chore: re-trigger CI checks

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

.gitignore

@@ -26,4 +26,3 @@ plan.md
 conceptual_plan.md
 build_image
 chromadb-*.lock
-.claude

docs/docs.json

@@ -291,7 +291,6 @@
                   "en/observability/arize-phoenix",
                   "en/observability/braintrust",
                   "en/observability/datadog",
-                  "en/observability/galileo",
                   "en/observability/langdb",
                   "en/observability/langfuse",
                   "en/observability/langtrace",
@@ -429,8 +428,7 @@
                 "group": "How-To Guides",
                 "pages": [
                   "en/enterprise/guides/build-crew",
-                  "en/enterprise/guides/prepare-for-deployment",
-                  "en/enterprise/guides/deploy-to-amp",
+                  "en/enterprise/guides/deploy-crew",
                   "en/enterprise/guides/kickoff-crew",
                   "en/enterprise/guides/update-crew",
                   "en/enterprise/guides/enable-crew-studio",
@@ -744,7 +742,6 @@
                   "pt-BR/observability/arize-phoenix",
                   "pt-BR/observability/braintrust",
                   "pt-BR/observability/datadog",
-                  "pt-BR/observability/galileo",
                   "pt-BR/observability/langdb",
                   "pt-BR/observability/langfuse",
                   "pt-BR/observability/langtrace",
@@ -865,8 +862,7 @@
                 "group": "Guias",
                 "pages": [
                   "pt-BR/enterprise/guides/build-crew",
-                  "pt-BR/enterprise/guides/prepare-for-deployment",
-                  "pt-BR/enterprise/guides/deploy-to-amp",
+                  "pt-BR/enterprise/guides/deploy-crew",
                   "pt-BR/enterprise/guides/kickoff-crew",
                   "pt-BR/enterprise/guides/update-crew",
                   "pt-BR/enterprise/guides/enable-crew-studio",
@@ -1207,7 +1203,6 @@
                   "ko/observability/arize-phoenix",
                   "ko/observability/braintrust",
                   "ko/observability/datadog",
-                  "ko/observability/galileo",
                   "ko/observability/langdb",
                   "ko/observability/langfuse",
                   "ko/observability/langtrace",
@@ -1328,8 +1323,7 @@
                 "group": "How-To Guides",
                 "pages": [
                   "ko/enterprise/guides/build-crew",
-                  "ko/enterprise/guides/prepare-for-deployment",
-                  "ko/enterprise/guides/deploy-to-amp",
+                  "ko/enterprise/guides/deploy-crew",
                   "ko/enterprise/guides/kickoff-crew",
                   "ko/enterprise/guides/update-crew",
                   "ko/enterprise/guides/enable-crew-studio",
@@ -1517,18 +1511,6 @@
       "source": "/enterprise/:path*",
       "destination": "/en/enterprise/:path*"
     },
-    {
-      "source": "/en/enterprise/guides/deploy-crew",
-      "destination": "/en/enterprise/guides/deploy-to-amp"
-    },
-    {
-      "source": "/ko/enterprise/guides/deploy-crew",
-      "destination": "/ko/enterprise/guides/deploy-to-amp"
-    },
-    {
-      "source": "/pt-BR/enterprise/guides/deploy-crew",
-      "destination": "/pt-BR/enterprise/guides/deploy-to-amp"
-    },
     {
       "source": "/api-reference/:path*",
       "destination": "/en/api-reference/:path*"

docs/en/concepts/flows.mdx

@@ -574,10 +574,6 @@ When you run this Flow, the output will change based on the random boolean value
 
 ### Human in the Loop (human feedback)
 
-<Note>
-The `@human_feedback` decorator requires **CrewAI version 1.8.0 or higher**.
-</Note>
-
 The `@human_feedback` decorator enables human-in-the-loop workflows by pausing flow execution to collect feedback from a human. This is useful for approval gates, quality review, and decision points that require human judgment.
 
 ```python Code

docs/en/enterprise/guides/deploy-crew.mdx

@@ -1,12 +1,12 @@
 ---
-title: "Deploy to AMP"
-description: "Deploy your Crew or Flow to CrewAI AMP"
+title: "Deploy Crew"
+description: "Deploying a Crew on CrewAI AMP"
 icon: "rocket"
 mode: "wide"
 ---
 
 <Note>
-  After creating a Crew or Flow locally (or through Crew Studio), the next step is
+  After creating a crew locally or through Crew Studio, the next step is
   deploying it to the CrewAI AMP platform. This guide covers multiple deployment
   methods to help you choose the best approach for your workflow.
 </Note>
@@ -14,26 +14,19 @@ mode: "wide"
 ## Prerequisites
 
 <CardGroup cols={2}>
-  <Card title="Project Ready for Deployment" icon="check-circle">
-    You should have a working Crew or Flow that runs successfully locally.
-    Follow our [preparation guide](/en/enterprise/guides/prepare-for-deployment) to verify your project structure.
+  <Card title="Crew Ready for Deployment" icon="users">
+    You should have a working crew either built locally or created through Crew
+    Studio
   </Card>
   <Card title="GitHub Repository" icon="github">
-    Your code should be in a GitHub repository (for GitHub integration
+    Your crew code should be in a GitHub repository (for GitHub integration
     method)
   </Card>
 </CardGroup>
 
-<Info>
-  **Crews vs Flows**: Both project types can be deployed as "automations" on CrewAI AMP.
-  The deployment process is the same, but they have different project structures.
-  See [Prepare for Deployment](/en/enterprise/guides/prepare-for-deployment) for details.
-</Info>
-
 ## Option 1: Deploy Using CrewAI CLI
 
-The CLI provides the fastest way to deploy locally developed Crews or Flows to the AMP platform.
-The CLI automatically detects your project type from `pyproject.toml` and builds accordingly.
+The CLI provides the fastest way to deploy locally developed crews to the Enterprise platform.
 
 <Steps>
   <Step title="Install CrewAI CLI">
@@ -135,7 +128,7 @@ crewai deploy remove <deployment_id>
 
 ## Option 2: Deploy Directly via Web Interface
 
-You can also deploy your Crews or Flows directly through the CrewAI AMP web interface by connecting your GitHub account. This approach doesn't require using the CLI on your local machine. The platform automatically detects your project type and handles the build appropriately.
+You can also deploy your crews directly through the CrewAI AMP web interface by connecting your GitHub account. This approach doesn't require using the CLI on your local machine.
 
 <Steps>
 
@@ -289,7 +282,68 @@ For automated deployments in CI/CD pipelines, you can use the CrewAI API to trig
 
   </Steps>
 
-## Interact with Your Deployed Automation
+## ⚠️ Environment Variable Security Requirements
+
+<Warning>
+  **Important**: CrewAI AMP has security restrictions on environment variable
+  names that can cause deployment failures if not followed.
+</Warning>
+
+### Blocked Environment Variable Patterns
+
+For security reasons, the following environment variable naming patterns are **automatically filtered** and will cause deployment issues:
+
+**Blocked Patterns:**
+
+- Variables ending with `_TOKEN` (e.g., `MY_API_TOKEN`)
+- Variables ending with `_PASSWORD` (e.g., `DB_PASSWORD`)
+- Variables ending with `_SECRET` (e.g., `API_SECRET`)
+- Variables ending with `_KEY` in certain contexts
+
+**Specific Blocked Variables:**
+
+- `GITHUB_USER`, `GITHUB_TOKEN`
+- `AWS_REGION`, `AWS_DEFAULT_REGION`
+- Various internal CrewAI system variables
+
+### Allowed Exceptions
+
+Some variables are explicitly allowed despite matching blocked patterns:
+
+- `AZURE_AD_TOKEN`
+- `AZURE_OPENAI_AD_TOKEN`
+- `ENTERPRISE_ACTION_TOKEN`
+- `CREWAI_ENTEPRISE_TOOLS_TOKEN`
+
+### How to Fix Naming Issues
+
+If your deployment fails due to environment variable restrictions:
+
+```bash
+# ❌ These will cause deployment failures
+OPENAI_TOKEN=sk-...
+DATABASE_PASSWORD=mypassword
+API_SECRET=secret123
+
+# ✅ Use these naming patterns instead
+OPENAI_API_KEY=sk-...
+DATABASE_CREDENTIALS=mypassword
+API_CONFIG=secret123
+```
+
+### Best Practices
+
+1. **Use standard naming conventions**: `PROVIDER_API_KEY` instead of `PROVIDER_TOKEN`
+2. **Test locally first**: Ensure your crew works with the renamed variables
+3. **Update your code**: Change any references to the old variable names
+4. **Document changes**: Keep track of renamed variables for your team
+
+<Tip>
+  If you encounter deployment failures with cryptic environment variable errors,
+  check your variable names against these patterns first.
+</Tip>
+
+### Interact with Your Deployed Crew
 
 Once deployment is complete, you can access your crew through:
 
@@ -333,108 +387,7 @@ The Enterprise platform also offers:
 - **Custom Tools Repository**: Create, share, and install tools
 - **Crew Studio**: Build crews through a chat interface without writing code
 
-## Troubleshooting Deployment Failures
-
-If your deployment fails, check these common issues:
-
-### Build Failures
-
-#### Missing uv.lock File
-
-**Symptom**: Build fails early with dependency resolution errors
-
-**Solution**: Generate and commit the lock file:
-
-```bash
-uv lock
-git add uv.lock
-git commit -m "Add uv.lock for deployment"
-git push
-```
-
-<Warning>
-  The `uv.lock` file is required for all deployments. Without it, the platform
-  cannot reliably install your dependencies.
-</Warning>
-
-#### Wrong Project Structure
-
-**Symptom**: "Could not find entry point" or "Module not found" errors
-
-**Solution**: Verify your project matches the expected structure:
-
-- **Both Crews and Flows**: Must have entry point at `src/project_name/main.py`
-- **Crews**: Use a `run()` function as entry point
-- **Flows**: Use a `kickoff()` function as entry point
-
-See [Prepare for Deployment](/en/enterprise/guides/prepare-for-deployment) for detailed structure diagrams.
-
-#### Missing CrewBase Decorator
-
-**Symptom**: "Crew not found", "Config not found", or agent/task configuration errors
-
-**Solution**: Ensure **all** crew classes use the `@CrewBase` decorator:
-
-```python
-from crewai.project import CrewBase, agent, crew, task
-
-@CrewBase  # This decorator is REQUIRED
-class YourCrew():
-    """Your crew description"""
-
-    @agent
-    def my_agent(self) -> Agent:
-        return Agent(
-            config=self.agents_config['my_agent'],  # type: ignore[index]
-            verbose=True
-        )
-
-    # ... rest of crew definition
-```
-
-<Info>
-  This applies to standalone Crews AND crews embedded inside Flow projects.
-  Every crew class needs the decorator.
-</Info>
-
-#### Incorrect pyproject.toml Type
-
-**Symptom**: Build succeeds but runtime fails, or unexpected behavior
-
-**Solution**: Verify the `[tool.crewai]` section matches your project type:
-
-```toml
-# For Crew projects:
-[tool.crewai]
-type = "crew"
-
-# For Flow projects:
-[tool.crewai]
-type = "flow"
-```
-
-### Runtime Failures
-
-#### LLM Connection Failures
-
-**Symptom**: API key errors, "model not found", or authentication failures
-
-**Solution**:
-1. Verify your LLM provider's API key is correctly set in environment variables
-2. Ensure the environment variable names match what your code expects
-3. Test locally with the exact same environment variables before deploying
-
-#### Crew Execution Errors
-
-**Symptom**: Crew starts but fails during execution
-
-**Solution**:
-1. Check the execution logs in the AMP dashboard (Traces tab)
-2. Verify all tools have required API keys configured
-3. Ensure agent configurations in `agents.yaml` are valid
-4. Check task configurations in `tasks.yaml` for syntax errors
-
 <Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
   Contact our support team for assistance with deployment issues or questions
-  about the AMP platform.
+  about the Enterprise platform.
 </Card>

docs/en/enterprise/guides/prepare-for-deployment.mdx

@@ -1,305 +0,0 @@
----
-title: "Prepare for Deployment"
-description: "Ensure your Crew or Flow is ready for deployment to CrewAI AMP"
-icon: "clipboard-check"
-mode: "wide"
----
-
-<Note>
-  Before deploying to CrewAI AMP, it's crucial to verify your project is correctly structured.
-  Both Crews and Flows can be deployed as "automations," but they have different project structures
-  and requirements that must be met for successful deployment.
-</Note>
-
-## Understanding Automations
-
-In CrewAI AMP, **automations** is the umbrella term for deployable Agentic AI projects. An automation can be either:
-
-- **A Crew**: A standalone team of AI agents working together on tasks
-- **A Flow**: An orchestrated workflow that can combine multiple crews, direct LLM calls, and procedural logic
-
-Understanding which type you're deploying is essential because they have different project structures and entry points.
-
-## Crews vs Flows: Key Differences
-
-<CardGroup cols={2}>
-  <Card title="Crew Projects" icon="users">
-    Standalone AI agent teams with `crew.py` defining agents and tasks. Best for focused, collaborative tasks.
-  </Card>
-  <Card title="Flow Projects" icon="diagram-project">
-    Orchestrated workflows with embedded crews in a `crews/` folder. Best for complex, multi-stage processes.
-  </Card>
-</CardGroup>
-
-| Aspect | Crew | Flow |
-|--------|------|------|
-| **Project structure** | `src/project_name/` with `crew.py` | `src/project_name/` with `crews/` folder |
-| **Main logic location** | `src/project_name/crew.py` | `src/project_name/main.py` (Flow class) |
-| **Entry point function** | `run()` in `main.py` | `kickoff()` in `main.py` |
-| **pyproject.toml type** | `type = "crew"` | `type = "flow"` |
-| **CLI create command** | `crewai create crew name` | `crewai create flow name` |
-| **Config location** | `src/project_name/config/` | `src/project_name/crews/crew_name/config/` |
-| **Can contain other crews** | No | Yes (in `crews/` folder) |
-
-## Project Structure Reference
-
-### Crew Project Structure
-
-When you run `crewai create crew my_crew`, you get this structure:
-
-```
-my_crew/
-├── .gitignore
-├── pyproject.toml          # Must have type = "crew"
-├── README.md
-├── .env
-├── uv.lock                  # REQUIRED for deployment
-└── src/
-    └── my_crew/
-        ├── __init__.py
-        ├── main.py          # Entry point with run() function
-        ├── crew.py          # Crew class with @CrewBase decorator
-        ├── tools/
-        │   ├── custom_tool.py
-        │   └── __init__.py
-        └── config/
-            ├── agents.yaml  # Agent definitions
-            └── tasks.yaml   # Task definitions
-```
-
-<Warning>
-  The nested `src/project_name/` structure is critical for Crews.
-  Placing files at the wrong level will cause deployment failures.
-</Warning>
-
-### Flow Project Structure
-
-When you run `crewai create flow my_flow`, you get this structure:
-
-```
-my_flow/
-├── .gitignore
-├── pyproject.toml          # Must have type = "flow"
-├── README.md
-├── .env
-├── uv.lock                  # REQUIRED for deployment
-└── src/
-    └── my_flow/
-        ├── __init__.py
-        ├── main.py          # Entry point with kickoff() function + Flow class
-        ├── crews/           # Embedded crews folder
-        │   └── poem_crew/
-        │       ├── __init__.py
-        │       ├── poem_crew.py  # Crew with @CrewBase decorator
-        │       └── config/
-        │           ├── agents.yaml
-        │           └── tasks.yaml
-        └── tools/
-            ├── __init__.py
-            └── custom_tool.py
-```
-
-<Info>
-  Both Crews and Flows use the `src/project_name/` structure.
-  The key difference is that Flows have a `crews/` folder for embedded crews,
-  while Crews have `crew.py` directly in the project folder.
-</Info>
-
-## Pre-Deployment Checklist
-
-Use this checklist to verify your project is ready for deployment.
-
-### 1. Verify pyproject.toml Configuration
-
-Your `pyproject.toml` must include the correct `[tool.crewai]` section:
-
-<Tabs>
-  <Tab title="For Crews">
-    ```toml
-    [tool.crewai]
-    type = "crew"
-    ```
-  </Tab>
-  <Tab title="For Flows">
-    ```toml
-    [tool.crewai]
-    type = "flow"
-    ```
-  </Tab>
-</Tabs>
-
-<Warning>
-  If the `type` doesn't match your project structure, the build will fail or
-  the automation won't run correctly.
-</Warning>
-
-### 2. Ensure uv.lock File Exists
-
-CrewAI uses `uv` for dependency management. The `uv.lock` file ensures reproducible builds and is **required** for deployment.
-
-```bash
-# Generate or update the lock file
-uv lock
-
-# Verify it exists
-ls -la uv.lock
-```
-
-If the file doesn't exist, run `uv lock` and commit it to your repository:
-
-```bash
-uv lock
-git add uv.lock
-git commit -m "Add uv.lock for deployment"
-git push
-```
-
-### 3. Validate CrewBase Decorator Usage
-
-**Every crew class must use the `@CrewBase` decorator.** This applies to:
-
-- Standalone crew projects
-- Crews embedded inside Flow projects
-
-```python
-from crewai import Agent, Crew, Process, Task
-from crewai.project import CrewBase, agent, crew, task
-from crewai.agents.agent_builder.base_agent import BaseAgent
-from typing import List
-
-@CrewBase  # This decorator is REQUIRED
-class MyCrew():
-    """My crew description"""
-
-    agents: List[BaseAgent]
-    tasks: List[Task]
-
-    @agent
-    def my_agent(self) -> Agent:
-        return Agent(
-            config=self.agents_config['my_agent'],  # type: ignore[index]
-            verbose=True
-        )
-
-    @task
-    def my_task(self) -> Task:
-        return Task(
-            config=self.tasks_config['my_task']  # type: ignore[index]
-        )
-
-    @crew
-    def crew(self) -> Crew:
-        return Crew(
-            agents=self.agents,
-            tasks=self.tasks,
-            process=Process.sequential,
-            verbose=True,
-        )
-```
-
-<Warning>
-  If you forget the `@CrewBase` decorator, your deployment will fail with
-  errors about missing agents or tasks configurations.
-</Warning>
-
-### 4. Check Project Entry Points
-
-Both Crews and Flows have their entry point in `src/project_name/main.py`:
-
-<Tabs>
-  <Tab title="For Crews">
-    The entry point uses a `run()` function:
-
-    ```python
-    # src/my_crew/main.py
-    from my_crew.crew import MyCrew
-
-    def run():
-        """Run the crew."""
-        inputs = {'topic': 'AI in Healthcare'}
-        result = MyCrew().crew().kickoff(inputs=inputs)
-        return result
-
-    if __name__ == "__main__":
-        run()
-    ```
-  </Tab>
-  <Tab title="For Flows">
-    The entry point uses a `kickoff()` function with a Flow class:
-
-    ```python
-    # src/my_flow/main.py
-    from crewai.flow import Flow, listen, start
-    from my_flow.crews.poem_crew.poem_crew import PoemCrew
-
-    class MyFlow(Flow):
-        @start()
-        def begin(self):
-            # Flow logic here
-            result = PoemCrew().crew().kickoff(inputs={...})
-            return result
-
-    def kickoff():
-        """Run the flow."""
-        MyFlow().kickoff()
-
-    if __name__ == "__main__":
-        kickoff()
-    ```
-  </Tab>
-</Tabs>
-
-### 5. Prepare Environment Variables
-
-Before deployment, ensure you have:
-
-1. **LLM API keys** ready (OpenAI, Anthropic, Google, etc.)
-2. **Tool API keys** if using external tools (Serper, etc.)
-
-<Tip>
-  Test your project locally with the same environment variables before deploying
-  to catch configuration issues early.
-</Tip>
-
-## Quick Validation Commands
-
-Run these commands from your project root to quickly verify your setup:
-
-```bash
-# 1. Check project type in pyproject.toml
-grep -A2 "\[tool.crewai\]" pyproject.toml
-
-# 2. Verify uv.lock exists
-ls -la uv.lock || echo "ERROR: uv.lock missing! Run 'uv lock'"
-
-# 3. Verify src/ structure exists
-ls -la src/*/main.py 2>/dev/null || echo "No main.py found in src/"
-
-# 4. For Crews - verify crew.py exists
-ls -la src/*/crew.py 2>/dev/null || echo "No crew.py (expected for Crews)"
-
-# 5. For Flows - verify crews/ folder exists
-ls -la src/*/crews/ 2>/dev/null || echo "No crews/ folder (expected for Flows)"
-
-# 6. Check for CrewBase usage
-grep -r "@CrewBase" . --include="*.py"
-```
-
-## Common Setup Mistakes
-
-| Mistake | Symptom | Fix |
-|---------|---------|-----|
-| Missing `uv.lock` | Build fails during dependency resolution | Run `uv lock` and commit |
-| Wrong `type` in pyproject.toml | Build succeeds but runtime fails | Change to correct type |
-| Missing `@CrewBase` decorator | "Config not found" errors | Add decorator to all crew classes |
-| Files at root instead of `src/` | Entry point not found | Move to `src/project_name/` |
-| Missing `run()` or `kickoff()` | Cannot start automation | Add correct entry function |
-
-## Next Steps
-
-Once your project passes all checklist items, you're ready to deploy:
-
-<Card title="Deploy to AMP" icon="rocket" href="/en/enterprise/guides/deploy-to-amp">
-  Follow the deployment guide to deploy your Crew or Flow to CrewAI AMP using
-  the CLI, web interface, or CI/CD integration.
-</Card>

docs/en/learn/a2a-agent-delegation.mdx

@@ -1,48 +1,43 @@
 ---
 title: Agent-to-Agent (A2A) Protocol
-description: Agents delegate tasks to remote A2A agents and/or operate as A2A-compliant server agents.
+description: Enable CrewAI agents to delegate tasks to remote A2A-compliant agents for specialized handling
 icon: network-wired
 mode: "wide"
 ---
 
 ## A2A Agent Delegation
 
-CrewAI treats [A2A protocol](https://a2a-protocol.org/latest/) as a first-class delegation primitive, enabling agents to delegate tasks, request information, and collaborate with remote agents, as well as act as A2A-compliant server agents.
-In client mode, agents autonomously choose between local execution and remote delegation based on task requirements.
+CrewAI supports the Agent-to-Agent (A2A) protocol, allowing agents to delegate tasks to remote specialized agents. The agent's LLM automatically decides whether to handle a task directly or delegate to an A2A agent based on the task requirements.
+
+<Note>
+  A2A delegation requires the `a2a-sdk` package. Install with: `uv add 'crewai[a2a]'` or `pip install 'crewai[a2a]'`
+</Note>
 
 ## How It Works
 
 When an agent is configured with A2A capabilities:
 
-1. The Agent analyzes each task
+1. The LLM analyzes each task
 2. It decides to either:
    - Handle the task directly using its own capabilities
    - Delegate to a remote A2A agent for specialized handling
 3. If delegating, the agent communicates with the remote A2A agent through the protocol
 4. Results are returned to the CrewAI workflow
 
-<Note>
-  A2A delegation requires the `a2a-sdk` package. Install with: `uv add 'crewai[a2a]'` or `pip install 'crewai[a2a]'`
-</Note>
-
 ## Basic Configuration
 
-<Warning>
-  `crewai.a2a.config.A2AConfig` is deprecated and will be removed in v2.0.0. Use `A2AClientConfig` for connecting to remote agents and/or `A2AServerConfig` for exposing agents as servers.
-</Warning>
-
 Configure an agent for A2A delegation by setting the `a2a` parameter:
 
 ```python Code
 from crewai import Agent, Crew, Task
-from crewai.a2a import A2AClientConfig
+from crewai.a2a import A2AConfig
 
 agent = Agent(
     role="Research Coordinator",
     goal="Coordinate research tasks efficiently",
     backstory="Expert at delegating to specialized research agents",
     llm="gpt-4o",
-    a2a=A2AClientConfig(
+    a2a=A2AConfig(
         endpoint="https://example.com/.well-known/agent-card.json",
         timeout=120,
         max_turns=10
@@ -59,9 +54,9 @@ crew = Crew(agents=[agent], tasks=[task], verbose=True)
 result = crew.kickoff()
 ```
 
-## Client Configuration Options
+## Configuration Options
 
-The `A2AClientConfig` class accepts the following parameters:
+The `A2AConfig` class accepts the following parameters:
 
 <ParamField path="endpoint" type="str" required>
   The A2A agent endpoint URL (typically points to `.well-known/agent-card.json`)
@@ -96,34 +91,14 @@ The `A2AClientConfig` class accepts the following parameters:
   Update mechanism for receiving task status. Options: `StreamingConfig`, `PollingConfig`, or `PushNotificationConfig`.
 </ParamField>
 
-<ParamField path="transport_protocol" type="Literal['JSONRPC', 'GRPC', 'HTTP+JSON']" default="JSONRPC">
-  Transport protocol for A2A communication. Options: `JSONRPC` (default), `GRPC`, or `HTTP+JSON`.
-</ParamField>
-
-<ParamField path="accepted_output_modes" type="list[str]" default='["application/json"]'>
-  Media types the client can accept in responses.
-</ParamField>
-
-<ParamField path="supported_transports" type="list[str]" default='["JSONRPC"]'>
-  Ordered list of transport protocols the client supports.
-</ParamField>
-
-<ParamField path="use_client_preference" type="bool" default="False">
-  Whether to prioritize client transport preferences over server.
-</ParamField>
-
-<ParamField path="extensions" type="list[str]" default="[]">
-  Extension URIs the client supports.
-</ParamField>
-
 ## Authentication
 
 For A2A agents that require authentication, use one of the provided auth schemes:
 
 <Tabs>
   <Tab title="Bearer Token">
-```python bearer_token_auth.py lines
-from crewai.a2a import A2AClientConfig
+    ```python Code
+from crewai.a2a import A2AConfig
 from crewai.a2a.auth import BearerTokenAuth
 
 agent = Agent(
@@ -131,18 +106,18 @@ agent = Agent(
     goal="Coordinate tasks with secured agents",
     backstory="Manages secure agent communications",
     llm="gpt-4o",
-    a2a=A2AClientConfig(
+    a2a=A2AConfig(
         endpoint="https://secure-agent.example.com/.well-known/agent-card.json",
         auth=BearerTokenAuth(token="your-bearer-token"),
         timeout=120
     )
 )
-```
+    ```
   </Tab>
 
   <Tab title="API Key">
-```python api_key_auth.py lines
-from crewai.a2a import A2AClientConfig
+    ```python Code
+from crewai.a2a import A2AConfig
 from crewai.a2a.auth import APIKeyAuth
 
 agent = Agent(
@@ -150,7 +125,7 @@ agent = Agent(
     goal="Coordinate with API-based agents",
     backstory="Manages API-authenticated communications",
     llm="gpt-4o",
-    a2a=A2AClientConfig(
+    a2a=A2AConfig(
         endpoint="https://api-agent.example.com/.well-known/agent-card.json",
         auth=APIKeyAuth(
             api_key="your-api-key",
@@ -160,12 +135,12 @@ agent = Agent(
         timeout=120
     )
 )
-```
+    ```
   </Tab>
 
   <Tab title="OAuth2">
-```python oauth2_auth.py lines
-from crewai.a2a import A2AClientConfig
+    ```python Code
+from crewai.a2a import A2AConfig
 from crewai.a2a.auth import OAuth2ClientCredentials
 
 agent = Agent(
@@ -173,7 +148,7 @@ agent = Agent(
     goal="Coordinate with OAuth-secured agents",
     backstory="Manages OAuth-authenticated communications",
     llm="gpt-4o",
-    a2a=A2AClientConfig(
+    a2a=A2AConfig(
         endpoint="https://oauth-agent.example.com/.well-known/agent-card.json",
         auth=OAuth2ClientCredentials(
             token_url="https://auth.example.com/oauth/token",
@@ -184,12 +159,12 @@ agent = Agent(
         timeout=120
     )
 )
-```
+    ```
   </Tab>
 
   <Tab title="HTTP Basic">
-```python http_basic_auth.py lines
-from crewai.a2a import A2AClientConfig
+    ```python Code
+from crewai.a2a import A2AConfig
 from crewai.a2a.auth import HTTPBasicAuth
 
 agent = Agent(
@@ -197,7 +172,7 @@ agent = Agent(
     goal="Coordinate with basic auth agents",
     backstory="Manages basic authentication communications",
     llm="gpt-4o",
-    a2a=A2AClientConfig(
+    a2a=A2AConfig(
         endpoint="https://basic-agent.example.com/.well-known/agent-card.json",
         auth=HTTPBasicAuth(
             username="your-username",
@@ -206,7 +181,7 @@ agent = Agent(
         timeout=120
     )
 )
-```
+    ```
   </Tab>
 </Tabs>
 
@@ -215,7 +190,7 @@ agent = Agent(
 Configure multiple A2A agents for delegation by passing a list:
 
 ```python Code
-from crewai.a2a import A2AClientConfig
+from crewai.a2a import A2AConfig
 from crewai.a2a.auth import BearerTokenAuth
 
 agent = Agent(
@@ -224,11 +199,11 @@ agent = Agent(
     backstory="Expert at delegating to the right specialist",
     llm="gpt-4o",
     a2a=[
-        A2AClientConfig(
+        A2AConfig(
             endpoint="https://research.example.com/.well-known/agent-card.json",
             timeout=120
         ),
-        A2AClientConfig(
+        A2AConfig(
             endpoint="https://data.example.com/.well-known/agent-card.json",
             auth=BearerTokenAuth(token="data-token"),
             timeout=90
@@ -244,7 +219,7 @@ The LLM will automatically choose which A2A agent to delegate to based on the ta
 Control how agent connection failures are handled using the `fail_fast` parameter:
 
 ```python Code
-from crewai.a2a import A2AClientConfig
+from crewai.a2a import A2AConfig
 
 # Fail immediately on connection errors (default)
 agent = Agent(
@@ -252,7 +227,7 @@ agent = Agent(
     goal="Coordinate research tasks",
     backstory="Expert at delegation",
     llm="gpt-4o",
-    a2a=A2AClientConfig(
+    a2a=A2AConfig(
         endpoint="https://research.example.com/.well-known/agent-card.json",
         fail_fast=True
     )
@@ -265,11 +240,11 @@ agent = Agent(
     backstory="Expert at working with available resources",
     llm="gpt-4o",
     a2a=[
-        A2AClientConfig(
+        A2AConfig(
             endpoint="https://primary.example.com/.well-known/agent-card.json",
             fail_fast=False
         ),
-        A2AClientConfig(
+        A2AConfig(
             endpoint="https://backup.example.com/.well-known/agent-card.json",
             fail_fast=False
         )
@@ -288,8 +263,8 @@ Control how your agent receives task status updates from remote A2A agents:
 
 <Tabs>
   <Tab title="Streaming (Default)">
-```python streaming_config.py lines
-from crewai.a2a import A2AClientConfig
+    ```python Code
+from crewai.a2a import A2AConfig
 from crewai.a2a.updates import StreamingConfig
 
 agent = Agent(
@@ -297,17 +272,17 @@ agent = Agent(
     goal="Coordinate research tasks",
     backstory="Expert at delegation",
     llm="gpt-4o",
-    a2a=A2AClientConfig(
+    a2a=A2AConfig(
         endpoint="https://research.example.com/.well-known/agent-card.json",
         updates=StreamingConfig()
     )
 )
-```
+    ```
   </Tab>
 
   <Tab title="Polling">
-```python polling_config.py lines
-from crewai.a2a import A2AClientConfig
+    ```python Code
+from crewai.a2a import A2AConfig
 from crewai.a2a.updates import PollingConfig
 
 agent = Agent(
@@ -315,7 +290,7 @@ agent = Agent(
     goal="Coordinate research tasks",
     backstory="Expert at delegation",
     llm="gpt-4o",
-    a2a=A2AClientConfig(
+    a2a=A2AConfig(
         endpoint="https://research.example.com/.well-known/agent-card.json",
         updates=PollingConfig(
             interval=2.0,
@@ -324,12 +299,12 @@ agent = Agent(
         )
     )
 )
-```
+    ```
   </Tab>
 
   <Tab title="Push Notifications">
-```python push_notifications_config.py lines
-from crewai.a2a import A2AClientConfig
+    ```python Code
+from crewai.a2a import A2AConfig
 from crewai.a2a.updates import PushNotificationConfig
 
 agent = Agent(
@@ -337,137 +312,19 @@ agent = Agent(
     goal="Coordinate research tasks",
     backstory="Expert at delegation",
     llm="gpt-4o",
-    a2a=A2AClientConfig(
+    a2a=A2AConfig(
         endpoint="https://research.example.com/.well-known/agent-card.json",
         updates=PushNotificationConfig(
-            url="{base_url}/a2a/callback",
+            url={base_url}/a2a/callback",
             token="your-validation-token",
             timeout=300.0
         )
     )
 )
-```
+    ```
   </Tab>
 </Tabs>
 
-## Exposing Agents as A2A Servers
-
-You can expose your CrewAI agents as A2A-compliant servers, allowing other A2A clients to delegate tasks to them.
-
-### Server Configuration
-
-Add an `A2AServerConfig` to your agent to enable server capabilities:
-
-```python a2a_server_agent.py lines
-from crewai import Agent
-from crewai.a2a import A2AServerConfig
-
-agent = Agent(
-    role="Data Analyst",
-    goal="Analyze datasets and provide insights",
-    backstory="Expert data scientist with statistical analysis skills",
-    llm="gpt-4o",
-    a2a=A2AServerConfig(url="https://your-server.com")
-)
-```
-
-### Server Configuration Options
-
-<ParamField path="name" type="str" default="None">
-  Human-readable name for the agent. Defaults to the agent's role if not provided.
-</ParamField>
-
-<ParamField path="description" type="str" default="None">
-  Human-readable description. Defaults to the agent's goal and backstory if not provided.
-</ParamField>
-
-<ParamField path="version" type="str" default="1.0.0">
-  Version string for the agent card.
-</ParamField>
-
-<ParamField path="skills" type="list[AgentSkill]" default="[]">
-  List of agent skills. Auto-generated from agent tools if not provided.
-</ParamField>
-
-<ParamField path="capabilities" type="AgentCapabilities" default="AgentCapabilities(streaming=True, push_notifications=False)">
-  Declaration of optional capabilities supported by the agent.
-</ParamField>
-
-<ParamField path="default_input_modes" type="list[str]" default='["text/plain", "application/json"]'>
-  Supported input MIME types.
-</ParamField>
-
-<ParamField path="default_output_modes" type="list[str]" default='["text/plain", "application/json"]'>
-  Supported output MIME types.
-</ParamField>
-
-<ParamField path="url" type="str" default="None">
-  Preferred endpoint URL. If set, overrides the URL passed to `to_agent_card()`.
-</ParamField>
-
-<ParamField path="preferred_transport" type="Literal['JSONRPC', 'GRPC', 'HTTP+JSON']" default="JSONRPC">
-  Transport protocol for the preferred endpoint.
-</ParamField>
-
-<ParamField path="protocol_version" type="str" default="0.3">
-  A2A protocol version this agent supports.
-</ParamField>
-
-<ParamField path="provider" type="AgentProvider" default="None">
-  Information about the agent's service provider.
-</ParamField>
-
-<ParamField path="documentation_url" type="str" default="None">
-  URL to the agent's documentation.
-</ParamField>
-
-<ParamField path="icon_url" type="str" default="None">
-  URL to an icon for the agent.
-</ParamField>
-
-<ParamField path="additional_interfaces" type="list[AgentInterface]" default="[]">
-  Additional supported interfaces (transport and URL combinations).
-</ParamField>
-
-<ParamField path="security" type="list[dict[str, list[str]]]" default="[]">
-  Security requirement objects for all agent interactions.
-</ParamField>
-
-<ParamField path="security_schemes" type="dict[str, SecurityScheme]" default="{}">
-  Security schemes available to authorize requests.
-</ParamField>
-
-<ParamField path="supports_authenticated_extended_card" type="bool" default="False">
-  Whether agent provides extended card to authenticated users.
-</ParamField>
-
-<ParamField path="signatures" type="list[AgentCardSignature]" default="[]">
-  JSON Web Signatures for the AgentCard.
-</ParamField>
-
-### Combined Client and Server
-
-An agent can act as both client and server by providing both configurations:
-
-```python Code
-from crewai import Agent
-from crewai.a2a import A2AClientConfig, A2AServerConfig
-
-agent = Agent(
-    role="Research Coordinator",
-    goal="Coordinate research and serve analysis requests",
-    backstory="Expert at delegation and analysis",
-    llm="gpt-4o",
-    a2a=[
-        A2AClientConfig(
-            endpoint="https://specialist.example.com/.well-known/agent-card.json",
-            timeout=120
-        ),
-        A2AServerConfig(url="https://your-server.com")
-    ]
-)
-```
-
 ## Best Practices
 
 <CardGroup cols={2}>

docs/en/learn/human-feedback-in-flows.mdx

@@ -7,10 +7,6 @@ mode: "wide"
 
 ## Overview
 
-<Note>
-The `@human_feedback` decorator requires **CrewAI version 1.8.0 or higher**. Make sure to update your installation before using this feature.
-</Note>
-
 The `@human_feedback` decorator enables human-in-the-loop (HITL) workflows directly within CrewAI Flows. It allows you to pause flow execution, present output to a human for review, collect their feedback, and optionally route to different listeners based on the feedback outcome.
 
 This is particularly valuable for:

docs/en/learn/human-in-the-loop.mdx

@@ -11,10 +11,10 @@ Human-in-the-Loop (HITL) is a powerful approach that combines artificial intelli
 
 CrewAI offers two main approaches for implementing human-in-the-loop workflows:
 
-| Approach | Best For | Integration | Version |
-|----------|----------|-------------|---------|
-| **Flow-based** (`@human_feedback` decorator) | Local development, console-based review, synchronous workflows | [Human Feedback in Flows](/en/learn/human-feedback-in-flows) | **1.8.0+** |
-| **Webhook-based** (Enterprise) | Production deployments, async workflows, external integrations (Slack, Teams, etc.) | This guide | - |
+| Approach | Best For | Integration |
+|----------|----------|-------------|
+| **Flow-based** (`@human_feedback` decorator) | Local development, console-based review, synchronous workflows | [Human Feedback in Flows](/en/learn/human-feedback-in-flows) |
+| **Webhook-based** (Enterprise) | Production deployments, async workflows, external integrations (Slack, Teams, etc.) | This guide |
 
 <Tip>
 If you're building flows and want to add human review steps with routing based on feedback, check out the [Human Feedback in Flows](/en/learn/human-feedback-in-flows) guide for the `@human_feedback` decorator.

docs/en/observability/galileo.mdx

@@ -1,115 +0,0 @@
----
-title: Galileo
-description: Galileo integration for CrewAI tracing and evaluation
-icon: telescope
-mode: "wide"
----
-
-## Overview
-
-This guide demonstrates how to integrate **Galileo** with **CrewAI**
-for comprehensive tracing and Evaluation Engineering.
-By the end of this guide, you will be able to trace your CrewAI agents,
-monitor their performance, and evaluate their behaviour with
-Galileo's powerful observability platform.
-
-> **What is Galileo?** [Galileo](https://galileo.ai) is AI evaluation and observability
-platform that delivers end-to-end tracing, evaluation,
-and monitoring for AI applications. It enables teams to capture ground truth,
-create robust guardrails, and run systematic experiments with
-built-in experiment tracking and performance analytics—ensuring reliability,
-transparency, and continuous improvement across the AI lifecycle.
-
-## Getting started
-
-This tutorial follows the [CrewAI quickstart](/en/quickstart) and shows how to add
-Galileo's [CrewAIEventListener](https://v2docs.galileo.ai/sdk-api/python/reference/handlers/crewai/handler),
-an event handler.
-For more information, see Galileo’s
-[Add Galileo to a CrewAI Application](https://v2docs.galileo.ai/how-to-guides/third-party-integrations/add-galileo-to-crewai/add-galileo-to-crewai)
-how-to guide.
-
-> **Note** This tutorial assumes you have completed the [CrewAI quickstart](/en/quickstart).
-If you want a completed comprehensive example, see the Galileo
-[CrewAI sdk-example repo](https://github.com/rungalileo/sdk-examples/tree/main/python/agent/crew-ai).
-
-### Step 1: Install dependencies
-
-Install the required dependencies for your app.
-Create a virtual environment using your preferred method,
-then install dependencies inside that environment using your
-preferred tool:
-
-```bash
-uv add galileo 
-```
-
-### Step 2: Add to the .env file from the [CrewAI quickstart](/en/quickstart)
-
-```bash
-# Your Galileo API key
-GALILEO_API_KEY="your-galileo-api-key"
-
-# Your Galileo project name
-GALILEO_PROJECT="your-galileo-project-name"
-
-# The name of the Log stream you want to use for logging
-GALILEO_LOG_STREAM="your-galileo-log-stream "
-```
-
-### Step 3: Add the Galileo event listener
-
-To enable logging with Galileo, you need to create an instance of the `CrewAIEventListener`.
-Import the Galileo CrewAI handler package by
-adding the following code at the top of your main.py file:
-
-```python
-from galileo.handlers.crewai.handler import CrewAIEventListener
-```
-
-At the start of your run function, create the event listener:
-
-```python
-def run():
-    # Create the event listener
-    CrewAIEventListener()
-    # The rest of your existing code goes here
-```
-
-When you create the listener instance, it is automatically
-registered with CrewAI.
-
-### Step 4: Run your crew
-
-Run your crew with the CrewAI CLI:
-
-```bash
-crewai run
-```
-
-### Step 5: View the traces in Galileo
-
-Once your crew has finished, the traces will be flushed and appear in Galileo.
-
-![Galileo trace view](/images/galileo-trace-veiw.png)
-  
-## Understanding the Galileo Integration
-
-Galileo integrates with CrewAI by registering an event listener
-that captures Crew execution events (e.g., agent actions, tool calls, model responses)
-and forwards them to Galileo for observability and evaluation.
-
-### Understanding the event listener
-
-Creating a `CrewAIEventListener()` instance is all that’s
-required to enable Galileo for a CrewAI run. When instantiated, the listener:
-
-- Automatically registers itself with CrewAI
-- Reads Galileo configuration from environment variables
-- Logs all run data to the Galileo project and log stream specified by
-  `GALILEO_PROJECT` and `GALILEO_LOG_STREAM`
-
-No additional configuration or code changes are required.
-All data from this run is logged to the Galileo project and
-log stream specified by your environment configuration
-(for example, GALILEO_PROJECT and GALILEO_LOG_STREAM).

docs/ko/concepts/flows.mdx

@@ -567,10 +567,6 @@ Fourth method running
 
 ### Human in the Loop (인간 피드백)
 
-<Note>
-`@human_feedback` 데코레이터는 **CrewAI 버전 1.8.0 이상**이 필요합니다.
-</Note>
-
 `@human_feedback` 데코레이터는 인간의 피드백을 수집하기 위해 플로우 실행을 일시 중지하는 human-in-the-loop 워크플로우를 가능하게 합니다. 이는 승인 게이트, 품질 검토, 인간의 판단이 필요한 결정 지점에 유용합니다.
 
