AgentBench: Evaluating LLMs as Agents
Large Language Models (LLMs) are becoming increasingly smart and autonomous, targeting real-world pragmatic missions beyond traditional NLP tasks. As a result, there has been an urgent need to evaluate LLMs as agents on challenging tasks in interactive environments. We present AgentBench, a multi-dimensional evolving benchmark that currently consists of 8 distinct environments to assess LLM-as-Age
GitHub - microsoft/autogen: A programming framework for agentic AI. Discord: https://aka.ms/autogen-dc. Roadmap: https://aka.ms/autogen-roadmap
📚 Cite paper. 🔥 Mar 26: Andrew Ng gave a shoutout to AutoGen in What's next for AI agentic workflows at Sequoia Capital's AI Ascent. 🔥 Mar 3: What's new in AutoGen? 📰Blog; 📺Youtube. 🔥 Mar 1: the first AutoGen multi-agent experiment on the challenging GAIA benchmark achieved the No. 1 accuracy in all the three levels. 🎉 Jan 30: AutoGen is highlighted by Peter Lee in Microsoft Research Forum
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GenAI_Agents/all_agents_tutorials/blog_writer_swarm.ipynb at main · NirDiamant/GenAI_Agents
GitHub - Codium-ai/cover-agent: CodiumAI Cover-Agent: An AI-Powered Tool for Automated Test Generation and Code Coverage Enhancement! 💻🤖🧪🐞
GitHub - naotaka1128/llm_app_codes
GitHub - GoogleCloudPlatform/genai-databases-retrieval-app
What's next for AI agentic workflows ft. Andrew Ng of AI Fund
いまこそ学ぶLLMベースのAIエージェント入門―基本的なしくみ／開発ツール／有名なOSSや論文の紹介