# NOAN vs supermemory NOAN records 3,663 monthly visits with -15.7% month-over-month growth and Paid pricing. supermemory records 156,579 monthly visits with -7.7% growth and Contact for Pricing pricing. | Metric | [NOAN](https://openaitools.app/tool/noan) | [supermemory](https://openaitools.app/tool/supermemory-1) | | :--- | :--- | :--- | | Website URL | https://getnoan.com | https://supermemory.ai | | Categories | AI Assistant, AI Knowledge Management, Large Language Models (LLMs), AI Marketing | AI API, Large Language Models (LLMs), AI Developer Tools, AI Agent | | Pricing Model | Paid | Contact for Pricing | | Monthly Visits | 3,663 | 156,579 | | MoM Growth Rate | -15.7% | -7.7% | | Primary Market | United States | United States | | Direct Visits | 1,450 | 71,163 | | Review Score | 4.4 | 4.5 | | Saved Count | 0 | 0 | | Supported Platforms | Website | Website | ## NOAN NOAN describes itself as fact infrastructure for agentic companies. The problem it addresses is a familiar one for teams running AI agents: the price lives in fourteen system prompts, three of which are wrong; retrieval surfaces last year's deck next to this year's; memory recalls what someone said on a call rather than what was decided. NOAN's answer is to keep your company's verified facts in one place and have every agent, app, and site read them live from a single API. Technically, NOAN exposes a plain REST API over api.getnoan.com/v1 and an MCP server at mcp.getnoan.com/mcp. You create a project, then issue a key for each agent, app, or site, scoped to the stacks it needs. A single GET returns the latest verified version of a fact block, along with who verified it and its sources. Agents can write back, posting new versions that supersede the previous ones while keeping the lineage — and if review-on-write is enabled, those writes wait for a person to approve them before anything can read them. Around that core, NOAN handles the surrounding mechanics: classification, permissions, partner sharing, and per-key activity auditing. The platform also ships the workspace pieces agents need day to day — a shared task board for people and agents, contacts with history and open work, notes linked to facts, and reusable assets and templates. It integrates with Claude, ChatGPT, Cursor, and other MCP clients, and supports multiple model providers because the model is a configuration choice. Beyond the tooling, NOAN publishes a startup program and grant, plus material for agencies that want to run a separate fact base per client. **Pricing & Plans:** Paid, with a free starting point: you can start free and scale when you ship, and pricing is presented as live/usage-based in the app. API access, MCP, versioned facts, review-on-write, and scoped keys are part of the platform, with higher tiers aimed at teams and agencies running the facts layer in production. A startup program and grant are also offered. - **Verified, versioned facts:** Each fact keeps its date, sources, and the person who verified it, so nothing is silently overwritten and agents can show the receipt behind an answer. - **Scoped keys and permissions:** Scope one key per agent, app, or site and limit it to the stacks it needs, so the right keys read the right facts and nothing else. - **Review on write:** Turn review on for a project or a single key so any write an agent proposes waits for a human to verify it before it can be read. - **Time Travel:** See your entire fact base as it stood on any past date, so you can answer what was true when a decision was made. - **Activity on the record:** Every agent action lands on one timeline, marked by the key that made it, giving you auditability across agents, apps, and sites. **Strengths:** Solves a real and specific agent problem — grounding — instead of adding another generic chatbot; Version history, sources, and named verification make outputs auditable and defensible; Model-agnostic and accessible over both REST and MCP, so it fits many stacks; Review-on-write lets agents propose updates without letting them silently change pricing or policy **Limitations:** The product is a new category (fact infrastructure), so new users need to absorb unfamiliar concepts before they see value; Value depends on teams actually maintaining facts, so adoption requires some process discipline; Pricing is presented as dynamic and depends on the scale you ship at; It complements rather than replaces memory or RAG layers, so it is another integration to wire up [Profile (Markdown)](https://openaitools.app/tool/noan.md) ## supermemory Most LLM applications treat memory as an afterthought: chat history is stuffed into the prompt until the window fills up, and everything is lost on the next session or the next model switch. supermemory attacks that problem as infrastructure. Its learner-1 model reads incoming material — conversations, files, URLs, raw data — and performs update, merge, infer and forget operations against a vector-graph database, so learnings stay dense, interconnected and time-aware rather than piling up as an ever-growing transcript. At request time the relevant memories are injected as tokens directly into the agent's context, which keeps the approach model-agnostic and harness-agnostic. Because learning and memory live outside the model providers, the same accumulated understanding travels with an application whether it runs on Claude, GPT-family models, Gemini or open-weight models, and whether it is driven by an SDK, a plugin or MCP. The company positions this as the hard part of agent infrastructure and backs it with published numbers: sub-second recall on production traffic, roughly a trillion tokens processed per month, benchmarks on LongMemEval, LoCoMo and ConvoMem, and cost comparisons against markdown-file memory at large corpora. Enterprise features include SOC 2 Type II certification, a HIPAA BAA on higher tiers, GDPR compliance and deployment options ranging from cloud to VPC, on-premises and air-gapped