codefuse chatbot

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Basic Information

CodeFuse-ChatBot is an open-source project that implements a configurable multi-agent framework and supporting toolchain to build AI assistants for the software development lifecycle, with a focus on DevOps tasks. The repository combines retrieval-augmented generation, tool learning and sandboxed execution so large language models can perform design, coding, testing, deployment and operations tasks across project repositories and documentation. It provides components for ingesting web and local sources, building domain-specific knowledge bases, and running private or offline deployments using open-source models or API-based models. The project is aimed at teams wanting an extensible, private DevOps assistant and includes examples, a muAgent Python package, deployment scripts and guidance for tested environments.

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App Details

Features
The README documents a multi-agent scheduling core (codefuse-muAgent) for configurable agent workflows, repository-level code analysis and project file generation, and document retrieval with knowledge-graph support. It includes multi-source web crawling, data processors for cleaning and chunking, text embedding and indexing, vector and graph database integration, prompt control and management, and a sandbox for safe code compilation and actions. The repo shows compatibility with open-source LLMs and OpenAI APIs, provides installation guidance including a pip-installable muAgent package and requirements, and offers Docker and example startup scripts and web UI configuration for running the chatbot in private environments.
Use Cases
CodeFuse-ChatBot helps development and operations teams by automating and augmenting tasks across the software lifecycle. It enables contextual code and documentation Q&A, repository-wide code understanding, and assisted code generation while keeping data private through offline deployments. The retrieval-augmented workflows and domain knowledge builders make documentation analysis and troubleshooting faster. Sandboxed execution reduces risk when testing code actions. Integration points for embedding models, vector and graph stores, and prompt management allow teams to tailor agents to specific DevOps workflows. The project also supplies demos, videos and contribution guidance to help teams adopt and extend the platform.

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