# EverMemOS vs supermemory

EverMemOS records 24,237 monthly visits with +12% month-over-month growth and Free Trial, Free pricing. supermemory records 156,579 monthly visits with -7.7% growth and Contact for Pricing pricing.

| Metric | [EverMemOS](https://openaitools.app/tool/evermemos) | [supermemory](https://openaitools.app/tool/supermemory-1) |
| :--- | :--- | :--- |
| Website URL | https://evermind.ai | https://supermemory.ai |
| Categories | AI Developer Tools, AI Agent, Open Source AI Models, AI Models | AI API, Large Language Models (LLMs), AI Developer Tools, AI Agent |
| Pricing Model | Free Trial, Free | Contact for Pricing |
| Monthly Visits | 24,237 | 156,579 |
| MoM Growth Rate | +12% | -7.7% |
| Primary Market | United States | United States |
| Direct Visits | 14,588 | 71,163 |
| Review Score | 4.4 | 4.5 |
| Saved Count | 1 | 0 |
| Supported Platforms | Website | Website |

## EverMemOS

Modern LLMs forget. Context windows cap out, RAG returns similar text without real understanding, and most memory systems are built for one user and one model. EverMemOS, the flagship product of EverMind's product family alongside the EverMe personal memory hub, is designed to close that gap by acting as a memory operating system for agents: persistent, proactive and self-evolving rather than stateless.

Two capabilities stand out. First, mRAG — a multimodal retrieval and ingestion layer that pulls in PDFs, images, Word documents, spreadsheets, presentations, emails, HTML pages and URLs through one API and supports cross-modal search. Second, self-evolving memory: agent trajectories are captured as Cases, repeated patterns are distilled into reusable Skills, and the agent applies those later instead of starting from scratch each session. A Memory Bank interface gives users visibility into user, group and agent memory and lets them inspect, manage and edit generated Skills.

EverMind frames memory as the difference between a tool and an agent, and backs the claim with reproducible open benchmarks — LoCoMo, LongMemEval and AMA are all listed on the site. The product family includes EverOS for developers building agent memory and EverMe as a personal memory hub. New users receive 2,000 credits on signup, and a Mac app is available.

**Pricing & Plans:** EverMind offers a free signup with 2,000 starter credits. No fixed public price list is shown for the cloud platform, and the company positions its memory infrastructure with free and open-source components alongside paid plans.

- **mRAG multimodal memory:** Multimodal retrieval and ingestion via a single API, covering PDFs, images, Word files, spreadsheets, decks, emails, HTML and URLs.
- **Self-evolving agent memory:** Agent trajectories are recorded as Cases, distilled into reusable Skills, and reused so performance improves over time.
- **Memory Bank transparency:** Inspect, manage and edit user, group and agent memories in a dedicated Memory Bank interface.
- **Open benchmark results:** EverMind publishes reproducible results on LoCoMo, LongMemEval and AMA alongside its memory research.
- **Team and temporal memory:** Supports multi-agent coordination and group context sharing, plus temporal tracking so outdated facts are not treated as current.

**Strengths:** Tackles a real gap: persistent memory and identity for AI agents; mRAG takes many document and media types through one API; Self-evolving Skills let agents improve instead of starting from zero; Transparent Memory Bank for inspecting and editing stored memory

**Limitations:** Memory infrastructure requires developer integration effort; The memory-OS space is crowded and fast-moving, so APIs may evolve; Self-hosting the open-source core puts operational load on your team; Benchmark leadership claims still need validation on your own workloads

[Profile (Markdown)](https://openaitools.app/tool/evermemos.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 EverMemOS and supermemory compare in monthly traffic?

EverMemOS has 24,237 monthly visits, while supermemory has 156,579. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

EverMemOS has a month-over-month growth rate of +12%; supermemory has -7.7%.

### How does pricing compare?

EverMemOS: Free Trial, Free. EverMind offers a free signup with 2,000 starter credits. No fixed public price list is shown for the cloud platform, and the company positions its memory infrastructure with free and open-source components alongside paid plans. 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 | [Gemma Open Models by Google](https://openaitools.app/tool/gemma-open-models-by-google) | 4,900,914 | -13.5% | Freemium, Paid | Google's family of lightweight, open-weight AI models | [.md](https://openaitools.app/tool/gemma-open-models-by-google.md) |
| 2 | [Hermes-Agent](https://openaitools.app/tool/hermes-agent-1) | 3,638,741 | -5.4% | Freemium | A free, open-source personal AI agent that learns your projects and works across every messaging app. | [.md](https://openaitools.app/tool/hermes-agent-1.md) |
| 3 | [ModelScope](https://openaitools.app/tool/modelscope-community) | 3,085,423 | +5.1% | Free | Open-source model and dataset hub with one-click online demos | [.md](https://openaitools.app/tool/modelscope-community.md) |
| 4 | [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) |
| 5 | [Unsloth AI](https://openaitools.app/tool/unsloth-ai) | 887,457 | -17.6% | Free | Open-source, memory-efficient fine-tuning and local model running | [.md](https://openaitools.app/tool/unsloth-ai.md) |
| 6 | [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) |
| 7 | [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) |
| 8 | [Pinecone](https://openaitools.app/tool/pinecone) | 286,732 | -31% | Freemium, Paid, Contact for Pricing | Offers enterprise-grade scalable vector search and RAG storage, but lacks supermemory's automated agent memory graph abstractions. | [.md](https://openaitools.app/tool/pinecone.md) |
| 9 | [Portkey](https://openaitools.app/tool/portkey-ai) | 195,650 | -9.4% | Freemium, Paid, Contact for Pricing | AI gateway, observability and guardrails for production LLM apps | [.md](https://openaitools.app/tool/portkey-ai.md) |
| 10 | [Agno](https://openaitools.app/tool/agno) | 140,302 | -3.3% | Contact for Pricing, Free | Self-driving agent platform you run in your own cloud | [.md](https://openaitools.app/tool/agno.md) |

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Source: https://openaitools.app/compare/evermemos-vs-supermemory-1

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