MiroFlow - A multi-agent system development framework that supports high concurrency processing.

What is MiroFlow?

MiroFlow is a powerful multi-agent system development framework designed for building complex, high-performance AI agents, generating high-quality agent trajectory data for models such as MiroThinker. The framework boasts reproducible performance, consistently achieving a 72.2% pass rate on the GAIA validation set. It supports high concurrency and fault tolerance, and includes built-in observability and evaluation tools for easy debugging and optimization. MiroFlow employs a modular design, supporting multi-turn dialogues, rich tool integration, and hierarchical sub-agent architectures, enabling flexible handling of complex tasks. MiroFlow provides a detailed configuration system, task logs, and output formatting features, facilitating rapid development and expansion for developers.

MiroFlow's main functions

• Reproducible high performanceMiroFlow achieves a stable 72.2% pass rate on the GAIA validation set, and provides complete scripts and configuration files to ensure reproducible results.
• High concurrency and fault toleranceIt supports efficient concurrent processing, has a strong fault tolerance mechanism, and can cope with rate-limited APIs and unstable networks.
• Built-in observability and evaluation toolsIt is equipped with benchmark scripts and a web interface for visualizing and debugging the trajectory of intelligent agents, while also providing a comprehensive logging system.
• Modular designBased on multi-turn dialogue, tool integration, and a hierarchical sub-agent architecture, it supports flexible handling of complex tasks.
• Flexible tool accessIt connects to various tools via the MCP server, supports functions such as code execution, visual perception, and web search, and is compatible with multiple LLM providers.
• Configuration and scalabilityHydra is used to configure and manage systems, supporting structured YAML configuration files, and its output formatting system can adapt to different benchmark testing needs.

MiroFlow's technical principles

• Multi-stage workflowUser input is enhanced through intent recognition and query augmentation using LLM. The master agent formulates an execution plan based on the enhanced query and coordinates the entire workflow, including invoking tools and delegating tasks to sub-agents. Sub-agents independently plan and execute tool invocations and return the results to the master agent. The master agent summarizes the results and generates output conforming to user instructions or benchmark test formats.
• Modular architectureThe core components include the Pipeline (task execution coordinator), the Orchestrator (dialogue flow manager), and the LLM Client (LLM interface). The Tool Manager is responsible for connecting to and managing the MCP server, supporting tool discovery, error handling, and tool blacklisting. Sub-Agents focus on domain-specific tasks, such as web browsing and code execution; each sub-agent has its own toolset and customized prompts.
• LLM and Tool IntegrationMiroFlow uses LLM for task planning and dialogue management, while executing specific tasks using tools provided by the MCP server. The MCP server is built on FastMCP and supports various tools, such as code execution, visual perception, and web search.
• High concurrency and fault toleranceIt enables high-concurrency task processing based on asynchronous processing and queue management. The built-in fault tolerance mechanism can handle issues such as API rate limiting and network instability, ensuring task continuity.
• Observability and Log SystemsProvides a web interface for visualizing the operational trajectory of intelligent agents. A comprehensive logging system records agent interactions, tool execution, performance metrics, and error messages, facilitating debugging and optimization.

MiroFlow project address

• GitHub repository: https://github.com/MiroMindAI/Miroflow

Application Scenarios of MiroFlow

• Automation of complex tasksMiroFlow can handle multi-step, multi-tool tasks, such as automating data processing and analysis, and improving enterprise efficiency.
• Intelligent Customer Service and SupportIt is used to build intelligent customer service systems, automatically answer user questions, handle complex tasks such as order inquiries, and improve customer experience.
• Education and learning supportIt provides students with personalized learning paths and homework tutoring, offering targeted solutions based on their learning progress, thus contributing to the intelligentization of education.
• Code generation and debuggingIt assists in software development by automatically generating code snippets and debugging code issues, thereby improving development efficiency and reducing manual intervention.
• Multimodal content creationIt combines multiple modalities such as text, images, and audio to generate content, thereby enhancing the diversity and appeal of content creation.