@oraclaw/solver

Your AI scheduler that matches tasks to your energy. Stop forcing deep work at 4pm. Let the optimizer plan your day around how you actually perform.

Industrial-grade LP/MIP solver (same tech behind airline scheduling and supply chains) — now in a simple API for daily planning, sprint planning, and resource allocation.

Quick Start

npm install @oraclaw/solver

import { OraSolver } from "@oraclaw/solver";

const solver = new OraSolver({ apiKey: "ok_live_..." });

const result = await solver.schedule(
  [
    { id: "report", name: "Q1 Financial Report", durationMinutes: 120, priority: 9, energyRequired: "high" },
    { id: "emails", name: "Process Inbox", durationMinutes: 30, priority: 3, energyRequired: "low" },
    { id: "design", name: "Review Design Specs", durationMinutes: 60, priority: 7, energyRequired: "medium" },
    { id: "standup", name: "Team Standup", durationMinutes: 15, priority: 8, energyRequired: "low" },
    { id: "research", name: "Competitor Analysis", durationMinutes: 90, priority: 6, energyRequired: "high" },
  ],
  [
    { id: "morning", startTime: 1711350000, durationMinutes: 120, energyLevel: "high" },
    { id: "mid-am", startTime: 1711357200, durationMinutes: 60, energyLevel: "medium" },
    { id: "lunch", startTime: 1711360800, durationMinutes: 30, energyLevel: "low" },
    { id: "afternoon", startTime: 1711364400, durationMinutes: 90, energyLevel: "medium" },
    { id: "late-pm", startTime: 1711369800, durationMinutes: 60, energyLevel: "low" },
  ],
);

// Result: Q1 Report → morning (high energy match)
//         Design Review → mid-am
//         Emails → lunch (low energy, low priority)
//         Research → afternoon
//         Standup → late-pm

Custom Optimization

Solve any resource allocation problem with hard constraints:

// Budget allocation: maximize ROI across 3 channels with constraints
const result = await solver.optimize({
  direction: "maximize",
  objective: { ads: 2.5, content: 1.8, events: 3.2 },  // ROI coefficients
  variables: [
    { name: "ads", lower: 0, upper: 50000 },
    { name: "content", lower: 0, upper: 30000 },
    { name: "events", lower: 0, upper: 20000, type: "integer" },
  ],
  constraints: [
    { name: "total_budget", coefficients: { ads: 1, content: 1, events: 1 }, upper: 80000 },
    { name: "min_content", coefficients: { content: 1 }, lower: 10000 },
  ],
});

console.log(result.solution);  // { ads: 50000, content: 10000, events: 20000 }
console.log(result.objectiveValue);  // 207000 (optimal ROI)

For AI Agents

{
  "mcpServers": {
    "oraclaw-solver": {
      "command": "npx",
      "args": ["tsx", "path/to/oraclaw-mcp/index.ts"],
      "description": "Task scheduling and constraint optimization for daily planning"
    }
  }
}

MCP Tools:

• solve_schedule — Optimal task-to-slot assignment with energy matching
• solve_constraints — General LP/MIP optimization for any resource problem

Agent use cases:

• "Plan my day optimally given these tasks and my energy curve"
• "Allocate this $50K budget across 4 marketing channels to maximize leads"
• "Schedule these 12 sprint tasks across 5 developers with skill matching"
• "Find the minimum-cost staffing plan that covers all shifts"

Pricing

| Plan | Price | Calls/mo | |------|-------|----------| | Free | $0 | 3,000 | | Starter | $9/mo | 10,000 | | Growth | $29/mo | 100,000 | | Scale | Custom | Unlimited |

Why OraClaw Solver vs. Motion/Reclaim?

| | OraClaw Solver | Motion | Reclaim | |--|---------------|--------|---------| | Custom constraints | Any LP/MIP problem | Calendar only | Calendar only | | Energy matching | Yes (high/med/low) | Basic | No | | Budget/resource optimization | Yes (general solver) | No | No | | API-first | Yes | No API | Limited API | | AI agent native (MCP) | Yes | No | No | | Price | $9/mo | $34/mo | $8/mo |

@oraclaw/solver is a thin API client. All optimization runs server-side. No algorithm source code is included in this package.