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Frugal Orchestrator

Token-efficient task orchestration system that delegates work to specialized subordinates while prioritizing system-level solutions over AI inference.
令牌高效的任务编排系统,将工作委托给专门的子模块,优先考虑系统层面的解决方案,而非 AI 推理。
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概述

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

MetricValue
---------------
Python Modules10
Shell Scripts6
Total Files58
Python LOC1,763
Token Reduction90%+

Token Efficiency

FeatureToken Reduction
------------------------
Auto-routing90-95%
Caching>99% for repeats
Batch processingLinear 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

版本历史

共 2 个版本

  • v1.0.0
    2026-03-30 11:40 安全
  • v1.0.1 当前
    2026-03-18 19:55 安全 安全

安全检测

腾讯云安全 (Keen)

安全,无风险
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腾讯云安全 (Sanbu)

安全,无风险
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