,70 @@
+"""Tests for lock_store.
+
+We verify our own logic: the _redis_available guard and which portalocker
+backend is selected. We trust portalocker to handle actual locking mechanics.
+"""
+
+from __future__ import annotations
+
+import sys
+from unittest import mock
+
+import pytest
+
+import crewai.utilities.lock_store as lock_store
+from crewai.utilities.lock_store import lock
+
+
+@pytest.fixture(autouse=True)
+def no_redis_url(monkeypatch):
+    monkeypatch.setattr(lock_store, "_REDIS_URL", None)
+
+
+# ---------------------------------------------------------------------------
+# _redis_available
+# ---------------------------------------------------------------------------
+
+
+def test_redis_not_available_without_url():
+    assert lock_store._redis_available() is False
+
+
+def test_redis_not_available_when_package_missing(monkeypatch):
+    monkeypatch.setattr(lock_store, "_REDIS_URL", "redis://localhost:6379")
+    monkeypatch.setitem(sys.modules, "redis", None)  # None → ImportError on import
+    assert lock_store._redis_available() is False
+
+
+def test_redis_available_with_url_and_package(monkeypatch):
+    monkeypatch.setattr(lock_store, "_REDIS_URL", "redis://localhost:6379")
+    monkeypatch.setitem(sys.modules, "redis", mock.MagicMock())
+    assert lock_store._redis_available() is True
+
+
+# ---------------------------------------------------------------------------
+# lock strategy selection
+# ---------------------------------------------------------------------------
+
+
+def test_uses_file_lock_when_redis_unavailable():
+    with mock.patch("portalocker.Lock") as mock_lock:
+        with lock("file_test"):
+            pass
+
+    mock_lock.assert_called_once()
+    assert "crewai:" in mock_lock.call_args.args[0]
+
+
+def test_uses_redis_lock_when_redis_available(monkeypatch):
+    fake_conn = mock.MagicMock()
+    monkeypatch.setattr(lock_store, "_redis_available", mock.Mock(return_value=True))
+    monkeypatch.setattr(lock_store, "_redis_connection", mock.Mock(return_value=fake_conn))
+
+    with mock.patch("portalocker.RedisLock") as mock_redis_lock:
+        with lock("redis_test"):
+            pass
+
+    mock_redis_lock.assert_called_once()
+    kwargs = mock_redis_lock.call_args.kwargs
+    assert kwargs["channel"].startswith("crewai:")
+    assert kwargs["connection"] is fake_conn

lib/devtools/src/crewai_devtools/__init__.py

@@ -1,3 +1,3 @@
 """CrewAI development tools."""
 
-__version__ = "1.11.0rc1"
+__version__ = "1.11.0"

lib/devtools/src/crewai_devtools/cli.py

@@ -5,6 +5,7 @@ from pathlib import Path
 import subprocess
 import sys
 import time
+from typing import Final, Literal
 
 import click
 from dotenv import load_dotenv
@@ -250,7 +251,9 @@ def add_docs_version(docs_json_path: Path, version: str) -> bool:
     return True
 
 
-_PT_BR_MONTHS = {
+ChangelogLang = Literal["en", "pt-BR", "ko"]
+
+_PT_BR_MONTHS: Final[dict[int, str]] = {
     1: "jan",
     2: "fev",
     3: "mar",
@@ -265,7 +268,9 @@ _PT_BR_MONTHS = {
     12: "dez",
 }
 
-_CHANGELOG_LOCALES: dict[str, dict[str, str]] = {
+_CHANGELOG_LOCALES: Final[
+    dict[ChangelogLang, dict[Literal["link_text", "language_name"], str]]
+] = {
     "en": {
         "link_text": "View release on GitHub",
         "language_name": "English",
@@ -283,7 +288,7 @@ _CHANGELOG_LOCALES: dict[str, dict[str, str]] = {
 
 def translate_release_notes(
     release_notes: str,
-    lang: str,
+    lang: ChangelogLang,
     client: OpenAI,
 ) -> str:
     """Translate release notes into the target language using OpenAI.
@@ -326,7 +331,7 @@ def translate_release_notes(
         return release_notes
 
 
-def _format_changelog_date(lang: str) -> str:
+def _format_changelog_date(lang: ChangelogLang) -> str:
     """Format today's date for a changelog entry in the given language."""
     from datetime import datetime
 
@@ -342,7 +347,7 @@ def update_changelog(
     changelog_path: Path,
     version: str,
     release_notes: str,
-    lang: str = "en",
+    lang: ChangelogLang = "en",
 ) -> bool:
     """Prepend a new release entry to a docs changelog file.
 
