MiroFlow v0.2 - MiroMind's open-source research agent framework

What is MiroFlow v0.2?

MiroFlow v0.2 is an open-source research agent framework from MiroMind that can upgrade any large Language Model (LLM) to OpenAI's deep research capabilities, focusing on reliably completing complex tool usage tasks. The framework boasts advantages such as open-source reproducibility, high concurrency, high reliability, and low deployment costs, achieving top-tier results in multiple benchmark tests including GAIA, HLE, xBench-DeepSearch, and BrowserComp. Based on a simple Grado front-end interface and powerful back-end multi-tool collaboration, MiroFlow v0.2 efficiently processes user queries through query enhancement, task planning, sub-agent delegation, tool invocation, and result synthesis, providing strong support for AI research and applications.

Main features of MiroFlow v0.2

• Task planning and executionIt can automatically plan and execute complex tasks, and coordinate multiple tools and sub-agents to complete the tasks.
• Multi-tool collaborationIt supports collaboration among various tools (such as web browsers, Python tools, etc.) to complete multi-step network research and resource analysis.
• High concurrency and high reliabilityIt features robust concurrency management and fault tolerance design, and efficiently handles rate-limited APIs and unstable network environments.
• Cost-effectivenessIt can run with a single RTX 4090 graphics card, is based on free and open-source tools, and is easy to deploy and expand.
• Open source and reproducibleAll performance metrics can be reproduced through publicly available code, ensuring the transparency and verifiability of the research.

Technical Principles of MiroFlow v0.2

• Query EnhancementBy analyzing user input through large-scale language models, we can clarify intent, enrich query details, and facilitate a more accurate understanding of needs.
• Task planningThe main intelligent agent formulates a detailed execution plan based on the enhanced query content and coordinates the entire workflow.
• Sub-agent delegationFor complex or specialized tasks, the main agent will delegate some of the work to sub-agents with relevant expertise, and the sub-agents can autonomously plan and execute the tasks.
• Tool callThe agent obtains and uses the necessary external tools by connecting to the MCP (Model Context Protocol) server.
• Result SynthesisAfter the task is completed, the system integrates the results from multiple information sources to ensure that the output content is of high quality and meets the user's needs.

MiroFlow v0.2 project address

• Project official websitehttps://miromind.ai/blog/miroflow
• HuggingFace model library: https://huggingface.co/collections/miromind-ai/mirothinker-v02-68af084a18035f57b17cd902

Application Scenarios of MiroFlow v0.2

• academic researchIt helps researchers quickly collect and analyze large amounts of academic resources, automatically generate literature reviews or research plans, and greatly improve the efficiency of academic research.
• Market AnalysisThrough multi-step network research and multi-tool collaboration, we can quickly analyze market trends and competitor information to provide data support for business decisions.
• News reportIt automatically collects and organizes multifaceted information about news events, helping journalists quickly generate news reporting frameworks and improve the timeliness of news production.
• Educational guidanceBased on students' questions, we can quickly search and organize educational resources, provide detailed answers and learning suggestions, and support teaching activities.
• Technology DevelopmentIt assists developers in quickly collecting resources such as technical documents and code examples, helping users better understand and apply new technologies and improve development efficiency.