 ```python Code

docs/ko/concepts/production-architecture.mdx

@@ -128,7 +128,7 @@ Flow를 배포할 때 다음을 고려하세요:
 ### CrewAI Enterprise
 Flow를 배포하는 가장 쉬운 방법은 CrewAI Enterprise를 사용하는 것입니다. 인프라, 인증 및 모니터링을 대신 처리합니다.
 
-시작하려면 [배포 가이드](/ko/enterprise/guides/deploy-to-amp)를 확인하세요.
+시작하려면 [배포 가이드](/ko/enterprise/guides/deploy-crew)를 확인하세요.
 
 ```bash
 crewai deploy create

docs/ko/enterprise/features/automations.mdx

@@ -91,7 +91,7 @@ Git 없이 빠르게 배포 — 프로젝트 ZIP 패키지를 업로드하세요
 ## 관련 문서
 
 <CardGroup cols={3}>
-  <Card title="크루 배포" href="/ko/enterprise/guides/deploy-to-amp" icon="rocket">
+  <Card title="크루 배포" href="/ko/enterprise/guides/deploy-crew" icon="rocket">
     GitHub 또는 ZIP 파일로 크루 배포
   </Card>
   <Card title="자동화 트리거" href="/ko/enterprise/guides/automation-triggers" icon="trigger">

docs/ko/enterprise/features/crew-studio.mdx

@@ -79,7 +79,7 @@ Crew Studio는 자연어와 시각적 워크플로 에디터로 처음부터 자
   <Card title="크루 빌드" href="/ko/enterprise/guides/build-crew" icon="paintbrush">
     크루를 빌드하세요.
   </Card>
-  <Card title="크루 배포" href="/ko/enterprise/guides/deploy-to-amp" icon="rocket">
+  <Card title="크루 배포" href="/ko/enterprise/guides/deploy-crew" icon="rocket">
     GitHub 또는 ZIP 파일로 크루 배포.
   </Card>
   <Card title="React 컴포넌트 내보내기" href="/ko/enterprise/guides/react-component-export" icon="download">

docs/ko/enterprise/guides/deploy-crew.mdx

@@ -0,0 +1,305 @@
+---
+title: "Crew 배포"
+description: "CrewAI 엔터프라이즈에서 Crew 배포하기"
+icon: "rocket"
+mode: "wide"
+---
+
+<Note>
+  로컬에서 또는 Crew Studio를 통해 crew를 생성한 후, 다음 단계는 이를 CrewAI AMP
+  플랫폼에 배포하는 것입니다. 본 가이드에서는 다양한 배포 방법을 다루며,
+  여러분의 워크플로우에 가장 적합한 방식을 선택할 수 있도록 안내합니다.
+</Note>
+
+## 사전 준비 사항
+
+<CardGroup cols={2}>
+  <Card title="배포 준비가 된 Crew" icon="users">
+    작동 중인 crew가 로컬에서 빌드되었거나 Crew Studio를 통해 생성되어 있어야
+    합니다.
+  </Card>
+  <Card title="GitHub 저장소" icon="github">
+    crew 코드가 GitHub 저장소에 있어야 합니다(GitHub 연동 방식의 경우).
+  </Card>
+</CardGroup>
+
+## 옵션 1: CrewAI CLI를 사용한 배포
+
+CLI는 로컬에서 개발된 crew를 Enterprise 플랫폼에 가장 빠르게 배포할 수 있는 방법을 제공합니다.
+
+<Steps>
+  <Step title="CrewAI CLI 설치">
+    아직 설치하지 않았다면 CrewAI CLI를 설치하세요:
+
+    ```bash
+    pip install crewai[tools]
+    ```
+
+    <Tip>
+    CLI는 기본 CrewAI 패키지에 포함되어 있지만, `[tools]` 추가 옵션을 사용하면 모든 배포 종속성을 함께 설치할 수 있습니다.
+    </Tip>
+
+  </Step>
+
+  <Step title="Enterprise 플랫폼에 인증">
+    먼저, CrewAI AMP 플랫폼에 CLI를 인증해야 합니다:
+
+    ```bash
+    # 이미 CrewAI AMP 계정이 있거나 새로 생성하고 싶을 때:
+    crewai login
+    ```
+
+    위 명령어를 실행하면 CLI가 다음을 진행합니다:
+    1. URL과 고유 기기 코드를 표시합니다
+    2. 브라우저를 열어 인증 페이지로 이동합니다
+    3. 기기 확인을 요청합니다
+    4. 인증 과정을 완료합니다
+
+    인증이 성공적으로 완료되면 터미널에 확인 메시지가 표시됩니다!
+
+  </Step>
+
+  <Step title="배포 생성">
+
+    프로젝트 디렉터리에서 다음 명령어를 실행하세요:
+
+    ```bash
+    crewai deploy create
+    ```
+
+    이 명령어는 다음을 수행합니다:
+    1. GitHub 저장소 정보를 감지합니다
+    2. 로컬 `.env` 파일의 환경 변수를 식별합니다
+    3. 이러한 변수를 Enterprise 플랫폼으로 안전하게 전송합니다
+    4. 고유 식별자가 부여된 새 배포를 만듭니다
+
+    성공적으로 생성되면 다음과 같은 메시지가 표시됩니다:
+    ```shell
+    Deployment created successfully!
+    Name: your_project_name
+    Deployment ID: 01234567-89ab-cdef-0123-456789abcdef
+    Current Status: Deploy Enqueued
+    ```
+
+  </Step>
+
+  <Step title="배포 진행 상황 모니터링">
+
+    다음 명령어로 배포 상태를 추적할 수 있습니다:
+
+    ```bash
+    crewai deploy status
+    ```
+
+    빌드 과정의 상세 로그가 필요하다면:
+
+    ```bash
+    crewai deploy logs
+    ```
+
+    <Tip>
+    첫 배포는 컨테이너 이미지를 빌드하므로 일반적으로 10~15분 정도 소요됩니다. 이후 배포는 훨씬 빠릅니다.
+    </Tip>
+
+  </Step>
+</Steps>
+
+## 추가 CLI 명령어
+
+CrewAI CLI는 배포를 관리하기 위한 여러 명령어를 제공합니다:
+
+```bash
+# 모든 배포 목록 확인
+crewai deploy list
+
+# 배포 상태 확인
+crewai deploy status
+
+# 배포 로그 보기
+crewai deploy logs
+
+# 코드 변경 후 업데이트 푸시
+crewai deploy push
+
+# 배포 삭제
+crewai deploy remove <deployment_id>
+```
+
+## 옵션 2: 웹 인터페이스를 통한 직접 배포
+
+GitHub 계정을 연결하여 CrewAI AMP 웹 인터페이스를 통해 crews를 직접 배포할 수도 있습니다. 이 방법은 로컬 머신에서 CLI를 사용할 필요가 없습니다.
+
+<Steps>
+
+  <Step title="GitHub로 푸시하기">
+
+crew를 GitHub 저장소에 푸시해야 합니다. 아직 crew를 만들지 않았다면, [이 튜토리얼](/ko/quickstart)을 따라할 수 있습니다.
+
+  </Step>
+
+  <Step title="GitHub를 CrewAI AOP에 연결하기">
+
+    1. [CrewAI AMP](https://app.crewai.com)에 로그인합니다.
+    2. "Connect GitHub" 버튼을 클릭합니다.
+
+    <Frame>
+      ![Connect GitHub Button](/images/enterprise/connect-github.png)
+    </Frame>
+
+  </Step>
+
+  <Step title="저장소 선택하기">
+
+    GitHub 계정을 연결한 후 배포할 저장소를 선택할 수 있습니다:
+
+    <Frame>
+      ![Select Repository](/images/enterprise/select-repo.png)
+    </Frame>
+
+  </Step>
+
+  <Step title="환경 변수 설정하기">
+
+    배포 전에, LLM 제공업체 또는 기타 서비스에 연결할 환경 변수를 설정해야 합니다:
+
+    1. 변수를 개별적으로 또는 일괄적으로 추가할 수 있습니다.
+    2. 환경 변수는 `KEY=VALUE` 형식(한 줄에 하나씩)으로 입력합니다.
+
+    <Frame>
+      ![Set Environment Variables](/images/enterprise/set-env-variables.png)
+    </Frame>
+
+  </Step>
+
+  <Step title="Crew 배포하기">
+
+    1. "Deploy" 버튼을 클릭하여 배포 프로세스를 시작합니다.
+    2. 진행 바를 통해 진행 상황을 모니터링할 수 있습니다.
+    3. 첫 번째 배포에는 일반적으로 약 10-15분 정도 소요되며, 이후 배포는 더 빠릅니다.
+
+    <Frame>
+      ![Deploy Progress](/images/enterprise/deploy-progress.png)
+    </Frame>
+
+    배포가 완료되면 다음을 확인할 수 있습니다:
+    - crew의 고유 URL
+    - crew API를 보호할 Bearer 토큰
+    - 배포를 삭제해야 하는 경우 "Delete" 버튼
+
+  </Step>
+
+</Steps>
+
+## ⚠️ 환경 변수 보안 요구사항
+
+<Warning>
+  **중요**: CrewAI AOP는 환경 변수 이름에 대한 보안 제한이 있으며, 이를 따르지
+  않을 경우 배포가 실패할 수 있습니다.
+</Warning>
+
+### 차단된 환경 변수 패턴
+
+보안상의 이유로, 다음과 같은 환경 변수 명명 패턴은 **자동으로 필터링**되며 배포에 문제가 발생할 수 있습니다:
+
+**차단된 패턴:**
+
+- `_TOKEN`으로 끝나는 변수 (예: `MY_API_TOKEN`)
+- `_PASSWORD`로 끝나는 변수 (예: `DB_PASSWORD`)
+- `_SECRET`로 끝나는 변수 (예: `API_SECRET`)
+- 특정 상황에서 `_KEY`로 끝나는 변수
+
+**특정 차단 변수:**
+
+- `GITHUB_USER`, `GITHUB_TOKEN`
+- `AWS_REGION`, `AWS_DEFAULT_REGION`
+- 다양한 내부 CrewAI 시스템 변수
+
+### 허용된 예외
+
+일부 변수는 차단된 패턴과 일치하더라도 명시적으로 허용됩니다:
+
+- `AZURE_AD_TOKEN`
+- `AZURE_OPENAI_AD_TOKEN`
+- `ENTERPRISE_ACTION_TOKEN`
+- `CREWAI_ENTEPRISE_TOOLS_TOKEN`
+
+### 네이밍 문제 해결 방법
+
+환경 변수 제한으로 인해 배포가 실패하는 경우:
+
+```bash
+# ❌ 이러한 이름은 배포 실패를 초래합니다
+OPENAI_TOKEN=sk-...
+DATABASE_PASSWORD=mypassword
+API_SECRET=secret123
+
+# ✅ 대신 다음과 같은 네이밍 패턴을 사용하세요
+OPENAI_API_KEY=sk-...
+DATABASE_CREDENTIALS=mypassword
+API_CONFIG=secret123
+```
+
+### 모범 사례
+
+1. **표준 명명 규칙 사용**: `PROVIDER_TOKEN` 대신 `PROVIDER_API_KEY` 사용
+2. **먼저 로컬에서 테스트**: crew가 이름이 변경된 변수로 제대로 동작하는지 확인
+3. **코드 업데이트**: 이전 변수 이름을 참조하는 부분을 모두 변경
+4. **변경 내용 문서화**: 팀을 위해 이름이 변경된 변수를 기록
+
+<Tip>
+  배포 실패 시, 환경 변수 에러 메시지가 난해하다면 먼저 변수 이름이 이 패턴을
+  따르는지 확인하세요.
+</Tip>
+
+### 배포된 Crew와 상호작용하기
+
+배포가 완료되면 다음을 통해 crew에 접근할 수 있습니다:
+
+1. **REST API**: 플랫폼에서 아래의 주요 경로가 포함된 고유한 HTTPS 엔드포인트를 생성합니다:
+
+   - `/inputs`: 필요한 입력 파라미터 목록
+   - `/kickoff`: 제공된 입력값으로 실행 시작
+   - `/status/{kickoff_id}`: 실행 상태 확인
+
+2. **웹 인터페이스**: [app.crewai.com](https://app.crewai.com)에 방문하여 다음을 확인할 수 있습니다:
+   - **Status 탭**: 배포 정보, API 엔드포인트 세부 정보 및 인증 토큰 확인
+   - **Run 탭**: crew 구조의 시각적 표현
+   - **Executions 탭**: 모든 실행 내역
+   - **Metrics 탭**: 성능 분석
+   - **Traces 탭**: 상세 실행 인사이트
+
+### 실행 트리거하기
+
+Enterprise 대시보드에서 다음 작업을 수행할 수 있습니다:
+
+1. crew 이름을 클릭하여 상세 정보를 엽니다
+2. 관리 인터페이스에서 "Trigger Crew"를 선택합니다
+3. 나타나는 모달에 필요한 입력값을 입력합니다
+4. 파이프라인을 따라 실행의 진행 상황을 모니터링합니다
+
+### 모니터링 및 분석
+
+Enterprise 플랫폼은 포괄적인 가시성 기능을 제공합니다:
+
+- **실행 관리**: 활성 및 완료된 실행 추적
+- **트레이스**: 각 실행의 상세 분해
+- **메트릭**: 토큰 사용량, 실행 시간, 비용
+- **타임라인 보기**: 작업 시퀀스의 시각적 표현
+
+### 고급 기능
+
+Enterprise 플랫폼은 또한 다음을 제공합니다:
+
+- **환경 변수 관리**: API 키를 안전하게 저장 및 관리
+- **LLM 연결**: 다양한 LLM 공급자와의 통합 구성
+- **Custom Tools Repository**: 도구 생성, 공유 및 설치
+- **Crew Studio**: 코드를 작성하지 않고 채팅 인터페이스를 통해 crew 빌드
+
+<Card
+  title="도움이 필요하신가요?"
+  icon="headset"
+  href="mailto:support@crewai.com"
+>
+  Enterprise 플랫폼의 배포 문제 또는 문의 사항이 있으시면 지원팀에 연락해
+  주십시오.
+</Card>

docs/ko/enterprise/guides/deploy-to-amp.mdx

@@ -1,438 +0,0 @@
----
-title: "AMP에 배포하기"
-description: "Crew 또는 Flow를 CrewAI AMP에 배포하기"
-icon: "rocket"
-mode: "wide"
----
-
-<Note>
-  로컬에서 또는 Crew Studio를 통해 Crew나 Flow를 생성한 후, 다음 단계는 이를 CrewAI AMP
-  플랫폼에 배포하는 것입니다. 본 가이드에서는 다양한 배포 방법을 다루며,
-  여러분의 워크플로우에 가장 적합한 방식을 선택할 수 있도록 안내합니다.
-</Note>
-
-## 사전 준비 사항
-
-<CardGroup cols={2}>
-  <Card title="배포 준비가 완료된 프로젝트" icon="check-circle">
-    로컬에서 성공적으로 실행되는 Crew 또는 Flow가 있어야 합니다.
-    [배포 준비 가이드](/ko/enterprise/guides/prepare-for-deployment)를 따라 프로젝트 구조를 확인하세요.
-  </Card>
-  <Card title="GitHub 저장소" icon="github">
-    코드가 GitHub 저장소에 있어야 합니다(GitHub 연동 방식의 경우).
-  </Card>
-</CardGroup>
-
-<Info>
-  **Crews vs Flows**: 두 프로젝트 유형 모두 CrewAI AMP에서 "자동화"로 배포할 수 있습니다.
-  배포 과정은 동일하지만, 프로젝트 구조가 다릅니다.
-  자세한 내용은 [배포 준비하기](/ko/enterprise/guides/prepare-for-deployment)를 참조하세요.
-</Info>
-
-## 옵션 1: CrewAI CLI를 사용한 배포
-
-CLI는 로컬에서 개발된 Crew 또는 Flow를 AMP 플랫폼에 가장 빠르게 배포할 수 있는 방법을 제공합니다.
-CLI는 `pyproject.toml`에서 프로젝트 유형을 자동으로 감지하고 그에 맞게 빌드합니다.
-
-<Steps>
-  <Step title="CrewAI CLI 설치">
-    아직 설치하지 않았다면 CrewAI CLI를 설치하세요:
-
-    ```bash
-    pip install crewai[tools]
-    ```
-
-    <Tip>
-    CLI는 기본 CrewAI 패키지에 포함되어 있지만, `[tools]` 추가 옵션을 사용하면 모든 배포 종속성을 함께 설치할 수 있습니다.
-    </Tip>
-
-  </Step>
-
-  <Step title="Enterprise 플랫폼에 인증">
-    먼저, CrewAI AMP 플랫폼에 CLI를 인증해야 합니다:
-
-    ```bash
-    # 이미 CrewAI AMP 계정이 있거나 새로 생성하고 싶을 때:
-    crewai login
-    ```
-
-    위 명령어를 실행하면 CLI가 다음을 진행합니다:
-    1. URL과 고유 기기 코드를 표시합니다
-    2. 브라우저를 열어 인증 페이지로 이동합니다
-    3. 기기 확인을 요청합니다
-    4. 인증 과정을 완료합니다
-
-    인증이 성공적으로 완료되면 터미널에 확인 메시지가 표시됩니다!
-
-  </Step>
-
-  <Step title="배포 생성">
-
-    프로젝트 디렉터리에서 다음 명령어를 실행하세요:
-
-    ```bash
-    crewai deploy create
-    ```
-
-    이 명령어는 다음을 수행합니다:
-    1. GitHub 저장소 정보를 감지합니다
-    2. 로컬 `.env` 파일의 환경 변수를 식별합니다
-    3. 이러한 변수를 Enterprise 플랫폼으로 안전하게 전송합니다
-    4. 고유 식별자가 부여된 새 배포를 만듭니다
-
-    성공적으로 생성되면 다음과 같은 메시지가 표시됩니다:
-    ```shell
-    Deployment created successfully!
-    Name: your_project_name
-    Deployment ID: 01234567-89ab-cdef-0123-456789abcdef
-    Current Status: Deploy Enqueued
-    ```
-
-  </Step>
-
-  <Step title="배포 진행 상황 모니터링">
-
-    다음 명령어로 배포 상태를 추적할 수 있습니다:
-
-    ```bash
-    crewai deploy status
-    ```
-
-    빌드 과정의 상세 로그가 필요하다면:
-
-    ```bash
-    crewai deploy logs
-    ```
-
-    <Tip>
-    첫 배포는 컨테이너 이미지를 빌드하므로 일반적으로 10~15분 정도 소요됩니다. 이후 배포는 훨씬 빠릅니다.
-    </Tip>
-
-  </Step>
-</Steps>
-
-## 추가 CLI 명령어
-
-CrewAI CLI는 배포를 관리하기 위한 여러 명령어를 제공합니다:
-
-```bash
-# 모든 배포 목록 확인
-crewai deploy list
-
-# 배포 상태 확인
-crewai deploy status
-
-# 배포 로그 보기
-crewai deploy logs
-
-# 코드 변경 후 업데이트 푸시
-crewai deploy push
-
-# 배포 삭제
-crewai deploy remove <deployment_id>
-```
-
-## 옵션 2: 웹 인터페이스를 통한 직접 배포
-
-GitHub 계정을 연결하여 CrewAI AMP 웹 인터페이스를 통해 Crew 또는 Flow를 직접 배포할 수도 있습니다. 이 방법은 로컬 머신에서 CLI를 사용할 필요가 없습니다. 플랫폼은 자동으로 프로젝트 유형을 감지하고 적절하게 빌드를 처리합니다.
-
-<Steps>
-
-  <Step title="GitHub로 푸시하기">
-
-Crew를 GitHub 저장소에 푸시해야 합니다. 아직 Crew를 만들지 않았다면, [이 튜토리얼](/ko/quickstart)을 따라할 수 있습니다.
-
-  </Step>
-
-  <Step title="GitHub를 CrewAI AMP에 연결하기">
-
-    1. [CrewAI AMP](https://app.crewai.com)에 로그인합니다.
-    2. "Connect GitHub" 버튼을 클릭합니다.
-
-    <Frame>
-      ![Connect GitHub Button](/images/enterprise/connect-github.png)
-    </Frame>
-
-  </Step>
-
-  <Step title="저장소 선택하기">
-
-    GitHub 계정을 연결한 후 배포할 저장소를 선택할 수 있습니다:
-
-    <Frame>
-      ![Select Repository](/images/enterprise/select-repo.png)
-    </Frame>
-
-  </Step>
-
-  <Step title="환경 변수 설정하기">
-
-    배포 전에, LLM 제공업체 또는 기타 서비스에 연결할 환경 변수를 설정해야 합니다:
-
-    1. 변수를 개별적으로 또는 일괄적으로 추가할 수 있습니다.
-    2. 환경 변수는 `KEY=VALUE` 형식(한 줄에 하나씩)으로 입력합니다.
-
-    <Frame>
-      ![Set Environment Variables](/images/enterprise/set-env-variables.png)
-    </Frame>
-
-  </Step>
-
-  <Step title="Crew 배포하기">
-
-    1. "Deploy" 버튼을 클릭하여 배포 프로세스를 시작합니다.
-    2. 진행 바를 통해 진행 상황을 모니터링할 수 있습니다.
-    3. 첫 번째 배포에는 일반적으로 약 10-15분 정도 소요되며, 이후 배포는 더 빠릅니다.
-
-    <Frame>
-      ![Deploy Progress](/images/enterprise/deploy-progress.png)
-    </Frame>
-
-    배포가 완료되면 다음을 확인할 수 있습니다:
-    - Crew의 고유 URL
-    - Crew API를 보호할 Bearer 토큰
-    - 배포를 삭제해야 하는 경우 "Delete" 버튼
-
-  </Step>
-
-</Steps>
-
-## 옵션 3: API를 통한 재배포 (CI/CD 통합)
-
-CI/CD 파이프라인에서 자동화된 배포를 위해 CrewAI API를 사용하여 기존 crew의 재배포를 트리거할 수 있습니다. 이 방법은 GitHub Actions, Jenkins 또는 기타 자동화 워크플로우에 특히 유용합니다.
-
-<Steps>
-  <Step title="개인 액세스 토큰 발급">
-
-    CrewAI AMP 계정 설정에서 API 토큰을 생성합니다:
-
-    1. [app.crewai.com](https://app.crewai.com)으로 이동합니다
-    2. **Settings** → **Account** → **Personal Access Token**을 클릭합니다
-    3. 새 토큰을 생성하고 안전하게 복사합니다
-    4. 이 토큰을 CI/CD 시스템의 시크릿으로 저장합니다
-
-  </Step>
-
-  <Step title="Automation UUID 찾기">
-
-    배포된 crew의 고유 식별자를 찾습니다:
-
-    1. CrewAI AMP 대시보드에서 **Automations**로 이동합니다
-    2. 기존 automation/crew를 선택합니다
-    3. **Additional Details**를 클릭합니다
-    4. **UUID**를 복사합니다 - 이것이 특정 crew 배포를 식별합니다
-
-  </Step>
-
-  <Step title="API를 통한 재배포 트리거">
-
-    Deploy API 엔드포인트를 사용하여 재배포를 트리거합니다:
-
-    ```bash
-    curl -i -X POST \
-         -H "Authorization: Bearer YOUR_PERSONAL_ACCESS_TOKEN" \
-         https://app.crewai.com/crewai_plus/api/v1/crews/YOUR-AUTOMATION-UUID/deploy
-
-    # HTTP/2 200
-    # content-type: application/json
-    #
-    # {
-    #   "uuid": "your-automation-uuid",
-    #   "status": "Deploy Enqueued",
-    #   "public_url": "https://your-crew-deployment.crewai.com",
-    #   "token": "your-bearer-token"
-    # }
-    ```
-
-    <Info>
-    Git에 연결되어 처음 생성된 automation의 경우, API가 재배포 전에 자동으로 저장소에서 최신 변경 사항을 가져옵니다.
-    </Info>
-
-  </Step>
-
-  <Step title="GitHub Actions 통합 예시">
-
-    더 복잡한 배포 트리거가 있는 GitHub Actions 워크플로우 예시입니다:
-
-    ```yaml
-    name: Deploy CrewAI Automation
-
-    on:
-      push:
-        branches: [ main ]
-      pull_request:
-        types: [ labeled ]
-      release:
-        types: [ published ]
-
-    jobs:
-      deploy:
-        runs-on: ubuntu-latest
-        if: |
-          (github.event_name == 'push' && github.ref == 'refs/heads/main') ||
-          (github.event_name == 'pull_request' && contains(github.event.pull_request.labels.*.name, 'deploy')) ||
-          (github.event_name == 'release')
-        steps:
-          - name: Trigger CrewAI Redeployment
-            run: |
-              curl -X POST \
-                   -H "Authorization: Bearer ${{ secrets.CREWAI_PAT }}" \
-                   https://app.crewai.com/crewai_plus/api/v1/crews/${{ secrets.CREWAI_AUTOMATION_UUID }}/deploy
-    ```
-
-    <Tip>
-    `CREWAI_PAT`와 `CREWAI_AUTOMATION_UUID`를 저장소 시크릿으로 추가하세요. PR 배포의 경우 "deploy" 라벨을 추가하여 워크플로우를 트리거합니다.
-    </Tip>
-
-    </Step>
-
-  </Steps>
-
-## 배포된 Automation과 상호작용하기
-
-배포가 완료되면 다음을 통해 crew에 접근할 수 있습니다:
-
-1. **REST API**: 플랫폼에서 아래의 주요 경로가 포함된 고유한 HTTPS 엔드포인트를 생성합니다:
-
-   - `/inputs`: 필요한 입력 파라미터 목록
-   - `/kickoff`: 제공된 입력값으로 실행 시작
-   - `/status/{kickoff_id}`: 실행 상태 확인
-
-2. **웹 인터페이스**: [app.crewai.com](https://app.crewai.com)에 방문하여 다음을 확인할 수 있습니다:
-   - **Status 탭**: 배포 정보, API 엔드포인트 세부 정보 및 인증 토큰 확인
-   - **Run 탭**: Crew 구조의 시각적 표현
-   - **Executions 탭**: 모든 실행 내역
-   - **Metrics 탭**: 성능 분석
-   - **Traces 탭**: 상세 실행 인사이트
-
-### 실행 트리거하기
-
-Enterprise 대시보드에서 다음 작업을 수행할 수 있습니다:
-
-1. Crew 이름을 클릭하여 상세 정보를 엽니다
-2. 관리 인터페이스에서 "Trigger Crew"를 선택합니다
-3. 나타나는 모달에 필요한 입력값을 입력합니다
-4. 파이프라인을 따라 실행의 진행 상황을 모니터링합니다
-
-### 모니터링 및 분석
-
-Enterprise 플랫폼은 포괄적인 가시성 기능을 제공합니다:
-
-- **실행 관리**: 활성 및 완료된 실행 추적
-- **트레이스**: 각 실행의 상세 분해
-- **메트릭**: 토큰 사용량, 실행 시간, 비용
-- **타임라인 보기**: 작업 시퀀스의 시각적 표현
-
-### 고급 기능
-
-Enterprise 플랫폼은 또한 다음을 제공합니다:
-
-- **환경 변수 관리**: API 키를 안전하게 저장 및 관리
-- **LLM 연결**: 다양한 LLM 공급자와의 통합 구성
-- **Custom Tools Repository**: 도구 생성, 공유 및 설치
-- **Crew Studio**: 코드를 작성하지 않고 채팅 인터페이스를 통해 crew 빌드
-
-## 배포 실패 문제 해결
-
-배포가 실패하면 다음과 같은 일반적인 문제를 확인하세요:
-
-### 빌드 실패
-
-#### uv.lock 파일 누락
-
-**증상**: 의존성 해결 오류와 함께 빌드 초기에 실패
-
-**해결책**: lock 파일을 생성하고 커밋합니다:
-
-```bash
-uv lock
-git add uv.lock
-git commit -m "Add uv.lock for deployment"
-git push
-```
-
-<Warning>
-  `uv.lock` 파일은 모든 배포에 필수입니다. 이 파일이 없으면 플랫폼에서
-  의존성을 안정적으로 설치할 수 없습니다.
-</Warning>
-
-#### 잘못된 프로젝트 구조
-
-**증상**: "Could not find entry point" 또는 "Module not found" 오류
-
-**해결책**: 프로젝트가 예상 구조와 일치하는지 확인합니다:
-
-- **Crews와 Flows 모두**: 진입점이 `src/project_name/main.py`에 있어야 합니다
-- **Crews**: 진입점으로 `run()` 함수 사용
-- **Flows**: 진입점으로 `kickoff()` 함수 사용
-
-자세한 구조 다이어그램은 [배포 준비하기](/ko/enterprise/guides/prepare-for-deployment)를 참조하세요.
-
-#### CrewBase 데코레이터 누락
-
-**증상**: "Crew not found", "Config not found" 또는 agent/task 구성 오류
-
-**해결책**: **모든** crew 클래스가 `@CrewBase` 데코레이터를 사용하는지 확인합니다:
-
-```python
-from crewai.project import CrewBase, agent, crew, task
-
-@CrewBase  # 이 데코레이터는 필수입니다
-class YourCrew():
-    """Crew 설명"""
-
-    @agent
-    def my_agent(self) -> Agent:
-        return Agent(
-            config=self.agents_config['my_agent'],  # type: ignore[index]
-            verbose=True
-        )
-
-    # ... 나머지 crew 정의
-```
-
-<Info>
-  이것은 독립 실행형 Crews와 Flow 프로젝트 내에 포함된 crews 모두에 적용됩니다.
-  모든 crew 클래스에 데코레이터가 필요합니다.
-</Info>
-
-#### 잘못된 pyproject.toml 타입
-
-**증상**: 빌드는 성공하지만 런타임에서 실패하거나 예상치 못한 동작
-
-**해결책**: `[tool.crewai]` 섹션이 프로젝트 유형과 일치하는지 확인합니다:
-
-```toml
-# Crew 프로젝트의 경우:
-[tool.crewai]
-type = "crew"
-
-# Flow 프로젝트의 경우:
-[tool.crewai]
-type = "flow"
-```
-
-### 런타임 실패
-
-#### LLM 연결 실패
-
-**증상**: API 키 오류, "model not found" 또는 인증 실패
-
-**해결책**:
-1. LLM 제공업체의 API 키가 환경 변수에 올바르게 설정되어 있는지 확인합니다
-2. 환경 변수 이름이 코드에서 예상하는 것과 일치하는지 확인합니다
-3. 배포 전에 동일한 환경 변수로 로컬에서 테스트합니다
-
-#### Crew 실행 오류
-
-**증상**: Crew가 시작되지만 실행 중에 실패
-
-**해결책**:
-1. AMP 대시보드에서 실행 로그를 확인합니다 (Traces 탭)
-2. 모든 도구에 필요한 API 키가 구성되어 있는지 확인합니다
-3. `agents.yaml`의 agent 구성이 유효한지 확인합니다
-4. `tasks.yaml`의 task 구성에 구문 오류가 없는지 확인합니다
-
-<Card title="도움이 필요하신가요?" icon="headset" href="mailto:support@crewai.com">
-  배포 문제 또는 AMP 플랫폼에 대한 문의 사항이 있으시면 지원팀에 연락해 주세요.
-</Card>

docs/ko/enterprise/guides/prepare-for-deployment.mdx

@@ -1,305 +0,0 @@
----
-title: "배포 준비하기"
-description: "Crew 또는 Flow가 CrewAI AMP에 배포될 준비가 되었는지 확인하기"
-icon: "clipboard-check"
-mode: "wide"
----
-
-<Note>
-  CrewAI AMP에 배포하기 전에, 프로젝트가 올바르게 구성되어 있는지 확인하는 것이 중요합니다.
-  Crews와 Flows 모두 "자동화"로 배포할 수 있지만, 성공적인 배포를 위해 충족해야 하는
-  서로 다른 프로젝트 구조와 요구 사항이 있습니다.
-</Note>
-
-## 자동화 이해하기
-
-CrewAI AMP에서 **자동화(automations)**는 배포 가능한 Agentic AI 프로젝트의 총칭입니다. 자동화는 다음 중 하나일 수 있습니다:
-
-- **Crew**: 작업을 함께 수행하는 AI 에이전트들의 독립 실행형 팀
-- **Flow**: 여러 crew, 직접 LLM 호출 및 절차적 로직을 결합할 수 있는 오케스트레이션된 워크플로우
-
-배포하는 유형을 이해하는 것은 프로젝트 구조와 진입점이 다르기 때문에 필수적입니다.
-
-## Crews vs Flows: 주요 차이점
-
-<CardGroup cols={2}>
-  <Card title="Crew 프로젝트" icon="users">
-    에이전트와 작업을 정의하는 `crew.py`가 있는 독립 실행형 AI 에이전트 팀. 집중적이고 협업적인 작업에 적합합니다.
-  </Card>
-  <Card title="Flow 프로젝트" icon="diagram-project">
-    `crews/` 폴더에 포함된 crew가 있는 오케스트레이션된 워크플로우. 복잡한 다단계 프로세스에 적합합니다.
-  </Card>
-</CardGroup>
-
-| 측면 | Crew | Flow |
-|------|------|------|
-| **프로젝트 구조** | `crew.py`가 있는 `src/project_name/` | `crews/` 폴더가 있는 `src/project_name/` |
-| **메인 로직 위치** | `src/project_name/crew.py` | `src/project_name/main.py` (Flow 클래스) |
-| **진입점 함수** | `main.py`의 `run()` | `main.py`의 `kickoff()` |
-| **pyproject.toml 타입** | `type = "crew"` | `type = "flow"` |
-| **CLI 생성 명령어** | `crewai create crew name` | `crewai create flow name` |
-| **설정 위치** | `src/project_name/config/` | `src/project_name/crews/crew_name/config/` |
-| **다른 crew 포함 가능** | 아니오 | 예 (`crews/` 폴더 내) |
-
-## 프로젝트 구조 참조
-
-### Crew 프로젝트 구조
-
-`crewai create crew my_crew`를 실행하면 다음 구조를 얻습니다:
-
-```
-my_crew/
-├── .gitignore
-├── pyproject.toml          # type = "crew"여야 함
-├── README.md
-├── .env
-├── uv.lock                  # 배포에 필수
-└── src/
-    └── my_crew/
-        ├── __init__.py
-        ├── main.py          # run() 함수가 있는 진입점
-        ├── crew.py          # @CrewBase 데코레이터가 있는 Crew 클래스
-        ├── tools/
-        │   ├── custom_tool.py
-        │   └── __init__.py
-        └── config/
-            ├── agents.yaml  # 에이전트 정의
-            └── tasks.yaml   # 작업 정의
-```
-
-<Warning>
-  중첩된 `src/project_name/` 구조는 Crews에 매우 중요합니다.
-  잘못된 레벨에 파일을 배치하면 배포 실패의 원인이 됩니다.
-</Warning>
-
-### Flow 프로젝트 구조
-
-`crewai create flow my_flow`를 실행하면 다음 구조를 얻습니다:
-
-```
-my_flow/
-├── .gitignore
-├── pyproject.toml          # type = "flow"여야 함
-├── README.md
-├── .env
-├── uv.lock                  # 배포에 필수
-└── src/
-    └── my_flow/
-        ├── __init__.py
-        ├── main.py          # kickoff() 함수 + Flow 클래스가 있는 진입점
-        ├── crews/           # 포함된 crews 폴더
-        │   └── poem_crew/
-        │       ├── __init__.py
-        │       ├── poem_crew.py  # @CrewBase 데코레이터가 있는 Crew
-        │       └── config/
-        │           ├── agents.yaml
-        │           └── tasks.yaml
-        └── tools/
-            ├── __init__.py
-            └── custom_tool.py
-```
-
-<Info>
-  Crews와 Flows 모두 `src/project_name/` 구조를 사용합니다.
-  핵심 차이점은 Flows는 포함된 crews를 위한 `crews/` 폴더가 있고,
-  Crews는 프로젝트 폴더에 직접 `crew.py`가 있다는 것입니다.
-</Info>
-
-## 배포 전 체크리스트
-
-이 체크리스트를 사용하여 프로젝트가 배포 준비가 되었는지 확인하세요.
-
-### 1. pyproject.toml 설정 확인
-
-`pyproject.toml`에 올바른 `[tool.crewai]` 섹션이 포함되어야 합니다:
-
-<Tabs>
-  <Tab title="Crews의 경우">
-    ```toml
-    [tool.crewai]
-    type = "crew"
-    ```
-  </Tab>
-  <Tab title="Flows의 경우">
-    ```toml
-    [tool.crewai]
-    type = "flow"
-    ```
-  </Tab>
-</Tabs>
-
-<Warning>
-  `type`이 프로젝트 구조와 일치하지 않으면 빌드가 실패하거나
-  자동화가 올바르게 실행되지 않습니다.
-</Warning>
-
-### 2. uv.lock 파일 존재 확인
-
-CrewAI는 의존성 관리를 위해 `uv`를 사용합니다. `uv.lock` 파일은 재현 가능한 빌드를 보장하며 배포에 **필수**입니다.
-
-```bash
-# lock 파일 생성 또는 업데이트
-uv lock
-
-# 존재 여부 확인
-ls -la uv.lock
-```
-
-파일이 존재하지 않으면 `uv lock`을 실행하고 저장소에 커밋하세요:
-
-```bash
-uv lock
-git add uv.lock
-git commit -m "Add uv.lock for deployment"
-git push
-```
-
-### 3. CrewBase 데코레이터 사용 확인
-
-**모든 crew 클래스는 `@CrewBase` 데코레이터를 사용해야 합니다.** 이것은 다음에 적용됩니다:
-
-- 독립 실행형 crew 프로젝트
-- Flow 프로젝트 내에 포함된 crews
-
-```python
-from crewai import Agent, Crew, Process, Task
-from crewai.project import CrewBase, agent, crew, task
-from crewai.agents.agent_builder.base_agent import BaseAgent
-from typing import List
-
-@CrewBase  # 이 데코레이터는 필수입니다
-class MyCrew():
-    """내 crew 설명"""
-
-    agents: List[BaseAgent]
-    tasks: List[Task]
-
-    @agent
-    def my_agent(self) -> Agent:
-        return Agent(
-            config=self.agents_config['my_agent'],  # type: ignore[index]
-            verbose=True
-        )
-
-    @task
-    def my_task(self) -> Task:
-        return Task(
-            config=self.tasks_config['my_task']  # type: ignore[index]
-        )
-
-    @crew
-    def crew(self) -> Crew:
-        return Crew(
-            agents=self.agents,
-            tasks=self.tasks,
-            process=Process.sequential,
-            verbose=True,
-        )
-```
-
-<Warning>
-  `@CrewBase` 데코레이터를 잊으면 에이전트나 작업 구성이 누락되었다는
-  오류와 함께 배포가 실패합니다.
-</Warning>
-
-### 4. 프로젝트 진입점 확인
-
-Crews와 Flows 모두 `src/project_name/main.py`에 진입점이 있습니다:
-
-<Tabs>
-  <Tab title="Crews의 경우">
-    진입점은 `run()` 함수를 사용합니다:
-
-    ```python
-    # src/my_crew/main.py
-    from my_crew.crew import MyCrew
-
-    def run():
-        """crew를 실행합니다."""
-        inputs = {'topic': 'AI in Healthcare'}
-        result = MyCrew().crew().kickoff(inputs=inputs)
-        return result
-
-    if __name__ == "__main__":
-        run()
-    ```
-  </Tab>
-  <Tab title="Flows의 경우">
-    진입점은 Flow 클래스와 함께 `kickoff()` 함수를 사용합니다:
-
-    ```python
-    # src/my_flow/main.py
-    from crewai.flow import Flow, listen, start
-    from my_flow.crews.poem_crew.poem_crew import PoemCrew
-
-    class MyFlow(Flow):
-        @start()
-        def begin(self):
-            # Flow 로직
-            result = PoemCrew().crew().kickoff(inputs={...})
-            return result
-
-    def kickoff():
-        """flow를 실행합니다."""
-        MyFlow().kickoff()
-
-    if __name__ == "__main__":
-        kickoff()
-    ```
-  </Tab>
-</Tabs>
-
-### 5. 환경 변수 준비
-
-배포 전에 다음을 준비해야 합니다:
-
-1. **LLM API 키** (OpenAI, Anthropic, Google 등)
-2. **도구 API 키** - 외부 도구를 사용하는 경우 (Serper 등)
-
-<Tip>
-  구성 문제를 조기에 발견하기 위해 배포 전에 동일한 환경 변수로
-  로컬에서 프로젝트를 테스트하세요.
-</Tip>
-
-## 빠른 검증 명령어
-
-프로젝트 루트에서 다음 명령어를 실행하여 설정을 빠르게 확인하세요:
-
-```bash
-# 1. pyproject.toml에서 프로젝트 타입 확인
-grep -A2 "\[tool.crewai\]" pyproject.toml
-
-# 2. uv.lock 존재 확인
-ls -la uv.lock || echo "오류: uv.lock이 없습니다! 'uv lock'을 실행하세요"
-
-# 3. src/ 구조 존재 확인
-ls -la src/*/main.py 2>/dev/null || echo "src/에서 main.py를 찾을 수 없습니다"
-
-# 4. Crews의 경우 - crew.py 존재 확인
-ls -la src/*/crew.py 2>/dev/null || echo "crew.py가 없습니다 (Crews에서 예상됨)"
-
-# 5. Flows의 경우 - crews/ 폴더 존재 확인
-ls -la src/*/crews/ 2>/dev/null || echo "crews/ 폴더가 없습니다 (Flows에서 예상됨)"
-
-# 6. CrewBase 사용 확인
-grep -r "@CrewBase" . --include="*.py"
-```
-
-## 일반적인 설정 실수
-
-| 실수 | 증상 | 해결 방법 |
-|------|------|----------|
-| `uv.lock` 누락 | 의존성 해결 중 빌드 실패 | `uv lock` 실행 후 커밋 |
-| pyproject.toml의 잘못된 `type` | 빌드 성공하지만 런타임 실패 | 올바른 타입으로 변경 |
-| `@CrewBase` 데코레이터 누락 | "Config not found" 오류 | 모든 crew 클래스에 데코레이터 추가 |
-| `src/` 대신 루트에 파일 배치 | 진입점을 찾을 수 없음 | `src/project_name/`으로 이동 |
-| `run()` 또는 `kickoff()` 누락 | 자동화를 시작할 수 없음 | 올바른 진입 함수 추가 |
-
-## 다음 단계
-
-프로젝트가 모든 체크리스트 항목을 통과하면 배포할 준비가 된 것입니다:
-
-<Card title="AMP에 배포하기" icon="rocket" href="/ko/enterprise/guides/deploy-to-amp">
-  CLI, 웹 인터페이스 또는 CI/CD 통합을 사용하여 Crew 또는 Flow를 CrewAI AMP에
-  배포하려면 배포 가이드를 따르세요.
-</Card>

docs/ko/enterprise/introduction.mdx

@@ -79,7 +79,7 @@ CrewAI AOP는 오픈 소스 프레임워크의 강력함에 프로덕션 배포,
     <Card
       title="Crew 배포"
       icon="rocket"
-      href="/ko/enterprise/guides/deploy-to-amp"
+      href="/ko/enterprise/guides/deploy-crew"
     >
       Crew 배포
     </Card>
@@ -96,4 +96,4 @@ CrewAI AOP는 오픈 소스 프레임워크의 강력함에 프로덕션 배포,
   </Step>
 </Steps>
 
-자세한 안내를 원하시면 [배포 가이드](/ko/enterprise/guides/deploy-to-amp)를 확인하거나 아래 버튼을 클릭해 시작하세요.
+자세한 안내를 원하시면 [배포 가이드](/ko/enterprise/guides/deploy-crew)를 확인하거나 아래 버튼을 클릭해 시작하세요.

docs/ko/learn/human-feedback-in-flows.mdx

@@ -7,10 +7,6 @@ mode: "wide"
 
 ## 개요
 
-<Note>
-`@human_feedback` 데코레이터는 **CrewAI 버전 1.8.0 이상**이 필요합니다. 이 기능을 사용하기 전에 설치를 업데이트하세요.
-</Note>
-
 `@human_feedback` 데코레이터는 CrewAI Flow 내에서 직접 human-in-the-loop(HITL) 워크플로우를 가능하게 합니다. Flow 실행을 일시 중지하고, 인간에게 검토를 위해 출력을 제시하고, 피드백을 수집하고, 선택적으로 피드백 결과에 따라 다른 리스너로 라우팅할 수 있습니다.
 
 이는 특히 다음과 같은 경우에 유용합니다:

docs/ko/learn/human-in-the-loop.mdx

@@ -5,22 +5,9 @@ icon: "user-check"
 mode: "wide"
 ---
 
-휴먼 인 더 루프(HITL, Human-in-the-Loop)는 인공지능과 인간의 전문 지식을 결합하여 의사결정을 강화하고 작업 결과를 향상시키는 강력한 접근 방식입니다. CrewAI는 필요에 따라 HITL을 구현하는 여러 가지 방법을 제공합니다.
+휴먼 인 더 루프(HITL, Human-in-the-Loop)는 인공지능과 인간의 전문 지식을 결합하여 의사결정을 강화하고 작업 결과를 향상시키는 강력한 접근 방식입니다. 이 가이드에서는 CrewAI 내에서 HITL을 구현하는 방법을 안내합니다.
 
-## HITL 접근 방식 선택
-
-CrewAI는 human-in-the-loop 워크플로우를 구현하기 위한 두 가지 주요 접근 방식을 제공합니다:
-
-| 접근 방식 | 적합한 용도 | 통합 | 버전 |
-|----------|----------|-------------|---------|
-| **Flow 기반** (`@human_feedback` 데코레이터) | 로컬 개발, 콘솔 기반 검토, 동기식 워크플로우 | [Flow에서 인간 피드백](/ko/learn/human-feedback-in-flows) | **1.8.0+** |
-| **Webhook 기반** (Enterprise) | 프로덕션 배포, 비동기 워크플로우, 외부 통합 (Slack, Teams 등) | 이 가이드 | - |
-
-<Tip>
-Flow를 구축하면서 피드백을 기반으로 라우팅하는 인간 검토 단계를 추가하려면 `@human_feedback` 데코레이터에 대한 [Flow에서 인간 피드백](/ko/learn/human-feedback-in-flows) 가이드를 참조하세요.
-</Tip>
-
-## Webhook 기반 HITL 워크플로우 설정
+## HITL 워크플로우 설정
 
 <Steps>
     <Step title="작업 구성">

docs/ko/observability/galileo.mdx

@@ -1,115 +0,0 @@
----
-title: Galileo 갈릴레오
-description: CrewAI 추적 및 평가를 위한 Galileo 통합
-icon: telescope
-mode: "wide"
----
-
-## 개요
-
-이 가이드는 **Galileo**를 **CrewAI**와 통합하는 방법을 보여줍니다.
-포괄적인 추적 및 평가 엔지니어링을 위한 것입니다.
-이 가이드가 끝나면 CrewAI 에이전트를 추적할 수 있게 됩니다.
-성과를 모니터링하고 행동을 평가합니다.
-Galileo의 강력한 관측 플랫폼.
-
-> **갈릴레오(Galileo)란 무엇인가요?**[Galileo](https://galileo.ai/)는 AI 평가 및 관찰 가능성입니다.
-엔드투엔드 추적, 평가,
-AI 애플리케이션 모니터링. 이를 통해 팀은 실제 사실을 포착할 수 있습니다.
-견고한 가드레일을 만들고 체계적인 실험을 실행하세요.
-내장된 실험 추적 및 성능 분석으로 신뢰성 보장
-AI 수명주기 전반에 걸쳐 투명성과 지속적인 개선을 제공합니다.
-
-## 시작하기
-
-이 튜토리얼은 [CrewAI 빠른 시작](/ko/quickstart.mdx)을 따르며 추가하는 방법을 보여줍니다.
-갈릴레오의 [CrewAIEventListener](https://v2docs.galileo.ai/sdk-api/python/reference/handlers/crewai/handler),
-이벤트 핸들러.
-자세한 내용은 갈릴레오 문서를 참고하세요.
-[CrewAI 애플리케이션에 Galileo 추가](https://v2docs.galileo.ai/how-to-guides/third-party-integrations/add-galileo-to-crewai/add-galileo-to-crewai)
-방법 안내.
-
-> **참고**이 튜토리얼에서는 [CrewAI 빠른 시작](/ko/quickstart.mdx)을 완료했다고 가정합니다.
-완전한 포괄적인 예제를 원한다면 Galileo
-[CrewAI SDK 예제 저장소](https://github.com/rungalileo/sdk-examples/tree/main/python/agent/crew-ai).
-
-### 1단계: 종속성 설치
-
-앱에 필요한 종속성을 설치합니다.
-원하는 방법으로 가상 환경을 생성하고,
-그런 다음 다음을 사용하여 해당 환경 내에 종속성을 설치하십시오.
-선호하는 도구:
-
-```bash
-uv add galileo 
-```
-
-### 2단계: [CrewAI 빠른 시작](/ko/quickstart.mdx)에서 .env 파일에 추가
-
-```bash
-# Your Galileo API key
-GALILEO_API_KEY="your-galileo-api-key"
-
-# Your Galileo project name
-GALILEO_PROJECT="your-galileo-project-name"
-
-# The name of the Log stream you want to use for logging
-GALILEO_LOG_STREAM="your-galileo-log-stream "
-```
-
-### 3단계: Galileo 이벤트 리스너 추가
-
-Galileo로 로깅을 활성화하려면 `CrewAIEventListener`의 인스턴스를 생성해야 합니다.
-다음을 통해 Galileo CrewAI 핸들러 패키지를 가져옵니다.
-main.py 파일 상단에 다음 코드를 추가하세요.
-
-```python
-from galileo.handlers.crewai.handler import CrewAIEventListener
-```
-
-실행 함수 시작 시 이벤트 리스너를 생성합니다.
-
-```python
-def run():
-    # Create the event listener
-    CrewAIEventListener()
-    # The rest of your existing code goes here
-```
-
-리스너 인스턴스를 생성하면 자동으로
-CrewAI에 등록되었습니다.
-
-### 4단계: Crew Agent 실행
-
-CrewAI CLI를 사용하여 Crew Agent를 실행하세요.
-
-```bash
-crewai run
-```
-
-### 5단계: Galileo에서 추적 보기
-
-승무원 에이전트가 완료되면 흔적이 플러시되어 Galileo에 나타납니다.
-
-![Galileo trace view](/images/galileo-trace-veiw.png)
-  
-## 갈릴레오 통합 이해
-
-Galileo는 이벤트 리스너를 등록하여 CrewAI와 통합됩니다.
-승무원 실행 이벤트(예: 에이전트 작업, 도구 호출, 모델 응답)를 캡처합니다.
-관찰 가능성과 평가를 위해 이를 갈릴레오에 전달합니다.
-
-### 이벤트 리스너 이해
-
-`CrewAIEventListener()` 인스턴스를 생성하는 것이 전부입니다.
-CrewAI 실행을 위해 Galileo를 활성화하는 데 필요합니다. 인스턴스화되면 리스너는 다음을 수행합니다.
-
--CrewAI에 자동으로 등록됩니다.
--환경 변수에서 Galileo 구성을 읽습니다.
--모든 실행 데이터를 Galileo 프로젝트 및 다음에서 지정한 로그 스트림에 기록합니다.
-  `GALILEO_PROJECT` 및 `GALILEO_LOG_STREAM`
-
-추가 구성이나 코드 변경이 필요하지 않습니다.
-이 실행의 모든 데이터는 Galileo 프로젝트에 기록되며
-환경 구성에 따라 지정된 로그 스트림
-(예: GALILEO_PROJECT 및 GALILEO_LOG_STREAM)

docs/pt-BR/concepts/flows.mdx

@@ -309,10 +309,6 @@ Ao executar esse Flow, a saída será diferente dependendo do valor booleano ale
 
 ### Human in the Loop (feedback humano)
 
-<Note>
-O decorador `@human_feedback` requer **CrewAI versão 1.8.0 ou superior**.
-</Note>
-
 O decorador `@human_feedback` permite fluxos de trabalho human-in-the-loop, pausando a execução do flow para coletar feedback de um humano. Isso é útil para portões de aprovação, revisão de qualidade e pontos de decisão que requerem julgamento humano.
 
