MemU
memU gives Codex, Claude, Cursor and other agents one shared, inspectable memory layer with source-linked recall.
If your problem is agent amnesia across a multi-agent stack, memU attacks it at the right layer: plain Markdown files you can read and diff, source links on every recalled fact, and a July 2026 memory path that runs in 474 lines with zero LLM calls. The tradeoff is real — installation is command-line, and there's no no-code plug-in here. Pick it for cross-agent continuity; pass if you need fully offline operation or memory bolted onto a plain RAG pipeline rather than agents.
Verified 2h ago · liveness 73/100 · cite: rightaichoice.com/tools/memu
- Developers running Codex, Cursor and Claude side by side who need conventions to carry across agents
- Teams running autonomous support agents that must reuse customer context without replaying conversations
- AI companion builders who want long-term personal memory synced across devices
- Knowledge workers moving research and source-linked decisions between different agent tools
- Non-technical users who want a no-code memory plug-in with zero configuration
- Projects that must run fully offline with no cross-device sync
- Teams looking for memory on non-agentic RAG or document-search pipelines
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Skip memU if you want a no-code memory plug-in, need a fully offline setup with no cloud sync, or want memory for a plain RAG pipeline rather than for AI agents.
Enterprise design-partner work is scoped as a pilot with architecture and data-boundary review, so custom memory systems, private deployment and host adapters are an engagement rather than an off-the-shelf purchase.
The free tier is aimed at individual developers running Codex, Claude, Cursor and similar agents, and covers shared memory, readable Markdown files, source-linked recall and cross-device sync. Teams that need custom memory scope, private deployment or internal-agent integration go through an enterprise design-partner pilot rather than a self-serve upgrade.
In short
MemU — memU gives Codex, Claude, Cursor and other agents one shared, inspectable memory layer with source-linked recall. Best for Developers running Codex, Cursor and Claude side by side who need conventions to carry across agents, Teams running autonomous support agents that must reuse customer context without replaying conversations, AI companion builders who want long-term personal memory synced across devices. Free to use.
What's new in MemU
Checked 5 days agoAcross the latest 1 update: 1 feature update.
What people actually say about MemU — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
55 mentions across 6 sources (Hacker News, YouTube, App Store, Stack Overflow, GitHub, Lemmy) · researched Aug 18, 2026.
Average across the 6 sources that answered — each source counts once, not each post.
- +Stores memory as readable Markdown — fully inspectable and transparent
- +Source-linked recall lets you see where every fact came from
- +Zero-LLM retrieval path means fast and cheap operation
- +Cross-agent and cross-device sharing — works with Codex, Claude, Cursor
- +Multimodal memory supports text, image, audio, and video
- −Docker Compose setup fails with obsolete 'version' warning
- −118 open issues raise maintenance and maturity concerns
- −Community feedback is thin — few real user reviews to judge by
- −Name collision with MEmu Android emulator causes confusion
- −Requires advanced skills — not approachable for non-coders
- • Pricing details for Pro and Enterprise tiers are not publicly listed
- • Self-hosting requires DevOps expertise and infrastructure costs
Viability Score
How well maintained and how widely used is MemU? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- Cross-agent shared memory across Codex, Claude, Cursor, OpenClaw, Hermes and WorkBuddy
- Readable Markdown memory files you can open, diff and edit by hand
- Source-linked recall attaching every remembered fact to its origin
- Agent-driven memory: the agent decides what to remember, memU stores and retrieves
- Zero LLM calls on the memory path (July 2026 release)
- Core runs in 474 lines with no retrieval pipelines or prompt chains
- Three-layer hierarchical memory: categories, items, actions
- Multimodal memory spanning text, image, audio and video
- Cross-device sync across laptop, desktop and home server
- Per-request retrieval that pulls only records relevant to the current task
- Context packs that replace repeated long prompts
- Automatic skill extraction from past sessions
- CLI install: fetch SKILL.md with an API key as a Bearer token
- Private deployment options: managed cloud or self-hosted
- SQLite or Postgres + pgvector storage backends
About MemU
