Here’s a simple framework for understanding agents: •Connect to any data source or vector store
•Use a code execution engine
•Orchestrate with other ML models
•LLM-agnostic for flexibility
•Designed for chat or task-based operations
AGENTS
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Simple Framework for Understanding and Building AI Agents
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On-Demand Software: AI Agents Automating Development
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On-demand software: we're entering a world where creating software is getting faster, cheaper, and automated. Replit Agent, Devin, GPTEngineer, and others are showing how quickly one can prototype software – making cool ideas into reality quickly. But what if software is
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Novel AI Control Approaches and Empathy Integration Challenges
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Could think up new approaches where control wouldn't be a problem – doesn't necessarily have to be a hack onto the existing approaches. I'm still thinking about empathy and how it is used in humans, but it requires tight integration..
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Building Account Qualification Agents: Cognitive Architectures Explained
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Agents are here, you just have to know how to build them Excited to share this VERY detailed blogpost from @unifygtm on how they built their recent account qualification agent This contains all sorts of nuggets like:
The three different cognitive architectures they considered -
Replit Agent showcased at YC SF Tech Week
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@amasad @pirroh @snowmaker @ycombinator @Techweek_ #SFTechWeek #Replit #Agent #Yc
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LangGraph Memory Features: Cross-Thread Persistence Implementation
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Conceptual video: https://
youtube.com/watch?v=JTL0yp
85FsE
… Conceptual docs: https://
langchain-ai.github.io/langgraph/conc
epts/memory/
… How-to docs: https://
langchain-ai.github.io/langgraph/how-
tos/cross-thread-persistence
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Memory Creation and Best Practices for AI Systems
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Of course – there's still a lot of complexity in how you create memories to store here, and how you use them We believe this will be application specific, but we want share best practices and conventions, so we spent a ton of time writing conceptual docs on this (next tweet)
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LangGraph Framework Enables Custom Cognitive Architectures Memory Management
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With LangGraph, we created a low-level, highly flexible framework to give users the power to create custom cognitive architectures We wanted to give the same low-level control and flexibility to how users created and used memory
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LangGraph Document Store for Memory Management
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After a few iterations, we found the best way to do that was to build a document store into LangGraph This comes with:
– Flexible namespacing (to support multiple types of memory)
– JSON Document Storage
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Defining Memory in AI Applications: Beyond the Hype
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The idea of memory is tantalizing, but also really vague What does it even mean for an application to have memory? Much like agents, there's a lot of hype and interest in this area, without a clear definition of what is actually means