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SciTeX Cloud (scitex-cloud)

SciTeX Cloud

GitHub for research — verifiable, AI-native, self-hosted infrastructure for scientists

For researchers and lab teams who want a unified, open-source platform
to manage the full research lifecycle — from literature to manuscript — under their own control.
scitex.ai is a live instance of this project.

PyPI version Documentation Tests License: AGPL-3.0

Full Documentation · pip install scitex-cloud


Problem and Solution

# Problem Solution
1

Fragmented tools

Literature, writing, analysis, and visualization require separate, often proprietary applications, forcing constant context-switching and making it difficult for AI agents to build sufficient context across the research workflow.

Unified platform

Scholar, Writer, FigRecipe, Console, Hub, and Clew in a single Django web application, deployable anywhere with Docker. All apps share the same project filesystem and integrate through the scitex Python package.
2

No custom tooling

Every research group needs domain-specific tools (e.g., clinical trial dashboards, spike-sorting interfaces, compound screening pipelines), yet building and sharing them requires deep computational knowledge and creating components from scratch.

App Maker and Store

Researchers create, publish, and install custom research tools on top of shared components — user/group permissions, AI infrastructure, containerized computation, and file operations are handled by the platform.
3

AI tools not research-aware

Existing tools often lack AI assistant capabilities and domain-specific skills for scientific work, unable to operate across the full research lifecycle (literature review, analysis, writing, verification).

Built-in AI co-pilot

Platform-aware context, skills, and tools such as MCP (Model Context Protocol) and CLI span the full research lifecycle, providing an AI assistant that understands the entire project from natural language.
4

Review crisis

The growing volume and heterogeneity of published papers overwhelms a limited, volunteer-based peer review process that cannot scale.

Open review via Issues and PRs

GitHub-style issue tracking and pull requests bring transparent, structured, and scalable peer review to research projects — anyone can inspect, comment, and propose changes.
5

Broken provenance

Papers, code, and execution environments are rarely tied together, making it difficult for reviewers to verify claims and for other researchers to replicate results — slowing cumulative scientific progress.

Verifiable provenance

Clew links papers, code, data, and execution environments into a hash-verified DAG (Directed Acyclic Graph) with visualization that serves as a compressed view of the research workflow and logic — reducing the decision points reviewers must check.
6

Lost knowledge on handoff

When researchers graduate or leave a project, successors inherit scattered files with little context, making it difficult to understand where to pick up and continue the work.

Seamless project handoff

The full project state — code, data, provenance graph, manuscript drafts, and execution environment — lives in one place, so successors can understand and continue work immediately.
7

No research community platform

No GitHub-like infrastructure exists for research-project-centric, fully traceable, parallel-working collaboration.

GitHub-style project hub

Repository hosting and ticket-based development with co-authors and the community enable efficient research advancement and collaboration.
8

No control

Researchers have no ownership over their infrastructure: vendor lock-in, opaque algorithms, unilateral pricing changes, and data policies they cannot influence.

Self-hosted, open-source, runnable from anywhere

Deploy on your laptop, lab server, or cloud. AGPL-3.0 licensed — inspect every line, customize freely, no vendor lock-in, no data surrender.

Table 1. Eight infrastructure challenges in scientific research and how SciTeX Cloud addresses each. These gaps fuel the reproducibility crisis, limit what AI can do for research, and leave knowledge stranded when people move on.

SciTeX Cloud is an AI-native infrastructure so that researchers can focus on science, not on tooling.

Screenshots

Writer
Writer

Scholar
Scholar

Apps
Apps

Figure 1. Core application modules. Writer provides a LaTeX manuscript environment with live compilation. Scholar offers literature discovery, BibTeX enrichment, and PDF management. The Apps panel shows the project-centric hub linking all modules.

