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Memory for OpenClaw is now Native! Our first OpenClaw Memory Skill was a massive success: 30k+ downloads in a week and 500k+ organic impressions overnight for launch post. But we knew memory needed to be native. On March 21, OpenClaw merged PR #50848, allowing us to go beyond the...

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andy nguyen

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169,075 Aufrufe • vor 2 Monaten •via X (Twitter)

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We analyzed Anthropic’s memory architecture and built something better: a persistent, human-inspectable, and token-efficient memory layer that scales with your projects. Today, it’s OPEN-SOURCE. ByteRover CLI gives agents (like OpenClaw, Claude Code, and Hermes) persistent, structured memory. Built on the exact architecture that became the #1 memory system for OpenClaw (30,000+ downloads in a week), it lets developers curate project knowledge into a file-based hierarchy. This guarantees highly accurate, lightning-fast retrieval, even with lightweight models. 👉Highly Accurate: >92% retrieval accuracy across long-running sessions - the highest proven production accuracy on the market. ⚡Fast: ~1.6s average retrieval time. 💰Economic: Maintains >90% accuracy even with lightweight models, saving 50-70% on token costs. ☁ Portable: Runs locally by default, with cloud-sync to share memory across agents and teammates.
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We analyzed Anthropic’s memory architecture and built something better: a persistent, human-inspectable, and token-efficient memory layer that scales with your projects. Today, it’s OPEN-SOURCE. ByteRover CLI gives agents (like OpenClaw, Claude Code, and Hermes) persistent, structured memory. Built on the exact architecture that became the #1 memory system for OpenClaw (30,000+ downloads in a week), it lets developers curate project knowledge into a file-based hierarchy. This guarantees highly accurate, lightning-fast retrieval, even with lightweight models. 👉Highly Accurate: >92% retrieval accuracy across long-running sessions - the highest proven production accuracy on the market. ⚡Fast: ~1.6s average retrieval time. 💰Economic: Maintains >90% accuracy even with lightweight models, saving 50-70% on token costs. ☁ Portable: Runs locally by default, with cloud-sync to share memory across agents and teammates.

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Autonomous AI agents can’t afford a memory problem. After OpenClaw, insane numbers of autonomous agents are coming online, but all of them have a memory layer that's breaking. I have been playing around with SureThing these days. It is an autonomous AI agent designed with a native memory layer. → it learns your patterns → integrates with 1000+ apps → fully autonomous They actually benchmarked memory depth. SureThing: 84.8, ChatGPT: 40, OpenClaw: 60. The gap speaks for itself. SureThing is invitation-only. The crazy part is that you have to talk to SureThing agent itself to get the invite code. The onboarding is the product demo. You feel the memory and judgment before you even have an account. Try it here: Partnered with SureThing for this post. Thanks to the team for the support.
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Autonomous AI agents can’t afford a memory problem. After OpenClaw, insane numbers of autonomous agents are coming online, but all of them have a memory layer that's breaking. I have been playing around with SureThing these days. It is an autonomous AI agent designed with a native memory layer. → it learns your patterns → integrates with 1000+ apps → fully autonomous They actually benchmarked memory depth. SureThing: 84.8, ChatGPT: 40, OpenClaw: 60. The gap speaks for itself. SureThing is invitation-only. The crazy part is that you have to talk to SureThing agent itself to get the invite code. The onboarding is the product demo. You feel the memory and judgment before you even have an account. Try it here: Partnered with SureThing for this post. Thanks to the team for the support.

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MemOS now has a plugin for OpenClaw that enables your AI agents to work on the common memory layer and cut down token usage. - Multiple agents read/write the same memory — no manual context handoff - 72% lower token costs (15.6M → 4.4M on LOCOMO dataset) - Cross-session and long-term memory that actually works Sponsored

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current OpenClaw🦞's memory system sucks. it's only hosted locally and you have to manually add memories. you can now connect Momo's memory as an OPENCLAW PLUGIN. > cloud-backed, encrypted, persistent > auto indexing, storage & memory management > shared across teams get an api key at Momo and run: openclaw plugins install Phil-memory/momo-memory
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LLMs are stateless. We built Dria Mem Agent to change that: Making memory a first-class feature. A 4B agent with local interoperable memory across Claude, ChatGPT and LM Studio. It turns LLMs from stateless chat into stateful agents with persistent human-readable memory.

