memora
★ 744
vs
engram-memory
★ 38
memora vs engram-memory
memora: Memora provides AI agents with a persistent memory layer, featuring structured storage, semantic retrieval, and graph relations for cross-session context. It allows agents to absorb work into a durable knowledge graph and retrieve relevant information, TODOs, and related edges using `memory_digest`.; engram-memory: Engram Memory provides AI agents with persistent, self-hosted memory, eliminating session-end forgetfulness and API costs. It features a three-tiered recall engine for efficient storage, search, and retrieval of semantic embeddings on your own hardware.
01
TL;DR
memoraMemory & Context · ★ 744
AI Agent Memory Management: Giving AI agents long-term, structured memory for complex, multi-session tasks.
engram-memoryMemory & Context · ★ 38
Providing long-term memory for AI agents (e.g., Claude Code, Cursor) to remember codebase details, user preferences, and architectural decisions across sessions.
02
Side-by-Side Comparison
Field
memoraengram-memoryCategory
Memory & Context
Memory & Context
Stars
★ 744
★ 38
License
MIT
NOASSERTION
Install Command
pip install git+https://github.com/agentic-box/memora.git
docker run -d --name engram-memory --restart unless-stopped -p 6333:6333 -p 11435:11435 -p 8585:8585 -v engram_data:/data engrammemory/engram-memory:latest
Updated
1d ago
4mo ago
Open Source
Yes
Yes
Tags
AI Agents, Persistent Memory, Knowledge Graph
AI Agent Memory, Self-hosted, Vector Database
03
Features
memora01Persistent Storage: SQLite with optional cloud sync (S3, R2, D1)
02Semantic Search: Vector embeddings for advanced queries (TF-IDF, sentence-transformers, OpenAI)
03Knowledge Graph: Interactive visualization with Mermaid rendering and cluster detection
04Chat with Memories: RAG-powered chat panel with LLM tool calling for search, create, update, delete
05LLM Deduplication: AI-powered semantic comparison to find and merge duplicate memories
engram-memory01Three-tiered recall engine (Hot-Tier Cache, Multi-Head Hash Index, Hybrid Vector Search) for fast and efficient memory retrieval.
02Persistent memory management with semantic embedding (store, search, recall, forget, consolidate) for AI agents.
03Local-first operation with self-hosted components (Qdrant, FastEmbed, MCP server) for data privacy and zero API costs.
04Kuzu-backed entity graph for tracking entities, co-retrieval patterns, and spreading activation.
05Advanced memory capabilities including conflict detection, temporal queries, and an "answer from memory" function.
06Hive Memory for multi-device sharing, enabling shared memory pools across different agents and devices.
04
Use Cases
memora↳AI Agent Memory Management: Giving AI agents long-term, structured memory for complex, multi-session tasks.
↳Knowledge Base for Agents: Building a retrievable knowledge base that agents can query semantically and contextually.
↳Project Management & Task Tracking: Automating the creation and tracking of TODOs and issues, and surfacing insights for project progress.
↳Document Analysis & Retrieval: Storing and searching structured documents (e.g., reports, plans) at a granular fragment level.
↳Contextual Conversation for AI: Powering RAG-based chat panels that allow agents or users to converse deeply about stored memories.
engram-memory↳Providing long-term memory for AI agents (e.g., Claude Code, Cursor) to remember codebase details, user preferences, and architectural decisions across sessions.
↳Enabling multi-agent systems to share a common knowledge base and collaborate effectively through shared memory pools (Hive Memory).
↳Answering complex questions by synthesizing information from an agent's stored memories, both locally and optionally with cloud enhancement.
↳Auditing an agent's knowledge at specific points in time or over date ranges using temporal queries for incident analysis or historical context.
↳Storing and retrieving user preferences, facts, decisions, goals, and insights efficiently and semantically for personalized agent interactions.
05
Best For
memoraTrendingHidden Gem
engram-memory—
FAQ
FAQ
What is the difference between memora and engram-memory?
Both memora and engram-memory are in the Memory & Context category. memora has 744 stars, while engram-memory has 38 stars.
Which is better, memora or engram-memory?
The best choice depends on your use case. Choose memora if AI Agent Memory Management: Giving AI agents long-term, structured memory for complex, multi-session tasks., and engram-memory if Providing long-term memory for AI agents (e.g., Claude Code, Cursor) to remember codebase details, user preferences, and architectural decisions across sessions..
Is memora free or open source?
Yes, memora is open source on GitHub (MIT).
Is engram-memory free or open source?
Yes, engram-memory is open source on GitHub (NOASSERTION).
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