MiroFish is a full-stack web application that implements a swarm intelligence prediction engine. It ingests real-world documents (PDF, Markdown, TXT), extracts seed information via LLM-driven ontology generation, builds a knowledge graph using Zep Cloud, and then generates agent profiles and simulation configurations for the OASIS multi-agent simulation framework. The system runs dual-platform simulations (Twitter-like and Reddit-like) to model social evolution and produce predictive reports through a ReACT-based report agent that queries the knowledge graph and interviews simulated agents. The frontend is a Vue 3 SPA with a 5-step wizard guiding users through graph building, environment setup, simulation execution, report generation, and deep interaction with agents. The architecture follows a monolith-with-subprocesses pattern: a Flask backend serves REST APIs for graph management, simulation lifecycle, and report generation, while simulation subprocesses run independently and communicate via a file-based IPC protocol. The system relies heavily on external services: an OpenAI-compatible LLM API for generation tasks, Zep Cloud for persistent graph storage and retrieval, and the OASIS simulation engine. The codebase is well-structured with clear separation into API routes, service modules, utility functions, and frontend components. It includes comprehensive i18n support (7 locales), Docker deployment, and CI/CD via GitHub Actions. While primarily an application, MiroFish has strong data/research characteristics: its core value proposition is predictive simulation driven by LLM-generated ontologies and agent profiles, and it uses GraphRAG techniques for report generation. The repository includes standalone scripts for star history tracking and validation, but these are auxiliary to the main application. The code demonstrates good engineering practices with reusable utilities (LLM client with retry, Zep pagination, file parser, retry decorators) and a consistent pattern for asynchronous task management and polling.
RVO2-3D is a C++ library implementing Optimal Reciprocal Collision Avoidance (ORCA) for multi-agent simulation in three-dimensional workspaces. It provides a simulator that manages agents with configurable parameters (radius, max speed, time horizon, etc.) and computes collision-free velocities each time step using a linear programming approach over ORCA half-planes. The library uses a k-d tree for efficient neighbor queries and optionally leverages OpenMP for parallel agent updates. The public API is exposed through the RVOSimulator class, with supporting types Vector3 and Plane. The codebase is written in C++98 for maximum compatibility, is thoroughly documented with Doxygen, and includes a single example (Sphere) that serves as both a demonstration and a test. The repository is well-maintained with CI across multiple platforms and build systems (CMake, Bazel), static analysis, and a quality declaration targeting ROS 2 Quality Level 2.
This repository implements a Chinese-localized version of the Stanford Generative Agents simulation. It creates a virtual town with 25 AI agents powered by LLMs (primarily via Ollama) that autonomously organize events, form relationships, and exhibit human-like behaviors. The system consists of a simulation engine that runs agent interactions over time, a compression tool to generate replay data, and a Flask-based web frontend for visualizing the simulation. Agents maintain memory, generate plans, reflect on experiences, and interact through LLM-driven conversations and actions. The application supports local deployment with configurable LLMs and includes features like breakpoint resumption and conversation logging.