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modelscope agent

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Multi-Agent Platforms & Frameworks
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Basic Information

MS-Agent is a lightweight framework to build and run autonomous agents that connect large language and other AI models to external tools and environments. It is designed to enable agents to perform complex tasks such as code generation, data analysis, document research, and tool calling while supporting the Model Context Protocol (MCP) for standardised model interactions. The repository bundles multi-agent orchestration, examples and applications, and specialized projects like Deep Research (Agentic Insight) and Doc Research for multimodal report generation. It targets developers who want an extensible, production-capable agent system that can run locally or integrated with ModelScope Studio. The project includes a Python package, installation instructions, demos, and example scripts to configure MCP servers, initialise LLMAgents, and run tasks with ModelScope API credentials.

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3214
App URL
https://github.com/modelscope/modelscope-agent
Github Repository
https://github.com/modelscope/modelscope-agent/blob/main/README.md

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App Details

Features
The project provides multi-agent chat with tool-calling capabilities driven by MCP, a deep research framework called Agentic Insight for autonomous exploration, and Doc Research for document analysis and multimodal report generation. It supports code generation with artifact management, lightweight search-then-execute flows to reduce token usage, and multimodal inputs including images and multi-file uploads. There are ready examples and demos for retrieval agents, registering tools, and codex-like graph code generation. The repo also documents integrations such as a Modelscope-Agent-Server enabling parallel tool calling and SDK compatibility, a Ray-based multi-agent option for distributed execution, Assistant and Tools APIs for isolated tool execution, and deployment-friendly patterns for local Gradio apps and ModelScope Studio.
Use Cases
MS-Agent helps teams and developers prototype, extend, and deploy agentic applications that require orchestration between models and external tools. It accelerates building research assistants that autonomously explore topics, produce multimodal reports, and analyse documents, while providing examples to shorten integration time. The MCP support standardises model-server interactions and the Tools API isolates utility execution for safer runs. Lightweight modes and search-then-execute reduce inference costs and token consumption. The codebase includes demos, apps and server patterns for scaling (parallel tool calling, Ray), enabling both local experimentation and Studio deployment. Overall it lowers the effort to create production-ready agents for research, code generation, and tool-enabled automation.

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