environments. **Pricing & Plans:** Pricing is split between self-serve tiers and enterprise contracts, which is why the site lists a contact-for-pricing model. Free, Pro and Max tiers cover standard PDF extraction and personal use, while Scale and Enterprise add advanced document extraction with OCR and figure/diagram descriptions, higher limits, security guarantees such as SOC 2 Type II and HIPAA BAA, and deployment choices including your VPC, your data center or air-gapped setups. Exact figures should be confirmed on the pricing page for current rates. - **learner-1 memory model:** A dedicated model extracts, merges, infers and forgets across stored context so the memory graph keeps improving instead of simply accumulating. - **Vector-graph memory store:** Learnings link to the entities, sources and moments around them, storing not just content but a time-aware map of how facts relate. - **Model, harness and MCP agnostic:** Memory lives outside model providers and is used through APIs, SDKs, plugins and MCP, so context carries across models and tools. - **Real-time context injection:** Relevant memories are injected as tokens into the prompt at request time, giving agents relevant context without unbounded transcripts. - **Enterprise deployment and compliance:** SOC 2 Type II, HIPAA BAA on higher tiers, GDPR compliance, plus deployment in the cloud, your VPC, your data center or air-gapped. **Strengths:** Memory is decoupled from model providers, so accumulated context survives model swaps; Rich engineering evidence, including published benchmarks, latency figures and cost comparisons; Multiple integration surfaces — REST API, SDKs, plugins and MCP; Strong enterprise posture with SOC 2 Type II, HIPAA BAA, GDPR and air-gapped deployment options **Limitations:** Self-serve pricing on the public site is thin, and exact tier costs may require a sales conversation; It is infrastructure rather than an end-user app, so there is real integration work before you see value; Benchmark results and adoption figures are vendor-reported and worth validating against your own workload [Profile (Markdown)](https://openaitools.app/tool/supermemory-1.md) ## Frequently Asked Questions ### How do NOAN and supermemory compare in monthly traffic? NOAN has 3,663 monthly visits, while supermemory has 156,579. Traffic estimates describe audience size, not product quality. ### How do their growth rates compare? NOAN has a month-over-month growth rate of -15.7%; supermemory has -7.7%. ### How does pricing compare? NOAN: Paid. Paid, with a free starting point: you can start free and scale when you ship, and pricing is presented as live/usage-based in the app. API access, MCP, versioned facts, review-on-write, and scoped keys are part of the platform, with higher tiers aimed at teams and agencies running the facts layer in production. A startup program and grant are also offered. supermemory: Contact for Pricing. Pricing is split between self-serve tiers and enterprise contracts, which is why the site lists a contact-for-pricing model. Free, Pro and Max tiers cover standard PDF extraction and personal use, while Scale and Enterprise add advanced document extraction with OCR and figure/diagram descriptions, higher limits, security guarantees such as SOC 2 Type II and HIPAA BAA, and deployment choices including your VPC, your data center or air-gapped setups. Exact figures should be confirmed on the pricing page for current rates. ## Alternatives | # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) | | :--- | :--- | :--- | :--- | :--- | :--- | :--- | | 1 | [Notion](https://openaitools.app/tool/t) | 173,441,670 | +0.1% | Freemium | The AI workspace where teams and agents get work done together | [.md](https://openaitools.app/tool/t.md) | | 2 | [Glean](https://openaitools.app/tool/glean) | 3,301,875 | +0.1% | Contact for Pricing | Enterprise AI grounded in your company's own knowledge | [.md](https://openaitools.app/tool/glean.md) | | 3 | [Composio](https://openaitools.app/tool/composio) | 2,782,603 | +22.8% | Freemium, Contact for Pricing | Focuses on tool integrations and authentication for agents, complementing memory engines rather than storing cross-session context. | [.md](https://openaitools.app/tool/composio.md) | | 4 | [Exa](https://openaitools.app/tool/exa-ai) | 1,062,574 | +16.8% | Freemium, Contact for Pricing | Web search API and crawler built for AI agents | [.md](https://openaitools.app/tool/exa-ai.md) | | 5 | [You.com](https://openaitools.app/tool/you) | 948,349 | +7% | Freemium | Search and inference APIs that give AI models real-time web data | [.md](https://openaitools.app/tool/you.md) | | 6 | [Scale](https://openaitools.app/tool/scale) | 616,915 | +12.5% | Contact for Pricing | The data, evaluations and systems behind AI that has to be right. | [.md](https://openaitools.app/tool/scale.md) | | 7 | [LiteLLM](https://openaitools.app/tool/litellm) | 576,967 | +7.9% | Freemium, Contact for Pricing | One open-source AI gateway for 100+ LLM providers | [.md](https://openaitools.app/tool/litellm.md) | | 8 | [Dust](https://openaitools.app/tool/dust) | 547,217 | +1.8% | Contact for Pricing | Multiplayer AI where people and agents work side by side | [.md](https://openaitools.app/tool/dust.md) | | 9 | [LlamaIndex](https://openaitools.app/tool/llamaindex) | 387,910 | +19.9% | Freemium, Contact for Pricing | Turn any document into AI-ready context with agentic parsing and extraction | [.md](https://openaitools.app/tool/llamaindex.md) | | 10 | [Merge](https://openaitools.app/tool/merge-dev) | 313,201 | +1.7% | Freemium, Contact for Pricing | The universal context engine connecting agents to data, tools, and models | [.md](https://openaitools.app/tool/merge-dev.md) | --- Source: https://openaitools.app/compare/noan-vs-supermemory-1 More Markdown: [Categories](https://openaitools.app/categories.md) · [Traffic rankings](https://openaitools.app/rankings.md) · [llms.txt](https://openaitools.app/llms.txt)