MemOS
MemOS gives AI agents persistent long-term memory through a cloud API or a fully local runtime.
Worth piloting if your agents keep forgetting context and you want either hosted memory or a fully local runtime without rewriting your integration. The current $0/mo tiers (Starter listed at an original $19, Pro at $286) make the trial cheap, but those are promotional. Decide early what metered memory API usage costs you at scale, especially before you build deep into the OpenClaw plugin path.
Verified 13h ago · liveness 80/100 · cite: rightaichoice.com/tools/memos
- Developers building agents that must remember user preferences and task state across sessions
- Startups adding long-term memory to a chatbot without building their own vector infrastructure
- Teams using OpenClaw that want memory via the Agent Cloud or Agent Local plugins
- Enterprises needing private deployment and governance for cross-session agent context
- Anyone looking for a standalone consumer chatbot — MemOS is infrastructure, not an end-user app
- Simple Q&A apps with no cross-session memory requirement
- Non-technical teams that can't manage API calls, a CLI, or a local runtime install
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Skip MemOS if your product has no cross-session memory requirement, or if your engineering team can't take on a developer-oriented integration via API, CLI, MCP, or plugin.
Free caps out at 50K memory adds and 20K searches per month — chatty production agents will burn through that allowance and need a paid tier.
MemOS prices at the developer-infrastructure end of the memory market. The $0/mo Free tier and the currently-$0/mo Starter and Pro tiers (originals $19 and $286) undercut hosted memory services that charge from day one, and MemOS Lite adds a local runtime most competitors don't offer at any price. Above that, Enterprise is custom-quoted for unlimited memory, chat, and knowledge base usage. Compared with Mem0's hosted plans, MemOS is cheaper to start and more expensive to fully scale past
In short
MemOS — MemOS gives AI agents persistent long-term memory through a cloud API or a fully local runtime. Best for Developers building agents that must remember user preferences and task state across sessions, Startups adding long-term memory to a chatbot without building their own vector infrastructure, Teams using OpenClaw that want memory via the Agent Cloud or Agent Local plugins. Free to use.
What's new in MemOS
Checked 6 days agoAcross the latest 1 update: 1 launch.
What people actually say about MemOS — 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.
97 mentions across 7 sources (Hacker News, YouTube, Product Hunt, Bluesky, Stack Overflow, GitHub, Lemmy) · researched Jul 18, 2026.
Average across the 7 sources that answered — each source counts once, not each post.
- +Promises hybrid retrieval combining multiple search strategies.
- +Offers cloud API with claimed 5-minute integration.
- +Supports local-first MemOS Lite with zero cloud dependency.
- +Claims cross-task skill reuse across different applications.
- +Achieved SOTA on LoCoMo and LongMemEval benchmarks.
- −No real user feedback available to validate any claims.
- −Tightly integrated with OpenClaw ecosystem, limiting flexibility.
- −Pricing details not clearly communicated (freemium structure vague).
- −Likely requires intermediate skill; beginner path unclear.
- −Reputation tied to a product name (Memos) that others use, causing confusion.
- • Potential overage fees for memory capacity beyond tier limits
- • Enterprise pricing not transparent; likely requires sales call
Viability Score
How well maintained and how widely used is MemOS? 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
- MemOS Cloud: hosted memory service with five-minute integration and millisecond-level add and search
- MemOS Lite: zero-cloud-dependency local memory runtime for local-first agent workflows
- Memory APIs for CRUD, batch cleanup, tagging, and governance
- Predictive, intent-aware scheduling that preloads relevant memory before it's queried
- Dynamic knowledge graph unifying memory types for adaptive retrieval
- Memory Interoperability Protocol (MIP) for sharing memory across models, devices, and apps
- Code integration via Harness Code, Plugin, CLI, and MCP
- Agent Cloud Plugin for OpenClaw: inject cloud persistent memory, reduce token usage
- Agent Local Plugin for OpenClaw: persistent memory plus skill evolution, fully local
- ClawForce enterprise agent platform with governance and cloud sandbox delivery
- Memmy personal memory hub with a dedicated agent
- Multi-scenario deployment: public cloud, private cloud, on-premises, and hybrid
- Model-agnostic compatibility with agent frameworks, RAG setups, and major model ecosystems
- Open source memory management OS with customizable APIs for secondary development
About MemOS
MemOS is a memory management operating system for AI applications. You wire it into an agent so it retains user preferences, task state, and domain knowledge across sessions instead of starting from scratch each time. The audience is developers and platform teams building assistants, multi-agent workflows, or RAG pipelines that need durable context. The MemOS 2.0 "Stardust" release puts two runtimes behind one API: MemOS Cloud, a hosted memory service the vendor pitches as a five-minute integration with millisecond-level add and search, and MemOS Lite, a zero-cloud-dependency runtime for local-first agent workflows. Both are model-agnostic, with documented compatibility across agent frameworks, RAG setups, and major model ecosystems. Under the hood the system is layered. An Application & API layer handles memory preservation, update, transfer, and rollback. A Memory Scheduling layer preloads relevant memory predictively using dialogue history, task semantics, or environmental cues. A Storage & Substrate layer holds containerized user, expert, and domain memory. A dynamic knowledge graph unifies memory types for adaptive retrieval, and the Memory Interoperability Protocol (MIP) moves memory across models, devices, and apps. Lifecycle controls cover CRUD, batch cleanup, tagging, and governance. The surrounding stack is bigger than a single API: ClawForce handles enterprise agent governance and cloud sandbox delivery, Memmy is a personal memory hub with a dedicated agent, and Agent Cloud / Agent Local plugins drop memory into OpenClaw agents. MemOS benchmarks against other memory layers on LoCoMo and LongMemEval using LLM-as-a-Judge metrics. It competes with Mem0 and LangChain memory modules, and its angle is shipping a hosted service and a genuinely local runtime under one API.
