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Introducing Long-term, multiplayer memory for your agents. #1 on BEAM, the largest public memory benchmark. Install it with a single prompt. Keep your stack. Add a past. Try it today →

714,636 次观看 • 9 天前 •via X (Twitter)

40 条评论

Paul Mit 的头像
Paul Mit9 天前

Long term memory is a new gold Congrats on the launch cool video btw

Chris Park 的头像
Chris Park9 天前

This is great

Mehdi Djabri 的头像
Mehdi Djabri9 天前

would love to chat Chris, we're helping some of the best agents in the world improve their long-term memory right now

Suryansh Tiwari 的头像
Suryansh Tiwari9 天前

launch videos are getting out of hand. this one made me want to flex my agent's memory at dinner

carpe 的头像
carpe9 天前

that’s cool man 👀

Aurélien Georget 的头像
Aurélien Georget9 天前

HUGE! Happy user 🙌🙌

Mehdi Djabri 的头像
Mehdi Djabri9 天前

we want to power the best agents in the world, and Fimo is one of them

KP 的头像
KP9 天前

“These idiots must be burning so many tokens” had to lol at that

Tisser Maxime 的头像
Tisser Maxime9 天前

Great video for a great product !

roman 的头像
roman9 天前

long term memory is just what i need. thank you man!

xIA 的头像
xIA9 天前

me interesa cómo resuelven que dos agentes se contradigan sobre lo mismo

Mehdi Djabri 的头像
Mehdi Djabri9 天前

That's one of the hardest things to do, read more here

Hasan Toor 的头像
Hasan Toor9 天前

The Acme budget example is the one that got me. Most agents would've confidently said 32k

Mehdi Djabri 的头像
Mehdi Djabri9 天前

yes, facts changing over time is one the hardest things to solve

Aaliya 的头像
Aaliya9 天前

hope 'past' is as good as the video is dope

Mehdi Djabri 的头像
Mehdi Djabri9 天前

it's even better

Aaliya 的头像
Aaliya9 天前

wow

Aman 🧋 的头像
Aman 🧋9 天前

great product

Ayan Barua 的头像
Ayan Barua9 天前

awesome! long term memory should be accurate and reliable

Mehdi Djabri 的头像
Mehdi Djabri9 天前

Ampersand + the perfect combo for world-class agents ❤️

shirish 的头像
shirish9 天前

did you guys buy the domain first or build the product

Mehdi Djabri 的头像
Mehdi Djabri9 天前

haha

Nav Toor 的头像
Nav Toor9 天前

i think memory will matter more than bigger context windows

Mehdi Djabri 的头像
Mehdi Djabri9 天前

yes, the time has come for long horizon and persistent agents

Val ⟠ 的头像
Val ⟠9 天前

definitely worth checking out

Nikunj Yaduvanshi 的头像
Nikunj Yaduvanshi9 天前

Keeping it accurate as facts change is the hard part. If a March email says one thing and a June meeting says the opposite, does it keep both with dates or overwrite with the newer one?

Flix Muller 的头像
Flix Muller9 天前

Giving AI agents reliable long-term memory could be a huge step forward. The multiplayer aspect is especially interesting when multiple agents need to share context and work together.

Tj Dunham 的头像
Tj Dunham9 天前

cool marketing bro, I got 96% on well over 10m context window for complicated data sets let’s chat

Mehdi Djabri 的头像
Mehdi Djabri9 天前

would love to chat

AshutoshShrivastava 的头像
AshutoshShrivastava9 天前

does this sit on top of your existing stack or does the agent route everything through it

Mehdi Djabri 的头像
Mehdi Djabri9 天前

You can add it to your existing memory system, combine with md files, RAG, grep, db search. We want to give devs full flexibility to leverage this.

Jiayang Liu 的头像
Jiayang Liu9 天前

Had such a blast directing this launch video! Excited for the future of

Veyra | AI & Tech 的头像
Veyra | AI & Tech9 天前

Persistent multiplayer memory with one prompt install is huge

Aman 🧋 的头像
Aman 🧋9 天前

check dm

Rodev 的头像
Rodev9 天前

just wow. long term memory >>>>>>>>>

Aditi 的头像
Aditi9 天前

agents that actually remember context over time would be such a game changer

Mehdi Djabri 的头像
Mehdi Djabri9 天前

it is possible now, and it looks crazy when you try it in real life scenarios

Prajwal 的头像
Prajwal9 天前

Great video Just sent you DM

M@tt 的头像
M@tt9 天前

Nice. I see ingest, recall, and delete but no export. If I keep my raws I can always rebuild, but can I get the cooked claims and what they superseded back out?

Mehdi Djabri 的头像
Mehdi Djabri9 天前

yes, we are offering export, never want to lock anyone!

相关视频

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

132,225 次观看 • 1 年前