Ori-Mnemos
★ 306
vs
optuna-mcp
★ 76
Ori-Mnemos vs optuna-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.; optuna-mcp: Optuna MCP Server exposes the Optuna hyperparameter optimization framework as a Model Context Protocol server. It lets LLMs and AI assistants run and manage optimization studies directly through a chat interface, create and analyze Optuna trials, and optimize parameters for any workflow. Compatible with Claude Desktop, Cursor, and other MCP clients via uv or Docker.
01
TL;DR
Choose Ori-Mnemos if…Multi-hop retrieval QA (e.g., HotpotQA)
Choose optuna-mcp if…Automated hyperparameter tuning for ML models driven by an LLM
02
Side-by-Side Comparison
03
Features
Ori-Mnemos01Persistent 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
optuna-mcp01Run Optuna optimization studies via natural language through any MCP client
02Create, manage, and analyze trials interactively in a chat session
03Persist optimization results with configurable Optuna storage backends
04Supports uv and Docker installation for Claude Desktop and Cursor
05Enables LLMs to optimize parameters of other MCP tools
04
Use Cases
Ori-Mnemos↳Multi-hop retrieval QA (e.g., HotpotQA)
↳Long-term conversational memory (e.g., LoCoMo)
↳Persistent AI agent identity and knowledge
optuna-mcp↳Automated hyperparameter tuning for ML models driven by an LLM
↳Interactive analysis of existing Optuna optimization results via chat
↳Optimizing inputs and outputs of other MCP tool pipelines
05
Best For
Ori-MnemosMemory & ContextRAG / Knowledge Base
optuna-mcp—
FAQ
FAQ
What is the difference between Ori-Mnemos and optuna-mcp?
Both Ori-Mnemos and optuna-mcp are in the Memory & Context category. Ori-Mnemos has 306 stars, while optuna-mcp has 76 stars.
Which is better, Ori-Mnemos or optuna-mcp?
The best choice depends on your use case. Choose Ori-Mnemos if Multi-hop retrieval QA (e.g., HotpotQA), and optuna-mcp if Automated hyperparameter tuning for ML models driven by an LLM.
Is Ori-Mnemos free or open source?
Yes, Ori-Mnemos is open source on GitHub (Apache-2.0).
Is optuna-mcp free or open source?
Yes, optuna-mcp is open source on GitHub (MIT).
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