#multi-agent-systems

A trilingual (繁中 / English / 简中) learning roadmap for agentic AI: from LLM basics to multi-agent systems, with 240+ curated resources and hands-on examples. 中文 AI agent 學習地圖。

Q-Learning Swarm Foraging 2026: Multi-Agent RL in Dynamic Grid Environments

🔁 Build reliable recurring AI-agent systems: 1025 resources, 22 operational patterns, 22 loop contracts, 8 runtime starters, an interactive atlas, and a structured dataset.

dsh-agent-team gives DeepSeek Harness agents that don't reset: durable Members with their own memory, notes, and skills across sessions, rollovers, and restarts. You set the direction; agents coordinate through Channels and Tasks.

An MCP server for orchestrating multi-agent workflows inside herdr, enabling real-time communication, pane management, and contextual handoffs.

Autonomy Composition Abstraction: a machine-readable charter format for defining authority, obligations, constraints, and escalation paths in multi-agent systems

Open-source Agentic AI Academy for engineering trustworthy AI agents from learning to production: agents, RAG, MCP, evaluation, security, observability, governance, enterprise architecture and production AI.

Bounded Agents: Delegation Security for Multi-Agent AI Systems - Reference Implementation and Evaluation Artifact

Build and operate production-grade AI agent systems through this 22-chapter technical course designed for collaborative learning with AI partners.

A practical 12-week Agentic AI learning roadmap covering LLM foundations, tool calling, RAG, memory, MCP, multi-agent systems, evaluation, security, and production deployment.

AgentBridge — an OpenClaw coordination dashboard and API for assigning, tracking, and reviewing work across AI agents.

Multi-Agent AI Orchestrator 2026 🚀 | YAML, 6+ LLM Providers, ReAct & Swarm

Run an autonomous AI company from your iPhone. Boardroom turns Hermes on your Mac into a local-first team that scouts, debates, builds, runs QA, and reports to you.

Tamper-evident audit logging for OpenClaw and other local AI agents: hash-chained, locally verifiable, zero cloud dependency.

Agentic loops for Claude Code that survive rate limits.

Battle-tested multi-agent dev team for Claude Code — 9 specialized sub-agent templates + a 6-stage pipeline with agent memory, verdict protocols, commit checkpoints & standing review prompts. CLAUDE.md constitution, bug-pattern library, full Laravel + React example. Bilingual EN/AR. Extracted from a production app with 200+ releases.

A multi-agent AI platform leveraging a Master Orchestrator and LLMs to unify and automate corporate finance, business intelligence, sales, and HR workflows.

Versioned operating loops and portable capability Capsules for persistent agent work.

Universal Agent Skill: builds anything with infinite disk memory and a two-brain team — typed consent gates, reproducible claims. Works with Claude Code, Codex, Antigravity.

Multi-agent orchestration preset for the DeepSeek Harness (DSH): cross-plane supervisor over Orca / dais (ex-zap) / DSH sessions — durable callback bridge (file+HTTP), fleet admission, SQLite ledger, flow compiler. Zero npm deps.

An AI-powered Indian Matrimonial Legal Assistant featuring grounded multi-agent deliberation, contract risk auditing, and adversarial stress-testing over 13,000+ verified statutory provisions and Supreme Court precedents.

Enterprise Multi-Agent Microgrid SCADA & Smart City Traffic Control Cockpit powered by Google Gemini 2.5 AI, 3/3 BFT Consensus, SHA-256 Cryptographic Ledger, Kaggle Machine Learning Predictors, and 10-Country Currency Matrix.

Agentic AI portfolio: planning agents, multi-agent orchestration, RAG, memory systems, semantic routing, evaluation pipelines, and business workflow automation.

Multi-agent newsroom pipeline: 6-agent hub-and-spoke architecture using Claude Sonnet

Synthetic benchmark for role-limited multi-agent deliberation in reservoir construction decision support

AI-powered hospital management system built with Python, Flask, multi-agent AI architecture, SQLite, and LLM integration.

A protocol for continuous AI operation through rotating active, shadow, and regeneration shifts across distributed agents and model replicas.

Multi-agent AI system that reviews arXiv research papers using LangGraph orchestration and Groq LLMs: reading, critiquing, and analyzing citations through a Streamlit UI where each user brings their own API key.

A privacy-aware multi-agent career management system with structured source governance, human approval workflows, and LLM output evaluation.

Tool-neutral Codex and Claude Code orchestration sample for Sitecore XM Cloud headless development

Multi-agent dialogue simulation combining generative agents (time-weighted FAISS memory) with a turn-taking DialogueSimulator, built on LangChain and Groq. Runs locally, in Colab, or as tests — no Colab lock-in. 21 fully-mocked unit tests.

Human-gated AI UX pipeline that takes a BRD through research, requirements challenge, ideation, design, prototyping and synthetic validation using specialized agents and reusable skills.

Revora AI is an intelligent AI-agent platform designed to automate decision-making through autonomous reasoning, tool usage, and multi-step workflows. It combines AI models, specialized agents, machine learning, and real-time processing to solve complex problems efficiently, securely, and intelligently.

A multi-agent LLM framework for explainable algorithmic trading on NSE-listed equities — DL forecaster, LLM news sentiment, and bull/bear debate, evaluated under a full Indian transaction-cost model.

Autonomous B2B lead intelligence and qualification system built with CrewAI, Python, DuckDuckGo Search, and Pydantic.

Self-healing AI reliability platform: LangGraph multi-agents + MCP tool execution + RAG-grounded Gemini evidence to detect, diagnose, and safely recover from LLM/RAG/agent failures — with human-in-the-loop approval and SLA-verified recovery.

Multi-agent AI operations copilot using RAG, MCP, tool calling, memory, workflow orchestration, and human-in-the-loop approvals.

Explore abandoned agent-harness research for local-first, model-agnostic AI orchestration with verification and sandboxing.

Engineering deterministic, production-grade systems around non-deterministic LLMs — FSM, durable execution, retries, DAGs, agent runtimes, model routing, edge inference, RAG, memory, multi-agent orchestration, security, and observability. 14 runnable proof-of-concept phases.