longmemeval

Local-first AI memory — runs offline on any machine with 8 GB+ RAM (SBC, mini PC, laptop, workstation). Zero-loss verbatim archive, knowledge graph, hybrid retrieval. Framework-agnostic, no cloud.

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

fidelis is zero-LLM agent memory for Claude Code and AI agents: a local-first memory layer whose default retrieval path uses BM25, dense vectors, and reciprocal rank fusion with no LLM call. It returns your original passages verbatim instead of paraphrasing and runs fully local. Benchmarked on LongMemEval-S. MIT, by Hermes Labs.

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.

Token-native agent memory retrieval for LLMs, without embedding APIs or vector databases.

Benchmark results, scorer, and reproducibility kit for Sibyl Memory. LongMemEval 95.6% (#2). Verify it yourself.

Multi-agent memory substrate for PostgreSQL — provenance-gated, vector-hybrid recall

Scope-isolated, graph-based long-term memory engine for AI agents.

Reproducible benchmarks for execution-intent memory in long-horizon AI coding agents. ID-RAG cross-corpus matrix + LongMemEval-S subset; BYO API keys.

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

LongMemEval 中文子集：识流基于 DeepSeek-V4-Flash 的 500 题公开评测结果与可复核数据。

The Cost of Remembering: filesystem memory matches long-context accuracy on LongMemEval while reading 97% fewer tokens and costing 95% less. Harness, run data, 129 agent-built memories, and paper source.

Retrain-free attention patch that makes Llama 3.3 70B ~1.3× more accurate on long-conversation memory

Benchmarks 20 agent-memory strategies through one lifecycle, one judge, one model. A 30-line vector store outranks every funded vendor SDK. Each result is stamped with commit, package versions, and seed so anyone can re-run it.

Smallest possible working example of CogmemAi (95.1% LongMemEval) wired into the Claude Agent SDK. Two-session demo: save in session 1, recall in session 2.

100-question 6-dimension long-conversation memory benchmark for Chinese-healthcare AI. Sivon reference: 92/100 mean (2026-05-27).

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).

The benchmark harness for kimetsu a local-first memory sidecar for AI coding agents. It answers one question: does kimetsu actually help the agents it attaches to, and how does the brain itself perform

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

LongMemEval-S results for Mnemo — 85.2% (426/500) under the official LongMemEval judge prompts (gpt-4o-mini); 80.8% strict exact-match cross-check. Per-question receipts + methodology.