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OpenClaw's memory out of the box sucks With this simple Obsidian system, it's actually awesome In this video I go over using Obsidian as a memory layer for your AI agent, making it SO much more smarter than it is now If you use OpenClaw or Hermes, you need to watch this:

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Your OpenClaw🦞 agent works perfectly for 20 minutes. Then it silently forgets your instructions and goes rogue. The fix isn't better prompts. It's understanding how memory actually works. I'm an OpenClaw maintainer. After 2+ months of daily use, I ended up with a memory system where my agent remembers decisions from weeks ago, checks its notes before acting, and picks up exactly where it left off after every restart: 0:00 The real reason your agent forgets 1:59 Quick start: 3 things that matter most 3:01 The 4 "memories" people confuse 4:52 3 failure modes: why your agent forgets 6:42 Compaction vs pruning 8:05 Prove what's loaded: /context list 10:06 What compaction actually does 13:42 Layer 1: pre-compaction memory flush 16:27 Layer 2: manual saves + /compact trick 19:12 Layer 3: the file architecture 23:22 The memory protocol for AGENTS.md 26:06 Retrieval: Track A, A+, and QMD by tobi lutke 31:59 Cost and cache 33:33 The complete config (copy and go) 36:35 5 things to remember
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Your OpenClaw🦞 agent works perfectly for 20 minutes. Then it silently forgets your instructions and goes rogue. The fix isn't better prompts. It's understanding how memory actually works. I'm an OpenClaw maintainer. After 2+ months of daily use, I ended up with a memory system where my agent remembers decisions from weeks ago, checks its notes before acting, and picks up exactly where it left off after every restart: 0:00 The real reason your agent forgets 1:59 Quick start: 3 things that matter most 3:01 The 4 "memories" people confuse 4:52 3 failure modes: why your agent forgets 6:42 Compaction vs pruning 8:05 Prove what's loaded: /context list 10:06 What compaction actually does 13:42 Layer 1: pre-compaction memory flush 16:27 Layer 2: manual saves + /compact trick 19:12 Layer 3: the file architecture 23:22 The memory protocol for AGENTS.md 26:06 Retrieval: Track A, A+, and QMD by tobi lutke 31:59 Cost and cache 33:33 The complete config (copy and go) 36:35 5 things to remember

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Career update: i'm joining supermemory to lead Dev GTM. supermemory is the memory layer for AI products, we got the best public results on AI memory by a wide margin, 95% on the leading benchmarks. my role will be to make it easy for everyone to use. demos, walkthroughs, educational content, and the cleanest possible path from "i need memory in my agent" to shipping. pumped to be working with the coolest team oh, and the team just shipped supermemory local. fully self-contained, open source, runs on any machine in a minute!!
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Career update: i'm joining supermemory to lead Dev GTM. supermemory is the memory layer for AI products, we got the best public results on AI memory by a wide margin, 95% on the leading benchmarks. my role will be to make it easy for everyone to use. demos, walkthroughs, educational content, and the cleanest possible path from "i need memory in my agent" to shipping. pumped to be working with the coolest team oh, and the team just shipped supermemory local. fully self-contained, open source, runs on any machine in a minute!!

Muskan Jain

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OPENCLAW JUST GOT A MEMORY UPGRADE THAT STOPS IT FROM LOSING CONTEXT.
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OPENCLAW JUST GOT A MEMORY UPGRADE THAT STOPS IT FROM LOSING CONTEXT.

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54,944 Aufrufe • vor 3 Monaten

We just launched a new primitive for AI Walrus Memory gives your agents persistent, portable memory that you own, and it drops straight into the platforms you already build on. Anthropic Claude, OpenAI ChatGPT, and Google Gemini, you name it plus native MCP and SDKs, added in a few lines of code LIVE TODAY
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We just launched a new primitive for AI Walrus Memory gives your agents persistent, portable memory that you own, and it drops straight into the platforms you already build on. Anthropic Claude, OpenAI ChatGPT, and Google Gemini, you name it plus native MCP and SDKs, added in a few lines of code LIVE TODAY

