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Autonomous research paper reproduction platform

Project description

Man1Lab

Engineering-first autonomous research paper reproduction platform.

Man1Lab transforms AI paper reproduction into a structured engineering workflow.

Starting from a research paper, it analyzes the methodology, discovers official repositories and resources, builds an execution strategy, and prepares everything required for software reproduction.

From paper → engineering decisions → reproducible implementation.

PyPI version Python Version Latest Release Tests License: MIT

Man1Lab Console


Why Man1Lab?

Reproducing modern AI papers is rarely just reading code.

A typical workflow requires:

  • Reading and understanding the paper
  • Finding the official implementation
  • Locating checkpoints and datasets
  • Preparing the runtime environment
  • Connecting paper concepts to source code
  • Planning how the reproduction should actually be executed

Man1Lab automates this engineering workflow.

Instead of starting from scattered resources, you start from structured engineering decisions.


Workflow

Paper
   │
   ▼
Analysis
   │
   ▼
Research Resource Discovery
   │
   ▼
Execution Planning
   │
   ▼
Execution (ongoing)

Each stage produces structured artifacts that become the input of the next stage.


Key Features

  • Structured paper analysis
  • Evidence-backed research resource discovery
  • Engineering-oriented execution planning
  • Interactive CLI and Console
  • Multi-provider LLM support (OpenAI / DeepSeek / Anthropic)
  • Python SDK
  • Workspace persistence and resume
  • Explainable Decision Trace
  • Execution Graph generation
  • Golden Benchmark framework

Quick Start

Install:

pip install man1lab

Initialize:

man1lab init

Validate your environment:

man1lab doctor

Run a complete planning pipeline:

man1lab reproduce paper.pdf

Or enter the interactive console:

man1lab

Interactive Console

The interactive console guides the complete engineering workflow.

man1lab
│
├── analyze <paper.pdf>
├── discover
├── plan
├── plan-all <paper.pdf>
├── doctor
├── model
└── profile

Workspace artifacts are persisted automatically, allowing interrupted sessions to resume.


Generated Workspace

Running Man1Lab produces structured engineering artifacts.

workspace/
├── analysis/
│   ├── analysis.json
│   └── analysis.md
├── discovery/
│   ├── resources.json
│   └── summary.md
├── planning/
│   ├── execution_strategy.json
│   └── summary.md
├── decision/
│   ├── decision_trace.json
│   └── execution_graph.json
└── logs/

These artifacts can be inspected, version-controlled, and reused in later stages.


Current Scope

Man1Lab focuses on software reproduction of AI research.

Supported domains include:

  • Computer Vision
  • Embodied AI (software stack)
  • LLM Systems
  • Agent Frameworks
  • Reinforcement Learning

Hardware deployment, robot calibration, and physical experiments are outside the current scope.


Architecture

Platform
        │
        ▼
Analysis
        │
        ▼
Discovery
        │
        ▼
Execution Planning
        │
        ▼
Execution

Interfaces (CLI, Console, SDK) share the same Platform Runtime and Decision Foundation.

Architecture documentation:

  • Architecture
  • Runtime
  • Execution Planning

Roadmap

Version Focus
v1.2.x Platform Runtime & Decision Foundation ✅
v1.3 Repository Understanding
v1.4 Repository Adaptation
v1.5 Knowledge Memory

Documentation

  • Getting Started
  • Architecture
  • Runtime
  • Current Status
  • Roadmap

Citation

@software{man1lab_2026,
  author  = {maniac1um},
  title   = {Man1Lab: An Autonomous Research Paper Reproduction Platform},
  year    = {2026},
  version = {1.2.4},
  url     = {https://github.com/maniac1um/Man1Lab}
}

See CITATION.cff for the preferred citation format.


License

Released under the MIT License.

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