MemoriLabs/Memori Alternatives

Choose if you want an Apache-2.0 universal memory layer with a large community and active development.

Universal memory layer for AI Agents

Choose if you want an MIT-licensed memory system with claimed benchmark leadership and permissive licensing.

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

Choose if you want an MIT-licensed agent memory that learns from experience rather than just storing state.

Hindsight: Agent Memory That Learns

Choose if you want a Rust-based, serverless single-file memory layer for low operational overhead instead of an enterprise infrastructure.

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 if you want a local-first, Markdown-native portable memory layer for user-owned, transparent storage.

One portable memory layer for every AI agent: local-first, Markdown-native, user-owned, and self-evolving across apps, tools, and workflows.

Choose if you accept AGPL-3.0 and want a library for building stateful agents.

Memory library for building stateful agents

Choose if you want an Apache-2.0 memory management kit with a focus on refinement and simplicity.

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

Choose if you want an Apache-2.0 universal memory layer with scalability and interoperability emphasis.

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 if you need a Java-native memory engine for JVM-based agent stacks.

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 if you want a TypeScript SDK with MCP/CLI/adapters for a portable semantic memory core.

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

Choose if you want an MIT-licensed, locally runnable memory API with high speed and scalability.

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

Choose if you want an Apache-2.0 memory OS with hybrid retrieval and cross-task skill reuse, promising token savings.

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 if you already use Milvus and want a Markdown-backed unified memory layer with an MIT license.

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

Choose if you want an MIT-licensed, research-focused agentic memory implementation with agentic memory capabilities.

A-MEM: Agentic Memory for LLM Agents

Choose if you want a single AGPL-licensed context database that unifies memory, knowledge RAG, and skills, rather than a separate memory layer.

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

Choose if you want a self-hosted knowledge graph engine for memory with explicit relationships and explainability, not just retrieval.

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 if you want a managed, team-level memory hub with prebuilt asset types (chat memory, skills, wiki, code graph) and governance.

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 if you want automatic session capture and AI-compressed context injection for coding agents, rather than a programmatic memory API.

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 if you are using LangGraph/CrewAI/AutoGen and want a REST knowledge-graph memory service with autonomous consolidation.

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

Choose if your use case is specifically coding agents and you want a benchmark-optimized persistent memory.

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

Choose if you need a lightweight local memory store for specific desktop agents like Claude Desktop, Copilot, or Codex.

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

Choose if you want unbounded, self-managing context for coding agents rather than a general-purpose memory API.

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

Choose if you use MCP agents and want a rollbackable, visual, graph-like memory server instead of a general enterprise layer.

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 if you want a free, local, personal memory engine for your own AI workflow, not for production multi-tenant systems.

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.