Memori and Awesome-AI-Memory
A SQL-native memory implementation and a curated knowledge base on AI memory systems are **ecosystem siblings**—one provides a concrete technical solution for agent memory persistence while the other serves as educational reference material documenting the design patterns and approaches that inform such implementations.
About Memori
MemoriLabs/Memori
SQL Native Memory Layer for LLMs, AI Agents & Multi-Agent Systems
Automatically intercepts and persists LLM conversations to SQL, then intelligently retrieves relevant context on subsequent queries—achieving 81.95% accuracy on long-context tasks while reducing token usage to ~5% of full-context approaches. Integrates directly with OpenAI, Anthropic, and other LLM providers via SDK wrappers, plus hooks into OpenClaw agents and MCP-compatible tools (Claude Code, Cursor) without requiring code changes. Supports bring-your-own-database deployments for self-hosted setups alongside cloud-hosted options.
About Awesome-AI-Memory
IAAR-Shanghai/Awesome-AI-Memory
Awesome AI Memory | LLM Memory | A curated knowledge base on AI memory for LLMs and agents, covering long-term memory, reasoning, retrieval, and memory-native system design. Awesome-AI-Memory 是一个 集中式、持续更新的 AI 记忆知识库，系统性整理了与 大模型记忆（LLM Memory）与智能体记忆（Agent Memory） 相关的前沿研究、工程框架、系统设计、评测基准与真实应用实践。
Organizes 285+ papers and 87 open-source projects across a multi-dimensional taxonomy covering parametric vs. external memory, episodic/semantic/procedural types, and operations like writing, retrieval, updating, and compression. The repository systematically maps memory mechanisms including RAG, summarization, vector retrieval, and symbolic-neural hybrid approaches, with explicit focus on agent systems, multi-agent collaboration, and evaluation benchmarks for long-term consistency and personalization tasks.
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