scitex-cloud 0.17.3a0

SciTeX Cloud - Deployment and management CLI for SciTeX

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.}

Full Documentation · pip install scitex-cloud

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Interfaces: Python ⭐ · CLI ⭐⭐⭐ · MCP ⭐⭐⭐ · Skills ⭐⭐ · Hook — · HTTP ⭐⭐

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

Scholar

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!

Four Interfaces

Python API

import scitex_cloud

# Version and health
scitex_cloud.__version__        # read from pyproject.toml (e.g. "0.17.0-alpha")
scitex_cloud.get_version()      # Version string
scitex_cloud.health_check()     # Local package info
scitex_cloud.health_check("https://scitex.ai/api/health/")  # Remote endpoint

# Clients / helpers
client = scitex_cloud.CloudClient()            # HTTP client
env = scitex_cloud.get_environment()           # Environment config
docker = scitex_cloud.DockerManager()          # Container helpers

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 up                 # Start containers
scitex-cloud docker down               # Stop containers
scitex-cloud docker ps                 # Container status
scitex-cloud docker build              # Build images
scitex-cloud docker restart            # Restart services

# 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
gitea	14	Git operations (clone, push, pull, PR, issues, auth)
sdk	14	DataStore, FileVault, JobQueue operations
api	9	Scholar search, CrossRef, BibTeX enrichment
app	7	App plugin lifecycle (init, validate, submit)
onsite	6	On-site platform operations
project_crud	5	Project create, list, rename, delete

_{Table 2. MCP tool categories — 55 tools total registered via register_all_tools in _mcp_tools/init.py. Use scitex-cloud mcp list-tools for the live list.}

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

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

Full MCP specification

Skills — for AI Agents

Skill files provide context-aware guidance to AI agents working within the SciTeX ecosystem.

# Export skills to dotfiles (sync to Claude)
scitex-dev skills export --package scitex-cloud

# List available skills
scitex-cloud skills list

Skills are stored in src/scitex_cloud/_skills/scitex-cloud/ and cover deployment, development, testing, and more.

Skills index

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
│   ├── workspace/          # Workspace modules
│   │   ├── apps_app/      # App marketplace & dev install
│   │   ├── scholar_app/   # Literature discovery
│   │   ├── writer_app/    # Scientific writing
│   │   ├── console_app/   # Terminal & code execution
│   │   ├── hub_app/       # Project hub & file browser
│   │   └── clew_app/      # Verification pipeline
│   ├── infra/             # Platform infrastructure
│   │   ├── workspace_app/ # Module registry & workspace shell
│   │   ├── platform_app/  # DataStore, FileVault, JobQueue APIs
│   │   └── project_app/   # Project management
│   └── public_app/        # Landing page & public 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 (platform CLI + MCP)
├── tests/                   # Test suite
└── Makefile                 # Thin dispatcher

For app developers: Use pip install scitex-app[cli] and the scitex-app app CLI. scitex-cloud is the platform server — app developers don't need to install it.

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

0. The freedom to run your research anywhere — your machine, your terms.
1. The freedom to study how every step works — from raw data to final manuscript.
2. The freedom to redistribute your workflows, not just your papers.
3. 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.

A2A Protocol Surface

scitex-cloud serves the Google A2A protocol at a2a.scitex.ai for the orochi agent fleet — AgentCard discovery, JSON-RPC dispatch, bearer-auth via Gitea PAT, and a Tier 3 forwarder to live agents. See apps/infra/a2a_app/README.md.

curl https://a2a.scitex.ai/v1/agents/ | jq '.agents | length'

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.

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