ChatWiki is an open-source, multi-service AI chatbot and workflow automation platform primarily targeting the WeChat ecosystem. In production, it operates as a suite of Go microservices (chatwiki, message_service, code_run, websocket, plugin, llm_runner) behind a Gin web server, with a Vue 3 admin panel, separate mobile/PC/Electron frontends, and an embeddable SDK. The core functionality includes knowledge base management (document ingestion, chunking, vector storage with pgvector), a LogicFlow-based visual workflow builder, multi-channel messaging (WeChat Official Account, Mini Program, Feishu, DingTalk, Telegram, WhatsApp, LINE), and an AI agent framework called Clawbot with skill reuse. External dependencies include PostgreSQL (with pgvector and AGE graph extension), Redis, NSQ message queue, and optional Neo4j and E2B sandbox. While the system is primarily an application, it incorporates data-intensive components such as RAG pipelines, LLM request logging, and daily statistics, giving it a hybrid character.

ChatClaw is a desktop AI assistant application built with Wails v3 (Go backend + Vue 3 frontend) that provides a local-first, multi-platform AI agent experience. It integrates with multiple messaging platforms (WeChat, QQ, DingTalk, Feishu, WeCom, WhatsApp) through gateway adapters and manages AI agents using the CloudWeGo Eino framework. The application features a knowledge base with full-text search (FTS5) and vector embeddings (sqlite-vec), document processing pipeline (parsing, splitting, RAPTOR hierarchical summarization), scheduled tasks with cron execution, a skill marketplace, MCP tool support, and native desktop capabilities like window snapping, floating ball, and text selection popups. It also bundles an OpenClaw Node.js runtime for advanced agent workflows. The frontend is Vue 3 with TypeScript, Pinia, and shadcn-vue UI components. The architecture is a monolith with service-oriented backend and multiple WebView windows. Note: The repo shows signs of hybridity as it includes substantial data/research components (RAPTOR, embeddings, document processing) but these serve the application's core functionality.