Video yükleniyor...

Video Yüklenemedi

Ana Sayfaya Dön

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:...

cailynyongyong's profile picture

Cailyn Y.

8,670 subscribers

59,618 görüntüleme • 7 ay önce •via X (Twitter)

LIVE

Anya Rossi• Live Now

Private livecam show

57 Yorum

Cailyn Y. profil fotoğrafı
Cailyn Y.7 ay önce

steps to use asap: 1. get api key here: 2. install plugin in terminal 3. go to openclaw dashboard > config > plugins > Momo memory > copy and paste api key and that's it. chat w your openclaw in deployed channels

Cailyn Y. profil fotoğrafı
Cailyn Y.7 ay önce

@getyourmomo ‼️if you don't know how to install openclaw or plugins DM me i'll set it up for you

Logan profil fotoğrafı
Logan7 ay önce

@openclaw @getyourmomo This is cool! Curious how big your team is? The product seems very professional

Cailyn Y. profil fotoğrafı
Cailyn Y.7 ay önce

@openclaw @getyourmomo 👀…its just me and claude..

Mehreen Omer profil fotoğrafı
Mehreen Omer7 ay önce

@quiet_node @openclaw @getyourmomo Wow girl that’s dam impressive! Do we need to integrate it with anything else or no?

Cailyn Y. profil fotoğrafı
Cailyn Y.7 ay önce

@quiet_node @openclaw @getyourmomo Nope! You can choose which apps to connect

Gabe | ガブ profil fotoğrafı
Gabe | ガブ7 ay önce

@openclaw @getyourmomo I just sent it the instructions in chat and it did it by itself!

Cailyn Y. profil fotoğrafı
Cailyn Y.7 ay önce

@openclaw @getyourmomo Omg really???

Gabe | ガブ profil fotoğrafı
Gabe | ガブ7 ay önce

@openclaw @getyourmomo Yeah!

Cailyn Y. profil fotoğrafı
Cailyn Y.7 ay önce

@openclaw @getyourmomo Oh thats so siiiiiickkkk Lemme know how it works out for you!!!!

Ninan Thampy 🌈💙💜 profil fotoğrafı
Ninan Thampy 🌈💙💜7 ay önce

@openclaw @getyourmomo Looks great. Only need to figure out a secure way use Open Claw for my SMB clients

Cailyn Y. profil fotoğrafı
Cailyn Y.7 ay önce

@openclaw @getyourmomo Hey, let me know if you have any security questions Our privacy docs are here:

Shreyans Bhansali profil fotoğrafı
Shreyans Bhansali7 ay önce

@openclaw @getyourmomo finally my agents can stop forgetting stuff like goldfish with wifi

Cailyn Y. profil fotoğrafı
Cailyn Y.7 ay önce

@openclaw @getyourmomo hahahaha

xdiep4474.eth profil fotoğrafı
xdiep4474.eth7 ay önce

@openclaw @getyourmomo Nice bro, cloud-backed memory = game changer, gonna test team sharing and encryption asap

Watzon profil fotoğrafı
Watzon7 ay önce

@openclaw @getyourmomo How did we end up with the same project name? Granted mine is different as a complete self-hosted SuperMemory alternative, but what are the fucking odds

Nour-Islam profil fotoğrafı
Nour-Islam7 ay önce

@openclaw @getyourmomo One of the main reasons why people love “ OpemClaw is because it stores everything on the server itself whether that be a Mac mini or a VPS.

Cailyn Y. profil fotoğrafı
Cailyn Y.7 ay önce

@openclaw @getyourmomo Yeah this is more focused on team shared memory - not just yours but you can connect it with ur teammtes as well

gz profil fotoğrafı
gz7 ay önce

@openclaw @getyourmomo Noice

Rohit T profil fotoğrafı
Rohit T7 ay önce

@openclaw @getyourmomo Pricing?

Cailyn Y. profil fotoğrafı
Cailyn Y.7 ay önce

@openclaw @getyourmomo here!

Christian Pickett profil fotoğrafı
Christian Pickett7 ay önce

@openclaw @getyourmomo openclaw memory is super important! dm'd!

Zoe profil fotoğrafı
Zoe7 ay önce

@openclaw @getyourmomo 👍

diegotorres.eth profil fotoğrafı
diegotorres.eth7 ay önce

@openclaw @getyourmomo Duuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuude! Yeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeees!

