ABES – a memory architecture for belief revision in AI agents
15-phase belief scheduler with decay mechanics, but unproven in production agent pipelines.
Open-source local-first cognitive memory. AGM-compliant belief revision (49/49 postulates). When a fact changes, downstream beliefs are automatically re-evaluated, not just flagged.
AGM-compliant belief revision that auto-re-evaluates downstream beliefs when facts change.
AI researchers, knowledge graph builders, cognitive system developers
Obsidian · Logseq · Neo4j
15-phase belief scheduler with decay mechanics, but unproven in production agent pipelines.
Turns chat history into structured 'belief' and 'cognitive pattern' blocks you can inject into prompts, with simple APIs like run_reflection and run_synthesis that read like a research prototype. It's smart about separating V1 (domain beliefs) from V2 (transferable cognitive patterns), but it's clearly early-stage — tiny repo, Ollama-only workflow, and few commits mean you should treat it as an experimental MVP rather than a drop-in production memory system.
Drift scoring beats Mem0's 0.94 with zero-inference provenance chains.
Knowledge graph compression (3,714x token ratio) is impressive, but 'persistent agent memory' is crowded territory.
SQL-like queries against beliefs beat vector search's 10% precision with claimed 100% accuracy.
Models behavior instead of facts — genuinely different from standard RAG memory.