Popular GitHub Repositories for Llm Agent

A programming framework for agentic AI ? PyPi: autogen-agentchat Discord: https://aka.ms/autogen-discord Office Hour: https://aka.ms/autogen-officehour

A generative speech model for daily dialogue.

? Open source implementation of the ChatGPT Code Interpreter

Your agent in your terminal, equipped with local tools: writes code, uses the terminal, browses the web, vision.

Harness LLMs with Multi-Agent Programming

总结Prompt&LLM论文，开源数据&模型，AIGC应用

A Comprehensive Benchmark to Evaluate LLMs as Agents (ICLR'24)

[ICML 2024] LLMCompiler: An LLM Compiler for Parallel Function Calling

A repo lists papers related to LLM based agent

Quark Agent - Your AI-powered Android APK Analyst

[GenAI Application Development Framework] ? Build GenAI application quick and easy ? Easy to interact with GenAI agent in code using structure data and chained-calls syntax ? Use Agently Workflow to manage complex GenAI working logic ? Switch to any model without rewrite application code

AIlice is a fully autonomous, general-purpose AI agent.

Official Repo for ICML 2024 paper "Executable Code Actions Elicit Better LLM Agents" by Xingyao Wang, Yangyi Chen, Lifan Yuan, Yizhe Zhang, Yunzhu Li, Hao Peng, Heng Ji.

LLM abstractions that aren't obstructions

[CVPR 2024 ?] Grounding Large Multimodal Model (GLaMM), the first-of-its-kind model capable of generating natural language responses that are seamlessly integrated with object segmentation masks.

Agentic-RAG explores advanced Retrieval-Augmented Generation systems enhanced with AI LLM agents.

? A curated list of papers and resources for multi-modal Graphical User Interface (GUI) agents.

Interactive LLM Powered NPCs, is an open-source project that completely transforms your interaction with non-player characters (NPCs) in any game! ???

Integrate LLM in any pipeline - fit/predict pattern, JSON driven flows, and built in concurency support.

The llama-cpp-agent framework is a tool designed for easy interaction with Large Language Models (LLMs). Allowing users to chat with LLM models, execute structured function calls and get structured output. Works also with models not fine-tuned to JSON output and function calls.