EvoScientist 0.0.1b0

EvoScientist: Towards Self-Evolving AI Scientists for End-to-End Scientific Discovery

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English | 简体中文

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. Going beyond traditional human-in-the-loop systems, EvoScientist introduces an AI-in-human’s-loop paradigm, where AI acts as a research buddy that co-evolves with human researchers and internalises scholarly taste and scientific judgement.

Unified Control, Different Surfaces

[TODO: Add a Demo to demonstrate the different interfaces (TUI, mobile) and how they connect to the same underlying proxy system.]

✨ Features

• 🤖 Multi-Agent Team — 6 sub-agents (plan, research, code, debug, analyze, write) working in concert.
• 🧠 Persistent Memory — Context, preferences, and findings survive across sessions.
• 🔬 Scientific Workflow — Intake → plan → execute → evaluate → write → verify.
• 🌐 Multi-Provider — Anthropic, OpenAI, Google, NVIDIA — one config to switch.
• 📱 Multi-Channel — CLI as the hub; Telegram, Discord, Slack, Feishu, WeChat, and more — one agent session.
• 🔌 MCP & Skills — Plug in MCP servers or install skills from GitHub on the fly.

🔥 News

• [27 Feb 2026] ⛳ EvoScientist officially debuts!

📖 Table of Contents

• 📦 Installation
• 🔑 Configuration
• ⚡ Quick Start
• 🔌 MCP Integration
• 📱 Channels
• 📚 Acknowledgments
• 🧪 EvoScientist Team
• 🤝 Contributing

📦 Installation

[!NOTE] Requires Python 3.11+. A virtual environment is strongly recommended — EvoScientist experiments may install ML libraries (PyTorch, transformers, etc.) that can conflict with your system packages. We recommend uv for fast, reliable dependency management — it handles Python versions, virtual environments, and packages in a single tool.

Install uv (if you don't have it)

# Always review scripts before piping to shell: https://astral.sh/uv/install.sh
curl -LsSf https://astral.sh/uv/install.sh | sh

Quick Install

uv pip install EvoScientist

Development Install

git clone https://github.com/EvoScientist/EvoScientist.git
cd EvoScientist
uv sync --dev

Using conda

conda create -n EvoSci python=3.11 -y
conda activate EvoSci
pip install -e ".[dev]"

Using pip

pip install EvoScientist          # quick install
pip install -e ".[dev]"           # development install

Upgrade to latest

git pull && uv sync --dev

Optional: Channel dependencies

Messaging channel integrations require extra dependencies. Install only what you need:

uv pip install "EvoScientist[telegram]"     # Telegram
uv pip install "EvoScientist[discord]"      # Discord
uv pip install "EvoScientist[slack]"        # Slack
uv pip install "EvoScientist[wechat]"       # WeChat
uv pip install "EvoScientist[qq]"           # QQ
uv pip install "EvoScientist[all-channels]" # everything

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🔑 Configuration

The easiest way to configure API keys is the interactive wizard:

EvoSci onboard

It walks you through provider selection, key validation, model choice, and workspace setup.

Manual configuration via environment variables

Set at least one LLM provider key and (optionally) a search key:

# Pick one LLM provider
export ANTHROPIC_API_KEY="sk-..."   # Claude — console.anthropic.com
export OPENAI_API_KEY="sk-..."      # GPT   — platform.openai.com
export GOOGLE_API_KEY="AI..."       # Gemini — aistudio.google.com/api-keys
export NVIDIA_API_KEY="nvapi-..."   # NIM   — build.nvidia.com

# Web search (optional)
export TAVILY_API_KEY="tvly-..."    # app.tavily.com

Or use EvoSci config set to persist keys in ~/.config/evoscientist/config.yaml.

Alternatively, copy the example .env file for project-level configuration:

cp .env.example .env  # then fill in your keys

[!WARNING] Never commit .env files with real keys. It is already in .gitignore.

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⚡ Quick Start

EvoSci  # or EvoScientist — interactive mode

Run EvoSci -h for all CLI options.

Common examples

EvoSci -p "your question"        # single-shot mode
EvoSci -m run                     # isolated per-session workspace
EvoSci --ui textual               # alternative TUI backend
EvoSci serve                      # headless mode — channels only, no interactive prompt

In-session commands

Command	Description
/new	Start a new session
/current	Show thread ID and workspace path
/channel	Start a messaging channel
/skills	List installed skills
/install-skill <src>	Install skill from path or GitHub
/mcp	List MCP servers and tool routing
/exit	Quit

Script Inference

from EvoScientist import EvoScientist_agent
from langchain_core.messages import HumanMessage
from EvoScientist.utils import format_messages

thread = {"configurable": {"thread_id": "1"}}
last_len = 0

for state in EvoScientist_agent.stream(
    {"messages": [HumanMessage(content="Hi?")]},
    config=thread,
    stream_mode="values",
):
    msgs = state["messages"]
    if len(msgs) > last_len:
        format_messages(msgs[last_len:])
        last_len = len(msgs)

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🔌 MCP Integration

Add external tools via MCP servers with a single command:

# Usage
EvoSci mcp add <name> <command> [-- args...]

# Example
EvoSci mcp add sequential-thinking npx -- -y @modelcontextprotocol/server-sequential-thinking

[!NOTE] For command options, config fields, tool routing, wildcard filtering, and troubleshooting, see the MCP Integration Guide.

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📱 Channels

Connect messaging platforms so they share the same agent session as the CLI:

# Usage
EvoSci channel setup <channel>

# Example
EvoSci channel setup telegram

Multiple channels can run concurrently — comma-separate names in the config:

channel_enabled: "telegram,discord,slack"

The channel can also be started interactively with /channel in the CLI session.

[!NOTE] For per-channel setup guides, capability matrix, architecture details, and troubleshooting, see the Channel Integration Guide.

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📚 Acknowledgments

This project builds upon the following outstanding open-source works:

• LangChain — A framework for building agents and LLM-powered applications.
• DeepAgents — The batteries-included agent harness.

We thank the authors for their valuable contributions to the open-source community.

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🧪 EvoScientist Team

Xi Zhang†

Ziheng Zhang‡

Dinos Papakostas‡

Yougang Lyu§

^† Project Leader ^‡ Core Developer ^§ Project Correspondent

For any enquiries or collaboration opportunities, please contact: [e-mail]

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🤝 Contributing

We welcome contributions from developers and researchers at all levels. Please refer to our Contributing Guidelines to get started and help make EvoScientist more accessible.

❤️ Thanks go to these awesome contributors:

📈 Star History

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📜 License

This project is licensed under the MIT License - see the LICENSE file for details.

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Made with ❤️ by the EvoScientist team and the open source community for the AI scientist community.