EvoScientist | Xi Zhang

⭐ EvoScientist Crosses 4.5k GitHub Stars!

Fri, 31 Jul 2026 00:00:00 +0000

Today we crossed 4.5k GitHub stars. 🚀🚀🚀

Thanks to everyone who’s been part of the journey — more to come.

Github: EvoScientist

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Star History

🎤 Invited Talk at EvoAgentX Community!

Wed, 01 Apr 2026 00:00:00 +0000

Honoured to be invited by the EvoAgentX community to present our latest project at this week’s EvoAgentX Talk! As a core contributor to the EvoAgentX community, I shared the design and progress behind EvoScientist — an open-source, long-horizon agent system for autonomous scientific discovery.

💡 Talk Title

EvoScientist: Toward Long-Horizon Agent Systems for Scientific Discovery

🖇️ Abstract

AI is rapidly shifting from dialogue models to agent systems. Platforms like OpenClaw have already demonstrated that AI can execute real-world tasks beyond simple Q&A. However, when we move to the scientific research domain, the challenges multiply dramatically. Research tasks are inherently long-horizon, multi-stage, strongly interdependent, and continuously iterative — far from a single-turn reasoning problem, they demand sustained decision-making and an evolving system architecture.

EvoScientist explores a long-horizon agent design paradigm tailored to research scenarios. Through multi-agent collaboration, the system progressively completes the full pipeline from idea generation to experiment execution to result summarization — an approach we call Vibe Research. Unlike many agent systems that remain at the demo stage, EvoScientist emphasizes executability in complex tasks and the ability to continuously evolve in real-world settings.

In this talk, I address three key questions: why current agent systems fall short in research scenarios, what the core design philosophy of EvoScientist is, and whether we truly need a new long-horizon agent paradigm to support increasingly complex task systems in the future.

📺 Slides

This talk was presented to the Chinese-speaking community, so the slides below are in Chinese. An English version will be shared in a future community session.

🔗 Project Link

EvoScientist — Harness Vibe Research with Self-evolving AI Scientists

EvoScientist aims to harness vibe research by enabling self-evolving AI scientists that autonomously explore, generate insights, and iteratively improve. It is designed to be opinionated and ready to use out of the box, offering a living research system that grows alongside evolving agent skills, toolsets, and memory bases. Moving beyond traditional human-in-the-loop systems, EvoScientist adopts a human-on-the-loop paradigm — AI acts as a research buddy that co-evolves with human researchers and internalizes scholarly taste and scientific judgment.

🚀 EvoScientist Debuts!

Fri, 13 Mar 2026 00:00:00 +0000

See what’s new on Github: EvoScientist

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🧬 If AI agents must be Self-Evolving, then AI Scientists should lead the way.

EvoScientist aims to harness vibe research by enabling self-evolving AI scientists that autonomously explore, generate insights, and iteratively improve. Moving beyond traditional human-in-the-loop systems, EvoScientist adopts a human-on-the-loop paradigm — AI acts as a research buddy that co-evolves with human researchers and internalizes scholarly taste and scientific judgment.

Why vibe research matters and how it drives EvoScientist: Harness Vibe Research 📟

🔬 End-to-End Scientific Workflow

🚀 Quick Start Demo

🏆 Awards & Recognition

🥇 #1 on DeepResearch Bench II
🥇 #1 on AstaBench Code & Execution
🥇 #1 on AstaBench Data Analysis
🏆 Best Paper & AI Reviewer’s Appraisal Award at ICAIS 2025

🪧 Join Our Community!

We’re building a community of researchers, developers, and visionaries dedicated to harnessing vibe research and self-evolving AI scientists. Together, we’re redefining autonomous scientific discovery.

Discord — Chat, discuss, and collaborate in real-time
GitHub — Source code and documentation
WeChat — Connect with our Chinese community

EvoScientist

Fri, 06 Mar 2026 00:00:00 +0000

Harnessing vibe research with self-evolving AI scientists that autonomously explore, generate insights, and iteratively improve.