cellar

CEL — the trust and execution layer for AI-operated computers.

CEL (Context Execution Layer) is an open-source platform that fuses accessibility trees, CDP, vision, network, and app-specific adapters into one structured device understanding - and exposes stable execution primitives, verification surfaces, and action receipts over MCP, CLI, SDK, and N-API. The planner is pluggable: use LangGraph, Mastra, Claude Code, Cursor, Codex, GPT, Gemini, n8n, a raw MCP client, or CEL's built-in cel_think fallback. CEL owns trusted device execution; you bring the agent.

Status: Active development. Core runtime fully functional on macOS. MCP server with 4 composable tools. Linux support available. Windows planned.

Three-layer architecture

+------------------------------------------------------------+
|  Agents       LangGraph | Mastra | Claude Code | Cursor    |
|               Codex | GPT | Gemini | n8n | MCP clients      |
+------------------------------------------------------------+
|  CEL / crates context fusion, stream normalization,         |
|               canonical execution, adapter dispatch,        |
|               receipts, stable MCP / CLI / SDK / N-API      |
+------------------------------------------------------------+
|  Adapters     browser | Numbers | Excel | Figma | Slack    |
|               Cursor | Docker Desktop | ...                 |
+------------------------------------------------------------+

• Adapters — where app-specific structured truth lives. Third-party extensible.

• CEL (this repo) — the durable core: fused context, execution, verification, receipts, adapter routing, tool surfaces.

• Agents — planners/orchestrators. Every framework is a first-class client; none of them defines the platform.

See docs/what-cel-is.md for the full platform boundary, docs/trust-execution-layer.md for the receipt/evidence contract, and docs/adapters-cel-agents.md for the north-star design doc.

What CEL owns vs. what's pluggable

Supported agents

First-class integrations. See docs/agents/README.md for the full matrix.

Adapters

First-party and community adapters. Full catalog in docs/adapter-catalog.md; build your own with docs/adapter-sdk.md.

Legend: Stable = shipping; In progress = active dev; Planned = on the roadmap.

Hybrid Runtime: What It Handles That Screenshots Can't

Run these scenarios yourself: ./scripts/demo.sh — see DEMO.md for the full walkthrough.

What Makes CEL Different

• Structure-first perception — reads what's actually on screen through OS-level APIs, not what pixels look like. Vision is the fallback, not the foundation.

• Hybrid runtime with strategy router — per-action routing: structured → semantic → vision → refresh → terminal failure. Escalation ceiling prevents infinite loops.

• Continuous awareness — Cortex tracks what changed, not just what's there now. Freshness model (fresh / soft-stale / hard-stale) prevents acting on stale state.

• Works everywhere — browsers, desktop apps, terminals, legacy software. One runtime, not separate products for browser vs. desktop.

• Model-agnostic — works with any LLM. Sends structured text, not screenshots. A local 7B model works for most workflows.

• Agent-agnostic — LangGraph, Mastra, Claude Code, Codex, GPT, Gemini, Cursor, n8n, or future runtimes should all be able to use CEL.

• 200x cheaper — structured context extraction eliminates expensive vision model inference on every step.

The Problem

Agentic computer use — AI that operates software through the UI — is the defining trend in AI. But it does not work reliably yet.

In browsers, agents have the DOM but still produce unstable results because they depend entirely on LLM interpretation. Outside the browser — on desktop apps, terminals, native software — it's far worse. Agents rely on screenshots alone, feeding pixels to vision models and hoping they correctly identify buttons, fields, and values.

Meanwhile, rich structured information already exists on every computer: accessibility trees, native application APIs, network traffic, input events. No tool combines these signals into a standard format that any agent can consume.

MCP solved this problem for tool access. CEL solves it for computer use.

The Solution: CEL

CEL (Context Execution Layer) is both a context extraction and execution layer. It fuses five streams into a single structured JSON output with per-element confidence scoring:

The agent calls getContext() and gets structured JSON with confidence scores — regardless of which source provided the data. Then it executes actions through CEL using the same multi-source approach. Workflows become replayable sequences of structured contexts and actions, not brittle screenshot-to-click chains.

Works on any interface: browser, terminal, Finder, Excel, SAP, Bloomberg — any OS, any application.

Unlike screenshot-only approaches that route every action through expensive LLM inference, CEL uses structured sources (accessibility tree, native APIs) first and escalates to vision models only when needed. Faster, cheaper, more predictable — and capable of running fully offline.

