# Mireye vs supermemory

Mireye records 10,770 monthly visits with +13.9% month-over-month growth and Freemium, Contact for Pricing pricing. supermemory records 156,579 monthly visits with -7.7% growth and Contact for Pricing pricing.

| Metric | [Mireye](https://openaitools.app/tool/mireye) | [supermemory](https://openaitools.app/tool/supermemory-1) |
| :--- | :--- | :--- |
| Website URL | https://www.mireye.com | https://supermemory.ai |
| Categories | AI API, AI Agent, AI Developer Tools, AI For Data Analytics | AI API, Large Language Models (LLMs), AI Developer Tools, AI Agent |
| Pricing Model | Freemium, Contact for Pricing | Contact for Pricing |
| Monthly Visits | 10,770 | 156,579 |
| MoM Growth Rate | +13.9% | -7.7% |
| Primary Market | United States | United States |
| Direct Visits | 8,612 | 71,163 |
| Review Score | 4.4 | 4.5 |
| Saved Count | 0 | 0 |
| Supported Platforms | Website | Website |

## Mireye

Mireye positions itself as infrastructure for physical-world AI agents. Rather than asking developers to stitch together federal downloads, county parcel files and GIS tooling, it exposes a small set of endpoints plus MCP tools that an agent can call in natural language. You can ask a question about a place, enrich a parcel with authoritative identity and attributes, search for parcels matching criteria, or fetch cited fields at any US coordinate. Every returned value carries its source name, fetch timestamp and a confidence level, which matters for insurance, lending and diligence workflows where provenance has to survive scrutiny.

The catalog leans heavily on authoritative US sources such as USGS, FEMA, NOAA, USDA, EPA, EIA, FCC, Census, NREL, BLM and others, complemented by open and licensed layers like Overture and Sentinel-2, for roughly 97 sources in total. Presets bundle common questions - flood risk, solar siting, data center siting, grid interconnection, wildfire underwriting, terrain, utilities and more - into one call. Bulk work is supported through batch fetch of many locations per request, and long jobs run through a separate runs endpoint.

A distinctive idea is the field request loop: if the catalog lacks a field you need, you file a request, Mireye researches, builds and validates it, and the new field becomes permanently available to every future caller. Coverage is the United States, with drive-time analysis extending to Canada. The service is backed by Y Combinator and presents itself as a paid, usage-metered API rather than a consumer app.

**Pricing & Plans:** Free tier with pay-as-you-go usage and a one-time $10 credit that never expires. Enrich costs about $0.02 per parcel, Search about $0.20 per page, Ask about $0.10 per answer, and driving proximity about $0.004 per calculation on the Free plan. Fetch, geocoding and straight-line proximity are free. Paid plans start at $49/month (Build) and $199/month (Growth) with included usage and 10-25% lower operation rates. You can add funds from $10; auto-recharge is optional.

- **Single API plus MCP server:** Agents such as Claude, ChatGPT, Gemini, Cursor or custom builds reach Mireye through one REST API and MCP tools, returning a replayable plan of the steps it ran.
- **Cited data everywhere:** Every returned field includes its source, fetch time and confidence level, so provenance can be forwarded to an end user, underwriter or auditor without extra work.
- **Parcel enrichment and search:** Attach authoritative parcel identity, ownership and attributes to a coordinate, or filter the US parcel universe by state, size and supported fields to get ranked candidates.
- **Preset and batch operations:** Ready-made presets cover flood risk, solar and wind siting, data center siting, grid interconnection, wildfire underwriting and terrain; hundreds of locations can be sent in one call.
- **Field request pipeline:** Request a field the catalog lacks, and once researched and validated it goes live permanently for all callers - requests that cannot be built are rejected honestly.

**Strengths:** Every field ships with a named source and confidence level, which is rare for geospatial APIs; Free Fetch, geocoding and straight-line proximity make prototyping cheap; Single API and MCP server reduce integration work across many agent frameworks; Field request loop lets you extend the catalog instead of switching vendors

**Limitations:** Data coverage is essentially US-only, with Canada limited to drive-time analysis; Per-operation pricing on Ask and Search can add up quickly at high volume; Real estate and infrastructure workflows still require domain knowledge to interpret results

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

Mireye has 10,770 monthly visits, while supermemory has 156,579. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Mireye has a month-over-month growth rate of +13.9%; supermemory has -7.7%.

### How does pricing compare?

Mireye: Freemium, Contact for Pricing. Free tier with pay-as-you-go usage and a one-time $10 credit that never expires. Enrich costs about $0.02 per parcel, Search about $0.20 per page, Ask about $0.10 per answer, and driving proximity about $0.004 per calculation on the Free plan. Fetch, geocoding and straight-line proximity are free. Paid plans start at $49/month (Build) and $199/month (Growth) with included usage and 10-25% lower operation rates. You can add funds from $10; auto-recharge is optional. 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 | [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) |
| 2 | [TinyFish](https://openaitools.app/tool/tinyfish) | 1,124,952 | +199.7% | Freemium, Contact for Pricing | One API key for agent-grade web search, fetch, browsing and automation | [.md](https://openaitools.app/tool/tinyfish.md) |
| 3 | [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) |
| 4 | [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) |
| 5 | [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) |
| 6 | [Treg](https://openaitools.app/tool/treg) | 452,935 | +223.4% | Freemium | One key for 4,100+ agent tools, pay per call | [.md](https://openaitools.app/tool/treg.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 | [Thor Data](https://openaitools.app/tool/thor-data) | 181,966 | -3.5% | Free Trial, Paid | Proxies and web data APIs for scraping and AI pipelines | [.md](https://openaitools.app/tool/thor-data.md) |

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

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