 ```python Code

docs/pt-BR/concepts/production-architecture.mdx

@@ -128,7 +128,7 @@ Ao implantar seu Flow, considere o seguinte:
 ### CrewAI Enterprise
 A maneira mais fácil de implantar seu Flow é usando o CrewAI Enterprise. Ele lida com a infraestrutura, autenticação e monitoramento para você.
 
-Confira o [Guia de Implantação](/pt-BR/enterprise/guides/deploy-to-amp) para começar.
+Confira o [Guia de Implantação](/pt-BR/enterprise/guides/deploy-crew) para começar.
 
 ```bash
 crewai deploy create

docs/pt-BR/enterprise/features/automations.mdx

@@ -91,7 +91,7 @@ Após implantar, você pode ver os detalhes da automação e usar o menu **Optio
 ## Relacionados
 
 <CardGroup cols={3}>
-  <Card title="Implantar um Crew" href="/pt-BR/enterprise/guides/deploy-to-amp" icon="rocket">
+  <Card title="Implantar um Crew" href="/pt-BR/enterprise/guides/deploy-crew" icon="rocket">
     Implante um Crew via GitHub ou arquivo ZIP.
   </Card>
   <Card title="Gatilhos de Automação" href="/pt-BR/enterprise/guides/automation-triggers" icon="trigger">

docs/pt-BR/enterprise/features/crew-studio.mdx

@@ -79,7 +79,7 @@ Após publicar, você pode visualizar os detalhes da automação e usar o menu *
   <Card title="Criar um Crew" href="/pt-BR/enterprise/guides/build-crew" icon="paintbrush">
     Crie um Crew.
   </Card>
-  <Card title="Implantar um Crew" href="/pt-BR/enterprise/guides/deploy-to-amp" icon="rocket">
+  <Card title="Implantar um Crew" href="/pt-BR/enterprise/guides/deploy-crew" icon="rocket">
     Implante um Crew via GitHub ou ZIP.
   </Card>
   <Card title="Exportar um Componente React" href="/pt-BR/enterprise/guides/react-component-export" icon="download">

docs/pt-BR/enterprise/guides/deploy-crew.mdx

@@ -0,0 +1,304 @@
+---
+title: "Deploy Crew"
+description: "Implantando um Crew na CrewAI AMP"
+icon: "rocket"
+mode: "wide"
+---
+
+<Note>
+  Depois de criar um crew localmente ou pelo Crew Studio, o próximo passo é
+  implantá-lo na plataforma CrewAI AMP. Este guia cobre múltiplos métodos de
+  implantação para ajudá-lo a escolher a melhor abordagem para o seu fluxo de
+  trabalho.
+</Note>
+
+## Pré-requisitos
+
+<CardGroup cols={2}>
+  <Card title="Crew Pronto para Implantação" icon="users">
+    Você deve ter um crew funcional, criado localmente ou pelo Crew Studio
+  </Card>
+  <Card title="Repositório GitHub" icon="github">
+    O código do seu crew deve estar em um repositório do GitHub (para o método
+    de integração com GitHub)
+  </Card>
+</CardGroup>
+
+## Opção 1: Implantar Usando o CrewAI CLI
+
+A CLI fornece a maneira mais rápida de implantar crews desenvolvidos localmente na plataforma Enterprise.
+
+<Steps>
+  <Step title="Instale o CrewAI CLI">
+    Se ainda não tiver, instale o CrewAI CLI:
+
+    ```bash
+    pip install crewai[tools]
+    ```
+
+    <Tip>
+    A CLI vem com o pacote principal CrewAI, mas o extra `[tools]` garante todas as dependências de implantação.
+    </Tip>
+
+  </Step>
+
+  <Step title="Autentique-se na Plataforma Enterprise">
+    Primeiro, você precisa autenticar sua CLI com a plataforma CrewAI AMP:
+
+    ```bash
+    # Se já possui uma conta CrewAI AMP, ou deseja criar uma:
+    crewai login
+    ```
+
+    Ao executar qualquer um dos comandos, a CLI irá:
+    1. Exibir uma URL e um código de dispositivo único
+    2. Abrir seu navegador para a página de autenticação
+    3. Solicitar a confirmação do dispositivo
+    4. Completar o processo de autenticação
+
+    Após a autenticação bem-sucedida, você verá uma mensagem de confirmação no terminal!
+
+  </Step>
+
+  <Step title="Criar uma Implantação">
+
+    No diretório do seu projeto, execute:
+
+    ```bash
+    crewai deploy create
+    ```
+
+    Este comando irá:
+    1. Detectar informações do seu repositório GitHub
+    2. Identificar variáveis de ambiente no seu arquivo `.env` local
+    3. Transferir essas variáveis com segurança para a plataforma Enterprise
+    4. Criar uma nova implantação com um identificador único
+
+    Com a criação bem-sucedida, você verá uma mensagem como:
+    ```shell
+    Deployment created successfully!
+    Name: your_project_name
+    Deployment ID: 01234567-89ab-cdef-0123-456789abcdef
+    Current Status: Deploy Enqueued
+    ```
+
+  </Step>
+
+  <Step title="Acompanhe o Progresso da Implantação">
+
+    Acompanhe o status da implantação com:
+
+    ```bash
+    crewai deploy status
+    ```
+
+    Para ver logs detalhados do processo de build:
+
+    ```bash
+    crewai deploy logs
+    ```
+
+    <Tip>
+    A primeira implantação normalmente leva de 10 a 15 minutos, pois as imagens dos containers são construídas. As próximas implantações são bem mais rápidas.
+    </Tip>
+
+  </Step>
+</Steps>
+
+## Comandos Adicionais da CLI
+
+O CrewAI CLI oferece vários comandos para gerenciar suas implantações:
+
+```bash
+# Liste todas as suas implantações
+crewai deploy list
+
+# Consulte o status de uma implantação
+crewai deploy status
+
+# Veja os logs da implantação
+crewai deploy logs
+
+# Envie atualizações após alterações no código
+crewai deploy push
+
+# Remova uma implantação
+crewai deploy remove <deployment_id>
+```
+
+## Opção 2: Implantar Diretamente pela Interface Web
+
+Você também pode implantar seus crews diretamente pela interface web da CrewAI AMP conectando sua conta do GitHub. Esta abordagem não requer utilizar a CLI na sua máquina local.
+
+<Steps>
+
+  <Step title="Enviar no GitHub">
+
+Você precisa subir seu crew para um repositório do GitHub. Caso ainda não tenha criado um crew, você pode [seguir este tutorial](/pt-BR/quickstart).
+
+  </Step>
+
+  <Step title="Conectando o GitHub ao CrewAI AMP">
+
+    1. Faça login em [CrewAI AMP](https://app.crewai.com)
+    2. Clique no botão "Connect GitHub"
+
+    <Frame>
+      ![Botão Connect GitHub](/images/enterprise/connect-github.png)
+    </Frame>
+
+  </Step>
+
+  <Step title="Selecionar o Repositório">
+
+    Após conectar sua conta GitHub, você poderá selecionar qual repositório deseja implantar:
+
+    <Frame>
+      ![Selecionar Repositório](/images/enterprise/select-repo.png)
+    </Frame>
+
+  </Step>
+
+  <Step title="Definir as Variáveis de Ambiente">
+
+    Antes de implantar, você precisará configurar as variáveis de ambiente para conectar ao seu provedor de LLM ou outros serviços:
+
+    1. Você pode adicionar variáveis individualmente ou em lote
+    2. Digite suas variáveis no formato `KEY=VALUE` (uma por linha)
+
+    <Frame>
+      ![Definir Variáveis de Ambiente](/images/enterprise/set-env-variables.png)
+    </Frame>
+
+  </Step>
+
+  <Step title="Implante Seu Crew">
+
+    1. Clique no botão "Deploy" para iniciar o processo de implantação
+    2. Você pode monitorar o progresso pela barra de progresso
+    3. A primeira implantação geralmente demora de 10 a 15 minutos; as próximas serão mais rápidas
+
+    <Frame>
+      ![Progresso da Implantação](/images/enterprise/deploy-progress.png)
+    </Frame>
+
+    Após a conclusão, você verá:
+    - A URL exclusiva do seu crew
+    - Um Bearer token para proteger sua API crew
+    - Um botão "Delete" caso precise remover a implantação
+
+  </Step>
+
+</Steps>
+
+## ⚠️ Requisitos de Segurança para Variáveis de Ambiente
+
+<Warning>
+  **Importante**: A CrewAI AMP possui restrições de segurança sobre os nomes de
+  variáveis de ambiente que podem causar falha na implantação caso não sejam
+  seguidas.
+</Warning>
+
+### Padrões de Variáveis de Ambiente Bloqueados
+
+Por motivos de segurança, os seguintes padrões de nome de variável de ambiente são **automaticamente filtrados** e causarão problemas de implantação:
+
+**Padrões Bloqueados:**
+
+- Variáveis terminando em `_TOKEN` (ex: `MY_API_TOKEN`)
+- Variáveis terminando em `_PASSWORD` (ex: `DB_PASSWORD`)
+- Variáveis terminando em `_SECRET` (ex: `API_SECRET`)
+- Variáveis terminando em `_KEY` em certos contextos
+
+**Variáveis Bloqueadas Específicas:**
+
+- `GITHUB_USER`, `GITHUB_TOKEN`
+- `AWS_REGION`, `AWS_DEFAULT_REGION`
+- Diversas variáveis internas do sistema CrewAI
+
+### Exceções Permitidas
+
+Algumas variáveis são explicitamente permitidas mesmo coincidindo com os padrões bloqueados:
+
+- `AZURE_AD_TOKEN`
+- `AZURE_OPENAI_AD_TOKEN`
+- `ENTERPRISE_ACTION_TOKEN`
+- `CREWAI_ENTEPRISE_TOOLS_TOKEN`
+
+### Como Corrigir Problemas de Nomeação
+
+Se sua implantação falhar devido a restrições de variáveis de ambiente:
+
+```bash
+# ❌ Estas irão causar falhas na implantação
+OPENAI_TOKEN=sk-...
+DATABASE_PASSWORD=mysenha
+API_SECRET=segredo123
+
+# ✅ Utilize estes padrões de nomeação
+OPENAI_API_KEY=sk-...
+DATABASE_CREDENTIALS=mysenha
+API_CONFIG=segredo123
+```
+
+### Melhores Práticas
+
+1. **Use convenções padrão de nomenclatura**: `PROVIDER_API_KEY` em vez de `PROVIDER_TOKEN`
+2. **Teste localmente primeiro**: Certifique-se de que seu crew funciona com as variáveis renomeadas
+3. **Atualize seu código**: Altere todas as referências aos nomes antigos das variáveis
+4. **Documente as mudanças**: Mantenha registro das variáveis renomeadas para seu time
+
+<Tip>
+  Se você se deparar com falhas de implantação com erros enigmáticos de
+  variáveis de ambiente, confira primeiro os nomes das variáveis em relação a
+  esses padrões.
+</Tip>
+
+### Interaja com Seu Crew Implantado
+
+Após a implantação, você pode acessar seu crew por meio de:
+
+1. **REST API**: A plataforma gera um endpoint HTTPS exclusivo com estas rotas principais:
+
+   - `/inputs`: Lista os parâmetros de entrada requeridos
+   - `/kickoff`: Inicia uma execução com os inputs fornecidos
+   - `/status/{kickoff_id}`: Consulta o status da execução
+
+2. **Interface Web**: Acesse [app.crewai.com](https://app.crewai.com) para visualizar:
+   - **Aba Status**: Informações da implantação, detalhes do endpoint da API e token de autenticação
+   - **Aba Run**: Visualização da estrutura do seu crew
+   - **Aba Executions**: Histórico de todas as execuções
+   - **Aba Metrics**: Análises de desempenho
+   - **Aba Traces**: Insights detalhados das execuções
+
+### Dispare uma Execução
+
+No dashboard Enterprise, você pode:
+
+1. Clicar no nome do seu crew para abrir seus detalhes
+2. Selecionar "Trigger Crew" na interface de gerenciamento
+3. Inserir os inputs necessários no modal exibido
+4. Monitorar o progresso à medida que a execução avança pelo pipeline
+
+### Monitoramento e Análises
+
+A plataforma Enterprise oferece recursos abrangentes de observabilidade:
+
+- **Gestão das Execuções**: Acompanhe execuções ativas e concluídas
+- **Traces**: Quebra detalhada de cada execução
+- **Métricas**: Uso de tokens, tempos de execução e custos
+- **Visualização em Linha do Tempo**: Representação visual das sequências de tarefas
+
+### Funcionalidades Avançadas
+
+A plataforma Enterprise também oferece:
+
+- **Gerenciamento de Variáveis de Ambiente**: Armazene e gerencie com segurança as chaves de API
+- **Conexões com LLM**: Configure integrações com diversos provedores de LLM
+- **Repositório Custom Tools**: Crie, compartilhe e instale ferramentas
+- **Crew Studio**: Monte crews via interface de chat sem escrever código
+
+<Card title="Precisa de Ajuda?" icon="headset" href="mailto:support@crewai.com">
+  Entre em contato com nossa equipe de suporte para ajuda com questões de
+  implantação ou dúvidas sobre a plataforma Enterprise.
+</Card>

docs/pt-BR/enterprise/guides/deploy-to-amp.mdx

@@ -1,439 +0,0 @@
----
-title: "Deploy para AMP"
-description: "Implante seu Crew ou Flow no CrewAI AMP"
-icon: "rocket"
-mode: "wide"
----
-
-<Note>
-  Depois de criar um Crew ou Flow localmente (ou pelo Crew Studio), o próximo passo é
-  implantá-lo na plataforma CrewAI AMP. Este guia cobre múltiplos métodos de
-  implantação para ajudá-lo a escolher a melhor abordagem para o seu fluxo de trabalho.
-</Note>
-
-## Pré-requisitos
-
-<CardGroup cols={2}>
-  <Card title="Projeto Pronto para Implantação" icon="check-circle">
-    Você deve ter um Crew ou Flow funcionando localmente com sucesso.
-    Siga nosso [guia de preparação](/pt-BR/enterprise/guides/prepare-for-deployment) para verificar a estrutura do seu projeto.
-  </Card>
-  <Card title="Repositório GitHub" icon="github">
-    Seu código deve estar em um repositório do GitHub (para o método de integração com GitHub).
-  </Card>
-</CardGroup>
-
-<Info>
-  **Crews vs Flows**: Ambos os tipos de projeto podem ser implantados como "automações" no CrewAI AMP.
-  O processo de implantação é o mesmo, mas eles têm estruturas de projeto diferentes.
-  Veja [Preparar para Implantação](/pt-BR/enterprise/guides/prepare-for-deployment) para detalhes.
-</Info>
-
-## Opção 1: Implantar Usando o CrewAI CLI
-
-A CLI fornece a maneira mais rápida de implantar Crews ou Flows desenvolvidos localmente na plataforma AMP.
-A CLI detecta automaticamente o tipo do seu projeto a partir do `pyproject.toml` e faz o build adequadamente.
-
-<Steps>
-  <Step title="Instale o CrewAI CLI">
-    Se ainda não tiver, instale o CrewAI CLI:
-
-    ```bash
-    pip install crewai[tools]
-    ```
-
-    <Tip>
-    A CLI vem com o pacote principal CrewAI, mas o extra `[tools]` garante todas as dependências de implantação.
-    </Tip>
-
-  </Step>
-
-  <Step title="Autentique-se na Plataforma Enterprise">
-    Primeiro, você precisa autenticar sua CLI com a plataforma CrewAI AMP:
-
-    ```bash
-    # Se já possui uma conta CrewAI AMP, ou deseja criar uma:
-    crewai login
-    ```
-
-    Ao executar qualquer um dos comandos, a CLI irá:
-    1. Exibir uma URL e um código de dispositivo único
-    2. Abrir seu navegador para a página de autenticação
-    3. Solicitar a confirmação do dispositivo
-    4. Completar o processo de autenticação
-
-    Após a autenticação bem-sucedida, você verá uma mensagem de confirmação no terminal!
-
-  </Step>
-
-  <Step title="Criar uma Implantação">
-
-    No diretório do seu projeto, execute:
-
-    ```bash
-    crewai deploy create
-    ```
-
-    Este comando irá:
-    1. Detectar informações do seu repositório GitHub
-    2. Identificar variáveis de ambiente no seu arquivo `.env` local
-    3. Transferir essas variáveis com segurança para a plataforma Enterprise
-    4. Criar uma nova implantação com um identificador único
-
-    Com a criação bem-sucedida, você verá uma mensagem como:
-    ```shell
-    Deployment created successfully!
-    Name: your_project_name
-    Deployment ID: 01234567-89ab-cdef-0123-456789abcdef
-    Current Status: Deploy Enqueued
-    ```
-
-  </Step>
-
-  <Step title="Acompanhe o Progresso da Implantação">
-
-    Acompanhe o status da implantação com:
-
-    ```bash
-    crewai deploy status
-    ```
-
-    Para ver logs detalhados do processo de build:
-
-    ```bash
-    crewai deploy logs
-    ```
-
-    <Tip>
-    A primeira implantação normalmente leva de 10 a 15 minutos, pois as imagens dos containers são construídas. As próximas implantações são bem mais rápidas.
-    </Tip>
-
-  </Step>
-</Steps>
-
-## Comandos Adicionais da CLI
-
-O CrewAI CLI oferece vários comandos para gerenciar suas implantações:
-
-```bash
-# Liste todas as suas implantações
-crewai deploy list
-
-# Consulte o status de uma implantação
-crewai deploy status
-
-# Veja os logs da implantação
-crewai deploy logs
-
-# Envie atualizações após alterações no código
-crewai deploy push
-
-# Remova uma implantação
-crewai deploy remove <deployment_id>
-```
-
-## Opção 2: Implantar Diretamente pela Interface Web
-
-Você também pode implantar seus Crews ou Flows diretamente pela interface web do CrewAI AMP conectando sua conta do GitHub. Esta abordagem não requer utilizar a CLI na sua máquina local. A plataforma detecta automaticamente o tipo do seu projeto e trata o build adequadamente.
-
-<Steps>
-
-  <Step title="Enviar para o GitHub">
-
-Você precisa enviar seu crew para um repositório do GitHub. Caso ainda não tenha criado um crew, você pode [seguir este tutorial](/pt-BR/quickstart).
-
-  </Step>
-
-  <Step title="Conectando o GitHub ao CrewAI AMP">
-
-    1. Faça login em [CrewAI AMP](https://app.crewai.com)
-    2. Clique no botão "Connect GitHub"
-
-    <Frame>
-      ![Botão Connect GitHub](/images/enterprise/connect-github.png)
-    </Frame>
-
-  </Step>
-
-  <Step title="Selecionar o Repositório">
-
-    Após conectar sua conta GitHub, você poderá selecionar qual repositório deseja implantar:
-
-    <Frame>
-      ![Selecionar Repositório](/images/enterprise/select-repo.png)
-    </Frame>
-
-  </Step>
-
-  <Step title="Definir as Variáveis de Ambiente">
-
-    Antes de implantar, você precisará configurar as variáveis de ambiente para conectar ao seu provedor de LLM ou outros serviços:
-
-    1. Você pode adicionar variáveis individualmente ou em lote
-    2. Digite suas variáveis no formato `KEY=VALUE` (uma por linha)
-
-    <Frame>
-      ![Definir Variáveis de Ambiente](/images/enterprise/set-env-variables.png)
-    </Frame>
-
-  </Step>
-
-  <Step title="Implante Seu Crew">
-
-    1. Clique no botão "Deploy" para iniciar o processo de implantação
-    2. Você pode monitorar o progresso pela barra de progresso
-    3. A primeira implantação geralmente demora de 10 a 15 minutos; as próximas serão mais rápidas
-
-    <Frame>
-      ![Progresso da Implantação](/images/enterprise/deploy-progress.png)
-    </Frame>
-
-    Após a conclusão, você verá:
-    - A URL exclusiva do seu crew
-    - Um Bearer token para proteger sua API crew
-    - Um botão "Delete" caso precise remover a implantação
-
-  </Step>
-
-</Steps>
-
-## Opção 3: Reimplantar Usando API (Integração CI/CD)
-
-Para implantações automatizadas em pipelines CI/CD, você pode usar a API do CrewAI para acionar reimplantações de crews existentes. Isso é particularmente útil para GitHub Actions, Jenkins ou outros workflows de automação.
-
-<Steps>
-  <Step title="Obtenha Seu Token de Acesso Pessoal">
-
-    Navegue até as configurações da sua conta CrewAI AMP para gerar um token de API:
-
-    1. Acesse [app.crewai.com](https://app.crewai.com)
-    2. Clique em **Settings** → **Account** → **Personal Access Token**
-    3. Gere um novo token e copie-o com segurança
-    4. Armazene este token como um secret no seu sistema CI/CD
-
-  </Step>
-
-  <Step title="Encontre o UUID da Sua Automação">
-
-    Localize o identificador único do seu crew implantado:
-
-    1. Acesse **Automations** no seu dashboard CrewAI AMP
-    2. Selecione sua automação/crew existente
-    3. Clique em **Additional Details**
-    4. Copie o **UUID** - este identifica sua implantação específica do crew
-
-  </Step>
-
-  <Step title="Acione a Reimplantação via API">
-
-    Use o endpoint da API de Deploy para acionar uma reimplantação:
-
-    ```bash
-    curl -i -X POST \
-         -H "Authorization: Bearer YOUR_PERSONAL_ACCESS_TOKEN" \
-         https://app.crewai.com/crewai_plus/api/v1/crews/YOUR-AUTOMATION-UUID/deploy
-
-    # HTTP/2 200
-    # content-type: application/json
-    #
-    # {
-    #   "uuid": "your-automation-uuid",
-    #   "status": "Deploy Enqueued",
-    #   "public_url": "https://your-crew-deployment.crewai.com",
-    #   "token": "your-bearer-token"
-    # }
-    ```
-
-    <Info>
-    Se sua automação foi criada originalmente conectada ao Git, a API automaticamente puxará as últimas alterações do seu repositório antes de reimplantar.
-    </Info>
-
-  </Step>
-
-  <Step title="Exemplo de Integração com GitHub Actions">
-
-    Aqui está um workflow do GitHub Actions com gatilhos de implantação mais complexos:
-
-    ```yaml
-    name: Deploy CrewAI Automation
-
-    on:
-      push:
-        branches: [ main ]
-      pull_request:
-        types: [ labeled ]
-      release:
-        types: [ published ]
-
-    jobs:
-      deploy:
-        runs-on: ubuntu-latest
-        if: |
-          (github.event_name == 'push' && github.ref == 'refs/heads/main') ||
-          (github.event_name == 'pull_request' && contains(github.event.pull_request.labels.*.name, 'deploy')) ||
-          (github.event_name == 'release')
-        steps:
-          - name: Trigger CrewAI Redeployment
-            run: |
-              curl -X POST \
-                   -H "Authorization: Bearer ${{ secrets.CREWAI_PAT }}" \
-                   https://app.crewai.com/crewai_plus/api/v1/crews/${{ secrets.CREWAI_AUTOMATION_UUID }}/deploy
-    ```
-
-    <Tip>
-    Adicione `CREWAI_PAT` e `CREWAI_AUTOMATION_UUID` como secrets do repositório. Para implantações de PR, adicione um label "deploy" para acionar o workflow.
-    </Tip>
-
-    </Step>
-
-  </Steps>
-
-## Interaja com Sua Automação Implantada
-
-Após a implantação, você pode acessar seu crew através de:
-
-1. **REST API**: A plataforma gera um endpoint HTTPS exclusivo com estas rotas principais:
-
-   - `/inputs`: Lista os parâmetros de entrada requeridos
-   - `/kickoff`: Inicia uma execução com os inputs fornecidos
-   - `/status/{kickoff_id}`: Consulta o status da execução
-
-2. **Interface Web**: Acesse [app.crewai.com](https://app.crewai.com) para visualizar:
-   - **Aba Status**: Informações da implantação, detalhes do endpoint da API e token de autenticação
-   - **Aba Run**: Visualização da estrutura do seu crew
-   - **Aba Executions**: Histórico de todas as execuções
-   - **Aba Metrics**: Análises de desempenho
-   - **Aba Traces**: Insights detalhados das execuções
-
-### Dispare uma Execução
-
-No dashboard Enterprise, você pode:
-
-1. Clicar no nome do seu crew para abrir seus detalhes
-2. Selecionar "Trigger Crew" na interface de gerenciamento
-3. Inserir os inputs necessários no modal exibido
-4. Monitorar o progresso à medida que a execução avança pelo pipeline
-
-### Monitoramento e Análises
-
-A plataforma Enterprise oferece recursos abrangentes de observabilidade:
-
-- **Gestão das Execuções**: Acompanhe execuções ativas e concluídas
-- **Traces**: Quebra detalhada de cada execução
-- **Métricas**: Uso de tokens, tempos de execução e custos
-- **Visualização em Linha do Tempo**: Representação visual das sequências de tarefas
-
-### Funcionalidades Avançadas
-
-A plataforma Enterprise também oferece:
-
-- **Gerenciamento de Variáveis de Ambiente**: Armazene e gerencie com segurança as chaves de API
-- **Conexões com LLM**: Configure integrações com diversos provedores de LLM
-- **Repositório Custom Tools**: Crie, compartilhe e instale ferramentas
-- **Crew Studio**: Monte crews via interface de chat sem escrever código
-
-## Solução de Problemas em Falhas de Implantação
-
-Se sua implantação falhar, verifique estes problemas comuns:
-
-### Falhas de Build
-
-#### Arquivo uv.lock Ausente
-
-**Sintoma**: Build falha no início com erros de resolução de dependências
-
-**Solução**: Gere e faça commit do arquivo lock:
-
-```bash
-uv lock
-git add uv.lock
-git commit -m "Add uv.lock for deployment"
-git push
-```
-
-<Warning>
-  O arquivo `uv.lock` é obrigatório para todas as implantações. Sem ele, a plataforma
-  não consegue instalar suas dependências de forma confiável.
-</Warning>
-
-#### Estrutura de Projeto Incorreta
-
-**Sintoma**: Erros "Could not find entry point" ou "Module not found"
-
-**Solução**: Verifique se seu projeto corresponde à estrutura esperada:
-
-- **Tanto Crews quanto Flows**: Devem ter ponto de entrada em `src/project_name/main.py`
-- **Crews**: Usam uma função `run()` como ponto de entrada
-- **Flows**: Usam uma função `kickoff()` como ponto de entrada
-
-Veja [Preparar para Implantação](/pt-BR/enterprise/guides/prepare-for-deployment) para diagramas de estrutura detalhados.
-
-#### Decorador CrewBase Ausente
-
-**Sintoma**: Erros "Crew not found", "Config not found" ou erros de configuração de agent/task
-
-**Solução**: Certifique-se de que **todas** as classes crew usam o decorador `@CrewBase`:
-
-```python
-from crewai.project import CrewBase, agent, crew, task
-
-@CrewBase  # Este decorador é OBRIGATÓRIO
-class YourCrew():
-    """Descrição do seu crew"""
-
-    @agent
-    def my_agent(self) -> Agent:
-        return Agent(
-            config=self.agents_config['my_agent'],  # type: ignore[index]
-            verbose=True
-        )
-
-    # ... resto da definição do crew
-```
-
-<Info>
-  Isso se aplica a Crews independentes E crews embutidos dentro de projetos Flow.
-  Toda classe crew precisa do decorador.
-</Info>
-
-#### Tipo Incorreto no pyproject.toml
-
-**Sintoma**: Build tem sucesso mas falha em runtime, ou comportamento inesperado
-
-**Solução**: Verifique se a seção `[tool.crewai]` corresponde ao tipo do seu projeto:
-
-```toml
-# Para projetos Crew:
-[tool.crewai]
-type = "crew"
-
-# Para projetos Flow:
-[tool.crewai]
-type = "flow"
-```
-
-### Falhas de Runtime
-
-#### Falhas de Conexão com LLM
-
-**Sintoma**: Erros de chave API, "model not found" ou falhas de autenticação
-
-**Solução**:
-1. Verifique se a chave API do seu provedor LLM está corretamente definida nas variáveis de ambiente
-2. Certifique-se de que os nomes das variáveis de ambiente correspondem ao que seu código espera
-3. Teste localmente com exatamente as mesmas variáveis de ambiente antes de implantar
-
-#### Erros de Execução do Crew
-
-**Sintoma**: Crew inicia mas falha durante a execução
-
-**Solução**:
-1. Verifique os logs de execução no dashboard AMP (aba Traces)
-2. Verifique se todas as ferramentas têm as chaves API necessárias configuradas
-3. Certifique-se de que as configurações de agents em `agents.yaml` são válidas
-4. Verifique se há erros de sintaxe nas configurações de tasks em `tasks.yaml`
-
-<Card title="Precisa de Ajuda?" icon="headset" href="mailto:support@crewai.com">
-  Entre em contato com nossa equipe de suporte para ajuda com questões de
-  implantação ou dúvidas sobre a plataforma AMP.
-</Card>

docs/pt-BR/enterprise/guides/prepare-for-deployment.mdx

@@ -1,305 +0,0 @@
----
-title: "Preparar para Implantação"
-description: "Certifique-se de que seu Crew ou Flow está pronto para implantação no CrewAI AMP"
-icon: "clipboard-check"
-mode: "wide"
----
-
-<Note>
-  Antes de implantar no CrewAI AMP, é crucial verificar se seu projeto está estruturado corretamente.
-  Tanto Crews quanto Flows podem ser implantados como "automações", mas eles têm estruturas de projeto
-  e requisitos diferentes que devem ser atendidos para uma implantação bem-sucedida.
-</Note>
-
-## Entendendo Automações
-
-No CrewAI AMP, **automações** é o termo geral para projetos de IA Agêntica implantáveis. Uma automação pode ser:
-
-- **Um Crew**: Uma equipe independente de agentes de IA trabalhando juntos em tarefas
-- **Um Flow**: Um workflow orquestrado que pode combinar múltiplos crews, chamadas diretas de LLM e lógica procedural
-
-Entender qual tipo você está implantando é essencial porque eles têm estruturas de projeto e pontos de entrada diferentes.
-
-## Crews vs Flows: Principais Diferenças
-
-<CardGroup cols={2}>
-  <Card title="Projetos Crew" icon="users">
-    Equipes de agentes de IA independentes com `crew.py` definindo agentes e tarefas. Ideal para tarefas focadas e colaborativas.
-  </Card>
-  <Card title="Projetos Flow" icon="diagram-project">
-    Workflows orquestrados com crews embutidos em uma pasta `crews/`. Ideal para processos complexos de múltiplas etapas.
-  </Card>
-</CardGroup>
-
-| Aspecto | Crew | Flow |
-|---------|------|------|
-| **Estrutura do projeto** | `src/project_name/` com `crew.py` | `src/project_name/` com pasta `crews/` |
-| **Localização da lógica principal** | `src/project_name/crew.py` | `src/project_name/main.py` (classe Flow) |
-| **Função de ponto de entrada** | `run()` em `main.py` | `kickoff()` em `main.py` |
-| **Tipo no pyproject.toml** | `type = "crew"` | `type = "flow"` |
-| **Comando CLI de criação** | `crewai create crew name` | `crewai create flow name` |
-| **Localização da configuração** | `src/project_name/config/` | `src/project_name/crews/crew_name/config/` |
-| **Pode conter outros crews** | Não | Sim (na pasta `crews/`) |
-
-## Referência de Estrutura de Projeto
-
-### Estrutura de Projeto Crew
-
-Quando você executa `crewai create crew my_crew`, você obtém esta estrutura:
-
-```
-my_crew/
-├── .gitignore
-├── pyproject.toml          # Deve ter type = "crew"
-├── README.md
-├── .env
-├── uv.lock                  # OBRIGATÓRIO para implantação
-└── src/
-    └── my_crew/
-        ├── __init__.py
-        ├── main.py          # Ponto de entrada com função run()
-        ├── crew.py          # Classe Crew com decorador @CrewBase
-        ├── tools/
-        │   ├── custom_tool.py
-        │   └── __init__.py
-        └── config/
-            ├── agents.yaml  # Definições de agentes
-            └── tasks.yaml   # Definições de tarefas
-```
-
-<Warning>
-  A estrutura aninhada `src/project_name/` é crítica para Crews.
-  Colocar arquivos no nível errado causará falhas na implantação.
-</Warning>
-
-### Estrutura de Projeto Flow
-
-Quando você executa `crewai create flow my_flow`, você obtém esta estrutura:
-
-```
-my_flow/
-├── .gitignore
-├── pyproject.toml          # Deve ter type = "flow"
-├── README.md
-├── .env
-├── uv.lock                  # OBRIGATÓRIO para implantação
-└── src/
-    └── my_flow/
-        ├── __init__.py
-        ├── main.py          # Ponto de entrada com função kickoff() + classe Flow
-        ├── crews/           # Pasta de crews embutidos
-        │   └── poem_crew/
-        │       ├── __init__.py
-        │       ├── poem_crew.py  # Crew com decorador @CrewBase
-        │       └── config/
-        │           ├── agents.yaml
-        │           └── tasks.yaml
-        └── tools/
-            ├── __init__.py
-            └── custom_tool.py
-```
-
-<Info>
-  Tanto Crews quanto Flows usam a estrutura `src/project_name/`.
-  A diferença chave é que Flows têm uma pasta `crews/` para crews embutidos,
-  enquanto Crews têm `crew.py` diretamente na pasta do projeto.
-</Info>
-
-## Checklist Pré-Implantação
-
-Use este checklist para verificar se seu projeto está pronto para implantação.
-
-### 1. Verificar Configuração do pyproject.toml
-
-Seu `pyproject.toml` deve incluir a seção `[tool.crewai]` correta:
-
-<Tabs>
-  <Tab title="Para Crews">
-    ```toml
-    [tool.crewai]
-    type = "crew"
-    ```
-  </Tab>
-  <Tab title="Para Flows">
-    ```toml
-    [tool.crewai]
-    type = "flow"
-    ```
-  </Tab>
-</Tabs>
-
-<Warning>
-  Se o `type` não corresponder à estrutura do seu projeto, o build falhará ou
-  a automação não funcionará corretamente.
-</Warning>
-
-### 2. Garantir que o Arquivo uv.lock Existe
-
-CrewAI usa `uv` para gerenciamento de dependências. O arquivo `uv.lock` garante builds reproduzíveis e é **obrigatório** para implantação.
-
-```bash
-# Gerar ou atualizar o arquivo lock
-uv lock
-
-# Verificar se existe
-ls -la uv.lock
-```
-
-Se o arquivo não existir, execute `uv lock` e faça commit no seu repositório:
-
-```bash
-uv lock
-git add uv.lock
-git commit -m "Add uv.lock for deployment"
-git push
-```
-
-### 3. Validar Uso do Decorador CrewBase
-
-**Toda classe crew deve usar o decorador `@CrewBase`.** Isso se aplica a:
-
-- Projetos crew independentes
-- Crews embutidos dentro de projetos Flow
-
-```python
-from crewai import Agent, Crew, Process, Task
-from crewai.project import CrewBase, agent, crew, task
-from crewai.agents.agent_builder.base_agent import BaseAgent
-from typing import List
-
-@CrewBase  # Este decorador é OBRIGATÓRIO
-class MyCrew():
-    """Descrição do meu crew"""
-
-    agents: List[BaseAgent]
-    tasks: List[Task]
-
-    @agent
-    def my_agent(self) -> Agent:
-        return Agent(
-            config=self.agents_config['my_agent'],  # type: ignore[index]
-            verbose=True
-        )
-
-    @task
-    def my_task(self) -> Task:
-        return Task(
-            config=self.tasks_config['my_task']  # type: ignore[index]
-        )
-
-    @crew
-    def crew(self) -> Crew:
-        return Crew(
-            agents=self.agents,
-            tasks=self.tasks,
-            process=Process.sequential,
-            verbose=True,
-        )
-```
-
-<Warning>
-  Se você esquecer o decorador `@CrewBase`, sua implantação falhará com
-  erros sobre configurações de agents ou tasks ausentes.
-</Warning>
-
-### 4. Verificar Pontos de Entrada do Projeto
-
-Tanto Crews quanto Flows têm seu ponto de entrada em `src/project_name/main.py`:
-
-<Tabs>
-  <Tab title="Para Crews">
-    O ponto de entrada usa uma função `run()`:
-
-    ```python
-    # src/my_crew/main.py
-    from my_crew.crew import MyCrew
-
-    def run():
-        """Executa o crew."""
-        inputs = {'topic': 'AI in Healthcare'}
-        result = MyCrew().crew().kickoff(inputs=inputs)
-        return result
-
-    if __name__ == "__main__":
-        run()
-    ```
-  </Tab>
-  <Tab title="Para Flows">
-    O ponto de entrada usa uma função `kickoff()` com uma classe Flow:
-
-    ```python
-    # src/my_flow/main.py
-    from crewai.flow import Flow, listen, start
-    from my_flow.crews.poem_crew.poem_crew import PoemCrew
-
-    class MyFlow(Flow):
-        @start()
-        def begin(self):
-            # Lógica do Flow aqui
-            result = PoemCrew().crew().kickoff(inputs={...})
-            return result
-
-    def kickoff():
-        """Executa o flow."""
-        MyFlow().kickoff()
-
-    if __name__ == "__main__":
-        kickoff()
-    ```
-  </Tab>
-</Tabs>
-
-### 5. Preparar Variáveis de Ambiente
-
-Antes da implantação, certifique-se de ter:
-
-1. **Chaves de API de LLM** prontas (OpenAI, Anthropic, Google, etc.)
-2. **Chaves de API de ferramentas** se estiver usando ferramentas externas (Serper, etc.)
-
-<Tip>
-  Teste seu projeto localmente com as mesmas variáveis de ambiente antes de implantar
-  para detectar problemas de configuração antecipadamente.
-</Tip>
-
-## Comandos de Validação Rápida
-
-Execute estes comandos a partir da raiz do seu projeto para verificar rapidamente sua configuração:
-
-```bash
-# 1. Verificar tipo do projeto no pyproject.toml
-grep -A2 "\[tool.crewai\]" pyproject.toml
-
-# 2. Verificar se uv.lock existe
-ls -la uv.lock || echo "ERRO: uv.lock ausente! Execute 'uv lock'"
-
-# 3. Verificar se estrutura src/ existe
-ls -la src/*/main.py 2>/dev/null || echo "Nenhum main.py encontrado em src/"
-
-# 4. Para Crews - verificar se crew.py existe
-ls -la src/*/crew.py 2>/dev/null || echo "Nenhum crew.py (esperado para Crews)"
-
-# 5. Para Flows - verificar se pasta crews/ existe
-ls -la src/*/crews/ 2>/dev/null || echo "Nenhuma pasta crews/ (esperado para Flows)"
-
-# 6. Verificar uso do CrewBase
-grep -r "@CrewBase" . --include="*.py"
-```
-
-## Erros Comuns de Configuração
-
-| Erro | Sintoma | Correção |
-|------|---------|----------|
-| `uv.lock` ausente | Build falha durante resolução de dependências | Execute `uv lock` e faça commit |
-| `type` errado no pyproject.toml | Build bem-sucedido mas falha em runtime | Altere para o tipo correto |
-| Decorador `@CrewBase` ausente | Erros "Config not found" | Adicione decorador a todas as classes crew |
-| Arquivos na raiz ao invés de `src/` | Ponto de entrada não encontrado | Mova para `src/project_name/` |
-| `run()` ou `kickoff()` ausente | Não é possível iniciar automação | Adicione a função de entrada correta |
-
-## Próximos Passos
-
-Uma vez que seu projeto passar por todos os itens do checklist, você está pronto para implantar:
-
-<Card title="Deploy para AMP" icon="rocket" href="/pt-BR/enterprise/guides/deploy-to-amp">
-  Siga o guia de implantação para implantar seu Crew ou Flow no CrewAI AMP usando
-  a CLI, interface web ou integração CI/CD.
-</Card>

docs/pt-BR/enterprise/introduction.mdx

@@ -82,7 +82,7 @@ CrewAI AMP expande o poder do framework open-source com funcionalidades projetad
     <Card
       title="Implantar Crew"
       icon="rocket"
-      href="/pt-BR/enterprise/guides/deploy-to-amp"
+      href="/pt-BR/enterprise/guides/deploy-crew"
     >
       Implantar Crew
     </Card>
@@ -92,11 +92,11 @@ CrewAI AMP expande o poder do framework open-source com funcionalidades projetad
     <Card
       title="Acesso via API"
       icon="code"
-      href="/pt-BR/enterprise/guides/kickoff-crew"
+      href="/pt-BR/enterprise/guides/deploy-crew"
     >
       Usar a API do Crew
     </Card>
   </Step>
 </Steps>
 
-Para instruções detalhadas, consulte nosso [guia de implantação](/pt-BR/enterprise/guides/deploy-to-amp) ou clique no botão abaixo para começar.
+Para instruções detalhadas, consulte nosso [guia de implantação](/pt-BR/enterprise/guides/deploy-crew) ou clique no botão abaixo para começar.

docs/pt-BR/learn/human-feedback-in-flows.mdx

@@ -7,10 +7,6 @@ mode: "wide"
 
 ## Visão Geral
 
-<Note>
-O decorador `@human_feedback` requer **CrewAI versão 1.8.0 ou superior**. Certifique-se de atualizar sua instalação antes de usar este recurso.
-</Note>
-
 O decorador `@human_feedback` permite fluxos de trabalho human-in-the-loop (HITL) diretamente nos CrewAI Flows. Ele permite pausar a execução do flow, apresentar a saída para um humano revisar, coletar seu feedback e, opcionalmente, rotear para diferentes listeners com base no resultado do feedback.
 
 Isso é particularmente valioso para:

docs/pt-BR/learn/human-in-the-loop.mdx

@@ -5,22 +5,9 @@ icon: "user-check"
 mode: "wide"
 ---
 
-Human-in-the-Loop (HITL) é uma abordagem poderosa que combina a inteligência artificial com a experiência humana para aprimorar a tomada de decisões e melhorar os resultados das tarefas. CrewAI oferece várias maneiras de implementar HITL dependendo das suas necessidades.
+Human-in-the-Loop (HITL) é uma abordagem poderosa que combina a inteligência artificial com a experiência humana para aprimorar a tomada de decisões e melhorar os resultados das tarefas. Este guia mostra como implementar HITL dentro da CrewAI.
 
-## Escolhendo Sua Abordagem HITL
-
-CrewAI oferece duas abordagens principais para implementar workflows human-in-the-loop:
-
-| Abordagem | Melhor Para | Integração | Versão |
-|----------|----------|-------------|---------|
-| **Baseada em Flow** (decorador `@human_feedback`) | Desenvolvimento local, revisão via console, workflows síncronos | [Feedback Humano em Flows](/pt-BR/learn/human-feedback-in-flows) | **1.8.0+** |
-| **Baseada em Webhook** (Enterprise) | Deployments em produção, workflows assíncronos, integrações externas (Slack, Teams, etc.) | Este guia | - |
-
-<Tip>
-Se você está construindo flows e deseja adicionar etapas de revisão humana com roteamento baseado em feedback, confira o guia [Feedback Humano em Flows](/pt-BR/learn/human-feedback-in-flows) para o decorador `@human_feedback`.
-</Tip>
-
-## Configurando Workflows HITL Baseados em Webhook
+## Configurando Workflows HITL
 
 <Steps>
     <Step title="Configure sua Tarefa">

docs/pt-BR/observability/galileo.mdx

@@ -1,115 +0,0 @@
----
-title: Galileo Galileu
-description: Integração Galileo para rastreamento e avaliação CrewAI
-icon: telescope
-mode: "wide"
----
-
-## Visão geral
-
-Este guia demonstra como integrar o **Galileo**com o **CrewAI**
-para rastreamento abrangente e engenharia de avaliação.
-Ao final deste guia, você será capaz de rastrear seus agentes CrewAI,
-monitorar seu desempenho e avaliar seu comportamento com
-A poderosa plataforma de observabilidade do Galileo.
-
-> **O que é Galileo?**[Galileo](https://galileo.ai/) é avaliação e observabilidade de IA
-plataforma que oferece rastreamento, avaliação e
-e monitoramento de aplicações de IA. Ele permite que as equipes capturem a verdade,
-criar grades de proteção robustas e realizar experimentos sistemáticos com
-rastreamento de experimentos integrado e análise de desempenho -garantindo confiabilidade,
-transparência e melhoria contínua em todo o ciclo de vida da IA.
-
-## Primeiros passos
-
-Este tutorial segue o [CrewAI Quickstart](pt-BR/quickstart) e mostra como adicionar
-[CrewAIEventListener] do Galileo(https://v2docs.galileo.ai/sdk-api/python/reference/handlers/crewai/handler),
-um manipulador de eventos.
-Para mais informações, consulte Galileu
-[Adicionar Galileo a um aplicativo CrewAI](https://v2docs.galileo.ai/how-to-guides/third-party-integrations/add-galileo-to-crewai/add-galileo-to-crewai)
-guia prático.
-
-> **Observação**Este tutorial pressupõe que você concluiu o [CrewAI Quickstart](pt-BR/quickstart).
-Se você quiser um exemplo completo e abrangente, consulte o Galileo
-[Repositório de exemplo SDK da CrewAI](https://github.com/rungalileo/sdk-examples/tree/main/python/agent/crew-ai).
-
-### Etapa 1: instalar dependências
-
-Instale as dependências necessárias para seu aplicativo.
-Crie um ambiente virtual usando seu método preferido,
-em seguida, instale dependências dentro desse ambiente usando seu
-ferramenta preferida:
-
-```bash
-uv add galileo 
-```
-
-### Etapa 2: adicione ao arquivo .env do [CrewAI Quickstart](/pt-BR/quickstart)
-
-```bash
-# Your Galileo API key
-GALILEO_API_KEY="your-galileo-api-key"
-
-# Your Galileo project name
-GALILEO_PROJECT="your-galileo-project-name"
-
-# The name of the Log stream you want to use for logging
-GALILEO_LOG_STREAM="your-galileo-log-stream "
-```
-
-### Etapa 3: adicionar o ouvinte de eventos Galileo
-
-Para habilitar o registro com Galileo, você precisa criar uma instância do `CrewAIEventListener`.
-Importe o pacote manipulador Galileo CrewAI por
-adicionando o seguinte código no topo do seu arquivo main.py:
-
-```python
-from galileo.handlers.crewai.handler import CrewAIEventListener
-```
-
-No início da sua função run, crie o ouvinte de evento:
-
-```python
-def run():
-    # Create the event listener
-    CrewAIEventListener()
-    # The rest of your existing code goes here
-```
-
-Quando você cria a instância do listener, ela é automaticamente
-registrado na CrewAI.
-
-### Etapa 4: administre sua Crew
-
-Administre sua Crew com o CrewAI CLI:
-
-```bash
-crewai run
-```
-
-### Passo 5: Visualize os traços no Galileo
-
-Assim que sua tripulação terminar, os rastros serão eliminados e aparecerão no Galileo.
-
-![Galileo trace view](/images/galileo-trace-veiw.png)
-  
-## Compreendendo a integração do Galileo
-
-Galileo se integra ao CrewAI registrando um ouvinte de evento
-que captura eventos de execução da tripulação (por exemplo, ações do agente, chamadas de ferramentas, respostas do modelo)
-e os encaminha ao Galileo para observabilidade e avaliação.
-
-### Compreendendo o ouvinte de eventos
-
-Criar uma instância `CrewAIEventListener()` é tudo o que você precisa
-necessário para habilitar o Galileo para uma execução do CrewAI. Quando instanciado, o ouvinte:
-
--Registra-se automaticamente no CrewAI
--Lê a configuração do Galileo a partir de variáveis de ambiente
--Registra todos os dados de execução no projeto Galileo e fluxo de log especificado por
-  `GALILEO_PROJECT` e `GALILEO_LOG_STREAM`
-
-Nenhuma configuração adicional ou alterações de código são necessárias.
-Todos os dados desta execução são registados no projecto Galileo e
-fluxo de log especificado pela configuração do seu ambiente
-(por exemplo, GALILEO_PROJECT e GALILEO_LOG_STREAM).

lib/crewai-tools/pyproject.toml

@@ -12,7 +12,7 @@ dependencies = [
     "pytube~=15.0.0",
     "requests~=2.32.5",
     "docker~=7.1.0",
-    "crewai==1.8.1",
+    "crewai==1.8.0",
     "lancedb~=0.5.4",
     "tiktoken~=0.8.0",
     "beautifulsoup4~=4.13.4",

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

@@ -291,4 +291,4 @@ __all__ = [
     "ZapierActionTools",
 ]
 
-__version__ = "1.8.1"
+__version__ = "1.8.0"

lib/crewai/pyproject.toml

@@ -49,7 +49,7 @@ Repository = "https://github.com/crewAIInc/crewAI"
 
 [project.optional-dependencies]
 tools = [
-    "crewai-tools==1.8.1",
+    "crewai-tools==1.8.0",
 ]
 embeddings = [
     "tiktoken~=0.8.0"

lib/crewai/src/crewai/__init__.py