Daniel Roman profil fotoğrafı
Daniel Roman7 ay önce

@openclaw @getyourmomo very nicee

AK profil fotoğrafı
AK7 ay önce

@openclaw @getyourmomo Finding it hard to keep up with all the different memory solutions for OpenClaw! Any advice on how to compare?

Cailyn Y. profil fotoğrafı
Cailyn Y.7 ay önce

@aekae8888 @openclaw @getyourmomo here!

wi/tch profil fotoğrafı
wi/tch7 ay önce

@openclaw @getyourmomo truly amazing

femto profil fotoğrafı
femto6 ay önce

@openclaw @getyourmomo I followed you because you're fun.

Zrewolwerowany Kaloryfer profil fotoğrafı
Zrewolwerowany Kaloryfer7 ay önce

@openclaw @getyourmomo and way too expensive for typical Clawdbot user - thanks, I'll pass

Cailyn Y. profil fotoğrafı
Cailyn Y.7 ay önce

@openclaw @getyourmomo cool

Doeon Kwon profil fotoğrafı
Doeon Kwon7 ay önce

@openclaw @getyourmomo let’s goooo

Cailyn Y. profil fotoğrafı
Cailyn Y.7 ay önce

@openclaw @getyourmomo Yayyy

dolev profil fotoğrafı
dolev7 ay önce

@openclaw @getyourmomo gonna try this out!

Cailyn Y. profil fotoğrafı
Cailyn Y.7 ay önce

@openclaw @getyourmomo Lezgooo

Jonathan Moon profil fotoğrafı
Jonathan Moon7 ay önce

@openclaw @getyourmomo Great, checking it out!

Cailyn Y. profil fotoğrafı
Cailyn Y.7 ay önce

@openclaw @getyourmomo yee lemme know how it goes

- Elijah Muraoka - profil fotoğrafı
- Elijah Muraoka -7 ay önce

@fdotinc @openclaw @getyourmomo Why use momo > @supermemory ? I’m deciding rn

Cailyn Y. profil fotoğrafı
Cailyn Y.7 ay önce

@fdotinc @openclaw @getyourmomo @supermemory momo indexes “decisions” that were made across the company immediately. From slack, notion github gmail etc. extracts ur team members decisions as well

Rafa profil fotoğrafı
Rafa7 ay önce

@openclaw @getyourmomo U da real claw <3

Brosnan Hoban profil fotoğrafı
Brosnan Hoban7 ay önce

@openclaw @getyourmomo Finally, a memory upgrade that makes sense.

Cailyn Y. profil fotoğrafı
Cailyn Y.7 ay önce

@openclaw @getyourmomo would love for you to try it out!

Just Jin profil fotoğrafı
Just Jin7 ay önce

@openclaw @getyourmomo Great to see you still at it! Haven't gotten at openclaw yet but using Cursor furiously. Does you app provide anything on the Cursor front? Trying to run everything locally.

Cailyn Y. profil fotoğrafı
Cailyn Y.7 ay önce

@openclaw @getyourmomo Omg how are you haha

Just Jin profil fotoğrafı
Just Jin7 ay önce

@openclaw @getyourmomo Lol on life support like everyone else.

Choureyyyy profil fotoğrafı
Choureyyyy7 ay önce

@openclaw @getyourmomo @cailynyongyong this is super duper cool! Ever since I noticed your passion for this, I’m rooting this succeeds. And what better than such use cases! 🎉

Cailyn Y. profil fotoğrafı
Cailyn Y.7 ay önce

@openclaw @getyourmomo Thanks so much, means a lot to me

Tommy profil fotoğrafı
Tommy7 ay önce

@openclaw @getyourmomo that's a game changer for claw users. tired of the local memory hassle too. gonna test this plugin asap.

Cailyn Y. profil fotoğrafı
Cailyn Y.7 ay önce

@openclaw @getyourmomo Lemme know how it goes!

Exell profil fotoğrafı
Exell7 ay önce

@openclaw @getyourmomo Is this free ?

Cailyn Y. profil fotoğrafı
Cailyn Y.7 ay önce

@openclaw @getyourmomo Theres a free limited storage!

