Multi-Agent Code Review & Refactoring
This challenge focuses on building an advanced multi-agent system using the OpenAI Agents SDK. The system will be designed to automate code review processes, identify potential bugs or inefficiencies in a given codebase, and suggest intelligent refactoring strategies. It will leverage the o4-mini model for its strong code understanding and generation capabilities, enabling nuanced analysis and creative solutions. The solution will incorporate Kiln AI for robust agent management and lifecycle, ensuring the agents operate reliably and can be scaled. Composio will be used for integrating various external developer tools, such as code analysis suites and version control systems, allowing agents to interact with real-world development environments. Metaflow will orchestrate the complex CI/CD workflow, from code ingestion to analysis, refactoring suggestions, and simulated integration. Optionally, Synthflow can be used to add a voice-based interaction layer for developers to query code status or request refactorings verbally. This project demonstrates cutting-edge multi-agent orchestration for significantly enhancing software development productivity and quality.
What you are building
The core problem, expected build, and operating context for this challenge.
Build an OpenAI Agents SDK-based multi-agent system with o4-mini to automate code review, refactoring, and CI/CD integration using Kiln AI, Composio, and Metaflow.
How work is evaluated
The solution will be evaluated on its ability to autonomously review a provided Python codebase, accurately identify issues, suggest correct and actionable refactorings, and demonstrate conceptual integration with a mock CI/CD environment.
Challenge datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
CorrectIssueIdentification
Checks if the agent correctly identified all expected issues from the input.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
ValidRefactoringSuggestions
Checks if refactoring suggestions are well-formed, relevant, and provide actionable advice.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
MockPRDescriptionPresent
Verifies that a simulated Pull Request description is generated.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
CodeQualityImprovementScore
A score indicating the comprehensiveness, accuracy, and impact of the suggested refactorings (0-100). • target: 85 • range: 0-100
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
AgentProcessingLatencyMS
Average time taken by the agent system to process a code review request, in milliseconds. • target: 2000 • range: 100-6000
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Master the OpenAI Agents SDK for defining agent roles, capabilities, and tool-calling functions for structured interactions.
Implement advanced prompting techniques with o4-mini for sophisticated code understanding, vulnerability detection, and transformation tasks.
Design and manage complex agent workflows using Kiln AI for scalable, observable, and resilient multi-agent deployments.
Integrate Composio to provide agents with programmatic access to Git repositories, linters, testing frameworks, and other developer tools.
Orchestrate complex AI-driven CI/CD pipelines using Metaflow for automated code quality gates, compliance checks, and deployment simulations.
Build robust error handling and feedback mechanisms within the agent system for continuous improvement and developer collaboration.
Reference links and supporting material
Sample data for 1 tasks
Challenge setup
The solution will be evaluated on its ability to autonomously review a provided Python codebase, accurately identify issues, suggest correct and actionable refactorings, and demonstrate conceptual integration with a m...
Challenge input
{"file_path": "str", "code_content": "str", "expected_issues": ["str"]}
OpenAI
OpenAI AI model provider
Composio
Tool integrations for AI agents
o4-mini
Policy Serving in the agent workflow.
Evaluated output
{"identified_issues": ["str"], "refactoring_suggestions": "str", "mock_pr_description": "str"}
- Checks if the agent correctly identified all expected issues from the input.
- Checks if refactoring suggestions are well-formed, relevant, and provide actionable advice.
- Verifies that a simulated Pull Request description is generated.
- CodeQualityImprovementScore target: 85
- 1 public reference case
- Python execution harness
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
View technical recipe
Configured tools
- OpenAI · Optional
- Composio · Optional
- o4-mini · Required
Evaluation contract
- CorrectIssueIdentification · Weight 1
- ValidRefactoringSuggestions · Weight 1
- MockPRDescriptionPresent · Weight 1
- CodeQualityImprovementScore · Weight 1
- AgentProcessingLatencyMS · Weight 1
Metrics
- CodeQualityImprovementScore · Target 85
- AgentProcessingLatencyMS · Target 2000
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: {"file_path": "str", "code_content": "str", "expected_issues": ["str"]}
Output: {"identified_issues": ["str"], "refactoring_suggestions": "str", "mock_pr_description": "str"}
Execution harness: Python
Recipe freeze marker
No recipe freeze time is recorded.