@@ -475,6 +480,23 @@ def get_packages(lib_dir: Path) -> list[Path]:
     return packages
 
 
+PrereleaseIndicator = Literal["a", "b", "rc", "alpha", "beta", "dev"]
+_PRERELEASE_INDICATORS: Final[tuple[PrereleaseIndicator, ...]] = (
+    "a",
+    "b",
+    "rc",
+    "alpha",
+    "beta",
+    "dev",
+)
+
+
+def _is_prerelease(version: str) -> bool:
+    """Check if a version string represents a pre-release."""
+    v = version.lower().lstrip("v")
+    return any(indicator in v for indicator in _PRERELEASE_INDICATORS)
+
+
 def get_commits_from_last_tag(tag_name: str, version: str) -> tuple[str, str]:
     """Get commits from the last tag, excluding current version.
 
@@ -489,6 +511,9 @@ def get_commits_from_last_tag(tag_name: str, version: str) -> tuple[str, str]:
         all_tags = run_command(["git", "tag", "--sort=-version:refname"]).split("\n")
         prev_tags = [t for t in all_tags if t and t != tag_name and t != f"v{version}"]
 
+        if not _is_prerelease(version):
+            prev_tags = [t for t in prev_tags if not _is_prerelease(t)]
+
         if prev_tags:
             last_tag = prev_tags[0]
             commit_range = f"{last_tag}..HEAD"
@@ -678,20 +703,28 @@ def _generate_release_notes(
 
     with console.status("[cyan]Generating release notes..."):
         try:
-            prev_bump_commit = run_command(
+            prev_bump_output = run_command(
                 [
                     "git",
                     "log",
                     "--grep=^feat: bump versions to",
-                    "--format=%H",
-                    "-n",
-                    "2",
+                    "--format=%H %s",
                 ]
             )
-            commits_list = prev_bump_commit.strip().split("\n")
+            bump_entries = [
+                line for line in prev_bump_output.strip().split("\n") if line.strip()
+            ]
 
-            if len(commits_list) > 1:
-                prev_commit = commits_list[1]
+            is_stable = not _is_prerelease(version)
+            prev_commit = None
+            for entry in bump_entries[1:]:
+                bump_ver = entry.split("feat: bump versions to", 1)[-1].strip()
+                if is_stable and _is_prerelease(bump_ver):
+                    continue
+                prev_commit = entry.split()[0]
+                break
+
+            if prev_commit:
                 commit_range = f"{prev_commit}..HEAD"
                 commits = run_command(
                     ["git", "log", commit_range, "--pretty=format:%s"]
@@ -777,10 +810,7 @@ def _generate_release_notes(
             "\n[green]✓[/green] Using generated release notes without editing"
         )
 
-    is_prerelease = any(
-        indicator in version.lower()
-        for indicator in ["a", "b", "rc", "alpha", "beta", "dev"]
-    )
+    is_prerelease = _is_prerelease(version)
 
     return release_notes, openai_client, is_prerelease
 
@@ -799,7 +829,7 @@ def _update_docs_and_create_pr(
         The docs branch name if a PR was created, None otherwise.
     """
     docs_json_path = cwd / "docs" / "docs.json"
-    changelog_langs = ["en", "pt-BR", "ko"]
+    changelog_langs: list[ChangelogLang] = ["en", "pt-BR", "ko"]
 
     if not dry_run:
         docs_files_staged: list[str] = []

