daseinpbc/SPL-FRAMEWORK
SUBSUMPTION PATTERN LEARNING (SPL) MULTI-AGENT FRAMEWORK: Hierarchical foundation model agent architecture that reduces costs by 10-50x through intelligent suppression of expensive foundation model calls. Grounded in R. Arkin's behavior-based robotics and R. Brooks' subsumption architecture, SPL brings 40+ years of proven autonomous systems design
This framework helps organizations that use multiple AI agents to solve complex problems by significantly reducing the cost of using large language models. It takes in requests for AI agents and efficiently returns solutions, often without needing to call expensive foundation models. The target users are data scientists, AI architects, or operations managers overseeing AI agent deployments who want to optimize performance and reduce expenditure.
Use this if you are running multiple AI agents that frequently call expensive foundation models and want to reduce operational costs and latency while maintaining accuracy.
Not ideal if your AI agents handle entirely novel problems every time and rarely encounter similar patterns, or if you only use a single, simple AI agent.
Stars
10
Forks
4
Language
Python
License
MIT
Category
Last pushed
Jan 31, 2026
Commits (30d)
0
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