Anthropic Technology Partner: Powering the @W-Architecture

Lopez.codes is proud to collaborate with Anthropic as an official technology partner, integrating Claude—Anthropic's frontier AI model—as a foundational node within our proprietary @W-Architecture framework.

Operating under the system persona "Claudia", Claude serves as our primary Anthropic node within an advanced multi-agent orchestration environment. Claudia works synchronously alongside our OpenAI-powered nodes (such as Omega and CodeX) to enable resilient, provider-agnostic AI workflows that dynamically route tasks to the most capable model.

What We Build with Claude

• Multi-Agent Orchestration: Complex task delegation within the @W-Architecture, allowing models to debate, verify, and execute code autonomously.

• AI Research & Development Pipelines: High-context data analysis and synthetic data generation.

• Full-Stack Engineering: Automated code review, architectural design, and deep technical documentation generation.

• Responsible AI Implementation: Structuring agentic behavior to strictly align with Swiss and EU AI governance and compliance standards.

Core Technologies & Implementation Details

To achieve a seamless, provider-agnostic multi-agent system, the @W-Architecture leverages several cutting-edge technologies:

• Model Context Protocol (MCP): We utilize MCP to standardize how Claudia and other agents interact with local filesystems, APIs, and databases. This isolates the LLM from the execution environment while giving it secure, unified tool access.

• Provider-Agnostic Router: A custom Node.js middleware layer that abstracts API differences between Anthropic's Messages API and OpenAI's Chat Completions, allowing agents to seamlessly hand off context to one another.

• Local System Integration (Shadows/Winframe): Real-time IPC (Inter-Process Communication) and WebSockets connect the web-based AI orchestrator directly to the local host OS, enabling models to interact with local applications and scripts.

• Vector Memory (RAG): Persistent memory structures utilizing dense vector embeddings to ensure long-term context retention across different agent sessions.

Recommended GitHub Repositories (Build It Yourself)

If you are looking to build a similar provider-agnostic, multi-agent orchestration system, we highly recommend exploring the following open-source projects:

1. Model Context Protocol (MCP) The official standard (championed by Anthropic) for connecting AI models to data sources and tools safely. Essential for giving your agents standardized access to local environments.

2. Microsoft AutoGen A framework that enables the development of LLM applications using multiple agents that can converse with each other to solve tasks. Perfect for recreating the "Claudia vs. Omega" multi-agent dynamic.

3. LangGraph by LangChain Ideal for building stateful, multi-actor applications with LLMs. It allows you to define cyclical graphs for agent reasoning, making it great for complex R&D pipelines.

4. AnythingLLM A full-stack application that turns any document, resource, or piece of content into context that any LLM can use as references during chatting.

5. Tauri / Electron If you want to build desktop-native AI assistants (similar to WensdayOS's Winframe approach) that have deep system access while maintaining web technologies for the UI.
   
   

Built with Claude — Anthropic Technology Partner

Authors: @Claudia & Lopez, N. V. (2026). Lopez.Codes