AgentIndex icon
AgentIndex
mnemosyne logo
mnemosyne
★ 151
vs
agents-remember logo
agents-remember
★ 29

mnemosyne vs agents-remember

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.; agents-remember: Agents Remember provides coding agents with durable, git-verified repository memory, ensuring project-specific rules are surfaced precisely when edits are made. It turns local invariants, naming rules, and other project facts into versioned Markdown files alongside the code, improving agents' ability to make safe and compliant changes.

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 →
agents-remember logo
agents-rememberMemory & Context · ★ 29

Ensuring coding agents adhere to project-specific rules and invariants when making changes deep within a codebase.

Read agents-remember full review →
02

Side-by-Side Comparison

Field
mnemosyne logomnemosyne
agents-remember logoagents-remember
Category
Memory & Context
Memory & Context
Stars
★ 151
★ 29
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
uvx agents-remember-mcp@latest --config /absolute/path/to/agents-remember-settings.json
Updated
2d ago
2d ago
Open Source
Yes
Yes
Website
↗ Visit
↗ Visit
GitHub
↗ GitHub
↗ GitHub
Tags
AI Agents, Persistent Memory, Context Management
AI Agents, Coding Assistant, Git Integration
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.
agents-remember logoagents-remember
01Path-addressed memory: Notes live at deterministic mirror paths, providing agents with relevant context without search or guesswork.
02Git-proven freshness: Memory (notes, overviews, catalogs) is drift-checked against source commits and scopes to ensure accuracy.
03Memory that lands with code: External memory repos use ledgers and dual worktrees to synchronize code and memory updates after approval.
04Repo-owned agent behavior: Each memory repo carries system files for path rules, tools, coding guidelines, and documentation sources.
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.
agents-remember logoagents-remember
↳Ensuring coding agents adhere to project-specific rules and invariants when making changes deep within a codebase.
↳Providing agents with critical context like naming rules, migration scars, or 'looks safe but is not' facts at the point of edit.
↳Maintaining synchronized documentation and code, with memory updates landing only after approved code changes.
↳Bootstrapping new projects or onboarding agents with predefined path rules, tools, and coding guidelines.
FAQ

FAQ

What is the difference between mnemosyne and agents-remember?
Both mnemosyne and agents-remember are in the Memory & Context category. mnemosyne has 151 stars, while agents-remember has 29 stars.
Which is better, mnemosyne or agents-remember?
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 agents-remember if Ensuring coding agents adhere to project-specific rules and invariants when making changes deep within a codebase..
Is mnemosyne free or open source?
Yes, mnemosyne is open source on GitHub (MIT).
Is agents-remember free or open source?
Yes, agents-remember is open source on GitHub (MIT).
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Related

Alternatives to mnemosyne →Alternatives to agents-remember →mnemosyne details →agents-remember details →