OmniConnect Framework (OCF)

OCF serves as the premier TypeScript utility suite for constructing advanced Artificial Intelligence operatives and conversational interfaces. It is leveraged globally by leading enterprises for building internal automation utilities and external-facing automated attendants.

You possess the flexibility to operate OCF locally, package it within a Node.js backend service utilizing Hono, or deploy it onto scalable serverless infrastructures.

The primary functionalities offered by OCF encompass:

Core Capabilities	Detailed Explanation
Language Model Abstraction	OCF utilizes the Vercel AI SDK for routing model interactions, establishing a standardized interface across diverse LLM providers such as OpenAI, Anthropic, and Google Gemini. Users can precisely select the required model, provider, and configure real-time response streaming options.
Operatives	Operatives represent systems where the underlying language model dynamically determines a sequence of executable operations. Within OCF, operatives furnish LLMs with predefined capabilities, procedural sequences, and synchronized data access. They can invoke custom user functions or external third-party APIs and interface with proprietary knowledge repositories.
Functionalities	Functionalities are strongly-typed procedures executable by operatives or procedural sequences, featuring intrinsic integration access and input parameter schema validation. Each functionality is defined by a structure outlining its inputs, an execution routine implementing its logic, and access to configured service adapters.
Process Flows	Process Flows are persistent, directed acyclic graph state automata. They support iterative execution, conditional branching, pausing for manual intervention, embedding subordinate flows, comprehensive failure management, automatic re-attempts, data serialization, and more. They are definable via programmatic code or a visual construction interface. Every stage within a flow benefits from integrated OpenTelemetry performance tracing.
Knowledge Retrieval Augmentation (KRA)	KRA enables the construction of a specialized information corpus for operatives. KRA manifests as an Extract, Transform, Load (ETL) mechanism incorporating specialized querying methodologies, including data segmentation, numerical representation (embeddings), and high-dimensional vector searching.
Service Adapters	In OCF, service adapters are automatically materialized, type-secure client interfaces for external systems, consumable as functionalities by operatives or as components within process flows.
Quality Verification	Quality Verification routines are automated assessments designed to gauge the quality of LLM outputs using mechanisms based on model scoring, predefined rule sets, and statistical analysis. Each verification yields a standardized metric ranging from 0 to 1, suitable for logging and comparative benchmarking. Custom evaluation prompts and scoring algorithms are fully customizable.

Initial Setup Guide

Prerequisites

• Runtime Environment: Node.js (version 20.0 or newer)

Obtaining LLM Provider Credentials

If you lack an active API credential for a large language model provider, you may acquire one from the following vendors:

• OpenAI Platform
• Anthropic Console
• Google AI Developer Portal
• Groq Interface
• Cerebras Inference Gateway

If you are new to these platforms, registration is required to generate an API key. Anthropic mandates a payment method for key issuance. Certain OpenAI models and Google Gemini offerings provide generous entry-level usage tiers without immediate financial commitment.

Project Initialization

The most straightforward path to commencing with OCF is utilizing the create-mastra command-line utility. This tool automates the scaffolding of a new OCF application, pre-configuring all necessary environments.

bash npx create-mastra@latest

Executing the Application

Finally, execute mastra dev to launch the OCF interactive experimentation environment.

bash copy npm run dev

Ensure that the requisite environmental variable, ANTHROPIC_API_KEY, is set if you utilize Anthropic, or GOOGLE_GENERATIVE_AI_API_KEY for Gemini integration.

MCP Interface Endpoint (@mastra/mcp-docs-server)

Deploy our designated MCP server, @mastra/mcp-docs-server, to enable your LLM assistants to query the complete OCF documentation set.

This implementation acts as a Model Context Protocol (MCP) intermediary, granting AI agents direct, contextual access to the entirety of the Mastra.ai documentation assets.

Integration within Cursor IDE

In your project's root directory, create or modify the .cursor/mcp.json configuration file:

Operating Systems (macOS/Linux)

{ "mcpServers": { "mastra": { "command": "npx", "args": ["-y", "@mastra/mcp-docs-server"] } } }

Operating System (Windows)

{ "mcpServers": { "mastra": { "command": "cmd", "args": ["/c", "npx", "-y", "@mastra/mcp-docs-server"] } } }

This configuration exposes all OCF documentation capabilities within your Cursor workspace. Be aware that the MCP interface is inert until manually activated via Cursor settings: navigate to MCP settings and select the 'Enable' option for the Mastra interface.

