CodeTeam: An LLM-Powered Multi-Agent Framework for Repository-Level Code Generation
📰 ArXiv cs.AI
Learn how CodeTeam, an LLM-powered multi-agent framework, generates entire software repositories from natural-language requirements documents, and apply its concepts to your own code generation tasks
Action Steps
- Implement a multi-agent framework using LLMs to generate code at the repository level
- Separate planning, decision-making, and execution phases in your code generation pipeline
- Use natural-language processing to parse requirements documents and generate stable interfaces across files
- Apply iterative debugging techniques to identify and resolve cross-file inconsistencies
- Integrate CodeTeam's concepts into your existing code generation tools and workflows
Who Needs to Know This
Software engineers, AI researchers, and DevOps teams can benefit from CodeTeam's approach to repository-level code generation, improving their productivity and code quality
Key Insight
💡 CodeTeam's multi-agent framework enables the generation of entire software repositories from natural-language requirements documents, leveraging LLMs for planning, decision-making, and execution
Share This
🤖 CodeTeam: LLM-powered multi-agent framework for repository-level code generation! 🚀 Improve your code gen tasks with this innovative approach 📈
Full Article
Title: CodeTeam: An LLM-Powered Multi-Agent Framework for Repository-Level Code Generation
Abstract:
arXiv:2606.22082v1 Announce Type: cross Abstract: Natural language to repository generation (NL2Repo) requires a system to construct an entire software repository from a natural-language requirements document. Compared with function-level code generation, this task demands longer planning horizons, stable interfaces across files, and iterative debugging of cross-file inconsistencies. To address these challenges, we propose CodeTeam, an LLM-based multi-agent framework that separates planning, dec
Abstract:
arXiv:2606.22082v1 Announce Type: cross Abstract: Natural language to repository generation (NL2Repo) requires a system to construct an entire software repository from a natural-language requirements document. Compared with function-level code generation, this task demands longer planning horizons, stable interfaces across files, and iterative debugging of cross-file inconsistencies. To address these challenges, we propose CodeTeam, an LLM-based multi-agent framework that separates planning, dec
DeepCamp AI