A Multi Agent Memory MCP That Connect Agents Across Systems and Machines

Local-first AI memory - runs on modest hardware like an SBC, scales across taOS clusters, and fits offline/compliance workflows or long-horizon agents. Verbatim transcript as source of truth, never overwritten. Zero-loss, framework-agnostic.

Your AI forgets everything between sessions. This fixes that — 98%+ retrieval accuracy, 100% on LongMemEval, 99% token savings. 44 MCP tools. Fully local, zero cost.

Persistent memory for AI agents. Single Rust CLI, hybrid Gemini + FTS5 + RRF retrieval. R@5 = 0.99 on LongMemEval S (beats MemPalace). Agent-native: no MCP, no server, just shell out.

Official Python SDK for RecallrAI – a revolutionary contextual memory system that enables AI assistants to form meaningful connections between conversations, just like human memory.

Public, reproducible benchmarks for Agent Brain on LongMemEval-M. 71.7% accuracy (Test 0). Companion code to https://doi.org/10.5281/zenodo.19673132 (Concept DOI → latest version, currently v3).

PredaCore — the apex autonomous agent. Hybrid Rust memory kernel, topped LongMemEval R@5 = 0.9574.

LENS - AI Memory Benchmark - Memory as Experience, Not Facts

Multi-agent strategic intelligence system with hybrid memory retrieval. Research project.

Anti-RAG dual-whitebox memory for LLM agents. 2.72 MB SQLite + Markdown kernel, no vector DB, no embeddings. Lifts qwen2.5:7b from 1.79% to 60.71% on NoLiMa-32k (+58.9pp), 88.71% on LV-Eval EN 256k, 84.8% on LongMemEval-S. Restart-safe, concurrency-bullet-proof, 100% transparent.

Reproducible evaluation harness for agent memory systems (LongMemEval and beyond).