RealityRouter

The Intelligent Decision Engine for AI Agents.

RealityRouter is a high-performance LLM routing gateway designed for the agentic era. Standard proxies pass requests through; RealityRouter uses Expected Utility Theory and real-time calibration to choose the best model for every individual prompt — balancing accuracy, cost, and latency.

In a world where model performance fluctuates and costs vary by orders of magnitude, RealityRouter puts the choice back in the hands of the user.

Why RealityRouter?

For developers building AI agents (Zed, Cursor, Claude Code, Roo Code, OpenClaw, AutoGPT), picking a model is usually a trade-off between "too expensive" (flagship models on every query) and "too unreliable" (small local models that miss). RealityRouter solves this by acting as smart middleware that:

1. Evaluates intelligence — uses the Reality Signal™ API to estimate the probability of success for each model on the specific task at hand.
2. Calculates utility — applies a mathematical formula to balance accuracy, cost, and speed.
3. Enforces quality — validates tool calls before they reach your agent, catching malformed JSON, ghost tool calls, and protocol leaks.

The core engine — Expected Utility

Every request is passed through a decision-theoretic engine. For each candidate model m in your configured pool, RealityRouter computes:

EU(m) = p · R − α · cost − β · latency

• p — calibrated probability of success on this specific prompt.
• R — constant reward for a correct answer.
• α — your cost sensitivity (tune in the dashboard).
• β — your latency sensitivity (tune in the dashboard).

The router picks argmax EU — every model, every query.

Dynamic tuning. The dashboard exposes α and β as live sliders. Slide left for maximum frugality; slide right for raw speed. Changes take effect on the next request — no restart, no redeploy.

Validation gateway

RealityRouter sits between the model and your agent to enforce protocol compliance:

• Leak protection — detects and scrubs raw tool tags that models accidentally leak into text output.
• Ghost tool detection — rejects responses that call tools you didn't expose to the model.
• Heuristic rescue — recovers valid JSON tool calls buried in conversational fluff.
• Schema validation — validates tool arguments against your schema before the agent ever sees them.

Key features

• Two routing strategies — single-shot (route to argmax-EU model immediately) or sequential (start cheap, escalate on validation failure).
• Automatic feedback loop — validated outcomes feed back into the calibration engine, sharpening future routing decisions.
• Multi-provider auto-discovery — bring your own keys; the router discovers and benchmarks models from OpenAI, Anthropic, Gemini, Mistral, DeepSeek, local Ollama, and any OpenAI-compatible endpoint.
• Live dashboard — track unit economics, savings vs. always-flagship, per-model reliability, and per-agent activity in a built-in web UI.

Documentation

• Quickstart — install and route your first request in about 60 seconds.
• Architecture — directory layout and component overview.
• How it works — Expected Utility math and the calibration feedback loop.
• Routing strategies — single-shot vs. sequential.
• Multi-agent support — protocol detection and sticky sessions.
• API reference — OpenAI-compatible endpoints.
• Dashboard — CLI event viewer + web dashboard.

Developer integration

RealityRouter is 100% OpenAI API compatible. You don't need to rewrite your agent — just change your environment variables:

• Base URL — http://localhost:8000/v1
• API Key — any (or your configured secret)
• Model — auto (or any model name; the router intercepts and chooses the best actual model for the job)

Contributing

We're building user-centric AI infrastructure. If you're interested in decision theory, agent protocols, or high-performance routing, contributions are welcome.

Built by Confidentia AI and the open-source community.