AgentIndex icon
AgentIndex
Ori-Mnemos logo
Ori-Mnemos
★ 333
vs
ogham-mcp logo
ogham-mcp
★ 116

Ori-Mnemos vs ogham-mcp

Ori-Mnemos: Ori Mnemos is an open-source persistent memory infrastructure for AI agents that implements human cognition models on a knowledge graph. It uses ACT-R decay, spreading activation, and Hebbian learning to manage memory, and achieves state-of-the-art retrieval performance while being zero-infrastructure and portable.; ogham-mcp: Ogham MCP is a persistent, searchable shared memory system for AI coding agents that works across different clients and sessions. It stores memories as vector embeddings in PostgreSQL, enabling hybrid search combining semantic and full-text retrieval with high accuracy. Ogham supports multiple embedding providers, knowledge graph traversal, wiki compilation, and automatic memory management features like scoring, condensing, and novelty detection.

01

TL;DR

Ori-Mnemos logo
Ori-MnemosMemory & Context · ★ 333

Multi-hop retrieval QA (e.g., HotpotQA)

Read Ori-Mnemos full review →
ogham-mcp logo
ogham-mcpMemory & Context · ★ 116

Store and retrieve architectural decisions and coding context across sessions and different AI coding agents

Read ogham-mcp full review →
02

Side-by-Side Comparison

Field
Ori-Mnemos logoOri-Mnemos
ogham-mcp logoogham-mcp
Category
Memory & Context
Memory & Context
Stars
★ 333
★ 116
License
Apache-2.0
MIT
Install Command
npm install -g ori-memory && ori init my-agent && cd my-agent
uvx --from ogham-mcp ogham init
Updated
3w ago
1w ago
Open Source
Yes
Yes
Website
↗ Visit
↗ Visit
GitHub
↗ GitHub
↗ GitHub
Tags
agent-memory, ai-agent, ai-agents
agent-memory, ai-memory, claude
03

Features

Ori-Mnemos logoOri-Mnemos
01Persistent memory across sessions, clients, and machines
02Knowledge graph with wiki-links, PageRank, and community detection
03Three memory spaces with cognitive forgetting (ACT-R decay)
04Four-signal fusion retrieval (semantic, BM25, PageRank, warmth)
05Retrieval intelligence with Q-learning and meta-learning
ogham-mcp logoogham-mcp
01Persistent shared memory across sessions and clients
02Hybrid search with Reciprocal Rank Fusion (semantic + full-text)
03Knowledge graph with entity linking, traversal, and contradiction detection
04Multi-provider embeddings (OpenAI, Ollama, Mistral, Voyage, Gemini, ONNX)
05Automatic condensing, scoring, novelty detection, and memory lifecycle management
04

Use Cases

Ori-Mnemos logoOri-Mnemos
↳Multi-hop retrieval QA (e.g., HotpotQA)
↳Long-term conversational memory (e.g., LoCoMo)
↳Persistent AI agent identity and knowledge
ogham-mcp logoogham-mcp
↳Store and retrieve architectural decisions and coding context across sessions and different AI coding agents
↳Provide consistent, context-aware assistance to AI coding agents by maintaining a shared knowledge base
↳Preserve project-specific information, preferences, and facts for long-term reference without repetition
FAQ

FAQ

What is the difference between Ori-Mnemos and ogham-mcp?
Both Ori-Mnemos and ogham-mcp are in the Memory & Context category. Ori-Mnemos has 333 stars, while ogham-mcp has 116 stars.
Which is better, Ori-Mnemos or ogham-mcp?
The best choice depends on your use case. Choose Ori-Mnemos if Multi-hop retrieval QA (e.g., HotpotQA), and ogham-mcp if Store and retrieve architectural decisions and coding context across sessions and different AI coding agents.
Is Ori-Mnemos free or open source?
Yes, Ori-Mnemos is open source on GitHub (Apache-2.0).
Is ogham-mcp free or open source?
Yes, ogham-mcp is open source on GitHub (MIT).
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Related

Alternatives to Ori-Mnemos →Alternatives to ogham-mcp →Ori-Mnemos details →ogham-mcp details →