FirasSX914 /
Routing & Cost OptimizationJanus
Framework for measuring when to employ Jev versus generative LLMs on proprietary datasets, routing queries based on measured benchmarks.
What Jev Does in this Architecture
Key Architecture Benefit
Replaces intuition with empirical data when adopting discrete decision models, optimizing overall cost while preserving task success.
Reviewed Source Code Evidence
Verified fixed-commit source files demonstrating genuine Jev integration:
Architecture Category
Routing & Cost Optimization
Complexity-based request routing to tier generative models and optimize token spend.
View all in this category →Related Projects in Routing & Cost Optimization
0xNatoshi
jev-codex-router
Uses Jev to classify each Codex turn, then applies local rules to choose the model, reasoning effort, and speed mode.
Classifies task difficulty and reasoning needs before local policy selects a model configuration.
↳ Keeps routing policy and decision logs local for inspection and tuning.
sherajdev
jev-research
A Jev and Herdr integration guide with a prototype for routing tasks to different Agents.
Uses task and repository state to choose an executor and assess risk and dispatch readiness.
↳ Provides a readable, editable example of multi-Agent task routing.
minghanminghan
jev-demo
A customer-service routing demo that batches Jev questions before following the resulting route.
Assesses classification levels, human-handoff intent and frustration, with low-confidence escalation.
↳ Combines routing and human handoff while leaving response generation to the application.