Michiel V profil fotoğrafı
Michiel V7 ay önce

@openclaw @getyourmomo the local-only memory limitation is real friction, especially for teams. cloud-backed + auto-indexing solves the two biggest pain points at once. the plugin approach is the right call too, keeps openclaw modular rather than bloated

TechE1111 profil fotoğrafı
TechE11117 ay önce

@openclaw @getyourmomo Memory is one of the last moats! Good-work Caitlyn $Momo forever

Est 1988 🐂🀄 profil fotoğrafı
Est 1988 🐂🀄7 ay önce

@openclaw @getyourmomo ngopi dulu neng biar canduuu

Moulaye Aly profil fotoğrafı
Moulaye Aly7 ay önce

@openclaw @getyourmomo @grok est ce gratuit ?

swaraj profil fotoğrafı
swaraj7 ay önce

@openclaw @getyourmomo but what's use of it being cloud based ?

Dev Sharma -e/acc profil fotoğrafı
Dev Sharma -e/acc7 ay önce

@openclaw @getyourmomo Greattt cailyn 👏

Benzer Videolar

You can now run supermemory locally. Introducing the supermemory local - Fully self-contained. Comes with our graph engine, embedding model, etc. - Run on any machine, with your OpenClaw🦞, hermes, claude, etc. - SDKs to add memory to your agent, or build your company brain.
1:16

You can now run supermemory locally. Introducing the supermemory local - Fully self-contained. Comes with our graph engine, embedding model, etc. - Run on any machine, with your OpenClaw🦞, hermes, claude, etc. - SDKs to add memory to your agent, or build your company brain.

Dhravya Shah

681,833 görüntüleme • 3 ay önce

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 skill layer and integrate directly into the agent’s context assembly flow. We try to make OpenClaw a truly 24/7 employee capable of complex workflows. The technical setup isn’t the hardest part but the real challenge is giving it a "brain" that remembers exact project details, past decisions, and team changes over time. The Native Memory Plugin is now live on NPM & ClawHub. Here is what it brings to your OpenClaw agents: 👉 Native Integration: Automatically manages a Three-Layer Memory architecture (Context Tree, Workspace Memory, Daily Memory). 👉 Git-like Stateful Memory: Organizes memory into a semantic hierarchy of human-readable, diffable Markdown files. You always get updated knowledge and can actually see and fix what your agent learns. 👉 Top Market Accuracy: Achieves an industry-leading 92.2% retrieval accuracy (LoCoMo & LongMemEval benchmarks), maintaining 90% accuracy even with cheap, lightweight models. 👉 Local-first & Portable: Local-by-default, fully portable for multi-agent teams. 👉 Super Easy Setup
1:46

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 skill layer and integrate directly into the agent’s context assembly flow. We try to make OpenClaw a truly 24/7 employee capable of complex workflows. The technical setup isn’t the hardest part but the real challenge is giving it a "brain" that remembers exact project details, past decisions, and team changes over time. The Native Memory Plugin is now live on NPM & ClawHub. Here is what it brings to your OpenClaw agents: 👉 Native Integration: Automatically manages a Three-Layer Memory architecture (Context Tree, Workspace Memory, Daily Memory). 👉 Git-like Stateful Memory: Organizes memory into a semantic hierarchy of human-readable, diffable Markdown files. You always get updated knowledge and can actually see and fix what your agent learns. 👉 Top Market Accuracy: Achieves an industry-leading 92.2% retrieval accuracy (LoCoMo & LongMemEval benchmarks), maintaining 90% accuracy even with cheap, lightweight models. 👉 Local-first & Portable: Local-by-default, fully portable for multi-agent teams. 👉 Super Easy Setup

andy nguyen

169,442 görüntüleme • 6 ay önce

Project LUCI = AI Pin + Real-world memories + 🦞 We're turning AI from something you prompt into an assistant that actually knows you. Powered by Memories AI for visual memory and OpenClaw for real-world action. Join the waitlist.
2:52

Project LUCI = AI Pin + Real-world memories + 🦞 We're turning AI from something you prompt into an assistant that actually knows you. Powered by Memories AI for visual memory and OpenClaw for real-world action. Join the waitlist.

Memories.ai

179,131 görüntüleme • 7 ay önce

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:
10:57

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:

Alex Finn

89,897 görüntüleme • 5 ay önce

So now we've an alternative to OpenClaw for enterprise with zero setup and a 70% lower token usage You can connect PokeeClaw to all your apps for: - Data analysis - Email management - PPT/XLSX generation - And much more And everything else like memory, 24/7 running, etc.
0:37

So now we've an alternative to OpenClaw for enterprise with zero setup and a 70% lower token usage You can connect PokeeClaw to all your apps for: - Data analysis - Email management - PPT/XLSX generation - And much more And everything else like memory, 24/7 running, etc.

