🌌 MARS — Multi-Agent Runtime System

Agentic AI is at the Peak of Inflated Expectations on the Gartner Hype Cycle for AI 2026. MARS is a research-grade runtime that puts engineering discipline behind the hype: lifecycle management, trust, governance, federated discovery, and goal-driven loops — grounded in papers written before "agent" became a buzzword again.

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🤖 What is MARS?

MARS is a Python asyncio platform for building production-grade multi-agent systems with LLM integration.

Gartner's 2026 analysis identifies agentic AI as the fastest-advancing technology on the curve, moving toward the peak on the back of:

• 🧠 Autonomous / semi-autonomous agents that perceive, decide, and act
• 🤝 Multi-agent collaboration with clear task delegation
• 🔒 AI Trust, Risk, and Security Management (AI TRiSM) as a foundational requirement
• 🏗️ The need for robust infrastructure, not just model access, to realise real business value

MARS addresses all four directly — not as an afterthought, but as core architecture inherited from research dating back to 1997.

✨ MARS Features

• Run an async multi-agent runtime with Platform, AMS, MessageBus, Directory, group rooms, and direct message routing across federated platforms.
• Spawn LLMAgent, ReactiveAgent, ProactiveAgent, and EchoBot instances with lifecycle hooks, BDI-style role / goal / behaviour fields, and session memory.
• Execute lifecycle FSM presets with StateMachine + StrategyEngine, including goal-driven loops and shared proactive tick scheduling in Agent.
• Connect to Anthropic Claude, GitHub Models / Copilot, or local Ollama — run multiple models in parallel, chat with each, and keep agents running persistently.
• Route work by skill: LLMAgent exposes list_skills / use_skill, while service agents advertise capabilities and can answer with messages or artifacts.
• Run mars-server as a headless TCP/REST/WebSocket backend with per-client focus, audit logging, and a browser UI.
• Auto-spawn five free service agents on server start: clock, screenshot, profiler, status, and bridge.copilot@1.
• Exchange text and binary artifacts with /artifact list, /artifact get, and /artifact send.
• Use the three-pane CLI with per-agent chat history, room chat, activity feed, visual mode badges (⚡ reactive / ⏰ proactive), and MXTY auto-naming.

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🗺️ Roadmap

Future work, focused on turning the runtime into business-shaped capabilities. The research papers define what each item means; delivery order is driven by the business model behind it.

🔬 Research / Long-term

Area	Feature	Paper origin
Trading	DIDF/DSDF specialist agent demo — hierarchical counterparty matching at runtime	Counterparty Matching
Location	Context-threaded federated retrieval — route queries by location + capability	Location-Aware Agent Retrieval
Federation	Multi-MARS network — authenticated clusters forming a global agent fabric	LARS
Governance	Formal Regelrahmen enforcement — hard policy boundaries with proof of compliance	Patient Technology
Self-organisation	Dynamic coalition formation — agents self-assemble teams for complex goals	EMIKA

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🚀 Getting started

• SETUP.md — prerequisites, install, configure providers, start the server.
• USER.md — CLI walkthrough: spawn agents, chat, scopes, artifacts, trust.
• ARCHITECTURE.md — runtime stack, service marketplace, scopes, game-theory FSM, providers.
• BUILD.md — building, running tests, packaging a wheel, Git LFS.
• AGENTS.md — service-agent catalogue and wire protocol.
• CONTRIBUTING.md — project layout, coding conventions, how to add providers and tests.

⚡ Quick start

After pip install -e ".[dev]" (see SETUP.md):

# Standalone CLI (in-process platform, no server needed)
python -m mars.cli.main --provider mock                        # offline test agent
python -m mars.cli.main --provider github-models               # free LLM (needs GITHUB_TOKEN)
python -m mars.cli.main --provider ollama                      # local Ollama — no API key, no limits
python -m mars.cli.main --provider ollama github-models        # both at once 🔀
python -m mars.cli.main --provider anthropic --model claude-3-5-sonnet-20241022  # Anthropic Claude (needs ANTHROPIC_API_KEY)

