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Multi-Agent Conversation Framework in TypeScript

CivAgent is an LLM-based Human-like Agent acting as a Digital Player within the Strategy Game Unciv.

langchain 工具,流程设计组件,服务,代理以及相关学习文档的合集(agent,service,tutorials,flow-design)

[NeurIPS 2024 D&B Track] GTA: A Benchmark for General Tool Agents

[EMNLP'24] EHRAgent: Code Empowers Large Language Models for Complex Tabular Reasoning on Electronic Health Records

Framework and toolkits for building and evaluating collaborative agents that can work together with humans.

ConcoLLMic: the first language- and theory-agonistic concolic execution engine via LLM agents

AI tool for automating Upwork job applications using AI agents to find and qualify jobs, write personalized cover letters, and prepare for interviews based on your skills and experience.

A curated list of tools, papers, and datasets for applying AI to cybersecurity tasks. This list primarily focuses on modern AI technologies like Large Language Models (LLMs), Agents, and Multi-Modal systems and their applications in security operations.

Multi-agent LLM driven cell type annotation for single-cell RNA-Seq data

A Pair Programming Framework for Code Generation via Multi-Plan Exploration and Feedback-Driven Refinement, ASE 2024 (Distinguished Paper Award)

DialOp: Decision-oriented dialogue environments for collaborative language agents

Ultra-Lightweight, Pure Python Multimodal Agent.

An Execution Isolation Architecture for LLM-Based Agentic Systems

AAAI24(Oral) ProAgent: Building Proactive Cooperative Agents with Large Language Models

[CVPR 2025 🔥]A Large Multimodal Model for Pixel-Level Visual Grounding in Videos

An agent based LLM assistant that extends RAG with batch entity extraction and SQL querying to improve performance on multi-step and analytical questions.

Promptdesk is a tool designed for effectively creating, organizing, and evaluating prompts and large language models (LLMs).

[ICML2025 Oral] LLM-SRBench: A New Benchmark for Scientific Equation Discovery with Large Language Models

AlgoTune is a NeurIPS 2025 benchmark made up of 154 math, physics, and computer science problems. The goal is write code that solves each problem, and is faster than existing implementations.