8 Best Mem0 Alternatives in 2026 (Open Source)

Mem0 — Universal memory layer for AI Agents. 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.

These 8 open-source tools do the same job. They are ordered by how closely they match Mem0, with live GitHub data so you can see which projects are actively maintained.

ToolGitHub starsStars / 30dLast commit
Mem0(original)66.4k+2,4292026-09-25
Cognee31.2k+2,6562026-09-29
Memary2.7k+122024-10-18
Letta25.0k+5152026-09-10
Letta25.0k+5152026-09-10
AgentLabs558+32025-02-06
Lagent2.3k+72026-04-20
AIOS6.4k+1672026-07-20
Microagents826+42024-03-15

  1. 1. 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

    ⭐ 31.2kMem0 vs Cognee →Full profile →

  2. 2. 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

    ⭐ 2.7kMem0 vs Memary →Full profile →

  3. 3. Letta

    Letta is the platform for building stateful agents: AI with advanced memory that can learn and self-improve over time.

    What sets it apart: Purpose-built memory architecture that enables agents to self-improve over time — vs generic agent frameworks that only have short-term chat history

    Best for: Building long-running AI agents that learn from interactions; Applications requiring persistent context across sessions

    ⭐ 25.0kMem0 vs Letta →Full profile →

  4. 4. Letta

    Letta is the platform for building stateful agents: AI with advanced memory that can learn and self-improve over time.

    What sets it apart: Pioneered virtual context management for LLMs — enabling unlimited conversation history through intelligent memory paging

    Best for: Long-running AI agents with persistent memory; Research on memory architectures for LLMs

    ⭐ 25.0kMem0 vs Letta →Full profile →

  5. 5. AgentLabs

    Universal AI Agent Frontend. Build your backend we handle the rest.

    What sets it apart: Open-source universal frontend for AI agents with built-in auth, real-time streaming SDK, and chat UI — focus on backend while it handles the rest (discontinued)

    Best for: rapid-agent-ui-deployment; adding-auth-to-ai-agents; chat-frontend-for-agents

    ⭐ 558Mem0 vs AgentLabs →Full profile →

  6. 6. Lagent

    A lightweight framework for building LLM-based agents

    What sets it apart: vs LangChain/CrewAI: PyTorch-inspired design with intuitive layer composition, dual sync/async interfaces, and built-in session-isolated memory for concurrent agent workloads

    Best for: Multi-agent workflows with iterative self-refinement; Research with InternLM/Qwen models and custom agents

    ⭐ 2.3kMem0 vs Lagent →Full profile →

  7. 7. AIOS

    AIOS: AI Agent Operating System

    What sets it apart: vs agent frameworks (LangChain/CrewAI): operates at OS-level abstraction with agent scheduling, memory management, and resource allocation — agents are 'apps' on an AI OS

    Best for: Research on OS-level agent infrastructure and scheduling; Building multi-agent systems with shared resource management

    ⭐ 6.4kMem0 vs AIOS →Full profile →

  8. 8. Microagents

    Agents Capable of Self-Editing Their Prompts / Python Code

    What sets it apart: vs pre-built tool agents: dynamically generates and stores agents for future reuse — the system independently develops new problem-solving methods rather than relying on manually defined tools

    Best for: Repetitive task automation that improves over time; Self-evolving agent systems that learn across sessions; Research into emergent agent specialization

    ⭐ 826Mem0 vs Microagents →Full profile →