# Llongterm vs supermemory

Llongterm records 0 monthly visits with 0% month-over-month growth and Freemium, Paid pricing. supermemory records 156,579 monthly visits with -7.7% growth and Contact for Pricing pricing.

| Metric | [Llongterm](https://openaitools.app/tool/llongterm) | [supermemory](https://openaitools.app/tool/supermemory-1) |
| :--- | :--- | :--- |
| Website URL | https://www.llongterm.com | https://supermemory.ai |
| Categories | AI Knowledge Management, AI Developer Tools, AI API, AI Knowledge Base | AI API, Large Language Models (LLMs), AI Developer Tools, AI Agent |
| Pricing Model | Freemium, Paid | Contact for Pricing |
| Monthly Visits | 0 | 156,579 |
| MoM Growth Rate | 0% | -7.7% |
| Primary Market | Global | United States |
| Direct Visits | 0 | 71,163 |
| Review Score | 4.2 | 4.5 |
| Saved Count | 0 | 0 |
| Supported Platforms | Website | Website |

## Llongterm

Llongterm answers a question every serious AI app eventually runs into: what happens to the conversation when the session ends? The product is a memory layer that sits between your application and its chatbot or agent. Instead of losing the thread, you create a "mind" for each user, pass in the relevant messages, and call remember — the API returns a memory object that captures what matters and can be fed back into future interactions.

What sets Llongterm apart is transparency. Its stored memories are fully human readable: nested structures with named keys that describe a user's learning style, goals, history or account status rather than an opaque embedding blob. The website demonstrates this with example profiles for AI teaching assistants, customer support, therapy notes and product management, showing how a long list of captured fields can be reviewed by a human. That makes the memory auditable and easier to correct.

Integration is deliberately lightweight. The primary SDK is JavaScript, with a sandbox available to experiment before wiring it into a real product, and a Python package listed as coming soon. Because Llongterm positions itself as the middle layer rather than a chatbot itself, it is intended to be compatible with all AI chatbots and agents.

**Pricing & Plans:** Freemium with paid plans. Llongterm offers a free entry point to build and test memory, with paid tiers for production usage. Specific plan prices are not prominently published, so higher-volume use may require contacting the vendor.

- **Persistent memory for AI apps:** Store and recall user details across sessions so an assistant remembers preferences, history and context.
- **Fully human-readable memory:** Memories are stored with named keys and nested structure, so you can read and audit exactly what is retained.
- **JavaScript SDK and sandbox:** A JavaScript SDK plus Python on the way, with a sandbox so you can try the flow before integrating.
- **Works with any chatbot or agent:** Llongterm sits between your app and any chatbot or agent, including assistants, support bots and analysis workflows.
- **Simple create and remember calls:** The API returns a memory object you control, so personalisation is programmatic rather than a black box.

**Strengths:** Adds genuine cross-session memory to chatbots with minimal integration work; Human-readable memory makes the AI's knowledge auditable and correctable; Framework-agnostic — works with any chatbot or agent; Sandbox lets you try the API before committing

**Limitations:** Python SDK is still marked 'coming soon' on the site; Requires application code to adopt the memory layer rather than being a drop-in chat product; Memory quality depends on how you structure the threads you pass in; Self-serve pricing details are not prominently published — larger use may need a conversation with the vendor

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

Llongterm has 0 monthly visits, while supermemory has 156,579. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Llongterm has a month-over-month growth rate of 0%; supermemory has -7.7%.

### How does pricing compare?

Llongterm: Freemium, Paid. Freemium with paid plans. Llongterm offers a free entry point to build and test memory, with paid tiers for production usage. Specific plan prices are not prominently published, so higher-volume use may require contacting the vendor. 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 | [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) |
| 2 | [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) |
| 3 | [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) |
| 4 | [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) |
| 5 | [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) |
| 6 | [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) |
| 7 | [Honcho](https://openaitools.app/tool/honcho) | 137,752 | +2.2% | Paid, Contact for Pricing | Provides open-source persistent user modelling for stateful agents, but focuses more on conversational psychology than universal vector graphs. | [.md](https://openaitools.app/tool/honcho.md) |
| 8 | [AIsa](https://openaitools.app/tool/aisa) | 89,162 | -10.3% | Freemium | One key, one schema, 6,000+ APIs for your AI agents | [.md](https://openaitools.app/tool/aisa.md) |
| 9 | [Qdrant](https://openaitools.app/tool/qdrant-io) | 72,992 | -38.4% | Freemium, Contact for Pricing | Provides high-performance open-source vector similarity search, requiring custom schema design for agent memory management. | [.md](https://openaitools.app/tool/qdrant-io.md) |
| 10 | [HydraDB OSS](https://openaitools.app/tool/hydradb-oss) | 29,635 | +5.8% | Freemium, Contact for Pricing | Delivers robust open-source graph storage on object stores, but is a database rather than a turnkey agent memory API. | [.md](https://openaitools.app/tool/hydradb-oss.md) |

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

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