正在加载视频...

视频加载失败

Introducing memorybench by supermemory 🔬 The standard, open source evaluation framework for all context systems, across any benchmark. Easily run and compare multiple memory providers. batteries included - comes with a Web UI, CLI, checkpoints and everything else.

44,455 次观看 • 9 个月前 •via X (Twitter)

25 条评论

Dhravya Shah 的头像
Dhravya Shah9 个月前

Over the last year, we have built several setups for running benchmarks. But we ran into the same problems. Setting up the benchmarks, but also checkpoints, comparisons, finding out the results, etc. is too time consuming.

Dhravya Shah 的头像
Dhravya Shah9 个月前

This also makes it harder to do "benchmarkhacking", as any provider which is truly SOTA, would be good across all the dimensions: - Multiple benchmarks - Multiple judges / answering models - Cost - Latency reports. We give everyone the grounds to find out, easily.

Dhravya Shah 的头像
Dhravya Shah9 个月前

It is fully open-source. You can view the results live in a web UI. Configure the answering / judge prompts Restart from checkpoints And view failed results, and exactly why they failed. Go star it :)

Dhravya Shah 的头像
Dhravya Shah9 个月前

We took inspiration from OpenBench by @GroqInc, and have included full documentation for playing around with it. Go ahead and add providers / benchmarks as you see fit!

weisser 的头像
weisser9 个月前

@supermemory This is great. Good work making it more open and easy to understand the options out there.

andrew pignanelli 的头像
andrew pignanelli9 个月前

@supermemory good work

Rakshith 的头像
Rakshith9 个月前

@supermemory We also are building a memory solution, and we have faced this issue ourselves, I can't thank you enough to make my life easier thanks brotha ❣️

Lhasa 的头像
Lhasa7 个月前

@supermemory will be great to evaluate memora

Anthony Ronning 的头像
Anthony Ronning9 个月前

@supermemory So where are the results?

Kaya Jones 的头像
Kaya Jones9 个月前

@supermemory nice

0xPK 的头像
0xPK9 个月前

@supermemory where the results are hosted

Anshuman Tiwari अंशुमान तिवारी 的头像
Anshuman Tiwari अंशुमान तिवारी9 个月前

@supermemory This is real work. All the best.

jia 的头像
jia9 个月前

@supermemory Lfg

Rob Imbeault 的头像
Rob Imbeault9 个月前

@supermemory Alright we’ll be on it asap! Thanks @DhravyaShah

Ayush Bansal 的头像
Ayush Bansal9 个月前

@supermemory This is exactly the kind of standardized tooling the AI/ML space needs. Having a unified way to evaluate context systems across different providers makes it way easier to make informed decisions instead of just going with whatever's trending.

Vedant 的头像
Vedant9 个月前

@supermemory this guys knows how to make demo videos, sick!

Ramon 🎹 的头像
Ramon 🎹9 个月前

@supermemory really cool

Jerry Jiang 的头像
Jerry Jiang9 个月前

@supermemory King move! Nice one

Aftab Khan 的头像
Aftab Khan9 个月前

@supermemory Good one

Kiran Sarpotdar 的头像
Kiran Sarpotdar9 个月前

@supermemory Superb

Gagan Ghotra 的头像
Gagan Ghotra9 个月前

@supermemory BASED drop over the holidays 🙌

Ranjana Jha 的头像
Ranjana Jha9 个月前

@supermemory shared benchmarks are how infra categories mature. comparison creates progress not noise.

Karan Jagtiani 的头像
Karan Jagtiani9 个月前

@supermemory Looks solid, especially the open-source aspect. The focus on simplifying benchmarks could save a lot of time for folks in the field. Nice work

paul 的头像
paul9 个月前

@supermemory exciting!

flâneur 的头像
flâneur9 个月前

@supermemory @lateinteraction @a1zhang its not exactly a long context benchmark but would still be interesting to see how RLM performs here

相关视频

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 次观看 • 1 年前