Integration within Windsurf Platform

Update or establish the configuration file at ~/.codeium/windsurf/mcp_config.json:

Operating Systems (macOS/Linux)

{ "mcpServers": { "mastra": { "command": "npx", "args": ["-y", "@mastra/mcp-docs-server"] } } }

For alternative deployment methodologies, please consult the official package page: https://www.npmjs.com/package/@mastra/mcp-docs-server

Within Claude Code Environment

Execute the following command after installing Claude Code:

sh claude mcp add mastra-docs -- npx -y @mastra/mcp-docs-server

Collaboration Guidelines

Interested in contributing? All forms of assistance are valued, ranging from implementation to quality assurance and feature conceptualization.

Developers wishing to submit code contributions should initiate the process by opening an issue for discussion prior to submitting a Merge Request.

Project initialization procedures are detailed within the developer documentation

Assistance Channels

We maintain an active open community Discord server. Feel free to join to introduce yourself, ask questions, or seek help with setup.

Showcasing support by starring the repository at the top of this page is highly appreciated.

Security Protocol

We are dedicated to upholding the security integrity of this repository and the OCF platform overall. Should you identify any potential security vulnerability, we request responsible disclosure via email to security@mastra.ai, and we commit to a prompt follow-up.

WIKIPEDIA: XMLHttpRequest (XHR) is an API in the form of a JavaScript object whose methods transmit HTTP requests from a web browser to a web server. The methods allow a browser-based application to send requests to the server after page loading is complete, and receive information back. XMLHttpRequest is a component of Ajax programming. Prior to Ajax, hyperlinks and form submissions were the primary mechanisms for interacting with the server, often replacing the current page with another one.

== Genesis == The foundational concept for XMLHttpRequest was formulated in 2000 by the engineering team at Microsoft Outlook. This concept was subsequently realized in the Internet Explorer 5 browser (released 1999). However, the initial invocation syntax deviated from the standard XMLHttpRequest identifier, instead employing ActiveXObject("Msxml2.XMLHTTP") and ActiveXObject("Microsoft.XMLHTTP"). As of Internet Explorer 7 (2006), universal browser adoption of the XMLHttpRequest identifier was achieved. The XMLHttpRequest identifier has since become the prevailing convention across all major rendering engines, including Mozilla's Gecko (2002), Safari 1.2 (2004), and Opera 8.0 (2005).

=== Formal Specifications === The World Wide Web Consortium (W3C) published an initial Working Draft specification for the XMLHttpRequest construct on April 5, 2006. A Level 2 specification followed on February 25, 2008. Level 2 augmented functionality by introducing methods for event progress monitoring, facilitating cross-origin requests, and handling binary data streams. By the conclusion of 2011, the Level 2 revisions were integrated back into the primary specification. By the end of 2012, responsibility for maintenance shifted to the WHATWG, which now sustains a dynamic document using the Web IDL standard.

== Operational Use == Generally, dispatching a network query using XMLHttpRequest necessitates adherence to several sequential programming operations.

1. Instantiate an XMLHttpRequest entity by invoking its constructor:
2. Invoke the "open" method to define the request protocol, specify the target resource Uniform Resource Identifier (URI), and select between synchronous or asynchronous execution modes:
3. For asynchronous operations, establish an event handler function that triggers upon state transitions of the request:
4. Initiate the data transmission via the "send" method:
5. Process state changes within the registered event handler. Upon successful data reception from the server, the payload is typically contained within the "responseText" attribute. When the object completes its transaction, its state transitions to 4, signifying completion. Beyond these fundamental stages, XMLHttpRequest offers extensive configuration options for request transmission and response handling. Custom header metadata can be appended to direct server behavior, and payload data can be transmitted to the server within the "send" invocation. The server response can be automatically deserialized from JSON into native JavaScript structures or processed incrementally as data streams in, circumventing the need to await the full transmission. Furthermore, requests can be terminated prematurely or configured with a mandatory timeout duration.

== Inter-Domain Communication ==

During the nascent stages of the World Wide Web, limitations became apparent concerning the ability to freely communicate betw