uv.lock

@@ -408,14 +408,14 @@ wheels = [
 
 [[package]]
 name = "authlib"
-version = "1.6.7"
+version = "1.6.9"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "cryptography" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/49/dc/ed1681bf1339dd6ea1ce56136bad4baabc6f7ad466e375810702b0237047/authlib-1.6.7.tar.gz", hash = "sha256:dbf10100011d1e1b34048c9d120e83f13b35d69a826ae762b93d2fb5aafc337b", size = 164950, upload-time = "2026-02-06T14:04:14.171Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/af/98/00d3dd826d46959ad8e32af2dbb2398868fd9fd0683c26e56d0789bd0e68/authlib-1.6.9.tar.gz", hash = "sha256:d8f2421e7e5980cc1ddb4e32d3f5fa659cfaf60d8eaf3281ebed192e4ab74f04", size = 165134, upload-time = "2026-03-02T07:44:01.998Z" }
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+    { url = "https://files.pythonhosted.org/packages/53/23/b65f568ed0c22f1efacb744d2db1a33c8068f384b8c9b482b52ebdbc3ef6/authlib-1.6.9-py2.py3-none-any.whl", hash = "sha256:f08b4c14e08f0861dc18a32357b33fbcfd2ea86cfe3fe149484b4d764c4a0ac3", size = 244197, upload-time = "2026-03-02T07:44:00.307Z" },
 ]
 
 [[package]]
@@ -5556,11 +5556,11 @@ wheels = [
 
 [[package]]
 name = "pyasn1"
-version = "0.6.2"
+version = "0.6.3"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/fe/b6/6e630dff89739fcd427e3f72b3d905ce0acb85a45d4ec3e2678718a3487f/pyasn1-0.6.2.tar.gz", hash = "sha256:9b59a2b25ba7e4f8197db7686c09fb33e658b98339fadb826e9512629017833b", size = 146586, upload-time = "2026-01-16T18:04:18.534Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/5c/5f/6583902b6f79b399c9c40674ac384fd9cd77805f9e6205075f828ef11fb2/pyasn1-0.6.3.tar.gz", hash = "sha256:697a8ecd6d98891189184ca1fa05d1bb00e2f84b5977c481452050549c8a72cf", size = 148685, upload-time = "2026-03-17T01:06:53.382Z" }
 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/5d/a0/7d793dce3fa811fe047d6ae2431c672364b462850c6235ae306c0efd025f/pyasn1-0.6.3-py3-none-any.whl", hash = "sha256:a80184d120f0864a52a073acc6fc642847d0be408e7c7252f31390c0f4eadcde", size = 83997, upload-time = "2026-03-17T01:06:52.036Z" },
 ]
 
 [[package]]
@@ -5940,11 +5940,14 @@ wheels = [
 
 [[package]]
 name = "pyjwt"
-version = "2.11.0"
+version = "2.12.1"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/5c/5a/b46fa56bf322901eee5b0454a34343cdbdae202cd421775a8ee4e42fd519/pyjwt-2.11.0.tar.gz", hash = "sha256:35f95c1f0fbe5d5ba6e43f00271c275f7a1a4db1dab27bf708073b75318ea623", size = 98019, upload-time = "2026-01-30T19:59:55.694Z" }
+dependencies = [
+    { name = "typing-extensions", marker = "python_full_version < '3.11'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/c2/27/a3b6e5bf6ff856d2509292e95c8f57f0df7017cf5394921fc4e4ef40308a/pyjwt-2.12.1.tar.gz", hash = "sha256:c74a7a2adf861c04d002db713dd85f84beb242228e671280bf709d765b03672b", size = 102564, upload-time = "2026-03-13T19:27:37.25Z" }
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+    { url = "https://files.pythonhosted.org/packages/e5/7a/8dd906bd22e79e47397a61742927f6747fe93242ef86645ee9092e610244/pyjwt-2.12.1-py3-none-any.whl", hash = "sha256:28ca37c070cad8ba8cd9790cd940535d40274d22f80ab87f3ac6a713e6e8454c", size = 29726, upload-time = "2026-03-13T19:27:35.677Z" },
 ]
 
 [package.optional-dependencies]
@@ -7307,7 +7310,7 @@ wheels = [
 
 [[package]]
 name = "snowflake-connector-python"
-version = "4.2.0"
+version = "4.3.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "asn1crypto" },
@@ -7329,28 +7332,28 @@ dependencies = [
     { name = "typing-extensions" },
     { name = "urllib3" },
 ]
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