Use CEL with Claude Code (MCP)

CEL ships as an MCP server with 4 tools. Connect it to Claude Code, Cursor, or any MCP client:

# Build everything
pnpm install && pnpm -r build

# Build native module (macOS)
cargo build --release -p cel-napi
cp target/release/libcel_napi.dylib cel/cel-napi/cel-napi.darwin-arm64.node
codesign -fs - cel/cel-napi/cel-napi.darwin-arm64.node

Pick an LLM provider — the fastest path is the interactive setup (writes ~/.cellar/config.toml):

cellar init

Options: paste a Gemini / Anthropic / OpenAI API key, or install Gemma 4 E4B locally via Ollama for fully-private runs. If you'd rather configure via .mcp.json directly (see below), skip init.

Configuration hierarchy

Environment variables override ~/.cellar/config.toml, which overrides compiled defaults.

# ~/.cellar/config.toml
[llm]
provider = "gemini"          # openai | anthropic | gemini | ollama | compatible
api_key  = "your-key"
model    = "gemini-2.0-flash"

[audio]                      # optional — enables audio transcription in the Cortex
whisper_endpoint = "https://api.openai.com/v1/audio/transcriptions"
whisper_api_key  = "sk-..."
whisper_model    = "whisper-1"
# whisper_language = "en"   # ISO 639-1 hint — improves accuracy

Full variable list: docs/api-reference.md.

Add to .mcp.json in your project root:

{
  "mcpServers": {
    "cellar": {
      "command": "node",
      "args": ["/path/to/cellar/mcp-server/dist/index.js"],
      "env": {
        "CEL_LLM_PROVIDER": "gemini",
        "CEL_LLM_API_KEY": "your-api-key",
        "CEL_LLM_MODEL": "gemini-2.0-flash"
      }
    }
  }
}

Restart Claude Code and you'll have four tools:

On startup, the Cortex boots automatically (screen model is warm before your first call) and Chrome CDP is auto-detected.

See docs/quickstart.md for the full setup guide and docs/mcp-server.md for the complete tool reference.

Current State

Cellar is in prototype phase on macOS. The bar for exit is defined in docs/PROTOTYPE_EXIT_CRITERIA.md; the curated regression suite that gates it lives in eval/prototype-subset/.

Gated today (macOS local):

• Local execution on macOS via AX + CDP + screen capture + input injection

• MCP server with 4 composable tools: cel_see / cel_act / cel_think / cel_perceive

• Cortex — always-on perception with background event streams

• Autonomous execution (run_goal) over the prototype scenario suite: browser happy-paths, grounding, ambiguity, recovery, browser-to-desktop handoff

• CLI entry points: cellar init (setup) and cellar run-goal "<goal>"

• BYOK providers (OpenAI, Anthropic, Gemini) and local Ollama (Gemma 4 E4B default)

• Per-role LLM routing — Planner / Observer / Vision / Validator

Built but outside the prototype exit bar:

• Audio capture + Whisper transcription fused into the Cortex world model

• Embedded SQLite + FTS5 for memory / semantic search

• First-party adapters — Excel, SAP GUI, Bloomberg, MetaTrader

• Recorder, live-view, and the wider benchmarks/ suite (50+ tasks + hybrid scenarios)

• napi-rs Rust ↔ Node.js bridge

Later phase — explicitly not prototype work (see docs/ROADMAP.md):

• Linux accessibility (AT-SPI2) and Windows UI Automation bridges

• Remote worker / Docker image / managed VMs (cellar-worker/ exists in-tree as a preview; not wired into prototype gates)

• Managed cloud, control plane, billing

• Production confidence calibration

• Portable context maps, community workflow registry

Architecture

cellar/
  cel/                  ← Cortex + perception layer (Rust, Apache 2.0)
    cel-accessibility/  ← accessibility bridge (AXUIElement, AT-SPI2)
    cel-context/        ← unified context API + multi-source fusion + references
    cel-display/        ← screen capture (xcap)
    cel-input/          ← input injection (enigo)
    cel-vision/         ← vision model integration (multi-provider)
    cel-network/        ← traffic monitoring + idle detection
    cel-store/          ← embedded SQLite + FTS5 (memory, knowledge)
    cel-llm/            ← LLM provider abstraction
    cel-planner/        ← built-in planner / runner code (useful, but not the repo's main value)
    cel-napi/           ← Node.js native bindings (napi-rs)
  agent/                ← agent integrations and runtime experiments
  mcp-server/           ← generic tool surface for external agents
  adapters/             ← app-specific adapters (browser, Excel, SAP)
  benchmarks/           ← eval harness (50+ tasks + 5 hybrid scenarios)
  live-view/            ← real-time debug surface (screen + runtime decisions)
  cli/                  ← `cellar` CLI

