Open Source Projects

TeXRA is built on the belief that great research tools benefit from community collaboration. We've open-sourced key components of our infrastructure so that developers, researchers, and tool builders can use and contribute to them independently.

llm-zoo

One package to lookup any LLM's pricing, capabilities, and context window.

Keeping track of LLM pricing, context windows, and capabilities across providers is a moving target. llm-zoo solves this by providing a single, typed, zero-dependency package covering 70+ models from Claude, GPT, Gemini, DeepSeek, Grok, and more.

Highlights

• 70+ models across Anthropic, OpenAI, Google, DeepSeek, xAI, Moonshot, DashScope, Copilot, and OpenRouter
• Zero dependencies — lightweight with full TypeScript support
• Tree-shakeable for efficient bundling
• Zod schemas for runtime validation (Zod v4)
• Always current — pricing and capabilities regularly updated

Quick Start

npm install llm-zoo

import { lookup, cost, from, cheapest, ModelProvider } from 'llm-zoo';

// Look up a model
const claude = lookup('sonnet');
console.log(claude.contextWindow); // 200000

// Calculate cost
const usage = { inputTokens: 1000, outputTokens: 500 };
console.log(cost('sonnet', usage)); // { input: 0.003, output: 0.0075, total: 0.0105 }

// Find the cheapest model with vision support
const model = cheapest({ capabilities: ['vision'] });

// Filter by provider
const anthropicModels = from(ModelProvider.ANTHROPIC);

Key APIs

Function	Description
lookup(model)	Get full model configuration
resolve(apiName)	Search by full API identifier
from(provider)	Filter models by provider
where(predicate)	Custom filtering by capabilities
supporting(capability)	Find models with a specific feature
withContext(tokens)	Models meeting context requirements
cheapest(filters)	Most cost-effective model for given constraints
cost(model, usage)	Calculate exact usage costs
compareCosts(models, usage)	Side-by-side cost comparison

Use Case

llm-zoo powers TeXRA's model selection and cost estimation internally. If you're building LLM-powered tools, routing logic, or cost dashboards, it saves you from maintaining your own model registry.

GitHub Repository · npm Package

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MCP Server for Mathematica

A Model Context Protocol server that bridges MCP clients to a local Mathematica installation.

Research in physics, mathematics, and engineering often requires symbolic computation. mcp-server-mathematica lets any MCP-compatible client (Cursor, Claude Desktop, and others) execute Mathematica code and verify mathematical derivations through a local wolframscript installation.

Highlights

• Execute Mathematica code from any MCP client
• Verify derivation steps — validate that algebraic transformations are correct
• Multiple output formats — text, LaTeX, or Mathematica expressions
• Lightweight — Node.js server communicating over stdio

Tools Provided

execute_mathematica

Run arbitrary Mathematica expressions and get results in your preferred format.

{
  "code": "Integrate[x^2, {x, 0, 1}]",
  "format": "latex"
}

verify_derivation

Validate a sequence of mathematical steps. The server checks each transition using Simplify[prev == current].

{
  "steps": ["x^2 - y^2", "(x-y)*(x+y)"],
  "format": "text"
}

Requirements

• Mathematica installed locally with wolframscript available in your PATH
• Node.js v16 or later

Quick Start

git clone https://github.com/texra-ai/mcp-server-mathematica.git
cd mcp-server-mathematica
npm install
npm run build
node build/index.js

Then configure your MCP client to connect to the running server.

Use Case

mcp-server-mathematica powers TeXRA's mathematical verification capabilities. It's especially useful for physicists and mathematicians who want AI assistants to check symbolic derivations against a proper computer algebra system rather than relying on LLM arithmetic alone.

GitHub Repository

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TeXRA Scientific Skills

A collection of agent skills for scientific writing, peer review, and figure creation.

The capabilities that power TeXRA, packaged as portable agent skills. texra-scientific-skills distributes specialized skills for working with LaTeX manuscripts, mathematical content, and scientific communication, usable as Claude Code plugins or Codex skill bundles.

Skills

• Inline Paper Critic — adds compile-safe review comments to LaTeX manuscripts
• Literature Search — discovers and synthesizes academic sources with proper grounding
• Manuscript Review — audits technical documents for mathematical accuracy and consistency
• Math OCR — converts handwritten math into LaTeX
• Mathematical Enhancer — improves proofs, derivations, and mathematical clarity
• Scientific Presenter — creates presentations and Beamer decks
• Scientific Simplifier — streamlines code, LaTeX, and writing while preserving meaning
• TikZ Figure Builder — creates and refines scientific diagrams with iterative compilation
• Writing Commenter — provides inline editorial feedback

Quick Start

# Claude Code
/plugin marketplace add texra-ai/texra-scientific-skills

Skills use the standardized SKILL.md format and can also be installed via Codex or by symlinking individual skill directories into your agent's skills location.

Use Case

These skills bring TeXRA's research workflows to any agent system that supports the SKILL.md format, so your own AI assistant can draft, review, and illustrate scientific work.

GitHub Repository

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TeXRA Lean Skills

Agent skills for Lean 4 and Mathlib formalization work.

texra-lean-skills helps AI agents assist with mathematical formalization in Lean 4, bridging informal mathematics and formal code. Distributed as a Claude Code plugin or Codex skills bundle following the standard skill format.

Skills

• lean-blueprint — authors and maintains documents connecting informal mathematics to Lean declarations
• lean-proof-assistant — develops and debugs Lean proofs with goal inspection and lemma search
• lean-search — locates existing Lean 4 and Mathlib lemmas, APIs, and formalization patterns
• lean-simplifier — refactors code toward Mathlib-quality style while preserving meaning

Quick Start

# Claude Code
/plugin marketplace add texra-ai/texra-lean-skills

Skills can also be installed via Codex or by symlinking individual skill directories into your agent's skills location.

Use Case

If you formalize mathematics in Lean 4, these skills let AI assistants search Mathlib, draft and debug proofs, and keep blueprints in sync with formal declarations.

GitHub Repository

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Contributing

All of these projects are MIT-licensed and welcome contributions. If you find a bug, want to add a model to llm-zoo, extend the Mathematica tools, or contribute a new agent skill — open an issue or submit a pull request on the respective GitHub repository.

• texra-ai on GitHub