# Server + client (recommended for multi-client / persistent runs)
# Terminal 1 — start the headless server (auto-spawns free service agents)
python -m mars.srv.main                                        # echo bots only
python -m mars.srv.main --provider github-models               # + free GitHub Models LLM
python -m mars.srv.main --provider ollama                      # + local llama3.2 (unlimited, no key)
python -m mars.srv.main --provider ollama github-models        # + one agent per provider 🔀

# Terminal 2 — connect a CLI client
python -m mars.cli.main --connect localhost:7432

The pip install -e ".[dev]" step also registers mars and mars-server console scripts. They work whenever your Python Scripts/ (Windows) or bin/ (Linux/macOS) directory is on PATH — but python -m … always works regardless of PATH configuration.

No API key? Use --provider ollama with Ollama installed locally — completely free, no rate limits. See SETUP.md for the full Ollama setup guide.

The server exposes TCP 7432 (JSON-line for CLI + agents), HTTP 7433 (REST), and WebSocket 7434 (browser UI). See USER.md for the full command cheat sheet.

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📈 Where MARS sits on the Hype Cycle

Gartner (2026) places agentic AI at the Peak of Inflated Expectations, warning that sustainable value requires:

1. Alignment with real business objectives — not demos for their own sake
2. Foundational infrastructure — lifecycle, messaging, trust, governance
3. AI TRiSM — trust, risk, and security built in from the start
4. Realistic scoping — iterative loops with defined exit criteria, not open-ended autonomy

MARS implements each of these:

Gartner requirement	MARS feature
Agent lifecycle management	AMS — INITIALIZING → ACTIVE → SUSPENDED → TERMINATED
Multi-agent communication	MessageBus — async unicast / broadcast / multicast
Trust & security	TrustManager — per-vendor trust (TRUSTED / NEUTRAL / SUSPICIOUS / BLOCKED)
Federated discovery	GlobalDirectory + DomainDirectory — two-level capability routing across domains and clusters
Goal-driven loops	StateMachine + LoopContext — build → review → fix → done, with exit conditions
Real business alignment	LLM agents with send_message, list_agents, and artifact tools — wired to real providers

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📖 Research foundations

The design is grounded in work by the author predating modern LLMs:

Paper	Contribution to MARS
Intelligent Mobile Agents in the Intra/Internet (Nopper, 1997)	Foundational diploma thesis: mobile agent theory, transport, lifecycle, platform-independence — the direct predecessor of LARS and the entire MARS runtime model
LARS — Living Agents Runtime System (Nopper, 2000)	Platform-independence, lifecycle FSM (INITIALIZING → ACTIVE → SUSPENDED → MIGRATED → TERMINATED), clustering, XML messaging
EMIKA (Müller, Eymann, Nopper et al., 2004)	Real-time sensor-to-agent middleware, self-organising coordination, Ist/Soll-Zustand loop
Agent-Based Counterparty Matching (Lohmann, Nopper, Henning, 1998)	Hierarchical DIDF/DSDF discovery, specialist agents over a shared runtime
Location-Aware Agent Retrieval (Nopper, Kammerer, 2000)	Context-threaded, federated multi-source agent retrieval
Patient Technology (Müller, Nopper et al., ~2003)	Self-organisation requires an explicit governance framework (Regelrahmen)

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📚 References

• Axelrod, R. (1984). The Evolution of Cooperation. Basic Books.
• Nopper, N. (1997). Intelligent Mobile Agents in the Intra/Internet. Diploma thesis, Hochschule Furtwangen University (HFU).
• Lohmann, Nopper, Henning (1998). Agent-Based Counterparty Matching in Financial Markets.
• Nopper, Kammerer (2000). Location-Aware Agent Retrieval.
• Nopper, N. (2000). LARS — Living Agents Runtime System. AgentLink.
• Müller, Nopper et al. (~2003). Patient Technology — Self-Organisation and Governance.
• Müller, Eymann, Nopper et al. (2004). EMIKA — Agent-Based Middleware for Real-Time Sensor Systems.
• Shoham & Leyton-Brown (2009). Multiagent Systems. Cambridge University Press.
• Gartner, Hype Cycle for Agentic AI, 2026 — https://www.gartner.com/en/articles/hype-cycle-for-agentic-ai