marlos 1.1.1

Autonomous Distributed Computing Operating System with Reinforcement Learning

MarlOS

Decentralized, self-organizing compute network powered by Multi-Agent Reinforcement Learning

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MarlOS is a peer-to-peer distributed computing OS where every node is autonomous, cryptographically authenticated, and makes its own bidding decisions using reinforcement learning — with no central orchestrator, no Kubernetes, no cloud controller.

When a job enters the network, nodes independently decide whether to Bid, Forward, or Defer. The winner is determined by a decentralized auction. Payment flows automatically via a token economy. Nodes that fail get replaced by backups. Nodes that misbehave get quarantined.

# Try it in 30 seconds
git clone https://github.com/ayush-jadaun/MarlOS.git && cd MarlOS
pip install -r requirements.txt
python scripts/demo.py

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Features

Core System

• RL-Driven Scheduling — PPO policy on 25D state vector (BID/FORWARD/DEFER)
• Decentralized Auctions — Ed25519-signed bids, deterministic winner selection
• Token Economy — MarlCredits with staking, progressive taxation, UBI
• Trust & Reputation — 0.0-1.0 scores, automatic quarantine of bad actors
• Self-Healing — Backup node takeover, heartbeat monitoring, checkpoint recovery
• Predictive Pre-Execution — RL-powered speculation cache for near-zero latency

New in v1.1

• REST API — HTTP endpoints for jobs, peers, wallet, trust, pipelines (docs)
• MCP Server — Claude and AI agents can submit jobs via Model Context Protocol (docs)
• Job Pipelines (DAGs) — Chain jobs with dependencies, output flows between steps (docs)
• Result Aggregation — Submit batch jobs, get combined results (docs)
• Plugin System — Drop a .py file in plugins/, restart, new runner available (docs)
• File Transfer — P2P chunked transfer with SHA-256 integrity verification
• Online Learning — Exploration decay, behavioral cloning from successful experiences
• D3.js Network Visualization — Force-directed graph in the dashboard
• JavaScript SDK — npm install marlos-sdk for Node.js/browser integration (docs)
• Economic Whitepaper — Formal token model with simulation results (docs)

Proven by Simulation

Metric	Fairness ON	Fairness OFF
Gini Coefficient	0.549	0.822
Participation Rate	100%	27%
Adversarial Detection	100%	—
False Positive Rate	0%	—
Online Learning Win Rate	+9.2pp improvement over 500 jobs

Charts: docs/charts/

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Architecture

┌─────────────────────────────────────────────────────────────────┐
│                         MarlOS Node                              │
│                                                                  │
│  ┌──────────┐  ┌──────────┐  ┌──────────┐  ┌──────────────┐    │
│  │  P2P     │  │  RL      │  │ Bidding  │  │  Executor    │    │
│  │ ZMQ      │  │ PPO      │  │ Auction  │  │  Shell       │    │
│  │ Ed25519  │  │ 25D state│  │ Scorer   │  │  Docker      │    │
│  │ Gossip   │  │ Online   │  │ Router   │  │  Security    │    │
│  │ FileTx   │  │ Learning │  │          │  │  Plugins     │    │
│  └──────────┘  └──────────┘  └──────────┘  └──────────────┘    │
│                                                                  │
│  ┌──────────┐  ┌──────────┐  ┌──────────┐  ┌──────────────┐    │
│  │  Token   │  │  Trust   │  │Pipeline  │  │  Interfaces  │    │
│  │ Economy  │  │ Reputat. │  │ DAG      │  │  Dashboard   │    │
│  │ Wallet   │  │ Quarant. │  │ Aggreg.  │  │  REST API    │    │
│  │ Tax/UBI  │  │ Watchdog │  │ FileTx   │  │  MCP Server  │    │
│  └──────────┘  └──────────┘  └──────────┘  └──────────────┘    │
└─────────────────────────────────────────────────────────────────┘

Data Flow

Job Submitted → P2P Broadcast → All Nodes Receive
                                      │
                          ┌───────────┼───────────┐
                          ▼           ▼           ▼
                        BID        FORWARD      DEFER
                    (RL decides) (RL decides) (RL decides)
                          │
                          ▼
                 Decentralized Auction
                 Ed25519-signed bids
                          │
                          ▼
                 Winner Claims Job → Stakes tokens → Executes
                          │
                    ┌─────┴─────┐
                    ▼           ▼
                Success       Failure
                Pay winner   Slash stake
                +trust       Backup takes over

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Quick Start

Demo (one command)

python scripts/demo.py --nodes 3 --jobs 2
# or
marl demo

This starts 3 nodes on localhost, submits jobs, shows the full auction lifecycle, token transfers, and trust updates.

Run a Node

# Single node
NODE_ID=my-node python -m agent.main

# With peers
NODE_ID=node-1 BOOTSTRAP_PEERS="tcp://192.168.1.100:5555" python -m agent.main

Submit Jobs

# Via CLI
marl execute "echo hello"

# Via REST API
curl -X POST http://localhost:3101/api/jobs \
  -H "Content-Type: application/json" \
  -d '{"job_type": "shell", "payload": {"command": "echo hello"}, "payment": 50}'

# Via JavaScript SDK
import { MarlOSClient } from 'marlos-sdk';
const client = new MarlOSClient('http://localhost:3101');
const result = await client.submitAndWait('shell', { command: 'echo hello' });

Submit a Pipeline (DAG)

curl -X POST http://localhost:3101/api/pipelines \
  -H "Content-Type: application/json" \
  -d '{
    "name": "security-scan",
    "steps": [
      {"id": "scan", "job_type": "port_scan", "payload": {"target": "10.0.0.0/24"}},
      {"id": "analyze", "job_type": "shell", "payload": {"command": "python analyze.py"}, "depends_on": ["scan"]}
    ]
  }'

AI Agent Integration (MCP)

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "marlos": {
      "command": "python",
      "args": ["-m", "agent.mcp.server"],
      "env": {"MARLOS_API_URL": "http://localhost:3101"}
    }
  }
}

Then Claude can: "Submit a port scan of 192.168.1.0/24 to MarlOS"

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Network Modes

Private Mode (default)

Connect to specific peers. Good for labs, teams, home networks.

