grahama1970

✅ Method Validator - AI Agent's API Discovery Tool

A specialized tool designed for AI agents to autonomously analyze Python packages, discover existing methods, and validate APIs before implementing new solutions. This tool helps prevent redundant code creation by identifying existing functionality in non-standard packages.

Features

• Smart Package Analysis: Automatically filters out standard library and common utility packages
• Method Discovery: Quick scanning of available methods with categorization
• Detailed Analysis: In-depth examination of method signatures, parameters, and return types
• Exception Analysis: Identifies and prioritizes relevant error handling patterns

grahama1970

README: MCP SDK Docker Deployment Issue

Problem Summary

MCP's Python and TypeScript SDKs fail to run persistently in Docker. Despite setting a lifespan in the Python SDK, mcp.run() exits immediately, shutting down the container. This issue affects all tested MCP projects, making real-world deployment impossible.

Reproducible Steps

1. Create an MCP server using the Python SDK with a lifespan function to manage resources.
2. Build and run the Docker container.
3. The MCP server connects to ArangoDB, logs success, and then exits immediately.
4. The same behavior is observed with the TypeScript SDK.

grahama1970

🚀 Latent Program Networks (LPN) for Tool Execution Strategies

This README explains how a Latent Program Networks (LPN)-inspired approach can generate tool execution strategies for multi-tool tasks in an agent workflow. This method offers advantages over relying solely on Large Language Models (LLMs) for tool strategy via prompting. A toy Python script demonstrates the concept in action.

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🤖 What is LPN?

Latent Program Networks (LPNs), originally designed for abstract reasoning (e.g., the ARC benchmark), embed "rules" or "completions" into a searchable latent space. Here, we adapt LPNs to embed tool configurations and execution order, enabling smooth coordination of multiple tools (e.g., generate_summary and create_pie_chart) for tasks like summarization and data visualization.

grahama1970

Cursor MCP Environment Limitations

Discovered Constraints

1. Package Access

   • Cannot access project's virtual environment
   • Limited to Python standard library
   • No ability to install additional packages

2. Environment Variables

• Cannot access project's .env files

grahama1970

#!/usr/bin/env python3
"""
Dynamic and Adaptive Python Environment in a Bubblewrap Sandbox

Overview:
    This project demonstrates a minimal, safe, and self-contained Python execution
    environment using bubblewrap (bwrap) and uv. The goal is to provide a lightweight
    alternative to Docker for running agent code—in this case, dynamically generated
    Python code along with its dependencies—within an isolated sandbox.

grahama1970

#!/usr/bin/env python3
"""
Dynamic and Adaptive Python Environment in a Bubblewrap Sandbox

Overview:
    This project demonstrates a minimal, safe, and self-contained Python execution
    environment using bubblewrap (bwrap) and uv. The goal is to provide a lightweight
    alternative to Docker for running agent code—in this case, dynamically generated
    Python code along with its dependencies—within an isolated sandbox.

grahama1970

#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Enhanced Unsloth GRPO with Student-Teacher Reward Mechanism
------------------------------------------------------------
This script extends Unsloth's GRPO by implementing a novel student-teacher reward
mechanism for improved reasoning chains.

Key Components:
1. Student Model (Unsloth GRPO):

grahama1970

import re
import json
from loguru import logger
from pathlib import Path
from arango import ArangoClient
from arango.database import StandardDatabase
from typing import Dict, Any

from search_tool.arangodb.generate_schema_for_llm.utils.arango_utils import initialize_database
from search_tool.shared_utils.json_utils import save_json_to_file

grahama1970

#!/bin/bash
# arangodump_db.sh
# This script dumps an ArangoDB database from a Docker container with a low memory batch size.
#
# Usage: ./arangodump_db.sh
# Make sure arangodump is in your PATH.
# This script dumps an ArangoDB database from a Docker container with a low memory batch size.
# The dump path follows the structure: backups/<database_name>/<timestamp>/arangodump
#
# Usage: ./arangodump_db.sh

grahama1970

"""
This script handles the creation and population of an ArangoDB collection containing error codes and messages.
It downloads an error code dataset from a specified URL, parses it into JSON format, and stores it in a collection
for easy reference and querying. The script is designed to be run as a standalone utility to initialize or update
the error code collection in an ArangoDB database.
"""

import requests
import csv
from arango import ArangoClient