Paul Couvert

42,977 görüntüleme • 5 ay önce

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.
1:19

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.

andy nguyen

28,980 görüntüleme • 6 ay önce

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.
2:06

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.

Pratham

80,634 görüntüleme • 7 ay önce

New course: Agent Memory: Building Memory-Aware Agents, built in partnership with Oracle and taught by Richmond Alake and Nacho Martínez. Many agents work well within a single session but their memory resets once the session ends. Consider a research agent working on dozens of papers across multiple days: without memory, it has no way to store and retrieve what it learned across sessions. This short course teaches you to build a memory system that enables agents to persist memory and thereby learn across sessions. You'll design a Memory Manager that handles different memory types, implement semantic tool retrieval that scales without bloating the context, and build write-back pipelines that let your agent autonomously update and refine what it knows over time. Skills you'll gain: - Build persistent memory stores for different agent memory types - Implement a Memory Manager that orchestrates how your agent reads, writes, and retrieves memory - Treat tools as procedural memory and retrieve only relevant ones at inference time using semantic search Join and learn to build agents that remember and improve over time!
1:37

New course: Agent Memory: Building Memory-Aware Agents, built in partnership with Oracle and taught by Richmond Alake and Nacho Martínez. Many agents work well within a single session but their memory resets once the session ends. Consider a research agent working on dozens of papers across multiple days: without memory, it has no way to store and retrieve what it learned across sessions. This short course teaches you to build a memory system that enables agents to persist memory and thereby learn across sessions. You'll design a Memory Manager that handles different memory types, implement semantic tool retrieval that scales without bloating the context, and build write-back pipelines that let your agent autonomously update and refine what it knows over time. Skills you'll gain: - Build persistent memory stores for different agent memory types - Implement a Memory Manager that orchestrates how your agent reads, writes, and retrieves memory - Treat tools as procedural memory and retrieve only relevant ones at inference time using semantic search Join and learn to build agents that remember and improve over time!

Andrew Ng

162,048 görüntüleme • 6 ay önce

OpenClaw Now Lives on Solana — Free AI Agents in Your Pocket We've integrated .molt domains with OpenClaw, bringing personal AI agents to the Solana ecosystem. Each .molt domain is a Metaplex Core NFT that unlocks your own AI agent instance. What you get: Wallet-based access — No accounts, no passwords. Connect your Solana wallet, prove NFT ownership, instant access Per-domain agents — Each domain NFT gets its own isolated AI agent with persistent memory Configure your channels — Set up Telegram, Discord, or Slack bots directly from the UI. Enter your bot token, click save, restart your agent Persistent storage — Your agent's memory and conversations persist in R2 storage, tied to your NFT The best part? It's free. You don't need a $600 MacBook. You don't need a $15/month VPS. You don't need to manage servers, Docker containers, or API keys. Just mint a .molt domain, connect your wallet, and you have OpenClaw in your pocket — accessible from any device, anywhere. Your domain. Your agent. Your data. All on-chain, all yours. Live on devnet:
2:06

OpenClaw Now Lives on Solana — Free AI Agents in Your Pocket We've integrated .molt domains with OpenClaw, bringing personal AI agents to the Solana ecosystem. Each .molt domain is a Metaplex Core NFT that unlocks your own AI agent instance. What you get: Wallet-based access — No accounts, no passwords. Connect your Solana wallet, prove NFT ownership, instant access Per-domain agents — Each domain NFT gets its own isolated AI agent with persistent memory Configure your channels — Set up Telegram, Discord, or Slack bots directly from the UI. Enter your bot token, click save, restart your agent Persistent storage — Your agent's memory and conversations persist in R2 storage, tied to your NFT The best part? It's free. You don't need a $600 MacBook. You don't need a $15/month VPS. You don't need to manage servers, Docker containers, or API keys. Just mint a .molt domain, connect your wallet, and you have OpenClaw in your pocket — accessible from any device, anywhere. Your domain. Your agent. Your data. All on-chain, all yours. Live on devnet:

Molt.id

315,151 görüntüleme • 7 ay önce

Clude is now available as a custom connector on Anthropic Claude. Giving it persistent memory across every conversation. It remembers your projects, your preferences, and the decisions you make, so you never start from zero again. Use Clude across Chat / Co-Work and Claude Code. Add it in 30 seconds, no API key: 1. In Claude: Customize, Add custom connector URL: Enter your email. That's it. Recap for people unfamiliar: Clude deviates from the traditional vector database that many others bolt onto Claude. We are a cognitive memory system modeled on how human memory works. Think traditional memory systems: Calculator > It computes. Fullstop. Excel > Dynamic, formula run, able to share and manipulate Clude scored 86% on LongMemEval, one of the toughest long-term memory benchmarks. This means less hallucinations to traditional platforms. It scores memories by relevance, recency, and importance, links them into a knowledge graph, and consolidates them over time. Tell Claude something, open a new chat, and watch it remember.
0:39