@@ -40,7 +40,7 @@ def _suppress_pydantic_deprecation_warnings() -> None:
 
 _suppress_pydantic_deprecation_warnings()
 
-__version__ = "1.8.1"
+__version__ = "1.8.0"
 _telemetry_submitted = False
 
 

lib/crewai/src/crewai/a2a/__init__.py

@@ -1,10 +1,8 @@
 """Agent-to-Agent (A2A) protocol communication module for CrewAI."""
 
-from crewai.a2a.config import A2AClientConfig, A2AConfig, A2AServerConfig
+from crewai.a2a.config import A2AConfig
 
 
 __all__ = [
-    "A2AClientConfig",
     "A2AConfig",
-    "A2AServerConfig",
 ]

lib/crewai/src/crewai/a2a/config.py

@@ -5,57 +5,45 @@ This module is separate from experimental.a2a to avoid circular imports.
 
 from __future__ import annotations
 
-from importlib.metadata import version
-from typing import Any, ClassVar, Literal
+from typing import Annotated, Any, ClassVar
 
-from pydantic import BaseModel, ConfigDict, Field
-from typing_extensions import deprecated
+from pydantic import (
+    BaseModel,
+    BeforeValidator,
+    ConfigDict,
+    Field,
+    HttpUrl,
+    TypeAdapter,
+)
 
 from crewai.a2a.auth.schemas import AuthScheme
-from crewai.a2a.types import TransportType, Url
 
 
 try:
-    from a2a.types import (
-        AgentCapabilities,
-        AgentCardSignature,
-        AgentInterface,
-        AgentProvider,
-        AgentSkill,
-        SecurityScheme,
-    )
-
     from crewai.a2a.updates import UpdateConfig
 except ImportError:
-    UpdateConfig = Any
-    AgentCapabilities = Any
-    AgentCardSignature = Any
-    AgentInterface = Any
-    AgentProvider = Any
-    SecurityScheme = Any
-    AgentSkill = Any
     UpdateConfig = Any  # type: ignore[misc,assignment]
 
 
+http_url_adapter = TypeAdapter(HttpUrl)
+
+Url = Annotated[
+    str,
+    BeforeValidator(
+        lambda value: str(http_url_adapter.validate_python(value, strict=True))
+    ),
+]
+
+
 def _get_default_update_config() -> UpdateConfig:
     from crewai.a2a.updates import StreamingConfig
 
     return StreamingConfig()
 
 
-@deprecated(
-    """
-    `crewai.a2a.config.A2AConfig` is deprecated and will be removed in v2.0.0,
-    use `crewai.a2a.config.A2AClientConfig` or `crewai.a2a.config.A2AServerConfig` instead.
-    """,
-    category=FutureWarning,
-)
 class A2AConfig(BaseModel):
     """Configuration for A2A protocol integration.
 
-    Deprecated:
-        Use A2AClientConfig instead. This class will be removed in a future version.
-
     Attributes:
         endpoint: A2A agent endpoint URL.
         auth: Authentication scheme.
@@ -65,7 +53,6 @@ class A2AConfig(BaseModel):
         fail_fast: If True, raise error when agent unreachable; if False, skip and continue.
         trust_remote_completion_status: If True, return A2A agent's result directly when completed.
         updates: Update mechanism config.
-        transport_protocol: A2A transport protocol (grpc, jsonrpc, http+json).
     """
 
     model_config: ClassVar[ConfigDict] = ConfigDict(extra="forbid")
@@ -95,180 +82,3 @@ class A2AConfig(BaseModel):
         default_factory=_get_default_update_config,
         description="Update mechanism config",
     )
-    transport_protocol: Literal["JSONRPC", "GRPC", "HTTP+JSON"] = Field(
-        default="JSONRPC",
-        description="Specified mode of A2A transport protocol",
-    )
-
-
-class A2AClientConfig(BaseModel):
-    """Configuration for connecting to remote A2A agents.
-
-    Attributes:
-        endpoint: A2A agent endpoint URL.
-        auth: Authentication scheme.
-        timeout: Request timeout in seconds.
-        max_turns: Maximum conversation turns with A2A agent.
-        response_model: Optional Pydantic model for structured A2A agent responses.
-        fail_fast: If True, raise error when agent unreachable; if False, skip and continue.
-        trust_remote_completion_status: If True, return A2A agent's result directly when completed.
-        updates: Update mechanism config.
-        accepted_output_modes: Media types the client can accept in responses.
-        supported_transports: Ordered list of transport protocols the client supports.
-        use_client_preference: Whether to prioritize client transport preferences over server.
-        extensions: Extension URIs the client supports.
-    """
-
-    model_config: ClassVar[ConfigDict] = ConfigDict(extra="forbid")
-
-    endpoint: Url = Field(description="A2A agent endpoint URL")
-    auth: AuthScheme | None = Field(
-        default=None,
-        description="Authentication scheme",
-    )
-    timeout: int = Field(default=120, description="Request timeout in seconds")
-    max_turns: int = Field(
-        default=10, description="Maximum conversation turns with A2A agent"
-    )
-    response_model: type[BaseModel] | None = Field(
-        default=None,
-        description="Optional Pydantic model for structured A2A agent responses",
-    )
-    fail_fast: bool = Field(
-        default=True,
-        description="If True, raise error when agent unreachable; if False, skip",
-    )
-    trust_remote_completion_status: bool = Field(
-        default=False,
-        description="If True, return A2A result directly when completed",
-    )
-    updates: UpdateConfig = Field(
-        default_factory=_get_default_update_config,
-        description="Update mechanism config",
-    )
-    accepted_output_modes: list[str] = Field(
-        default_factory=lambda: ["application/json"],
-        description="Media types the client can accept in responses",
-    )
-    supported_transports: list[str] = Field(
-        default_factory=lambda: ["JSONRPC"],
-        description="Ordered list of transport protocols the client supports",
-    )
-    use_client_preference: bool = Field(
-        default=False,
-        description="Whether to prioritize client transport preferences over server",
-    )
-    extensions: list[str] = Field(
-        default_factory=list,
-        description="Extension URIs the client supports",
-    )
-    transport_protocol: Literal["JSONRPC", "GRPC", "HTTP+JSON"] = Field(
-        default="JSONRPC",
-        description="Specified mode of A2A transport protocol",
-    )
-
-
-class A2AServerConfig(BaseModel):
-    """Configuration for exposing a Crew or Agent as an A2A server.
-
-    All fields correspond to A2A AgentCard fields. Fields like name, description,
-    and skills can be auto-derived from the Crew/Agent if not provided.
-
-    Attributes:
-        name: Human-readable name for the agent.
-        description: Human-readable description of the agent.
-        version: Version string for the agent card.
-        skills: List of agent skills/capabilities.
-        default_input_modes: Default supported input MIME types.
-        default_output_modes: Default supported output MIME types.
-        capabilities: Declaration of optional capabilities.
-        preferred_transport: Transport protocol for the preferred endpoint.
-        protocol_version: A2A protocol version this agent supports.
-        provider: Information about the agent's service provider.
-        documentation_url: URL to the agent's documentation.
-        icon_url: URL to an icon for the agent.
-        additional_interfaces: Additional supported interfaces.
-        security: Security requirement objects for all interactions.
-        security_schemes: Security schemes available to authorize requests.
-        supports_authenticated_extended_card: Whether agent provides extended card to authenticated users.
-        url: Preferred endpoint URL for the agent.
-        signatures: JSON Web Signatures for the AgentCard.
-    """
-
-    model_config: ClassVar[ConfigDict] = ConfigDict(extra="forbid")
-
-    name: str | None = Field(
-        default=None,
-        description="Human-readable name for the agent. Auto-derived from Crew/Agent if not provided.",
-    )
-    description: str | None = Field(
-        default=None,
-        description="Human-readable description of the agent. Auto-derived from Crew/Agent if not provided.",
-    )
-    version: str = Field(
-        default="1.0.0",
-        description="Version string for the agent card",
-    )
-    skills: list[AgentSkill] = Field(
-        default_factory=list,
-        description="List of agent skills. Auto-derived from tasks/tools if not provided.",
-    )
-    default_input_modes: list[str] = Field(
-        default_factory=lambda: ["text/plain", "application/json"],
-        description="Default supported input MIME types",
-    )
-    default_output_modes: list[str] = Field(
-        default_factory=lambda: ["text/plain", "application/json"],
-        description="Default supported output MIME types",
-    )
-    capabilities: AgentCapabilities = Field(
-        default_factory=lambda: AgentCapabilities(
-            streaming=True,
-            push_notifications=False,
-        ),
-        description="Declaration of optional capabilities supported by the agent",
-    )
-    preferred_transport: TransportType = Field(
-        default="JSONRPC",
-        description="Transport protocol for the preferred endpoint",
-    )
-    protocol_version: str = Field(
-        default_factory=lambda: version("a2a-sdk"),
-        description="A2A protocol version this agent supports",
-    )
-    provider: AgentProvider | None = Field(
-        default=None,
-        description="Information about the agent's service provider",
-    )
-    documentation_url: Url | None = Field(
-        default=None,
-        description="URL to the agent's documentation",
-    )
-    icon_url: Url | None = Field(
-        default=None,
-        description="URL to an icon for the agent",
-    )
-    additional_interfaces: list[AgentInterface] = Field(
-        default_factory=list,
-        description="Additional supported interfaces (transport and URL combinations)",
-    )
-    security: list[dict[str, list[str]]] = Field(
-        default_factory=list,
-        description="Security requirement objects for all agent interactions",
-    )
-    security_schemes: dict[str, SecurityScheme] = Field(
-        default_factory=dict,
-        description="Security schemes available to authorize requests",
-    )
-    supports_authenticated_extended_card: bool = Field(
-        default=False,
-        description="Whether agent provides extended card to authenticated users",
-    )
-    url: Url | None = Field(
-        default=None,
-        description="Preferred endpoint URL for the agent. Set at runtime if not provided.",
-    )
-    signatures: list[AgentCardSignature] = Field(
-        default_factory=list,
-        description="JSON Web Signatures for the AgentCard",
-    )

lib/crewai/src/crewai/a2a/types.py

@@ -1,17 +1,7 @@
 """Type definitions for A2A protocol message parts."""
 
-from __future__ import annotations
+from typing import Any, Literal, Protocol, TypedDict, runtime_checkable
 
-from typing import (
-    Annotated,
-    Any,
-    Literal,
-    Protocol,
-    TypedDict,
-    runtime_checkable,
-)
-
-from pydantic import BeforeValidator, HttpUrl, TypeAdapter
 from typing_extensions import NotRequired
 
 from crewai.a2a.updates import (
@@ -25,18 +15,6 @@ from crewai.a2a.updates import (
 )
 
 
-TransportType = Literal["JSONRPC", "GRPC", "HTTP+JSON"]
-
-http_url_adapter: TypeAdapter[HttpUrl] = TypeAdapter(HttpUrl)
-
-Url = Annotated[
-    str,
-    BeforeValidator(
-        lambda value: str(http_url_adapter.validate_python(value, strict=True))
-    ),
-]
-
-
 @runtime_checkable
 class AgentResponseProtocol(Protocol):
     """Protocol for the dynamically created AgentResponse model."""

lib/crewai/src/crewai/a2a/utils.py

@@ -1,14 +1,16 @@
-"""A2A delegation utilities for executing tasks on remote agents."""
+"""Utility functions for A2A (Agent-to-Agent) protocol delegation."""
 
 from __future__ import annotations
 
 import asyncio
 from collections.abc import AsyncIterator, MutableMapping
 from contextlib import asynccontextmanager
-from typing import TYPE_CHECKING, Any, Literal
+from functools import lru_cache
+import time
+from typing import TYPE_CHECKING, Any
 import uuid
 
-from a2a.client import Client, ClientConfig, ClientFactory
+from a2a.client import A2AClientHTTPError, Client, ClientConfig, ClientFactory
 from a2a.types import (
     AgentCard,
     Message,
@@ -16,16 +18,21 @@ from a2a.types import (
     PushNotificationConfig as A2APushNotificationConfig,
     Role,
     TextPart,
+    TransportProtocol,
 )
+from aiocache import cached  # type: ignore[import-untyped]
+from aiocache.serializers import PickleSerializer  # type: ignore[import-untyped]
 import httpx
-from pydantic import BaseModel
+from pydantic import BaseModel, Field, create_model
 
 from crewai.a2a.auth.schemas import APIKeyAuth, HTTPDigestAuth
 from crewai.a2a.auth.utils import (
     _auth_store,
     configure_auth_client,
+    retry_on_401,
     validate_auth_against_agent_card,
 )
+from crewai.a2a.config import A2AConfig
 from crewai.a2a.task_helpers import TaskStateResult
 from crewai.a2a.types import (
     HANDLER_REGISTRY,
@@ -39,7 +46,6 @@ from crewai.a2a.updates import (
     StreamingHandler,
     UpdateConfig,
 )
-from crewai.a2a.utils.agent_card import _afetch_agent_card_cached
 from crewai.events.event_bus import crewai_event_bus
 from crewai.events.types.a2a_events import (
     A2AConversationStartedEvent,
@@ -47,6 +53,7 @@ from crewai.events.types.a2a_events import (
     A2ADelegationStartedEvent,
     A2AMessageSentEvent,
 )
+from crewai.types.utils import create_literals_from_strings
 
 
 if TYPE_CHECKING:
@@ -69,9 +76,189 @@ def get_handler(config: UpdateConfig | None) -> HandlerType:
     return HANDLER_REGISTRY.get(type(config), StreamingHandler)
 
 
+@lru_cache()
+def _fetch_agent_card_cached(
+    endpoint: str,
+    auth_hash: int,
+    timeout: int,
+    _ttl_hash: int,
+) -> AgentCard:
+    """Cached sync version of fetch_agent_card."""
+    auth = _auth_store.get(auth_hash)
+
+    loop = asyncio.new_event_loop()
+    asyncio.set_event_loop(loop)
+    try:
+        return loop.run_until_complete(
+            _afetch_agent_card_impl(endpoint=endpoint, auth=auth, timeout=timeout)
+        )
+    finally:
+        loop.close()
+
+
+def fetch_agent_card(
+    endpoint: str,
+    auth: AuthScheme | None = None,
+    timeout: int = 30,
+    use_cache: bool = True,
+    cache_ttl: int = 300,
+) -> AgentCard:
+    """Fetch AgentCard from an A2A endpoint with optional caching.
+
+    Args:
+        endpoint: A2A agent endpoint URL (AgentCard URL)
+        auth: Optional AuthScheme for authentication
+        timeout: Request timeout in seconds
+        use_cache: Whether to use caching (default True)
+        cache_ttl: Cache TTL in seconds (default 300 = 5 minutes)
+
+    Returns:
+        AgentCard object with agent capabilities and skills
+
+    Raises:
+        httpx.HTTPStatusError: If the request fails
+        A2AClientHTTPError: If authentication fails
+    """
+    if use_cache:
+        if auth:
+            auth_data = auth.model_dump_json(
+                exclude={
+                    "_access_token",
+                    "_token_expires_at",
+                    "_refresh_token",
+                    "_authorization_callback",
+                }
+            )
+            auth_hash = hash((type(auth).__name__, auth_data))
+        else:
+            auth_hash = 0
+        _auth_store[auth_hash] = auth
+        ttl_hash = int(time.time() // cache_ttl)
+        return _fetch_agent_card_cached(endpoint, auth_hash, timeout, ttl_hash)
+
+    loop = asyncio.new_event_loop()
+    asyncio.set_event_loop(loop)
+    try:
+        return loop.run_until_complete(
+            afetch_agent_card(endpoint=endpoint, auth=auth, timeout=timeout)
+        )
+    finally:
+        loop.close()
+
+
+async def afetch_agent_card(
+    endpoint: str,
+    auth: AuthScheme | None = None,
+    timeout: int = 30,
+    use_cache: bool = True,
+) -> AgentCard:
+    """Fetch AgentCard from an A2A endpoint asynchronously.
+
+    Native async implementation. Use this when running in an async context.
+
+    Args:
+        endpoint: A2A agent endpoint URL (AgentCard URL).
+        auth: Optional AuthScheme for authentication.
+        timeout: Request timeout in seconds.
+        use_cache: Whether to use caching (default True).
+
+    Returns:
+        AgentCard object with agent capabilities and skills.
+
+    Raises:
+        httpx.HTTPStatusError: If the request fails.
+        A2AClientHTTPError: If authentication fails.
+    """
+    if use_cache:
+        if auth:
+            auth_data = auth.model_dump_json(
+                exclude={
+                    "_access_token",
+                    "_token_expires_at",
+                    "_refresh_token",
+                    "_authorization_callback",
+                }
+            )
+            auth_hash = hash((type(auth).__name__, auth_data))
+        else:
+            auth_hash = 0
+        _auth_store[auth_hash] = auth
+        agent_card: AgentCard = await _afetch_agent_card_cached(
+            endpoint, auth_hash, timeout
+        )
+        return agent_card
+
+    return await _afetch_agent_card_impl(endpoint=endpoint, auth=auth, timeout=timeout)
+
+
+@cached(ttl=300, serializer=PickleSerializer())  # type: ignore[untyped-decorator]
+async def _afetch_agent_card_cached(
+    endpoint: str,
+    auth_hash: int,
+    timeout: int,
+) -> AgentCard:
+    """Cached async implementation of AgentCard fetching."""
+    auth = _auth_store.get(auth_hash)
+    return await _afetch_agent_card_impl(endpoint=endpoint, auth=auth, timeout=timeout)
+
+
+async def _afetch_agent_card_impl(
+    endpoint: str,
+    auth: AuthScheme | None,
+    timeout: int,
+) -> AgentCard:
+    """Internal async implementation of AgentCard fetching."""
+    if "/.well-known/agent-card.json" in endpoint:
+        base_url = endpoint.replace("/.well-known/agent-card.json", "")
+        agent_card_path = "/.well-known/agent-card.json"
+    else:
+        url_parts = endpoint.split("/", 3)
+        base_url = f"{url_parts[0]}//{url_parts[2]}"
+        agent_card_path = f"/{url_parts[3]}" if len(url_parts) > 3 else "/"
+
+    headers: MutableMapping[str, str] = {}
+    if auth:
+        async with httpx.AsyncClient(timeout=timeout) as temp_auth_client:
+            if isinstance(auth, (HTTPDigestAuth, APIKeyAuth)):
+                configure_auth_client(auth, temp_auth_client)
+            headers = await auth.apply_auth(temp_auth_client, {})
+
+    async with httpx.AsyncClient(timeout=timeout, headers=headers) as temp_client:
+        if auth and isinstance(auth, (HTTPDigestAuth, APIKeyAuth)):
+            configure_auth_client(auth, temp_client)
+
+        agent_card_url = f"{base_url}{agent_card_path}"
+
+        async def _fetch_agent_card_request() -> httpx.Response:
+            return await temp_client.get(agent_card_url)
+
+        try:
+            response = await retry_on_401(
+                request_func=_fetch_agent_card_request,
+                auth_scheme=auth,
+                client=temp_client,
+                headers=temp_client.headers,
+                max_retries=2,
+            )
+            response.raise_for_status()
+
+            return AgentCard.model_validate(response.json())
+
+        except httpx.HTTPStatusError as e:
+            if e.response.status_code == 401:
+                error_details = ["Authentication failed"]
+                www_auth = e.response.headers.get("WWW-Authenticate")
+                if www_auth:
+                    error_details.append(f"WWW-Authenticate: {www_auth}")
+                if not auth:
+                    error_details.append("No auth scheme provided")
+                msg = " | ".join(error_details)
+                raise A2AClientHTTPError(401, msg) from e
+            raise
+
+
 def execute_a2a_delegation(
     endpoint: str,
-    transport_protocol: Literal["JSONRPC", "GRPC", "HTTP+JSON"],
     auth: AuthScheme | None,
     timeout: int,
     task_description: str,
@@ -95,23 +282,6 @@ def execute_a2a_delegation(
     use aexecute_a2a_delegation directly.
 
     Args:
-        endpoint: A2A agent endpoint URL (AgentCard URL)
-        transport_protocol: Optional A2A transport protocol (grpc, jsonrpc, http+json)
-        auth: Optional AuthScheme for authentication (Bearer, OAuth2, API Key, HTTP Basic/Digest)
-        timeout: Request timeout in seconds
-        task_description: The task to delegate
-        context: Optional context information
-        context_id: Context ID for correlating messages/tasks
-        task_id: Specific task identifier
-        reference_task_ids: List of related task IDs
-        metadata: Additional metadata (external_id, request_id, etc.)
-        extensions: Protocol extensions for custom fields
-        conversation_history: Previous Message objects from conversation
-        agent_id: Agent identifier for logging
-        agent_role: Role of the CrewAI agent delegating the task
-        agent_branch: Optional agent tree branch for logging
-        response_model: Optional Pydantic model for structured outputs
-        turn_number: Optional turn number for multi-turn conversations
         endpoint: A2A agent endpoint URL.
         auth: Optional AuthScheme for authentication.
         timeout: Request timeout in seconds.
@@ -153,7 +323,6 @@ def execute_a2a_delegation(
                 agent_role=agent_role,
                 agent_branch=agent_branch,
                 response_model=response_model,
-                transport_protocol=transport_protocol,
                 turn_number=turn_number,
                 updates=updates,
             )
@@ -164,7 +333,6 @@ def execute_a2a_delegation(
 
 async def aexecute_a2a_delegation(
     endpoint: str,
-    transport_protocol: Literal["JSONRPC", "GRPC", "HTTP+JSON"],
     auth: AuthScheme | None,
     timeout: int,
     task_description: str,
@@ -188,23 +356,6 @@ async def aexecute_a2a_delegation(
     in an async context (e.g., with Crew.akickoff() or agent.aexecute_task()).
 
     Args:
-        endpoint: A2A agent endpoint URL
-        transport_protocol: Optional A2A transport protocol (grpc, jsonrpc, http+json)
-        auth: Optional AuthScheme for authentication
-        timeout: Request timeout in seconds
-        task_description: Task to delegate
-        context: Optional context
-        context_id: Context ID for correlation
-        task_id: Specific task identifier
-        reference_task_ids: Related task IDs
-        metadata: Additional metadata
-        extensions: Protocol extensions
-        conversation_history: Previous Message objects
-        turn_number: Current turn number
-        agent_branch: Agent tree branch for logging
-        agent_id: Agent identifier for logging
-        agent_role: Agent role for logging
-        response_model: Optional Pydantic model for structured outputs
         endpoint: A2A agent endpoint URL.
         auth: Optional AuthScheme for authentication.
         timeout: Request timeout in seconds.
@@ -263,7 +414,6 @@ async def aexecute_a2a_delegation(
         agent_role=agent_role,
         response_model=response_model,
         updates=updates,
-        transport_protocol=transport_protocol,
     )
 
     crewai_event_bus.emit(
@@ -281,7 +431,6 @@ async def aexecute_a2a_delegation(
 
 async def _aexecute_a2a_delegation_impl(
     endpoint: str,
-    transport_protocol: Literal["JSONRPC", "GRPC", "HTTP+JSON"],
     auth: AuthScheme | None,
     timeout: int,
     task_description: str,
@@ -375,6 +524,7 @@ async def _aexecute_a2a_delegation_impl(
         extensions=extensions,
     )
 
+    transport_protocol = TransportProtocol("JSONRPC")
     new_messages: list[Message] = [*conversation_history, message]
     crewai_event_bus.emit(
         None,
@@ -446,7 +596,7 @@ async def _aexecute_a2a_delegation_impl(
 @asynccontextmanager
 async def _create_a2a_client(
     agent_card: AgentCard,
-    transport_protocol: Literal["JSONRPC", "GRPC", "HTTP+JSON"],
+    transport_protocol: TransportProtocol,
     timeout: int,
     headers: MutableMapping[str, str],
     streaming: bool,
@@ -457,18 +607,19 @@ async def _create_a2a_client(
     """Create and configure an A2A client.
 
     Args:
-        agent_card: The A2A agent card.
-        transport_protocol: Transport protocol to use.
-        timeout: Request timeout in seconds.
-        headers: HTTP headers (already with auth applied).
-        streaming: Enable streaming responses.
-        auth: Optional AuthScheme for client configuration.
-        use_polling: Enable polling mode.
-        push_notification_config: Optional push notification config.
+        agent_card: The A2A agent card
+        transport_protocol: Transport protocol to use
+        timeout: Request timeout in seconds
+        headers: HTTP headers (already with auth applied)
+        streaming: Enable streaming responses
+        auth: Optional AuthScheme for client configuration
+        use_polling: Enable polling mode
+        push_notification_config: Optional push notification config to include in requests
 
     Yields:
-        Configured A2A client instance.
+        Configured A2A client instance
     """
+
     async with httpx.AsyncClient(
         timeout=timeout,
         headers=headers,
@@ -489,7 +640,7 @@ async def _create_a2a_client(
 
         config = ClientConfig(
             httpx_client=httpx_client,
-            supported_transports=[transport_protocol],
+            supported_transports=[str(transport_protocol.value)],
             streaming=streaming and not use_polling,
             polling=use_polling,
             accepted_output_modes=["application/json"],
@@ -499,3 +650,78 @@ async def _create_a2a_client(
         factory = ClientFactory(config)
         client = factory.create(agent_card)
         yield client
+
+
+def create_agent_response_model(agent_ids: tuple[str, ...]) -> type[BaseModel]:
+    """Create a dynamic AgentResponse model with Literal types for agent IDs.
+
+    Args:
+        agent_ids: List of available A2A agent IDs
+
+    Returns:
+        Dynamically created Pydantic model with Literal-constrained a2a_ids field
+    """
+
+    DynamicLiteral = create_literals_from_strings(agent_ids)  # noqa: N806
+
+    return create_model(
+        "AgentResponse",
+        a2a_ids=(
+            tuple[DynamicLiteral, ...],  # type: ignore[valid-type]
+            Field(
+                default_factory=tuple,
+                max_length=len(agent_ids),
+                description="A2A agent IDs to delegate to.",
+            ),
+        ),
+        message=(
+            str,
+            Field(
+                description="The message content. If is_a2a=true, this is sent to the A2A agent. If is_a2a=false, this is your final answer ending the conversation."
+            ),
+        ),
+        is_a2a=(
+            bool,
+            Field(
+                description="Set to false when the remote agent has answered your question - extract their answer and return it as your final message. Set to true ONLY if you need to ask a NEW, DIFFERENT question. NEVER repeat the same request - if the conversation history shows the agent already answered, set is_a2a=false immediately."
+            ),
+        ),
+        __base__=BaseModel,
+    )
+
+
+def extract_a2a_agent_ids_from_config(
+    a2a_config: list[A2AConfig] | A2AConfig | None,
+) -> tuple[list[A2AConfig], tuple[str, ...]]:
+    """Extract A2A agent IDs from A2A configuration.
+
+    Args:
+        a2a_config: A2A configuration
+
+    Returns:
+        List of A2A agent IDs
+    """
+    if a2a_config is None:
+        return [], ()
+
+    if isinstance(a2a_config, A2AConfig):
+        a2a_agents = [a2a_config]
+    else:
+        a2a_agents = a2a_config
+    return a2a_agents, tuple(config.endpoint for config in a2a_agents)
+
+
+def get_a2a_agents_and_response_model(
+    a2a_config: list[A2AConfig] | A2AConfig | None,
+) -> tuple[list[A2AConfig], type[BaseModel]]:
+    """Get A2A agent IDs and response model.
+
+    Args:
+        a2a_config: A2A configuration
+
+    Returns:
+        Tuple of A2A agent IDs and response model
+    """
+    a2a_agents, agent_ids = extract_a2a_agent_ids_from_config(a2a_config=a2a_config)
+
+    return a2a_agents, create_agent_response_model(agent_ids)

lib/crewai/src/crewai/a2a/utils/__init__.py

@@ -1 +0,0 @@
-"""A2A utility modules for client operations."""

lib/crewai/src/crewai/a2a/utils/agent_card.py

@@ -1,399 +0,0 @@
-"""AgentCard utilities for A2A client and server operations."""
-
-from __future__ import annotations
-
-import asyncio
-from collections.abc import MutableMapping
-from functools import lru_cache
-import time
-from types import MethodType
-from typing import TYPE_CHECKING
-
-from a2a.client.errors import A2AClientHTTPError
-from a2a.types import AgentCapabilities, AgentCard, AgentSkill
-from aiocache import cached  # type: ignore[import-untyped]
-from aiocache.serializers import PickleSerializer  # type: ignore[import-untyped]
-import httpx
-
-from crewai.a2a.auth.schemas import APIKeyAuth, HTTPDigestAuth
-from crewai.a2a.auth.utils import (
-    _auth_store,
-    configure_auth_client,
-    retry_on_401,
-)
-from crewai.a2a.config import A2AServerConfig
-from crewai.crew import Crew
-
-
-if TYPE_CHECKING:
-    from crewai.a2a.auth.schemas import AuthScheme
-    from crewai.agent import Agent
-    from crewai.task import Task
-
-
-def _get_server_config(agent: Agent) -> A2AServerConfig | None:
-    """Get A2AServerConfig from an agent's a2a configuration.
-
-    Args:
-        agent: The Agent instance to check.
-
-    Returns:
-        A2AServerConfig if present, None otherwise.
-    """
-    if agent.a2a is None:
-        return None
-    if isinstance(agent.a2a, A2AServerConfig):
-        return agent.a2a
-    if isinstance(agent.a2a, list):
-        for config in agent.a2a:
-            if isinstance(config, A2AServerConfig):
-                return config
-    return None
-
-
-def fetch_agent_card(
-    endpoint: str,
-    auth: AuthScheme | None = None,
-    timeout: int = 30,
-    use_cache: bool = True,
-    cache_ttl: int = 300,
-) -> AgentCard:
-    """Fetch AgentCard from an A2A endpoint with optional caching.
-
-    Args:
-        endpoint: A2A agent endpoint URL (AgentCard URL).
-        auth: Optional AuthScheme for authentication.
-        timeout: Request timeout in seconds.
-        use_cache: Whether to use caching (default True).
-        cache_ttl: Cache TTL in seconds (default 300 = 5 minutes).
-
-    Returns:
-        AgentCard object with agent capabilities and skills.
-
-    Raises:
-        httpx.HTTPStatusError: If the request fails.
-        A2AClientHTTPError: If authentication fails.
-    """
-    if use_cache:
-        if auth:
-            auth_data = auth.model_dump_json(
-                exclude={
-                    "_access_token",
-                    "_token_expires_at",
-                    "_refresh_token",
-                    "_authorization_callback",
-                }
-            )
-            auth_hash = hash((type(auth).__name__, auth_data))
-        else:
-            auth_hash = 0
-        _auth_store[auth_hash] = auth
-        ttl_hash = int(time.time() // cache_ttl)
-        return _fetch_agent_card_cached(endpoint, auth_hash, timeout, ttl_hash)
-
-    loop = asyncio.new_event_loop()
-    asyncio.set_event_loop(loop)
-    try:
-        return loop.run_until_complete(
-            afetch_agent_card(endpoint=endpoint, auth=auth, timeout=timeout)
-        )
-    finally:
-        loop.close()
-
-
-async def afetch_agent_card(
-    endpoint: str,
-    auth: AuthScheme | None = None,
-    timeout: int = 30,
-    use_cache: bool = True,
-) -> AgentCard:
-    """Fetch AgentCard from an A2A endpoint asynchronously.
-
-    Native async implementation. Use this when running in an async context.
-
-    Args:
-        endpoint: A2A agent endpoint URL (AgentCard URL).
-        auth: Optional AuthScheme for authentication.
-        timeout: Request timeout in seconds.
-        use_cache: Whether to use caching (default True).
-
-    Returns:
-        AgentCard object with agent capabilities and skills.
-
-    Raises:
-        httpx.HTTPStatusError: If the request fails.
-        A2AClientHTTPError: If authentication fails.
-    """
-    if use_cache:
-        if auth:
-            auth_data = auth.model_dump_json(
-                exclude={
-                    "_access_token",
-                    "_token_expires_at",
-                    "_refresh_token",
-                    "_authorization_callback",
-                }
-            )
-            auth_hash = hash((type(auth).__name__, auth_data))
-        else:
-            auth_hash = 0
-        _auth_store[auth_hash] = auth
-        agent_card: AgentCard = await _afetch_agent_card_cached(
-            endpoint, auth_hash, timeout
-        )
-        return agent_card
-
-    return await _afetch_agent_card_impl(endpoint=endpoint, auth=auth, timeout=timeout)
-
-
-@lru_cache()
-def _fetch_agent_card_cached(
-    endpoint: str,
-    auth_hash: int,
-    timeout: int,
-    _ttl_hash: int,
-) -> AgentCard:
-    """Cached sync version of fetch_agent_card."""
-    auth = _auth_store.get(auth_hash)
-
-    loop = asyncio.new_event_loop()
-    asyncio.set_event_loop(loop)
-    try:
-        return loop.run_until_complete(
-            _afetch_agent_card_impl(endpoint=endpoint, auth=auth, timeout=timeout)
-        )
-    finally:
-        loop.close()
-
-
-@cached(ttl=300, serializer=PickleSerializer())  # type: ignore[untyped-decorator]
-async def _afetch_agent_card_cached(
-    endpoint: str,
-    auth_hash: int,
-    timeout: int,
-) -> AgentCard:
-    """Cached async implementation of AgentCard fetching."""
-    auth = _auth_store.get(auth_hash)
-    return await _afetch_agent_card_impl(endpoint=endpoint, auth=auth, timeout=timeout)
-
-
-async def _afetch_agent_card_impl(
-    endpoint: str,
-    auth: AuthScheme | None,
-    timeout: int,
-) -> AgentCard:
-    """Internal async implementation of AgentCard fetching."""
-    if "/.well-known/agent-card.json" in endpoint:
-        base_url = endpoint.replace("/.well-known/agent-card.json", "")
-        agent_card_path = "/.well-known/agent-card.json"
-    else:
-        url_parts = endpoint.split("/", 3)
-        base_url = f"{url_parts[0]}//{url_parts[2]}"
-        agent_card_path = f"/{url_parts[3]}" if len(url_parts) > 3 else "/"
-
-    headers: MutableMapping[str, str] = {}
-    if auth:
-        async with httpx.AsyncClient(timeout=timeout) as temp_auth_client:
-            if isinstance(auth, (HTTPDigestAuth, APIKeyAuth)):
-                configure_auth_client(auth, temp_auth_client)
-            headers = await auth.apply_auth(temp_auth_client, {})
-
-    async with httpx.AsyncClient(timeout=timeout, headers=headers) as temp_client:
-        if auth and isinstance(auth, (HTTPDigestAuth, APIKeyAuth)):
-            configure_auth_client(auth, temp_client)
-
-        agent_card_url = f"{base_url}{agent_card_path}"
-
-        async def _fetch_agent_card_request() -> httpx.Response:
-            return await temp_client.get(agent_card_url)
-
-        try:
-            response = await retry_on_401(
-                request_func=_fetch_agent_card_request,
-                auth_scheme=auth,
-                client=temp_client,
-                headers=temp_client.headers,
-                max_retries=2,
-            )
-            response.raise_for_status()
-
-            return AgentCard.model_validate(response.json())
-
-        except httpx.HTTPStatusError as e:
-            if e.response.status_code == 401:
-                error_details = ["Authentication failed"]
-                www_auth = e.response.headers.get("WWW-Authenticate")
-                if www_auth:
-                    error_details.append(f"WWW-Authenticate: {www_auth}")
-                if not auth:
-                    error_details.append("No auth scheme provided")
-                msg = " | ".join(error_details)
-                raise A2AClientHTTPError(401, msg) from e
-            raise
-
-
-def _task_to_skill(task: Task) -> AgentSkill:
-    """Convert a CrewAI Task to an A2A AgentSkill.
-
-    Args:
-        task: The CrewAI Task to convert.
-
-    Returns:
-        AgentSkill representing the task's capability.
-    """
-    task_name = task.name or task.description[:50]
-    task_id = task_name.lower().replace(" ", "_")
-
-    tags: list[str] = []
-    if task.agent:
-        tags.append(task.agent.role.lower().replace(" ", "-"))
-
-    return AgentSkill(
-        id=task_id,
-        name=task_name,
-        description=task.description,
-        tags=tags,
-        examples=[task.expected_output] if task.expected_output else None,
-    )
-
-
-def _tool_to_skill(tool_name: str, tool_description: str) -> AgentSkill:
-    """Convert an Agent's tool to an A2A AgentSkill.
-
-    Args:
-        tool_name: Name of the tool.
-        tool_description: Description of what the tool does.
-
-    Returns:
-        AgentSkill representing the tool's capability.
-    """
-    tool_id = tool_name.lower().replace(" ", "_")
-
-    return AgentSkill(
-        id=tool_id,
-        name=tool_name,
-        description=tool_description,
-        tags=[tool_name.lower().replace(" ", "-")],
-    )
-
-
-def _crew_to_agent_card(crew: Crew, url: str) -> AgentCard:
-    """Generate an A2A AgentCard from a Crew instance.
-
-    Args:
-        crew: The Crew instance to generate a card for.
-        url: The base URL where this crew will be exposed.
-
-    Returns:
-        AgentCard describing the crew's capabilities.
-    """
-    crew_name = getattr(crew, "name", None) or crew.__class__.__name__
-
-    description_parts: list[str] = []
-    crew_description = getattr(crew, "description", None)
-    if crew_description:
-        description_parts.append(crew_description)
-    else:
-        agent_roles = [agent.role for agent in crew.agents]
-        description_parts.append(
-            f"A crew of {len(crew.agents)} agents: {', '.join(agent_roles)}"
-        )
-
-    skills = [_task_to_skill(task) for task in crew.tasks]
-
-    return AgentCard(
-        name=crew_name,
-        description=" ".join(description_parts),
-        url=url,
-        version="1.0.0",
-        capabilities=AgentCapabilities(
-            streaming=True,
-            push_notifications=True,
-        ),
-        default_input_modes=["text/plain", "application/json"],
-        default_output_modes=["text/plain", "application/json"],
-        skills=skills,
-    )
-
-
-def _agent_to_agent_card(agent: Agent, url: str) -> AgentCard:
-    """Generate an A2A AgentCard from an Agent instance.
-
-    Uses A2AServerConfig values when available, falling back to agent properties.
-
-    Args:
-        agent: The Agent instance to generate a card for.
-        url: The base URL where this agent will be exposed.
-
-    Returns:
-        AgentCard describing the agent's capabilities.
-    """
-    server_config = _get_server_config(agent) or A2AServerConfig()
-
-    name = server_config.name or agent.role
-
-    description_parts = [agent.goal]
-    if agent.backstory:
-        description_parts.append(agent.backstory)
-    description = server_config.description or " ".join(description_parts)
-
-    skills: list[AgentSkill] = (
-        server_config.skills.copy() if server_config.skills else []
-    )
-
-    if not skills:
-        if agent.tools:
-            for tool in agent.tools:
-                tool_name = getattr(tool, "name", None) or tool.__class__.__name__
-                tool_desc = getattr(tool, "description", None) or f"Tool: {tool_name}"
-                skills.append(_tool_to_skill(tool_name, tool_desc))
-
-        if not skills:
-            skills.append(
-                AgentSkill(
-                    id=agent.role.lower().replace(" ", "_"),
-                    name=agent.role,
-                    description=agent.goal,
-                    tags=[agent.role.lower().replace(" ", "-")],
-                )
-            )
-
-    return AgentCard(
-        name=name,
-        description=description,
-        url=server_config.url or url,
-        version=server_config.version,
-        capabilities=server_config.capabilities,
-        default_input_modes=server_config.default_input_modes,
-        default_output_modes=server_config.default_output_modes,
-        skills=skills,
-        protocol_version=server_config.protocol_version,
-        provider=server_config.provider,
-        documentation_url=server_config.documentation_url,
-        icon_url=server_config.icon_url,
-        additional_interfaces=server_config.additional_interfaces,
-        security=server_config.security,
-        security_schemes=server_config.security_schemes,
-        supports_authenticated_extended_card=server_config.supports_authenticated_extended_card,
-        signatures=server_config.signatures,
-    )
-
-
-def inject_a2a_server_methods(agent: Agent) -> None:
-    """Inject A2A server methods onto an Agent instance.
-
-    Adds a `to_agent_card(url: str) -> AgentCard` method to the agent
-    that generates an A2A-compliant AgentCard.
-
-    Only injects if the agent has an A2AServerConfig.
-
-    Args:
-        agent: The Agent instance to inject methods onto.
-    """
-    if _get_server_config(agent) is None:
-        return
-
-    def _to_agent_card(self: Agent, url: str) -> AgentCard:
-        return _agent_to_agent_card(self, url)
-
-    object.__setattr__(agent, "to_agent_card", MethodType(_to_agent_card, agent))

lib/crewai/src/crewai/a2a/utils/response_model.py

@@ -1,101 +0,0 @@
-"""Response model utilities for A2A agent interactions."""
-
-from __future__ import annotations
-
-from typing import TypeAlias
-
-from pydantic import BaseModel, Field, create_model
-
-from crewai.a2a.config import A2AClientConfig, A2AConfig, A2AServerConfig
-from crewai.types.utils import create_literals_from_strings
-
-
-A2AConfigTypes: TypeAlias = A2AConfig | A2AServerConfig | A2AClientConfig
-A2AClientConfigTypes: TypeAlias = A2AConfig | A2AClientConfig
-
-
-def create_agent_response_model(agent_ids: tuple[str, ...]) -> type[BaseModel] | None:
-    """Create a dynamic AgentResponse model with Literal types for agent IDs.
-
-    Args:
-        agent_ids: List of available A2A agent IDs.
-
-    Returns:
-        Dynamically created Pydantic model with Literal-constrained a2a_ids field,
-        or None if agent_ids is empty.
-    """
-    if not agent_ids:
-        return None
-
-    DynamicLiteral = create_literals_from_strings(agent_ids)  # noqa: N806
-
-    return create_model(
-        "AgentResponse",
-        a2a_ids=(
-            tuple[DynamicLiteral, ...],  # type: ignore[valid-type]
-            Field(
-                default_factory=tuple,
-                max_length=len(agent_ids),
-                description="A2A agent IDs to delegate to.",
-            ),
-        ),
-        message=(
-            str,
-            Field(
-                description="The message content. If is_a2a=true, this is sent to the A2A agent. If is_a2a=false, this is your final answer ending the conversation."
-            ),
-        ),
-        is_a2a=(
-            bool,
-            Field(
-                description="Set to false when the remote agent has answered your question - extract their answer and return it as your final message. Set to true ONLY if you need to ask a NEW, DIFFERENT question. NEVER repeat the same request - if the conversation history shows the agent already answered, set is_a2a=false immediately."
