Python SDK

Cross-chain route discovery, simulation, and analysis in Python.

Installation

Requires Python 3.10+.

bash

1pip install mnmx-sdk

Basic Usage

python

1from mnmx import MnmxRouter
2
3router = MnmxRouter(strategy="minimax")
4
5route = router.find_route(
6    from_chain="ethereum",
7    from_token="ETH",
8    amount="1.0",
9    to_chain="solana",
10    to_token="SOL",
11)
12
13print(f"Path: {' → '.join(route.path)}")
14print(f"Expected output: {route.expected_output} SOL")
15print(f"Guaranteed minimum: {route.guaranteed_minimum} SOL")
16print(f"Total fees: {route.total_fees}")
17print(f"Estimated time: {route.estimated_time}s")

Compare All Routes

python

1routes = router.find_all_routes(
2    from_chain="ethereum",
3    from_token="ETH",
4    amount="1.0",
5    to_chain="solana",
6    to_token="SOL",
7)
8
9for route in routes:
10    print(f"{' → '.join(route.path)}")
11    print(f"  Expected: {route.expected_output} SOL")
12    print(f"  Minimum:  {route.guaranteed_minimum} SOL")
13    print(f"  Score:    {route.minimax_score:.4f}")
14    print()

Route Simulation

Simulate routes under various market conditions without executing:

python

1from mnmx import RouteSimulator
2
3sim = RouteSimulator()
4
5# Simulate with adversarial conditions
6result = sim.simulate(route, conditions={
7    "slippage_multiplier": 2.0,    # 2x quoted slippage
8    "gas_multiplier": 1.5,         # 1.5x current gas
9    "bridge_delay_multiplier": 3.0, # 3x median delay
10    "mev_extraction": 0.003,        # 0.3% MEV
11})
12
13print(f"Simulated output: {result.output}")
14print(f"Fees paid: {result.total_fees}")
15print(f"Time: {result.total_time}s")

Monte Carlo Analysis

Run thousands of simulations with randomized conditions to understand the outcome distribution:

python

1mc = sim.monte_carlo(
2    route=route,
3    iterations=10_000,
4    seed=42,
5)
6
7print(f"Mean output: {mc.mean_output:.4f} SOL")
8print(f"5th percentile: {mc.percentile_5:.4f} SOL")
9print(f"95th percentile: {mc.percentile_95:.4f} SOL")
10print(f"Worst observed: {mc.min_output:.4f} SOL")
11print(f"Guaranteed minimum: {route.guaranteed_minimum} SOL")

Batch Route Analysis

Compare strategies across multiple token pairs:

python

1from mnmx import BatchAnalyzer
2
3analyzer = BatchAnalyzer(router)
4
5pairs = [
6    ("ethereum", "ETH", "solana", "SOL", "1.0"),
7    ("ethereum", "USDC", "arbitrum", "USDC", "5000"),
8    ("solana", "SOL", "base", "ETH", "10.0"),
9]
10
11report = analyzer.compare_strategies(
12    pairs=pairs,
13    strategies=["minimax", "cheapest", "fastest"],
14)
15
16print(report.summary())
17# Shows side-by-side comparison of strategies across all pairs

CLI

bash

1# Find optimal route
2mnmx route --from ethereum:ETH:1.0 --to solana:SOL
3
4# Compare all routes
5mnmx route --from ethereum:ETH:1.0 --to solana:SOL --all
6
7# Monte Carlo simulation
8mnmx simulate --from ethereum:ETH:1.0 --to solana:SOL --iterations 10000
9
10# Compare strategies
11mnmx compare --from ethereum:ETH:1.0 --to solana:SOL

Error Handling

Exception	When
`MnmxError`	Base class for all SDK errors
`NoRouteFoundError`	No viable path exists
`InsufficientLiquidityError`	Bridge liquidity below transfer amount
`SimulationError`	Invalid simulation parameters
`TimeoutError`	Route discovery exceeded time limit

python

1from mnmx.exceptions import NoRouteFoundError
2
3try:
4    route = router.find_route(
5        from_chain="ethereum", from_token="ETH", amount="1.0",
6        to_chain="solana", to_token="SOL",
7    )
8except NoRouteFoundError as e:
9    print(f"No route available: {e.reason}")

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