Supermemory
Memory and continual learning for agents
From the Supermemory blog
Published by Supermemory, not by us. Every card opens the original post.
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.
supermemory.aiJev 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.
supermemory.aiI 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.
supermemory.aiAn 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.
supermemory.aiScaling 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.
supermemory.aiHow 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%.
supermemory.aiSMFS: 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…
supermemory.aiIntroducing 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.
supermemory.aiDear 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.
supermemory.aiIntroducing @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.
supermemory.aiSolving 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.
supermemory.aiOpenClaw 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…
supermemory.ai