feat: Foundry agent adapter #2852

bassmang · 2025-05-16T20:32:30Z

No description provided.

joaomdmoura · 2025-05-16T20:35:30Z

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

Code Review Comment: Foundry Agent Adapter Implementation

Overview

This code implements a Foundry Agent adapter for crewAI that integrates with Azure AI services. It introduces important functionality for handling Azure-based AI agents across multiple files.

Code Quality Findings

1. Import Organization and Dependencies

Findings:

Inconsistent import grouping (e.g., standard library, third-party, local).
Some areas lack type hints, which can lead to ambiguity in code usage.
Organization of conditional imports could be improved.

Suggestions:

Group imports by category and add necessary type hints for better clarity.

Example Recommendation:

# Standard library imports
import inspect
import json

# Third-party imports
from azure.ai.projects import AIProjectClient

# Local imports
from crewai.agents.agent_adapters.base_agent_adapter import BaseAgentAdapter

2. Error Handling and Logging

Findings:

Basic error handling without specification of error types.
Logging lacks specificity which can obscure debugging efforts.

Suggestions:

Enhance error handling by raising specific exceptions and improving logging granularity.

Example Improvement:

class FoundryAgentAdapter(BaseAgentAdapter):
    def execute_task(self, task: Any) -> str:
        try:
            # Execution logic...
        except FoundryClientError as e:
            self._logger.error(f"Foundry client error: {e}", exc_info=True)
            raise

3. Configuration Management

Findings:

Hardcoded values and undocumented environment variable dependencies.

Suggestions:

Use configuration models and ensure documentation of environment variables.

Example Code:

class FoundryConfig(BaseModel):
    connection_string: str

4. Tool Adapter Implementation

Findings:

Commented-out code and incomplete tool handling logic.

Suggestions:

Remove commented-out code and ensure robust tool validation.

5. Thread Management

Findings:

Possible resource leaks due to inadequate thread cleanup.

Suggestions:

Implement cleanup methods to manage thread lifecycle effectively.

Example Addition:

async def cleanup_threads(self):
    """Cleanup active threads."""

6. Result Processing

Findings:

Insufficient response handling and validation of structures.

Suggestions:

Include thorough validation mechanisms for message and response structures.

Example Enhancement:

def _process_messages(self, messages: Any) -> str:
    if not messages.data:
        raise ValueError("No messages received.")

Historical Context and Related PRs

Although the specific pull request history isn’t accessible, referencing similar changes in related PRs may demonstrate how previous issues were resolved or improved upon. It’s essential to maintain best practices and learn from historical decisions regarding error management, configuration handling, and modular design.

Implications for Related Files

The modifications within this PR will likely influence how tools are adapted and how configuration settings are handled across the crewAI framework. Further integration tests should be implemented to ensure that these changes work seamlessly with existing code.

General Recommendations

Introduce comprehensive docstrings for classes and methods for better documentation.
Establish detailed logging practices.
Implement more robust error handling mechanisms across the board.
Develop unit tests to cover newly introduced functionalities.
Use secure logging and implement proper input sanitization practices.

Security Considerations

Ensure secure handling of credentials and sensitive information.
Implement access control mechanisms and input validation.
Maintain proper error handling to avoid leaking sensitive information.

In summary, while this implementation establishes a solid foundation for the integration of Foundry services, it must undergo several enhancements in error handling, configuration management, and resource cleaning to achieve production readiness.

This structured review encapsulates specific code improvements, suggestions, and potential implications of changes. Please make sure to integrate community feedback as well for continuous enhancement.