AgentIndex icon
AgentIndex
mnemosyne logo
mnemosyne
★ 151
vs
state-memory-mcp logo
state-memory-mcp
★ 48

mnemosyne vs state-memory-mcp

mnemosyne: Mnemosyne provides persistent memory for AI agents, enabling them to carry work forward, recover original evidence, and adapt by retiring obsolete lessons when their source changes. It connects memory to its consequences, allowing agents to resume from handoffs, withhold skills after failures, and benefit from a wiser start.; state-memory-mcp: @putervision/state-memory-mcp is a zero-infrastructure, deterministic Model Context Protocol (MCP) server designed to provide AI coding assistants with a structured, persistent SQLite graph for tracking workflow state. It offers efficient context management, reduces latency by offloading to a local database, and supports multi-agent collaboration with a shared blackboard.

01

TL;DR

mnemosyne logo
mnemosyneMemory & Context · ★ 151

Building AI agents that require long-term, verifiable memory for decision-making and learning.

Read mnemosyne full review →
state-memory-mcp logo
state-memory-mcpMemory & Context · ★ 48

Providing persistent workflow state to AI coding assistants like Cursor and Copilot

Read state-memory-mcp full review →
02

Side-by-Side Comparison

Field
mnemosyne logomnemosyne
state-memory-mcp logostate-memory-mcp
Category
Memory & Context
Memory & Context
Stars
★ 151
★ 48
License
MIT
MIT
Install Command
git clone --branch v2.0.0-rc.9 --depth 1 https://github.com/28naem-del/mnemosyne.git && cd mnemosyne && npm ci --ignore-scripts && npm run build
npm install -g @putervision/state-memory-mcp && state-memory-mcp init
Updated
2d ago
2w ago
Open Source
Yes
Yes
Website
↗ Visit
↗ Visit
GitHub
↗ GitHub
↗ GitHub
Tags
AI Agents, Persistent Memory, Context Management
State Management, Context Management, SQLite
03

Features

mnemosyne logomnemosyne
01Memory with evidence: Private-by-default records, explicit sharing, corrections, conflicts, source history, and dependent retirement.
02Continuity across agents: Structured checkpoints preserve goals, decisions, artifacts, and next actions; source changes can retire obsolete handoffs.
03Adaptive context: Budgeted observations, hierarchical summaries, and expandable original source bytes, with evidence revalidation.
04Recall across words and meaning: Scoped BM25 by default; optional embeddings, hybrid fusion, and reranking over authorized records.
05Evidence-gated skills: Candidate procedures, explicit trial requirements, and retirement after failed or changed evidence.
state-memory-mcp logostate-memory-mcp
01Deterministic State Memory & Compact TaskSlices for fast evaluation
02Efficient Context Management & Decision Thresholding to prevent graph bloat
03Multi-Agent Blackboard for shared context and parallel subagent updates
04100% Local & Private architecture, ensuring all state stays within your workspace
05Interactive 3D Visualizer for graph exploration
04

Use Cases

mnemosyne logomnemosyne
↳Building AI agents that require long-term, verifiable memory for decision-making and learning.
↳Enabling agents to resume complex tasks or projects across multiple sessions or handoffs without losing context.
↳Developing AI systems that adapt to new information by invalidating or correcting outdated knowledge based on source changes.
↳Creating self-improving agents where skills are refined or retired based on trial outcomes and evidence.
↳Managing dynamic context for large language models, ensuring relevance and adherence to evidence within token budgets.
state-memory-mcp logostate-memory-mcp
↳Providing persistent workflow state to AI coding assistants like Cursor and Copilot
↳Managing context and decision-making for autonomous agents
↳Facilitating multi-agent collaboration on complex tasks with a shared context store
↳Auditing and tracing agent activities with a tamper-evident event ledger
↳Improving agent efficiency and reducing latency in task retrieval and context processing
FAQ

FAQ

What is the difference between mnemosyne and state-memory-mcp?
Both mnemosyne and state-memory-mcp are in the Memory & Context category. mnemosyne has 151 stars, while state-memory-mcp has 48 stars.
Which is better, mnemosyne or state-memory-mcp?
The best choice depends on your use case. Choose mnemosyne if Building AI agents that require long-term, verifiable memory for decision-making and learning., and state-memory-mcp if Providing persistent workflow state to AI coding assistants like Cursor and Copilot.
Is mnemosyne free or open source?
Yes, mnemosyne is open source on GitHub (MIT).
Is state-memory-mcp free or open source?
Yes, state-memory-mcp is open source on GitHub (MIT).
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Related

Alternatives to mnemosyne →Alternatives to state-memory-mcp →mnemosyne details →state-memory-mcp details →