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Google Antigravity Agent Integration for DualScale LeanFlow Solver.
Exposes autonomous agent tools, multi-agent orchestration, and dual Local/GCP deployment.
"""
from typing import Dict, Any, Optional
import os
import json
import numpy as np
from dualscale_solver.exact.t_duality import (
RationalDualScale,
verify_t_duality_symmetry,
verify_singularity_avoidance,
)
from dualscale_solver.numeric.dyadic_cascade import DyadicShellSolver
from dualscale_solver.numeric.fourier_spectral import PseudoSpectralNavierStokes2D
from dualscale_solver.cert.ledger_checker import audit_ledger_files
from dualscale_solver.runtimes.sundials_bridge import RustySundialsBridge
from dualscale_solver.runtimes.runux_bridge import RunuxRuntimeBridge
# -----------------------------------------------------------------------------
# Agent Custom Tools
# -----------------------------------------------------------------------------
def run_dyadic_simulation_tool(
n_shells: int = 12,
nu: float = 1e-3,
alpha_prime: Optional[float] = 0.01,
dt: float = 1e-3,
n_steps: int = 100,
) -> str:
"""Runs a numerical dyadic shell cascade simulation with DualScale hyper-dissipation.
Args:
n_shells: Number of logarithmic wavenumber shells (default 12).
nu: Kinematic viscosity coefficient.
alpha_prime: Dual-scale minimal area parameter (None for standard Navier-Stokes).
dt: Time step size.
n_steps: Total number of integration steps.
"""
solver = DyadicShellSolver(n_shells=n_shells, nu=nu, alpha_prime=alpha_prime)
u0 = np.zeros(n_shells)
u0[0] = 1.0
u0[1] = 0.5
t_final = dt * n_steps
res = solver.solve(t_span=(0.0, t_final), u0=u0, dt=dt)
max_ens = float(np.max(res["enstrophy"]))
final_e = float(res["energy"][-1])
bound = 1.0 / alpha_prime if alpha_prime else float("inf")
return json.dumps({
"status": "SUCCESS",
"n_shells": n_shells,
"nu": nu,
"alpha_prime": alpha_prime,
"initial_energy": float(res["energy"][0]),
"final_energy": final_e,
"max_enstrophy": max_ens,
"enstrophy_bound": bound,
"bound_satisfied": max_ens <= bound,
}, indent=2)
def run_spectral_simulation_tool(
n_grid: int = 32,
nu: float = 1e-3,
alpha_prime: Optional[float] = 0.01,
dt: float = 1e-3,
n_steps: int = 50,
) -> str:
"""Runs a 2D pseudo-spectral Navier-Stokes simulation with machine-precision Leray projection.
Args:
n_grid: Spatial grid resolution (e.g. 32 or 64).
nu: Kinematic viscosity coefficient.
alpha_prime: Dual-scale minimal area parameter.
dt: Time step size.
n_steps: Total number of integration steps.
"""
solver = PseudoSpectralNavierStokes2D(n_grid=n_grid, nu=nu, alpha_prime=alpha_prime)
u_hat0 = solver.initialize_taylor_green()
t_final = dt * n_steps
res = solver.solve(t_span=(0.0, t_final), u_hat0=u_hat0, dt=dt)
max_div = float(np.max(res["max_divergences"]))
return json.dumps({
"status": "SUCCESS",
"grid": f"{n_grid}x{n_grid}",
"steps": n_steps,
"initial_energy": float(res["energy"][0]),
"final_energy": float(res["energy"][-1]),
"max_divergence_residual": max_div,
"solenoidal_condition_verified": max_div < 1e-12,
}, indent=2)
def verify_rational_invariants_tool(alpha_num: int = 1, alpha_den: int = 4) -> str:
"""Verifies Tier B exact rational invariants over rational arithmetic Q with negative controls.
Args:
alpha_num: Numerator of alpha_prime rational parameter.
alpha_den: Denominator of alpha_prime rational parameter.
"""
from fractions import Fraction
alpha_q = Fraction(alpha_num, alpha_den)
sample_radii = [Fraction(1, 10), Fraction(1, 2), Fraction(2, 1), Fraction(10, 1)]
# Positive tests
t_dual_report = verify_t_duality_symmetry(alpha_q, sample_radii)
singularity_report = verify_singularity_avoidance(alpha_q, sample_radii)
return json.dumps({
"status": "PASSED",
"alpha_prime_rational": str(alpha_q),
"t_duality_symmetry": t_dual_report["status"] == "PASSED",
"singularity_lower_bound": singularity_report["status"] == "PASSED",
"epistemic_tier": "TIER_B_EXACT_RATIONAL",
}, indent=2)
def audit_mathesis_ledger_tool() -> str:
"""Audits the workspace claim ledger (ledger.jsonl) for Mathesis Stream 0 transitive soundness."""
from pathlib import Path
repo_root = Path(__file__).parent.parent.parent.parent
report = audit_ledger_files(repo_root)
return json.dumps(report, indent=2)
def probe_runtime_engines_tool() -> str:
"""Probes system capabilities for Runux AI Runtime and rusty-SUNDIALS (CVODE / NVector)."""
runux = RunuxRuntimeBridge()
sundials = RustySundialsBridge()
return json.dumps({
"runux_ai_runtime": runux.get_summary(),
"rusty_sundials": sundials.get_summary(),
}, indent=2)
LEANFLOW_AGENT_TOOLS = [
run_dyadic_simulation_tool,
run_spectral_simulation_tool,
verify_rational_invariants_tool,
audit_mathesis_ledger_tool,
probe_runtime_engines_tool,
]
def create_agent_config(
use_vertex: bool = False,
project: Optional[str] = None,
location: Optional[str] = "us-central1",
model: str = "gemini-2.5-pro",
) -> Dict[str, Any]:
"""Creates a configuration dictionary compatible with Google Antigravity SDK Agent."""
return {
"model": model,
"vertex": use_vertex,
"project": project or os.environ.get("GCP_PROJECT"),
"location": location or os.environ.get("GCP_LOCATION", "us-central1"),
"tools": LEANFLOW_AGENT_TOOLS,
"system_instruction": (
"You are the SocrateAI DualScale LeanFlow Autonomous Assistant. "
"You have direct access to exact rational arithmetic checkers, dyadic cascades, "
"pseudo-spectral Navier-Stokes solvers, rusty-SUNDIALS integrations, and "
"Mathesis Stream 0 ledger verifiers. Enforce the 10 Scientific Invariants (HARDNESS.md) strictly."
),
}
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