Remember when AJAX felt revolutionary? When suddenly web pages could update without that jarring full-page refresh? I’m getting similar vibes watching Microsoft’s NLWeb roll out. Not because it’s flashy—it’s actually pretty understated—but because it’s addressing something fundamental about how we interact with information online.
Earlier this year, Microsoft introduced NLWeb (Natural Language Web), a project that brings conversational AI directly into websites. NLWeb is an open project developed by Microsoft that aims to make it simple to create a rich, natural language interface for websites using the model of their choice and their own data.
But here’s what caught my attention: this isn’t just another chatbot wrapper.
NLWeb is more than just a concept; it’s an open-source initiative comprising a Python-based framework and a set of protocols, collectively designed to embed natural language interfaces directly into web experiences. NLWeb is a collection of open protocols and associated open source tools.
What NLWeb Actually Does (Beyond the Buzz)
Let’s cut through the marketing speak.
NLWeb brings together protocols, Schema.org formats, and sample code to help sites quickly implement conversational endpoints — benefitting both users through natural interfaces and agents through structured interaction. NLWeb simplifies the process of building conversational interfaces for websites.
And what does this mean for your website?
rather than relying on complex front-end components, developers can use the NLWeb backend to handle natural language queries like “find sci-fi movies released after 2000 directed by Spielberg.” The front-end’s is now just to present these rich results, taking care of unnecessary UI complexity.
I’ve been working with complex search interfaces for years, and this hits different. You know that feeling when you’re building yet another faceted search component, complete with dropdowns, checkboxes, and that inevitable date range picker that nobody uses correctly? NLWeb basically says: “What if users could just ask for what they want?”
Every NLWeb instance also acts as an MCP server and supports a core method, ask, which allows a natural language question to be posed to a website. The response returned uses Schema.org — a widely adopted vocabulary for describing web data. In short, MCP is to NLWeb what HTTP is to HTML.
The MCP Connection: Why This Matters for Architecture
This is where it gets interesting from a systems perspective.
The Model Context Protocol (MCP) is an open standard for connecting AI assistants to the systems where data lives, including content repositories, business tools, and development environments. It provides a universal, open standard for connecting AI systems with data sources, replacing fragmented integrations with a single protocol.
Think about the integration nightmare we’ve all dealt with. Every new service needs its own connector, its own authentication flow, its own quirks.
Gone are the days of building custom connectors for every single data source—now you can just write against one standard protocol and call it a day. Over time, as more systems adopt MCP, AI tools will actually remember what they learned from one system when they jump to another. It’s like finally having a universal translator for all your scattered data sources.
It’s designed to be surprisingly lightweight and scalable—you can run this thing on a beefy data center cluster or just fire it up on your laptop. Mobile support is coming soon, which honestly makes sense given where everything’s headed. It natively supports MCP (Model Context Protocol), allowing the same natural language APIs to serve both humans and AI agents.
The extensibility is what caught my eye.
The true power of NLWeb for developers lies in its extensibility: Adding Data Sources: Use tools/db_load.py with different arguments to index various types of content. For unsupported types, developers might need to write custom pre-processing scripts. Implement the LLMProvider interface (from code/llm/llm_provider.py) for a new LLM.
The lightweight code that controls the core service to handle natural language queries, as well as documentation on how this can be extended and customized. Connectors to some of the most popular models and vector databases, as well as documentation to add other models of your choice.
But let’s talk about the practical stuff.
Performance & Costs: Embedding and LLM calls add latency and compute costs. Mitigation: Batch warm-up, client-side caching, or edge deployment of lightweight NLWeb servers can mitigate spikes and cut round-trip times.
The Developer Reality CheckNLWeb was conceived and developed by R.V. Guha, who recently joined Microsoft as CVP and Technical Fellow. Guha is the creator of widely used web standards such as RSS, RDF and Schema.org. When the person who created RSS is working on this, it’s worth paying attention.
In NLWeb’s documentation, Microsoft said that it has tested the protocol on Windows, MacOS, Linux, Qdrant, Snowflake, Milvus, Azure AI Search, and with LLMs, including Deepseek, Gemini, and Anthropic’s Claude.
The semantic markup requirement is real though.
Here’s the thing…