EdgeClaw

GitHub: Stackbilt-dev/edgeclaw · MIT

Part of the Stackbilt ecosystem. OpenClaw on Cloudflare Workers — persistent personal AI assistant running on Durable Objects + Workers AI. No server. No machine to babysit. 300 edges. Deploy in 5 minutes.

For the full production cognitive kernel with multi-tier memory, autonomous goals, and scheduled tasks, see AEGIS Core. EdgeClaw is the clean deployable template for personal use — same Cloudflare primitives, packaged for solo deployment.

What This Is

OpenClaw runs as a local daemon: SQLite state, a long-running process, your own hardware for LLM inference. EdgeClaw takes that model to the edge:

OpenClaw (local)	EdgeClaw (CF Workers)
SQLite per agent	Durable Object per agent (SQLite-backed)
Local daemon	DO hibernation — always available, no idle cost
LanceDB vector memory	Vectorize (coming)
LLM API calls	Workers AI (Llama 4 Scout)
Channel socket listeners	Incoming webhooks
`~/.openclaw/` filesystem	KV + R2

Deploy in 5 Minutes

git clone https://github.com/Stackbilt-dev/edgeclaw
cd edgeclaw && npm install

# Create KV namespace
npx wrangler kv:namespace create edgeclaw-skills
# Paste the returned id into wrangler.toml → kv_namespaces[0].id

# Deploy
npx wrangler deploy

# Set your channel secrets
npx wrangler secret put TELEGRAM_BOT_TOKEN
npx wrangler secret put TELEGRAM_SECRET

# Wire Telegram webhook
curl "https://api.telegram.org/bot<TOKEN>/setWebhook" \
  -d "url=https://edgeclaw.<your-subdomain>.workers.dev/channels/telegram&secret_token=<SECRET>"

Message your bot and it responds.

Channels

Channel	Status	Setup
Telegram	Ready	`wrangler secret put TELEGRAM_BOT_TOKEN` + setWebhook
Slack	Ready	`wrangler secret put SLACK_SIGNING_SECRET SLACK_BOT_TOKEN`
HTTP REST	Ready	`POST /chat` — for testing and integrations
WhatsApp	Coming soon	—
Discord	Coming soon	—

Architecture

Channel webhook → Hono router → AgentSession DO (per user)
                                      ↓
                               Workers AI (Llama 4 Scout)
                                      ↓
                          SQLite history + KV memory

Each user gets their own AgentSession Durable Object — persistent conversation history, isolated state, hibernation when idle (no compute cost). Workers AI handles inference free on Cloudflare’s network.

Adding Skills

Skills are functions registered on the AgentSession. Drop a file in src/skills/ and import it in agent-session.ts. Skills can read/write KV, call external APIs, or query D1. The model invokes them via tool use.

EdgeClaw vs. AEGIS Core

	EdgeClaw	AEGIS Core
Purpose	Personal assistant, 5-min deploy	Production cognitive kernel
Memory	SQLite history + KV	Multi-tier (working/episodic/semantic/long-term)
Goals	None	Autonomous goals + dreaming cycles
Governance	None	ADF-governed, MCP native
Audience	Solo deployment	Production platform

Start with EdgeClaw. Graduate to AEGIS when you need the full system.