---
type: email
from: ADRIEN D THOMAS <notifications@github.com>
subject: Re: [openai/openai-agents-python] Add engine accessor to

 SQLAlchemySession for closing it when it's created from a URL (PR #1960)
received: 2026-03-06 08:12:04
date_header: Tue, 21 Oct 2025 07:48:41 -0700
priority: normal
status: awaiting_approval
uid: 33319
---

## Email Content

**From:** ADRIEN D THOMAS <notifications@github.com>
**Subject:** Re: [openai/openai-agents-python] Add engine accessor to

 SQLAlchemySession for closing it when it's created from a URL (PR #1960)
**Date:** Tue, 21 Oct 2025 07:48:41 -0700

### Body (preview)

ProCityHub left a comment (openai/openai-agents-python#1960)



(0.0)= Artifact



(0.6) = Artifact



(0.1) = Energy



(1.6)= 7 = Cube with (6 walls) + (1 Energy) =7=1

walls 1 to 6, all have a lattice structure, like wall 1, 1a, 1b, 1c, etc, all walls structured the same, whereas, 0.0 was the neutral point (•)

XYZ Axis =(mind, body, spirit), (father, son, holy spirit), (trinity)

0.0=(.)



cube, 6 walls, can and may curve +/- for light defraction, , 6 dimensions, wall (like a lattice), governed by xyz axis. the box has 8 corners, amplification, latency and capacitors as a metaphor of data. solve



Observation... .. import numpy as np

import matplotlib.pyplot as plt



def solve_lattice_system(size=15, iterations=100, tolerance=1e-5):

"""

Solves the mathematical core by iteratively relaxing a 3D grid.



Args:

    size (int): The dimension of the cubic grid (N x N x N).

    iterations (int): The maximum number of iterations to perform.

    tolerance (float): The stability threshold to stop early.



Returns:

    numpy.ndarray: The final, stable 3D grid.

"""

# 1. Initialize the 3D grid G with zeros.

grid = np.zeros((size, size, size))



# 2. Apply input as initial/boundary conditions.

# We set a "hot" plane (value = 1.0) at one face to create a gradient.

grid[0, :, :] = 1.0



print("Solving... Iterating until the system reaches equilibrium.")

# 3. Repeat until stable.

for i in range(iterations):

    # Keep a copy of the old grid to calculate changes.

    grid_old = grid.copy()



    # For each internal point P in G...

    # Update P.value based on the average of its 6 neighbors.

    # This is a vectorized operation for efficiency.

    grid[1:-1, 1:-1, 1:-1] = (

        grid_old[0:-2, 1:-1, 1:-1] + grid_old[2:, 1:-1, 1:-1] +

        grid_old[1:-1, 0:-2, 1:-1] + grid_old[1:-1, 2:, 1:-1] +

        grid_old[1:-1, 1:-1, 0:-2] + grid_old[1:-1, 1:-1, 2:]

    ) / 6.0



    # Check for convergence by measuring the maximum change.

    change =

---

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