Loading video...

Video Failed to Load

There was a problem loading this video. This could be due to a temporary network issue or the video might be unavailable.

🚨 KEEP CALM AND AI - AUTONOMOUS AGENTS WITH INFINITE MEMORY We are launching the ability to create arbitrary agents that run on schedule and have access to a persistent and infinite memory The agents will be able to store, retrieve, and update information across sessions and perform repetitive... show more

Bindu Reddy

172,638 subscribers

12,626 views • 5 months ago •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

0 Comments

No comments available

Comments from the original post will appear here

Related Videos

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!

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

158,947 views • 3 months ago

INTELLIGENT TASKS ARE A STEPPING STONE TO AGI Today, we are launching ChatLLM Tasks. We have hooked up tools like web search, email, and web scrappers to mini-agents that can be triggered on a schedule. These tasks combine intelligent tool use with crons! We think this is the first step towards AGI. The next step is to connect our AI engineer to create more complex tasks. AGI STEP ONE - DONE!

INTELLIGENT TASKS ARE A STEPPING STONE TO AGI Today, we are launching ChatLLM Tasks. We have hooked up tools like web search, email, and web scrappers to mini-agents that can be triggered on a schedule. These tasks combine intelligent tool use with crons! We think this is the first step towards AGI. The next step is to connect our AI engineer to create more complex tasks. AGI STEP ONE - DONE!

Bindu Reddy

18,486 views • 1 year ago

5 huge AI agents, MCP, and LLM updates today: 1. Mem0 just dropped OpenMemory MCP server that adds persistent memory across MCP clients. Create a shared memory layer with style, topics, emotions, etc. that works Claude, Cursor, Windsurf, and others. 100% local and opensource.

5 huge AI agents, MCP, and LLM updates today: 1. Mem0 just dropped OpenMemory MCP server that adds persistent memory across MCP clients. Create a shared memory layer with style, topics, emotions, etc. that works Claude, Cursor, Windsurf, and others. 100% local and opensource.

Unwind AI

63,243 views • 1 year ago

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:

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 views • 1 year ago

🚀 Meet your new AI team in the Multi-Agents Sidebar! With dynamic memory and specialized tools, your AI agents are ready to follow you into every project. 🧠 Collaborate with your AI workforce across Taskade, ready to tackle tasks at any time! Reply 'AI' for early access! ✨

🚀 Meet your new AI team in the Multi-Agents Sidebar! With dynamic memory and specialized tools, your AI agents are ready to follow you into every project. 🧠 Collaborate with your AI workforce across Taskade, ready to tackle tasks at any time! Reply 'AI' for early access! ✨

Taskade

28,231 views • 2 years ago

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.

Sensitive content

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.

Dria

169,981 views • 9 months ago

The first step towards AGI and automating white-collar work is automating your routine tasks I have a dozen agents that run daily that do my job. Here is one that summarizes my news. The most useful one wakes up every day, looks for new Jira tickets, fixes bugs, and submits PRs!

The first step towards AGI and automating white-collar work is automating your routine tasks I have a dozen agents that run daily that do my job. Here is one that summarizes my news. The most useful one wakes up every day, looks for new Jira tickets, fixes bugs, and submits PRs!

Bindu Reddy

15,700 views • 5 months ago

Introducing mem0 CLI 🚀 Give your agents memory from the terminal - add, search, and retrieve memory - remember agents + developers's preference - agent-first, JSON schema-driven We built mem0 CLI to help agents remember Get started npm install -g @mem0/cli Open source 👇

Introducing mem0 CLI 🚀 Give your agents memory from the terminal - add, search, and retrieve memory - remember agents + developers's preference - agent-first, JSON schema-driven We built mem0 CLI to help agents remember Get started npm install -g @mem0/cli Open source 👇

Taranjeet

20,567 views • 3 months ago

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!

