AgentIndex icon
AgentIndex
caura-memclaw logo
caura-memclaw
★ 504
vs
engram-memory logo
engram-memory
★ 38

caura-memclaw vs engram-memory

caura-memclaw: MemClaw is an open-source, multi-tenant, multi-agent AI fleet memory system designed for agents to store what they learn, find fleet knowledge, and improve with every interaction. It transforms plain text into searchable, governed, and self-improving memory, enabling agents to learn from each other and avoid repeating mistakes.; 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

caura-memclaw logo
caura-memclawMemory & Context · ★ 504

Enabling large fleets of AI agents within an enterprise to share learned knowledge under strict governance.

Read caura-memclaw full review →
engram-memory logo
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.

Read engram-memory full review →
02

Side-by-Side Comparison

Field
caura-memclaw logocaura-memclaw
engram-memory logoengram-memory
Category
Memory & Context
Memory & Context
Stars
★ 504
★ 38
License
Apache-2.0
NOASSERTION
Install Command
git clone https://github.com/caura-ai/caura-memclaw.git && cd caura-memclaw && cp .env.example .env && docker compose up -d
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
Website
↗ Visit
↗ Visit
GitHub
↗ GitHub
↗ GitHub
Tags
AI Agent Memory, Multi-Agent System, Knowledge Graph
AI Agent Memory, Self-hosted, Vector Database
03

Features

caura-memclaw logocaura-memclaw
01Row-level tenant isolation with PII detection and quarantine.
02Hybrid search combining semantic similarity, full-text, and knowledge graph expansion.
03Outcome-based learning (Karpathy Loop) to reinforce successful actions and learn from failures.
04Contradiction detection and supersession for conflicting memories.
05Built-in MCP (Model Context Protocol) server for client integrations.
engram-memory logoengram-memory
01Three-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

caura-memclaw logocaura-memclaw
↳Enabling large fleets of AI agents within an enterprise to share learned knowledge under strict governance.
↳Deploying a unified, governed memory system for hundreds of production AI agents to share skills and memories efficiently.
↳Providing an auditable memory plane for multiple agents, teams, and even different vendors to collaborate and share information securely.
engram-memory logoengram-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

caura-memclaw logocaura-memclaw
Trending
engram-memory logoengram-memory
—
FAQ

FAQ

What is the difference between caura-memclaw and engram-memory?
Both caura-memclaw and engram-memory are in the Memory & Context category. caura-memclaw has 504 stars, while engram-memory has 38 stars.
Which is better, caura-memclaw or engram-memory?
The best choice depends on your use case. Choose caura-memclaw if Enabling large fleets of AI agents within an enterprise to share learned knowledge under strict governance., 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 caura-memclaw free or open source?
Yes, caura-memclaw is open source on GitHub (Apache-2.0).
Is engram-memory free or open source?
Yes, engram-memory is open source on GitHub (NOASSERTION).
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