Getting Started

Quickstart — Claude Code (recommended)

See docs/quickstart.md for the full step-by-step guide. The short version:

# 1. Build
pnpm install && pnpm -r build
cargo build --release -p cel-napi
cp target/release/libcel_napi.dylib cel/cel-napi/cel-napi.darwin-arm64.node
codesign -fs - cel/cel-napi/cel-napi.darwin-arm64.node

# 2. Configure .mcp.json (see quickstart for full config)

# 3. Grant Accessibility permissions in System Settings

# 4. Restart Claude Code — tools are ready

Quickstart — see what the agent sees

No Rust build needed. Just Node.js 20+ and pnpm:

pnpm install && pnpm -r build
npx tsx examples/quickstart.ts https://github.com/login

This launches a browser, extracts DOM elements as structured ContextElements with confidence scores, and shows the kind of context any external agent runtime would receive.

Prerequisites

• Node.js 20+ and pnpm 9+ (TypeScript packages)

• Rust 1.75+ (CEL core, accessibility bridge, native bindings)

• macOS 13+ with Accessibility permissions

• Chrome (optional, for CDP features)

Build

# Build everything
make build

# Or separately
make build-rust    # cargo build --workspace
make build-ts      # pnpm install && pnpm build

# Run tests
make test

CLI

cellar init                    # Interactive first-run setup (pick LLM provider or install Gemma 4)
cellar setup                   # Configure AX + CDP permissions on this machine
cellar context                 # Show unified context with confidence scores
cellar context --json          # Output raw JSON
cellar context --watch         # Live-update context in terminal
cellar capture                 # Capture screenshot to file
cellar action click 500 300    # Click at coordinates
cellar action type "Hello"     # Type text
cellar action key Enter        # Press a key
cellar action combo Ctrl C     # Key combination
cellar mcp                     # Start MCP server (stdio)
cellar mcp install             # Print Claude Desktop config
cellar run <workflow>          # Execute a saved workflow
cellar train                   # Enter training mode

Benchmarks

Hybrid Runtime Scenarios (CEL advantage)

5 scenarios designed to test where multi-source perception matters. Run them: ./scripts/demo.sh

General Web Tasks

We also benchmark on 50+ general web tasks against other tools:

Measured on Apple M-series (arm64, 12 cores, 18GB RAM), April 2026. Hybrid suite: 5 tasks testing browser-desktop handoff, stale state recovery, ambiguous targets, side-effect detection, terminal failure. All local tools use Gemini 2.5 Flash. Computer Use locked to Claude Sonnet.

Benchmark results (April 2026 — Hybrid Suite, 5 tasks)

CEL vs the field:

Why CEL wins:

• 1 LLM call per task — structured context means most tasks extract data in a single pass. Competitors need 3-18 calls.

• $0.0005/task — Gemini Flash + context distillation. At 1000 tasks: CEL $0.50 vs Computer Use $155.

• Structured context is free — 500+ elements extracted in 100-400ms via Rust-native DOM fusion, no LLM required.

• Full Rust execution loop — perceive, plan, execute, verify all in Rust. No FFI in the hot path.

• For building reliable automation (not one-off tasks), structured context is the foundation

See benchmarks/README.md for full methodology, per-task breakdown, and how to reproduce.

Roadmap

The forward plan lives in docs/ROADMAP.md. Two related references:

• docs/deployment.md — topology: local / remote worker / managed cloud, Docker scope, model backends.

• docs/oss-boundary.md — what's OSS vs commercial, mirror strategy.

Contributing

See CONTRIBUTING.md for how to get started, and DEVELOPMENT.md for build instructions and conventions.

We welcome contributions — especially:

• Accessibility bridges (macOS AXUIElement, Windows UI Automation)

• New application adapters — see docs/building-adapters.md

• MCP tool improvements

• Test coverage for platform-specific code

• Documentation and examples

Platform Support

License

• Everything OSS-destined (cel/, agent/, cli/, mcp-server/, cellar-worker/, live-view/, recorder/, registry/, docs/, benchmarks/, examples/, e2e/, tests/, box/): Apache License 2.0.

• Community adapters (adapters/): MIT.

• Commercial-only (app/, future control-plane/, cloud/, billing/): proprietary — not covered by this license.

See docs/oss-boundary.md for the full license map and what stays private.