Installation

pip install scitex-cloud              # CLI only
pip install scitex-cloud[mcp]         # CLI + MCP server
pip install scitex-cloud[all]         # Everything

Quick Start

git clone https://github.com/ywatanabe1989/scitex-cloud.git
cd scitex-cloud
make start                    # Start development environment

# Access at: http://localhost:8000
# Gitea: http://localhost:3000
# Test user: test-user / Password123!

Three Interfaces

Python API
import scitex_cloud

# Version and health
scitex_cloud.__version__        # "0.15.0"
scitex_cloud.get_version()      # Version string
scitex_cloud.health_check()     # Service health status

Full API reference

CLI Commands
scitex-cloud --help                    # Help
scitex-cloud --help-recursive          # All commands recursively
scitex-cloud --version                 # Version

# Git hosting (Gitea)
scitex-cloud gitea list                # List repositories
scitex-cloud gitea clone user/repo     # Clone repository
scitex-cloud gitea push                # Push changes
scitex-cloud gitea pr create           # Create pull request
scitex-cloud gitea issue create        # Create issue

# Docker management
scitex-cloud docker status             # Container status
scitex-cloud docker logs               # View logs

# MCP server
scitex-cloud mcp start                 # Start MCP server
scitex-cloud mcp list-tools            # List available tools
scitex-cloud mcp doctor                # Diagnose setup
scitex-cloud mcp installation          # Client config instructions

# Utilities
scitex-cloud status                    # Deployment status
scitex-cloud completion                # Shell completion setup
scitex-cloud list-python-apis          # List all Python APIs

Full CLI reference

MCP Server — for AI Agents

AI agents can interact with the SciTeX Cloud platform autonomously via MCP (Model Context Protocol) tools.

Category Tools Description
cloud 14 Git operations (clone, push, pull, PR, issues)
api 9 Scholar search, CrossRef, BibTeX enrichment

Table 2. MCP tool categories. All tools accept JSON parameters and return JSON results. Use scitex-cloud mcp list-tools for the full list.

Claude Desktop (~/.config/claude/claude_desktop_config.json):

{
  "mcpServers": {
    "scitex-cloud": {
      "command": "scitex-cloud",
      "args": ["mcp", "start"]
    }
  }
}

Full MCP specification

Web Platform

Deployment
make start                    # Development (default)
make ENV=prod start           # Production
make ENV=prod status          # Health check
make ENV=prod db-backup       # Backup database
make help                     # All available commands
Configuration

.env files in deployment/docker/envs/ (gitignored):

.env.dev        # Development
.env.prod       # Production
.env.staging    # Staging
.env.example    # Template (tracked)

Key variables:

SCITEX_CLOUD_DJANGO_SECRET_KEY=your-secret-key
SCITEX_CLOUD_POSTGRES_PASSWORD=strong-password
SCITEX_CLOUD_GITEA_TOKEN=your-token
Project Structure
scitex-cloud/
├── apps/                    # Django applications
│   ├── scholar_app/        # Literature discovery
│   ├── writer_app/         # Scientific writing
│   ├── console_app/        # Terminal & code execution
│   ├── figrecipe_app/      # Data visualization
│   ├── hub_app/            # Project hub & file browser
│   ├── project_app/        # Project management
│   ├── clew_app/           # Verification pipeline
│   └── public_app/         # Landing page & tools
│
├── deployment/docker/
│   ├── docker_dev/         # Development compose
│   ├── docker_prod/        # Production compose
│   └── envs/               # .env files (gitignored)
│
├── config/                  # Django settings
├── static/                  # Shared frontend assets
├── src/scitex_cloud/        # pip package (CLI + MCP)
├── tests/                   # Test suite
└── Makefile                 # Thin dispatcher

Part of SciTeX

SciTeX Cloud is part of SciTeX. When modules work together, each output feeds naturally into the next:

From Produces To Outcome
Scholar Citations as cards Writer Convenient, evidence-based referencing
SciTeX-followed Analysis Artifacts Writer AI writes a manuscript based on actual results
FigRecipe Style-editable, composable figures Writer Publication-ready figures in context
Clew Verification and DAG visualization Writer Proven reproducibility for every claim

The SciTeX system follows the Four Freedoms for Research below, inspired by the Free Software Definition:

Four Freedoms for Research

  1. The freedom to run your research anywhere — your machine, your terms.
  2. The freedom to study how every step works — from raw data to final manuscript.
  3. The freedom to redistribute your workflows, not just your papers.
  4. The freedom to modify any module and share improvements with the community.

AGPL-3.0 — because we believe research infrastructure deserves the same freedoms as the software it runs on.

Status

SciTeX Cloud is in alpha. Core functionality is working and under active development. Data formats may change between releases — back up important work.

Contributing

We welcome contributions! See CONTRIBUTING.md.


SciTeX