Frugal Orchestrator

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

nelohenriq@nelohenriq

Install

openclaw skills install @nelohenriq/frugal-orchestrator

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

bash

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

Python Integration

python

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