Clude is now available as a custom connector on Anthropic Claude. Giving it persistent memory across every conversation. It remembers your projects, your preferences, and the decisions you make, so you never start from zero again. Use Clude across Chat / Co-Work and Claude Code. Add it in 30 seconds, no API key: 1. In Claude: Customize, Add custom connector URL: Enter your email. That's it. Recap for people unfamiliar: Clude deviates from the traditional vector database that many others bolt onto Claude. We are a cognitive memory system modeled on how human memory works. Think traditional memory systems: Calculator > It computes. Fullstop. Excel > Dynamic, formula run, able to share and manipulate Clude scored 86% on LongMemEval, one of the toughest long-term memory benchmarks. This means less hallucinations to traditional platforms. It scores memories by relevance, recency, and importance, links them into a knowledge graph, and consolidates them over time. Tell Claude something, open a new chat, and watch it remember.

seb

20,611 görüntüleme • 3 ay önce

🦞 OpenClaw 3.7 JUST DROPPED and it's HUGE. ✅ Context Engine Plugin API - Say goodbye to context loss. Plugins like lossless-claw can now preserve full memory without touching core logic. ✅ 30% Faster with LongCat - New official integration solves the Google/Anthropic ban problem. Faster tasks, stable API, zero account risk. ✅ Threads Survive Restarts - Discord/Telegram bindings now persist. Your conversations stay connected even after reboot. ✅ Docker Slim + i18n - Multi-stage builds, Spanish locale support, iOS App Store prep. OpenClaw is going mainstream. Your AI agent just got a serious upgrade. The AI OS war has begun.
8:47

🦞 OpenClaw 3.7 JUST DROPPED and it's HUGE. ✅ Context Engine Plugin API - Say goodbye to context loss. Plugins like lossless-claw can now preserve full memory without touching core logic. ✅ 30% Faster with LongCat - New official integration solves the Google/Anthropic ban problem. Faster tasks, stable API, zero account risk. ✅ Threads Survive Restarts - Discord/Telegram bindings now persist. Your conversations stay connected even after reboot. ✅ Docker Slim + i18n - Multi-stage builds, Spanish locale support, iOS App Store prep. OpenClaw is going mainstream. Your AI agent just got a serious upgrade. The AI OS war has begun.

Boxmining

127,735 görüntüleme • 6 ay önce

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:
2:06

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

132,058 görüntüleme • 1 yıl önce

OpenClaw just got a plugin nobody is talking about. And it can replace a whole operations team. It is called Paperclip. It lets you build a full AI company on your own computer. You get an AI CEO. You get a marketing team. You get a tech team. They have real memory so they never forget a task. They wake up every day and do the work for you. You do not have to manage them or pay them a salary. Your data stays safe because it runs on your hardware. Here is how to start today. Go to GitHub and download OpenClaw. Install the Paperclip dashboard inside it. Pick one task and hire your first AI worker.
8:46

OpenClaw just got a plugin nobody is talking about. And it can replace a whole operations team. It is called Paperclip. It lets you build a full AI company on your own computer. You get an AI CEO. You get a marketing team. You get a tech team. They have real memory so they never forget a task. They wake up every day and do the work for you. You do not have to manage them or pay them a salary. Your data stays safe because it runs on your hardware. Here is how to start today. Go to GitHub and download OpenClaw. Install the Paperclip dashboard inside it. Pick one task and hire your first AI worker.

Julian Goldie SEO

33,889 görüntüleme • 6 ay önce

SK Hynix's Hoshik Kim admits high bandwidth memory "is not the answer" to the memory-wall problem, and SK is working on different solutions to solve the bottleneck. "No, actually, I kind of agree with Pat. So HBM is not the answer." [SK Hynix is admitting that HBM is a bad memory?] "Nothing is a bad memory. It's a good memory, but it's not the final answer. So actually, HBM will not solve the memory-wall problem. Memory-wall problem is an inherent AI problem, which you cannot avoid." "So we try our best to provide higher bandwidth with this memory, with HBM technology even though it has some limitation, as Pat already mentioned, with thermal, IOs, cost, a lot of stuff. "But that's the only thing we got right now to help ease this memory-wall problem, memory-bottleneck problem. But at the same time, we are working in different directions to solve this memory-wall problem with different solutions."
0:51

