MemU
One shared, inspectable memory layer for all your AI agents
MemU is a practical fix for cross-agent memory amnesia, and the July 2026 rewrite (zero LLM calls, 474 lines) directly attacks the cost and latency that plague embedding-based systems. Pick it if you run multiple coding agents and want durable, inspectable context. Skip it if you need plug-and-play no-code or offline-only operation.
Verified 3d ago · liveness 73/100 · cite: rightaichoice.com/tools/memu
- Developers running multiple coding agents who need consistent context across sessions
- Teams building autonomous support agents with durable customer context
- AI companion developers requiring long-term memory across devices
- Enterprise teams needing traceable, source-linked agent memory
- Beginners wanting a no-code memory plug-in with zero configuration
- Projects requiring fully offline operation (standard use is cloud-based)
- Teams needing memory for non-agentic RAG pipelines
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Skip MemU if you need plug-and-play no-code setup, fully offline operation, or non-agentic memory for RAG pipelines.
Standard usage is cloud-based, so you may need a paid plan for heavy cross-device sync or advanced features like custom memory systems.
MemU's free tier covers personal memory across sessions, agents, and devices with readable Markdown storage—ideal for solo developers. Startup and Enterprise tiers are contact-based, so costs scale with your needs; cheaper than building your own memory layer, but more expensive than simple embedding tools if you only need basic recall.
In short
MemU — One shared, inspectable memory layer for all your AI agents. Best for Developers running multiple coding agents who need consistent context across sessions, Teams building autonomous support agents with durable customer context, AI companion developers requiring long-term memory across devices. Free to use.
What's new in MemU
Checked 4 days agoAcross the latest 6 updates: 6 feature updates.
memU's July release: agent decides what to remember; memU stores, embeds, retrieves — in 474 lines, zero LLM calls
MemU's July release removes pipelines and LLM retrieval chains; agent-driven memory now in 474 lines with zero LLM calls.
CLAUDE.md gives repo constitution; MemU remembers incidents beyond static markdown
CLAUDE.md teaches repos fast; MemU remembers migrations and incidents markdown can't update.
Claude Computer Use automates UIs; MemU persists selectors and outcomes
Claude Computer Use drives real UIs; MemU remembers selector maps when portals redesign.
Claude Projects scope chats but lack shared agent memory
Claude Projects organize file context; MemU adds API-addressable memory across agents.
Claude Code harnesses measure PRs but lack persistent debugging memory
CI harnesses for Claude Code quantify quality; MemU compounds results into long-term debugging memory.
Anthropic Agent Skills: static skills need MemU for cross-team memory
Claude Agent Skills package runbooks; MemU adds memory of real outcomes across teams.
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.
- +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: September 2026
How we score →Key Features
- Cross-agent shared memory across Codex, Claude, Cursor, OpenClaw, Hermes, WorkBuddy
- Readable Markdown storage for full inspectability
- Source-linked recall—every fact attaches to its origin
- Agent-driven memory: the agent decides what to remember
- Zero LLM calls on the memory path (July 2026 release)
- Core runs in 474 lines with no pipelines or prompts
- Hierarchical memory structure: categories, items, actions
- Multimodal memory support for text, image, audio, video
- Cross-device sync across desktop, laptop, and home server
- Retrieval scoped per request—only relevant records are pulled
- Small context packs replace full prompts
- CLI install via SKILL.md fetch
- Private deployment options: managed cloud or self-hosted
- Supports SQLite or Postgres + pgvector for storage
About MemU
MemU is an agentic memory layer that gives your AI agents one shared, inspectable memory across sessions, agents, and devices. It solves the problem of agents forgetting context between sessions: instead of re-explaining your preferences, decisions, and coding habits every time you switch from Codex to Claude or move from desktop to home server, MemU stores durable facts in readable Markdown files that any connected agent can pull up before acting. This means the next agent starts with the right context, not a blank slate. MemU is built for developers running multiple coding agents, teams building autonomous support agents, AI companions, and knowledge workflows that span multiple tools. Installation is simple: paste a SKILL.md file into a new session, and MemU works with Codex, Claude, Cursor, OpenClaw, Hermes, and WorkBuddy. Once installed, each agent retrieves only records relevant to the current request, attaches source links to every recalled fact, and memorizes new decisions after a task. Memory is stored as plain Markdown, so you can read exactly what an agent knows and where that fact came from—no black box. The July 2026 release reworked the architecture: the agent itself decides what to remember, and MemU just stores, embeds, and retrieves. The new version runs in 474 lines with zero LLM calls on the memory path, removing previous pipelines, prompts, and retrieval chains—making it faster and cheaper. The system uses a three-layer hierarchical structure (categories, items, actions) and supports multimodal memory (text, image, audio, video), so you can store more than plain notes. For enterprises, MemU offers custom memory systems, private deployment (managed cloud or self-hosted, with SQLite or Postgres + pgvector), and integration with existing agents through scoped design-partner engagements. Enterprise capabilities are delivered via design-partner pilots, with deployment, integration, and support terms agreed per engagement. MemU is positioned for
Behind the Verdict
