[Blog](https://supermemory.ai/blog) · [Engineering](https://supermemory.ai/blog/tag/engineering) # Architecting a memory engine inspired by the human brain By Dhravya Shah June 5, 2025 · 5 min read ![Supermemory header reading "Human memory inspired our memory engine" — A translucent leaf reveals the connected branches of its vein network](https://supermemory.ai/_astro/cover.DzVkNP7z_1h5qN8.webp) Language is at the heart of intelligence, but what truly powers meaningful interaction is memory — the ability to accumulate, recall, and contextualize information over time. Large Language Models (LLMs) have mastered language, but memory remains their Achilles’ heel. Every leap in context window size is quickly outpaced by real-world demands: users upload massive documents, have lengthy conversations, and expect seamless recall of preferences and history. The result? LLMs that forget, hallucinate, and frustrate users to the point of wanting to just start over with a clean slate. At Supermemory, the idea wasn’t always to help models remember better. It started off as a way to make \*you\* remember your bookmarks. Then, it was a cost and accuracy efficient way to easily implement RAG in your product. The more experience we had with this pain point, the more we were led to tackle context, and that’s what it currently stands as — a drop-in memory layer for your LLMs that is extremely scalable, with sub-400ms latency and enterprise-grade reliability. Long context does not really solve this either - as found in popular [papers](https://arxiv.org/abs/2502.05167) and proven time and time again in real-world use-cases. But why? ### What Makes Memory for LLMs So Hard? Building a memory layer for AI isn’t just about one more layer of data storage. You need to optimize for five uncompromising requirements: * **High Recall & Precision:** Always surface the right information — even across years of chat history or thousands of documents, and filtering out irrelevant, outdated, or noisy data to keep responses accurate. * **Low Latency:** Memory shouldn’t slow you down. It needs to work fast, especially at scale. This is particularly a challenge - because all current solutions for memory are _not_ built for scale. * _Vector Databases_: Either get too expensive, or too slow as they grow. There are new, proprietary, server-less options now - but we'll get into this shortly * _Graph_: To add every node, or for every query, one typically has to traverse factors or magnitudes more edges than nodes. * _Key-value:_ The entire KV pair has to fit inside the context length of models. Which moves the problem from one context length to another. * **Ease of Integration:** Developers need APIs and SDKs that require minimal changes to integrate — not weeks of onboarding or complex migrations. Managing embeddings, migrating between them, doing research for new improvements, etc. is usually not the primary business goal for apps needing memory. Lots of engineering hours are wasted. * **Semantic & Non-Literal Queries:** Memory must understand nuances, metaphors, and ambiguity — not just literal matches. Humans don’t search against a corpus of data with search terms they kinda know. What is "Non-literal match", here's an example ![Diagram contrasting a literal searchable query against a non-literal semantic query that current memory solutions fail](https://supermemory.ai/_astro/image-1.CqUpiZ59_1xJU2r.webp) Most solutions optimize for some of these, but fall short on others — especially when it comes to semantic understanding and scaling to billions of data points - For example, a user asking questions about all their internet life may ask more “general” questions that require knowledge of the entire dataset, not just ability to fuzzy search keywords. ## The Supermemory Approach: Human Memory at Scale Your brain doesn't store everything perfectly as you see it—and that's actually a feature, not a bug. It forgets the mundane, emphasizes what you've used recently, and rewrites memories based on current context. Our architecture works the same way, but engineered for AI at scale. **Smart Forgetting & Decay** Just like you naturally forget where you parked three weeks ago but remember yesterday's important meeting, our system applies intelligent decay. Less relevant information gradually fades while important, frequently-accessed content stays sharp. No more drowning in irrelevant context. **Recency & Relevance Bias** That thing you just talked about? It gets priority. That document you reference constantly? It stays top-of-mind. We mirror your brain's natural tendency to surface what's actually useful right now, not just what's technically "relevant" to a search query. **Context Rewriting & Broad Connections** Your brain doesn't just file away facts—it rewrites them based on new