add new tool for accessing bedrock inline agents as a tool from crew …

[raju-rangan]

As an addition to the invoke agents tool, adding a new tool that would allow crew AI agents to create and invoke inline bedrock agents. https://docs.aws.amazon.com/bedrock/latest/userguide/agents-create-inline.html

[joaomdmoura]

Disclaimer: This review was made by a crew of AI Agents.

Code Review Comment: Integration of Bedrock Inline Agents

Overview

The recent pull request successfully integrates Amazon Bedrock Inline Agents into the CrewAI framework, showcasing a well-organized structure and robust functionalities that enhance the capabilities of CrewAI. Overall, this implementation appears solid, but there are areas for improvement that could significantly enhance code maintainability, security, and performance.

Strengths

1. Documentation: The comprehensive documentation within README.md provides clear setup instructions and examples of usage, which are essential for user onboarding and understanding the integration.
2. Modular Design: The clear separation of concerns with utility classes enhances maintainability and allows for easy modifications or additions in the future.
3. Error Handling: The implementation showcases robust error handling mechanisms, which are crucial for reliable performance during bad input or failures in external service calls.
4. Configurability: Detailed configuration options, including environment variable support, enable flexible setups tailored to different environments.

Suggested Improvements

1. Import Structure Optimization

The import structure in __init__.py files could benefit from enhanced clarity. For example:

from .knowledge_base.retriever_tool import BedrockKBRetrieverTool
from .agents.invoke_agent_tool import BedrockInvokeAgentTool
from .inline_agents import BedrockInlineAgentTool, ConfigLoader

__all__ = [
    "BedrockKBRetrieverTool",
    "BedrockInvokeAgentTool", 
    "BedrockInlineAgentTool",
    "ConfigLoader",
]

This structure improves clarity on the exported components, aiding developers in understanding the module’s capabilities.

2. Response Handler Enhancements

Type hints and detailed error handling would improve the ResponseHandler class:

from typing import Dict, List, Optional, Union
from dataclasses import dataclass

@dataclass
class FileInfo:
    name: str
    type: str
    path: str
    source: str

class ResponseHandler:
    ...

Type hints and data class usage enhance readability and make static type checking more effective, reducing runtime errors.

3. Configuration Validation

The ConfigLoader could be enhanced with validation for configuration inputs to prevent issues caused by invalid setups:

from typing import Dict, Any, Optional
from pydantic import BaseModel, Field

class BedrockAgentConfig(BaseModel):
    model_id: str
    instruction: str
    ...

Using a model class with Pydantic allows built-in validation and better control over configuration management.

4. Parameter Validation for BedrockInlineAgentTool

Incorporating stronger parameter validation in BedrockInlineAgentTool would enhance security and reliability:

from pydantic import BaseModel, Field, validator

class BedrockInlineAgentTool(BaseTool):
    ...
    @validator("model_id")
    def validate_model_id(cls, v):
        ...

This ensures that only valid configurations are allowed, preventing misuse.

5. Unit Testing

It would be beneficial to implement unit tests for the new functionalities introduced in this PR. For instance:

import pytest
from unittest.mock import Mock, patch

def test_bedrock_inline_agent_tool():
    ...

Incorporating tests ensures that changes do not unintentionally break existing functionalities, ultimately leading to a more robust codebase.

6. Enhanced Documentation

It is crucial to elaborate on error handling strategies and advanced usage scenarios within the README.md. Suggested additions include:

## Error Handling

```python
try:
    ...
except BedrockClientError as e:
    print(f"Bedrock API error: {e}")

Providing these examples can empower users to handle potential issues effectively.

Security Recommendations

• Input Validation: Implement input validation for all user inputs to prevent vulnerabilities associated with incorrect data processing.
• Rate Limiting: Consider adding rate limiting on API calls to avoid potential abuse and excessive load on the Bedrock service.
• Encryption: Implement request/response encryption for sensitive data to bolster security measures.

Conclusion

The integration of the BedrockInlineAgentTool into CrewAI represents a significant advancement in capabilities, with a well-laid-out structure that supports extensibility and adaptability. By addressing the mentioned improvements, especially regarding security, validation, and testing, we can enhance both the reliability and the user experience of the tool.

Approval: The PR can be approved upon addressing the suggested modifications and enhancements. Thank you for the hard work that went into this integration!

[lorenzejay]

can you use logger here ? @raju-rangan vs print

[lorenzejay]

logger vs print everywhere 🙏🏼

[lorenzejay]

@raju-rangan can we get these tests fixed 🙏🏼

[raju-rangan]

The tests are failing because of an unrelated issue in serper_dev_tool.py.