If you've ever watched Claude or Codex forget a project decision you made ten minutes ago, you know the pain MemU targets. It gives your agents a shared memory layer that survives session resets, device switches, and even agent changes: a preference learned in Codex stays available when you pick up in Cursor. That's the core promise, and the readable Markdown storage makes it more than a black-box vector store—you can actually open a file and see what your agent thinks it knows about you. We'd reach for MemU when you're juggling multiple coding agents and want consistent context without rewriting long prompts every session. The July 2026 architecture shift is a big deal: the agent decides what to remember, and MemU just stores, embeds, and retrieves. With zero LLM calls on the memory path, it's both faster and cheaper than embedding-based systems that make a request every time. That's a meaningful advantage if you're paying per token. Where it bites: the installation is not no-code. You need to paste a SKILL.md file into a new session and follow the instructions, and standard use is cloud-based—if you need fully offline operation, you'll be looking at the enterprise self-hosted route, which is a design-partner engagement, not a self-serve option. That's fine for developers, but it rules out beginners who want a plug-and-play memory plugin. Compared to Anthropic's Agent Skills, which package runbooks and guardrails as static files, MemU adds memory of real outcomes across teams—skills can't update themselves when a portal redesign breaks a selector map. MemU's memory can. That's the differentiator: it's not just a knowledge base, it's a record of what actually happened, with source links to prove it. In practice, the enterprise path is still a pilot. You won't find
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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.
You install MemU in Codex, and over a week it learns your coding conventions (prefer type hints, run lint after changes). When you switch to Claude for a different task, it recalls those conventions without you re-explaining.
Outcome: Claude starts with your coding habits already in memory, reducing back-and-forth and keeping code style consistent.
You connect MemU to a customer support agent across sessions. It memorizes a customer's preferences and past issues, so each new session starts with that context.
Outcome: The agent resolves tickets faster by not re-asking for background, improving customer satisfaction.
You're building a virtual companion that runs on your desktop and home server. MemU syncs conversation memories across devices, so the companion remembers shared stories and preferences regardless of where you talk.
Outcome: The companion maintains consistent long-term memory, making interactions feel more natural and personal.
Use Cases
- Deploy a 24/7 customer support agent that remembers past interactions and user preferences
- Build an AI coding assistant that retains context across multiple development sessions
- Create an intelligent onboarding bot that proactively guides new users based on behavior
- Develop a personalized content recommendation engine that learns user interests over time
- Power a virtual companion that maintains long-term memory of conversations and shared experiences
- Enable enterprise knowledge management with traceable, hierarchical memory files
Models Under the Hood
as of 2026-08-28
Limitations
- MemU is a shared, inspectable memory layer for AI agents, storing memory in readable Markdown with source-linked recall.
- Setup requires fetching SKILL.md via an API key and configuring via the command line.
- Some blog articles describe an earlier-version architecture and may not reflect the current setup.
as of 2026-08-25
Verification history
We have re-verified MemU 5 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-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
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/mo
Ideal for
Solo developers and hobbyists who want to try MemU with multiple agents without paying, needing personal memory across sessions and devices with readable Markdown storage.
What this tier adds
Starting free tier: includes personal memory, cross-device sync, source-linked recall, and SKILL.md installation.
Startup
Contact for pricing
Ideal for
Early-stage startups or small teams that need advanced memory features and priority support, but don't yet require custom enterprise deployment.
What this tier adds
Adds advanced memory features and priority support over the Free tier.
Enterprise
Contact for pricing
Ideal for
Large organizations that need custom memory systems, private deployment (cloud or self-hosted), and integration with existing agents, with a scoped design-partner engagement.
What this tier adds
Adds custom memory systems, private deployment (SQLite or Postgres + pgvector), and agent memory integration via design-partner pilots.
Where the pricing makes sense
The company stage and team size where MemU's pricing actually pencils out — and where peers do it cheaper.
MemU's free tier covers personal memory across sessions, agents, and devices with readable Markdown storage—ideal for solo developers. Startup and Enterprise tiers are contact-based, so costs scale with your needs; cheaper than building your own memory layer, but more expensive than simple embedding tools if you only need basic recall.
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 solo developer comfortable with CLI: about 10 minutes to fetch SKILL.md, get an API key, and configure MemU in one agent like Codex. Adding each new agent (Claude, Cursor, etc.) takes another few minutes. Non-technical users may need 30+ minutes to follow the SKILL.md instructions.
Integrations
Resources & Guides
Tutorials & Learning
Official links
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 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.
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.
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