experiences and draws unexpected connections. Our system does the same, continuously updating summaries and finding links between seemingly unrelated information. That random insight from last month might be exactly what you need for today's problem. **Hierarchical Memory Layers** Like how you have working memory, short-term memory, and long-term storage, we use Cloudflare's infrastructure to create memory layers that match how you actually think. Hot, recent stuff stays instantly accessible (we personally use [KV](https://developers.cloudflare.com/kv/)). Deeper memories get retrieved when you need them, not before. ## Building on top of the engine Supermemory isn’t just another vector database or RAG toolkit. It’s a universal memory layer that gives your LLMs the power of infinite context, with near-instant plug-and-play integration. Add our endpoint to your existing AI provider, plug in your API key, and boom — you’re done. On top of this engine, we've been building some interesting products and experiences - * **Memory as a service:** Storing and querying multimodal data, at scale, with support for external connectors and sync with Google Drive, Notion, OneDrive, etc. It's a few API calls - just /add, /connect, /search [docs.supermemory.ai/api-reference/manag…](https://docs.supermemory.ai/api-reference/manage-memories/add-memory) [![Diagram showing Documents, Videos, Images, Text, and Connectors flowing into Supermemory, then queried for results](https://supermemory.ai/_astro/image-2.-pOMsvyX_1p7p60.webp) ](https://docs.supermemory.ai/api-reference/manage-memories/add-memory) * **The Supermemory MCP:** Model-interoperable MCP server that lets users carry their memories, and chats, through LLM apps without losing context. This is actually built on top of our own Memory as a service X content is paused until you allow embedded content. [Open on X ↗](https://twitter.com/Supermemoryai/status/1930465720325771420?ref%5Fsrc=twsrc%5Etfw) > And our latest launch, called Infinite Chat API, manages memories inline with the conversation history to only send what's needed to the model providers. Leading to less token usage, cost savings, better latencies and better quality responses. You heard that right. You can use it _today_ with just [one line of code!](https://docs.supermemory.ai/infinite-chat) X content is paused until you allow embedded content. [Open on X ↗](https://twitter.com/Supermemoryai/status/1923122703009186217?ref%5Fsrc=twsrc%5Etfw) > We've been constantly improving this with our latest benchmarks, work and progress in the field. ![Line chart: Supermemory-enhanced GPT-4o, Llama, Claude, and Gemini score higher than baselines, especially past 20K tokens](https://supermemory.ai/_astro/image-3.3OCdOHsJ_Z1isdUf.webp) Memory is a huge missing piece on the road to AGI. With Supermemory, you can finally build products that remember, reason, and respond like never before. If you believe in the mission, [we're hiring](https://x.com/i/jobs/1928194391946186862). If you want better memory for your LLMs and apps - you can use all these products _today_. Check out our [docs](https://docs.supermemory.ai/), [MCP](https://mcp.supermemory.ai/), and get an API key on our [dashboard](https://console.supermemory.ai/) ## Applying this in an application For the application architecture decision, see [RAG versus agent memory](https://supermemory.ai/blog/rag-vs-agent-memory/): when retrieval is sufficient and when changing user context needs a memory lifecycle. For historical questions and changing facts, use [temporal agent memory](https://supermemory.ai/blog/temporal-knowledge-graphs-agent-memory/) to distinguish event time, validity, and ingestion time before selecting a graph. For a concrete source-to-answer walkthrough, read [the RAG pipeline explained](https://supermemory.ai/blog/rag-pipeline-explained/). Design [correctable memory records](https://supermemory.ai/blog/agent-memory-schema-design/) before connecting those records into a wider system. ## Other posts. 1. [We're open sourcing the company brain. Here's how we designed the multiplayer harness Company Brain is now open source. A walkthrough of the multiplayer harness behind its Slack experience, from proactivity and memory boundaries to approvals and recovery. NewsSep 25, 2026 ](https://supermemory.ai/blog/open-sourcing-company-brain) 2. [Jev changes a lot in memory & context engineering. Here's exactly how. We tested Jev across reranking, chunking, observation, and harness decisions. Here is where fast decision models help memory systems, where they cost more, and where they still fall short. EngineeringSep 24, 2026 ](https://supermemory.ai/blog/jev-memory-context-engineering) 3. [I reverse-engineered Instinct's memory. Here's exactly how it works Instinct keeps its memory as git-tracked markdown