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

243,319 views • 1 month ago

Today we’re launching the first and only human-like AI agents in the world. Super Agents™ are the first agents with human‑level skills – they DM you, take @ mentions, send emails, manage docs, tasks, and more. Not just tools or API calls, but real skills fine‑tuned for how teams actually work. The first agents with 100% context – fully native in ClickUp and fully synced from other apps. Super Agents see your work the same way that humans do: tasks, docs, schedules, and conversations all in one place. The first agents that learn from human interactions automatically, without any setup or configuration – when you give feedback, they listen and improve how they work. The first agents with human‑level memory for custom agents – historical memory for every interaction, short-term working memory, and even long‑term memory stored in docs you can literally open, inspect, and edit. The first agents that are literally the same as users – our agentic user model is the same as our user data model. This gives you permissions and capabilities that you and your systems are already familiar with. The first infinite agent catalog – where anyone can create and customize agents in minutes, for literally any type of work imaginable. It's the most intuitive way to build agents on the planet. 95% of companies are failing in AI adoption. The reality is that AI isn't meant to be adopted, it's meant to be adapted – to you. Super Agents are automatically personalized to you and your company using proprietary state-of-the-art agent architecture, orchestration, and tooling. Today is the largest step forward we've ever made towards our mission of making people more productive. Maximize human productivity, with ClickUp Super Agents. Available NOW. For everyone.

Today we’re launching the first and only human-like AI agents in the world. Super Agents™ are the first agents with human‑level skills – they DM you, take @ mentions, send emails, manage docs, tasks, and more. Not just tools or API calls, but real skills fine‑tuned for how teams actually work. The first agents with 100% context – fully native in ClickUp and fully synced from other apps. Super Agents see your work the same way that humans do: tasks, docs, schedules, and conversations all in one place. The first agents that learn from human interactions automatically, without any setup or configuration – when you give feedback, they listen and improve how they work. The first agents with human‑level memory for custom agents – historical memory for every interaction, short-term working memory, and even long‑term memory stored in docs you can literally open, inspect, and edit. The first agents that are literally the same as users – our agentic user model is the same as our user data model. This gives you permissions and capabilities that you and your systems are already familiar with. The first infinite agent catalog – where anyone can create and customize agents in minutes, for literally any type of work imaginable. It's the most intuitive way to build agents on the planet. 95% of companies are failing in AI adoption. The reality is that AI isn't meant to be adopted, it's meant to be adapted – to you. Super Agents are automatically personalized to you and your company using proprietary state-of-the-art agent architecture, orchestration, and tooling. Today is the largest step forward we've ever made towards our mission of making people more productive. Maximize human productivity, with ClickUp Super Agents. Available NOW. For everyone.

Zeb Evans

320,554 views • 6 months ago

🚀 Say hello to the new & improved phidata 🚀 Build, ship, and monitor Agents with blazing-fast memory, knowledge, tools & reasoning 🔥 ⚡️ 70% faster memory & knowledge 🛠 100+ tools 🧠 Reasoning Agents 🤝 Multi-agent collaboration 📊 Built-in monitoring and Agent UI

🚀 Say hello to the new & improved phidata 🚀 Build, ship, and monitor Agents with blazing-fast memory, knowledge, tools & reasoning 🔥 ⚡️ 70% faster memory & knowledge 🛠 100+ tools 🧠 Reasoning Agents 🤝 Multi-agent collaboration 📊 Built-in monitoring and Agent UI

Ashpreet Bedi

114,531 views • 1 year ago

HERMES Agent just launched a desktop app for managing autonomous AI agents at scale You can now: - Run multiple agents from one interface - Maintain persistent memory - Automate long-running workflows - Keep agents operating continuously

HERMES Agent just launched a desktop app for managing autonomous AI agents at scale You can now: - Run multiple agents from one interface - Maintain persistent memory - Automate long-running workflows - Keep agents operating continuously

Vaishnavi

58,133 views • 1 month ago

⚒️ How to build an AI agent with persistent memory? Watch Shaw (spirit/acc) building an agent from scratch with elizaOS & showing how external, queryable memory based on OriginTrail gives agents context and extends their memory beyond a single session. 02:15 – Set up your first agent 08:55 – Initializing runtime & memory foundations 12:50 – Wiring message handling & agent memory 24:45 – Persistent conversational memory in action 38:00 – Extending agents with plugins

⚒️ How to build an AI agent with persistent memory? Watch Shaw (spirit/acc) building an agent from scratch with elizaOS & showing how external, queryable memory based on OriginTrail gives agents context and extends their memory beyond a single session. 02:15 – Set up your first agent 08:55 – Initializing runtime & memory foundations 12:50 – Wiring message handling & agent memory 24:45 – Persistent conversational memory in action 38:00 – Extending agents with plugins

