UltraContext – A simple context API for AI agents with auto-versioning
Hey HN! I'm Fabio and I built UltraContext, a simple context API for AI agents with automatic versioning. After two years building AI agents in production, I experienced firsthand how frustrating it is to manage context at scale. Storing messages, iterating system prompts, debugging behavior and multi-agent patterns—all while keeping track of everything without breaking anything. It was driving me insane. So I built UltraContext. The mental model is git for context: - Updates and deletes automatically create versions (history is never lost) - Replay state at any point The API is 5 methods:…
In plain words
UltraContext is a context API designed for AI agents that automatically versions all updates and deletions, ensuring change history is never lost. Built by someone with two years of production AI agent experience, it provides five core methods for creating, appending, retrieving, updating, and deleting messages while maintaining full version control. The tool is for developers building multi-agent systems who need to manage context at scale, debug behavior patterns, and iterate on system prompts without losing previous states. Messages are schema-free, and users can replay context at any point in its history.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hey HN! I'm Fabio and I built UltraContext, a simple context API for AI agents with automatic versioning. After two years building AI agents in production, I experienced firsthand how frustrating it is to manage context at scale. Storing messages, iterating system prompts, debugging behavior and multi-agent patterns—all while keeping track of everything without breaking anything. It was driving me insane. So I built UltraContext. The mental model is git for context: - Updates and deletes automatically create versions (history is never lost) - Replay state at any point The API is 5 methods: uc.create() // new context (can fork from existing) uc.append() // add message uc.get() // retrieve by version, timestamp, or index uc.update() // edit message → creates version uc.delete() // remove message → creates version Messages are schema-free. Store conversation history, tool calls, system prompts—whatever shape you need. Pass it straight to your LLM using any framework you'd like. What it's for: - Persisting conversation state across sessions - Debugging agent behavior (rewind to decision point) - Forking contexts to test different flows - Audit trails without building audit infrastructure - Multi-agent and sub-agent patterns What it's NOT: - Not a memory/RAG system (no semantic search) - Not a vector database - Not an Orchestration/LLM framework UltraContext handles versioning, branching, history. You get time-travel with one line. Docs: https://ultracontext.ai/docs Early access: https://ultracontext.ai Would love feedback! Especially from anyone who's rolled their own context engineering and can tell me what I'm missing.
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, January 2026
the whole month →- IN
Hey HN! I wanted to share something I built over the last few weeks: isometric.nyc is a massive isometric pixel art map of NYC, built with nano banana and coding agents. I didn't write a single line of code. Of course no-code doesn't mean no-engineering. This project took a lot more manual labor than I'd hoped! I wrote a deep dive on the workflow and some thoughts about the future of AI coding and creativity: http://cannoneyed.com/projects/isometric-nyc
AI · Jan 2026 · cannoneyed.com
Automatic AI-powered code reviews the moment you open a PR
Dev tools · Jan 2026 · kilo.ai
