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.

License: Apache-2.0 npm (core) npm (mcp) npm (cli) Tests MCP tools Hosts Website

Why defer non-urgent AI work?

US data centers are projected to consume 6.7–12% of national electricity grid load by 2028 (DOE 2024), driven primarily by AI compute. Most agent workload that lands on that load is deferrable (overnight summaries, batch analyses, scheduled compliance scans, multi-step report generation) — agent code just dispatches it synchronously by default. ebb-ai makes the choice automatic. Four parallel wins:

1. Lighter on the grid. Spreads load away from peak hours, which is what ISOs and grid operators want from large compute users.
2. 50% cheaper. Auto-routes through Anthropic and OpenAI Batch APIs (24-hour SLA) when the deadline allows. Same prompt, half the bill.
3. Faster at off-peak. Anthropic explicitly expanded off-peak capacity in 2026 (rate-limit policy) — doubled usage limits outside peak hours, citing smoothed demand. Sync calls observe shorter queues.
4. 40–70% lower carbon. Per-task carbon receipts against the actual grid intensity used at dispatch, persisted to a local SQLite ledger. Auditable, region-aware, reproducible. v0.10+ uses per-model Wh/token coefficients (Patterson 2021, Luccioni 2024, Hugging Face AI Energy Score) so receipts reflect what the agent actually ran — not a flat placeholder. v0.11+ signs every receipt with Ed25519 so consumers can verify them offline via ebb verify (B2B ESG export path).

ebb-ai is the same code that would have fired a sync LLM call — now deferred to the cleanest, cheapest, fastest hour inside the deadline. Apache-2.0.

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: v0.12.0 · 2026-07-08 · @ebb-ai/{core,mcp,cli} published to npm under the @ebb-ai org; ebb-ai on PyPI; @vitalini/ebb OpenClaw plugin shares the queue. One-command Claude Code plugin via /plugin marketplace add Vitalini/ebb-ai && /plugin install ebb-ai. Four real-data grid feeds across 31 regions (NA/EU/APAC): 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 / ES / IT / NL / …, free with token), and Electricity Maps as universal fallback. v0.10: per-model Wh/token coefficients across 37 LLMs (Patterson 2021, Luccioni 2024, HF AI Energy Score) replace the v0.1–v0.9 flat placeholder. v0.11: Ed25519-signed carbon receipts (offline verifiable via ebb verify) plus WAL multi-writer SQLite so ebb tick and the MCP server can share ~/.ebb-ai/queue.db. v0.12 ("trust repairs"): Batch API routing is real — deferrable tasks (deadline > 24h) are submitted to the provider's Batch API and polled to completion (submitted status), instead of the previous dead-code gate; receipts record grid-data provenance (intensity, feed source — synthetic mock is disclosed, not silently signed) and the energy-coefficient confidence tier; Python- and TS-signed receipts verify in either language (one canonical signing form, JCS numbers); daemons installed by ebb install actually dispatch (real node/script paths, ~/.config/ebb/env secrets); hardened ledger (0600, redacted body_json); honest feed labeling (forecast vs persistence). Anthropic + OpenAI Batch adapters, 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 / cancel_all), receipt redaction, file output, retry-with-backoff. 605 tests passing (368 TS + 237 Python) across 5 packages and 2 languages. See QUICKSTART.md.

Live demo

Deploy with Vercel

Or visit the maintainer-hosted dashboard at ebb-ai.com to see live grid-load and carbon-intensity forecasts across 31 grid regions (NA / EU / APAC — CAISO, ERCOT, ISO-NE, PJM, Great Britain, France, Germany, and more) 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); US ISOs use the EIA open-data feed, EU zones ENTSO-E, with Electricity Maps as the universal fallback and a clearly-labelled deterministic mock when no key is configured.

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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."

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Components

Package	Purpose
@ebb-ai/core	TypeScript core library. defer() API, AnthropicAdapter, OpenAIAdapter, opt-in SQLite-backed durable queue, per-model energy coefficients (estimateEnergyKwh, MODEL_ENERGY_COEFFICIENTS).
@ebb-ai/mcp	Model Context Protocol server. Drop-in for Claude Desktop, Claude Code, OpenClaw, Cursor.
ebb-ai (Python)	Python 3.11+ port. asyncio scheduler, aiosqlite persistence, Anthropic + OpenAI adapters, mirrored ebb_ai.energy module.
apps/web	Next.js 15 website at https://www.ebb-ai.com — install picker (13 hosts), live carbon-intensity map, best-window planner, docs.
packages/claude-code-plugin	Claude Code plugin tree (8 /ebb-ai:* slash commands + auto-invocation skill + MCP wiring).
packages/openclaw-plugin	OpenClaw plugin (@vitalini/ebb-ai on ClawHub). Native OpenClaw tools mirroring the MCP surface.
docs/spec	Upstream MCP spec proposal for priority, deadline, carbon_budget fields.

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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):

Zone	Source	Auth	Notes
GB	UK National Grid ESO Carbon Intensity API	None	Real 48h forecast, always on
US-CAL-CISO, US-TEX-ERCO, US-NE-ISNE, US-MIDA-PJM	US EIA Open Data	Free API key (EBB_EIA_API_KEY)	Hourly fuel-mix → carbon intensity via IPCC AR5 factors
FR, DE (plus ES, IT, NL opt-in)	ENTSO-E Transparency Platform	Free token (EBB_ENTSOE_SECURITY_TOKEN)	Realised generation by type → carbon intensity
any zone (universal fallback)	Electricity Maps free-tier	EBB_ELECTRICITY_MAPS_API_KEY	Used when zone-specific source missing
anything else	Deterministic mock curve	None	Used when no key set or upstream fails

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.

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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.

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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.

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License

Apache License 2.0 — patent grant included.

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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.

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Built by Vitalii Borovyk.