ALI: A Python agent architecture where the normative evaluator is

Independent researcher Wolfgang Stegemann released v0.4 of the open-source reference implementation of the Artificial Local Intelligence (ALI) architecture, a Python-based agent framework that separates five organizational responsibilities into independent components. The implementation satisfies all 44 verifiable SHALL requirements defined in the architectural specification and requires no third-party packages, running on Python 3.11 or later.

Artificial Local Intelligence — Reference Implementation v0.4 This repository contains the open-source reference implementation of the ALI architecture as described in: Stegemann, W. 2026 . Artificial Local Intelligence: Architecture and Reference Implementation. Zenodo. https://doi.org/10.5281/zenodo.21631699 Artificial Local Intelligence ALI is a reference architecture for autonomous agents that separates five organisational responsibilities into independent components: Causal Core — evaluates operational viability before planning begins Ego — generates behavioural proposals without evaluating them Super-Ego — evaluates proposals without generating them Memory — preserves every decision as a complete, immutable Event Runtime — coordinates the cycle without participating in reasoning The architecture is defined by a compliance specification of 44 verifiable SHALL requirements. This implementation satisfies all 44. - Python 3.11 or later - No third-party packages Create demonstration files python main.py init Run one cycle dry run — no files changed python main.py run Execute approved actions python main.py run --apply Show current viability state and recent Events python main.py status Undo the last executed action python main.py rollback Run the full test suite python -m unittest discover -s tests -v ali/ Core architecture causal core.py Operational viability assessment ego.py Behavioural proposal generation super ego.py Normative evaluation memory.py Immutable Event storage SQLite runtime.py Cycle coordination interfaces.py Abstract base classes for all components plugins.py ComponentFactory — plugin system models.py Architectural data objects frozen dataclasses configuration.py Configuration loading examples/ llm ego.py Plugin example: LLM-based Ego stub mode README.md Plugin development guide config/ ali config.json Standard configuration ali config llm example.json Example: LLM Ego via plugin tests/ test architecture.py Hard norm and component boundary tests test runtime.py Operational cycle and learning tests test plugins.py Plugin system tests test compliance.py All 44 SHALL requirements verified workspace/ Default operational domain local files main.py Command-line interface Any architectural component can be replaced without touching the rest of the code. Add a components entry to ali config.json : { "components": { "ego": { "class": "examples.llm ego.LLMEgo", "params": { "model": "claude-sonnet-4-6", "stub": true } } } } See examples/README.md for a complete guide to writing plugins. 72 tests — 0 failures test compliance.py 41 tests — all 44 SHALL requirements verified test plugins.py 15 tests — plugin loading and integration test architecture.py 9 tests — component boundaries and hard norms test runtime.py 7 tests — operational cycle and learning Full architectural specification book : Stegemann, W. 2026 . Artificial Local Intelligence: Architecture and Reference Implementation. Zenodo. https://doi.org/10.5281/zenodo.21631699 https://doi.org/10.5281/zenodo.21631699 Scientific paper: Stegemann, W. 2026 . Artificial Local Intelligence: Architecture and Reference Implementation. Zenodo. https://doi.org/10.5281/zenodo.21632233 https://doi.org/10.5281/zenodo.21632233 Wolfgang Stegemann Independent Researcher ORCID: 0009-0000-1346-1170 https://orcid.org/0009-0000-1346-1170 https://orcid.org/0009-0000-1346-1170 MIT License. See LICENSE file.