MAMA logo
MAMA
★ 15
vs
daimon-memory logo
daimon-memory
★ 14

MAMA vs daimon-memory

MAMA: MAMA OS provides local operating memory for AI agents, enabling them to connect diverse data sources like chats and documents into a searchable memory substrate. It allows agents to understand context, cite evidence, track decision evolution, and operate within defined boundaries to produce better briefings and actions.; daimon-memory: Daimon-memory is a self-hostable memory backend for AI agents, storing typed records in PostgreSQL with Qdrant as a semantic index. It provides fast, cheap, and reproducible hybrid keyword + vector search recall without an LLM in the recall path.

01

TL;DR

MAMA logo
MAMAMemory & Context · ★ 15

Assess Project Risk: An agent can assess project risk by synthesizing information from emails, Slack, PR status, and QA checklists.

daimon-memory logo
daimon-memoryMemory & Context · ★ 14

Providing persistent, shared memory for AI agents across different tools.

02

Side-by-Side Comparison

Field
MAMA logoMAMA
daimon-memory logodaimon-memory
Category
Memory & Context
Memory & Context
Stars
★ 15
★ 14
License
MIT
MIT
Install Command
claude auth login && npx @jungjaehoon/mama-os init && mama start
git clone https://github.com/wakbijok/daimon-memory && cd daimon-memory && ./install.sh --yes
Updated
6d ago
2mo ago
Open Source
Yes
Yes
Tags
Local Operating Memory, AI Agents, Knowledge Graph
AI Agents, Memory, Persistence
03

Features

MAMA logoMAMA
01Enables AI agents to read the board, cite evidence, and operate within explicit boundaries using local operating memory.
02Integrates diverse data sources like chats, documents, decisions, and work logs into a unified local memory substrate.
03Builds a knowledge graph that tracks decision evolution, including relationships like 'supersedes', 'builds on', and 'debates'.
04Features strict and balanced search modes that reject vector-only noise unless confirmed by lexical, entity, raw, or seed evidence.
05Ensures agent security through signed runtime envelopes, scope boundaries, and destination limits enforced before tool calls.
daimon-memory logodaimon-memory
01Persistent and typed memory for AI agents
02Deterministic and LLM-free recall using hybrid keyword + vector search
03Self-hostable backend using PostgreSQL for canonical storage and Qdrant for semantic indexing
04Single-subject memory space shared across multiple AI tools
04

Use Cases

MAMA logoMAMA
↳Assess Project Risk: An agent can assess project risk by synthesizing information from emails, Slack, PR status, and QA checklists.
↳Generate Cited Reports: Produce detailed situation reports and briefings for humans, explicitly citing evidence and separating judgment from inference.
↳Automate Workflow Triggers: An agent authors its own triggers from recurring situations, recalling relevant memory and folding everything into owner situation reports.
↳Manage Company Memory Twin: Maintain an append-only company memory twin that agents can inspect, simulate, and cite, preserving raw evidence and its evolution.
daimon-memory logodaimon-memory
↳Providing persistent, shared memory for AI agents across different tools.
↳Maintaining consistent personas and operating disciplines for AI agents.
↳Storing and retrieving AI agent decisions, lessons, incidents, and reminders.
↳Integrating memory capabilities with existing AI agent platforms like Claude Code, Codex, and Hermes.
FAQ

FAQ

What is the difference between MAMA and daimon-memory?
Both MAMA and daimon-memory are in the Memory & Context category. MAMA has 15 stars, while daimon-memory has 14 stars.
Which is better, MAMA or daimon-memory?
The best choice depends on your use case. Choose MAMA if Assess Project Risk: An agent can assess project risk by synthesizing information from emails, Slack, PR status, and QA checklists., and daimon-memory if Providing persistent, shared memory for AI agents across different tools..
Is MAMA free or open source?
Yes, MAMA is open source on GitHub (MIT).
Is daimon-memory free or open source?
Yes, daimon-memory is open source on GitHub (MIT).
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