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

nanosearch

Nanosearch is an in-memory search engine designed for small (< 10,000 URL) websites.

With Nanosearch, you can build a search engine in a few lines of code.

Nanosearch supports the BM25 and TF/IDF algorithms.

Nanosearch also computes a link graph and uses the number of inlinks to a page as a ranking factor. This is useful for ranking... See more

💸🤑 Announcing our Bounty Program: Help the Julep community fix bugs and ship features and get paid. More details here.

Start your project with conversation history, support for any LLM, agentic workflows, integrations & more.

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Why Julep?

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

📖 Introduction

ReactAgent is an experimental autonomous agent that uses GPT-4 language model to generate and compose React components from user stories. It is built with React, TailwindCSS, Typescript, Radix UI, Shandcn UI, and OpenAI API.

🚀 Features

• Generate React Components from user stories


• Compose React Components from existing components


• Use a

... 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

🤖 Create perpetual chatbots with self-editing memory!

🗃️ Chat with your data - talk to your SQL database or your local files!

📄 You can also talk to docs - for example ask about LlamaIndex!

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

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

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