Adeniyi.sui

45,781 Aufrufe • vor 12 Tagen

🚨 memU bot is live. A better alternative to OpenClaw🦞 (formerly Moltbot / Clawdbot) 👉Get instant access to the memU bot: 🕒 A 24/7 proactive assistant memU bot runs continuously on your machine and works as a proactive assistant. It takes action based on your behavior and context — instead of waiting for explicit commands. 🧠 Highly personal, built for you memU bot learns from your long-term usage and memory, and gradually adapts to your work style and preferences. It becomes your assistant — not a generic AI. ⚡ Very easy to use — download and run No complex setup. No configuration. Even non-technical users can simply download and run memU bot. 🔒 Local-first and secure, with no server dependency memU bot runs locally on your device. Your data never needs to be uploaded to public networks or third-party servers. 💸 Lower LLM token cost (more efficient than OpenClaw🦞) While supporting always-on and proactive behavior, memU bot is designed to reduce LLM calls and token usage — so it runs cheaper than OpenClaw, without sacrificing performance. 🧠 "Always-on" is the real key to a proactive agent. And memory is what gives it true proactivity. With memory, an agent is no longer generic. It becomes personal — shaped by who you are. This is how a user-intention-driven proactive agent is born: before you even issue a command, it can already anticipate what kind of help you’ll need, based on your past, your habits, your context. 🔮 A 24/7 process that can observe 👀, remember 📝, and act ⚡ — not just wait for prompts. 🤖 memU bot is our attempt at a user-intention-driven proactive agent — one that lives beyond the chat box.
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🚨 memU bot is live. A better alternative to OpenClaw🦞 (formerly Moltbot / Clawdbot) 👉Get instant access to the memU bot: 🕒 A 24/7 proactive assistant memU bot runs continuously on your machine and works as a proactive assistant. It takes action based on your behavior and context — instead of waiting for explicit commands. 🧠 Highly personal, built for you memU bot learns from your long-term usage and memory, and gradually adapts to your work style and preferences. It becomes your assistant — not a generic AI. ⚡ Very easy to use — download and run No complex setup. No configuration. Even non-technical users can simply download and run memU bot. 🔒 Local-first and secure, with no server dependency memU bot runs locally on your device. Your data never needs to be uploaded to public networks or third-party servers. 💸 Lower LLM token cost (more efficient than OpenClaw🦞) While supporting always-on and proactive behavior, memU bot is designed to reduce LLM calls and token usage — so it runs cheaper than OpenClaw, without sacrificing performance. 🧠 "Always-on" is the real key to a proactive agent. And memory is what gives it true proactivity. With memory, an agent is no longer generic. It becomes personal — shaped by who you are. This is how a user-intention-driven proactive agent is born: before you even issue a command, it can already anticipate what kind of help you’ll need, based on your past, your habits, your context. 🔮 A 24/7 process that can observe 👀, remember 📝, and act ⚡ — not just wait for prompts. 🤖 memU bot is our attempt at a user-intention-driven proactive agent — one that lives beyond the chat box.

memU

817,868 Aufrufe • vor 4 Monaten

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New short course: Long-Term Agentic Memory with LangGraph. Learn to build an agent with long-term memory in this course developed in collaboration with taught by its Co-Founder and CEO, Harrison Chase! Personal assistance and productivity tasks have become important use cases for agents. An important feature of an AI assistant, such as a coding or calendar assistant, is its ability to keep improving over time from its experience. Agent memory is the key capability that enables this. To add memory to an agent, you must first figure out what to store and what to retrieve when it is time to use the information. Additionally, you’ll have to decide when to update the stored information. For example, you might update in each iteration loop of the agent or perform updates in the background, with a helper agent. In this course, you will learn a mental framework to build agents with long-term memory. You'll create a useful email assistant that can respond, ignore, and notify using writing, scheduling, and memory-management tools. You’ll develop your agent's memory by adding facts to its memory store, provide examples to learn the user's preferences, and optimize system prompts to evolve instructions based on previous responses. In detail, you’ll: - Learn how the three types of memory--semantic, episodic, and procedural–and the two update mechanisms–via hot path and in the background–apply to your agents. - Build an email agent with writing, scheduling, and availability tools, along with a router that triages incoming email and handles it accordingly by ignoring, responding, or notifying the user. - Add tools to your email agent that allow it to operate on semantic memory by learning facts about the user, storing them in a long-term memory store, and searching over them in future interactions. - Incorporate episodic memory, in the form of few-shot examples, in the triage step of your agents to help them learn and update user preferences. - Add procedural memory as system prompts, optimized with feedback to improve the instructions the agent follows. Learn how to approach memory in agents, and start building agents with long-term memory with LangGraph! Please sign up here:

Andrew Ng

131,640 Aufrufe • vor 1 Jahr

Memory Store (Memory Store) gives your team and AI agents a shared company brain. Your team's knowledge & decisions are scattered across slack, emails, and people's heads. Memory Store turns them into a living wiki for your agents and teammates. Congrats on the launch, Ishita Jindal & Diwank Singh Tomer!
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Memory Store (Memory Store) gives your team and AI agents a shared company brain. Your team's knowledge & decisions are scattered across slack, emails, and people's heads. Memory Store turns them into a living wiki for your agents and teammates. Congrats on the launch, Ishita Jindal & Diwank Singh Tomer!