files, found with grep rather than vectors. Here is the whole system as far as black-box probing can reconstruct it, and how to rebuild it on supermemory in about 60 lines. EngineeringSep 20, 2026 ](https://supermemory.ai/blog/reverse-engineering-instinct-memory) 4. [An update to supermemory We've discontinued the supermemory company brain and Nova. Everyone who was charged has been refunded, our MCP and plugins continue to run, and we're going all in on the memory engine. NewsSep 10, 2026 ](https://supermemory.ai/blog/an-update-to-supermemory) 5. [Scaling Conversations: How Adapta Grew Usage Without Losing Context Adapta added Supermemory as a persistent memory layer so every conversation keeps its context — letting the team scale usage without losing the thread. Case StudyJun 12, 2026 ](https://supermemory.ai/blog/adapta-scaling-conversations) 6. [How Chatarmin Ditched RAG and Went Memory-Only with Supermemory Chatarmin replaced a heavy RAG pipeline with Supermemory's memory layer — cutting average AI response time from 40s to 12s and token usage by 40–50%. Case StudyJun 10, 2026 ](https://supermemory.ai/blog/chatarmin-memory-only) 7. [SMFS: making agentic retrieval 55% cheaper AND more accurate We launched SMFS.ai (Supermemory Filesystem) a few weeks ago, with a simple bet: We can redesign the filesystem specifically for agents, with special files, structures, and commands that it can use for it's tasks. Today, SMFS is used by hundreds of companies to power their agents. EngineeringMay 28, 2026 ](https://supermemory.ai/blog/smfs-making-agentic-retrieval-55-cheaper-and-more-accurate) 8. [Introducing Dynamic Dreaming: supermemory now connects the dots, for you. Dreaming is magical. TLDR: We're launching Dynamic Dreaming in supermemory today, which automatically works if you're using supermemory in any way - API, OpenClaw, Hermes agent, etc. EngineeringMay 25, 2026 ](https://supermemory.ai/blog/introducing-dynamic-dreaming-supermemory-now-connects-the-dots-for-you) 9. [Dear reader, we just made supermemory insanely cheap... the Context Cloud When I first started building supermemory, I had one goal: To build the best memory system for AI. I would talk to customers, and find out that memory was not the only thing they needed - They were all setting up 7-8 different vendors at the same time. EngineeringMay 18, 2026 ](https://supermemory.ai/blog/dear-reader-we-just-made-supermemory-insanely-cheap-the-context-cloud) 10. [Introducing @supermemory/tools v2.0.0 Today we're releasing v2.0.0\. This release unifies the API across all agents sdk integrations from AI SDK to Mastra, makes conversation identity a first-class concept, and ships with memory saving on by default. EngineeringApr 27, 2026 ](https://supermemory.ai/blog/introducing-supermemory-tools-v2-0-0) 11. [Solving the Precision-Recall Tradeoff: Search Result Aggregation When you're building memory for AI, search is your foundational layer. The way search generally works is straightforward: the user defines a query, and then sets a limit (top-K) on how many search results they want returned. Usually, this is set to 10 or 20. EngineeringApr 5, 2026 ](https://supermemory.ai/blog/solving-the-precision-recall-tradeoff-search-result-aggregation) 12. [OpenClaw Memory Problems: Why It Forgets and How to Fix It (2026) TLDR: Today, we are releasing a new version of our openclaw plugin - https://github.com/supermemoryai/openclaw-supermemory. This post is going to be a bit technical, so bear with me (or bookmark for later!) In this post, I will talk about what we do about OpenClaw memory, and how we fix it. EngineeringFeb 19, 2026 ](https://supermemory.ai/blog/why-everyone-is-complaining-about-openclaws-memory-it-sucks-and-why-supermemory-fixes-it) 13. [Stateful Coding Agents with Memory: Build Long-Running Agents (2026) We built a plugin for Claude Code and OpenCode that gives your coding agent persistent memory. It remembers your preferences, learns your codebase, and never loses context mid-conversation. The result is an agent you can run for months without starting over. EngineeringFeb 18, 2026 ](https://supermemory.ai/blog/infinitely-running-stateful-coding-agents) 14. [Clawd / Molt bot's memory SUCKS. We gave it supermemory. I'm the founder of supermemory. Clawd/Molt bot is blowing up right now, with many, many use cases. I set it up, too, and have been using it through telegram. TLDR: just go to https://supermemory.ai/docs/integrations/clawdbot to set up supermemory for your clawd bot. EngineeringJan 28, 2026 ](https://supermemory.ai/blog/clawd-molt-bots-memory-sucks-we-gave-it-supermemory) 15. [Catch up with our UNFORGETTABLE Launch Week Over the last year, one belief has guided almost everything we’ve built at Supermemory AI becomes meaningfully useful only when it remembers. Memory shouldn’t