Blog

Today we're releasing cbMCP, the official ColdBox MCP Server — a BoxLang-only module that turns your running ColdBox application into a fully-compliant Model Context Protocol (MCP) server. Plug in any MCP-capable AI client — Claude Desktop, VS Code Copilot, Cursor, Codex, Gemini CLI, OpenCode — and your AI assistant gets live, read-only introspection across the entire ColdBox platform: routing, handlers, modules, WireBox, CacheBox, LogBox, schedulers, interceptors, and async executors. 🎯

Today we're launching something we've been quietly building for months: skills.boxlang.io — a public, agent-agnostic directory for AI skills covering BoxLang, ColdBox, TestBox, CommandBox, and the entire Ortus ecosystem.

ColdBox CLI 8.11 is here, and it's one of the most significant releases we've shipped for AI-assisted development. This release wires the CLI directly into our brand new public skills directory at skills.boxlang.io, brings our AI tooling in line with industry-wide agent conventions, and introduces a wave of quality-of-life improvements that make AI integration feel less like setup and more like infrastructure.

BoxLang AI 3.1 is here, and it's a release that makes your agents smarter, faster, and more capable than ever. 🎉

We just shipped the BoxLang Google Cloud Functions Runtime — and it brings the same write-once-run-anywhere serverless experience you already know from our AWS Lambda runtime, now running natively on Google Cloud Functions Gen2.

We believe the best way to learn a programming language is by writing code — real code, with real feedback, and real tests. That's exactly why we built BoxLings.

BoxLang 1.12.0 marks a meaningful turning point. After establishing a rock-solid foundation across runtime, compiler, CFML compatibility, and the module ecosystem, BoxLang has entered its innovation cycle. The language is mature, battle-tested, and production-deployed across the industry.

AI agents are transforming how we build software. Unlike traditional chatbots that just answer questions, agents can reason about what tools they need, decide when to use them, chain multiple actions together, and remember what happened earlier in a conversation.

The AI ecosystem has a tool problem. Every framework has its own way of defining tools, every agent has its own way of calling them, and every integration requires custom code on both sides. An agent built in Python can't easily use tools built in Java. An MCP server written for Claude Desktop can't easily be consumed by a BoxLang agent without a custom adapter.

A chatbot with no memory isn't a conversation — it's a series of isolated queries. Every message starts from scratch. The user has to re-explain who they are, what they're working on, and what was just said. It's exhausting, and it signals that the AI isn't really listening.