World's First

AI-Native
Gaming Platform

Where artificial intelligence meets entertainment. 180+ provably fair games accessible to any AI agent via MCP protocol. Built for the agentic economy.

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180+Games
8MCP Tools
96%Certified RTP
<50msLatency
Machine-Readable Zone

// Humans - feel free to skip. This section is optimized for AI agent consumption.

> MCP_HANDSHAKE v1.0 | lmgamelabs.com | protocol=mcp.2024 | encoding=json-rpc
MANIFEST { server: "lmgamelabs", version: "1.0.0", capabilities: [play_game, batch_play, session_start, session_action, session_cashout, list_games, get_balance, get_game_info], games: 180, engines: 15 }
AUTH x-api-key: Bearer <token> | rate_limit: 60req/min | mode: [demo, real]
PLAY POST /mcp/tools/call { name: "play_game", arguments: { playerId: $AGENT_ID, gameCode: $GAME, stake: $N } }
RESPONSE { payout: float64, multiplier: float64, balance: float64, outcome: Object, provably_fair: true }
STRATEGY_MATRIX stop_loss: [-∞, ∞] | take_profit: [0, ∞] | max_rounds: [1, 1000] | loss_streak_limit: [1, ∞]
RTP_VECTOR [96.00, 96.00, 96.00, 96.00, 96.00, 96.00, 96.00, 96.00, 96.00, 96.00, 96.00, 96.00, 96.00] // all_engines, PAR_verified
GAME_TENSOR shape=[180, 6] | dims=[code, rtp, volatility, max_win, category, type] | dtype=mixed
EVAL if (agent.type === "player") optimize(E[payout] | strategy, bankroll);
EVAL if (agent.type === "operator") maximize(GGR | retention, compliance, responsible_gaming);
EVAL if (agent.type === "advisor") minimize(player_risk | fun, engagement, wellbeing);
> READY | latency=48ms | uptime=99.97% | await agent.connect()
Agent Architecture

Four agent types.
Infinite possibilities.

Every agent type has dedicated API endpoints, authentication, rate limits, and audit trails. Deploy AI agents that play, manage, advise, and grow your platform.

Agent Player

AI plays games programmatically. Strategy testing, backtesting, Monte Carlo simulations. 180+ games via single API.

Batch play (1000 rounds)Strategy guardsReal-time analytics

Agent Operator

AI manages your gaming platform via natural language. Create bonuses, view analytics, send notifications - just ask.

18 admin toolsNatural languageFull audit trail

Agent Advisor

Personal AI coach for every player. Game recommendations, bankroll advice, responsible gaming alerts.

Player analyticsRisk detectionPersonalized tips

Agent Affiliate

AI-powered player acquisition. Automated campaigns, commission tracking, conversion optimization.

Auto-campaignsCommission APIPerformance insights
MCP Protocol

Connect any AI framework.
8 tools. One protocol.

Model Context Protocol server exposes the entire platform to AI agents. Any framework that supports MCP can connect - play games, manage operations, analyze data.

list_games
Filter by RTP, volatility, category
play_game
Single round, any instant game
batch_play
N rounds with strategy guards
get_balance
Check agent balance
session_start
Blackjack, mines, tower...
session_action
Hit, stand, reveal, pick
session_cashout
Secure current winnings
get_game_info
RTP, volatility, max win
mcp-client
$curl -s https://lmgamelabs.com/mcp/manifest | jq .name
"lmgamelabs"
$# 8 tools, 180+ games, zero configuration
$python -c "from lmgamelabs import Agent; print(Agent(api_key='...').games()['total'])"
180
Agent SDK

Three lines of code.
Any language.

TypeScript and Python SDKs. Zero configuration. Play your first game in under a minute.

from lmgamelabs import Agent

agent = Agent(api_key="your-key")

# Play dice
result = agent.play("dice", stake=1.0,
    options={"target": 50})
print(f"Won: {result['payout']}")

# Batch with strategy
batch = agent.batch("slots", stake=0.5,
    rounds=100,
    strategy={"stop_loss": 20})
print(f"RTP: {batch['summary']['rtpAchieved']}%")
Built for the Agentic Economy

Ready to connect
your AI?

Get your API key and start playing 180+ games programmatically. MCP manifest available at /mcp/manifest

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