NODE_ID=node-1 BOOTSTRAP_PEERS="tcp://192.168.1.100:5555,tcp://192.168.1.101:5555" python -m agent.main

Public Mode

DHT-based global discovery. Anyone can join.

NODE_ID=node-1 NETWORK_MODE=public DHT_ENABLED=true \
  DHT_BOOTSTRAP="bootstrap1.example.com:5559" python -m agent.main

Sybil resistance: minimum stake (10 AC) and per-subnet peer limits.

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Job Types

Type	Runner	Example
shell	Shell commands	{"command": "echo hello"}
docker	Docker containers	{"image": "python:3.11", "command": "python -c 'print(1)'"}
docker_build	Container builds	{"dockerfile": ".", "tag": "myapp"}
port_scan	Network scanning	{"target": "192.168.1.0/24"}
malware_scan	File analysis	{"file_path": "/tmp/suspicious.bin"}
hash_crack	Hash cracking	{"hash": "5f4dcc3b...", "algorithm": "md5"}
threat_intel	Threat lookups	{"indicator": "evil.com"}
Custom plugins	Drop in plugins/	See Plugin Guide

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Running Tests

# Unit tests (fast, ~30s)
python -m pytest test/ --ignore=test/integration -v

# API tests
python -m pytest test/api/ -v

# Pipeline/DAG tests
python -m pytest test/pipeline/ -v

# Plugin tests
python -m pytest test/plugins/ -v

# Integration tests (3-node real network, ~3 min)
python -m pytest test/integration/ -v

# Benchmark
python scripts/benchmark.py --nodes 3 --jobs 10

# Economic simulation (generates charts)
python scripts/economic_simulation.py

# Adversarial resistance demo
python scripts/adversarial_demo.py

236+ tests passing across unit, API, pipeline, plugin, economic, adversarial, and integration tests.

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Configuration

Three layers (highest priority wins):

Layer	Source	Example
1 (lowest)	Dataclass defaults	pub_port=5555
2	YAML: ~/.marlos/nodes/{NODE_ID}/config.yaml	Custom per-node
3 (highest)	Environment variables	PUB_PORT=6000

Key env vars: NODE_ID, PUB_PORT, SUB_PORT, DASHBOARD_PORT, NETWORK_MODE, BOOTSTRAP_PEERS, DHT_ENABLED, DHT_BOOTSTRAP

See docs/CONFIG_ARCHITECTURE.md for details.

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Documentation

Getting Started

• docs/LOCAL_TESTING.md — Run demos, benchmarks, and tests locally
• docs/CLI_GUIDE.md — All marl CLI commands
• docs/QUICKSTART.md — 5-minute manual setup

Deployment

• docs/DEPLOYMENT.md — Single node, LAN, cloud, worldwide
• docs/DISTRIBUTED_DEPLOYMENT.md — Real device deployment

API & Integration

• docs/API_GUIDE.md — REST API, MCP server, JavaScript SDK
• docs/PLUGINS.md — Writing custom runners
• sdk/js/README.md — JavaScript SDK reference

Architecture

• docs/ARCHITECTURE_RL.md — 25D state vector, PPO, fairness metrics
• docs/ARCHITECTURE_TOKEN.md — Token economy, taxation, UBI
• docs/ECONOMIC_WHITEPAPER.md — Formal economic model
• docs/NETWORK_DESIGN.md — P2P protocol design
• docs/CHECKPOINT_RECOVERY_GUIDE.md — Fault tolerance

Configuration

• docs/CONFIG_ARCHITECTURE.md — 3-tier config system
• docs/FULL_CONFIG_USAGE.md — All parameters
• docs/USER_GUIDE_NETWORK_MODES.md — Private vs Public

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Technology Stack

Layer	Technology
Language	Python 3.11+ (async/await)
P2P	ZeroMQ PUB/SUB
Crypto	Ed25519 (PyNaCl)
RL	PyTorch + Stable-Baselines3 (PPO)
API	aiohttp (REST) + MCP SDK
Execution	Shell, Docker, custom plugins
Storage	SQLite (wallet), JSON (ledger)
Dashboard	React + D3.js + WebSockets
SDK	JavaScript/TypeScript

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Project Structure

agent/                 # Core agent
  p2p/                 # ZMQ networking, Ed25519, file transfer
  rl/                  # PPO policy, online learner, state calc
  bidding/             # Auction, scoring, routing
  executor/            # Job runners (shell, docker, security)
  tokens/              # Wallet, ledger, economy
  trust/               # Reputation, watchdog, quarantine
  pipeline/            # DAGs, aggregator
  plugins/             # Plugin loader
  api/                 # REST API server
  mcp/                 # MCP server for AI agents
  dashboard/           # WebSocket server
  predictive/          # Speculation cache
cli/                   # marl CLI
scripts/               # Demo, benchmark, simulations
plugins/               # Custom runner plugins
sdk/js/                # JavaScript SDK
dashboard/             # React + D3.js frontend
test/                  # 236+ tests
docs/                  # Documentation

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Contributing

See CONTRIBUTING.md for guidelines, code style, and how to write a new runner.

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Contributors

Team async_await

• Ayush Jadaun
• Shreeya Srivastava
• Arnav Raj

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License

Apache-2.0