memU is a cross-agent memory layer for people who run more than one AI agent and are tired of re-explaining themselves. It gives Codex, Claude, Cursor, OpenClaw, Hermes and WorkBuddy one shared store of durable context — preferences, decisions, coding habits, tool patterns, things to avoid — that any connected agent can pull from before it starts a task. Setup is a single paste: fetch https://memu.so/SKILL.md with your API key as a Bearer token in the Authorization header, then follow the retrieved instructions. What makes the storage model unusual is that agents write to readable Markdown files — project-preferences.md, launch-decisions.md, coding-habits.md, tool-patterns.md, things-to-avoid.md — so you can open a file and see exactly what an agent believes about you. The retrieval contract in tool-patterns.md is explicit: before a task, retrieve only records related to the current request, prefer recent decisions over stale ones, and keep source links attached; after a task, memorize durable decisions and skip temporary detail. Every recalled fact carries a link back to the session, brief or review that produced it. The July 2026 release pushed memory decisions into the agent itself: memU stores, embeds and retrieves, running in 474 lines with zero LLM calls on the memory path after dropping the earlier retrieval pipelines and prompt chains. Memory is organized in three layers — categories, items, actions — and spans text, image, audio and video. Because it syncs across devices, a convention learned in Codex on a laptop is waiting when the next task opens in Cursor on a desktop or an agent on a home server. It fits developers juggling several coding agents, teams running autonomous support or knowledge-work flows that need durable customer context, and companion builders who want long-term memory. Enterprises can go further as design partners: custom memory systems, private deployment on managed cloud or self-hosted, SQLite or Postgres + pgvector storage, and
Behind the Verdict
Most memory products sell you a black box and ask you to trust it. memU goes the other direction: the memory is a folder of Markdown files you can open, read and edit. That single design choice is why we'd shortlist it for anyone running multiple coding agents. When to pick it: you juggle Codex, Claude, Cursor or a home-server agent and keep pasting the same project background into every session. The coding-habits and launch-decisions files travel with you, so the next agent starts already briefed. The source-link requirement on every recalled fact is the part that matters for teams — when an agent says "you prefer X," you can trace where that came from. The July 2026 release is the current reason to look again if you bounced off earlier versions. Memory decisions now live in the agent, with memU storing, embedding and retrieving in 474 lines and zero LLM calls on the memory path. Fewer moving parts means less to break and less to audit. Where it bites: you need a CLI comfort level. Fetching SKILL.md with a Bearer token is a one-liner for a developer and a wall for a non-technical user. Cross-device sync is a strength, but it also means your memory lives off-machine — if a project must run fully offline, this isn't the shape you want. Closest alternative depends on your axis. If you have an agent stack and want transparent, editable, source-linked memory, memU is the more inspectable option. If what you actually need is retrieval over documents rather than agent state, you're in a different category — plain RAG tooling solves that and memU isn't built for it. My practical advice: start with the free tier on one messy multi-agent workflow, watch what lands in the files for a week, then judge. The files themselves are the product's clearest argument.
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Real-world workflow fit
Concrete scenarios for the personas MemU actually fits — and what changes day-one when you adopt it.
Start a Codex session on a MacBook and have the agent write coding habits — read the repository before editing, reuse existing components, run lint and focused tests — into coding-habits.md; later open Cursor on a desktop and let it pull the same file before the next task.
Outcome: The convention learned in the first session is already in context in the second, with a source link back to the Codex session that produced it.
Let an agent memorize durable customer context and support playbooks after each resolved conversation, then have the next agent retrieve only the records related to the incoming request instead of replaying the full prior transcript.
Outcome: Customer context carries forward across agents without pasting conversation histories into every prompt.
Store launch decisions and research notes in launch-decisions.md, keep the source link to the original brief attached, and retrieve the relevant decisions when work moves to a different agent.
Outcome: Decisions stay consistent and traceable across tools, and stale assumptions lose priority to recent ones.
Use Cases
- Automatically extract and store reusable skills from repeated agent sessions
- Keep a coding convention learned in Codex available when the next task starts in Cursor or Claude
- Carry personal preferences and decisions across assistants and devices
- Keep research context and source-linked decisions available when work moves to another agent
- Reuse customer context and durable support playbooks without replaying prior conversations
- Preserve learning goals, progress and reusable guidance across sessions
- Keep voice, constraints, references and creative decisions consistent between drafts and agents
- Maintain traceable, hierarchical memory files for enterprise knowledge work
Models Under the Hood
as of 2026-10-18
Limitations
- Setup is command-line: you fetch SKILL.md using an API key as a Bearer token, follow the retrieved instructions, and reuse the same key wherever required — there is no no-code install path.
- Some blog articles on the site are marked as describing an earlier-version architecture and may not reflect the current agent-driven setup.
- Memory is a layer for agents rather than a drop-in backend for a non-agentic RAG pipeline.
- Organizations whose agent is not on the supported host-adapter list need a scoped custom integration engagement rather than a self-serve path.