Behind the Verdict
Pick MemOS when persistent memory is the bottleneck and you want to outsource the plumbing. The strongest case is a team that needs both cloud and local modes: prototypes run against MemOS Cloud in minutes, then the same integration moves to MemOS Lite for on-prem or privacy-sensitive deployments. That dual-runtime story is the main reason to look here instead of Mem0. The memory scheduling layer is the part I'd actually test. Preloading context before a query based on dialogue history or task semantics is where you either save tokens or create noise. Run your own eval on a slice of your traffic rather than trusting the LoCoMo and LongMemEval numbers. Pass if you're building a simple Q&A bot with no cross-session requirement. You don't need a memory operating system for that, and the API surface will be overhead. Non-technical teams should also look elsewhere: there's a CLI, SDK integration, and a local runtime install in the picture. Watch the pricing. Free, Starter, and Pro all show $0/mo right now, with Starter "original $19" and Pro "original $286." Promotions end. Model your per-call add and search volume against the published limits (50K/20K on Free up to 80M/30M on Pro) before you commit architecture. If you're already invested in Mem0 or LangChain memory modules, switching only pays off if you specifically need the local runtime or cross-model memory portability via MIP. Otherwise the migration cost probably exceeds the gain. The OpenClaw plugins are convenient but they pull you into a specific ecosystem. Keep that layer thin so you can swap runtimes later.
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Real-world workflow fit
Concrete scenarios for the personas MemOS actually fits — and what changes day-one when you adopt it.
You wire the MemOS Cloud API into your agent so each end user's prior tickets, preferences, and unresolved issues are stored as memory and searched on every new message. You start on the Free tier's 50K adds and 20K searches per month to validate the pattern.
Outcome: Your agent greets returning users with their history intact instead of asking the same qualifying questions twice, and you can measure search latency in milliseconds before committing to a paid tier.
You install the Agent Cloud Plugin to inject persistent memory into your existing OpenClaw agents and cut token usage on context re-sending, then trial the Agent Local Plugin for a workflow that can't leave the machine.
Outcome: Memory lives outside the prompt, token spend drops, and the same integration path works whether a given agent runs in the cloud or fully local.
You evaluate ClawForce for team knowledge capture with governance and cloud sandbox delivery, and scope an Enterprise MemOS plan with private deployment so memory stays in your own environment.
Outcome: Cross-session context is retained under your governance rules, and memory portability via MIP keeps you from being pinned to one model or device.
Use Cases
- Give your AI chatbot persistent memory so it remembers user preferences across sessions.
- Add cross-task skill reuse so a behavior learned in one agent application transfers to another.
- Deploy a memory layer for an OpenClaw agent platform to cut token usage and improve context.
- Run MemOS Lite in a local-first agent workflow to keep all memory processing offline.
- Build a personal memory assistant with Memmy for long-term conversation history.
- Scale enterprise AI workflows with ClawForce team knowledge capture and governance.
- Move memory between models or devices with MIP instead of rebuilding context per platform.
Limitations
- MemOS is a memory management operating system for AI applications, not an AI model itself — it has no underlying model of its own.
- Cloud tiers are usage-metered: the Free plan covers 50K memory adds and 20K searches per month, Starter (originally $19) covers 600K adds and 200K searches, and Pro (originally $286) covers 80M adds and 30M searches; only Enterprise offers unlimited Memory API, Chat API, and Knowledge Base usage with private deployment and custom integration.
- Knowledge Base limits also scale by tier — 10 items at 1G each on Free, 30 items at 10G on Starter, and 100 items at 100G on Pro.
- Integration is developer-oriented via Cloud API, Plugin, CLI, and MCP (including OpenClaw-focused Agent Cloud and Agent Local plugins), so validate your integration route against your stack.
as of 2026-10-11
Verification history
We have re-verified MemOS 10 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
- — 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 10 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 MemOS 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
Students, individual developers, and proof-of-concept builds that need to prove agent memory works before spending anything.
