Graham 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.

🤖 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.

Cursor MCP Environment Limitations

Discovered Constraints

Package Access
- Cannot access project's virtual environment
- Limited to Python standard library
- No ability to install additional packages
Environment Variables

Cannot access project's .env files

🛠️ Async Tools for SmolAgents (LiteLLM)

This directory contains tools for handling LLM operations—most notably for document summarization—using asynchronous (async) techniques. Async Tools allow SmolAgents to process large inputs efficiently by splitting work into smaller chunks and running parallel API calls, all while keeping integration with the agent framework simple.

📏 Preventing Context Length Issues

The Problem

Large text documents can exceed the fixed context window of LLMs (e.g. GPT-4), which may result in:

🚨 Bug Report: SmolAgents CodeAgent Truncates Input Before Tool Execution

Summary

SmolAgents' CodeAgent incorrectly processes input before invoking tools, leading to truncation of long text even when a tool is explicitly designed to handle it. This defeats the purpose of tools and prevents reliable execution in workflows requiring large text processing.

Steps to Reproduce

Define a Tool (e.g., SummarizationTool) that accepts long text and chunks it internally.
Register the Tool with CodeAgent.
Send a large text input via agent.run(prompt).
Observe the Issue:

	#!/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.

	#!/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.

	#!/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):

	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

	#!/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

	"""
	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