Excited to announce @Letta_AI, the company @charlespacker and I started for building stateful LLM agents
We're building out an incredible (in-person) team in SF, and are actively hiring founding engineer/researchers jobs.ashbyhq.com/letta
techcrunch.com/2024/09/23/let…
- the hardest part about being a female founder.. in my experience is finding a boyfriend / partner that understands the short term gains that we are forgoing and the long-term optimization game that we are playing. most men in SF (as we women known) insist on paying for dates,the hardest part about being a male founder.. in my experience is finding a girlfriend / partner that understands the short term gains that we are forgoing and the long-term optimization game that we are playing. most women expect to have most dates paid for, their vacations
- Excited to release the first course on agents with memory with @AndrewYNg @DeepLearningAI @Letta_AI We cover customizing agent memory management beyond MemGPT, integrating external tools @langchain @composio @crewAIInc, agentic RAG with @llama_index, and multi-agent.New short course: LLMs as Operating Systems: Agent Memory, created with @Letta_AI, and taught by its founders @charlespacker and @sarahwooders. An LLM's input context window has limited space. Using a longer input context also costs more and results in slower processing. So,
00:00 - We've been trying to wrangle Responses API, and here's my response to this: Claim #1 -> Responses is just ChatCompletitions++ Wrong! Responses makes managing your own context window a PINA and also introduces a brand new (not generally supported) API that is very obviouslyfeeling frustrated (and a little guilty): there’s still way too much confusion about @OpenAI's Responses API. this is partly on us: we haven’t always been clear about why we built it, how to use it, and why it matters. here's my attempt at setting the record straight. 👇
- Pretty crazy to see Anthropic API being implemented over ChatCompletions, which used to be the standard (until OpenAI refused to properly support reasoning)Hey guys, thank you all for your love and passion for M2. Lot asking why we recommend Anthropic API. I think need to explain a little bit. M2 is a agentic thinking model, it do interleaved thinking like sonnet 4.5, which means every response will contain its thought content. Its
- We are moving from a stateless to stateful LLM programming paradigm: from “agents” more akin to stateless workflows, to agents that can actually learn, evolve, and persist over time.
- All I want is a model that - can properly call tools - API support - does what I tell it to lmk when there's an alternative to claude 3.5 sonnet
- Most "AI danger" stuff is just marketing to portray models are more powerful than they actually are imo. Now we're in a ridiculous situation where the foundation model marketers + AI doomers have teamed up to use the government to try to stifle OSS innovation. Thank youA decision on SB-1047 is due soon. Governor @GavinNewsom has said he's concerned about its "chilling effect, particularly in the open source community". He's right, and I hope he will veto this. If you agree, please like/retweet this to show your support for VETOing SB-1047!
- Sounds like OpenAI wants to make it harder to directly access models via APIs - which means more black box systems that are hard to debug and understand. This is why we need open models and model providers who aren't obsessed with hiding everything from developers.OPENAI ROADMAP UPDATE FOR GPT-4.5 and GPT-5: We want to do a better job of sharing our intended roadmap, and a much better job simplifying our product offerings. We want AI to “just work” for you; we realize how complicated our model and product offerings have gotten. We hate
- A few months ago, @mem0ai published benchmarking numbers for MemGPT and claimed "SOTA" in memory. The weird part? I have no idea how they even could have run that benchmark, without making significant modifications to MemGPT (they did not respond to our requests for details onSimply storing the conversation history in a file outperforms specialized tools for agent memory on LoCoMo, a popular retrieval benchmark (74.0% versus reported 68.5%)
- Don't let LLM providers own your data - separate agent storage from compute with @Letta_AI















