HASHIRU: Hierarchical Agent System for Hybrid Intelligent Resource
  Utilization
Authors: name: Kunal Pai, name: Parth Shah, name: Harshil Patel

Rapid Large Language Model (LLM) advancements are fueling autonomous
Multi-Agent System (MAS) development. However, current frameworks often lack
flexibility, resource awareness, model diversity, and autonomous tool creation.
This paper introduces HASHIRU (Hierarchical Agent System for Hybrid Intelligent
Resource Utilization), a novel MAS framework enhancing flexibility, resource
efficiency, and adaptability. HASHIRU features a "CEO" agent dynamically
managing specialized "employee" agents, instantiated based on task needs and
resource constraints (cost, memory). Its hybrid intelligence prioritizes
smaller, local LLMs (via Ollama) while flexibly using external APIs and larger
models when necessary. An economic model with hiring/firing costs promotes team
stability and efficient resource allocation. The system also includes
autonomous API tool creation and a memory function. Evaluations on tasks like
academic paper review (58% success), safety assessments (100% on a
JailbreakBench subset), and complex reasoning (outperforming Gemini 2.0 Flash
on GSM8K: 96% vs. 61%; JEEBench: 80% vs. 68.3%; SVAMP: 92% vs. 84%) demonstrate
HASHIRU's capabilities. Case studies illustrate its self-improvement via
autonomous cost model generation, tool integration, and budget management.
HASHIRU offers a promising approach for more robust, efficient, and adaptable
MAS through dynamic hierarchical control, resource-aware hybrid intelligence,
and autonomous functional extension. Source code and benchmarks are available
at https://github.com/HASHIRU-AI/HASHIRU and
https://github.com/HASHIRU-AI/HASHIRUBench respectively, and a live demo is
available at https://hashiruagentx-hashiruai.hf.space upon request.