Frugal Orchestrator

Token-efficient task orchestration system that delegates work to specialized subordinates while prioritizing system-level solutions over AI inference.

Skill: Frugal Orchestrator

Metadata

• Name: frugal-orchestrator
• Version: 0.5.0
• Author: Agent Zero Project
• Tags: orchestration, efficiency, token-optimization, delegation, caching, batch-processing, learning
• Description: Complete token-efficient task orchestration platform with auto-routing, caching, batch processing, A2A mesh, and learning engine. Achieves 90%+ token reduction.

Problem Statement

AI agents often waste tokens on tasks better solved by system tools (Linux commands, Python scripts). This creates unnecessary costs and slower execution.

Solution: Frugal Orchestrator v0.5.0 with intelligent task routing, caching layer, and specialized subordinate delegation.

Result: 90%+ token reduction while maintaining full functionality

Core Capabilities

Module 1: Auto-Router

Purpose: Automatically detect task type and route optimally

• System commands → Terminal (95% token reduction)
• Scripts → Python/Node.js execution
• Complex logic → AI delegation
• Class: TaskRouter

Module 2: Token Tracker

Purpose: TOON-format token metrics logging

• Track delegation vs direct execution
• Generate savings reports
• Class: TokenTracker

Module 3: Cache Manager

Purpose: Content-addressable result caching with TTL

• CRC32 hash-based keys
• LRU eviction, 7-day default TTL
• Class: CacheManager

Module 4: Error Recovery

Purpose: Resilient execution with retry/fallback chains

• Exponential backoff, circuit breaker
• Classes: ErrorRecovery, FailureType

Module 5: Batch Processor

Purpose: Parallel task execution

• Concurrent worker pool
• Manifest-based processing
• Class: BatchProcessor

Module 6: A2A Adapter

Purpose: Agent-to-Agent mesh communication

• Service discovery, load balancing
• Class: A2AAdapter

Module 7: Learning Engine

Purpose: Pattern recognition for routing decisions

• Confidence scoring, history analysis
• Class: LearningEngine

Module 8: Scheduler Integration

Purpose: Recurring task scheduling

• Cron-style scheduling
• Class: SchedulerClient

Quick Start

# Run demonstration
cd /a0/usr/projects/frugal_orchestrator/demo && bash run_demo.sh

Python Integration

from scripts.auto_router import TaskRouter
from scripts.cache_manager import CacheManager
from scripts.token_tracker import TokenTracker

# Initialize
router = TaskRouter(TokenTracker())
result = router.route("file_operations", task_input)

Project Statistics

Metric	Value
Python Modules	10
Shell Scripts	6
Total Files	58
Python LOC	1,763
Token Reduction	90%+

Token Efficiency

Feature	Token Reduction
Auto-routing	90-95%
Caching	>99% for repeats
Batch processing	Linear scaling

GitHub Repository

https://github.com/nelohenriq/frugal_orchestrator (v0.5.0)

Version History

• 0.5.0: Complete orchestration platform (10 modules, full infrastructure)
• 0.2.0: Standardized agentskills.io format, Git repo
• 0.1.0: Initial implementation