-            ),
-        ),
-        __base__=BaseModel,
-    )
-
-
-def extract_a2a_agent_ids_from_config(
-    a2a_config: list[A2AConfigTypes] | A2AConfigTypes | None,
-) -> tuple[list[A2AClientConfigTypes], tuple[str, ...]]:
-    """Extract A2A agent IDs from A2A configuration.
-
-    Filters out A2AServerConfig since it doesn't have an endpoint for delegation.
-
-    Args:
-        a2a_config: A2A configuration (any type).
-
-    Returns:
-        Tuple of client A2A configs list and agent endpoint IDs.
-    """
-    if a2a_config is None:
-        return [], ()
-
-    configs: list[A2AConfigTypes]
-    if isinstance(a2a_config, (A2AConfig, A2AClientConfig, A2AServerConfig)):
-        configs = [a2a_config]
-    else:
-        configs = a2a_config
-
-    # Filter to only client configs (those with endpoint)
-    client_configs: list[A2AClientConfigTypes] = [
-        config for config in configs if isinstance(config, (A2AConfig, A2AClientConfig))
-    ]
-
-    return client_configs, tuple(config.endpoint for config in client_configs)
-
-
-def get_a2a_agents_and_response_model(
-    a2a_config: list[A2AConfigTypes] | A2AConfigTypes | None,
-) -> tuple[list[A2AClientConfigTypes], type[BaseModel] | None]:
-    """Get A2A agent configs and response model.
-
-    Args:
-        a2a_config: A2A configuration (any type).
-
-    Returns:
-        Tuple of client A2A configs and response model.
-    """
-    a2a_agents, agent_ids = extract_a2a_agent_ids_from_config(a2a_config=a2a_config)
-
-    return a2a_agents, create_agent_response_model(agent_ids)

lib/crewai/src/crewai/a2a/utils/task.py

@@ -1,284 +0,0 @@
-"""A2A task utilities for server-side task management."""
-
-from __future__ import annotations
-
-import asyncio
-from collections.abc import Callable, Coroutine
-from functools import wraps
-import logging
-import os
-from typing import TYPE_CHECKING, Any, ParamSpec, TypeVar, cast
-
-from a2a.server.agent_execution import RequestContext
-from a2a.server.events import EventQueue
-from a2a.types import (
-    InternalError,
-    InvalidParamsError,
-    Message,
-    Task as A2ATask,
-    TaskState,
-    TaskStatus,
-    TaskStatusUpdateEvent,
-)
-from a2a.utils import new_agent_text_message, new_text_artifact
-from a2a.utils.errors import ServerError
-from aiocache import SimpleMemoryCache, caches  # type: ignore[import-untyped]
-
-from crewai.events.event_bus import crewai_event_bus
-from crewai.events.types.a2a_events import (
-    A2AServerTaskCanceledEvent,
-    A2AServerTaskCompletedEvent,
-    A2AServerTaskFailedEvent,
-    A2AServerTaskStartedEvent,
-)
-from crewai.task import Task
-
-
-if TYPE_CHECKING:
-    from crewai.agent import Agent
-
-
-logger = logging.getLogger(__name__)
-
-P = ParamSpec("P")
-T = TypeVar("T")
-
-
-def _parse_redis_url(url: str) -> dict[str, Any]:
-    from urllib.parse import urlparse
-
-    parsed = urlparse(url)
-    config: dict[str, Any] = {
-        "cache": "aiocache.RedisCache",
-        "endpoint": parsed.hostname or "localhost",
-        "port": parsed.port or 6379,
-    }
-    if parsed.path and parsed.path != "/":
-        try:
-            config["db"] = int(parsed.path.lstrip("/"))
-        except ValueError:
-            pass
-    if parsed.password:
-        config["password"] = parsed.password
-    return config
-
-
-_redis_url = os.environ.get("REDIS_URL")
-
-caches.set_config(
-    {
-        "default": _parse_redis_url(_redis_url)
-        if _redis_url
-        else {
-            "cache": "aiocache.SimpleMemoryCache",
-        }
-    }
-)
-
-
-def cancellable(
-    fn: Callable[P, Coroutine[Any, Any, T]],
-) -> Callable[P, Coroutine[Any, Any, T]]:
-    """Decorator that enables cancellation for A2A task execution.
-
-    Runs a cancellation watcher concurrently with the wrapped function.
-    When a cancel event is published, the execution is cancelled.
-
-    Args:
-        fn: The async function to wrap.
-
-    Returns:
-        Wrapped function with cancellation support.
-    """
-
-    @wraps(fn)
-    async def wrapper(*args: P.args, **kwargs: P.kwargs) -> T:
-        """Wrap function with cancellation monitoring."""
-        context: RequestContext | None = None
-        for arg in args:
-            if isinstance(arg, RequestContext):
-                context = arg
-                break
-        if context is None:
-            context = cast(RequestContext | None, kwargs.get("context"))
-
-        if context is None:
-            return await fn(*args, **kwargs)
-
-        task_id = context.task_id
-        cache = caches.get("default")
-
-        async def poll_for_cancel() -> bool:
-            """Poll cache for cancellation flag."""
-            while True:
-                if await cache.get(f"cancel:{task_id}"):
-                    return True
-                await asyncio.sleep(0.1)
-
-        async def watch_for_cancel() -> bool:
-            """Watch for cancellation events via pub/sub or polling."""
-            if isinstance(cache, SimpleMemoryCache):
-                return await poll_for_cancel()
-
-            try:
-                client = cache.client
-                pubsub = client.pubsub()
-                await pubsub.subscribe(f"cancel:{task_id}")
-                async for message in pubsub.listen():
-                    if message["type"] == "message":
-                        return True
-            except Exception as e:
-                logger.warning("Cancel watcher error for task_id=%s: %s", task_id, e)
-                return await poll_for_cancel()
-            return False
-
-        execute_task = asyncio.create_task(fn(*args, **kwargs))
-        cancel_watch = asyncio.create_task(watch_for_cancel())
-
-        try:
-            done, _ = await asyncio.wait(
-                [execute_task, cancel_watch],
-                return_when=asyncio.FIRST_COMPLETED,
-            )
-
-            if cancel_watch in done:
-                execute_task.cancel()
-                try:
-                    await execute_task
-                except asyncio.CancelledError:
-                    pass
-                raise asyncio.CancelledError(f"Task {task_id} was cancelled")
-            cancel_watch.cancel()
-            return execute_task.result()
-        finally:
-            await cache.delete(f"cancel:{task_id}")
-
-    return wrapper
-
-
-@cancellable
-async def execute(
-    agent: Agent,
-    context: RequestContext,
-    event_queue: EventQueue,
-) -> None:
-    """Execute an A2A task using a CrewAI agent.
-
-    Args:
-        agent: The CrewAI agent to execute the task.
-        context: The A2A request context containing the user's message.
-        event_queue: The event queue for sending responses back.
-
-    TODOs:
-        * need to impl both of structured output and file inputs, depends on `file_inputs` for
-          `crewai.task.Task`, pass the below two to Task. both utils in `a2a.utils.parts`
-        * structured outputs ingestion, `structured_inputs = get_data_parts(parts=context.message.parts)`
-        * file inputs ingestion, `file_inputs = get_file_parts(parts=context.message.parts)`
-    """
-
-    user_message = context.get_user_input()
-    task_id = context.task_id
-    context_id = context.context_id
-    if task_id is None or context_id is None:
-        msg = "task_id and context_id are required"
-        crewai_event_bus.emit(
-            agent,
-            A2AServerTaskFailedEvent(a2a_task_id="", a2a_context_id="", error=msg),
-        )
-        raise ServerError(InvalidParamsError(message=msg)) from None
-
-    task = Task(
-        description=user_message,
-        expected_output="Response to the user's request",
-        agent=agent,
-    )
-
-    crewai_event_bus.emit(
-        agent,
-        A2AServerTaskStartedEvent(a2a_task_id=task_id, a2a_context_id=context_id),
-    )
-
-    try:
-        result = await agent.aexecute_task(task=task, tools=agent.tools)
-        result_str = str(result)
-        history: list[Message] = [context.message] if context.message else []
-        history.append(new_agent_text_message(result_str, context_id, task_id))
-        await event_queue.enqueue_event(
-            A2ATask(
-                id=task_id,
-                context_id=context_id,
-                status=TaskStatus(state=TaskState.input_required),
-                artifacts=[new_text_artifact(result_str, f"result_{task_id}")],
-                history=history,
-            )
-        )
-        crewai_event_bus.emit(
-            agent,
-            A2AServerTaskCompletedEvent(
-                a2a_task_id=task_id, a2a_context_id=context_id, result=str(result)
-            ),
-        )
-    except asyncio.CancelledError:
-        crewai_event_bus.emit(
-            agent,
-            A2AServerTaskCanceledEvent(a2a_task_id=task_id, a2a_context_id=context_id),
-        )
-        raise
-    except Exception as e:
-        crewai_event_bus.emit(
-            agent,
-            A2AServerTaskFailedEvent(
-                a2a_task_id=task_id, a2a_context_id=context_id, error=str(e)
-            ),
-        )
-        raise ServerError(
-            error=InternalError(message=f"Task execution failed: {e}")
-        ) from e
-
-
-async def cancel(
-    context: RequestContext,
-    event_queue: EventQueue,
-) -> A2ATask | None:
-    """Cancel an A2A task.
-
-    Publishes a cancel event that the cancellable decorator listens for.
-
-    Args:
-        context: The A2A request context containing task information.
-        event_queue: The event queue for sending the cancellation status.
-
-    Returns:
-        The canceled task with updated status.
-    """
-    task_id = context.task_id
-    context_id = context.context_id
-    if task_id is None or context_id is None:
-        raise ServerError(InvalidParamsError(message="task_id and context_id required"))
-
-    if context.current_task and context.current_task.status.state in (
-        TaskState.completed,
-        TaskState.failed,
-        TaskState.canceled,
-    ):
-        return context.current_task
-
-    cache = caches.get("default")
-
-    await cache.set(f"cancel:{task_id}", True, ttl=3600)
-    if not isinstance(cache, SimpleMemoryCache):
-        await cache.client.publish(f"cancel:{task_id}", "cancel")
-
-    await event_queue.enqueue_event(
-        TaskStatusUpdateEvent(
-            task_id=task_id,
-            context_id=context_id,
-            status=TaskStatus(state=TaskState.canceled),
-            final=True,
-        )
-    )
-
-    if context.current_task:
-        context.current_task.status = TaskStatus(state=TaskState.canceled)
-        return context.current_task
-    return None

lib/crewai/src/crewai/a2a/wrapper.py

@@ -15,7 +15,7 @@ from typing import TYPE_CHECKING, Any
 from a2a.types import Role, TaskState
 from pydantic import BaseModel, ValidationError
 
-from crewai.a2a.config import A2AClientConfig, A2AConfig
+from crewai.a2a.config import A2AConfig
 from crewai.a2a.extensions.base import ExtensionRegistry
 from crewai.a2a.task_helpers import TaskStateResult
 from crewai.a2a.templates import (
@@ -26,16 +26,13 @@ from crewai.a2a.templates import (
     UNAVAILABLE_AGENTS_NOTICE_TEMPLATE,
 )
 from crewai.a2a.types import AgentResponseProtocol
-from crewai.a2a.utils.agent_card import (
-    afetch_agent_card,
-    fetch_agent_card,
-    inject_a2a_server_methods,
-)
-from crewai.a2a.utils.delegation import (
+from crewai.a2a.utils import (
     aexecute_a2a_delegation,
+    afetch_agent_card,
     execute_a2a_delegation,
+    fetch_agent_card,
+    get_a2a_agents_and_response_model,
 )
-from crewai.a2a.utils.response_model import get_a2a_agents_and_response_model
 from crewai.events.event_bus import crewai_event_bus
 from crewai.events.types.a2a_events import (
     A2AConversationCompletedEvent,
@@ -125,12 +122,10 @@ def wrap_agent_with_a2a_instance(
         agent, "aexecute_task", MethodType(aexecute_task_with_a2a, agent)
     )
 
-    inject_a2a_server_methods(agent)
-
 
 def _fetch_card_from_config(
-    config: A2AConfig | A2AClientConfig,
-) -> tuple[A2AConfig | A2AClientConfig, AgentCard | Exception]:
+    config: A2AConfig,
+) -> tuple[A2AConfig, AgentCard | Exception]:
     """Fetch agent card from A2A config.
 
     Args:
@@ -151,7 +146,7 @@ def _fetch_card_from_config(
 
 
 def _fetch_agent_cards_concurrently(
-    a2a_agents: list[A2AConfig | A2AClientConfig],
+    a2a_agents: list[A2AConfig],
 ) -> tuple[dict[str, AgentCard], dict[str, str]]:
     """Fetch agent cards concurrently for multiple A2A agents.
 
@@ -186,7 +181,7 @@ def _fetch_agent_cards_concurrently(
 
 def _execute_task_with_a2a(
     self: Agent,
-    a2a_agents: list[A2AConfig | A2AClientConfig],
+    a2a_agents: list[A2AConfig],
     original_fn: Callable[..., str],
     task: Task,
     agent_response_model: type[BaseModel],
@@ -275,7 +270,7 @@ def _execute_task_with_a2a(
 
 
 def _augment_prompt_with_a2a(
-    a2a_agents: list[A2AConfig | A2AClientConfig],
+    a2a_agents: list[A2AConfig],
     task_description: str,
     agent_cards: dict[str, AgentCard],
     conversation_history: list[Message] | None = None,
@@ -528,11 +523,11 @@ def _prepare_delegation_context(
     task: Task,
     original_task_description: str | None,
 ) -> tuple[
-    list[A2AConfig | A2AClientConfig],
+    list[A2AConfig],
     type[BaseModel],
     str,
     str,
-    A2AConfig | A2AClientConfig,
+    A2AConfig,
     str | None,
     str | None,
     dict[str, Any] | None,
@@ -596,7 +591,7 @@ def _handle_task_completion(
     task: Task,
     task_id_config: str | None,
     reference_task_ids: list[str],
-    agent_config: A2AConfig | A2AClientConfig,
+    agent_config: A2AConfig,
     turn_num: int,
 ) -> tuple[str | None, str | None, list[str]]:
     """Handle task completion state including reference task updates.
@@ -636,7 +631,7 @@ def _handle_agent_response_and_continue(
     a2a_result: TaskStateResult,
     agent_id: str,
     agent_cards: dict[str, AgentCard] | None,
-    a2a_agents: list[A2AConfig | A2AClientConfig],
+    a2a_agents: list[A2AConfig],
     original_task_description: str,
     conversation_history: list[Message],
     turn_num: int,
@@ -776,7 +771,6 @@ def _delegate_to_a2a(
                 response_model=agent_config.response_model,
                 turn_number=turn_num + 1,
                 updates=agent_config.updates,
-                transport_protocol=agent_config.transport_protocol,
             )
 
             conversation_history = a2a_result.get("history", [])
@@ -873,8 +867,8 @@ def _delegate_to_a2a(
 
 
 async def _afetch_card_from_config(
-    config: A2AConfig | A2AClientConfig,
-) -> tuple[A2AConfig | A2AClientConfig, AgentCard | Exception]:
+    config: A2AConfig,
+) -> tuple[A2AConfig, AgentCard | Exception]:
     """Fetch agent card from A2A config asynchronously."""
     try:
         card = await afetch_agent_card(
@@ -888,7 +882,7 @@ async def _afetch_card_from_config(
 
 
 async def _afetch_agent_cards_concurrently(
-    a2a_agents: list[A2AConfig | A2AClientConfig],
+    a2a_agents: list[A2AConfig],
 ) -> tuple[dict[str, AgentCard], dict[str, str]]:
     """Fetch agent cards concurrently for multiple A2A agents using asyncio."""
     agent_cards: dict[str, AgentCard] = {}
@@ -913,7 +907,7 @@ async def _afetch_agent_cards_concurrently(
 
 async def _aexecute_task_with_a2a(
     self: Agent,
-    a2a_agents: list[A2AConfig | A2AClientConfig],
+    a2a_agents: list[A2AConfig],
     original_fn: Callable[..., Coroutine[Any, Any, str]],
     task: Task,
     agent_response_model: type[BaseModel],
@@ -992,7 +986,7 @@ async def _ahandle_agent_response_and_continue(
     a2a_result: TaskStateResult,
     agent_id: str,
     agent_cards: dict[str, AgentCard] | None,
-    a2a_agents: list[A2AConfig | A2AClientConfig],
+    a2a_agents: list[A2AConfig],
     original_task_description: str,
     conversation_history: list[Message],
     turn_num: int,
@@ -1091,7 +1085,6 @@ async def _adelegate_to_a2a(
                 agent_branch=agent_branch,
                 response_model=agent_config.response_model,
                 turn_number=turn_num + 1,
-                transport_protocol=agent_config.transport_protocol,
                 updates=agent_config.updates,
             )
 

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

@@ -17,6 +17,7 @@ from urllib.parse import urlparse
 from pydantic import BaseModel, Field, InstanceOf, PrivateAttr, model_validator
 from typing_extensions import Self
 
+from crewai.a2a.config import A2AConfig
 from crewai.agent.utils import (
     ahandle_knowledge_retrieval,
     apply_training_data,
@@ -72,19 +73,11 @@ from crewai.utilities.constants import TRAINED_AGENTS_DATA_FILE, TRAINING_DATA_F
 from crewai.utilities.converter import Converter
 from crewai.utilities.guardrail_types import GuardrailType
 from crewai.utilities.llm_utils import create_llm
-from crewai.utilities.prompts import Prompts, StandardPromptResult, SystemPromptResult
+from crewai.utilities.prompts import Prompts
 from crewai.utilities.token_counter_callback import TokenCalcHandler
 from crewai.utilities.training_handler import CrewTrainingHandler
 
 
-try:
-    from crewai.a2a.config import A2AClientConfig, A2AConfig, A2AServerConfig
-except ImportError:
-    A2AClientConfig = Any
-    A2AConfig = Any
-    A2AServerConfig = Any
-
-
 if TYPE_CHECKING:
     from crewai_tools import CodeInterpreterTool
 
@@ -225,18 +218,9 @@ class Agent(BaseAgent):
     guardrail_max_retries: int = Field(
         default=3, description="Maximum number of retries when guardrail fails"
     )
-    a2a: (
-        list[A2AConfig | A2AServerConfig | A2AClientConfig]
-        | A2AConfig
-        | A2AServerConfig
-        | A2AClientConfig
-        | None
-    ) = Field(
+    a2a: list[A2AConfig] | A2AConfig | None = Field(
         default=None,
-        description="""
-        A2A (Agent-to-Agent) configuration for delegating tasks to remote agents.
-        Can be a single A2AConfig/A2AClientConfig/A2AServerConfig, or a list of any number of A2AConfig/A2AClientConfig with a single A2AServerConfig.
-        """,
+        description="A2A (Agent-to-Agent) configuration for delegating tasks to remote agents. Can be a single A2AConfig or a dict mapping agent IDs to configs.",
     )
     executor_class: type[CrewAgentExecutor] | type[CrewAgentExecutorFlow] = Field(
         default=CrewAgentExecutor,
@@ -725,17 +709,9 @@ class Agent(BaseAgent):
         raw_tools: list[BaseTool] = tools or self.tools or []
         parsed_tools = parse_tools(raw_tools)
 
-        use_native_tool_calling = (
-            hasattr(self.llm, "supports_function_calling")
-            and callable(getattr(self.llm, "supports_function_calling", None))
-            and self.llm.supports_function_calling()
-            and len(raw_tools) > 0
-        )
-
         prompt = Prompts(
             agent=self,
             has_tools=len(raw_tools) > 0,
-            use_native_tool_calling=use_native_tool_calling,
             i18n=self.i18n,
             use_system_prompt=self.use_system_prompt,
             system_template=self.system_template,
@@ -743,8 +719,6 @@ class Agent(BaseAgent):
             response_template=self.response_template,
         ).task_execution()
 
-        print("prompt", prompt)
-
         stop_words = [self.i18n.slice("observation")]
 
         if self.response_template:
@@ -759,7 +733,7 @@ class Agent(BaseAgent):
         if self.agent_executor is not None:
             self._update_executor_parameters(
                 task=task,
-                tools=parsed_tools,  # type: ignore[arg-type]
+                tools=parsed_tools,
                 raw_tools=raw_tools,
                 prompt=prompt,
                 stop_words=stop_words,
@@ -768,7 +742,7 @@ class Agent(BaseAgent):
         else:
             self.agent_executor = self.executor_class(
                 llm=cast(BaseLLM, self.llm),
-                task=task,  # type: ignore[arg-type]
+                task=task,
                 i18n=self.i18n,
                 agent=self,
                 crew=self.crew,
@@ -791,11 +765,11 @@ class Agent(BaseAgent):
     def _update_executor_parameters(
         self,
         task: Task | None,
-        tools: list[BaseTool],
+        tools: list,
         raw_tools: list[BaseTool],
-        prompt: SystemPromptResult | StandardPromptResult,
+        prompt: dict,
         stop_words: list[str],
-        rpm_limit_fn: Callable | None,  # type: ignore[type-arg]
+        rpm_limit_fn: Callable | None,
     ) -> None:
         """Update executor parameters without recreating instance.
 

lib/crewai/src/crewai/agent/utils.py

@@ -236,30 +236,14 @@ def process_tool_results(agent: Agent, result: Any) -> Any:
 def save_last_messages(agent: Agent) -> None:
     """Save the last messages from agent executor.
 
-    Sanitizes messages to be compatible with TaskOutput's LLMMessage type,
-    which only accepts 'user', 'assistant', 'system' roles and requires
-    content to be a string or list (not None).
-
     Args:
         agent: The agent instance.
     """
-    if not agent.agent_executor or not hasattr(agent.agent_executor, "messages"):
-        agent._last_messages = []
-        return
-
-    sanitized_messages = []
-    for msg in agent.agent_executor.messages:
-        role = msg.get("role", "")
-        # Only include messages with valid LLMMessage roles
-        if role not in ("user", "assistant", "system"):
-            continue
-        # Ensure content is not None (can happen with tool call assistant messages)
-        content = msg.get("content")
-        if content is None:
-            content = ""
-        sanitized_messages.append({"role": role, "content": content})
-
-    agent._last_messages = sanitized_messages
+    agent._last_messages = (
+        agent.agent_executor.messages.copy()
+        if agent.agent_executor and hasattr(agent.agent_executor, "messages")
+        else []
+    )
 
 
 def prepare_tools(

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

@@ -30,7 +30,6 @@ from crewai.hooks.llm_hooks import (
 )
 from crewai.utilities.agent_utils import (
     aget_llm_response,
-    convert_tools_to_openai_schema,
     enforce_rpm_limit,
     format_message_for_llm,
     get_llm_response,
@@ -216,33 +215,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
     def _invoke_loop(self) -> AgentFinish:
         """Execute agent loop until completion.
 
-        Checks if the LLM supports native function calling and uses that
-        approach if available, otherwise falls back to the ReAct text pattern.
-
-        Returns:
-            Final answer from the agent.
-        """
-        # Check if model supports native function calling
-        use_native_tools = (
-            hasattr(self.llm, "supports_function_calling")
-            and callable(getattr(self.llm, "supports_function_calling", None))
-            and self.llm.supports_function_calling()
-            and self.original_tools
-        )
-
-        if use_native_tools:
-            return self._invoke_loop_native_tools()
-
-        # Fall back to ReAct text-based pattern
-        return self._invoke_loop_react()
-
-    def _invoke_loop_react(self) -> AgentFinish:
-        """Execute agent loop using ReAct text-based pattern.
-
-        This is the traditional approach where tool definitions are embedded
-        in the prompt and the LLM outputs Action/Action Input text that is
-        parsed to execute tools.
-
         Returns:
             Final answer from the agent.
         """
@@ -272,10 +244,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                     response_model=self.response_model,
                     executor_context=self,
                 )
-                print("--------------------------------")
-                print("get_llm_response answer", answer)
-                print("--------------------------------")
-                # breakpoint()
                 if self.response_model is not None:
                     try:
                         self.response_model.model_validate_json(answer)
@@ -365,338 +333,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         self._show_logs(formatted_answer)
         return formatted_answer
 
-    def _invoke_loop_native_tools(self) -> AgentFinish:
-        """Execute agent loop using native function calling.
-
-        This method uses the LLM's native tool/function calling capability
-        instead of the text-based ReAct pattern. The LLM directly returns
-        structured tool calls which are executed and results fed back.
-
-        Returns:
-            Final answer from the agent.
-        """
-        print("--------------------------------")
-        print("invoke_loop_native_tools")
-        print("--------------------------------")
-        # Convert tools to OpenAI schema format
-        if not self.original_tools:
-            # No tools available, fall back to simple LLM call
-            return self._invoke_loop_native_no_tools()
-
-        openai_tools, available_functions = convert_tools_to_openai_schema(
-            self.original_tools
-        )
-
-        while True:
-            try:
-                if has_reached_max_iterations(self.iterations, self.max_iter):
-                    formatted_answer = handle_max_iterations_exceeded(
-                        None,
-                        printer=self._printer,
-                        i18n=self._i18n,
-                        messages=self.messages,
-                        llm=self.llm,
-                        callbacks=self.callbacks,
-                    )
-                    self._show_logs(formatted_answer)
-                    return formatted_answer
-
-                enforce_rpm_limit(self.request_within_rpm_limit)
-
-                # Debug: Show messages being sent to LLM
-                print("--------------------------------")
-                print(f"Messages count: {len(self.messages)}")
-                for i, msg in enumerate(self.messages):
-                    role = msg.get("role", "unknown")
-                    content = msg.get("content", "")
-                    if content:
-                        preview = (
-                            content[:200] + "..." if len(content) > 200 else content
-                        )
-                    else:
-                        preview = "(no content)"
-                    print(f"  [{i}] {role}: {preview}")
-                print("--------------------------------")
-
-                # Call LLM with native tools
-                # Pass available_functions=None so the LLM returns tool_calls
-                # without executing them. The executor handles tool execution
-                # via _handle_native_tool_calls to properly manage message history.
-                answer = get_llm_response(
-                    llm=self.llm,
-                    messages=self.messages,
-                    callbacks=self.callbacks,
-                    printer=self._printer,
-                    tools=openai_tools,
-                    available_functions=None,
-                    from_task=self.task,
-                    from_agent=self.agent,
-                    response_model=self.response_model,
-                    executor_context=self,
-                )
-                print("--------------------------------")
-                print("invoke_loop_native_tools answer", answer)
-                print("--------------------------------")
-                # print("get_llm_response answer", answer[:500] + "...")
-
-                # Check if the response is a list of tool calls
-                if (
-                    isinstance(answer, list)
-                    and answer
-                    and self._is_tool_call_list(answer)
-                ):
-                    # Handle tool calls - execute tools and add results to messages
-                    self._handle_native_tool_calls(answer, available_functions)
-                    # Continue loop to let LLM analyze results and decide next steps
-                    continue
-
-                # Text or other response - handle as potential final answer
-                if isinstance(answer, str):
-                    # Text response - this is the final answer
-                    formatted_answer = AgentFinish(
-                        thought="",
-                        output=answer,
-                        text=answer,
-                    )
-                    self._invoke_step_callback(formatted_answer)
-                    self._append_message(answer)  # Save final answer to messages
-                    self._show_logs(formatted_answer)
-                    return formatted_answer
-
-                # Unexpected response type, treat as final answer
-                formatted_answer = AgentFinish(
-                    thought="",
-                    output=str(answer),
-                    text=str(answer),
-                )
-                self._invoke_step_callback(formatted_answer)
-                self._append_message(str(answer))  # Save final answer to messages
-                self._show_logs(formatted_answer)
-                return formatted_answer
-
-            except Exception as e:
-                if e.__class__.__module__.startswith("litellm"):
-                    raise e
-                if is_context_length_exceeded(e):
-                    handle_context_length(
-                        respect_context_window=self.respect_context_window,
-                        printer=self._printer,
-                        messages=self.messages,
-                        llm=self.llm,
-                        callbacks=self.callbacks,
-                        i18n=self._i18n,
-                    )
-                    continue
-                handle_unknown_error(self._printer, e)
-                raise e
-            finally:
-                self.iterations += 1
-
-    def _invoke_loop_native_no_tools(self) -> AgentFinish:
-        """Execute a simple LLM call when no tools are available.
-
-        Returns:
-            Final answer from the agent.
-        """
-        enforce_rpm_limit(self.request_within_rpm_limit)
-
-        answer = get_llm_response(
-            llm=self.llm,
-            messages=self.messages,
-            callbacks=self.callbacks,
-            printer=self._printer,
-            from_task=self.task,
-            from_agent=self.agent,
-            response_model=self.response_model,
-            executor_context=self,
-        )
-
-        formatted_answer = AgentFinish(
-            thought="",
-            output=str(answer),
-            text=str(answer),
-        )
-        self._show_logs(formatted_answer)
-        return formatted_answer
-
-    def _is_tool_call_list(self, response: list[Any]) -> bool:
-        """Check if a response is a list of tool calls.
-
-        Args:
-            response: The response to check.
-
-        Returns:
-            True if the response appears to be a list of tool calls.
-        """
-        if not response:
-            return False
-        first_item = response[0]
-        # OpenAI-style
-        if hasattr(first_item, "function") or (
-            isinstance(first_item, dict) and "function" in first_item
-        ):
-            return True
-        # Anthropic-style
-        if (
-            hasattr(first_item, "type")
-            and getattr(first_item, "type", None) == "tool_use"
-        ):
-            return True
-        if hasattr(first_item, "name") and hasattr(first_item, "input"):
-            return True
-        # Gemini-style
-        if hasattr(first_item, "function_call") and first_item.function_call:
-            return True
-        return False
-
-    def _handle_native_tool_calls(
-        self,
-        tool_calls: list[Any],
-        available_functions: dict[str, Callable[..., Any]],
-    ) -> None:
-        """Handle a single native tool call from the LLM.
-
-        Executes only the FIRST tool call and appends the result to message history.
-        This enables sequential tool execution with reflection after each tool,
-        allowing the LLM to reason about results before deciding on next steps.
-
-        Args:
-            tool_calls: List of tool calls from the LLM (only first is processed).
-            available_functions: Dict mapping function names to callables.
-        """
-        from datetime import datetime
-        import json
-
-        from crewai.events import crewai_event_bus
-        from crewai.events.types.tool_usage_events import (
-            ToolUsageFinishedEvent,
-            ToolUsageStartedEvent,
-        )
-
-        if not tool_calls:
-            return
-
-        # Only process the FIRST tool call for sequential execution with reflection
-        tool_call = tool_calls[0]
-
-        # Extract tool call info - handle OpenAI-style, Anthropic-style, and Gemini-style
-        if hasattr(tool_call, "function"):
-            # OpenAI-style: has .function.name and .function.arguments
-            call_id = getattr(tool_call, "id", f"call_{id(tool_call)}")
-            func_name = tool_call.function.name
-            func_args = tool_call.function.arguments
-        elif hasattr(tool_call, "function_call") and tool_call.function_call:
-            # Gemini-style: has .function_call.name and .function_call.args
-            call_id = f"call_{id(tool_call)}"
-            func_name = tool_call.function_call.name
-            func_args = (
-                dict(tool_call.function_call.args)
-                if tool_call.function_call.args
-                else {}
-            )
-        elif hasattr(tool_call, "name") and hasattr(tool_call, "input"):
-            # Anthropic format: has .name and .input (ToolUseBlock)
-            call_id = getattr(tool_call, "id", f"call_{id(tool_call)}")
-            func_name = tool_call.name
-            func_args = tool_call.input  # Already a dict in Anthropic
-        elif isinstance(tool_call, dict):
-            call_id = tool_call.get("id", f"call_{id(tool_call)}")
-            func_info = tool_call.get("function", {})
-            func_name = func_info.get("name", "") or tool_call.get("name", "")
-            func_args = func_info.get("arguments", "{}") or tool_call.get("input", {})
-        else:
-            return
-
-        # Append assistant message with single tool call
-        assistant_message: LLMMessage = {
-            "role": "assistant",
-            "content": None,
-            "tool_calls": [
-                {
-                    "id": call_id,
-                    "type": "function",
-                    "function": {
-                        "name": func_name,
-                        "arguments": func_args
-                        if isinstance(func_args, str)
-                        else json.dumps(func_args),
-                    },
-                }
-            ],
-        }
-
-        self.messages.append(assistant_message)
-
-        # Parse arguments for the single tool call
-        if isinstance(func_args, str):
-            try:
-                args_dict = json.loads(func_args)
-            except json.JSONDecodeError:
-                args_dict = {}
-        else:
-            args_dict = func_args
-
-        # Emit tool usage started event
-        started_at = datetime.now()
-        crewai_event_bus.emit(
-            self,
-            event=ToolUsageStartedEvent(
-                tool_name=func_name,
-                tool_args=args_dict,
-                from_agent=self.agent,
-                from_task=self.task,
-            ),
-        )
-
-        # Execute the tool
-        print(f"Using Tool: {func_name}")
-        result = "Tool not found"
-        if func_name in available_functions:
-            try:
-                tool_func = available_functions[func_name]
-                result = tool_func(**args_dict)
-                if not isinstance(result, str):
-                    result = str(result)
-            except Exception as e:
-                result = f"Error executing tool: {e}"
-
-        # Emit tool usage finished event
-        crewai_event_bus.emit(
-            self,
-            event=ToolUsageFinishedEvent(
-                output=result,
-                tool_name=func_name,
-                tool_args=args_dict,
-                from_agent=self.agent,
-                from_task=self.task,
-                started_at=started_at,
-                finished_at=datetime.now(),
-            ),
-        )
-
-        # Append tool result message
-        tool_message: LLMMessage = {
-            "role": "tool",
-            "tool_call_id": call_id,
-            "content": result,
-        }
-        self.messages.append(tool_message)
-
-        # Log the tool execution
-        if self.agent and self.agent.verbose:
-            self._printer.print(
-                content=f"Tool {func_name} executed with result: {result[:200]}...",
-                color="green",
-            )
-
-        # Inject post-tool reasoning prompt to enforce analysis
-        reasoning_prompt = self._i18n.slice("post_tool_reasoning")
-        reasoning_message: LLMMessage = {
-            "role": "user",
-            "content": reasoning_prompt,
-        }
-        self.messages.append(reasoning_message)
-
     async def ainvoke(self, inputs: dict[str, Any]) -> dict[str, Any]:
         """Execute the agent asynchronously with given inputs.
 
@@ -746,29 +382,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
     async def _ainvoke_loop(self) -> AgentFinish:
         """Execute agent loop asynchronously until completion.
 
-        Checks if the LLM supports native function calling and uses that
-        approach if available, otherwise falls back to the ReAct text pattern.
-
-        Returns:
-            Final answer from the agent.
-        """
-        # Check if model supports native function calling
-        use_native_tools = (
-            hasattr(self.llm, "supports_function_calling")
-            and callable(getattr(self.llm, "supports_function_calling", None))
-            and self.llm.supports_function_calling()
-            and self.original_tools
-        )
-
-        if use_native_tools:
-            return await self._ainvoke_loop_native_tools()
-
-        # Fall back to ReAct text-based pattern
-        return await self._ainvoke_loop_react()
-
-    async def _ainvoke_loop_react(self) -> AgentFinish:
-        """Execute agent loop asynchronously using ReAct text-based pattern.
-
         Returns:
             Final answer from the agent.
         """
@@ -882,139 +495,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         self._show_logs(formatted_answer)
         return formatted_answer
 
-    async def _ainvoke_loop_native_tools(self) -> AgentFinish:
-        """Execute agent loop asynchronously using native function calling.
-
-        This method uses the LLM's native tool/function calling capability
-        instead of the text-based ReAct pattern.
-
-        Returns:
-            Final answer from the agent.
-        """
-        # Convert tools to OpenAI schema format
-        if not self.original_tools:
-            return await self._ainvoke_loop_native_no_tools()
-
-        openai_tools, available_functions = convert_tools_to_openai_schema(
-            self.original_tools
-        )
-
-        while True:
-            try:
-                if has_reached_max_iterations(self.iterations, self.max_iter):
-                    formatted_answer = handle_max_iterations_exceeded(
-                        None,
-                        printer=self._printer,
-                        i18n=self._i18n,
-                        messages=self.messages,
-                        llm=self.llm,
-                        callbacks=self.callbacks,
-                    )
-                    self._show_logs(formatted_answer)
-                    return formatted_answer
-
-                enforce_rpm_limit(self.request_within_rpm_limit)
-
-                # Call LLM with native tools
-                # Pass available_functions=None so the LLM returns tool_calls
-                # without executing them. The executor handles tool execution
-                # via _handle_native_tool_calls to properly manage message history.
-                answer = await aget_llm_response(
-                    llm=self.llm,
-                    messages=self.messages,
-                    callbacks=self.callbacks,
-                    printer=self._printer,
-                    tools=openai_tools,
-                    available_functions=None,
-                    from_task=self.task,
-                    from_agent=self.agent,
-                    response_model=self.response_model,
-                    executor_context=self,
-                )
-                print("--------------------------------")
-                print("native llm completion answer", answer)
-                print("--------------------------------")
-
-                # Check if the response is a list of tool calls
-                if (
-                    isinstance(answer, list)
-                    and answer
-                    and self._is_tool_call_list(answer)
-                ):
-                    # Handle tool calls - execute tools and add results to messages
-                    self._handle_native_tool_calls(answer, available_functions)
-                    # Continue loop to let LLM analyze results and decide next steps
-                    continue
-
-                # Text or other response - handle as potential final answer
-                if isinstance(answer, str):
-                    # Text response - this is the final answer
-                    formatted_answer = AgentFinish(
-                        thought="",
-                        output=answer,
-                        text=answer,
-                    )
-                    self._invoke_step_callback(formatted_answer)
-                    self._append_message(answer)  # Save final answer to messages
-                    self._show_logs(formatted_answer)
-                    return formatted_answer
-
-                # Unexpected response type, treat as final answer
-                formatted_answer = AgentFinish(
-                    thought="",
-                    output=str(answer),
-                    text=str(answer),
-                )
-                self._invoke_step_callback(formatted_answer)
-                self._append_message(str(answer))  # Save final answer to messages
-                self._show_logs(formatted_answer)
-                return formatted_answer
-
-            except Exception as e:
-                if e.__class__.__module__.startswith("litellm"):
-                    raise e
-                if is_context_length_exceeded(e):
-                    handle_context_length(
-                        respect_context_window=self.respect_context_window,
-                        printer=self._printer,
-                        messages=self.messages,
-                        llm=self.llm,
-                        callbacks=self.callbacks,
-                        i18n=self._i18n,
-                    )
-                    continue
-                handle_unknown_error(self._printer, e)
-                raise e
-            finally:
-                self.iterations += 1
-
-    async def _ainvoke_loop_native_no_tools(self) -> AgentFinish:
