Axiom Engine

Axiom Engine (axiomai-engine on PyPI) is a headless, LLM-driven narrative game engine. It powers the Axiom AI desktop application, but it has zero GUI dependencies: you can drive it from a Python script, a terminal, a server — or build your own front-end on top of it.

import axiom
from axiom.config import load_config, build_llm_from_config
from axiom.db_helpers import create_new_save

llm = build_llm_from_config(load_config())
save_id = create_new_save("MyWorld.db", "Alice", "Normal")

session = axiom.Session("MyWorld.db", save_id, llm=llm)
result = session.take_turn("I open the tavern door.")
print(result.narrative_text)

What it does

  • Persistent universes — each world is a self-contained SQLite database holding entities, stats, rules, lore, locations and scheduled events.

  • Universe-as-Code — a universe can be defined as a versionable tree of plain-text files (TOML + Markdown); the .db is just a compiled cache. Compile, decompile, hot-reload, share on Git.

  • LLM-arbitrated narration — a configurable LLM backend (Google Gemini, or any OpenAI-compatible API such as Ollama) narrates each turn, applies your universe’s rules, and tracks entity stats.

  • Event sourcing & time travel — every turn is an event in a journal. Rewind to any past turn, fork a save into an alternate timeline, or edit a save state by hand.

  • Causal time — an in-game clock advances with the story; an off-screen “Chronicler” simulates the rest of the world while the player is away.

  • Hybrid narrative memory — long-term recall over a local vector store, combining semantic and lexical (BM25) search with rank fusion, turn-aware recency and an optional neural reranker. An opt-in living mode additionally distils each turn into structured facts the world can remember. See the Memory guide.

  • Content generation — populate a universe (entities, lore, map, rules…) with an LLM, from the command line or from Python.

  • Optional scene illustration — generate an image per turn via Stable Diffusion WebUI, ComfyUI or the Gemini image API.

Where to start

  • New here? Follow the Quickstart: install the engine, configure an LLM backend and play your first turn — in Python or straight from the terminal.

  • Building a universe? Read the Universe-as-Code format and the CLI guide.

  • Integrating the engine? Head to the API reference, generated from the source docstrings.

Getting started

Guides

Reference