If you’re in NYC next week come hang out with @lmstudio and @Letta_AI (of memGPT fame) teams and learn about stateful local agents

the new frontier: AI agent hosting/serving 👾🛸 the AI/LLM agents stack is a significant departure from the standard LLM stack. the key difference between the two lies in managing state: LLM serving platforms are generally stateless, whereas agent serving platforms need to be stateful (retain the agent state server-side)

thank you @trychroma 🟦🟥🟨 for hosting the 2nd @MemGPT LLM agents meetup at their beautiful mission office if you're interested in (1) in-person events (2) LLM agents (3) AI research.. follow our luma calendar so you don't miss the next one 👉 lu.ma/berkeley-llm-meetup

✨The second ever @MemGPT LLM agents developer meetup is happening today at @trychroma ! ✨ Expect to learn about agents-as-a-service, agents APIs, persistent agents and giving LLMs long-term memory! lu.ma/memgpt-meetup-aug

✨ MemGPT now supports custom memory modules! ✨ This allows developers to both define custom memory fields (instead of just human/persona) as well as custom memory editing functions (rather than core_memory_append/replace). 🧵

@MemGPT at the @arizeai conference!

🌐 Memory Layer of AI Agents 🌐 Personalization (Memory)🧠 Personalization dynamically adjusts and customizes AI agents' responses and functions based on users' historical behavior, preferences, and specific needs. This enhances user experience, making AI agents more relevant and responsive. 🔹 WhyHowAI @chiajy2000 - Features: Offers personalized recommendations and response optimization. - Pros: Automatically creates knowledge graphs and integrates with existing workflows, building effective RAG solutions. 🔹 Cognee @cognee_ - Features: Provides personalized services by analyzing user interaction data. 🔹 Graphlit @graphlit - Features: Uses user data for personalized recommendations. 🔹 LangMem - Features: Focuses on personalized memory functions, enabling AI agents to remember user preferences and historical interactions. 🔹 MemGPT @MemGPT - Features: Combines GPT models for personalized response generation. - Pros: Represents Memory-GPT, a system designed to improve LLM performance through advanced memory management, overcoming fixed context window challenges. Storage 💾 Storage provides efficient and reliable data storage solutions for agents. These systems must handle large amounts of data and support fast read/write operations to ensure efficient AI model operation. 🔹 Pinecone @pinecone - Features: High-performance vector database, supports fast data retrieval. 🔹 Chroma @trychroma - Features: Efficient data storage solutions, open-source vector database designed for AI and embedded applications. 🔹 Weaviate @weaviate_io - Features: Open-source vector database, supports content-based retrieval and storage. 🔹 MongoDB @MongoDB - Features: Popular NoSQL database, provides flexible storage and retrieval functions. Context 🗣️ Context enables AI agents to understand and utilize contextual information in conversations or tasks to provide more accurate and relevant responses. This ensures agents maintain coherence and understand more complex user needs. 🔹 Unstructure @UnstructuredIO - Features: Open-source project focused on providing robust context management functions, allowing AI agents to understand and utilize context information in conversations or tasks, resulting in more coherent and intelligent responses. #AI #MemoryLayer #Personalization #Storage #Context #TechStack #Innovation #Startup

🤖 @MemGPT = Easily build and deploy stateful LLM agents! 🗄️ #Milvus = Powerful vector database for your data! 🔥 MemGPT Milvus = Create agents with strong memory and access to massive-scale external knowledge base. 🔗 bit.ly/45NSRJN #AI #RAG #Tech

running live demos with @MemGPT agents at @MindsDB 🧠✍️