-        """Execute a simple async LLM call when no tools are available.
-
-        Returns:
-            Final answer from the agent.
-        """
-        enforce_rpm_limit(self.request_within_rpm_limit)
-
-        answer = await aget_llm_response(
-            llm=self.llm,
-            messages=self.messages,
-            callbacks=self.callbacks,
-            printer=self._printer,
-            from_task=self.task,
-            from_agent=self.agent,
-            response_model=self.response_model,
-            executor_context=self,
-        )
-
-        formatted_answer = AgentFinish(
-            thought="",
-            output=str(answer),
-            text=str(answer),
-        )
-        self._show_logs(formatted_answer)
-        return formatted_answer
-
     def _handle_agent_action(
         self, formatted_answer: AgentAction, tool_result: ToolResult
     ) -> AgentAction | AgentFinish:

lib/crewai/src/crewai/cli/authentication/providers/keycloak.py

@@ -1,32 +0,0 @@
-from crewai.cli.authentication.providers.base_provider import BaseProvider
-
-
-class KeycloakProvider(BaseProvider):
-    def get_authorize_url(self) -> str:
-        return f"{self._oauth2_base_url()}/realms/{self.settings.extra.get('realm')}/protocol/openid-connect/auth/device"
-
-    def get_token_url(self) -> str:
-        return f"{self._oauth2_base_url()}/realms/{self.settings.extra.get('realm')}/protocol/openid-connect/token"
-
-    def get_jwks_url(self) -> str:
-        return f"{self._oauth2_base_url()}/realms/{self.settings.extra.get('realm')}/protocol/openid-connect/certs"
-
-    def get_issuer(self) -> str:
-        return f"{self._oauth2_base_url()}/realms/{self.settings.extra.get('realm')}"
-
-    def get_audience(self) -> str:
-        return self.settings.audience or "no-audience-provided"
-
-    def get_client_id(self) -> str:
-        if self.settings.client_id is None:
-            raise ValueError(
-                "Client ID is required. Please set it in the configuration."
-            )
-        return self.settings.client_id
-
-    def get_required_fields(self) -> list[str]:
-        return ["realm"]
-
-    def _oauth2_base_url(self) -> str:
-        domain = self.settings.domain.removeprefix("https://").removeprefix("http://")
-        return f"https://{domain}"

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]==1.8.1"
+    "crewai[tools]==1.8.0"
 ]
 
 [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]==1.8.1"
+    "crewai[tools]==1.8.0"
 ]
 
 [project.scripts]

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

@@ -209,9 +209,10 @@ class EventListener(BaseEventListener):
         @crewai_event_bus.on(TaskCompletedEvent)
         def on_task_completed(source: Any, event: TaskCompletedEvent) -> None:
             # Handle telemetry
-            span = self.execution_spans.pop(source, None)
+            span = self.execution_spans.get(source)
             if span:
                 self._telemetry.task_ended(span, source, source.agent.crew)
+            self.execution_spans[source] = None
 
             # Pass task name if it exists
             task_name = get_task_name(source)
@@ -221,10 +222,11 @@ class EventListener(BaseEventListener):
 
         @crewai_event_bus.on(TaskFailedEvent)
         def on_task_failed(source: Any, event: TaskFailedEvent) -> None:
-            span = self.execution_spans.pop(source, None)
+            span = self.execution_spans.get(source)
             if span:
                 if source.agent and source.agent.crew:
                     self._telemetry.task_ended(span, source, source.agent.crew)
+                self.execution_spans[source] = None
 
             # Pass task name if it exists
             task_name = get_task_name(source)
@@ -378,12 +380,6 @@ class EventListener(BaseEventListener):
                 self.formatter.handle_llm_tool_usage_finished(
                     event.tool_name,
                 )
-            else:
-                self.formatter.handle_tool_usage_finished(
-                    event.tool_name,
-                    event.output,
-                    getattr(event, "run_attempts", None),
-                )
 
         @crewai_event_bus.on(ToolUsageErrorEvent)
         def on_tool_usage_error(source: Any, event: ToolUsageErrorEvent) -> None:

lib/crewai/src/crewai/events/event_types.py

@@ -1,20 +1,3 @@
-from crewai.events.types.a2a_events import (
-    A2AConversationCompletedEvent,
-    A2AConversationStartedEvent,
-    A2ADelegationCompletedEvent,
-    A2ADelegationStartedEvent,
-    A2AMessageSentEvent,
-    A2APollingStartedEvent,
-    A2APollingStatusEvent,
-    A2APushNotificationReceivedEvent,
-    A2APushNotificationRegisteredEvent,
-    A2APushNotificationTimeoutEvent,
-    A2AResponseReceivedEvent,
-    A2AServerTaskCanceledEvent,
-    A2AServerTaskCompletedEvent,
-    A2AServerTaskFailedEvent,
-    A2AServerTaskStartedEvent,
-)
 from crewai.events.types.agent_events import (
     AgentExecutionCompletedEvent,
     AgentExecutionErrorEvent,
@@ -93,22 +76,7 @@ from crewai.events.types.tool_usage_events import (
 
 
 EventTypes = (
-    A2AConversationCompletedEvent
-    | A2AConversationStartedEvent
-    | A2ADelegationCompletedEvent
-    | A2ADelegationStartedEvent
-    | A2AMessageSentEvent
-    | A2APollingStartedEvent
-    | A2APollingStatusEvent
-    | A2APushNotificationReceivedEvent
-    | A2APushNotificationRegisteredEvent
-    | A2APushNotificationTimeoutEvent
-    | A2AResponseReceivedEvent
-    | A2AServerTaskCanceledEvent
-    | A2AServerTaskCompletedEvent
-    | A2AServerTaskFailedEvent
-    | A2AServerTaskStartedEvent
-    | CrewKickoffStartedEvent
+    CrewKickoffStartedEvent
     | CrewKickoffCompletedEvent
     | CrewKickoffFailedEvent
     | CrewTestStartedEvent

lib/crewai/src/crewai/events/types/a2a_events.py

@@ -210,37 +210,3 @@ class A2APushNotificationTimeoutEvent(A2AEventBase):
     type: str = "a2a_push_notification_timeout"
     task_id: str
     timeout_seconds: float
-
-
-class A2AServerTaskStartedEvent(A2AEventBase):
-    """Event emitted when an A2A server task execution starts."""
-
-    type: str = "a2a_server_task_started"
-    a2a_task_id: str
-    a2a_context_id: str
-
-
-class A2AServerTaskCompletedEvent(A2AEventBase):
-    """Event emitted when an A2A server task execution completes."""
-
-    type: str = "a2a_server_task_completed"
-    a2a_task_id: str
-    a2a_context_id: str
-    result: str
-
-
-class A2AServerTaskCanceledEvent(A2AEventBase):
-    """Event emitted when an A2A server task execution is canceled."""
-
-    type: str = "a2a_server_task_canceled"
-    a2a_task_id: str
-    a2a_context_id: str
-
-
-class A2AServerTaskFailedEvent(A2AEventBase):
-    """Event emitted when an A2A server task execution fails."""
-
-    type: str = "a2a_server_task_failed"
-    a2a_task_id: str
-    a2a_context_id: str
-    error: str

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

@@ -366,32 +366,6 @@ To enable tracing, do any one of these:
 
         self.print_panel(content, f"🔧 Tool Execution Started (#{iteration})", "yellow")
 
-    def handle_tool_usage_finished(
-        self,
-        tool_name: str,
-        output: str,
-        run_attempts: int | None = None,
-    ) -> None:
-        """Handle tool usage finished event with panel display."""
-        if not self.verbose:
-            return
-
-        iteration = self.tool_usage_counts.get(tool_name, 1)
-
-        content = Text()
-        content.append("Tool Completed\n", style="green bold")
-        content.append("Tool: ", style="white")
-        content.append(f"{tool_name}\n", style="green bold")
-
-        if output:
-            content.append("Output: ", style="white")
-
-            content.append(f"{output}\n", style="green")
-
-        self.print_panel(
-            content, f"✅ Tool Execution Completed (#{iteration})", "green"
-        )
-
     def handle_tool_usage_error(
         self,
         tool_name: str,

lib/crewai/src/crewai/experimental/crew_agent_executor_flow.py

@@ -1,8 +1,6 @@
 from __future__ import annotations
 
 from collections.abc import Callable
-from datetime import datetime
-import json
 import threading
 from typing import TYPE_CHECKING, Any, Literal, cast
 from uuid import uuid4
@@ -19,24 +17,16 @@ from crewai.agents.parser import (
     OutputParserError,
 )
 from crewai.events.event_bus import crewai_event_bus
-from crewai.events.listeners.tracing.utils import (
-    is_tracing_enabled_in_context,
-)
 from crewai.events.types.logging_events import (
     AgentLogsExecutionEvent,
     AgentLogsStartedEvent,
 )
-from crewai.events.types.tool_usage_events import (
-    ToolUsageFinishedEvent,
-    ToolUsageStartedEvent,
-)
 from crewai.flow.flow import Flow, listen, or_, router, start
 from crewai.hooks.llm_hooks import (
     get_after_llm_call_hooks,
     get_before_llm_call_hooks,
 )
 from crewai.utilities.agent_utils import (
-    convert_tools_to_openai_schema,
     enforce_rpm_limit,
     format_message_for_llm,
     get_llm_response,
@@ -81,8 +71,6 @@ class AgentReActState(BaseModel):
     current_answer: AgentAction | AgentFinish | None = Field(default=None)
     is_finished: bool = Field(default=False)
     ask_for_human_input: bool = Field(default=False)
-    use_native_tools: bool = Field(default=False)
-    pending_tool_calls: list[Any] = Field(default_factory=list)
 
 
 class CrewAgentExecutorFlow(Flow[AgentReActState], CrewAgentExecutorMixin):
@@ -191,10 +179,6 @@ class CrewAgentExecutorFlow(Flow[AgentReActState], CrewAgentExecutorMixin):
                 )
             )
 
-        # Native tool calling support
-        self._openai_tools: list[dict[str, Any]] = []
-        self._available_functions: dict[str, Callable[..., Any]] = {}
-
         self._state = AgentReActState()
 
     def _ensure_flow_initialized(self) -> None:
@@ -205,66 +189,14 @@ class CrewAgentExecutorFlow(Flow[AgentReActState], CrewAgentExecutorMixin):
         Only the instance that actually executes via invoke() will emit events.
         """
         if not self._flow_initialized:
-            current_tracing = is_tracing_enabled_in_context()
             # Now call Flow's __init__ which will replace self._state
             # with Flow's managed state. Suppress flow events since this is
             # an agent executor, not a user-facing flow.
             super().__init__(
                 suppress_flow_events=True,
-                tracing=current_tracing if current_tracing else None,
             )
             self._flow_initialized = True
 
-    def _check_native_tool_support(self) -> bool:
-        """Check if LLM supports native function calling.
-
-        Returns:
-            True if the LLM supports native function calling and tools are available.
-        """
-        return (
-            hasattr(self.llm, "supports_function_calling")
-            and callable(getattr(self.llm, "supports_function_calling", None))
-            and self.llm.supports_function_calling()
-            and bool(self.original_tools)
-        )
-
-    def _setup_native_tools(self) -> None:
-        """Convert tools to OpenAI schema format for native function calling."""
-        if self.original_tools:
-            self._openai_tools, self._available_functions = (
-                convert_tools_to_openai_schema(self.original_tools)
-            )
-
-    def _is_tool_call_list(self, response: list[Any]) -> bool:
-        """Check if a response is a list of tool calls.
-
-        Args:
-            response: The response to check.
-
-        Returns:
-            True if the response appears to be a list of tool calls.
-        """
-        if not response:
-            return False
-        first_item = response[0]
-        # Check for OpenAI-style tool call structure
-        if hasattr(first_item, "function") or (
-            isinstance(first_item, dict) and "function" in first_item
-        ):
-            return True
-        # Check for Anthropic-style tool call structure (ToolUseBlock)
-        if (
-            hasattr(first_item, "type")
-            and getattr(first_item, "type", None) == "tool_use"
-        ):
-            return True
-        if hasattr(first_item, "name") and hasattr(first_item, "input"):
-            return True
-        # Check for Gemini-style function call (Part with function_call)
-        if hasattr(first_item, "function_call") and first_item.function_call:
-            return True
-        return False
-
     @property
     def use_stop_words(self) -> bool:
         """Check to determine if stop words are being used.
@@ -297,11 +229,6 @@ class CrewAgentExecutorFlow(Flow[AgentReActState], CrewAgentExecutorMixin):
     def initialize_reasoning(self) -> Literal["initialized"]:
         """Initialize the reasoning flow and emit agent start logs."""
         self._show_start_logs()
-        # Check for native tool support on first iteration
-        if self.state.iterations == 0:
-            self.state.use_native_tools = self._check_native_tool_support()
-            if self.state.use_native_tools:
-                self._setup_native_tools()
         return "initialized"
 
     @listen("force_final_answer")
@@ -376,69 +303,6 @@ class CrewAgentExecutorFlow(Flow[AgentReActState], CrewAgentExecutorMixin):
             handle_unknown_error(self._printer, e)
             raise
 
-    @listen("continue_reasoning_native")
-    def call_llm_native_tools(
-        self,
-    ) -> Literal["native_tool_calls", "native_finished", "context_error"]:
-        """Execute LLM call with native function calling.
-
-        Returns routing decision based on whether tool calls or final answer.
-        """
-        try:
-            enforce_rpm_limit(self.request_within_rpm_limit)
-
-            # Call LLM with native tools
-            # Pass available_functions=None so the LLM returns tool_calls
-            # without executing them. The executor handles tool execution.
-            answer = get_llm_response(
-                llm=self.llm,
-                messages=list(self.state.messages),
-                callbacks=self.callbacks,
-                printer=self._printer,
-                tools=self._openai_tools,
-                available_functions=None,
-                from_task=self.task,
-                from_agent=self.agent,
-                response_model=self.response_model,
-                executor_context=self,
-            )
-
-            # Check if the response is a list of tool calls
-            if isinstance(answer, list) and answer and self._is_tool_call_list(answer):
-                # Store tool calls for sequential processing
-                self.state.pending_tool_calls = list(answer)
-                return "native_tool_calls"
-
-            # Text response - this is the final answer
-            if isinstance(answer, str):
-                self.state.current_answer = AgentFinish(
-                    thought="",
-                    output=answer,
-                    text=answer,
-                )
-                self._invoke_step_callback(self.state.current_answer)
-                self._append_message_to_state(answer)
-                return "native_finished"
-
-            # Unexpected response type, treat as final answer
-            self.state.current_answer = AgentFinish(
-                thought="",
-                output=str(answer),
-                text=str(answer),
-            )
-            self._invoke_step_callback(self.state.current_answer)
-            self._append_message_to_state(str(answer))
-            return "native_finished"
-
-        except Exception as e:
-            if is_context_length_exceeded(e):
-                self._last_context_error = e
-                return "context_error"
-            if e.__class__.__module__.startswith("litellm"):
-                raise e
-            handle_unknown_error(self._printer, e)
-            raise
-
     @router(call_llm_and_parse)
     def route_by_answer_type(self) -> Literal["execute_tool", "agent_finished"]:
         """Route based on whether answer is AgentAction or AgentFinish."""
@@ -494,14 +358,6 @@ class CrewAgentExecutorFlow(Flow[AgentReActState], CrewAgentExecutorMixin):
                 self.state.is_finished = True
                 return "tool_result_is_final"
 
-            # Inject post-tool reasoning prompt to enforce analysis
-            reasoning_prompt = self._i18n.slice("post_tool_reasoning")
-            reasoning_message: LLMMessage = {
-                "role": "user",
-                "content": reasoning_prompt,
-            }
-            self.state.messages.append(reasoning_message)
-
             return "tool_completed"
 
         except Exception as e:
@@ -511,143 +367,6 @@ class CrewAgentExecutorFlow(Flow[AgentReActState], CrewAgentExecutorMixin):
             self._console.print(error_text)
             raise
 
-    @listen("native_tool_calls")
-    def execute_native_tool(self) -> Literal["native_tool_completed"]:
-        """Execute a single native tool call and inject reasoning prompt.
-
-        Processes only the FIRST tool call from pending_tool_calls for
-        sequential execution with reflection after each tool.
-        """
-        if not self.state.pending_tool_calls:
-            return "native_tool_completed"
-
-        tool_call = self.state.pending_tool_calls[0]
-        self.state.pending_tool_calls = []  # Clear pending calls
-
-        # Extract tool call info - handle OpenAI, Anthropic, and Gemini formats
-        if hasattr(tool_call, "function"):
-            # OpenAI format: has .function.name and .function.arguments
-            call_id = getattr(tool_call, "id", f"call_{id(tool_call)}")
-            func_name = tool_call.function.name
-            func_args = tool_call.function.arguments
-        elif hasattr(tool_call, "function_call") and tool_call.function_call:
-            # Gemini format: has .function_call.name and .function_call.args
-            call_id = f"call_{id(tool_call)}"
-            func_name = tool_call.function_call.name
-            func_args = (
-                dict(tool_call.function_call.args)
-                if tool_call.function_call.args
-                else {}
-            )
-        elif hasattr(tool_call, "name") and hasattr(tool_call, "input"):
-            # Anthropic format: has .name and .input (ToolUseBlock)
-            call_id = getattr(tool_call, "id", f"call_{id(tool_call)}")
-            func_name = tool_call.name
-            func_args = tool_call.input  # Already a dict in Anthropic
-        elif isinstance(tool_call, dict):
-            call_id = tool_call.get("id", f"call_{id(tool_call)}")
-            func_info = tool_call.get("function", {})
-            func_name = func_info.get("name", "") or tool_call.get("name", "")
-            func_args = func_info.get("arguments", "{}") or tool_call.get("input", {})
-        else:
-            return "native_tool_completed"
-
-        # Append assistant message with single tool call
-        assistant_message: LLMMessage = {
-            "role": "assistant",
-            "content": None,
-            "tool_calls": [
-                {
-                    "id": call_id,
-                    "type": "function",
-                    "function": {
-                        "name": func_name,
-                        "arguments": func_args
-                        if isinstance(func_args, str)
-                        else json.dumps(func_args),
-                    },
-                }
-            ],
-        }
-        self.state.messages.append(assistant_message)
-
-        # Parse arguments for the single tool call
-        if isinstance(func_args, str):
-            try:
-                args_dict = json.loads(func_args)
-            except json.JSONDecodeError:
-                args_dict = {}
-        else:
-            args_dict = func_args
-
-        # Emit tool usage started event
-        started_at = datetime.now()
-        crewai_event_bus.emit(
-            self,
-            event=ToolUsageStartedEvent(
-                tool_name=func_name,
-                tool_args=args_dict,
-                from_agent=self.agent,
-                from_task=self.task,
-            ),
-        )
-
-        # Execute the tool
-        result = "Tool not found"
-        if func_name in self._available_functions:
-            try:
-                tool_func = self._available_functions[func_name]
-                result = tool_func(**args_dict)
-                if not isinstance(result, str):
-                    result = str(result)
-            except Exception as e:
-                result = f"Error executing tool: {e}"
-
-        # Emit tool usage finished event
-        crewai_event_bus.emit(
-            self,
-            event=ToolUsageFinishedEvent(
-                output=result,
-                tool_name=func_name,
-                tool_args=args_dict,
-                from_agent=self.agent,
-                from_task=self.task,
-                started_at=started_at,
-                finished_at=datetime.now(),
-            ),
-        )
-
-        # Append tool result message
-        tool_message: LLMMessage = {
-            "role": "tool",
-            "tool_call_id": call_id,
-            "content": result,
-        }
-        self.state.messages.append(tool_message)
-
-        # Log the tool execution
-        if self.agent and self.agent.verbose:
-            self._printer.print(
-                content=f"Tool {func_name} executed with result: {result[:200]}...",
-                color="green",
-            )
-
-        # Inject post-tool reasoning prompt to enforce analysis
-        reasoning_prompt = self._i18n.slice("post_tool_reasoning")
-        reasoning_message: LLMMessage = {
-            "role": "user",
-            "content": reasoning_prompt,
-        }
-        self.state.messages.append(reasoning_message)
-
-        return "native_tool_completed"
-
-    @router(execute_native_tool)
-    def increment_native_and_continue(self) -> Literal["initialized"]:
-        """Increment iteration counter after native tool execution."""
-        self.state.iterations += 1
-        return "initialized"
-
     @listen("initialized")
     def continue_iteration(self) -> Literal["check_iteration"]:
         """Bridge listener that connects iteration loop back to iteration check."""
@@ -656,14 +375,10 @@ class CrewAgentExecutorFlow(Flow[AgentReActState], CrewAgentExecutorMixin):
     @router(or_(initialize_reasoning, continue_iteration))
     def check_max_iterations(
         self,
-    ) -> Literal[
-        "force_final_answer", "continue_reasoning", "continue_reasoning_native"
-    ]:
+    ) -> Literal["force_final_answer", "continue_reasoning"]:
         """Check if max iterations reached before proceeding with reasoning."""
         if has_reached_max_iterations(self.state.iterations, self.max_iter):
             return "force_final_answer"
-        if self.state.use_native_tools:
-            return "continue_reasoning_native"
         return "continue_reasoning"
 
     @router(execute_tool_action)
@@ -672,7 +387,7 @@ class CrewAgentExecutorFlow(Flow[AgentReActState], CrewAgentExecutorMixin):
         self.state.iterations += 1
         return "initialized"
 
-    @listen(or_("agent_finished", "tool_result_is_final", "native_finished"))
+    @listen(or_("agent_finished", "tool_result_is_final"))
     def finalize(self) -> Literal["completed", "skipped"]:
         """Finalize execution and emit completion logs."""
         if self.state.current_answer is None:
@@ -760,8 +475,6 @@ class CrewAgentExecutorFlow(Flow[AgentReActState], CrewAgentExecutorMixin):
             self.state.iterations = 0
             self.state.current_answer = None
             self.state.is_finished = False
-            self.state.use_native_tools = False
-            self.state.pending_tool_calls = []
 
             if "system" in self.prompt:
                 prompt = cast("SystemPromptResult", self.prompt)

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

@@ -1,5 +1,4 @@
 import inspect
-from typing import Any
 
 from pydantic import BaseModel, Field, InstanceOf, model_validator
 from typing_extensions import Self
@@ -15,14 +14,14 @@ class FlowTrackable(BaseModel):
     inspecting the call stack.
     """
 
-    parent_flow: InstanceOf[Flow[Any]] | None = Field(
+    parent_flow: InstanceOf[Flow] | None = Field(
         default=None,
         description="The parent flow of the instance, if it was created inside a flow.",
     )
 
     @model_validator(mode="after")
     def _set_parent_flow(self) -> Self:
-        max_depth = 8
+        max_depth = 5
         frame = inspect.currentframe()
 
         try:

lib/crewai/src/crewai/llm.py

@@ -931,6 +931,7 @@ class LLM(BaseLLM):
             self._handle_streaming_callbacks(callbacks, usage_info, last_chunk)
 
             if not tool_calls or not available_functions:
+
                 if response_model and self.is_litellm:
                     instructor_instance = InternalInstructor(
                         content=full_response,
@@ -1143,12 +1144,8 @@ class LLM(BaseLLM):
             if response_model:
                 params["response_model"] = response_model
             response = litellm.completion(**params)
-
-            if (
-                hasattr(response, "usage")
-                and not isinstance(response.usage, type)
-                and response.usage
-            ):
+            
+            if hasattr(response,"usage") and not isinstance(response.usage, type) and response.usage:
                 usage_info = response.usage
                 self._track_token_usage_internal(usage_info)
 
@@ -1202,19 +1199,16 @@ class LLM(BaseLLM):
             )
             return text_response
 
-        # --- 6) If there are tool calls but no available functions, return the tool calls
-        # This allows the caller (e.g., executor) to handle tool execution
-        if tool_calls and not available_functions:
+        # --- 6) If there is no text response, no available functions, but there are tool calls, return the tool calls
+        if tool_calls and not available_functions and not text_response:
             return tool_calls
 
-        # --- 7) Handle tool calls if present (execute when available_functions provided)
-        if tool_calls and available_functions:
-            tool_result = self._handle_tool_call(
-                tool_calls, available_functions, from_task, from_agent
-            )
-            if tool_result is not None:
-                return tool_result
-
+        # --- 7) Handle tool calls if present
+        tool_result = self._handle_tool_call(
+            tool_calls, available_functions, from_task, from_agent
+        )
+        if tool_result is not None:
+            return tool_result
         # --- 8) If tool call handling didn't return a result, emit completion event and return text response
         self._handle_emit_call_events(
             response=text_response,
@@ -1279,11 +1273,7 @@ class LLM(BaseLLM):
                 params["response_model"] = response_model
             response = await litellm.acompletion(**params)
 
-            if (
-                hasattr(response, "usage")
-                and not isinstance(response.usage, type)
-                and response.usage
-            ):
+            if hasattr(response,"usage") and not isinstance(response.usage, type) and response.usage:
                 usage_info = response.usage
                 self._track_token_usage_internal(usage_info)
 
@@ -1331,18 +1321,14 @@ class LLM(BaseLLM):
             )
             return text_response
 
-        # If there are tool calls but no available functions, return the tool calls
-        # This allows the caller (e.g., executor) to handle tool execution
-        if tool_calls and not available_functions:
+        if tool_calls and not available_functions and not text_response:
             return tool_calls
 
-        # Handle tool calls if present (execute when available_functions provided)
-        if tool_calls and available_functions:
-            tool_result = self._handle_tool_call(
-                tool_calls, available_functions, from_task, from_agent
-            )
-            if tool_result is not None:
-                return tool_result
+        tool_result = self._handle_tool_call(
+            tool_calls, available_functions, from_task, from_agent
+        )
+        if tool_result is not None:
+            return tool_result
 
         self._handle_emit_call_events(
             response=text_response,
@@ -1377,7 +1363,7 @@ class LLM(BaseLLM):
         """
         full_response = ""
         chunk_count = 0
-
+        
         usage_info = None
 
         accumulated_tool_args: defaultdict[int, AccumulatedToolArgs] = defaultdict(

lib/crewai/src/crewai/llms/base_llm.py

@@ -445,7 +445,7 @@ class BaseLLM(ABC):
                 from_agent=from_agent,
             )
 
-            return result
+            return str(result)
 
         except Exception as e:
             error_msg = f"Error executing function '{function_name}': {e!s}"

lib/crewai/src/crewai/llms/providers/anthropic/completion.py

@@ -418,7 +418,6 @@ class AnthropicCompletion(BaseLLM):
         - System messages are separate from conversation messages
         - Messages must alternate between user and assistant
         - First message must be from user
-        - Tool results must be in user messages with tool_result content blocks
         - When thinking is enabled, assistant messages must start with thinking blocks
 
         Args:
@@ -432,7 +431,6 @@ class AnthropicCompletion(BaseLLM):
 
         formatted_messages: list[LLMMessage] = []
         system_message: str | None = None
-        pending_tool_results: list[dict[str, Any]] = []
 
         for message in base_formatted:
             role = message.get("role")
@@ -443,47 +441,16 @@ class AnthropicCompletion(BaseLLM):
                     system_message += f"\n\n{content}"
                 else:
                     system_message = cast(str, content)
-            elif role == "tool":
-                # Convert OpenAI-style tool message to Anthropic tool_result format
-                # These will be collected and added as a user message
-                tool_call_id = message.get("tool_call_id", "")
-                tool_result = {
-                    "type": "tool_result",
-                    "tool_use_id": tool_call_id,
-                    "content": content if content else "",
-                }
-                pending_tool_results.append(tool_result)
-            elif role == "assistant":
-                # First, flush any pending tool results as a user message
-                if pending_tool_results:
-                    formatted_messages.append(
-                        {"role": "user", "content": pending_tool_results}
-                    )
-                    pending_tool_results = []
+            else:
+                role_str = role if role is not None else "user"
 
-                # Handle assistant message with tool_calls (convert to Anthropic format)
-                tool_calls = message.get("tool_calls", [])
-                if tool_calls:
-                    assistant_content: list[dict[str, Any]] = []
-                    for tc in tool_calls:
-                        if isinstance(tc, dict):
-                            func = tc.get("function", {})
-                            tool_use = {
-                                "type": "tool_use",
-                                "id": tc.get("id", ""),
-                                "name": func.get("name", ""),
-                                "input": json.loads(func.get("arguments", "{}"))
-                                if isinstance(func.get("arguments"), str)
-                                else func.get("arguments", {}),
-                            }
-                            assistant_content.append(tool_use)
-                    if assistant_content:
-                        formatted_messages.append(
-                            {"role": "assistant", "content": assistant_content}
-                        )
-                elif isinstance(content, list):
-                    formatted_messages.append({"role": "assistant", "content": content})
-                elif self.thinking and self.previous_thinking_blocks:
+                if isinstance(content, list):
+                    formatted_messages.append({"role": role_str, "content": content})
+                elif (
+                    role_str == "assistant"
+                    and self.thinking
+                    and self.previous_thinking_blocks
+                ):
                     structured_content = cast(
                         list[dict[str, Any]],
                         [
@@ -492,34 +459,14 @@ class AnthropicCompletion(BaseLLM):
                         ],
                     )
                     formatted_messages.append(
-                        LLMMessage(role="assistant", content=structured_content)
+                        LLMMessage(role=role_str, content=structured_content)
                     )
-                else:
-                    content_str = content if content is not None else ""
-                    formatted_messages.append(
-                        LLMMessage(role="assistant", content=content_str)
-                    )
-            else:
-                # User message - first flush any pending tool results
-                if pending_tool_results:
-                    formatted_messages.append(
-                        {"role": "user", "content": pending_tool_results}
-                    )
-                    pending_tool_results = []
-
-                role_str = role if role is not None else "user"
-                if isinstance(content, list):
-                    formatted_messages.append({"role": role_str, "content": content})
                 else:
                     content_str = content if content is not None else ""
                     formatted_messages.append(
                         LLMMessage(role=role_str, content=content_str)
                     )
 
-        # Flush any remaining pending tool results
-        if pending_tool_results:
-            formatted_messages.append({"role": "user", "content": pending_tool_results})
-
         # Ensure first message is from user (Anthropic requirement)
         if not formatted_messages:
             # If no messages, add a default user message
@@ -579,19 +526,13 @@ class AnthropicCompletion(BaseLLM):
                 return structured_json
 
         # Check if Claude wants to use tools
-        if response.content:
+        if response.content and available_functions:
             tool_uses = [
                 block for block in response.content if isinstance(block, ToolUseBlock)
             ]
 
             if tool_uses:
-                # If no available_functions, return tool calls for executor to handle
-                # This allows the executor to manage tool execution with proper
-                # message history and post-tool reasoning prompts
-                if not available_functions:
-                    return list(tool_uses)
-
-                # Handle tool use conversation flow internally
+                # Handle tool use conversation flow
                 return self._handle_tool_use_conversation(
                     response,
                     tool_uses,
@@ -755,7 +696,7 @@ class AnthropicCompletion(BaseLLM):
 
                 return structured_json
 
-        if final_message.content:
+        if final_message.content and available_functions:
             tool_uses = [
                 block
                 for block in final_message.content
@@ -763,11 +704,7 @@ class AnthropicCompletion(BaseLLM):
             ]
 
             if tool_uses:
-                # If no available_functions, return tool calls for executor to handle
-                if not available_functions:
-                    return list(tool_uses)
-
-                # Handle tool use conversation flow internally
+                # Handle tool use conversation flow
                 return self._handle_tool_use_conversation(
                     final_message,
                     tool_uses,
@@ -996,16 +933,12 @@ class AnthropicCompletion(BaseLLM):
 
                 return structured_json
 
-        if response.content:
+        if response.content and available_functions:
             tool_uses = [
                 block for block in response.content if isinstance(block, ToolUseBlock)
             ]
 
             if tool_uses:
-                # If no available_functions, return tool calls for executor to handle
-                if not available_functions:
-                    return list(tool_uses)
-
                 return await self._ahandle_tool_use_conversation(
                     response,
                     tool_uses,
@@ -1146,7 +1079,7 @@ class AnthropicCompletion(BaseLLM):
 
                 return structured_json
 
-        if final_message.content:
+        if final_message.content and available_functions:
             tool_uses = [
                 block
                 for block in final_message.content
@@ -1154,10 +1087,6 @@ class AnthropicCompletion(BaseLLM):
             ]
 
             if tool_uses:
-                # If no available_functions, return tool calls for executor to handle
-                if not available_functions:
-                    return list(tool_uses)
-
                 return await self._ahandle_tool_use_conversation(
                     final_message,
                     tool_uses,

lib/crewai/src/crewai/llms/providers/azure/completion.py

@@ -443,7 +443,7 @@ class AzureCompletion(BaseLLM):
             params["presence_penalty"] = self.presence_penalty
         if self.max_tokens is not None:
             params["max_tokens"] = self.max_tokens
-        if self.stop and self.supports_stop_words():
+        if self.stop:
             params["stop"] = self.stop
 
         # Handle tools/functions for Azure OpenAI models
@@ -514,31 +514,10 @@ class AzureCompletion(BaseLLM):
 
         for message in base_formatted:
             role = message.get("role", "user")  # Default to user if no role
-            # Handle None content - Azure requires string content
-            content = message.get("content") or ""
+            content = message.get("content", "")
 
-            # Handle tool role messages - keep as tool role for Azure OpenAI
-            if role == "tool":
-                tool_call_id = message.get("tool_call_id", "unknown")
-                azure_messages.append(
-                    {
-                        "role": "tool",
-                        "tool_call_id": tool_call_id,
-                        "content": content,
-                    }
-                )
-            # Handle assistant messages with tool_calls
-            elif role == "assistant" and message.get("tool_calls"):
-                tool_calls = message.get("tool_calls", [])
-                azure_msg: LLMMessage = {
-                    "role": "assistant",
-                    "content": content,  # Already defaulted to "" above
-                    "tool_calls": tool_calls,
-                }
-                azure_messages.append(azure_msg)
-            else:
-                # Azure AI Inference requires both 'role' and 'content'
-                azure_messages.append({"role": role, "content": content})
+            # Azure AI Inference requires both 'role' and 'content'
+            azure_messages.append({"role": role, "content": content})
 
         return azure_messages
 
@@ -625,11 +604,6 @@ class AzureCompletion(BaseLLM):
                 from_agent=from_agent,
             )
 
-        # If there are tool_calls but no available_functions, return the tool_calls
-        # This allows the caller (e.g., executor) to handle tool execution
-        if message.tool_calls and not available_functions:
-            return list(message.tool_calls)
-
         # Handle tool calls
         if message.tool_calls and available_functions:
             tool_call = message.tool_calls[0]  # Handle first tool call
@@ -801,21 +775,6 @@ class AzureCompletion(BaseLLM):
                 from_agent=from_agent,
             )
 
-        # If there are tool_calls but no available_functions, return them
-        # in OpenAI-compatible format for executor to handle
-        if tool_calls and not available_functions:
-            return [
-                {
-                    "id": call_data.get("id", f"call_{idx}"),
-                    "type": "function",
-                    "function": {
-                        "name": call_data["name"],
-                        "arguments": call_data["arguments"],
-                    },
-                }
-                for idx, call_data in tool_calls.items()
-            ]
-
         # Handle completed tool calls
         if tool_calls and available_functions:
             for call_data in tool_calls.values():
@@ -972,28 +931,8 @@ class AzureCompletion(BaseLLM):
         return self.is_openai_model
 
     def supports_stop_words(self) -> bool:
-        """Check if the model supports stop words.
-
-        Models using the Responses API (GPT-5 family, o-series reasoning models,
-        computer-use-preview) do not support stop sequences.
-        See: https://learn.microsoft.com/en-us/azure/ai-foundry/foundry-models/concepts/models-sold-directly-by-azure
-        """
-        model_lower = self.model.lower() if self.model else ""
-
-        if "gpt-5" in model_lower:
-            return False
-
-        o_series_models = ["o1", "o3", "o4", "o1-mini", "o3-mini", "o4-mini"]
-
-        responses_api_models = ["computer-use-preview"]
-
-        unsupported_stop_models = o_series_models + responses_api_models
-
-        for unsupported in unsupported_stop_models:
-            if unsupported in model_lower:
-                return False
-
-        return True
+        """Check if the model supports stop words."""
+        return True  # Most Azure models support stop sequences
 
     def get_context_window_size(self) -> int:
         """Get the context window size for the model."""

lib/crewai/src/crewai/llms/providers/gemini/completion.py

@@ -606,17 +606,6 @@ class GeminiCompletion(BaseLLM):
         if response.candidates and (self.tools or available_functions):
             candidate = response.candidates[0]
             if candidate.content and candidate.content.parts:
-                # Collect function call parts
-                function_call_parts = [
-                    part for part in candidate.content.parts if part.function_call
-                ]
-
-                # If there are function calls but no available_functions,
-                # return them for the executor to handle (like OpenAI/Anthropic)
-                if function_call_parts and not available_functions:
-                    return function_call_parts
-
-                # Otherwise execute the tools internally
                 for part in candidate.content.parts:
                     if part.function_call:
                         function_name = part.function_call.name
@@ -731,7 +720,7 @@ class GeminiCompletion(BaseLLM):
         from_task: Any | None = None,
         from_agent: Any | None = None,
         response_model: type[BaseModel] | None = None,
-    ) -> str | list[dict[str, Any]]:
+    ) -> str:
         """Finalize streaming response with usage tracking, function execution, and events.
 
