Ruflo is a large, modular AI agent orchestration platform that extends Claude Code with a plugin ecosystem, swarm coordination, memory systems, neural learning, security enforcement, and federation. The core is a CLI tool (`npx ruflo`) that provides 26+ commands and 215+ MCP tools for managing agents, memory, security, hooks, and deployments. It includes a console UI inside Claude Code, a Svelte-based chat UI (ruvocal), and a React-based goal UI. The architecture is ADR-driven with clean separation into domain, application, and infrastructure layers across 35+ plugins and 20+ core packages. The system implements advanced patterns: CQRS for memory and swarm, circuit breakers for resilience, capability envelopes for security, Ed25519 signing for release integrity, and a harness loop for automated champion selection. It supports multi-model routing, hybrid vector search (HNSW + SQLite), watermarking (WASM-based Gumbel scheme), and neural learning (PPO, DQN, Decision Transformer). Federation enables cross-installation agent collaboration with zero-trust, HMAC-signed envelopes, and PII-gated data flow. While the codebase is well-structured and actively maintained, it has some maintenance risks: duplicate helper scripts across multiple directories, hardcoded version numbers in smoke tests, experimental alpha plugins, and a large regression test suite that requires ongoing upkeep. The plugin ecosystem is extensive but some plugins may be aspirational rather than production-ready.
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.
This repository is a structured, comprehensive AI engineering curriculum spanning 20 phases and over 500 lessons, from math foundations and ML fundamentals to deep learning, computer vision, NLP, speech, transformers, generative AI, reinforcement learning, LLMs from scratch, agent engineering, multi-agent systems, production infrastructure, and ethics/safety. It is an educational resource, not a production application or data research project. The code consists of self-contained pedagogical implementations (primarily Python, with some TypeScript, Rust, and Julia) that demonstrate core algorithms from scratch—such as attention mechanisms, BPE tokenization, diffusion models, RLHF, and agent loops—using synthetic data and deterministic stubs. Capstone projects include small web servers (Hono/TypeScript) and test harnesses. The curriculum is actively maintained with CI tooling, certification prep materials, and translation support. While the code is simplified for learning, it exhibits clean patterns, consistent lesson structure, and testing practices that make individual components (e.g., tool registries, state machines, evaluation harnesses) reusable in other contexts.
Pentest-Swarm-AI is an open-source autonomous penetration testing tool built on a stigmergic swarm architecture. It coordinates multiple LLM-driven specialist agents (recon, classifier, exploit, report) via a shared blackboard with pheromone decay, enabling emergent attack paths without a central planner. The system integrates 30+ external security tools (nmap, nuclei, sqlmap, etc.), multiple LLM providers (Claude, OpenAI, Ollama, Gemini, LM Studio), and bug bounty platforms (HackerOne, Bugcrowd, Intigriti) for automated scope import and report submission. The architecture is a multi-agent system with a scheduler that dispatches agents based on blackboard predicates and pheromone levels. Key entry points include a CLI (Cobra commands: scan, init, playbook, submit, etc.), an API server (Fiber), a WebSocket hub for live streaming, a Next.js web dashboard, and an MCP server for IDE integration. Data flows: agents write findings to the blackboard with pheromone weights; the scheduler reads the blackboard and triggers agents whose predicates match; agents read findings, process them, and write new findings, creating a stigmergic feedback loop. External dependencies include PostgreSQL with pgvector for durable blackboard storage, LLM API endpoints, and the installed security tool binaries. The codebase demonstrates high craftsmanship with clean abstractions (tool adapter pattern, LLM provider interface, blackboard interface), extensive testing (unit, integration, e2e, benchmarks, LLM eval), and safety features (scope enforcement, provenance signing, rate limiting, false-positive caching). It is actively maintained with recent commits, CI/CD pipelines, and community engagement.