memora and MegaMemory

These two tools are competitors, as both "agentic-box/memora" and "0xK3vin/MegaMemory" describe themselves as MCP servers for providing persistent memory and knowledge graph capabilities to AI agents, offering similar core functionalities like semantic storage and knowledge graph management.

memora
54
Established
MegaMemory
50
Established
Maintenance 13/25
Adoption 10/25
Maturity 15/25
Community 16/25
Maintenance 10/25
Adoption 8/25
Maturity 20/25
Community 12/25
Stars: 322
Forks: 34
Downloads:
Commits (30d): 0
Language: Python
License: MIT
Stars: 59
Forks: 7
Downloads:
Commits (30d): 0
Language: TypeScript
License: MIT
No Package No Dependents
No risk flags

About memora

agentic-box/memora

Give your AI agents persistent memory — MCP server for semantic storage, knowledge graphs, and cross-session context

This project helps AI agents remember information across different tasks and conversations, acting like a persistent brain. It takes in structured notes, conversations, and observations, then organizes them into a searchable memory and a visual knowledge graph. AI developers or researchers building sophisticated agents that need long-term context and recall would use this.

AI Agent Development Conversational AI Knowledge Management Contextual AI Semantic Search

About MegaMemory

0xK3vin/MegaMemory

Persistent project knowledge graph for coding agents. MCP server with semantic search, in-process embeddings, and web explorer.

MegaMemory helps AI coding agents remember project details across different work sessions. It takes natural language descriptions of code concepts, architecture, and decisions, then allows the agent to semantically search and recall these details for future tasks. This tool is for developers who use AI coding assistants like OpenAI Codex, Claude Code, or Antigravity and want them to maintain a consistent understanding of a project over time.

AI coding assistant developer tools knowledge management software development agent workflow

Scores updated daily from GitHub, PyPI, and npm data. How scores work