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.