HASHIRU: Hierarchical Agent System for Hybrid Intelligent Resource
  Utilization
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.
684312988f9ec8394c514883
Kunal Pai
claimed_verified
2025-06-07T05:45:12.032Z
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Kunal Pai
user
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684312988f9ec8394c514884
Parth Shah
extracted_pending
2025-06-06T16:08:57.423Z
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Parth Shah
user
helloparthshah
684312988f9ec8394c514885
Harshil Patel
HASHIRU: Hierarchical Agent System for Hybrid Intelligent Resource
  Utilization
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.
HASHIRU, a novel MAS framework, enhances flexibility, resource efficiency, and adaptability by dynamically managing specialized agents and using a hybrid intelligence approach with smaller, local LLMs and external APIs.
Hierarchical Agent System Hybrid Intelligent Resource Utilization HASHIRU CEO agent employee agents Ollama external APIs economic model hiring/firing costs autonomous API tool creation academic paper review safety assessments GSM8K JEEBench SVAMP Gemini 2.0 Flash self-improvement autonomous cost model generation tool integration budget management