# supermemory vs Treg

supermemory records 156,579 monthly visits with -7.7% month-over-month growth and Contact for Pricing pricing. Treg records 452,935 monthly visits with +223.4% growth and Freemium pricing.

| Metric | [supermemory](https://openaitools.app/tool/supermemory-1) | [Treg](https://openaitools.app/tool/treg) |
| :--- | :--- | :--- |
| Website URL | https://supermemory.ai | https://treg.to |
| Categories | AI API, Large Language Models (LLMs), AI Developer Tools, AI Agent | AI API, AI Agent, AI SEO Tools, AI Advertising |
| Pricing Model | Contact for Pricing | Freemium |
| Monthly Visits | 156,579 | 452,935 |
| MoM Growth Rate | -7.7% | +223.4% |
| Primary Market | United States | United States |
| Direct Visits | 71,163 | 235,299 |
| Review Score | 4.5 | 4.5 |
| Saved Count | 0 | 0 |
| Supported Platforms | Website | Website |

## 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)

## Treg

Treg acts as a unified credential and routing layer for AI agents. Instead of signing up for dozens of SaaS APIs, you connect your agent to one MCP server and receive a single treg token that answers for every tool in the catalog. The catalog spans SEO keyword and rank tracking, backlink analysis, AI search visibility, social media and advertising data, lead enrichment, email verification, company data, web scraping, and AI video, image, and voice generation.

A core idea is evidence-based tool selection. Multiple providers often sell the same capability - nineteen sell backlinks, nine sell email lookup - so treg shows each one's listed price alongside measured success rate, speed, and last-answer time, letting the agent pick a vendor on data rather than guesswork. Pricing is pay-per-result with the cost quoted before each call, funded by a starting balance, and there is no subscription required for tools you'd otherwise buy seats for.

For teams that already pay providers directly, treg supports bring-your-own-key (BYOK): your own keys route first and run unmetered, while credentials are injected server-side so raw keys never sit on a machine. It is fully open source under Apache 2.0, integrates with coding agents and automation workflows, and lets orgs publish internal SKILL.md-based tools to teammates by token.

**Pricing & Plans:** Pay-per-call with no subscription; prices are quoted before each call and start with $1.00 of free credit. Bringing your own provider keys makes those calls unmetered.

- **One key, 127 providers:** A single treg token routes to 4,100+ endpoints across 127 providers, so no per-provider signups or scattered key files.
- **Evidence-based provider comparison:** See each provider's price plus measured success rate, speed, and last-answer time, and pin a preferred vendor when you want.
- **Pay-per-call pricing:** Pay fractions of a cent per call with the price shown before you commit, no monthly seats required.
- **Bring your own keys (BYOK):** Route calls through your existing provider keys at no markup; credentials are injected server-side and never stored on a machine.
- **Open source and agent-ready:** Apache 2.0 licensed with MCP support, coding-agent integration, and the ability to publish your own SKILL.md tools.

**Strengths:** Single key across 4,100+ endpoints and 127 providers; Transparent pay-per-call pricing quoted before each call; Measured success rate and latency help pick the right vendor; Open source (Apache 2.0) with BYOK and server-side credential injection

**Limitations:** The breadth of the catalog can be overwhelming for newcomers; Coverage and reliability depend on the many third-party providers behind it; Usage is metered, so heavy callers still accumulate real costs

[Profile (Markdown)](https://openaitools.app/tool/treg.md)

## Frequently Asked Questions

### How do supermemory and Treg compare in monthly traffic?

supermemory has 156,579 monthly visits, while Treg has 452,935. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

supermemory has a month-over-month growth rate of -7.7%; Treg has +223.4%.

### How does pricing compare?

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. Treg: Freemium. Pay-per-call with no subscription; prices are quoted before each call and start with $1.00 of free credit. Bringing your own provider keys makes those calls unmetered.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [OpenRouter](https://openaitools.app/tool/openrouter-ai) | 19,941,437 | -6.2% | Paid | One API key for hundreds of AI models from every major provider | [.md](https://openaitools.app/tool/openrouter-ai.md) |
| 2 | [Composio](https://openaitools.app/tool/composio) | 2,782,603 | +22.8% | Freemium, Contact for Pricing | Provides comprehensive pre-built tool integrations for AI agents, but lacks Treg's open-source pay-per-call API catalog. | [.md](https://openaitools.app/tool/composio.md) |
| 3 | [TinyFish](https://openaitools.app/tool/tinyfish) | 1,124,952 | +199.7% | Freemium, Contact for Pricing | Provides advanced browser automation and live web fetching, but lacks a general pay-per-call API marketplace. | [.md](https://openaitools.app/tool/tinyfish.md) |
| 4 | [Exa](https://openaitools.app/tool/exa-ai) | 1,062,574 | +16.8% | Freemium, Contact for Pricing | Excels at AI-optimized web search and crawling data, but lacks broader multi-app tool integration capabilities. | [.md](https://openaitools.app/tool/exa-ai.md) |
| 5 | [OrcaRouter](https://openaitools.app/tool/orcarouter) | 975,300 | -5.2% | Freemium | One OpenAI-compatible gateway for adaptive LLM routing, guardrails, and spend control | [.md](https://openaitools.app/tool/orcarouter.md) |
| 6 | [LiteLLM](https://openaitools.app/tool/litellm) | 576,967 | +7.9% | Freemium, Contact for Pricing | Offers an open-source AI gateway for model routing, but focuses on LLMs rather than general data endpoints. | [.md](https://openaitools.app/tool/litellm.md) |
| 7 | [Merge](https://openaitools.app/tool/merge-dev) | 313,201 | +1.7% | Freemium, Contact for Pricing | Offers universal B2B data and tool integrations for agents, but focuses less on pay-per-call API monetization. | [.md](https://openaitools.app/tool/merge-dev.md) |
| 8 | [Pipedream](https://openaitools.app/tool/pipedream) | 312,216 | -14.3% | Freemium, Paid, Contact for Pricing | Delivers robust managed integration workflows and triggers for agents, but offers fewer native MCP-specific endpoints. | [.md](https://openaitools.app/tool/pipedream.md) |
| 9 | [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) |
| 10 | [AI/ML API](https://openaitools.app/tool/ai-ml-api) | 232,946 | -20.1% | Pay-as-you-go credits (free to start); enterprise custom pricing on request | One universal API for 500+ AI models and 1,000+ agentic tools | [.md](https://openaitools.app/tool/ai-ml-api.md) |

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

More Markdown: [Categories](https://openaitools.app/categories.md) · [Traffic rankings](https://openaitools.app/rankings.md) · [llms.txt](https://openaitools.app/llms.txt)