Y Combinator

239,716 Aufrufe • vor 27 Tagen

🚨 The OriginTrail Bounty is UNDERWAY! 🔥 Round 1 is OPEN NOW — 50,000 TRAC Tokens up for grabs! OriginTrail is paying builders 1,000 to 10,000 TRAC Tokens to connect AI agents (ChatGPT, Claude, OpenClaw, Hermes, etc.) to the new Working Memory and Shared Memory layers in DKG v10. Just dropped a clear breakdown video explaining: ✅ - How the 3 memory layers work ✅ - What makes a Flagship integration (10k TRAC) ✅ - Real examples you can actually build ✅ - How to submit your project If you're a developer or agent builder → this is your moment. Watch the full explainer 👇
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🚨 The OriginTrail Bounty is UNDERWAY! 🔥 Round 1 is OPEN NOW — 50,000 TRAC Tokens up for grabs! OriginTrail is paying builders 1,000 to 10,000 TRAC Tokens to connect AI agents (ChatGPT, Claude, OpenClaw, Hermes, etc.) to the new Working Memory and Shared Memory layers in DKG v10. Just dropped a clear breakdown video explaining: ✅ - How the 3 memory layers work ✅ - What makes a Flagship integration (10k TRAC) ✅ - Real examples you can actually build ✅ - How to submit your project If you're a developer or agent builder → this is your moment. Watch the full explainer 👇

dkg://TriniZone

39,323 Aufrufe • vor 1 Monat

Love OpenClaw but hate the token burn? 💸 Running a 24/7 agent on GPT-4/Claude is overkill. You don't need SOTA reasoning to handle a greeting or a simple lookup. LLMRouter 🩷 OpenClaw The first production-ready, agentic router designed to plug directly into OpenClaw. LLMRouter fully supports Multimodal, Memory-Equipped routing that adapts 100% to your needs—compatible with FREE open-source models. The Logic is Simple:🔹 Simple query → Cheap/Local model 🔹 Complex reasoning → SOTA model (GPT-4/Claude 3.5) 🔹 Multimodal input → Vision/Audio specialized model Why this isn't just a switch: 📉 30–50% drop in inference costs 🧠 Zero loss in response quality 🔓 100% compatible with OpenAI-style APIs 🚀 Deploy in Seconds General Usage: Get the library and serve any model: pip install llmrouter-lib llmrouter serve OpenClaw Native Integration: Want the full agent experience? LLMRouter built a dedicated integration for OpenClaw users: LLMRouter Resources: 🔗 Repo: 📦 PyPI: 🤝 Works with: Route smarter. Train your own. Pay less. More on LLMRouter: Most routers are static if/else. LLMRouter is an intelligent, learning system. 🤖 Agentic & Memory-Aware: Decisions aren't stateless. We use RAG-powered memory to route based on context and history. 👤 Fully Personalized: It learns from your usage patterns via RL feedback loops. 🔬 Research-Grade: Switch between 16+ routing strategies (KNN, SVM, BERT, Graph, RL) with a single flag.
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Love OpenClaw but hate the token burn? 💸 Running a 24/7 agent on GPT-4/Claude is overkill. You don't need SOTA reasoning to handle a greeting or a simple lookup. LLMRouter 🩷 OpenClaw The first production-ready, agentic router designed to plug directly into OpenClaw. LLMRouter fully supports Multimodal, Memory-Equipped routing that adapts 100% to your needs—compatible with FREE open-source models. The Logic is Simple:🔹 Simple query → Cheap/Local model 🔹 Complex reasoning → SOTA model (GPT-4/Claude 3.5) 🔹 Multimodal input → Vision/Audio specialized model Why this isn't just a switch: 📉 30–50% drop in inference costs 🧠 Zero loss in response quality 🔓 100% compatible with OpenAI-style APIs 🚀 Deploy in Seconds General Usage: Get the library and serve any model: pip install llmrouter-lib llmrouter serve OpenClaw Native Integration: Want the full agent experience? LLMRouter built a dedicated integration for OpenClaw users: LLMRouter Resources: 🔗 Repo: 📦 PyPI: 🤝 Works with: Route smarter. Train your own. Pay less. More on LLMRouter: Most routers are static if/else. LLMRouter is an intelligent, learning system. 🤖 Agentic & Memory-Aware: Decisions aren't stateless. We use RAG-powered memory to route based on context and history. 👤 Fully Personalized: It learns from your usage patterns via RL feedback loops. 🔬 Research-Grade: Switch between 16+ routing strategies (KNN, SVM, BERT, Graph, RL) with a single flag.

Jiaxuan You

31,271 Aufrufe • vor 4 Monaten