ebb-ai

Workload scheduling for the agentic-AI economy. Defer non-urgent LLM tasks to cheap, low-load grid windows. ~50% cheaper inference via Batch APIs, smoother data-center load curves, auditable carbon receipts. MCP-native, ships as an npm package and a one-command Claude Code plugin.

AI inference is on track to consume 6.7–12% of US grid load by 2028 (DOE projections). A lot of agentic-AI workload is deferrable (overnight summaries, batch analysis, scheduled compliance scans, document processing) but agent code dispatches it synchronously by default. ebb-ai makes the choice automatic: the same code that would have fired a sync LLM call now defers to a cheap, off-peak grid window — and writes an auditable receipt (cost + carbon + provider + duration) for every dispatch.

import { recommendWindow } from "@ebb-ai/core";

const plan = await recommendWindow({
  deadline: "2026-05-14T08:00:00-04:00",
  region: "US-CAL-CISO",
});

// {
//   scheduledFor:                "2026-05-14T05:00:00.000Z",
//   intensityGCo2PerKwh:         60,
//   band:                        "very_clean",
//   estimatedCarbonGCo2:         0.1,
//   estimatedSavingsVsNowPct:    73,
//   batchEligible:               true,
//   reasoning:
//     "cleanest in-deadline window is 05:00 UTC (very clean mix); " +
//     "~73% cleaner than dispatching now; Batch API saves an " +
//     "additional 50% on cost (24h SLA)"
// }

Same call surfaces as an MCP tool to any compatible agent host (Claude Desktop, Claude Code, Cursor, Cline, Continue, Zed, Windsurf, OpenClaw, OpenAI Codex CLI, Pi). The agent asks recommend_window, sees the plan, then commits via schedule_task — or doesn't.

Status: v0.7 · 2026-05-14 · @ebb-ai/{core,mcp,cli} published to npm under the @ebb-ai org. One-command Claude Code plugin via claude plugin install ebb-ai. Four real-data grid feeds: UK National Grid ESO Carbon Intensity API (GB, free no key), US EIA Open Data (CAISO / ERCOT / ISO-NE / PJM, free with key), ENTSO-E Transparency Platform (FR / DE, free with token), and Electricity Maps as universal fallback. Anthropic + OpenAI Batch adapters, durable SQLite queue, Python port at parity, live dashboard, recommend_window planning endpoint, always-on ebb tick CLI with macOS launchd + Linux systemd + pmset/rtcwake wake events, full control surface (cancel_task / expedite_task / update_deadline / retry_task), receipt redaction, file output, retry-with-backoff. 88 + Python tests passing across 4 packages and 2 languages. See QUICKSTART.md.

Live demo

Or visit the maintainer-hosted dashboard at ebb-ai.com to see live grid-load and carbon-intensity forecasts across the seven regions where the major LLM providers run inference (CAISO, ERCOT, ISO-NE, PJM, Great Britain, France, Germany) and to try the best-window planner without installing anything. Great Britain is powered by the free National Grid ESO Carbon Intensity API (real data, no key required); the other zones use Electricity Maps when a key is configured and a deterministic mock otherwise.

Why

AI inference is becoming a major load on US grid infrastructure. Data-center electricity demand has doubled since 2020 and is projected to keep rising as agentic workloads scale. But the same agent code that triggers this load dispatches it synchronously by default — even when the task is "summarize my inbox overnight" or "rewrite these 5,000 product descriptions by Friday." Three things follow:

• Cost. Anthropic and OpenAI both offer Batch APIs at a flat 50% discount for tasks that can wait up to 24 hours. Almost no agent code uses them, because the choice has to be made at the call site. ebb-ai makes the choice automatic — and routes deadline-tolerant work through the cheaper path.
• Grid load. Data-center AI compute is concentrated in a few US regions (PJM Mid-Atlantic / Virginia, ERCOT Texas, CAISO California). Peak-hour AI workloads compete with hospitals, industrial users, and residential customers for capacity that is already constrained — Virginia regulators have flagged data-center load growth as a top-tier reliability concern. Time-shifting deferrable workloads to off-peak windows reduces the peak the grid has to plan for.
• Carbon, as a measurable side effect. Grid carbon intensity varies 30–60% inside a single day. The same dispatch decision that saves cost and smooths load also emits less CO₂. ebb-ai writes an auditable receipt for every dispatch — useful for ESG reporting, cost-accounting, and upcoming compute-disclosure regulations.

ebb-ai automates the choice for any task that is not "answer me right now."

Components

Architecture

The MCP server is a thin stdio process the agent host (Claude Code, Claude Desktop, Cursor, Cline, Zed, OpenClaw) spawns. It enqueues work to a SQLite-backed queue at ~/.ebb-ai/queue.db; an off-process ebb tick daemon (launchd / systemd / cron) reads scheduled rows and dispatches them to the LLM provider at the chosen window. The grid feed is a side-channel — the router picks per-zone between four real-data sources before falling back to mock.