         Args:
@@ -749,21 +738,6 @@ class GeminiCompletion(BaseLLM):
         """
         self._track_token_usage_internal(usage_data)
 
-        # If there are function calls but no available_functions,
-        # return them for the executor to handle
-        if function_calls and not available_functions:
-            return [
-                {
-                    "id": call_data["id"],
-                    "function": {
-                        "name": call_data["name"],
-                        "arguments": json.dumps(call_data["args"]),
-                    },
-                    "type": "function",
-                }
-                for call_data in function_calls.values()
-            ]
-
         # Handle completed function calls
         if function_calls and available_functions:
             for call_data in function_calls.values():

lib/crewai/src/crewai/llms/providers/openai/completion.py

@@ -428,12 +428,6 @@ class OpenAICompletion(BaseLLM):
             choice: Choice = response.choices[0]
             message = choice.message
 
-            # If there are tool_calls but no available_functions, return the tool_calls
-            # This allows the caller (e.g., executor) to handle tool execution
-            if message.tool_calls and not available_functions:
-                return list(message.tool_calls)
-
-            # If there are tool_calls and available_functions, execute the tools
             if message.tool_calls and available_functions:
                 tool_call = message.tool_calls[0]
                 function_name = tool_call.function.name
@@ -731,15 +725,6 @@ class OpenAICompletion(BaseLLM):
             choice: Choice = response.choices[0]
             message = choice.message
 
-            # If there are tool_calls but no available_functions, return the tool_calls
-            # This allows the caller (e.g., executor) to handle tool execution
-            if message.tool_calls and not available_functions:
-                print("--------------------------------")
-                print("lorenze tool_calls", list(message.tool_calls))
-                print("--------------------------------")
-                return list(message.tool_calls)
-
-            # If there are tool_calls and available_functions, execute the tools
             if message.tool_calls and available_functions:
                 tool_call = message.tool_calls[0]
                 function_name = tool_call.function.name

lib/crewai/src/crewai/translations/en.json

@@ -11,9 +11,6 @@
     "role_playing": "You are {role}. {backstory}\nYour personal goal is: {goal}",
     "tools": "\nYou ONLY have access to the following tools, and should NEVER make up tools that are not listed here:\n\n{tools}\n\nIMPORTANT: Use the following format in your response:\n\n```\nThought: you should always think about what to do\nAction: the action to take, only one name of [{tool_names}], just the name, exactly as it's written.\nAction Input: the input to the action, just a simple JSON object, enclosed in curly braces, using \" to wrap keys and values.\nObservation: the result of the action\n```\n\nOnce all necessary information is gathered, return the following format:\n\n```\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n```",
     "no_tools": "\nTo give my best complete final answer to the task respond using the exact following format:\n\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described.\n\nI MUST use these formats, my job depends on it!",
-    "native_tools": "\nUse available tools to gather information and complete your task.",
-    "native_task": "\nCurrent Task: {input}\n\nThis is VERY important to you, your job depends on it!",
-    "post_tool_reasoning": "PAUSE and THINK before responding.\n\nInternally consider (DO NOT output these steps):\n- What key insights did the tool provide?\n- Have I fulfilled ALL requirements from my original instructions (e.g., minimum tool calls, specific sources)?\n- Do I have enough information to fully answer the task?\n\nIF you have NOT met all requirements or need more information: Call another tool now.\n\nIF you have met all requirements and have sufficient information: Provide ONLY your final answer in the format specified by the task's expected output. Do NOT include reasoning steps, analysis sections, or meta-commentary. Just deliver the answer.",
     "format": "I MUST either use a tool (use one at time) OR give my best final answer not both at the same time. When responding, I must use the following format:\n\n```\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action, dictionary enclosed in curly braces\nObservation: the result of the action\n```\nThis Thought/Action/Action Input/Result can repeat N times. Once I know the final answer, I must return the following format:\n\n```\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described\n\n```",
     "final_answer_format": "If you don't need to use any more tools, you must give your best complete final answer, make sure it satisfies the expected criteria, use the EXACT format below:\n\n```\nThought: I now can give a great answer\nFinal Answer: my best complete final answer to the task.\n\n```",
     "format_without_tools": "\nSorry, I didn't use the right format. I MUST either use a tool (among the available ones), OR give my best final answer.\nHere is the expected format I must follow:\n\n```\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action\nObservation: the result of the action\n```\n This Thought/Action/Action Input/Result process can repeat N times. Once I know the final answer, I must return the following format:\n\n```\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described\n\n```",

lib/crewai/src/crewai/types/utils.py

@@ -1,6 +1,8 @@
 """Utilities for creating and manipulating types."""
 
-from typing import Annotated, Final, Literal, cast
+from typing import Annotated, Final, Literal
+
+from typing_extensions import TypeAliasType
 
 
 _DYNAMIC_LITERAL_ALIAS: Final[Literal["DynamicLiteral"]] = "DynamicLiteral"
@@ -18,11 +20,6 @@ def create_literals_from_strings(
 
     Returns:
         Literal type for each A2A agent ID
-
-    Raises:
-        ValueError: If values is empty (Literal requires at least one value)
     """
     unique_values: tuple[str, ...] = tuple(dict.fromkeys(values))
-    if not unique_values:
-        raise ValueError("Cannot create Literal type from empty values")
-    return cast(type, Literal.__getitem__(unique_values))
+    return Literal.__getitem__(unique_values)

lib/crewai/src/crewai/utilities/agent_utils.py

@@ -108,65 +108,6 @@ def render_text_description_and_args(
     return "\n".join(tool_strings)
 
 
-def convert_tools_to_openai_schema(
-    tools: Sequence[BaseTool | CrewStructuredTool],
-) -> tuple[list[dict[str, Any]], dict[str, Callable[..., Any]]]:
-    """Convert CrewAI tools to OpenAI function calling format.
-
-    This function converts CrewAI BaseTool and CrewStructuredTool objects
-    into the OpenAI-compatible tool schema format that can be passed to
-    LLM providers for native function calling.
-
-    Args:
-        tools: List of CrewAI tool objects to convert.
-
-    Returns:
-        Tuple containing:
-        - List of OpenAI-format tool schema dictionaries
-        - Dict mapping tool names to their callable run() methods
-
-    Example:
-        >>> tools = [CalculatorTool(), SearchTool()]
-        >>> schemas, functions = convert_tools_to_openai_schema(tools)
-        >>> # schemas can be passed to llm.call(tools=schemas)
-        >>> # functions can be passed to llm.call(available_functions=functions)
-    """
-    openai_tools: list[dict[str, Any]] = []
-    available_functions: dict[str, Callable[..., Any]] = {}
-
-    for tool in tools:
-        # Get the JSON schema for tool parameters
-        parameters: dict[str, Any] = {}
-        if hasattr(tool, "args_schema") and tool.args_schema is not None:
-            try:
-                parameters = tool.args_schema.model_json_schema()
-                # Remove title and description from schema root as they're redundant
-                parameters.pop("title", None)
-                parameters.pop("description", None)
-            except Exception:
-                parameters = {}
-
-        # Extract original description from formatted description
-        # BaseTool formats description as "Tool Name: ...\nTool Arguments: ...\nTool Description: {original}"
-        description = tool.description
-        if "Tool Description:" in description:
-            # Extract the original description after "Tool Description:"
-            description = description.split("Tool Description:")[-1].strip()
-
-        schema: dict[str, Any] = {
-            "type": "function",
-            "function": {
-                "name": tool.name,
-                "description": description,
-                "parameters": parameters,
-            },
-        }
-        openai_tools.append(schema)
-        available_functions[tool.name] = tool.run
-
-    return openai_tools, available_functions
-
-
 def has_reached_max_iterations(iterations: int, max_iterations: int) -> bool:
     """Check if the maximum number of iterations has been reached.
 
@@ -293,13 +234,11 @@ def get_llm_response(
     messages: list[LLMMessage],
     callbacks: list[TokenCalcHandler],
     printer: Printer,
-    tools: list[dict[str, Any]] | None = None,
-    available_functions: dict[str, Callable[..., Any]] | None = None,
     from_task: Task | None = None,
     from_agent: Agent | LiteAgent | None = None,
     response_model: type[BaseModel] | None = None,
     executor_context: CrewAgentExecutor | LiteAgent | None = None,
-) -> str | Any:
+) -> str:
     """Call the LLM and return the response, handling any invalid responses.
 
     Args:
@@ -307,16 +246,13 @@ def get_llm_response(
         messages: The messages to send to the LLM.
         callbacks: List of callbacks for the LLM call.
         printer: Printer instance for output.
-        tools: Optional list of tool schemas for native function calling.
-        available_functions: Optional dict mapping function names to callables.
         from_task: Optional task context for the LLM call.
         from_agent: Optional agent context for the LLM call.
         response_model: Optional Pydantic model for structured outputs.
         executor_context: Optional executor context for hook invocation.
 
     Returns:
-        The response from the LLM as a string, or tool call results if
-        native function calling is used.
+        The response from the LLM as a string.
 
     Raises:
         Exception: If an error occurs.
@@ -331,9 +267,7 @@ def get_llm_response(
     try:
         answer = llm.call(
             messages,
-            tools=tools,
             callbacks=callbacks,
-            available_functions=available_functions,
             from_task=from_task,
             from_agent=from_agent,  # type: ignore[arg-type]
             response_model=response_model,
@@ -355,13 +289,11 @@ async def aget_llm_response(
     messages: list[LLMMessage],
     callbacks: list[TokenCalcHandler],
     printer: Printer,
-    tools: list[dict[str, Any]] | None = None,
-    available_functions: dict[str, Callable[..., Any]] | None = None,
     from_task: Task | None = None,
     from_agent: Agent | LiteAgent | None = None,
     response_model: type[BaseModel] | None = None,
     executor_context: CrewAgentExecutor | None = None,
-) -> str | Any:
+) -> str:
     """Call the LLM asynchronously and return the response.
 
     Args:
@@ -369,16 +301,13 @@ async def aget_llm_response(
         messages: The messages to send to the LLM.
         callbacks: List of callbacks for the LLM call.
         printer: Printer instance for output.
-        tools: Optional list of tool schemas for native function calling.
-        available_functions: Optional dict mapping function names to callables.
         from_task: Optional task context for the LLM call.
         from_agent: Optional agent context for the LLM call.
         response_model: Optional Pydantic model for structured outputs.
         executor_context: Optional executor context for hook invocation.
 
     Returns:
-        The response from the LLM as a string, or tool call results if
-        native function calling is used.
+        The response from the LLM as a string.
 
     Raises:
         Exception: If an error occurs.
@@ -392,9 +321,7 @@ async def aget_llm_response(
     try:
         answer = await llm.acall(
             messages,
-            tools=tools,
             callbacks=callbacks,
-            available_functions=available_functions,
             from_task=from_task,
             from_agent=from_agent,  # type: ignore[arg-type]
             response_model=response_model,

lib/crewai/src/crewai/utilities/file_handler.py

@@ -2,8 +2,11 @@ from datetime import datetime
 import json
 import os
 import pickle
+import tempfile
+import threading
 from typing import Any, TypedDict
 
+import portalocker
 from typing_extensions import Unpack
 
 
@@ -123,10 +126,15 @@ class FileHandler:
 
 
 class PickleHandler:
-    """Handler for saving and loading data using pickle.
+    """Thread-safe handler for saving and loading data using pickle.
+
+    This class provides thread-safe file operations using portalocker for
+    cross-process file locking and atomic write operations to prevent
+    data corruption during concurrent access.
 
     Attributes:
         file_path: The path to the pickle file.
+        _lock: Threading lock for thread-safe operations within the same process.
     """
 
     def __init__(self, file_name: str) -> None:
@@ -141,34 +149,62 @@ class PickleHandler:
             file_name += ".pkl"
 
         self.file_path = os.path.join(os.getcwd(), file_name)
+        self._lock = threading.Lock()
 
     def initialize_file(self) -> None:
         """Initialize the file with an empty dictionary and overwrite any existing data."""
         self.save({})
 
     def save(self, data: Any) -> None:
-        """
-        Save the data to the specified file using pickle.
+        """Save the data to the specified file using pickle with thread-safe atomic writes.
+
+        This method uses a two-phase approach for thread safety:
+        1. Threading lock for same-process thread safety
+        2. Atomic write (write to temp file, then rename) for cross-process safety
+           and data integrity
 
         Args:
-          data: The data to be saved to the file.
+            data: The data to be saved to the file.
         """
-        with open(self.file_path, "wb") as f:
-            pickle.dump(obj=data, file=f)
+        with self._lock:
+            dir_name = os.path.dirname(self.file_path) or os.getcwd()
+            fd, temp_path = tempfile.mkstemp(
+                suffix=".pkl.tmp", prefix="pickle_", dir=dir_name
+            )
+            try:
+                with os.fdopen(fd, "wb") as f:
+                    pickle.dump(obj=data, file=f)
+                    f.flush()
+                    os.fsync(f.fileno())
+                os.replace(temp_path, self.file_path)
+            except Exception:
+                if os.path.exists(temp_path):
+                    os.unlink(temp_path)
+                raise
 
     def load(self) -> Any:
-        """Load the data from the specified file using pickle.
+        """Load the data from the specified file using pickle with thread-safe locking.
+
+        This method uses portalocker for cross-process read locking to ensure
+        data consistency when multiple processes may be accessing the file.
 
         Returns:
-            The data loaded from the file.
+            The data loaded from the file, or an empty dictionary if the file
+            does not exist or is empty.
         """
-        if not os.path.exists(self.file_path) or os.path.getsize(self.file_path) == 0:
-            return {}  # Return an empty dictionary if the file does not exist or is empty
+        with self._lock:
+            if (
+                not os.path.exists(self.file_path)
+                or os.path.getsize(self.file_path) == 0
+            ):
+                return {}
 
-        with open(self.file_path, "rb") as file:
-            try:
-                return pickle.load(file)  # noqa: S301
-            except EOFError:
-                return {}  # Return an empty dictionary if the file is empty or corrupted
-            except Exception:
-                raise  # Raise any other exceptions that occur during loading
+            with portalocker.Lock(
+                self.file_path, "rb", flags=portalocker.LOCK_SH
+            ) as file:
+                try:
+                    return pickle.load(file)  # noqa: S301
+                except EOFError:
+                    return {}
+                except Exception:
+                    raise

lib/crewai/src/crewai/utilities/prompts.py

@@ -22,9 +22,7 @@ class SystemPromptResult(StandardPromptResult):
     user: Annotated[str, "The user prompt component"]
 
 
-COMPONENTS = Literal[
-    "role_playing", "tools", "no_tools", "native_tools", "task", "native_task"
-]
+COMPONENTS = Literal["role_playing", "tools", "no_tools", "task"]
 
 
 class Prompts(BaseModel):
@@ -38,10 +36,6 @@ class Prompts(BaseModel):
     has_tools: bool = Field(
         default=False, description="Indicates if the agent has access to tools"
     )
-    use_native_tool_calling: bool = Field(
-        default=False,
-        description="Whether to use native function calling instead of ReAct format",
-    )
     system_template: str | None = Field(
         default=None, description="Custom system prompt template"
     )
@@ -64,24 +58,12 @@ class Prompts(BaseModel):
             A dictionary containing the constructed prompt(s).
         """
         slices: list[COMPONENTS] = ["role_playing"]
-        # When using native tool calling with tools, use native_tools instructions
-        # When using ReAct pattern with tools, use tools instructions
-        # When no tools are available, use no_tools instructions
         if self.has_tools:
-            if self.use_native_tool_calling:
-                slices.append("native_tools")
-            else:
-                slices.append("tools")
+            slices.append("tools")
         else:
             slices.append("no_tools")
         system: str = self._build_prompt(slices)
-
-        # Use native_task for native tool calling (no "Thought:" prompt)
-        # Use task for ReAct pattern (includes "Thought:" prompt)
-        task_slice: COMPONENTS = (
-            "native_task" if self.use_native_tool_calling else "task"
-        )
-        slices.append(task_slice)
+        slices.append("task")
 
         if (
             not self.system_template
@@ -90,7 +72,7 @@ class Prompts(BaseModel):
         ):
             return SystemPromptResult(
                 system=system,
-                user=self._build_prompt([task_slice]),
+                user=self._build_prompt(["task"]),
                 prompt=self._build_prompt(slices),
             )
         return StandardPromptResult(

lib/crewai/tests/a2a/utils/test_agent_card.py

@@ -1,325 +0,0 @@
-"""Tests for A2A agent card utilities."""
-
-from __future__ import annotations
-
-from a2a.types import AgentCard, AgentSkill
-
-from crewai import Agent
-from crewai.a2a.config import A2AClientConfig, A2AServerConfig
-from crewai.a2a.utils.agent_card import inject_a2a_server_methods
-
-
-class TestInjectA2AServerMethods:
-    """Tests for inject_a2a_server_methods function."""
-
-    def test_agent_with_server_config_gets_to_agent_card_method(self) -> None:
-        """Agent with A2AServerConfig should have to_agent_card method injected."""
-        agent = Agent(
-            role="Test Agent",
-            goal="Test goal",
-            backstory="Test backstory",
-            a2a=A2AServerConfig(),
-        )
-
-        assert hasattr(agent, "to_agent_card")
-        assert callable(agent.to_agent_card)
-
-    def test_agent_without_server_config_no_injection(self) -> None:
-        """Agent without A2AServerConfig should not get to_agent_card method."""
-        agent = Agent(
-            role="Test Agent",
-            goal="Test goal",
-            backstory="Test backstory",
-            a2a=A2AClientConfig(endpoint="http://example.com"),
-        )
-
-        assert not hasattr(agent, "to_agent_card")
-
-    def test_agent_without_a2a_no_injection(self) -> None:
-        """Agent without any a2a config should not get to_agent_card method."""
-        agent = Agent(
-            role="Test Agent",
-            goal="Test goal",
-            backstory="Test backstory",
-        )
-
-        assert not hasattr(agent, "to_agent_card")
-
-    def test_agent_with_mixed_configs_gets_injection(self) -> None:
-        """Agent with list containing A2AServerConfig should get to_agent_card."""
-        agent = Agent(
-            role="Test Agent",
-            goal="Test goal",
-            backstory="Test backstory",
-            a2a=[
-                A2AClientConfig(endpoint="http://example.com"),
-                A2AServerConfig(name="My Agent"),
-            ],
-        )
-
-        assert hasattr(agent, "to_agent_card")
-        assert callable(agent.to_agent_card)
-
-    def test_manual_injection_on_plain_agent(self) -> None:
-        """inject_a2a_server_methods should work when called manually."""
-        agent = Agent(
-            role="Test Agent",
-            goal="Test goal",
-            backstory="Test backstory",
-        )
-        # Manually set server config and inject
-        object.__setattr__(agent, "a2a", A2AServerConfig())
-        inject_a2a_server_methods(agent)
-
-        assert hasattr(agent, "to_agent_card")
-        assert callable(agent.to_agent_card)
-
-
-class TestToAgentCard:
-    """Tests for the injected to_agent_card method."""
-
-    def test_returns_agent_card(self) -> None:
-        """to_agent_card should return an AgentCard instance."""
-        agent = Agent(
-            role="Test Agent",
-            goal="Test goal",
-            backstory="Test backstory",
-            a2a=A2AServerConfig(),
-        )
-
-        card = agent.to_agent_card("http://localhost:8000")
-
-        assert isinstance(card, AgentCard)
-
-    def test_uses_agent_role_as_name(self) -> None:
-        """AgentCard name should default to agent role."""
-        agent = Agent(
-            role="Data Analyst",
-            goal="Analyze data",
-            backstory="Expert analyst",
-            a2a=A2AServerConfig(),
-        )
-
-        card = agent.to_agent_card("http://localhost:8000")
-
-        assert card.name == "Data Analyst"
-
-    def test_uses_server_config_name(self) -> None:
-        """AgentCard name should prefer A2AServerConfig.name over role."""
-        agent = Agent(
-            role="Data Analyst",
-            goal="Analyze data",
-            backstory="Expert analyst",
-            a2a=A2AServerConfig(name="Custom Agent Name"),
-        )
-
-        card = agent.to_agent_card("http://localhost:8000")
-
-        assert card.name == "Custom Agent Name"
-
-    def test_uses_goal_as_description(self) -> None:
-        """AgentCard description should include agent goal."""
-        agent = Agent(
-            role="Test Agent",
-            goal="Accomplish important tasks",
-            backstory="Has extensive experience",
-            a2a=A2AServerConfig(),
-        )
-
-        card = agent.to_agent_card("http://localhost:8000")
-
-        assert "Accomplish important tasks" in card.description
-
-    def test_uses_server_config_description(self) -> None:
-        """AgentCard description should prefer A2AServerConfig.description."""
-        agent = Agent(
-            role="Test Agent",
-            goal="Accomplish important tasks",
-            backstory="Has extensive experience",
-            a2a=A2AServerConfig(description="Custom description"),
-        )
-
-        card = agent.to_agent_card("http://localhost:8000")
-
-        assert card.description == "Custom description"
-
-    def test_uses_provided_url(self) -> None:
-        """AgentCard url should use the provided URL."""
-        agent = Agent(
-            role="Test Agent",
-            goal="Test goal",
-            backstory="Test backstory",
-            a2a=A2AServerConfig(),
-        )
-
-        card = agent.to_agent_card("http://my-server.com:9000")
-
-        assert card.url == "http://my-server.com:9000"
-
-    def test_uses_server_config_url(self) -> None:
-        """AgentCard url should prefer A2AServerConfig.url over provided URL."""
-        agent = Agent(
-            role="Test Agent",
-            goal="Test goal",
-            backstory="Test backstory",
-            a2a=A2AServerConfig(url="http://configured-url.com"),
-        )
-
-        card = agent.to_agent_card("http://fallback-url.com")
-
-        assert card.url == "http://configured-url.com/"
-
-    def test_generates_default_skill(self) -> None:
-        """AgentCard should have at least one skill based on agent role."""
-        agent = Agent(
-            role="Research Assistant",
-            goal="Help with research",
-            backstory="Skilled researcher",
-            a2a=A2AServerConfig(),
-        )
-
-        card = agent.to_agent_card("http://localhost:8000")
-
-        assert len(card.skills) >= 1
-        skill = card.skills[0]
-        assert skill.name == "Research Assistant"
-        assert skill.description == "Help with research"
-
-    def test_uses_server_config_skills(self) -> None:
-        """AgentCard skills should prefer A2AServerConfig.skills."""
-        custom_skill = AgentSkill(
-            id="custom-skill",
-            name="Custom Skill",
-            description="A custom skill",
-            tags=["custom"],
-        )
-        agent = Agent(
-            role="Test Agent",
-            goal="Test goal",
-            backstory="Test backstory",
-            a2a=A2AServerConfig(skills=[custom_skill]),
-        )
-
-        card = agent.to_agent_card("http://localhost:8000")
-
-        assert len(card.skills) == 1
-        assert card.skills[0].id == "custom-skill"
-        assert card.skills[0].name == "Custom Skill"
-
-    def test_includes_custom_version(self) -> None:
-        """AgentCard should include version from A2AServerConfig."""
-        agent = Agent(
-            role="Test Agent",
-            goal="Test goal",
-            backstory="Test backstory",
-            a2a=A2AServerConfig(version="2.0.0"),
-        )
-
-        card = agent.to_agent_card("http://localhost:8000")
-
-        assert card.version == "2.0.0"
-
-    def test_default_version(self) -> None:
-        """AgentCard should have default version 1.0.0."""
-        agent = Agent(
-            role="Test Agent",
-            goal="Test goal",
-            backstory="Test backstory",
-            a2a=A2AServerConfig(),
-        )
-
-        card = agent.to_agent_card("http://localhost:8000")
-
-        assert card.version == "1.0.0"
-
-
-class TestAgentCardJsonStructure:
-    """Tests for the JSON structure of AgentCard."""
-
-    def test_json_has_required_fields(self) -> None:
-        """AgentCard JSON should contain all required A2A protocol fields."""
-        agent = Agent(
-            role="Test Agent",
-            goal="Test goal",
-            backstory="Test backstory",
-            a2a=A2AServerConfig(),
-        )
-
-        card = agent.to_agent_card("http://localhost:8000")
-        json_data = card.model_dump()
-
-        assert "name" in json_data
-        assert "description" in json_data
-        assert "url" in json_data
-        assert "version" in json_data
-        assert "skills" in json_data
-        assert "capabilities" in json_data
-        assert "defaultInputModes" in json_data
-        assert "defaultOutputModes" in json_data
-
-    def test_json_skills_structure(self) -> None:
-        """Each skill in JSON should have required fields."""
-        agent = Agent(
-            role="Test Agent",
-            goal="Test goal",
-            backstory="Test backstory",
-            a2a=A2AServerConfig(),
-        )
-
-        card = agent.to_agent_card("http://localhost:8000")
-        json_data = card.model_dump()
-
-        assert len(json_data["skills"]) >= 1
-        skill = json_data["skills"][0]
-        assert "id" in skill
-        assert "name" in skill
-        assert "description" in skill
-        assert "tags" in skill
-
-    def test_json_capabilities_structure(self) -> None:
-        """Capabilities in JSON should have expected fields."""
-        agent = Agent(
-            role="Test Agent",
-            goal="Test goal",
-            backstory="Test backstory",
-            a2a=A2AServerConfig(),
-        )
-
-        card = agent.to_agent_card("http://localhost:8000")
-        json_data = card.model_dump()
-
-        capabilities = json_data["capabilities"]
-        assert "streaming" in capabilities
-        assert "pushNotifications" in capabilities
-
-    def test_json_serializable(self) -> None:
-        """AgentCard should be JSON serializable."""
-        agent = Agent(
-            role="Test Agent",
-            goal="Test goal",
-            backstory="Test backstory",
-            a2a=A2AServerConfig(),
-        )
-
-        card = agent.to_agent_card("http://localhost:8000")
-        json_str = card.model_dump_json()
-
-        assert isinstance(json_str, str)
-        assert "Test Agent" in json_str
-        assert "http://localhost:8000" in json_str
-
-    def test_json_excludes_none_values(self) -> None:
-        """AgentCard JSON with exclude_none should omit None fields."""
-        agent = Agent(
-            role="Test Agent",
-            goal="Test goal",
-            backstory="Test backstory",
-            a2a=A2AServerConfig(),
-        )
-
-        card = agent.to_agent_card("http://localhost:8000")
-        json_data = card.model_dump(exclude_none=True)
-
-        assert "provider" not in json_data
-        assert "documentationUrl" not in json_data
-        assert "iconUrl" not in json_data

lib/crewai/tests/a2a/utils/test_task.py

@@ -1,370 +0,0 @@
-"""Tests for A2A task utilities."""
-
-from __future__ import annotations
-
-import asyncio
-from typing import Any
-from unittest.mock import AsyncMock, MagicMock, patch
-
-import pytest
-import pytest_asyncio
-from a2a.server.agent_execution import RequestContext
-from a2a.server.events import EventQueue
-from a2a.types import Message, Task as A2ATask, TaskState, TaskStatus
-
-from crewai.a2a.utils.task import cancel, cancellable, execute
-
-
-@pytest.fixture
-def mock_agent() -> MagicMock:
-    """Create a mock CrewAI agent."""
-    agent = MagicMock()
-    agent.role = "Test Agent"
-    agent.tools = []
-    agent.aexecute_task = AsyncMock(return_value="Task completed successfully")
-    return agent
-
-
-@pytest.fixture
-def mock_task() -> MagicMock:
-    """Create a mock Task."""
-    return MagicMock()
-
-
-@pytest.fixture
-def mock_context() -> MagicMock:
-    """Create a mock RequestContext."""
-    context = MagicMock(spec=RequestContext)
-    context.task_id = "test-task-123"
-    context.context_id = "test-context-456"
-    context.get_user_input.return_value = "Test user message"
-    context.message = MagicMock(spec=Message)
-    context.current_task = None
-    return context
-
-
-@pytest.fixture
-def mock_event_queue() -> AsyncMock:
-    """Create a mock EventQueue."""
-    queue = AsyncMock(spec=EventQueue)
-    queue.enqueue_event = AsyncMock()
-    return queue
-
-
-@pytest_asyncio.fixture(autouse=True)
-async def clear_cache(mock_context: MagicMock) -> None:
-    """Clear cancel flag from cache before each test."""
-    from aiocache import caches
-
-    cache = caches.get("default")
-    await cache.delete(f"cancel:{mock_context.task_id}")
-
-
-class TestCancellableDecorator:
-    """Tests for the cancellable decorator."""
-
-    @pytest.mark.asyncio
-    async def test_executes_function_without_context(self) -> None:
-        """Function executes normally when no RequestContext is provided."""
-        call_count = 0
-
-        @cancellable
-        async def my_func(value: int) -> int:
-            nonlocal call_count
-            call_count += 1
-            return value * 2
-
-        result = await my_func(5)
-
-        assert result == 10
-        assert call_count == 1
-
-    @pytest.mark.asyncio
-    async def test_executes_function_with_context(self, mock_context: MagicMock) -> None:
-        """Function executes normally with RequestContext when not cancelled."""
-        @cancellable
-        async def my_func(context: RequestContext) -> str:
-            await asyncio.sleep(0.01)
-            return "completed"
-
-        result = await my_func(mock_context)
-
-        assert result == "completed"
-
-    @pytest.mark.asyncio
-    async def test_cancellation_raises_cancelled_error(
-        self, mock_context: MagicMock
-    ) -> None:
-        """Function raises CancelledError when cancel flag is set."""
-        from aiocache import caches
-
-        cache = caches.get("default")
-
-        @cancellable
-        async def slow_func(context: RequestContext) -> str:
-            await asyncio.sleep(1.0)
-            return "should not reach"
-
-        await cache.set(f"cancel:{mock_context.task_id}", True)
-
-        with pytest.raises(asyncio.CancelledError):
-            await slow_func(mock_context)
-
-    @pytest.mark.asyncio
-    async def test_cleanup_removes_cancel_flag(self, mock_context: MagicMock) -> None:
-        """Cancel flag is cleaned up after execution."""
-        from aiocache import caches
-
-        cache = caches.get("default")
-
-        @cancellable
-        async def quick_func(context: RequestContext) -> str:
-            return "done"
-
-        await quick_func(mock_context)
-
-        flag = await cache.get(f"cancel:{mock_context.task_id}")
-        assert flag is None
-
-    @pytest.mark.asyncio
-    async def test_extracts_context_from_kwargs(self, mock_context: MagicMock) -> None:
-        """Context can be passed as keyword argument."""
-        @cancellable
-        async def my_func(value: int, context: RequestContext | None = None) -> int:
-            return value + 1
-
-        result = await my_func(10, context=mock_context)
-
-        assert result == 11
-
-
-class TestExecute:
-    """Tests for the execute function."""
-
-    @pytest.mark.asyncio
-    async def test_successful_execution(
-        self,
-        mock_agent: MagicMock,
-        mock_context: MagicMock,
-        mock_event_queue: AsyncMock,
-        mock_task: MagicMock,
-    ) -> None:
-        """Execute completes successfully and enqueues completed task."""
-        with (
-            patch("crewai.a2a.utils.task.Task", return_value=mock_task),
-            patch("crewai.a2a.utils.task.crewai_event_bus") as mock_bus,
-        ):
-            await execute(mock_agent, mock_context, mock_event_queue)
-
-        mock_agent.aexecute_task.assert_called_once()
-        mock_event_queue.enqueue_event.assert_called_once()
-        assert mock_bus.emit.call_count == 2
-
-    @pytest.mark.asyncio
-    async def test_emits_started_event(
-        self,
-        mock_agent: MagicMock,
-        mock_context: MagicMock,
-        mock_event_queue: AsyncMock,
-        mock_task: MagicMock,
-    ) -> None:
-        """Execute emits A2AServerTaskStartedEvent."""
-        with (
-            patch("crewai.a2a.utils.task.Task", return_value=mock_task),
-            patch("crewai.a2a.utils.task.crewai_event_bus") as mock_bus,
-        ):
-            await execute(mock_agent, mock_context, mock_event_queue)
-
-        first_call = mock_bus.emit.call_args_list[0]
-        event = first_call[0][1]
-
-        assert event.type == "a2a_server_task_started"
-        assert event.a2a_task_id == mock_context.task_id
-        assert event.a2a_context_id == mock_context.context_id
-
-    @pytest.mark.asyncio
-    async def test_emits_completed_event(
-        self,
-        mock_agent: MagicMock,
-        mock_context: MagicMock,
-        mock_event_queue: AsyncMock,
-        mock_task: MagicMock,
-    ) -> None:
-        """Execute emits A2AServerTaskCompletedEvent on success."""
-        with (
-            patch("crewai.a2a.utils.task.Task", return_value=mock_task),
-            patch("crewai.a2a.utils.task.crewai_event_bus") as mock_bus,
-        ):
-            await execute(mock_agent, mock_context, mock_event_queue)
-
-        second_call = mock_bus.emit.call_args_list[1]
-        event = second_call[0][1]
-
-        assert event.type == "a2a_server_task_completed"
-        assert event.a2a_task_id == mock_context.task_id
-        assert event.result == "Task completed successfully"
-
-    @pytest.mark.asyncio
-    async def test_emits_failed_event_on_exception(
-        self,
-        mock_agent: MagicMock,
-        mock_context: MagicMock,
-        mock_event_queue: AsyncMock,
-        mock_task: MagicMock,
-    ) -> None:
-        """Execute emits A2AServerTaskFailedEvent on exception."""
-        mock_agent.aexecute_task = AsyncMock(side_effect=ValueError("Test error"))
-
-        with (
-            patch("crewai.a2a.utils.task.Task", return_value=mock_task),
-            patch("crewai.a2a.utils.task.crewai_event_bus") as mock_bus,
-        ):
-            with pytest.raises(Exception):
-                await execute(mock_agent, mock_context, mock_event_queue)
-
-        failed_call = mock_bus.emit.call_args_list[1]
-        event = failed_call[0][1]
-
-        assert event.type == "a2a_server_task_failed"
-        assert "Test error" in event.error
-
-    @pytest.mark.asyncio
-    async def test_emits_canceled_event_on_cancellation(
-        self,
-        mock_agent: MagicMock,
-        mock_context: MagicMock,
-        mock_event_queue: AsyncMock,
-        mock_task: MagicMock,
-    ) -> None:
-        """Execute emits A2AServerTaskCanceledEvent on CancelledError."""
-        mock_agent.aexecute_task = AsyncMock(side_effect=asyncio.CancelledError())
-
-        with (
-            patch("crewai.a2a.utils.task.Task", return_value=mock_task),
-            patch("crewai.a2a.utils.task.crewai_event_bus") as mock_bus,
-        ):
-            with pytest.raises(asyncio.CancelledError):
-                await execute(mock_agent, mock_context, mock_event_queue)
-
-        canceled_call = mock_bus.emit.call_args_list[1]
-        event = canceled_call[0][1]
-
-        assert event.type == "a2a_server_task_canceled"
-        assert event.a2a_task_id == mock_context.task_id
-
-
-class TestCancel:
-    """Tests for the cancel function."""
-
-    @pytest.mark.asyncio
-    async def test_sets_cancel_flag_in_cache(
-        self,
-        mock_context: MagicMock,
-        mock_event_queue: AsyncMock,
-    ) -> None:
-        """Cancel sets the cancel flag in cache."""
-        from aiocache import caches
-
-        cache = caches.get("default")
-
-        await cancel(mock_context, mock_event_queue)
-
-        flag = await cache.get(f"cancel:{mock_context.task_id}")
-        assert flag is True
-
-    @pytest.mark.asyncio
-    async def test_enqueues_task_status_update_event(
-        self,
-        mock_context: MagicMock,
-        mock_event_queue: AsyncMock,
-    ) -> None:
-        """Cancel enqueues TaskStatusUpdateEvent with canceled state."""
-        await cancel(mock_context, mock_event_queue)
-
-        mock_event_queue.enqueue_event.assert_called_once()
-        event = mock_event_queue.enqueue_event.call_args[0][0]
-
-        assert event.task_id == mock_context.task_id
-        assert event.context_id == mock_context.context_id
-        assert event.status.state == TaskState.canceled
-        assert event.final is True
-
-    @pytest.mark.asyncio
-    async def test_returns_none_when_no_current_task(
-        self,
-        mock_context: MagicMock,
-        mock_event_queue: AsyncMock,
-    ) -> None:
-        """Cancel returns None when context has no current_task."""
-        mock_context.current_task = None
-
-        result = await cancel(mock_context, mock_event_queue)
-
-        assert result is None
-
-    @pytest.mark.asyncio
-    async def test_returns_updated_task_when_current_task_exists(
-        self,
-        mock_context: MagicMock,
-        mock_event_queue: AsyncMock,
-    ) -> None:
-        """Cancel returns updated task when context has current_task."""
-        current_task = MagicMock(spec=A2ATask)
-        current_task.status = TaskStatus(state=TaskState.working)
-        mock_context.current_task = current_task
-
-        result = await cancel(mock_context, mock_event_queue)
-
-        assert result is current_task
-        assert result.status.state == TaskState.canceled
-
-    @pytest.mark.asyncio
-    async def test_cleanup_after_cancel(
-        self,
-        mock_context: MagicMock,
-        mock_event_queue: AsyncMock,
-    ) -> None:
-        """Cancel flag persists for cancellable decorator to detect."""
-        from aiocache import caches
-
-        cache = caches.get("default")
-
-        await cancel(mock_context, mock_event_queue)
-
-        flag = await cache.get(f"cancel:{mock_context.task_id}")
-        assert flag is True
-
-        await cache.delete(f"cancel:{mock_context.task_id}")
-
-
-class TestExecuteAndCancelIntegration:
-    """Integration tests for execute and cancel working together."""
-
-    @pytest.mark.asyncio
-    async def test_cancel_stops_running_execute(
-        self,
-        mock_agent: MagicMock,
-        mock_context: MagicMock,
-        mock_event_queue: AsyncMock,
-        mock_task: MagicMock,
-    ) -> None:
-        """Calling cancel stops a running execute."""
-        async def slow_task(**kwargs: Any) -> str:
-            await asyncio.sleep(2.0)
-            return "should not complete"
-
-        mock_agent.aexecute_task = slow_task
-
-        with (
-            patch("crewai.a2a.utils.task.Task", return_value=mock_task),
-            patch("crewai.a2a.utils.task.crewai_event_bus"),
-        ):
-            execute_task = asyncio.create_task(
-                execute(mock_agent, mock_context, mock_event_queue)
-            )
-
-            await asyncio.sleep(0.1)
-            await cancel(mock_context, mock_event_queue)
-
-            with pytest.raises(asyncio.CancelledError):
-                await execute_task

lib/crewai/tests/agents/test_native_tool_calling.py

@@ -1,479 +0,0 @@
-"""Integration tests for native tool calling functionality.
-
-These tests verify that agents can use native function calling
-when the LLM supports it, across multiple providers.
-"""
-
-from __future__ import annotations
-
-import os
-from typing import Any
-from unittest.mock import patch, MagicMock
-
-import pytest
-from pydantic import BaseModel, Field
-
-from crewai import Agent, Crew, Task
-from crewai.llm import LLM
-from crewai.tools.base_tool import BaseTool
-
-
-# Check for optional provider availability
-try:
-    import anthropic
-    HAS_ANTHROPIC = True
-except ImportError:
-    HAS_ANTHROPIC = False
-
-try:
-    import google.genai
-    HAS_GOOGLE_GENAI = True
-except ImportError:
-    HAS_GOOGLE_GENAI = False
-
-try:
-    import boto3
-    HAS_BOTO3 = True
-except ImportError:
-    HAS_BOTO3 = False
-
-
-class CalculatorInput(BaseModel):
-    """Input schema for calculator tool."""
-
