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remembra-ai/remembra

by Various

Universal memory layer for AI applications. Self-host in minutes. Open source.

R

MCP

remembra-ai/remembra

Added 1 June 2026

#ai #ai-agents #ai-memory #claude #developer-tools #embeddings #knowledge-graph #llm

Overview

Remembra is an open-source universal memory layer for AI applications. It can be self-hosted in minutes, providing a persistent memory backend for AI agents and chatbots.

Best for

Best for
Developers who want a simple, self-hosted memory solution for AI applications

Use cases

  • Persisting conversation context across sessions for chatbots
  • Sharing memory between multiple AI agents or tools
  • Self-hosting a private memory backend for data control

How to use

Install

pip install remembra

Tools exposed

  • store_memory
  • recall_memories
  • update_memory
  • forget_memories
  • list_memories
  • search_entities
  • share_memory
  • relationships_at
  • ingest_conversation
  • health_check

Tested with

Claude Desktop, Claude Code, Cursor, Windsurf, VS Code, ChatGPT

Notes

Remembra is an open-source universal memory layer for AI applications. It can be self-hosted in minutes, providing a persistent memory backend for AI agents and chatbots.

13 stars on GitHub. Last updated 2026-04-30. Licensed MIT.

Use cases

  • Persisting conversation context across sessions for chatbots
  • Sharing memory between multiple AI agents or tools
  • Self-hosting a private memory backend for data control

Pros

  • Open-source and self-hosted for full data ownership
  • Quick setup with minimal configuration
  • Designed as a universal drop-in memory layer

Cons

  • Very small community (13 GitHub stars) indicating early stage
  • Limited ecosystem and third-party integrations
  • Potential lack of documentation and support

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Open-source and self-hosted for full data ownership
  • Quick setup with minimal configuration
  • Designed as a universal drop-in memory layer

Cons

  • Very small community (13 GitHub stars) indicating early stage
  • Limited ecosystem and third-party integrations
  • Potential lack of documentation and support
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