autonomous-lab 0.5.1

MCP server that turns any Senior-Junior workflow into an autonomous loop with a human decision maker. Seamless integration with Cursor, Claude Code, Codex, and any MCP client.

Autonomous Lab

MCP server that turns any senior-junior workflow into an autonomous loop. AI handles the execution. You make the decisions.

The server runs inside your existing agentic coding tool (Cursor, Claude Code, Codex CLI, Windsurf, or any MCP client). Your SKILL.md files become character configurations. The loop runs for hours without stopping. You step in as editor when the work is ready for judgment.

Install

# From PyPI
uv pip install autonomous-lab

# With biomedical toolkit (optional)
uv pip install autonomous-lab[biotools]

Quick start

Add to your MCP client config (e.g. Cursor ~/.cursor/mcp.json):

{
  "mcpServers": {
    "autonomous-lab": {
      "command": "uvx",
      "args": ["autonomous-lab"],
      "timeout": 600,
      "env": {
        "MCP_WEB_PORT": "8766"
      }
    }
  }
}

Or if you installed via uv pip install:

{
  "mcpServers": {
    "autonomous-lab": {
      "command": "autonomous-lab",
      "timeout": 600,
      "env": {
        "MCP_WEB_PORT": "8766"
      }
    }
  }
}

Then tell your agent: "Initialize an autonomous lab project on [your topic]."

What it does

Two AI personas (senior + junior) iterate on your project in a loop. They design, execute, write, and revise. You sit above them as the decision maker: editor, code reviewer, creative director, or whatever the domain calls for.

The loop:

autolab_next → (AI acts as role) → autolab_record → lab_meeting → autolab_next → ...

When work is ready, you review it. Accept, request revisions, or reject. The loop continues until you're satisfied.

Key capabilities

• Skill containers: configure characters with any combination of SKILL.md files you already have. A PI with scanpy + scientific-writing + statistical-analysis skills behaves differently from a Tech Lead with react + typescript + code-review skills.
• 24-hour sessions: the loop runs indefinitely. No timeout, no context loss. Sessions persist across disconnects with autolab_resume.
• Fully configurable: YAML character profiles control personality, expertise, goals, and available tools. Swap them in seconds.
• Domain-agnostic: research, software, consulting, legal, medical, creative, or anything with a senior-junior structure.
• Expert consultation: invite domain specialists mid-session for one-off advice without breaking the loop.
• Verified citations: built-in CrossRef integration for real, validated references (no hallucinated papers).
• Game-style monitoring UI: browser dashboard shows live progress, iteration history, and editorial controls.

MCP tools

Tool	What it does
autolab_init	Initialize a new project
autolab_resume	Resume an interrupted session
autolab_next	Get the next role prompt (PI or Trainee)
autolab_record	Record a completed turn
autolab_status	Check project state
autolab_cite	Search, validate, and format citations
autolab_consult	Invite a domain expert
autolab_editorial	Wait for editor decision
autolab_editor_act	Execute editorial decision (AI fallback)
autolab_create_character	Build a character profile
lab_meeting	Pause for user feedback between turns

Character example

name: Dr. Maria Chen
role: pi
title: Computational Biology PI
expertise: single-cell genomics, machine learning
goal: discover cell-type-specific regulatory programs
skills:
  - scanpy
  - scvi-tools
  - scientific-writing
  - statistical-analysis
personality:
  - "Visionary: spots novel research directions"
  - "Rigorous: demands statistical reproducibility"

Requirements

• Python >= 3.11
• An MCP-compatible client (Cursor, Claude Code, Codex CLI, Windsurf, etc.)

License

Apache 2.0. See LICENSE and NOTICE.