SK Hynix's Hoshik Kim admits high bandwidth memory "is not the answer" to the memory-wall problem, and SK is working on different solutions to solve the bottleneck. "No, actually, I kind of agree with Pat. So HBM is not the answer." [SK Hynix is admitting that HBM is a bad memory?] "Nothing is a bad memory. It's a good memory, but it's not the final answer. So actually, HBM will not solve the memory-wall problem. Memory-wall problem is an inherent AI problem, which you cannot avoid." "So we try our best to provide higher bandwidth with this memory, with HBM technology even though it has some limitation, as Pat already mentioned, with thermal, IOs, cost, a lot of stuff. "But that's the only thing we got right now to help ease this memory-wall problem, memory-bottleneck problem. But at the same time, we are working in different directions to solve this memory-wall problem with different solutions."

Fireside Alpha

140,571 görüntüleme • 2 ay önce

🚨 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 👇
6:06

🚨 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

39,451 görüntüleme • 4 ay önce

Day 5 of OpenClaw 🦞 My entire AI team is now building relationships with one another IN REALTIME!! I added new md, soul, and memory files per ai employee so that they: - Know what to do - Have distinct and unique personalities - Remember and build relationships overtime. The VugolaAI team just got 100x better, and we are only 5 days into OpenClaw. I’m actually thinking about getting a second Mac Mini and trying to run a small AI model locally, this feels like the future!! P.S I have no coding experience or knowledge at all. 🙃
5:08

Day 5 of OpenClaw 🦞 My entire AI team is now building relationships with one another IN REALTIME!! I added new md, soul, and memory files per ai employee so that they: - Know what to do - Have distinct and unique personalities - Remember and build relationships overtime. The VugolaAI team just got 100x better, and we are only 5 days into OpenClaw. I’m actually thinking about getting a second Mac Mini and trying to run a small AI model locally, this feels like the future!! P.S I have no coding experience or knowledge at all. 🙃

Vadim

38,088 görüntüleme • 7 ay önce

Chase Hughes just revealed a wild way to edit traumatic memories using hypnosis on the Joe Rogan Experience. HUGHES: “I figured out a way to edit memory… I can take you back and change your perspective on trauma.” “You go back to that event when you got kicked in the nuts and everybody laughed at you in elementary school… and you can reprocess that memory in a very short amount of time as an adult with the perspective of an adult.” “Instead of modifying the memory like ‘oh that never happened’ — the memory stays. The perspective changes.” “And it’s exactly what psychedelics do. This massive perspective shift on memory… their whole life can be different after that day.” He says he’s done this hundreds of times and it creates real downstream effects.
5:35

Chase Hughes just revealed a wild way to edit traumatic memories using hypnosis on the Joe Rogan Experience. HUGHES: “I figured out a way to edit memory… I can take you back and change your perspective on trauma.” “You go back to that event when you got kicked in the nuts and everybody laughed at you in elementary school… and you can reprocess that memory in a very short amount of time as an adult with the perspective of an adult.” “Instead of modifying the memory like ‘oh that never happened’ — the memory stays. The perspective changes.” “And it’s exactly what psychedelics do. This massive perspective shift on memory… their whole life can be different after that day.” He says he’s done this hundreds of times and it creates real downstream effects.

