Entity: Ringer
Ringer is a multi-agent harness, local proxy, and task-estimation framework designed by nate-b-jones to bring cost-optimized, highly reliable AI execution to individual operators.
Core Architecture & Cost Optimization
Ringer implements a highly optimized hierarchical design to address both the memory and evaluation constraints of single-agent systems:
- Manager-Worker Hierarchy: Uses an expensive frontier model like fable-5 to write initial specifications, plans, and judge final outputs. Token-heavy execution, coding, and research are farmed out to much cheaper worker agents (such as qwen). This corporate-style routing reduces overall Fable 5 costs by approximately 10x while maintaining high cognitive quality.
- Heterogeneous Model Swarms: Allows users to plug in specialized, cost-effective models (e.g., qwen or distilled open-weight variants) as task execution agents within the swarm, reserving frontier reasoning solely for orchestration and review.
- Mechanical Verification: Every finished task is verified mechanically. Source documents must be attached and match the task, or the entry is rejected. The agent’s own opinion of its work is not accepted as evidence.
- Self-Healing Retries: Failed tasks automatically trigger a retry with failure details included.
- Dashboard Scorecard: All results feed a live running scorecard dashboard, allowing users to easily monitor multi-agent runs.
Level 3 Intermediary Proxy & Token Minimization
In addition to multi-agent orchestration, Ringer operates as a local intermediary proxy running between user AI clients (such as claude-code or codex) and LLM API providers:
- Pre-Request Interception: Intercepts outgoing requests before prompt envelopes, system prompts, or tool schemas (which often burn ~55,000 tokens for tools like GitHub, Slack, Sentry, and Grafana) are assembled and sent.
- Local Recipe Execution: Executes fixed local recipes or returns deterministic local answers without invoking external LLM API calls.
- Hard Packet Caps: Enforces strict token payload boundaries on inbound and outbound network calls.
- OpenBrain Cache Matching: Intercepts requests by matching queries against previously accepted answers stored in open-brain-stack, returning cached answers locally and saving 100% of LLM API calls.