Klavis AI – Open-source MCP integration for AI applications
Hi HN, we are excited to show you Klavis AI. It is an open source project and we provide hosted versions with API access as well. (Website: https://www.klavis.ai/, Github repo: https://github.com/Klavis-AI/klavis) We're addressing a couple of key problems with using MCPs. First, many available MCP servers lack native or used-based authentications, creating security vulnerabilities and adding complexity during development. Second, many MCP servers are personal projects, not designed for the reliability needed in production. Connecting to these servers…
In plain words
Klavis AI is an open-source project that integrates Model Context Protocol (MCP) servers with AI applications through a standardized API. It solves security and reliability issues common in MCP implementations by providing hosted, production-ready servers with built-in authentication and eliminating the need for custom MCP client code. Developers can access these servers via API without writing protocol-level code, making integration simpler for applications already using function calling systems.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hi HN, we are excited to show you Klavis AI. It is an open source project and we provide hosted versions with API access as well. (Website: https://www.klavis.ai/, Github repo: https://github.com/Klavis-AI/klavis) We're addressing a couple of key problems with using MCPs. First, many available MCP servers lack native or used-based authentications, creating security vulnerabilities and adding complexity during development. Second, many MCP servers are personal projects, not designed for the reliability needed in production. Connecting to these servers usually requires writing custom MCP client code for the MCP protocol itself, which is a barrier, especially if you already have function calling systems in place. Klavis AI aims to address these issues. To simplify access, we offer an API to launch production-ready, hosted MCP servers quickly via our API. The API also provides built-in OAuth and multi-tenancy auth support for MCP servers. We also want to remove the need for developers to write MCP client code. You can use our API to interact with any remote MCP servers directly from your existing backend infrastructure. For faster prototyping or direct user interaction, we also provide open-source client interfaces for Web, Slack, and Discord. The MCP servers and clients code is open source because we want to contribute to the MCP community. For a quick start in the hosted verions, log in to our website and generate an API key. Then start calling our APIs directly. You can find more details in our doc: https://docs.klavis.ai For a quick start in the open source version, go to our github repository and check out the detailed readme on each MCP server and client. A little note about myself: my background includes working on the function calling for Google Gemini. During that time, I saw firsthand the challenges teams face when trying to connect AI agents to external tools. I want to bring my insights and energy to accelerate MCP adoption. This is an early release, and we’d appreciate feedback from the community. What are your worst pain points related to MCPs, either as a developer or a general user? What other MCP servers or features would be most valuable to you? We'll be around in the comments. Thanks for reading!
More ai this month
the category →
I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
AI · 17d ago · simedw.com
Astute▲585Automate your B2B brand going viral, with new media creators
AI · 18d ago · company-app.joinastute.com
Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 26d ago · cactuscompute.com
Launched alongside, May 2025
the whole month →
- C9
Life & fun · 2025 · felixrieseberg.github.io