Defiant Ghost

26,912 görüntüleme • 3 ay önce

HERMES AGENT CAN SHARE MEMORY WITH CODEX AND CLAUDE CODE THROUGH HINDSIGHT. ONE MEMORY BANK. ONE AGENT REMEMBERS, EVERY OTHER AGENT KNOWS. the problem: you use Hermes for orchestration. Codex for coding. Claude Code for debugging. each has its own memory. switch between them and you explain the same project three times. Hindsight fixes this. one shared memory bank that every agent reads and writes to. tell Codex: "the test color for this project is purple." switch to Hermes. ask: "what test color did I pick?" Hermes answers: "purple." no copy-paste. no re-explaining. instant recall. HOW IT WORKS: Hindsight runs as a Docker container on your machine. self-hosted. your data stays local. an LLM powers the memory processing (retain, recall, reflect). RETAIN: extracts facts from your conversations. entities, decisions, preferences, project context. saved to the memory bank automatically. RECALL: when you ask a question, Hindsight pulls from semantic search, keywords, graph connections, and temporal data. fused into one answer. REFLECT: deeper reasoning layer. connects memories across sessions. identifies patterns in your work. produces observations that get smarter over time. CONNECT TO HERMES: Desktop app: Settings → Memory and Context → switch provider from Namosin to Hindsight. set API URL to your local Docker container. set bank ID. done. CLI: hermes memory setup → select Hindsight. verify: hermes memory status should show: provider: hindsight, installed, available. CONNECT TO CODEX: npx hindsight-coding-agents install codex \ --self-hosted --server this installs lifecycle hooks: initialize memory on session start. recall context during work. retain the session when done. enable hooks in Codex: Settings → Hooks → trust all three. CONNECT TO CLAUDE CODE (same command): npx hindsight-coding-agents install all "all" connects every detected agent on your machine. Claude Code, Codex, Cursor, and others. one command. every agent shares the same bank. TAGS FOR FILTERING: every memory gets tagged by harness (Hermes, Codex, Claude Code) and optionally by project name. in the Hindsight control plane: filter by harness. see only Hermes memories. or only Codex memories. or search across everything. soft partitions inside one bank. not hard walls. cross-reference when you need to. ONE BANK OR MANY: one global bank: solo dev, related projects. all agents share everything. patterns emerge across projects. per-project banks: unrelated codebases. each project gets its own memory. no cross-contamination. your call. start with one. split when projects diverge. KNOWLEDGE PAGES (v0.9.0): Hindsight auto-generates living summaries from your accumulated memories. components, concepts, conventions, decisions. not static docs. projected from real agent conversations. auto-refresh as new memories land. WHAT TO KNOW: self-hosted via Docker. your data never leaves your machine. backup system built in (admin CLI + scheduled exports). works with any LLM (local Ollama, OpenAI, Codex subscription). memory defense: redact or block sensitive content automatically. 33,000+ memories accumulated in ~10 days of normal use.
34:12

HERMES AGENT CAN SHARE MEMORY WITH CODEX AND CLAUDE CODE THROUGH HINDSIGHT. ONE MEMORY BANK. ONE AGENT REMEMBERS, EVERY OTHER AGENT KNOWS. the problem: you use Hermes for orchestration. Codex for coding. Claude Code for debugging. each has its own memory. switch between them and you explain the same project three times. Hindsight fixes this. one shared memory bank that every agent reads and writes to. tell Codex: "the test color for this project is purple." switch to Hermes. ask: "what test color did I pick?" Hermes answers: "purple." no copy-paste. no re-explaining. instant recall. HOW IT WORKS: Hindsight runs as a Docker container on your machine. self-hosted. your data stays local. an LLM powers the memory processing (retain, recall, reflect). RETAIN: extracts facts from your conversations. entities, decisions, preferences, project context. saved to the memory bank automatically. RECALL: when you ask a question, Hindsight pulls from semantic search, keywords, graph connections, and temporal data. fused into one answer. REFLECT: deeper reasoning layer. connects memories across sessions. identifies patterns in your work. produces observations that get smarter over time. CONNECT TO HERMES: Desktop app: Settings → Memory and Context → switch provider from Namosin to Hindsight. set API URL to your local Docker container. set bank ID. done. CLI: hermes memory setup → select Hindsight. verify: hermes memory status should show: provider: hindsight, installed, available. CONNECT TO CODEX: npx hindsight-coding-agents install codex \ --self-hosted --server this installs lifecycle hooks: initialize memory on session start. recall context during work. retain the session when done. enable hooks in Codex: Settings → Hooks → trust all three. CONNECT TO CLAUDE CODE (same command): npx hindsight-coding-agents install all "all" connects every detected agent on your machine. Claude Code, Codex, Cursor, and others. one command. every agent shares the same bank. TAGS FOR FILTERING: every memory gets tagged by harness (Hermes, Codex, Claude Code) and optionally by project name. in the Hindsight control plane: filter by harness. see only Hermes memories. or only Codex memories. or search across everything. soft partitions inside one bank. not hard walls. cross-reference when you need to. ONE BANK OR MANY: one global bank: solo dev, related projects. all agents share everything. patterns emerge across projects. per-project banks: unrelated codebases. each project gets its own memory. no cross-contamination. your call. start with one. split when projects diverge. KNOWLEDGE PAGES (v0.9.0): Hindsight auto-generates living summaries from your accumulated memories. components, concepts, conventions, decisions. not static docs. projected from real agent conversations. auto-refresh as new memories land. WHAT TO KNOW: self-hosted via Docker. your data never leaves your machine. backup system built in (admin CLI + scheduled exports). works with any LLM (local Ollama, OpenAI, Codex subscription). memory defense: redact or block sensitive content automatically. 33,000+ memories accumulated in ~10 days of normal use.