Quick start

See QUICKSTART.md — four steps, five minutes.

As a Claude Code plugin (one-command install)

claude plugin marketplace add Vitalini/ebb-ai
claude plugin install ebb-ai

That ships three slash commands (/ebb-ai:defer, /ebb-ai:check, /ebb-ai:grid), a carbon-aware-coding skill, and auto-wires the @ebb-ai/mcp MCP server. Full plugin reference: PLUGIN.md.

> /ebb-ai:defer "Summarize today's GitHub notifications" --by 4h
Deferred ✓ 38% cleaner than now, scheduled for 22:15 UTC, est. 0.34 g CO2e

> /ebb-ai:check
2 tasks queued · oldest in 1h · cleanest at 03:00 UTC

Full command surface: /ebb-ai:{defer, plan, check, cancel, expedite, reschedule, retry, grid}. Tasks persist to ~/.ebb-ai/queue.db and survive Claude Code restarts.

As an MCP server (Claude Desktop / Cursor / Cline / Zed)

npm install -g @ebb-ai/mcp     # or run via npx -y @ebb-ai/mcp

Then add to Claude Desktop's MCP config (~/Library/Application Support/Claude/claude_desktop_config.json on macOS):

{
  "mcpServers": {
    "ebb-ai": {
      "command": "npx",
      "args": ["-y", "@ebb-ai/mcp"],
      "env": {
        "EBB_ELECTRICITY_MAPS_API_KEY": "optional; falls back to mock data without it. GB is always live via the free UK Carbon Intensity API."
      }
    }
  }
}

The MCP server exposes three tools to the agent:

• get_grid_forecast(region, hours?) — returns the next N hours of carbon intensity for a grid region (e.g. US-CAL-CISO).
• schedule_task(prompt, deadline, model?, carbon_budget_g?) — queues a task for execution at the cleanest window inside the deadline.
• check_queue_status(task_id?) — lists pending tasks and any completed receipts.

As a library

import { defer } from "@ebb-ai/core";

const result = await defer(
  () => anthropic.messages.create({ /* … */ }),
  {
    deadline: "2026-05-13T08:00:00-04:00",
    carbonBudgetG: 5,
    region: "US-CAL-CISO",
  },
);

With a provider adapter and the Batch API (v0.2)

import { Scheduler, AnthropicAdapter } from "@ebb-ai/core";

const scheduler = new Scheduler({ dbPath: "/var/lib/ebb/queue.sqlite" });
const adapter = new AnthropicAdapter();

await scheduler.defer(
  () => adapter.dispatch("claude-sonnet-4-5", "Summarize today's git commits."),
  { deadline: "2026-05-13T08:00:00-04:00", region: "US-CAL-CISO" },
);

// or — submit 100 prompts via Anthropic Message Batches for a 50% discount:
const handle = await adapter.dispatchBatch("claude-sonnet-4-5", prompts);
console.log(handle.batchId);

The SQLite-backed queue is opt-in via dbPath; without it the scheduler runs in-memory as in v0.1. The Anthropic and OpenAI SDKs are peer dependencies — install them only if you use the corresponding adapter.

Python

pip install -e "packages/core-py[anthropic,openai]"

import asyncio
from ebb_ai import defer

asyncio.run(defer(
    lambda: do_work(),
    deadline="2026-05-13T08:00:00-04:00",
    carbon_budget_g=5,
    region="US-CAL-CISO",
))

Dashboard

pnpm --filter @ebb-ai/web dev
# → http://localhost:3000

Pages: live carbon-intensity map (7 regions: CAISO, ERCOT, ISO-NE, PJM, GB, FR, DE), 72-hour forecast charts, best-window planner, queue viewer.

Grid data sources (per zone, falls back to mock on failure):

WattTime marginal-emissions support is on the v0.8 roadmap. Want to add another free public source? Each adapter is a single function in packages/core-ts/src/grid.ts (~80 lines) — open a PR.

Install (development)

# from the repo root
pnpm install        # installs all workspace packages
pnpm build          # builds @ebb-ai/core and @ebb-ai/mcp
pnpm test           # runs vitest across packages

Requirements: Node 20+, pnpm 9+. Python 3.11+ if working on the Python package.

Documentation

• ROADMAP.md — 24-week execution plan, architecture, roadmap, success metrics.
• docs/ — design docs, MCP spec proposals (forthcoming).
• examples/ — OpenClaw demo skill, Claude Code config.

License

Apache License 2.0 — patent grant included.

Contributing

This project is in active early development. Issues and PRs welcome; see the ROADMAP.md roadmap for current scope. Major new features should be discussed in an issue first to avoid duplicate effort.

Built by Vitalii Borovyk.