-    expression: str = Field(description="Mathematical expression to evaluate")
-
-
-class CalculatorTool(BaseTool):
-    """A calculator tool that performs mathematical calculations."""
-
-    name: str = "calculator"
-    description: str = "Perform mathematical calculations. Use this for any math operations."
-    args_schema: type[BaseModel] = CalculatorInput
-
-    def _run(self, expression: str) -> str:
-        """Execute the calculation."""
-        try:
-            # Safe evaluation for basic math
-            result = eval(expression)  # noqa: S307
-            return f"The result of {expression} is {result}"
-        except Exception as e:
-            return f"Error calculating {expression}: {e}"
-
-
-class WeatherInput(BaseModel):
-    """Input schema for weather tool."""
-
-    location: str = Field(description="City name to get weather for")
-
-
-class WeatherTool(BaseTool):
-    """A mock weather tool for testing."""
-
-    name: str = "get_weather"
-    description: str = "Get the current weather for a location"
-    args_schema: type[BaseModel] = WeatherInput
-
-    def _run(self, location: str) -> str:
-        """Get weather (mock implementation)."""
-        return f"The weather in {location} is sunny with a temperature of 72°F"
-
-
-@pytest.fixture
-def calculator_tool() -> CalculatorTool:
-    """Create a calculator tool for testing."""
-    return CalculatorTool()
-
-
-@pytest.fixture
-def weather_tool() -> WeatherTool:
-    """Create a weather tool for testing."""
-    return WeatherTool()
-
-
-# =============================================================================
-# OpenAI Provider Tests
-# =============================================================================
-
-
-class TestOpenAINativeToolCalling:
-    """Tests for native tool calling with OpenAI models."""
-
-    @pytest.mark.vcr()
-    def test_openai_agent_with_native_tool_calling(
-        self, calculator_tool: CalculatorTool
-    ) -> None:
-        """Test OpenAI agent can use native tool calling."""
-        agent = Agent(
-            role="Math Assistant",
-            goal="Help users with mathematical calculations",
-            backstory="You are a helpful math assistant.",
-            tools=[calculator_tool],
-            llm=LLM(model="gpt-4o-mini"),
-            verbose=False,
-            max_iter=3,
-        )
-
-        task = Task(
-            description="Calculate what is 15 * 8",
-            expected_output="The result of the calculation",
-            agent=agent,
-        )
-
-        crew = Crew(agents=[agent], tasks=[task])
-        result = crew.kickoff()
-
-        assert result is not None
-        assert result.raw is not None
-        assert "120" in str(result.raw)
-
-    def test_openai_agent_kickoff_with_tools_mocked(
-        self, calculator_tool: CalculatorTool
-    ) -> None:
-        """Test OpenAI agent kickoff with mocked LLM call."""
-        llm = LLM(model="gpt-4o-mini")
-
-        with patch.object(llm, "call", return_value="The answer is 120.") as mock_call:
-            agent = Agent(
-                role="Math Assistant",
-                goal="Calculate math",
-                backstory="You calculate.",
-                tools=[calculator_tool],
-                llm=llm,
-                verbose=False,
-            )
-
-            task = Task(
-                description="Calculate 15 * 8",
-                expected_output="Result",
-                agent=agent,
-            )
-
-            crew = Crew(agents=[agent], tasks=[task])
-            result = crew.kickoff()
-
-            assert mock_call.called
-            assert result is not None
-
-
-# =============================================================================
-# Anthropic Provider Tests
-# =============================================================================
-
-
-@pytest.mark.skipif(not HAS_ANTHROPIC, reason="anthropic package not installed")
-class TestAnthropicNativeToolCalling:
-    """Tests for native tool calling with Anthropic models."""
-
-    @pytest.fixture(autouse=True)
-    def mock_anthropic_api_key(self):
-        """Mock ANTHROPIC_API_KEY for tests."""
-        if "ANTHROPIC_API_KEY" not in os.environ:
-            with patch.dict(os.environ, {"ANTHROPIC_API_KEY": "test-key"}):
-                yield
-        else:
-            yield
-
-    @pytest.mark.vcr()
-    def test_anthropic_agent_with_native_tool_calling(
-        self, calculator_tool: CalculatorTool
-    ) -> None:
-        """Test Anthropic agent can use native tool calling."""
-        agent = Agent(
-            role="Math Assistant",
-            goal="Help users with mathematical calculations",
-            backstory="You are a helpful math assistant.",
-            tools=[calculator_tool],
-            llm=LLM(model="anthropic/claude-3-5-haiku-20241022"),
-            verbose=False,
-            max_iter=3,
-        )
-
-        task = Task(
-            description="Calculate what is 15 * 8",
-            expected_output="The result of the calculation",
-            agent=agent,
-        )
-
-        crew = Crew(agents=[agent], tasks=[task])
-        result = crew.kickoff()
-
-        assert result is not None
-        assert result.raw is not None
-
-    def test_anthropic_agent_kickoff_with_tools_mocked(
-        self, calculator_tool: CalculatorTool
-    ) -> None:
-        """Test Anthropic agent kickoff with mocked LLM call."""
-        llm = LLM(model="anthropic/claude-3-5-haiku-20241022")
-
-        with patch.object(llm, "call", return_value="The answer is 120.") as mock_call:
-            agent = Agent(
-                role="Math Assistant",
-                goal="Calculate math",
-                backstory="You calculate.",
-                tools=[calculator_tool],
-                llm=llm,
-                verbose=False,
-            )
-
-            task = Task(
-                description="Calculate 15 * 8",
-                expected_output="Result",
-                agent=agent,
-            )
-
-            crew = Crew(agents=[agent], tasks=[task])
-            result = crew.kickoff()
-
-            assert mock_call.called
-            assert result is not None
-
-
-# =============================================================================
-# Google/Gemini Provider Tests
-# =============================================================================
-
-
-@pytest.mark.skipif(not HAS_GOOGLE_GENAI, reason="google-genai package not installed")
-class TestGeminiNativeToolCalling:
-    """Tests for native tool calling with Gemini models."""
-
-    @pytest.fixture(autouse=True)
-    def mock_google_api_key(self):
-        """Mock GOOGLE_API_KEY for tests."""
-        with patch.dict(os.environ, {"GOOGLE_API_KEY": "test-key"}):
-            yield
-
-    @pytest.mark.vcr()
-    def test_gemini_agent_with_native_tool_calling(
-        self, calculator_tool: CalculatorTool
-    ) -> None:
-        """Test Gemini agent can use native tool calling."""
-        agent = Agent(
-            role="Math Assistant",
-            goal="Help users with mathematical calculations",
-            backstory="You are a helpful math assistant.",
-            tools=[calculator_tool],
-            llm=LLM(model="gemini/gemini-2.0-flash-001"),
-            verbose=False,
-            max_iter=3,
-        )
-
-        task = Task(
-            description="Calculate what is 15 * 8",
-            expected_output="The result of the calculation",
-            agent=agent,
-        )
-
-        crew = Crew(agents=[agent], tasks=[task])
-        result = crew.kickoff()
-
-        assert result is not None
-        assert result.raw is not None
-
-    def test_gemini_agent_kickoff_with_tools_mocked(
-        self, calculator_tool: CalculatorTool
-    ) -> None:
-        """Test Gemini agent kickoff with mocked LLM call."""
-        llm = LLM(model="gemini/gemini-2.0-flash-001")
-
-        with patch.object(llm, "call", return_value="The answer is 120.") as mock_call:
-            agent = Agent(
-                role="Math Assistant",
-                goal="Calculate math",
-                backstory="You calculate.",
-                tools=[calculator_tool],
-                llm=llm,
-                verbose=False,
-            )
-
-            task = Task(
-                description="Calculate 15 * 8",
-                expected_output="Result",
-                agent=agent,
-            )
-
-            crew = Crew(agents=[agent], tasks=[task])
-            result = crew.kickoff()
-
-            assert mock_call.called
-            assert result is not None
-
-
-# =============================================================================
-# Azure Provider Tests
-# =============================================================================
-
-
-class TestAzureNativeToolCalling:
-    """Tests for native tool calling with Azure OpenAI models."""
-
-    @pytest.fixture(autouse=True)
-    def mock_azure_env(self):
-        """Mock Azure environment variables for tests."""
-        env_vars = {
-            "AZURE_API_KEY": "test-key",
-            "AZURE_API_BASE": "https://test.openai.azure.com",
-            "AZURE_API_VERSION": "2024-02-15-preview",
-        }
-        with patch.dict(os.environ, env_vars):
-            yield
-
-    def test_azure_agent_kickoff_with_tools_mocked(
-        self, calculator_tool: CalculatorTool
-    ) -> None:
-        """Test Azure agent kickoff with mocked LLM call."""
-        llm = LLM(
-            model="azure/gpt-4o-mini",
-            api_key="test-key",
-            base_url="https://test.openai.azure.com",
-        )
-
-        with patch.object(llm, "call", return_value="The answer is 120.") as mock_call:
-            agent = Agent(
-                role="Math Assistant",
-                goal="Calculate math",
-                backstory="You calculate.",
-                tools=[calculator_tool],
-                llm=llm,
-                verbose=False,
-            )
-
-            task = Task(
-                description="Calculate 15 * 8",
-                expected_output="Result",
-                agent=agent,
-            )
-
-            crew = Crew(agents=[agent], tasks=[task])
-            result = crew.kickoff()
-
-            assert mock_call.called
-            assert result is not None
-
-
-# =============================================================================
-# Bedrock Provider Tests
-# =============================================================================
-
-
-@pytest.mark.skipif(not HAS_BOTO3, reason="boto3 package not installed")
-class TestBedrockNativeToolCalling:
-    """Tests for native tool calling with AWS Bedrock models."""
-
-    @pytest.fixture(autouse=True)
-    def mock_aws_env(self):
-        """Mock AWS environment variables for tests."""
-        env_vars = {
-            "AWS_ACCESS_KEY_ID": "test-key",
-            "AWS_SECRET_ACCESS_KEY": "test-secret",
-            "AWS_REGION": "us-east-1",
-        }
-        with patch.dict(os.environ, env_vars):
-            yield
-
-    def test_bedrock_agent_kickoff_with_tools_mocked(
-        self, calculator_tool: CalculatorTool
-    ) -> None:
-        """Test Bedrock agent kickoff with mocked LLM call."""
-        llm = LLM(model="bedrock/anthropic.claude-3-haiku-20240307-v1:0")
-
-        with patch.object(llm, "call", return_value="The answer is 120.") as mock_call:
-            agent = Agent(
-                role="Math Assistant",
-                goal="Calculate math",
-                backstory="You calculate.",
-                tools=[calculator_tool],
-                llm=llm,
-                verbose=False,
-            )
-
-            task = Task(
-                description="Calculate 15 * 8",
-                expected_output="Result",
-                agent=agent,
-            )
-
-            crew = Crew(agents=[agent], tasks=[task])
-            result = crew.kickoff()
-
-            assert mock_call.called
-            assert result is not None
-
-
-# =============================================================================
-# Cross-Provider Native Tool Calling Behavior Tests
-# =============================================================================
-
-
-class TestNativeToolCallingBehavior:
-    """Tests for native tool calling behavior across providers."""
-
-    def test_supports_function_calling_check(self) -> None:
-        """Test that supports_function_calling() is properly checked."""
-        # OpenAI should support function calling
-        openai_llm = LLM(model="gpt-4o-mini")
-        assert hasattr(openai_llm, "supports_function_calling")
-        assert openai_llm.supports_function_calling() is True
-
-    @pytest.mark.skipif(not HAS_ANTHROPIC, reason="anthropic package not installed")
-    def test_anthropic_supports_function_calling(self) -> None:
-        """Test that Anthropic models support function calling."""
-        with patch.dict(os.environ, {"ANTHROPIC_API_KEY": "test-key"}):
-            llm = LLM(model="anthropic/claude-3-5-haiku-20241022")
-            assert hasattr(llm, "supports_function_calling")
-            assert llm.supports_function_calling() is True
-
-    @pytest.mark.skipif(not HAS_GOOGLE_GENAI, reason="google-genai package not installed")
-    def test_gemini_supports_function_calling(self) -> None:
-        """Test that Gemini models support function calling."""
-        # with patch.dict(os.environ, {"GOOGLE_API_KEY": "test-key"}):
-        print("GOOGLE_API_KEY", os.getenv("GOOGLE_API_KEY"))
-        llm = LLM(model="gemini/gemini-2.5-flash")
-        assert hasattr(llm, "supports_function_calling")
-        # Gemini uses supports_tools property
-        assert llm.supports_function_calling() is True
-
-
-# =============================================================================
-# Token Usage Tests
-# =============================================================================
-
-
-class TestNativeToolCallingTokenUsage:
-    """Tests for token usage with native tool calling."""
-
-    @pytest.mark.vcr()
-    def test_openai_native_tool_calling_token_usage(
-        self, calculator_tool: CalculatorTool
-    ) -> None:
-        """Test token usage tracking with OpenAI native tool calling."""
-        agent = Agent(
-            role="Calculator",
-            goal="Perform calculations efficiently",
-            backstory="You calculate things.",
-            tools=[calculator_tool],
-            llm=LLM(model="gpt-4o-mini"),
-            verbose=False,
-            max_iter=3,
-        )
-
-        task = Task(
-            description="What is 100 / 4?",
-            expected_output="The result",
-            agent=agent,
-        )
-
-        crew = Crew(agents=[agent], tasks=[task])
-        result = crew.kickoff()
-
-        assert result is not None
-        assert result.token_usage is not None
-        assert result.token_usage.total_tokens > 0
-        assert result.token_usage.successful_requests >= 1
-
-        print(f"\n[OPENAI NATIVE TOOL CALLING TOKEN USAGE]")
-        print(f"  Prompt tokens: {result.token_usage.prompt_tokens}")
-        print(f"  Completion tokens: {result.token_usage.completion_tokens}")
-        print(f"  Total tokens: {result.token_usage.total_tokens}")

lib/crewai/tests/cli/authentication/providers/test_keycloak.py

@@ -1,138 +0,0 @@
-import pytest
-
-from crewai.cli.authentication.main import Oauth2Settings
-from crewai.cli.authentication.providers.keycloak import KeycloakProvider
-
-
-class TestKeycloakProvider:
-    @pytest.fixture(autouse=True)
-    def setup_method(self):
-        self.valid_settings = Oauth2Settings(
-            provider="keycloak",
-            domain="keycloak.example.com",
-            client_id="test-client-id",
-            audience="test-audience",
-            extra={
-                "realm": "test-realm"
-            }
-        )
-        self.provider = KeycloakProvider(self.valid_settings)
-
-    def test_initialization_with_valid_settings(self):
-        provider = KeycloakProvider(self.valid_settings)
-        assert provider.settings == self.valid_settings
-        assert provider.settings.provider == "keycloak"
-        assert provider.settings.domain == "keycloak.example.com"
-        assert provider.settings.client_id == "test-client-id"
-        assert provider.settings.audience == "test-audience"
-        assert provider.settings.extra.get("realm") == "test-realm"
-
-    def test_get_authorize_url(self):
-        expected_url = "https://keycloak.example.com/realms/test-realm/protocol/openid-connect/auth/device"
-        assert self.provider.get_authorize_url() == expected_url
-
-    def test_get_authorize_url_with_different_domain(self):
-        settings = Oauth2Settings(
-            provider="keycloak",
-            domain="auth.company.com",
-            client_id="test-client",
-            audience="test-audience",
-            extra={
-                "realm": "my-realm"
-            }
-        )
-        provider = KeycloakProvider(settings)
-        expected_url = "https://auth.company.com/realms/my-realm/protocol/openid-connect/auth/device"
-        assert provider.get_authorize_url() == expected_url
-
-    def test_get_token_url(self):
-        expected_url = "https://keycloak.example.com/realms/test-realm/protocol/openid-connect/token"
-        assert self.provider.get_token_url() == expected_url
-
-    def test_get_token_url_with_different_domain(self):
-        settings = Oauth2Settings(
-            provider="keycloak",
-            domain="sso.enterprise.com",
-            client_id="test-client",
-            audience="test-audience",
-            extra={
-                "realm": "enterprise-realm"
-            }
-        )
-        provider = KeycloakProvider(settings)
-        expected_url = "https://sso.enterprise.com/realms/enterprise-realm/protocol/openid-connect/token"
-        assert provider.get_token_url() == expected_url
-
-    def test_get_jwks_url(self):
-        expected_url = "https://keycloak.example.com/realms/test-realm/protocol/openid-connect/certs"
-        assert self.provider.get_jwks_url() == expected_url
-
-    def test_get_jwks_url_with_different_domain(self):
-        settings = Oauth2Settings(
-            provider="keycloak",
-            domain="identity.org",
-            client_id="test-client",
-            audience="test-audience",
-            extra={
-                "realm": "org-realm"
-            }
-        )
-        provider = KeycloakProvider(settings)
-        expected_url = "https://identity.org/realms/org-realm/protocol/openid-connect/certs"
-        assert provider.get_jwks_url() == expected_url
-
-    def test_get_issuer(self):
-        expected_issuer = "https://keycloak.example.com/realms/test-realm"
-        assert self.provider.get_issuer() == expected_issuer
-
-    def test_get_issuer_with_different_domain(self):
-        settings = Oauth2Settings(
-            provider="keycloak",
-            domain="login.myapp.io",
-            client_id="test-client",
-            audience="test-audience",
-            extra={
-                "realm": "app-realm"
-            }
-        )
-        provider = KeycloakProvider(settings)
-        expected_issuer = "https://login.myapp.io/realms/app-realm"
-        assert provider.get_issuer() == expected_issuer
-
-    def test_get_audience(self):
-        assert self.provider.get_audience() == "test-audience"
-
-    def test_get_client_id(self):
-        assert self.provider.get_client_id() == "test-client-id"
-
-    def test_get_required_fields(self):
-        assert self.provider.get_required_fields() == ["realm"]
-
-    def test_oauth2_base_url(self):
-        assert self.provider._oauth2_base_url() == "https://keycloak.example.com"
-
-    def test_oauth2_base_url_strips_https_prefix(self):
-        settings = Oauth2Settings(
-            provider="keycloak",
-            domain="https://keycloak.example.com",
-            client_id="test-client-id",
-            audience="test-audience",
-            extra={
-                "realm": "test-realm"
-            }
-        )
-        provider = KeycloakProvider(settings)
-        assert provider._oauth2_base_url() == "https://keycloak.example.com"
-
-    def test_oauth2_base_url_strips_http_prefix(self):
-        settings = Oauth2Settings(
-            provider="keycloak",
-            domain="http://keycloak.example.com",
-            client_id="test-client-id",
-            audience="test-audience",
-            extra={
-                "realm": "test-realm"
-            }
-        )
-        provider = KeycloakProvider(settings)
-        assert provider._oauth2_base_url() == "https://keycloak.example.com"

lib/crewai/tests/llms/azure/test_azure.py

@@ -515,94 +515,6 @@ def test_azure_supports_stop_words():
     assert llm.supports_stop_words() == True
 
 
-def test_azure_gpt5_models_do_not_support_stop_words():
-    """
-    Test that GPT-5 family models do not support stop words.
-    GPT-5 models use the Responses API which doesn't support stop sequences.
-    See: https://learn.microsoft.com/en-us/azure/ai-foundry/foundry-models/concepts/models-sold-directly-by-azure
-    """
-    # GPT-5 base models
-    gpt5_models = [
-        "azure/gpt-5",
-        "azure/gpt-5-mini",
-        "azure/gpt-5-nano",
-        "azure/gpt-5-chat",
-        # GPT-5.1 series
-        "azure/gpt-5.1",
-        "azure/gpt-5.1-chat",
-        "azure/gpt-5.1-codex",
-        "azure/gpt-5.1-codex-mini",
-        # GPT-5.2 series
-        "azure/gpt-5.2",
-        "azure/gpt-5.2-chat",
-    ]
-
-    for model_name in gpt5_models:
-        llm = LLM(model=model_name)
-        assert llm.supports_stop_words() == False, f"Expected {model_name} to NOT support stop words"
-
-
-def test_azure_o_series_models_do_not_support_stop_words():
-    """
-    Test that o-series reasoning models do not support stop words.
-    """
-    o_series_models = [
-        "azure/o1",
-        "azure/o1-mini",
-        "azure/o3",
-        "azure/o3-mini",
-        "azure/o4",
-        "azure/o4-mini",
-    ]
-
-    for model_name in o_series_models:
-        llm = LLM(model=model_name)
-        assert llm.supports_stop_words() == False, f"Expected {model_name} to NOT support stop words"
-
-
-def test_azure_responses_api_models_do_not_support_stop_words():
-    """
-    Test that models using the Responses API do not support stop words.
-    """
-    responses_api_models = [
-        "azure/computer-use-preview",
-    ]
-
-    for model_name in responses_api_models:
-        llm = LLM(model=model_name)
-        assert llm.supports_stop_words() == False, f"Expected {model_name} to NOT support stop words"
-
-
-def test_azure_stop_words_not_included_for_unsupported_models():
-    """
-    Test that stop words are not included in completion params for models that don't support them.
-    """
-    with patch.dict(os.environ, {
-        "AZURE_API_KEY": "test-key",
-        "AZURE_ENDPOINT": "https://models.inference.ai.azure.com"
-    }):
-        # Test GPT-5 model - stop should NOT be included even if set
-        llm_gpt5 = LLM(
-            model="azure/gpt-5-nano",
-            stop=["STOP", "END"]
-        )
-        params = llm_gpt5._prepare_completion_params(
-            messages=[{"role": "user", "content": "test"}]
-        )
-        assert "stop" not in params, "stop should not be included for GPT-5 models"
-
-        # Test regular model - stop SHOULD be included
-        llm_gpt4 = LLM(
-            model="azure/gpt-4",
-            stop=["STOP", "END"]
-        )
-        params = llm_gpt4._prepare_completion_params(
-            messages=[{"role": "user", "content": "test"}]
-        )
-        assert "stop" in params, "stop should be included for GPT-4 models"
-        assert params["stop"] == ["STOP", "END"]
-
-
 def test_azure_context_window_size():
     """
     Test that Azure models return correct context window sizes

lib/crewai/tests/test_crew.py

@@ -4500,71 +4500,6 @@ def test_crew_copy_with_memory():
         pytest.fail(f"Copying crew raised an unexpected exception: {e}")
 
 
-def test_sets_parent_flow_when_using_crewbase_pattern_inside_flow():
-    @CrewBase
-    class TestCrew:
-        agents_config = None
-        tasks_config = None
-
-        agents: list[BaseAgent]
-        tasks: list[Task]
-
-        @agent
-        def researcher(self) -> Agent:
-            return Agent(
-                role="Researcher",
-                goal="Research things",
-                backstory="Expert researcher",
-            )
-
-        @agent
-        def writer_agent(self) -> Agent:
-            return Agent(
-                role="Writer",
-                goal="Write things",
-                backstory="Expert writer",
-            )
-
-        @task
-        def research_task(self) -> Task:
-            return Task(
-                description="Test task for researcher",
-                expected_output="output",
-                agent=self.researcher(),
-            )
-
-        @task
-        def write_task(self) -> Task:
-            return Task(
-                description="Test task for writer",
-                expected_output="output",
-                agent=self.writer_agent(),
-            )
-
-        @crew
-        def crew(self) -> Crew:
-            return Crew(
-                agents=self.agents,
-                tasks=self.tasks,
-                process=Process.sequential,
-            )
-
-    captured_crew = None
-
-    class MyFlow(Flow):
-        @start()
-        def start_method(self):
-            nonlocal captured_crew
-            captured_crew = TestCrew().crew()
-            return captured_crew
-
-    flow = MyFlow()
-    flow.kickoff()
-
-    assert captured_crew is not None
-    assert captured_crew.parent_flow is flow
-
-
 def test_sets_parent_flow_when_outside_flow(researcher, writer):
     crew = Crew(
         agents=[researcher, writer],

lib/crewai/tests/utilities/test_agent_utils.py

@@ -1,214 +0,0 @@
-"""Tests for agent utility functions."""
-
-from __future__ import annotations
-
-from typing import Any
-
-import pytest
-from pydantic import BaseModel, Field
-
-from crewai.tools.base_tool import BaseTool
-from crewai.utilities.agent_utils import convert_tools_to_openai_schema
-
-
-class CalculatorInput(BaseModel):
-    """Input schema for calculator tool."""
-
-    expression: str = Field(description="Mathematical expression to evaluate")
-
-
-class CalculatorTool(BaseTool):
-    """A simple calculator tool for testing."""
-
-    name: str = "calculator"
-    description: str = "Perform mathematical calculations"
-    args_schema: type[BaseModel] = CalculatorInput
-
-    def _run(self, expression: str) -> str:
-        """Execute the calculation."""
-        try:
-            result = eval(expression)  # noqa: S307
-            return str(result)
-        except Exception as e:
-            return f"Error: {e}"
-
-
-class SearchInput(BaseModel):
-    """Input schema for search tool."""
-
-    query: str = Field(description="Search query")
-    max_results: int = Field(default=10, description="Maximum number of results")
-
-
-class SearchTool(BaseTool):
-    """A search tool for testing."""
-
-    name: str = "web_search"
-    description: str = "Search the web for information"
-    args_schema: type[BaseModel] = SearchInput
-
-    def _run(self, query: str, max_results: int = 10) -> str:
-        """Execute the search."""
-        return f"Search results for '{query}' (max {max_results})"
-
-
-class NoSchemaTool(BaseTool):
-    """A tool without an args schema for testing edge cases."""
-
-    name: str = "simple_tool"
-    description: str = "A simple tool with no schema"
-
-    def _run(self, **kwargs: Any) -> str:
-        """Execute the tool."""
-        return "Simple tool executed"
-
-
-class TestConvertToolsToOpenaiSchema:
-    """Tests for convert_tools_to_openai_schema function."""
-
-    def test_converts_single_tool(self) -> None:
-        """Test converting a single tool to OpenAI schema."""
-        tools = [CalculatorTool()]
-        schemas, functions = convert_tools_to_openai_schema(tools)
-
-        assert len(schemas) == 1
-        assert len(functions) == 1
-
-        schema = schemas[0]
-        assert schema["type"] == "function"
-        assert schema["function"]["name"] == "calculator"
-        assert schema["function"]["description"] == "Perform mathematical calculations"
-        assert "properties" in schema["function"]["parameters"]
-        assert "expression" in schema["function"]["parameters"]["properties"]
-
-    def test_converts_multiple_tools(self) -> None:
-        """Test converting multiple tools to OpenAI schema."""
-        tools = [CalculatorTool(), SearchTool()]
-        schemas, functions = convert_tools_to_openai_schema(tools)
-
-        assert len(schemas) == 2
-        assert len(functions) == 2
-
-        # Check calculator
-        calc_schema = next(s for s in schemas if s["function"]["name"] == "calculator")
-        assert calc_schema["function"]["description"] == "Perform mathematical calculations"
-
-        # Check search
-        search_schema = next(s for s in schemas if s["function"]["name"] == "web_search")
-        assert search_schema["function"]["description"] == "Search the web for information"
-        assert "query" in search_schema["function"]["parameters"]["properties"]
-        assert "max_results" in search_schema["function"]["parameters"]["properties"]
-
-    def test_functions_dict_contains_callables(self) -> None:
-        """Test that the functions dict maps names to callable run methods."""
-        tools = [CalculatorTool(), SearchTool()]
-        schemas, functions = convert_tools_to_openai_schema(tools)
-
-        assert "calculator" in functions
-        assert "web_search" in functions
-        assert callable(functions["calculator"])
-        assert callable(functions["web_search"])
-
-    def test_function_can_be_called(self) -> None:
-        """Test that the returned function can be called."""
-        tools = [CalculatorTool()]
-        schemas, functions = convert_tools_to_openai_schema(tools)
-
-        result = functions["calculator"](expression="2 + 2")
-        assert result == "4"
-
-    def test_empty_tools_list(self) -> None:
-        """Test with an empty tools list."""
-        schemas, functions = convert_tools_to_openai_schema([])
-
-        assert schemas == []
-        assert functions == {}
-
-    def test_schema_has_required_fields(self) -> None:
-        """Test that the schema includes required fields information."""
-        tools = [SearchTool()]
-        schemas, functions = convert_tools_to_openai_schema(tools)
-
-        schema = schemas[0]
-        params = schema["function"]["parameters"]
-
-        # Should have required array
-        assert "required" in params
-        assert "query" in params["required"]
-
-    def test_tool_without_args_schema(self) -> None:
-        """Test converting a tool that doesn't have an args_schema."""
-        # Create a minimal tool without args_schema
-        class MinimalTool(BaseTool):
-            name: str = "minimal"
-            description: str = "A minimal tool"
-
-            def _run(self) -> str:
-                return "done"
-
-        tools = [MinimalTool()]
-        schemas, functions = convert_tools_to_openai_schema(tools)
-
-        assert len(schemas) == 1
-        schema = schemas[0]
-        assert schema["function"]["name"] == "minimal"
-        # Parameters should be empty dict or have minimal schema
-        assert isinstance(schema["function"]["parameters"], dict)
-
-    def test_schema_structure_matches_openai_format(self) -> None:
-        """Test that the schema structure matches OpenAI's expected format."""
-        tools = [CalculatorTool()]
-        schemas, functions = convert_tools_to_openai_schema(tools)
-
-        schema = schemas[0]
-
-        # Top level must have "type": "function"
-        assert schema["type"] == "function"
-
-        # Must have "function" key with nested structure
-        assert "function" in schema
-        func = schema["function"]
-
-        # Function must have name and description
-        assert "name" in func
-        assert "description" in func
-        assert isinstance(func["name"], str)
-        assert isinstance(func["description"], str)
-
-        # Parameters should be a valid JSON schema
-        assert "parameters" in func
-        params = func["parameters"]
-        assert isinstance(params, dict)
-
-    def test_removes_redundant_schema_fields(self) -> None:
-        """Test that redundant title and description are removed from parameters."""
-        tools = [CalculatorTool()]
-        schemas, functions = convert_tools_to_openai_schema(tools)
-
-        params = schemas[0]["function"]["parameters"]
-        # Title should be removed as it's redundant with function name
-        assert "title" not in params
-
-    def test_preserves_field_descriptions(self) -> None:
-        """Test that field descriptions are preserved in the schema."""
-        tools = [SearchTool()]
-        schemas, functions = convert_tools_to_openai_schema(tools)
-
-        params = schemas[0]["function"]["parameters"]
-        query_prop = params["properties"]["query"]
-
-        # Field description should be preserved
-        assert "description" in query_prop
-        assert query_prop["description"] == "Search query"
-
-    def test_preserves_default_values(self) -> None:
-        """Test that default values are preserved in the schema."""
-        tools = [SearchTool()]
-        schemas, functions = convert_tools_to_openai_schema(tools)
-
-        params = schemas[0]["function"]["parameters"]
-        max_results_prop = params["properties"]["max_results"]
-
-        # Default value should be preserved
-        assert "default" in max_results_prop
-        assert max_results_prop["default"] == 10

lib/crewai/tests/utilities/test_file_handler.py

@@ -1,6 +1,8 @@
 import os
+import threading
 import unittest
 import uuid
+from concurrent.futures import ThreadPoolExecutor, as_completed
 
 import pytest
 from crewai.utilities.file_handler import PickleHandler
@@ -8,7 +10,6 @@ from crewai.utilities.file_handler import PickleHandler
 
 class TestPickleHandler(unittest.TestCase):
     def setUp(self):
-        # Use a unique file name for each test to avoid race conditions in parallel test execution
         unique_id = str(uuid.uuid4())
         self.file_name = f"test_data_{unique_id}.pkl"
         self.file_path = os.path.join(os.getcwd(), self.file_name)
@@ -47,3 +48,234 @@ class TestPickleHandler(unittest.TestCase):
 
         assert str(exc.value) == "pickle data was truncated"
         assert "<class '_pickle.UnpicklingError'>" == str(exc.type)
+
+
+class TestPickleHandlerThreadSafety(unittest.TestCase):
+    """Tests for thread-safety of PickleHandler operations."""
+
+    def setUp(self):
+        unique_id = str(uuid.uuid4())
+        self.file_name = f"test_thread_safe_{unique_id}.pkl"
+        self.file_path = os.path.join(os.getcwd(), self.file_name)
+        self.handler = PickleHandler(self.file_name)
+
+    def tearDown(self):
+        if os.path.exists(self.file_path):
+            os.remove(self.file_path)
+
+    def test_concurrent_writes_same_handler(self):
+        """Test that concurrent writes from multiple threads using the same handler don't corrupt data."""
+        num_threads = 10
+        num_writes_per_thread = 20
+        errors: list[Exception] = []
+        write_count = 0
+        count_lock = threading.Lock()
+
+        def write_data(thread_id: int) -> None:
+            nonlocal write_count
+            for i in range(num_writes_per_thread):
+                try:
+                    data = {"thread": thread_id, "iteration": i, "data": f"value_{thread_id}_{i}"}
+                    self.handler.save(data)
+                    with count_lock:
+                        write_count += 1
+                except Exception as e:
+                    errors.append(e)
+
+        threads = []
+        for i in range(num_threads):
+            t = threading.Thread(target=write_data, args=(i,))
+            threads.append(t)
+            t.start()
+
+        for t in threads:
+            t.join()
+
+        assert len(errors) == 0, f"Errors occurred during concurrent writes: {errors}"
+        assert write_count == num_threads * num_writes_per_thread
+        loaded_data = self.handler.load()
+        assert isinstance(loaded_data, dict)
+        assert "thread" in loaded_data
+        assert "iteration" in loaded_data
+
+    def test_concurrent_reads_same_handler(self):
+        """Test that concurrent reads from multiple threads don't cause issues."""
+        test_data = {"key": "value", "nested": {"a": 1, "b": 2}}
+        self.handler.save(test_data)
+
+        num_threads = 20
+        results: list[dict] = []
+        errors: list[Exception] = []
+        results_lock = threading.Lock()
+
+        def read_data() -> None:
+            try:
+                data = self.handler.load()
+                with results_lock:
+                    results.append(data)
+            except Exception as e:
+                errors.append(e)
+
+        threads = []
+        for _ in range(num_threads):
+            t = threading.Thread(target=read_data)
+            threads.append(t)
+            t.start()
+
+        for t in threads:
+            t.join()
+
+        assert len(errors) == 0, f"Errors occurred during concurrent reads: {errors}"
+        assert len(results) == num_threads
+        for result in results:
+            assert result == test_data
+
+    def test_concurrent_read_write_same_handler(self):
+        """Test that concurrent reads and writes don't corrupt data or cause errors."""
+        initial_data = {"counter": 0}
+        self.handler.save(initial_data)
+
+        num_writers = 5
+        num_readers = 10
+        writes_per_thread = 10
+        reads_per_thread = 20
+        write_errors: list[Exception] = []
+        read_errors: list[Exception] = []
+        read_results: list[dict] = []
+        results_lock = threading.Lock()
+
+        def writer(thread_id: int) -> None:
+            for i in range(writes_per_thread):
+                try:
+                    data = {"writer": thread_id, "write_num": i}
+                    self.handler.save(data)
+                except Exception as e:
+                    write_errors.append(e)
+
+        def reader() -> None:
+            for _ in range(reads_per_thread):
+                try:
+                    data = self.handler.load()
+                    with results_lock:
+                        read_results.append(data)
+                except Exception as e:
+                    read_errors.append(e)
+
+        threads = []
+        for i in range(num_writers):
+            t = threading.Thread(target=writer, args=(i,))
+            threads.append(t)
+
+        for _ in range(num_readers):
+            t = threading.Thread(target=reader)
+            threads.append(t)
+
+        for t in threads:
+            t.start()
+
+        for t in threads:
+            t.join()
+
+        assert len(write_errors) == 0, f"Write errors: {write_errors}"
+        assert len(read_errors) == 0, f"Read errors: {read_errors}"
+        for result in read_results:
+            assert isinstance(result, dict)
+
+    def test_atomic_write_no_partial_data(self):
+        """Test that atomic writes prevent partial/corrupted data from being read."""
+        large_data = {"key": "x" * 100000, "numbers": list(range(10000))}
+        num_iterations = 50
+        errors: list[Exception] = []
+        corruption_detected = False
+        corruption_lock = threading.Lock()
+
+        def writer() -> None:
+            for _ in range(num_iterations):
+                try:
+                    self.handler.save(large_data)
+                except Exception as e:
+                    errors.append(e)
+
+        def reader() -> None:
+            nonlocal corruption_detected
+            for _ in range(num_iterations * 2):
+                try:
+                    data = self.handler.load()
+                    if data and data != {} and data != large_data:
+                        with corruption_lock:
+                            corruption_detected = True
+                except Exception as e:
+                    errors.append(e)
+
+        writer_thread = threading.Thread(target=writer)
+        reader_thread = threading.Thread(target=reader)
+
+        writer_thread.start()
+        reader_thread.start()
+
+        writer_thread.join()
+        reader_thread.join()
+
+        assert len(errors) == 0, f"Errors occurred: {errors}"
+        assert not corruption_detected, "Partial/corrupted data was read"
+
+    def test_thread_pool_concurrent_operations(self):
+        """Test thread safety using ThreadPoolExecutor for more realistic concurrent access."""
+        num_operations = 100
+        errors: list[Exception] = []
+
+        def operation(op_id: int) -> str:
+            try:
+                if op_id % 3 == 0:
+                    self.handler.save({"op_id": op_id, "type": "write"})
+                    return f"write_{op_id}"
+                else:
+                    data = self.handler.load()
+                    return f"read_{op_id}_{type(data).__name__}"
+            except Exception as e:
+                errors.append(e)
+                return f"error_{op_id}"
+
+        with ThreadPoolExecutor(max_workers=20) as executor:
+            futures = [executor.submit(operation, i) for i in range(num_operations)]
+            results = [f.result() for f in as_completed(futures)]
+
+        assert len(errors) == 0, f"Errors occurred: {errors}"
+        assert len(results) == num_operations
+
+    def test_multiple_handlers_same_file(self):
+        """Test that multiple PickleHandler instances for the same file work correctly."""
+        handler1 = PickleHandler(self.file_name)
+        handler2 = PickleHandler(self.file_name)
+
+        num_operations = 50
+        errors: list[Exception] = []
+
+        def use_handler1() -> None:
+            for i in range(num_operations):
+                try:
+                    handler1.save({"handler": 1, "iteration": i})
+                except Exception as e:
+                    errors.append(e)
+
+        def use_handler2() -> None:
+            for i in range(num_operations):
+                try:
+                    handler2.save({"handler": 2, "iteration": i})
+                except Exception as e:
+                    errors.append(e)
+
+        t1 = threading.Thread(target=use_handler1)
+        t2 = threading.Thread(target=use_handler2)
+
+        t1.start()
+        t2.start()
+
+        t1.join()
+        t2.join()
+
+        assert len(errors) == 0, f"Errors occurred: {errors}"
+        final_data = self.handler.load()
+        assert isinstance(final_data, dict)
+        assert "handler" in final_data
+        assert "iteration" in final_data

lib/devtools/src/crewai_devtools/__init__.py

@@ -1,3 +1,3 @@
 """CrewAI development tools."""
 
-__version__ = "1.8.1"
+__version__ = "1.8.0"

pyproject.toml

@@ -117,7 +117,7 @@ show_error_codes = true
 warn_unused_ignores = true
 python_version = "3.12"
 exclude = "(?x)(^lib/crewai/src/crewai/cli/templates/ | ^lib/crewai/tests/ | ^lib/crewai-tools/tests/)"
-plugins = ["pydantic.mypy"]
+plugins = ["pydantic.mypy", "crewai.mypy"]
 
 
 [tool.bandit]