be something developers rebuild from scratch. It shouldn’t be fragile, expensive, or trapped inside a single tool. NewsJan 4, 2026 ](https://supermemory.ai/blog/catch-up-with-our-unforgettable-launch-week) 16. [Empowering the Next Generation of Founders: Supermemory Startup Program If there’s one thing we’ve learned while building Supermemory, it’s that most startups don’t fail because they didn't build features; they fail when infrastructure slows them down, or they built too slow. NewsDec 31, 2025 ](https://supermemory.ai/blog/empowering-the-next-generation-of-founders-supermemory-startup-program) 17. [Building code-chunk: AST Aware Code Chunking At Supermemory, we're building context engineering infrastructure for AI. A huge part of that is dealing with code: ingesting repos, understanding structure, and making it searchable. The problem is that most code chunking solutions are terrible. We built code-chunk to fix this. EngineeringDec 29, 2025 ](https://supermemory.ai/blog/building-code-chunk-ast-aware-code-chunking) 18. [Supermemory raises $3 million with the best memory engine for LLMs Today, I am excited to announce our first funding round to accelerate our mission of building an interoperable, scalable and reliable memory for LLMs and agents. Memory is one of the hardest challenges in AI right now. NewsOct 6, 2025 ](https://supermemory.ai/blog/supermemory-raises-3-million-and-building-the-best-memory-engine-for-llms) 19. [Mem0 vs Supermemory: Why Scira Switched Scira AI moved its production memory layer from Mem0 to Supermemory. This is what failed, what improved, and how the team evaluated the two systems. Case StudyOct 2, 2025 ](https://supermemory.ai/blog/why-scira-ai-switched) 20. [Never Record Again: How Montra Uses Supermemory to Rethink Video Creation Campbell Baron, the founder of Montra, has been making videos since he was twelve. By thirteen, he was already doing brand work. Today, he’s betting on a very different future for creators: a world where recording is the exception, and most videos are generated from scratch. Case StudyAug 21, 2025 ](https://supermemory.ai/blog/never-record-again-how-montra-uses-supermemory-to-rethink-video-creation) 21. [Unified Memory That Works Where You Work: Your Second Brain With Supermemory Hi everyone, I’m Dhravya, the founder of Supermemory. I want to start with a little story behind why this product means so much to me. You can also skip straight to what it is and how it works below. EngineeringJul 25, 2025 ](https://supermemory.ai/blog/unified-memory-that-works-where-you-work-your-second-brain-with-supermemory) 22. [Supermemory just got faster on PlanetScale What is Supermemory? Supermemory completes the missing part of the LLM puzzle: memory. Just as memory is crucial for human intelligence, it's essential for truly intelligent AI systems. EngineeringJul 18, 2025 ](https://supermemory.ai/blog/supermemory-just-got-faster-on-planetscale) 23. [Faster, smarter, reliable infinite chat: Supermemory IS context engineering. People are obsessed with prompts and prompt engineering. Sure, what you say is important, but what the model knows when you say it is the difference between a stateless text generator and an intelligent AI system. In short, context is the most crucial component. NewsJul 9, 2025 ](https://supermemory.ai/blog/faster-smarter-reliable-infinite-chat-supermemory-is-context-engineering) 24. [We solved AI API interoperability One API to rule them all, One spec to find them, One library to bring them all and in the TypeScript, bind them. When we were building the Infinite Chat API, initially, we only supported the OpenAI format. This was fine, until a lot of our customers started asking for more. EngineeringJul 7, 2025 ](https://supermemory.ai/blog/we-solved-ai-api-interoperability) 25. [The Wow Factor of Memory - How Flow Used Supermemory To Build Smarter, Stickier Products Overview: Flow is a note-taking app built around a bold vision: to create a more personal, context-aware writing experience powered by AI. At the heart of this mission is memory. Case StudyJun 14, 2025 ](https://supermemory.ai/blog/the-wow-factor-of-memory-how-flow-used-supermemory-to-build-smarter-stickier-products) 26. [The UX and technicalities of awesome MCPs Last month, we launched the Supermemory MCP, mostly to test our own infrastructure and get some initial traction. It blew up. To my absolute surprise, the initial launch itself got half a million impressions (!!!). Then, we launched and got #2 on ProductHunt too. EngineeringJun 8, 2025 ](https://supermemory.ai/blog/the-ux-and-technicalities-of-awesome-mcps) ## Start building with supermemory. Memory and continual learning for any model, any harness. Available through our API, plugins, and MCP. [Build with supermemory ](https://console.supermemory.ai/)