Agents

The problem I've found with all of them is it is difficult to get consistent solid results that you would want to build around. Even listening to podcasts this seems to be a theme with the people at the forefront of MAS LLMs.

The two most important problems (at least how I am thinking about them currently), are:

Finding a rigorous scientific framework for how different agent skills, personalities, and instructions combine to be most capable for different problems (think of this as social management science for AI agents) .

Figuring out how you formally validate and verify... See more

The LLM doesn’t call the tool directly (yet), but it does pass back to the application what functions should be called — and with which parameters. And, now, OpenAI lets multiple function calls be “invoked” at once.

But, this idea is not just about GPT. The open source world is moving towards this model as well.

This Is The Future…It’s Just Not Here... See more

The Semantic Layer

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Connect data silos, drive consistent metrics, and power your AI and analytics with context.

📖 Introduction

XAgent is an open-source experimental Large Language Model (LLM) driven autonomous agent that can automatically solve various tasks. It is designed to be a general-purpose agent that can be applied to a wide range of tasks. XAgent is still in its early stages, and we are working hard to improve it.

🏆 Our goal is to create a... See more

In an Agentic UX, an AI agent pulls weight across 3 categories: cognitive, creative, and logistical.

• Cognitive weight — the built-in agent pulls analysis and decision-making weight


• Creative weight — the built-in agent takes on visualization and media creation weight


• Logistical weight — the built-in agent takes tasks on operational and workflow

... See more

Here are example ways to increase the success rate of these custom agents:

Provide a more focused, use-case specific system prompt to kick things off. Since you know what kinds of goals the agent supports, you can improve the odds of coming up with the right set of tasks initially. In fact, there’s nothing wrong with actually defining the set of... See more

Introduction

VT.ai is a multi-modal AI Chatbot Assistant, offering a chat interface to interact with Large Language Models (LLMs) from various providers. Both via remote API or running locally with Ollama.

The application supports multi-modal conversations, seamlessly integrating text, images, and vision processing with LLMs.

[Beta] Multi-modal AI... See more

In the open-source community, there are huge numbers of people leveraging AutoGen in creative ways, and solving surprising problems. One pattern that we see as fundamental is the "generator+critic" pattern, where one agent generates content (writing, code, etc.) and another agent critiques it (finds bugs, etc.) They can iterate until the solution... See more