8 Best Memori Alternatives in 2026 (Open Source)

Memori is agent-native memory infrastructure. A LLM-agnostic layer that turns agent execution and conversation into structured, persistent state for production . Provides agent-native memory infrastructure that works with existing data systems without requiring rip-and-replace.

These 8 open-source tools do the same job. They are ordered by how closely they match Memori, with live GitHub data so you can see which projects are actively maintained.

1. 1. Mem0

   Universal memory layer for AI Agents

   What sets it apart: Unlike Zep (session-focused memory) or ChatGPT's built-in memory (closed, limited), Mem0 provides a standalone, open-source memory layer with proven +26% accuracy gains over OpenAI Memory, multi-level (user/session/agent) state management, and 90% token reduction via intelligent memory retrieval.

   Best for: AI assistant developers who need persistent, personalized memory across conversations without building custom infrastructure; Customer support chatbots that need to recall past tickets and user preferences

   ⭐ 66.4kMemori vs Mem0 →Full profile →

2. 2. Hindsight

   Hindsight: Agent Memory That Learns

   What sets it apart: Focuses on enabling agents to learn from experience rather than just retrieving stored conversation history.

   Best for: Building agents that need to learn from past interactions; Improving agent performance over long conversations; Implementing sophisticated memory systems

   ⭐ 43.9kMemori vs Hindsight →Full profile →

3. 3. memvid

   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.

   What sets it apart: Packages complete memory system into a single portable file without requiring databases or complex infrastructure.

   Best for: Long-running AI agents; Offline-first AI systems; Auditable AI workflows

   ⭐ 16.6kMemori vs memvid →Full profile →

4. 4. Supermemory

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

   What sets it apart: Claims #1 performance on three major AI memory benchmarks with 95% recall and 99.4% context reduction.

   Best for: Adding persistent memory to AI agents; Building AI products with memory capabilities; Running memory systems locally

   ⭐ 31.0kMemori vs Supermemory →Full profile →

5. 5. MemPalace

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

   What sets it apart: Open-source local-first memory system with verbatim storage and 96.6% R@5 performance on LongMemEval benchmarks without API calls.

   Best for: developers building AI agents with persistent memory; projects requiring local-first conversation history storage; systems needing structured semantic retrieval

   ⭐ 59.4kMemori vs MemPalace →Full profile →

6. 6. MemOS

   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 Harne

   What sets it apart: Provides a unified memory operating system with graph-structured memory that's inspectable and editable, not just a black-box embedding store.

   Best for: AI agents needing long-term memory; multi-agent collaboration systems; developers building context-aware agents

   ⭐ 11.7kMemori vs MemOS →Full profile →

7. 7. Memary

   The Open Source Memory Layer For Autonomous Agents

   What sets it apart: vs LangChain Memory / Mem0: graph-database-backed memory system emulating human memory (breadth + depth tracking) — agents automatically build and query knowledge graphs rather than flat conversation history

   Best for: Building persistent, context-aware AI agents with evolving memory; User preference tracking and personalization across sessions; Multi-user agent management with separate knowledge contexts

   ⭐ 2.7kMemori vs Memary →Full profile →

8. 8. Cognee

   Knowledge Engine for AI Agent Memory in 6 lines of code

   What sets it apart: Unlike Mem0 (conversation memory) or Chroma (pure vector search), Cognee builds an evolving knowledge graph from documents, combining vector + graph search with cognitive science approaches, ontology grounding, and cross-agent knowledge sharing — making it AI memory infrastructure rather than just a vector database.

   Best for: AI agent developers who need persistent, learning memory that combines vector search with knowledge graph relationships; Enterprise use cases requiring tenant isolation, audit trails, and cross-agent knowledge sharing

   ⭐ 31.2kMemori vs Cognee →Full profile →