Memori and mengram
These tools are competitors, with MemoriLabs/Memori providing a robust, SQL-native memory layer focused on data persistence and retrieval for LLMs and multi-agent systems, while alibaizhanov/mengram offers a more psychologically inspired, human-like memory system with semantic, episodic, and procedural components for learning and experience-driven procedures in AI agents.
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 mengram
alibaizhanov/mengram
Human-like memory for AI agents — semantic, episodic & procedural. Experience-driven procedures that learn from failures. Free API, Python & JS SDKs, LangChain & CrewAI integrations.
Supports semantic/episodic/procedural memory extraction through conversational APIs and file uploads (PDF, DOCX, TXT, MD using vision AI), with automatic procedure evolution triggered by failure feedback or implicit detection from conversation context. Offers multi-user isolation, cognitive profiling via system prompts, and Claude Code hooks for zero-config auto-save/recall in Claude IDE; integrates with LangChain, CrewAI, and MCP, plus data import from ChatGPT and Obsidian.
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