Agents

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

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

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

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Why Julep?

We've built a lot of AI apps and... See more

AgentTuning: Enabling Generalized Agent Abilities For LLMs

🤗 Model (AgentLM-70B) • 🤗 Dataset (AgentInstruct) • 📃 Paper • 🌐 Project Page

中文版(Chinese)

AgentTuning represents the very first attempt to instruction-tune LLMs using interaction trajectories across multiple agent tasks. Evaluation results indicate that AgentTuning enables the agent... See more

🔎 GPT Researcher

GPT Researcher is an autonomous agent designed for comprehensive online research on a variety of tasks.

The agent can produce detailed, factual and unbiased research reports, with customization options for focusing on relevant resources, outlines, and lessons. Inspired by the recent Plan-and-Solve and RAG (Retrieval Augmented... See more

Large Language Model State Machine (llmstatemachine)

Introduction

llmstatemachine is a library for creating agents with GPT-based language models and state machine logic.

• Chat History as Memory : Leverages large context window models, making chat history the primary source of agent memory.


• Custom Python Functions with JSON Generation : Allows the

... See more

So right now, LLMs (Large Language Models) are all the rage. But in the future, it’s possible that the way we get things done is composing things with a combination of LLMs, SMMs (Small, Mighty Models), agents and tools.

It’s what I call Cognitive Composition (because it sounds cool and I have a longtime love affair with alliteration).

This is how we... See more

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.