modelscope/ms-agent
Ms Agent
ms-agent is an LLM agent framework and multi-agent orchestration system designed to build autonomous entities that combine large language models with tool calling and structured workflows. It serves as a tool integration platform and workflow engine for executing complex tasks through the coordination of specialized agents.
The project distinguishes itself through a multimodal agent workflow engine capable of automating the production of text, images, and video. It features a sandboxed code execution environment for running generated code and quantitative data analysis in isolated containers, alongside a self-healing execution model that uses language models to analyze runtime failures and automatically generate fixes.
The system covers broad capability areas including deep research automation, automated software generation, and financial research analysis. It implements these through a dependency-aware directed acyclic graph for task scheduling, hybrid-search skill matching, and state-aware memory management to maintain context across extended interactions.
An interactive web interface is provided for real-time chatting and monitoring agent execution via bidirectional connections.
Features
- Agentic LLM Frameworks - Ships a framework for building autonomous agents powered by LLMs with native support for tool use and structured workflows.
- Autonomous Agent Creation - Provides a comprehensive framework for building autonomous agent loops that combine LLMs with tool calling and structured workflows.
- AI Agent Integration Platforms - Provides a unified platform for managing the connection between autonomous agents and custom external tool interfaces.
- AI Agent Workflow Definition - Enables the configuration-based definition of structured agent workflows to handle multi-step reasoning.
- Language Model Integrations - Connects various reasoning engines and language model providers to power autonomous agent operations.
- Multi-Agent Orchestration - Implements high-level orchestration for decomposing complex goals and delegating them across multiple specialized agents.
- Multi-Agent Orchestration Systems - Implements a system for coordinating multiple autonomous agents to execute collaborative, multi-step workflows.
- Multi-Agent Orchestrators - Coordinates teams of specialized AI agents to decompose complex tasks into manageable sub-tasks.
- Tool Calling - Implements mechanisms for language models to request and execute external functions via a standardized protocol.
- AI Agent Development Tools - Provides a comprehensive environment for building autonomous entities that combine LLMs with tool calling and structured protocols.
- Custom Tool Definitions - Implements frameworks for defining typed, executable functions that agents use to interact with external systems.
- Agent Skill DAG Execution - Executes complex agent skill sequences using a dependency-aware directed acyclic graph for precise task orchestration.
- Code Sandboxing Environments - Provides isolated container environments to securely execute model-generated code and data processing tasks.
- Automated Software Engineering Agents - Coordinates specialized agents to translate natural language requirements into complete and validated software codebases.
- Hybrid Short-and-Long Term Memory - Integrates short-term session context with long-term persistent storage and memory compression to track state.
- Deep Research Execution - Autonomously executes complex analytical tasks to generate evidence-based research reports combining market data and sentiment analysis.
- Hybrid Search Retrievers - Combines keyword and vector search to match user intents with the most relevant agent tools and capabilities.
- Multimodal Workflow Orchestrators - Coordinates agents across diverse modalities to automate the production of text, images, and video.
- Runtime Bug Healing - Uses LLMs to automatically repair runtime bugs occurring during the execution of agent tasks in sandboxed environments.
- Code Execution Environments - Provides an isolated container environment specifically for agents to safely execute generated code and data analysis.
- External Tool Integration - Provides the connectivity layer for agents to interact with external APIs and perform real-world actions.
- Iterative Report Generators - Generates professional long-form reports using iterative search, outlining, and consistency checks.
- Financial Analysis Frameworks - Coordinates specialized agents to combine quantitative market data and sentiment analysis for professional financial research.
- Multimodal Content Generation - Processes and generates content across multiple modalities, including text, images, and video.
- Multimodal Model Integrations - Sets up interfaces that allow LLMs and VLMs to process and respond to multiple data types simultaneously.
- Short-Form Video Generation - Automates the end-to-end synthesis of short-form videos, including scripting, storyboarding, and rendering.
- Sandboxed Execution - Executes quantitative data processing and visualization tasks within isolated environments for security and reproducibility.
- Code Execution Sandboxes - Runs generated data processing tasks inside isolated containers to ensure environment security and reproducibility.
- DAG Workflow Executions - Uses directed acyclic graphs to map dependencies between agent skills and ensure correct execution order.
- LLM-Driven Self-Healing - Employs language models to analyze runtime failures and automatically generate fixes to recover failed execution steps.
- Requirement to Code Generators - Automates the translation of natural language requirements into complete, validated codebases using dependency-aware scheduling.
