scitex 2.25.1

A comprehensive Python library for scientific computing and data analysis

SciTeX (scitex)

Modular Python Toolkit for Scientific Research Automation

Docs · Quick Start · API · pip install scitex[all]

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Problem and Solution

#	Problem	Solution
1	Fragmented tools -- literature search, statistics, figures, and writing each require separate tools with incompatible formats	Unified toolkit -- import scitex as stx provides 50+ modules under one namespace, accessible via Python API, CLI, and MCP
2	No verification -- AI-accelerated research compounds volume and opacity; existing tools address whether work could be reproduced, not whether it has been	Cryptographic verification -- Clew builds SHA-256 hash-chain DAGs linking every manuscript claim back to source data
3	AI agents lack context -- general-purpose LLMs cannot operate across the full research lifecycle without domain-specific tools	293 MCP tools -- AI agents run statistics, create figures, search literature, and compile manuscripts through structured tool calls
4	No custom tooling -- every lab needs domain-specific tools, but building and sharing them requires deep infrastructure knowledge	App Maker and Store -- researchers create custom apps with scitex-app SDK and share via SciTeX Cloud

SciTeX serves as the computational engine behind SciTeX Cloud (scitex.ai) -- a self-hostable web platform for collaborative research.

SciTeX Ecosystem

                                               SciTeX Ecosystem
                     ┌─────────────────────────────────────────────────────────────┐
                     │  SciTeX Cloud (scitex.ai) -- self-hosted research platform  │
                     │    Writer | Scholar | FigRecipe | Clew | Hub | Apps         |
                     ├─────────────────────────────────────────────────────────────┤
                     │  scitex (this package) -- Python engine & orchestrator      │
                     │    @session | io | stats | plt | scholar | writer | clew    │
                     ├──────────┬──────────┬──────────┬──────────┬─────────────────┤
                     │ scitex-  │ scitex-  │ fig-     │ scitex-  │  scitex-clew    │
                     │ io       │ stats    │ recipe   │ writer   │  (verification) │
                     │ 30+ fmt  │ 23 tests │ figures  │ LaTeX    │  SHA-256 DAG    │
                     └──────────┴──────────┴──────────┴──────────┴─────────────────┘
                 Each package: standalone (pip install scitex-io) or unified (scitex.io)

SciTeX and Research Workflow

_{Figure 1. SciTeX research pipeline -- from literature search to manuscript compilation, with every step cryptographically linked.}

Demo

_{Video 1. 40 min, minimal human intervention -- AI agent conducts: literature search -> data analysis -> statistics -> figures -> 21-page manuscript -> peer review simulation}

Installation

pip install scitex[all]                # Recommended: everything

Per-module extras

pip install scitex                     # Core only (minimal)
pip install scitex[plt,stats,scholar]  # Typical research setup
pip install scitex[plt]                # Publication-ready figures (figrecipe)
pip install scitex[stats]              # Statistical testing (23+ tests)
pip install scitex[scholar]            # Literature search, PDF download, BibTeX enrichment
pip install scitex[writer]             # LaTeX manuscript compilation
pip install scitex[audio]              # Text-to-speech
pip install scitex[ai]                 # LLM APIs (OpenAI, Anthropic, Google) + ML tools
pip install scitex[dataset]            # Scientific datasets (DANDI, OpenNeuro, PhysioNet)
pip install scitex[browser]            # Web automation (Playwright)
pip install scitex[capture]            # Screenshot capture and monitoring
pip install scitex[cloud]              # Cloud platform integration

Requires Python 3.10+. We recommend uv for fast installs.

Quick Start

@scitex.session -- Reproducible Experiment Tracking

One decorator gives you: auto-CLI, YAML config injection, random seed fixation, structured output, and logging.

import scitex as stx
import numpy as np
    
@stx.session
def main(
    data_path,                       # Positional arg: python script.py data.csv
    n_samples=100,                   # Keyword arg:    python script.py data.csv --n-samples 200
    CONFIG=stx.session.INJECTED,     # Aggregated ./config/*.yaml
    plt=stx.session.INJECTED,        # Pre-configured matplotlib
    logger=stx.session.INJECTED,     # Session logger
):
    """Analyze data. Docstring becomes --help text."""
    
