Mirothinker — AI Agent Framework: Live Stats & TrendScore

Live GitHub stats, community sentiment, and trend data for Mirothinker. TrendingBots tracks star velocity, fork activity, and what developers are saying — updated from real data sources.

GitHub data synced: Apr 25, 2026 • Sentiment updated: Apr 12, 2026

GitHub Statistics

Community Sentiment

Community Buzz: MiroThinker is proposed to support "interactive scaling" as mentioned on GitHub, and some users like it, for example, on GitHub, a user said "MiroThinker-1.7-mini 的int8或者int4量化有没有，想在有限的显存上玩一玩" which translates to "Is there int8 or int4 quantization for MiroThinker-1.7-mini, want to try it on limited VRAM".

Pros & Cons

What People Love

MiroThinker's ability to support 600+ tool calls for deep reasoning as mentioned on GitHub, The potential of MiroThinker for interactive self-improvement as discussed on GitHub

Common Complaints

Difficulty in using MiroThinker with OpenClaw as mentioned on GitHub, Limited availability of MiroThinker's quantization for limited VRAM as discussed on GitHub

Biggest Positive: Interactive Scaling

Biggest Negative: Compatibility Issues

Why Mirothinker Stands Out

MiroThinker is different from alternatives due to its enhanced post-training pipeline, which enables SOTA performance in deep research tasks among open-source models. Its support for interactive scaling, long-horizon reasoning, and deep multi-step analysis makes it a unique solution for research agents. Additionally, MiroThinker's comprehensive suite of tools and workflows provides flexibility and customizability for diverse research scenarios.

Built With

Build a deep research agent for financial prediction — MiroThinker's enhanced post-training pipeline enables SOTA performance in deep research tasks among open-source models, Build an interactive scaling system for research agents — MiroThinker's support for 256K context window and up to 300 tool interactions per task allows for long-horizon reasoning and deep multi-step analysis, Build a reliable agent for long-chain tasks — MiroThinker's comprehensive suite of tools and workflows flexibly support diverse research scenarios, Build a search agent that achieves state-of-the-art performance on BrowseComp and GAIA-Val-165 — MiroThinker's proprietary agent MiroThinker-H1 achieves leading performance among open-source and commercial models, Build a research report generation system with extended document upload types — MiroThinker online supports generation, preview, and sharing of deep research reports

Getting Started

  1. Install MiroThinker using pip: `pip install miromind-ai`
  2. Configure the environment variables: `export MIROMIND_AI_HOME=/path/to/miromind-ai`
  3. Download the pre-trained models: `python download_pretrained_models.py`
  4. Start the MiroThinker server: `python start_server.py`
  5. Try the interactive demo to verify it works: `http://localhost:8080/demo`

About

MiroThinker is a deep research agent optimized for complex research and prediction tasks. Our latest models, MiroThinker-1.7, achieves 74.0 and 75.3 on the BrowseComp and BrowseComp Zh, respectively.

Official site: https://miromind.ai/

Category & Tags

Category: research

Tags: agent, agent-framework, browsecomp, deep-research, futurex, gaia, hle, research-agent, search-agent, xbench

Market Context

MiroThinker is positioned as a competitive deep research agent in the AI market, with mentions on GitHub and other platforms