awesome-llm-agents — Agent Tool by junhua

by junhua · Agent Tool · ★ 83

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About awesome-llm-agents

Awesome-LLM-Agents: Recent Trends and Advancement in Agentic AI This Awesome-LLM-Agents contains A hand-picked and carefully categorised reading list. Furthermore, I will conduct review on each paper and project, and (hopefully) put them in anto a survey paper. The detailed thought process of forming this project is documented at this Medium Post. It's put behind a paywall to prevent the evil LLMs' crawling. The full category breakdown. LLM Core — Foundation Models Scaling laws for neural language models (OpenAI, 2020, arXiv) LLaMA: Open and Efficient Foundation Language Models (Meta, Feb 2023, arXiv) The Llama 3 Herd of Models (Meta, July 2024, arXiv) Sparks of Artificial General Intelligence: Early experiments with GPT-4 (Microsoft, Apr 2023, arXiv) Apple Intelligence Foundation Language Models (Apple, Doc) StarCoder (Dec 2023, arXiv) Gemma 2B: Improving Open Language Models at a Practical Size (Jul, 2024, [arXiv](https://arxi

Quick Facts

Stars83
Forks13
CategoryAgent Tool
LicenseApache-2.0
Quality Score51.3169014280933/100
Open Issues4
Last Updated2024-11-01
Created2024-07-28
Est. Tokens~8k

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Frequently Asked Questions

What is awesome-llm-agents?

awesome-llm-agents is A Collection of High Quality research papers and open-source projects about LLM-agents. It is categorized as a Agent Tool with 83 GitHub stars.

How do I install or use awesome-llm-agents?

You can find installation instructions and usage details in the awesome-llm-agents GitHub repository at github.com/junhua/awesome-llm-agents. The project has 83 stars and 13 forks, indicating an active community.

What license does awesome-llm-agents use?

awesome-llm-agents is released under the Apache-2.0 license, making it free to use and modify according to the license terms.

What are the best alternatives to awesome-llm-agents?

The top alternatives to awesome-llm-agents on Agent Skills Hub include tutor-skills, lenny-skills, repren. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.

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