Ratelord
Active Development - Phase 5

Budget-Literate Autonomy for Agentic Systems

Ratelord provides a "sensory organ" and "prefrontal cortex" for resource availability and budget planning, enabling systems to negotiate, forecast, govern, and adapt under constraints.

Core Capabilities

Built to solve the "blind agent" problem by making resource constraints a first-class citizen in autonomous systems.

Local-First Architecture

Zero-ops daemon that runs alongside your agents, providing low-latency constraint management without external dependencies.

Event-Sourced System

Complete audit trail of all constraint decisions and budget allocations, fully replayable for debugging and analysis.

Predictive Modeling

Time-to-exhaustion forecasts help agents plan their activities based on real-time resource availability.

Intent Negotiation

Standardized protocol for agents to request resources and negotiate budgets before execution.

Hierarchical Modeling

Model complex constraint relationships with nested scopes and resource pools.

Provider Agnostic

Extensible design that supports any API or resource type, starting with GitHub API integration.

The Vision

Autonomous agents today are "blind" to the economic and rate-limiting realities of the APIs they consume. They hit limits unexpectedly, burn through budgets, and fail to plan effectively.

Ratelord introduces Budget-Literate Autonomy. By acting as a sensory organ for constraints, it allows agents to "feel" the pressure of limits before they are hit, enabling sophisticated negotiation and planning strategies.

Core Principles

  • Local-First: No external SaaS dependencies for critical path decisions.
  • Daemon Authority: Centralized truth for resource state.
  • Negotiation Mandate: Agents must ask before they act.
  • Prediction: Reactive is not enough; we must forecast.

Current Status

Phase5: Remediation
Core EngineGo + SQLite
InterfacesTUI & Web UI
IntegrationsGitHub API (In Progress)

Join the Development

We are actively looking for contributors to help build the future of agentic constraint management.

View Open Issues →