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Basic Information

ARGO is an open-source AI Agent client designed to make it easy to build and use AI-powered assistants capable of autonomous thinking, task planning, and multi-stage execution. The project emphasizes a local-first approach so users can run agents privately and offline with all data stored locally. It supports integration with both open-source and closed-source models, including one-click model downloads and Ollama integration, and is compatible with APIs that follow the OpenAI format. ARGO provides a Multi-Agent task engine, local RAG knowledge base support, MCP protocol tooling, and a visual Agent Factory to customize scenario-specific assistants. The software targets desktop and server environments and offers native clients for Windows, macOS, and Linux as well as Docker deployment options for CPU and GPU environments.

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Features
ARGO bundles features for model management, multi-agent coordination, knowledge retrieval, and extensible tool integration. Model features include one-click Ollama integration, HuggingFace GGUF support, adaptive chat templates, and the ability to switch between local and API models. The multi-agent engine performs intent recognition, task planning, tool calling, task execution, self-reflection, and structured result summarization. The local RAG knowledge engine ingests files, folders, and websites, supports dynamic folder synchronization, multi-format document parsing, and answer traceability. ARGO ships a built-in tool library (web crawlers, browser control, file management), supports MCP protocol for STDIO and SSE tools, and offers visual agent creation, sharing, KaTeX and Mermaid rendering in chat, and branchable conversations.
Use Cases
ARGO helps users and teams create private, customizable AI assistants for a range of scenarios without relying on external cloud services. Individuals can use it as a personal or study assistant, creators can accelerate content generation and editing, and developers can get code generation and debugging support. Businesses can build industry-specific agents such as legal or analyst assistants, and teams can construct RAG-enabled knowledge systems for traceable answers and document-driven insights. Offline operation and local storage provide privacy and compliance benefits. Deployment options include simple desktop installers and Docker compose files for CPU or GPU setups, enabling flexible local, private cloud, or server installations.

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