    # Load
    data = stx.io.load(data_path)
    
    # Demo data
    x = np.linspace(0, 2 * np.pi, n_samples)
    y = np.sin(x) + np.random.randn(n_samples) * 0.1
    
    # FigRecipe Plot
    fig, ax = stx.plt.subplots()
    ax.plot_line(x, y)
    ax.set_xyt("Time", "Amplitude", "Noisy Sine Wave")
    
    # Save sine.png + sine.csv with logging message
    stx.io.save(fig, "sine.png")
    
    return 0

if __name__ == "__main__":
    main()

$ python script.py data.csv --n-samples 200
$ python script.py --help
# usage: script.py [-h] [--n-samples N_SAMPLES] data_path
# Analyze data. Docstring becomes --help text.

script_out/FINISHED_SUCCESS/2026-03-18_14-30-00_Z5MR/
├── sine.png, sine.csv         # Figure + auto-exported plot data
├── CONFIGS/CONFIG.yaml        # Frozen parameters
└── logs/{stdout,stderr}.log   # Execution logs

scitex.clew -- Cryptographic Verification for AI-Driven Science

As AI agents produce research at scale, the question shifts from "could this be reproduced?" to "has this been verified?". Clew builds a SHA-256 hash-chain DAG linking every manuscript claim back to source data.

import scitex as stx

# Every stx.io.load/save automatically records file hashes -- zero config
stx.clew.status()                          # {'verified': 12, 'mismatched': 0, 'missing': 0}
stx.clew.chain("results/figure1.png")      # Trace one file back to source data
stx.clew.dag(claims=True)                  # Verify all manuscript claims

# Register traceable assertions
stx.clew.add_claim(
    file_path="paper/main.tex", claim_type="statistic", line_number=142,
    claim_value="t(58) = 2.34, p = .021",
    source_session="2026-03-18_14-30-00_Z5MR", source_file="results/stats.csv",
)

stx.clew.mermaid(claims=True)              # Visualize provenance DAG

Mode	Function	Answers
Project	clew.dag()	Is the whole project intact?
File	clew.chain("output.csv")	Can I trust this specific file?
Claim	clew.verify_claim("Fig 1")	Is this manuscript assertion valid?

L1 hash comparison (ms) / L2 sandbox re-execution (min) / L3 registered timestamp proof (optional).

_{Figure 2. Clew verification DAG -- green nodes are verified (hash match), red nodes have mismatches. Each node shows its SHA-256 hash prefix.}

scitex.io -- Unified File I/O (30+ Formats)

import scitex as stx

# Save and load -- format detected from extension
stx.io.save(df, "results.csv")
df = stx.io.load("results.csv")

stx.io.save(arr, "data.npy")
arr = stx.io.load("data.npy")

stx.io.save(fig, "figure.png")       # Also exports figure data as CSV
stx.io.save(config, "config.yaml")
stx.io.save(model, "model.pkl")

# Aggregate ./config/*.yaml into a single DotDict
CONF = stx.io.load_configs("./config/*.yaml")
print(CONF.MODEL.hidden_size)        # Dot-notation access

# Register custom formats
@stx.io.register_saver(".custom")
def save_custom(obj, path, **kw):
    with open(path, "w") as f:
        f.write(str(obj))

@stx.io.register_loader(".custom")
def load_custom(path, **kw):
    with open(path) as f:
        return f.read()

Supports: CSV, JSON, YAML, TOML, HDF5, NPY, NPZ, PKL, PNG, JPG, SVG, PDF, Excel, Parquet, Zarr, INI, TXT, MAT, WAV, MP3, BibTeX, and more.

scitex.plt -- Reproducible, Restylable Figures

Powered by figrecipe. Figures are reproducible nodes in the Clew verification DAG -- scientific data and visual style are decomposed, so figures can be restyled (fonts, colors, layout) without altering the underlying data hash. Every figure auto-exports its data as CSV + a YAML recipe for exact reproduction.

import scitex as stx
fig, axes = stx.plt.subplots(1, 3)
axes[0].stx_line(x, y)
axes[0].set_xyt("Time", "Value", "Line")

axes[1].stx_violin([g1, g2, g3])
axes[1].set_xyt("Group", "Score", "Violin")

axes[2].stx_heatmap(corr_matrix)
axes[2].set_xyt("X", "Y", "Heatmap")
stx.io.save(fig, "analysis.png")  # Saves analysis.png + analysis.csv + analysis.yaml

# Restyle without changing data (hash stays valid for Clew verification)
stx.plt.reproduce("analysis.yaml", style="nature")

scitex.stats -- Publication-Ready Statistics (23+ Tests)

import scitex as stx
result = stx.stats.run_test("ttest_ind", group1, group2, return_as="dataframe")
# Returns: p-value, effect size (Cohen's d), CI, normality check, power
recommendations = stx.stats.recommend_tests(data)
stx.stats.format_results(result, style="apa")   # "t(58) = 2.34, p = .021, d = 0.60"

scitex.scholar -- Literature Management

Search, download, enrich papers. Backed by local CrossRef (167M+) and OpenAlex (250M+) databases.

import scitex as stx
papers = stx.scholar.search("neural oscillations working memory", n=20)
stx.scholar.fetch("10.1038/s41586-024-07804-3")
stx.scholar.enrich_bibtex("references.bib", output="enriched.bib")

scitex scholar search "neural oscillations" --n 20
scitex scholar bibtex references.bib --output enriched.bib

scitex.writer -- LaTeX Manuscript Compilation

import scitex as stx
stx.writer.compile_manuscript("paper/")
stx.writer.add_figure("paper/", "results.png", caption="Main results")
stx.writer.add_table("paper/", "stats.csv", caption="Statistical summary")

scitex.notification -- Multi-Backend Notifications

Get notified when experiments finish -- via desktop, phone call, SMS, or email -- with automatic fallback.

import scitex as stx
stx.notification.alert("Experiment complete: accuracy = 94.2%")
stx.notification.call("Training diverged -- loss is NaN")
stx.notification.sms("GPU job finished on node-42")

@stx.session(notify=True)   # Notifies on completion or failure
def main(CONFIG=stx.session.INJECTED): ...