What this tier adds
Starting tier. 50K memory adds, 20K searches, 3M chat input tokens, 1M output tokens per month, and 10 knowledge base items at 1G each.
Starter
$0/mo (original $19)
Ideal for
Growing teams running a live agent product that has already outgrown proof-of-concept volumes.
What this tier adds
Raises allowances to 600K adds, 200K searches, 12M chat input and 4M output tokens per month, and 30 knowledge base items at 10G each. Listed at $0/mo now, original $19.
Pro
$0/mo (original $286)
Ideal for
Scaling teams with high-volume agent traffic that need far larger memory and knowledge base ceilings.
What this tier adds
Jumps to 80M adds, 30M searches, 90M chat input and 30M output tokens per month, 100 knowledge base items at 100G each, plus dedicated support. Listed at $0/mo now, original $286.
Enterprise
Custom
Ideal for
Enterprises that need unlimited usage, private deployment with their own data, and custom integration.
What this tier adds
Removes all Memory API, Chat API, and Knowledge Base caps and adds private deployment, custom integration, and lower latency at custom pricing.
Where the pricing makes sense
The company stage and team size where MemOS's pricing actually pencils out — and where peers do it cheaper.
MemOS prices at the developer-infrastructure end of the memory market. The $0/mo Free tier and the currently-$0/mo Starter and Pro tiers (originals $19 and $286) undercut hosted memory services that charge from day one, and MemOS Lite adds a local runtime most competitors don't offer at any price. Above that, Enterprise is custom-quoted for unlimited memory, chat, and knowledge base usage. Compared with Mem0's hosted plans, MemOS is cheaper to start and more expensive to fully scale past
Setup time & first value
How long it actually takes to get something useful out of MemOS — broken out by persona, not the marketing-page minute.
For a developer, MemOS Cloud is pitched as a 5-minute integration using the Code, Plugin, CLI, or MCP path, meaning first memory add and search can land inside one working session. MemOS Lite takes longer because you install and run the local runtime. Enterprise deployment with private infrastructure and custom integration is a scoping project measured in weeks, not minutes. Non-developers should
Switching to or from MemOS
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From Mem0: port your existing memory store into the MemOS Cloud API and re-point adds and searches at the MemOS endpoint.
- →From LangChain memory modules: replace the in-chain memory component with MemOS API calls so memory survives outside the chain.
- →From a self-built vector store: migrate stored embeddings and context into MemOS and let the dynamic knowledge graph handle retrieval.
- →From prompt-only context: start writing durable facts via the Memory API so they persist past the session window.
- ↗To Mem0: extract memories through the MemOS API and reload them into Mem0's store, re-implementing any MIP-based cross-device sharing yourself.
- ↗To a self-hosted vector store: export memory records and re-embed them, losing the Memory Scheduling preload behavior.
- ↗To MemOS Lite: move from the hosted Cloud service to the local runtime if your constraint shifts to keeping everything on-prem.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “MemOS”, and we withheld 6: 6 could not be judged, because “MemOS” 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 MemOS.
Official links
Tools that pair well with MemOS
Common stack mates teams adopt alongside MemOS, with the specific reason each pairing earns its keep.
Featured Head-to-Head Comparisons
Memos vs Spider Cloud
Choose Spider Cloud if your AI agent's priority is fetching fresh, structured web data at scale—its Rust engine and Browser AI commands deliver speed and reliability at low cost. Pick MemOS if the bottleneck is memory persistence across sessions; its hybrid retrieval and knowledge graph excel at maintaining context, but it requires deeper integration. They solve different problems—combining both could create a powerful autonomous agent stack.
Memos vs Presto Voice
These two don't compete. Presto Voice is an operations purchase: you run drive-thru lanes on a Toast POS, you want a vendor to own voice ordering and upselling end-to-end, and you're prepared to talk to sales because pricing isn't published. MemOS is a build decision: you're shipping an AI agent that forgets everything between sessions and you want a memory layer you call from code — MemOS Cloud for a fast API integration, MemOS Lite when data can't leave the machine. If you're a QSR operator, only one of these is even a candidate. If you're a developer, the drive-thru upsell metrics are irrelevant to your backlog.
Memos vs Temporal Ai
Choose Temporal AI if your priority is reliable, fault-tolerant execution of multi-step workflows and AI agents with automatic state persistence and retry. Choose MemOS if your main need is adding long-term memory and recall across sessions to existing AI agents, without building infrastructure. They solve different problems: Temporal handles flow, MemOS handles memory.
Alternatives to MemOS
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Zep Memory
Zep Memory gives production AI agents governed long-term memory through temporal context graphs.
Agentmemory
Open-source memory runtime that gives coding agents persistent recall between sessions, stored on your machine.
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Frequently Asked Questions
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