Multi-Agent Collaborative Development Framework: 7-Step Closed-Loop Workflow with Claude, Codex, and Gemini

The multi-agent-dev-loop project builds a multi-agent collaborative development framework, integrating the advantages of models like Claude, Codex, and Gemini. Through a 7-step closed-loop workflow including planning, review, coding, etc., it achieves end-to-end automation from requirements to delivery, significantly improving the quality of completing complex development tasks. This framework represents a new paradigm for AI-assisted development, addressing the capability limits of single models in handling complex tasks.

With the enhancement of large language model capabilities, single models face capability limits when handling complex development tasks. Different models have their own advantages in code generation, architecture design, code review, and other links. Coordinating multiple models to complete tasks collaboratively has become an important research direction in AI-assisted development. The multi-agent-dev-loop project integrates the advantages of multiple models into a structured 7-step workflow to achieve end-to-end automation.

Step 1: Requirements Analysis and Task Planning

Led by models good at context understanding, analyze requirements to generate a structured implementation plan and dependency diagram.

Step 2: Architecture Design and Technology Selection

Multi-agents discuss technology selection, module division, etc., to determine the optimal solution and avoid the thinking limitations of a single model.

Step 3: Code Generation and Implementation

Models good at code generation are responsible for module implementation, and ensure code consistency through interface collaboration.

Step 4: Code Review and Quality Evaluation

Review agents check the code (static analysis, bug identification, etc.), and feedback fixes to form an iterative loop.

Step 5: Test Case Generation and Execution

Automatically generate test cases to verify correctness; failures trigger diagnosis and repair.

Step 6: Deployment and Environment Configuration

Automatically generate deployment scripts to complete target environment configuration and deployment.

Step 7: Verification and Feedback Collection

Simulate user scenarios to verify functions, record results to optimize the workflow and model performance.

Fixed Artifacts and Clear Interfaces

Each step produces structured and verifiable artifacts; agents communicate through clear interfaces to reduce uncertainty.

Explicit Failure Routing Mechanism

When a failure occurs, select a fallback strategy (retry, manual intervention, etc.) based on the type instead of simple retries.

Complementary Advantages of Multiple Models

Claude excels at long-context reasoning, Codex at code generation, and Gemini at multimodal processing; tasks are reasonably assigned to maximize efficiency.

Complex Function Development

Reduce the cognitive burden on developers; AI independently handles most implementation details.

Legacy System Maintenance

Analyze code intent from multiple angles, generate accurate modification plans to reduce the risk of new bugs.

Rapid Prototype Verification

Complete the conversion from requirements to a runnable prototype in a short time, accelerating innovation iteration.

Adopt a modular design; each step can be independently configured and extended. Users can replace agent implementations or add new steps to support scenarios for teams of different sizes.

multi-agent-dev-loop represents a new paradigm for AI-assisted development, building a multi-agent collaborative ecosystem to complete complex software engineering tasks. With the improvement of model capabilities and collaboration mechanisms, it is expected to become a standard practice in future software development.