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

43
/ 100
Emerging

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.

AI-operations cost-optimization multi-agent-systems large-language-models swarm-intelligence
No Package No Dependents
Maintenance 10 / 25
Adoption 5 / 25
Maturity 13 / 25
Community 15 / 25

How are scores calculated?

Stars

10

Forks

4

Language

Python

License

MIT

Category

framework

Last pushed

Jan 31, 2026

Commits (30d)

0

Get this data via API

curl "https://pt-edge.onrender.com/api/v1/quality/mcp/daseinpbc/SPL-FRAMEWORK"

Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.