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ms agent

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Multi-Agent Platforms & Frameworks
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Basic Information

MS-Agent is a lightweight Python framework designed to empower autonomous agents with exploration and complex task execution capabilities. It provides a flexible architecture for building multi-agent systems that can chat, call tools, and interact with models using the MCP Model Context Protocol. The repository targets developers who need agent orchestration for research workflows, document analysis, code generation, and multimodal report production. It includes installation instructions for PyPI and source installs, a Python quickstart demonstrating LLMAgent initialization and MCP server configuration, and examples and demos such as Gradio applications and ModelScope Studio deployments. The project emphasizes extensibility so teams can customize agents, register new tools, integrate LLMs via ModelScope APIs, and run local or hosted agent instances for both experimentation and production.

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Stars
3330
App URL
https://github.com/modelscope/ms-agent
Github Repository
https://github.com/modelscope/ms-agent/blob/main/README.md

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App Details

Features
The project ships core features to support autonomous and extensible agent workflows. It offers multi-agent support with tool-calling powered by MCP, a deep research module called Agentic Insight for autonomous exploration and report generation, and a Doc Research pipeline for multi-file and URL inputs producing multimodal Markdown reports. It supports code generation and artifact handling, efficient search-then-execute research flows to reduce token usage, callback mechanisms for agent chat, and configurable MCP servers for SSE interactions. The repo includes demos, Gradio apps, examples for registering tools and retrieval agents, a ModelScope-Agent-Server for isolated tool execution and parallel tool calling, and compatibility layers for hybrid retrieval and RAG flows.
Use Cases
MS-Agent helps teams and researchers build agent-driven applications that perform complex automated tasks with minimal integration effort. It consolidates multi-agent orchestration, tool calling, and model interactions under MCP to standardize how agents fetch data and invoke services. The Deep Research and Doc Research features automate literature or document analysis, produce multimodal reports, and optimize token consumption through a search-then-execute pattern. Developers can prototype locally using provided Gradio demos or deploy via ModelScope Studio and leverage free or hosted model inference for ModelScope users. Examples, tutorials, and modular components make it easier to extend agents for code generation, data analysis, mobile automation, and production tool execution while supporting secure isolated tool runtimes.

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