llms-txt-hub

ProxyLLM is an OpenAI-compatible LLM gateway designed to route workloads across multiple provider lanes, including flat-fee ChatGPT/Codex subscriptions, self-hosted Claude Code bridges, and metered API keys. It provides features such as fallback logic, budget management, and request logging.

Key resources:

• Website: https://proxyllm.ai/
• llms.txt: https://proxyllm.ai/llms.txt
• Machine Manifest: https://proxyllm.ai/auth.md
• OpenAPI Specification: https://proxyllm.ai/openapi.json

The Crypto APIs REST platform provides a single integration point for over 20 blockchains (including UTXO, EVM, Solana, XRP, Kaspa, and Tezos).

Supported Data and Operations:

• Blockchain Data: Address Latest, Address History, Block Data, Transactions Data, and Blockchain Events.
• Wallet & Transaction Management: HD Wallets, Prepare Transactions, Broadcast Transactions, and Simulate Transactions.
• Market & Utilities: Blockchain Fees, Market Data, and Utils.
• Advanced Features: Contracts and AML address screening.

Integration Details:

• The API includes standard authentication and pagination conventions.
• Documentation includes explicit guardrails to distinguish between the current API surface and deprecated legacy v1 or retired SDK documentation.

The llm-cost-optimizer-node is a Node.js SDK designed for LLM Cost & Context Optimization. It provides serverless prompt preprocessing to reduce token costs for providers like OpenAI, Anthropic, and LLaMA by up to 50% or more.

Key Details:

• Category: infrastructure-cloud
• Website: https://github.com/Buddy-Henderson/llm-cost-optimizer-node
• llms.txt URL: https://github.com/Buddy-Henderson/llm-cost-optimizer-node/blob/main/llms.txt

Live Tennis API is a read-only HTTP API providing real-time professional tennis data for ATP, WTA, Challenger, and ITF (singles and doubles).

Key Data Areas:

• Real-time match scores and state: Point-by-point scores and live match updates.
• Players and Rankings: Player profiles, rankings, and cached season statistics.
• Fixtures: Forward fixture schedules.
• Win-Probability: Model-driven win-probability for tracked matches.

Access Methods:

• REST: Standard HTTP endpoints.
• WebSocket: For real-time data streaming.
• Official Clients: MIT-licensed clients are available for JavaScript and Python.
• MCP Server: An Model Context Protocol (MCP) server is available for agentic integration.

llmstxt.studio is a platform designed to help website owners create and manage llms.txt files. These files are standardized configuration files intended to improve how AI models interact with and comprehend website content.

Key capabilities include:

• Intelligent Form Builder: Generates llms.txt files that are compliant with the specification.
• Management Dashboard: Allows for the management of multiple files.
• AI-Driven Analysis: Provides content analysis and structure enhancement.
• Performance Tracking: Monitors how well AI models comprehend the provided content.

The llms-txt-hub repository maintains a curated collection of links to llms.txt and llms-full.txt files. These files are specialized documentation formats designed to provide Large Language Models (LLMs) with high-density, context-rich information about a project, tool, or service.

When browsing the hub, you can find links to:

• llms.txt: Typically a concise summary or entry point for LLM consumption.
• llms-full.txt: A more comprehensive version containing expanded documentation for deeper context.

This hub serves as a directory for developers and AI agents to quickly locate machine-readable documentation for various developer tools, frameworks, and services.