YanXbt

29,658 görüntüleme • 1 ay önce

CLAUDE CODE IS NOW FREE AND YOU CAN BUILD AN ENTIRE AI CODING SYSTEM WITH 1M CONTEXT, AGENT OS AND OBSIDIAN MEMORY WITHOUT PAYING FOR ANY OF IT. The stack gets smarter over time and costs you nothing to run.
0:34

CLAUDE CODE IS NOW FREE AND YOU CAN BUILD AN ENTIRE AI CODING SYSTEM WITH 1M CONTEXT, AGENT OS AND OBSIDIAN MEMORY WITHOUT PAYING FOR ANY OF IT. The stack gets smarter over time and costs you nothing to run.

0xMarioNawfal

51,265 görüntüleme • 3 ay önce

New short course: LLMs as Operating Systems: Agent Memory, created with Letta, and taught by its founders Charles Packer and Sarah Wooders. An LLM's input context window has limited space. Using a longer input context also costs more and results in slower processing. So, managing what's stored in this context window is important. In the innovative paper MemGPT: Towards LLMs as Operating Systems, its authors (which include the instructors) proposed using an LLM agent to manage this context window. Their system uses a large persistent memory that stores everything that could be included in the input context, and an agent decides what is actually included. Take the example of building a chatbot that needs to remember what's been said earlier in a conversation (perhaps over many days of interaction with a user). As the conversation's length grows, the memory management agent will move information from the input context to a persistent searchable database; summarize information to keep relevant facts in the input context; and restore relevant conversation elements from further back in time. This allows a chatbot to keep what's currently most relevant in its input context memory to generate the next response. When I read the original MemGPT paper, I thought it was an innovative technique for handling memory for LLMs. The open-source Letta framework, which we'll use in this course, makes MemGPT easy to implement. It adds memory to your LLM agents and gives them transparent long-term memory. In detail, you’ll learn: - How to build an agent that can edit its own limited input context memory, using tools and multi-step reasoning - What is a memory hierarchy (an idea from computer operating systems, which use a cache to speed up memory access), and how these ideas apply to managing the LLM input context (where the input context window is a "cache" storing the most relevant information; and an agent decides what to move in and out of this to/from a larger persistent storage system) - How to implement multi-agent collaboration by letting different agents share blocks of memory This course will give you a sophisticated understanding of memory management for LLMs, which is important for chatbots having long conversations, and for complex agentic workflows. Please sign up here!
2:57

New short course: LLMs as Operating Systems: Agent Memory, created with Letta, and taught by its founders Charles Packer and Sarah Wooders. An LLM's input context window has limited space. Using a longer input context also costs more and results in slower processing. So, managing what's stored in this context window is important. In the innovative paper MemGPT: Towards LLMs as Operating Systems, its authors (which include the instructors) proposed using an LLM agent to manage this context window. Their system uses a large persistent memory that stores everything that could be included in the input context, and an agent decides what is actually included. Take the example of building a chatbot that needs to remember what's been said earlier in a conversation (perhaps over many days of interaction with a user). As the conversation's length grows, the memory management agent will move information from the input context to a persistent searchable database; summarize information to keep relevant facts in the input context; and restore relevant conversation elements from further back in time. This allows a chatbot to keep what's currently most relevant in its input context memory to generate the next response. When I read the original MemGPT paper, I thought it was an innovative technique for handling memory for LLMs. The open-source Letta framework, which we'll use in this course, makes MemGPT easy to implement. It adds memory to your LLM agents and gives them transparent long-term memory. In detail, you’ll learn: - How to build an agent that can edit its own limited input context memory, using tools and multi-step reasoning - What is a memory hierarchy (an idea from computer operating systems, which use a cache to speed up memory access), and how these ideas apply to managing the LLM input context (where the input context window is a "cache" storing the most relevant information; and an agent decides what to move in and out of this to/from a larger persistent storage system) - How to implement multi-agent collaboration by letting different agents share blocks of memory This course will give you a sophisticated understanding of memory management for LLMs, which is important for chatbots having long conversations, and for complex agentic workflows. Please sign up here!

Andrew Ng

201,127 görüntüleme • 1 yıl önce