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

Zero-LLM agent memory for Claude Code and AI agents: local-first retrieval via BM25, dense vectors, and reciprocal rank fusion, with no LLM call in the default path. Returns your original passages verbatim, not paraphrased. Benchmarked on LongMemEval-S. Pre-release, MIT.

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.

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.

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。

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).

Auditable memory layer for AI agents: zero-LLM-call local ingest (~10ms/msg, air-gapped), matches Mem0 on accuracy at ~1000x lower ingest cost, bi-temporal belief-state, MCP server. Honest LoCoMo/LongMemEval benchmarks. Open source (Apache-2.0).

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