EverMind-AI/EverOS Alternatives

choose it when you need a widely adopted memory API with pluggable vector/graph backends instead of Markdown-native files.

Universal memory layer for AI Agents

choose it when you want a benchmark-focused memory layer with an alternative storage/retrieval design.

The best-benchmarked open-source AI memory system. And it's free.

choose it when you want memory that actively learns from agent behavior rather than static Markdown.

Hindsight: Agent Memory That Learns

choose it when you prefer a single-file Rust serverless memory layer over a Python local stack.

Memory layer for AI Agents. Replace complex RAG pipelines with a serverless, single-file memory layer. Give your agents instant retrieval and long-term memory.

choose it when you want a hosted-ready memory library for stateful agent workflows.

Memory library for building stateful agents

choose it when you need a memory management kit with explicit refinement and consolidation.

ReMe: Memory Management Kit for Agents - Remember Me, Refine Me.

choose it when you need an interoperable, highly scalable memory layer for autonomous agent systems.

Universal memory layer for AI Agents. It provides scalable, extensible, and interoperable memory storage and retrieval to streamline AI agent state management for next-generation autonomous systems.

choose it when you are building agents in Java and want a JVM-native memory engine.

Self-evolving cognitive memory and context engine for AI agents in Java. Empowering 24/7 proactive agents like OpenClaw with understanding and SOTA performance.

choose it when you want a TypeScript core with framework adapters and an MCP server.

Portable semantic memory for AI agents: core engine, TypeScript SDK, framework adapters, MCP server, CLI, and host plugins.

choose it when you want one system that combines agent memory, RAG, and skills.

Self-evolving Context Database for AI Agents. Unify Agent Memory, Knowledge RAG and Skills.

choose it when you want a knowledge-graph-based memory platform with built-in ETL.

Cognee is the open-source AI memory platform for agents. Give your AI agents persistent long-term memory across sessions with a self-hosted knowledge graph engine.

choose it when you want a fast, locally runnable memory API plus an included client.

Memory and context engine + app that is extremely fast, scalable, and can be run fully locally. The Memory API for the AI era.

choose it when you need a managed, team-level memory hub that turns artifacts into reusable assets.

TencentDB Agent Memory is a team-level memory hub for AI Agents — turning conversations, docs, and code into four reusable memory assets (Chat Memory, Skill, LLM-Wiki, Code-Graph) that are governed, shared, and equipped across agents and frameworks.

choose it when you need enterprise-grade memory infrastructure with structured state and governance.

Memori is agent-native memory infrastructure. A LLM-agnostic layer that turns agent execution and conversation into structured, persistent state for production systems. Built for enterprise, Memori works with the data infrastructure you already run, no rip-and-replace, and deploys across managed cloud, single-tenant cloud, VPC, and on-premises.

choose it when you want a self-evolving memory OS with hybrid retrieval and DeepSeek Harness support.

Self-evolving memory OS for LLM & AI Agents: ultra-persistent memory, hybrid-retrieval, and cross-task skill reuse, with 35.24% token savings and DeepSeek Harness support.

choose it when you prefer Milvus for vector search but still want a Markdown-backed memory layer.

A persistent, unified memory layer for all your AI agents (e.g. Claude Code, Codex, DSH), backed by Markdown and Milvus.

choose it when you want a self-hosted REST + knowledge-graph memory service for MCP pipelines.

Open-source persistent memory for AI agent pipelines (LangGraph, CrewAI, AutoGen) and Claude. REST API + knowledge graph + autonomous consolidation.

choose it when you want a research-oriented agentic memory with procedural memory support.

A-MEM: Agentic Memory for LLM Agents

choose it when you want a self-hosted MCP memory server with graph visualization and rollback.

A lightweight, rollbackable, and visual Long-Term Memory Server for MCP Agents. Say goodbye to Vector RAG and amnesia. Empower your AI with persistent, graph-like structured memory across any model, session, or tool. Drop-in replacement for OpenClaw.

choose it when you need a TypeScript persistent memory specialized for coding agents.

#1 Persistent memory for AI coding agents based on real-world benchmarks

choose it when you want self-managing unbounded context inside coding agents.

Unbounded context. Memory that manages itself. One session, for life. The hippocampus for coding agents, part of CortexKit.

choose it when you want a ready-made CLI that captures and reinjects context for Claude Code.

Persistent Context Across Sessions for Every Agent – Captures everything your agent does during sessions, compresses it with AI, and injects relevant context back into future sessions. Works with Claude Code, OpenClaw, Codex, Gemini, Hermes, Copilot, OpenCode + More

choose it when you want a turnkey local memory store for desktop coding assistants.

Local persistent memory store for LLM applications including claude desktop, github copilot, codex, antigravity, etc.

choose it when you want a free local personal memory app for your coding AI tools.

A cyber brain for your AI. It never forgets a detail, remembers exactly what you said, and learns how you work over time. Free, local, works with Cursor, Claude Code, Codex, OpenClaw, Hermes and more. MIT.