Full API reference · Module status

Module Overview

Category	Modules	Description
Core	session, io, config, clew	Experiment tracking, file I/O, config, cryptographic verification
Analysis	stats, plt, dsp, linalg	Statistics, plotting, signal processing, linear algebra
Research	scholar, writer, diagram, canvas	Literature, manuscripts, diagrams, figure composition
ML/AI	ai, nn, torch, cv, benchmark	LLM APIs, neural networks, PyTorch, computer vision
Data	pd, db, dataset, schema	Pandas utilities, databases, scientific datasets
Infra	app, cloud, tunnel, container	App SDK, cloud, SSH tunnels, containers
Automation	browser, capture, audio, notification	Web automation, screenshots, TTS, notifications
Dev	dev, template, linter, introspect	Ecosystem tools, scaffolding, code analysis

CLI Commands

scitex --help-recursive                  # Show all commands
scitex scholar search "topic"            # Search literature
scitex scholar fetch "10.1038/..."       # Download paper by DOI
scitex stats recommend                   # Suggest statistical tests
scitex clew status                       # Project verification overview
scitex clew dag --claims                 # Verify all manuscript claims
scitex audio speak "Analysis complete"   # Text-to-speech
scitex notification alert "Job finished" # Multi-backend notification
scitex template clone research my_proj   # Scaffold a project
scitex dev versions                      # Check ecosystem versions
scitex mcp list-tools                    # List all MCP tools (293)

Full CLI reference

MCP Server (293 tools across 23 modules)

Turn AI agents into autonomous researchers via MCP.

Category	Tools		Category	Tools		Category	Tools
plt	73		crossref	15		io	5
cloud	50		dev	13		template	4
writer	38		introspect	12		openalex	4
scholar	22		stats	10		linter	3
clew	9		dataset	8		social	3
project	6		notify	5		tunnel	3
docs	4		ui	2		usage	2

{"mcpServers": {"scitex": {"command": "scitex", "args": ["mcp", "start"],
  "env": {"SCITEX_ENV_SRC": "${SCITEX_ENV_SRC}"}}}}

Full MCP reference

SciTeX Platform

scitex (this package) is the Python engine. It powers the full SciTeX platform:

Layer	Package	Role
Cloud	scitex-cloud	Self-hosted Django web application (scitex.ai) -- Writer, Scholar, App Store, Hub, Clew verification
Frontend	scitex-ui	React/TypeScript components -- workspace, data tables, editors
App SDK	scitex-app	Runtime SDK for building and sharing custom research apps
Engine	scitex (this)	Python orchestrator -- @session, io, stats, plt, clew, CLI, MCP

Each standalone package (pip install scitex-io) also works unified (stx.io).

Full Ecosystem (17 packages)

Package	Module	Description
scitex-clew	stx.clew	SHA-256 hash-chain DAG for provenance
scitex-io	stx.io	Unified file I/O (30+ formats)
scitex-stats	stx.stats	Publication-ready statistics
figrecipe	stx.plt	Publication-ready matplotlib figures
scitex-writer	stx.writer	LaTeX manuscript compilation
scitex-scholar	stx.scholar	Literature management
scitex-notification	stx.notification	Multi-backend notifications
scitex-audio	stx.audio	Text-to-speech and audio
scitex-dev	stx.dev	Developer tools, ecosystem management
scitex-linter	stx.linter	AST-based code pattern checking
scitex-dataset	stx.dataset	Scientific datasets
scitex-cloud	stx.cloud	Self-hosted research platform
scitex-app	stx.app	Runtime SDK for research apps
scitex-ui	stx.ui	React/TS frontend components
crossref-local	stx.scholar	Local CrossRef (167M+ papers)
openalex-local	stx.scholar	Local OpenAlex (250M+ works)
socialia	stx.social	Social media (Twitter, LinkedIn)

Configuration

cp -r .env.d.examples .env.d  # 1. Copy examples
$EDITOR .env.d/                # 2. Edit credentials
source .env.d/entry.src        # 3. Source in shell

Full configuration reference

Part of SciTeX

SciTeX is an open-source research automation platform at scitex.ai. SciTeX Cloud provides a self-hostable web application -- GitHub for research -- with Writer, Scholar, App Store, and Clew verification built in.

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 research infrastructure deserves the same freedoms as the software it runs on.

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