OriginTrail

37,242 views • 5 months ago

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

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

🚨 AI News | TestingCatalog

41,040 views • 4 months ago

Announcing... Voice x Memory! We’re unpacking what makes agents listen, respond, and remember, or sometimes forget, and what that means for building better voice systems. We will move towards a world of large LLMs remembering a lot of information to smaller LMs with finite real-time intelligence and infinite memory. Here's a short teaser as we walk down the streets of SF having a chat with @shortkingceo on how we see the future

Announcing... Voice x Memory! We’re unpacking what makes agents listen, respond, and remember, or sometimes forget, and what that means for building better voice systems. We will move towards a world of large LLMs remembering a lot of information to smaller LMs with finite real-time intelligence and infinite memory. Here's a short teaser as we walk down the streets of SF having a chat with @shortkingceo on how we see the future

Sudarshan Kamath

23,452 views • 4 months ago

Open sourcing Dynamic Graph Memory by mem0. Memory is fundamental to human reasoning, shaping how we approach tasks and make decisions. At Mem0, we believe that AI agents & apps should reflect this principle. Our Dynamic Graph Memory emulates human memory, advancing AI agents toward more intelligent, human-like reasoning. This is a significant step forward in building AI that truly understands and interacts with the world like we do. All credit to Dev Khant Deshraj Yadav Prateek Chhikara for their countless nights spent on bringing this to life. Link:

Open sourcing Dynamic Graph Memory by mem0. Memory is fundamental to human reasoning, shaping how we approach tasks and make decisions. At Mem0, we believe that AI agents & apps should reflect this principle. Our Dynamic Graph Memory emulates human memory, advancing AI agents toward more intelligent, human-like reasoning. This is a significant step forward in building AI that truly understands and interacts with the world like we do. All credit to Dev Khant Deshraj Yadav Prateek Chhikara for their countless nights spent on bringing this to life. Link:

Taranjeet

51,129 views • 1 year ago

🚀 Introducing the first-ever Agent UI 🚀 This is hands-down my favorite product! Chat with local Agents tailored to my needs. Local memory, storage, knowledge and tools 🔥 ⚡️ Your data, your control 🧠 Compatible with any LLM 🤝 Run multiple agents or a team of agents

🚀 Introducing the first-ever Agent UI 🚀 This is hands-down my favorite product! Chat with local Agents tailored to my needs. Local memory, storage, knowledge and tools 🔥 ⚡️ Your data, your control 🧠 Compatible with any LLM 🤝 Run multiple agents or a team of agents

Ashpreet Bedi

68,970 views • 1 year ago

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.

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 views • 3 months ago

We are launching today Tyran AI - the easiest way to create health agents. Tyran makes it super easy to: - connect any health source - prompt and use AI - export everything directly to your app It's actually surprising what we saw it doing early: - Detect jet lag - Detect recovery - Detect signs of asthma (NOT medical diagnosis, obviously!) - Run multiple experiments with different fitness metrics After the first release, we added memory to understand the user better, and the ability to have multiple agents running in parallel. Memory improvements, speed improvements, and end user access are on the way For the full release, check the Y Combinator launch in the 🧵

We are launching today Tyran AI - the easiest way to create health agents. Tyran makes it super easy to: - connect any health source - prompt and use AI - export everything directly to your app It's actually surprising what we saw it doing early: - Detect jet lag - Detect recovery - Detect signs of asthma (NOT medical diagnosis, obviously!) - Run multiple experiments with different fitness metrics After the first release, we added memory to understand the user better, and the ability to have multiple agents running in parallel. Memory improvements, speed improvements, and end user access are on the way For the full release, check the Y Combinator launch in the 🧵

Kyriakos Eleftheriou

22,881 views • 9 months ago

We are partnering up with zauth to secure the next generation of 3D AI agents. These agents have memory, emotion, voice, and on-chain identity and can be embedded anywhere. These agents use the x402 protocol to make real payments for skills and services. To make this ecosystem production-ready and safe, is teaming up with zauth, the security layer for the agentic internet. Bringing safety and trust to the next wave of 3D AI agents 🔐

We are partnering up with zauth to secure the next generation of 3D AI agents. These agents have memory, emotion, voice, and on-chain identity and can be embedded anywhere. These agents use the x402 protocol to make real payments for skills and services. To make this ecosystem production-ready and safe, is teaming up with zauth, the security layer for the agentic internet. Bringing safety and trust to the next wave of 3D AI agents 🔐

three.ws

50,259 views • 27 days ago