- The July 2026 release runs its memory path with zero LLM calls, so claims about the older retrieval-pipeline architecture no longer describe the current product.
as of 2026-10-13
Verification history
We have re-verified MemU 8 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 8 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published MemU tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Free
$0
Ideal for
Individual developer running Codex, Claude, Cursor or similar agents who wants one shared, inspectable memory across sessions and devices.
What this tier adds
Starting tier: cross-agent shared memory, readable Markdown files, source-linked recall, CLI install via SKILL.md and cross-device sync.
Enterprise design partner
Custom
Ideal for
Organizations that need custom memory scope, private deployment on managed cloud or self-hosted infrastructure, and integration with their internally approved agents.
What this tier adds
Adds custom memory and skill design, retrieval and storage configuration, a supported self-hosted pilot, SQLite or Postgres + pgvector backends, packated host adapters or scoped custom integration, plus architecture and data-boundary review.
Where the pricing makes sense
The company stage and team size where MemU's pricing actually pencils out — and where peers do it cheaper.
The free tier is aimed at individual developers running Codex, Claude, Cursor and similar agents, and covers shared memory, readable Markdown files, source-linked recall and cross-device sync. Teams that need custom memory scope, private deployment or internal-agent integration go through an enterprise design-partner pilot rather than a self-serve upgrade.
Setup time & first value
How long it actually takes to get something useful out of MemU — broken out by persona, not the marketing-page minute.
For a developer already comfortable with a terminal, first value lands quickly: paste the SKILL.md fetch command into a Codex or Claude session, authenticate with your API key as a Bearer token, follow the retrieved install instructions, and the agent can start writing memory files in the same session. Teams wiring several agents plus cross-device sync should budget an afternoon; enterprise
Switching to or from MemU
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From repeated long prompts: replace the boilerplate you paste into each agent with small context packs that memU retrieves per request.
- →From per-agent built-in memory: move durable preferences, decisions and coding habits into shared Markdown files so every connected agent reads the same store.
- →From manually maintained notes: let agents memorize durable decisions after a task and skip temporary detail instead of you copying context between sessions.
- ↗To a single-agent built-in memory: point that one agent at the files it needs and drop cross-agent sync, accepting that other agents stop seeing the shared store.
- ↗To a non-agentic RAG or search backend: rebuild retrieval around embeddings and queries rather than the Markdown memory files agents write and read.
- ↗To a self-managed stack: run the storage and embedding layer yourself with SQLite or Postgres + pgvector and own the sync across devices.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “MemU”, and we withheld 6: 6 could not be judged, because “MemU” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about MemU.
Official links
Tools that pair well with MemU
Common stack mates teams adopt alongside MemU, with the specific reason each pairing earns its keep.
Agentmemory
Agentmemory gives coding agents persistent memory between sessions — local, open-source recall for Claude Code, Cursor and Codex.
Powermem
Open-source, self-hosted memory layer that gives AI agents persistent recall via hybrid retrieval.
Signetai
Local-first agent memory and secrets layer that gives Claude Code, Codex, and OpenCode one shared context on your own machine.
Featured Head-to-Head Comparisons
Memu vs Spider Cloud
Spider Cloud and MemU serve entirely different needs. If you need to feed fresh web data into AI agents or RAG pipelines, Spider Cloud’s low-cost scraping ($0.03/1K pages) and new Browser AI commands make it the clear choice. If your AI agent needs persistent memory that predicts user intentions and operates 24/7 autonomously, MemU’s three-layer memory architecture is purpose-built for that. Choose based on whether your bottleneck is data ingestion or memory persistence.
Memu vs Temporal Ai
If your priority is building fault-tolerant AI agents or orchestrating multi-step workflows that survive crashes, Temporal AI is the clear choice—it's battle-tested by OpenAI and Replit. If you need a persistent, proactive memory layer that predicts user intent and works 24/7, MemU offers a unique three-layer architecture. For most developers, start with Temporal’s durable execution and add MemU if your agent requires long-term memory.
Memu vs Presto Voice
Choose Presto Voice if you operate a QSR chain and need a proven drive-thru voice AI that boosts revenue via upselling (up to 6% monthly lift) with 95% non-intervention. Choose MemU if you're a developer building proactive AI agents that need persistent, hierarchical memory and intention prediction — it's free to start and open-source. They serve completely different domains.
Alternatives to MemU
View allAgentmemory
Agentmemory gives coding agents persistent memory between sessions — local, open-source recall for Claude Code, Cursor and Codex.
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