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Basic Information

MaiCore-MaiBot is an open-source, Python-based interactive agent project that implements a lifelike chatbot designed primarily for group chat scenarios such as QQ groups and similar multi-platform contexts. The repository provides the core runtime and assets to run an LLM-driven conversational entity that emphasizes human-like presence rather than purely utilitarian assistance. It integrates components for persistent long-term memory, adaptive personality, real-time internal reasoning, emotion and sticker expression, and learning of individual users' speaking styles. The project ships deployment instructions, release builds and branches for stable and development versions, documentation and community channels. It is licensed under GPL-3.0 and intended both for operators who want a companion-style bot in social chat environments and for contributors extending its plugin ecosystem.

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App Details

Features
The project documents a set of focused capabilities: an LLM-based conversational system with unified handling for normal and focused dialog flows. A redesigned plugin architecture supports managed plugins with APIs and permission control for extending abilities. A real-time thinking subsystem simulates internal reasoning and a dynamic personality system adapts expression and traits. Expression learning captures user speaking styles and an emotion system plus sticker integration support expressive responses. Persistent long-term memory is implemented with a graph-based storage approach. The repo shows Python compatibility and provides demo material, deployment guides, a launcher, contributor information, and changelogs to help setup and extension.
Use Cases
MaiBot helps teams and individuals deploy a more human-like chatbot for group interactions by combining conversation, personality and memory into a single system. Operators can add capabilities through plugins and control access via the plugin management API, reducing development overhead when integrating new features. Persistent graph memory and expression learning produce more consistent, personalized conversations over time, while the emotion and sticker systems increase engagement. The project includes documentation, deployment tutorials and a launcher to simplify installation and updates. Community channels, contribution guidelines and changelogs support collaboration. The README also warns about token use and platform restrictions and points to license and privacy requirements.

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