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OpenClaw

OpenClaw is an open-source AI agent that runs locally and executes tasks autonomously. It manages emails, browses the web, and runs shell commands through chat apps like WhatsApp, Telegram, and Discord.

By combining OpenClaw with Deeplake CLI, you give the agent persistent, cloud-backed memory that survives restarts, syncs across devices, and is searchable.

Objective

Set up OpenClaw with a Deeplake-mounted directory so the agent can store and retrieve files, notes, and context persistently, without any code changes.

Architecture

┌──────────────────────────────────┐
│  You (WhatsApp / Telegram / CLI) │
└──────────────┬───────────────────┘
               │  Chat messages
┌──────────────▼───────────────────┐
│  OpenClaw Agent                  │
│  ├─ Skills (file I/O, shell)     │
│  ├─ Persistent memory system     │
│  └─ Browser automation           │
└──────────────┬───────────────────┘
               │  Standard file read/write
┌──────────────▼───────────────────┐
│  ~/agent-memory/ (FUSE mount)    │
│  Backed by Deeplake             │
└──────────────┬───────────────────┘
               │  Synced to cloud
┌──────────────▼───────────────────┐
│  Deeplake Managed Service       │
│  (persistent, searchable, shared)│
└──────────────────────────────────┘

OpenClaw reads and writes files through its built-in filesystem skills. The FUSE mount transparently stores everything in Deeplake.

Prerequisites

  • A Deeplake account and API token
  • Linux or macOS with FUSE support

Step 1: Install Deeplake CLI

curl -fsSL deeplake.ai/install | bash

Step 2: Install OpenClaw

curl -fsSL https://openclaw.ai/install.sh | bash
openclaw onboard

Follow the onboarding wizard to connect your preferred chat platform (WhatsApp, Telegram, Discord, etc.).

Step 3: Initialize and Mount

Run deeplake init. It handles authentication, workspace selection, and mounting in one interactive flow:

deeplake init

Verify the mount is active:

deeplake list

Step 4: Configure OpenClaw to Use the Mount

Tell OpenClaw where its persistent memory lives. During a chat session, send:

Remember: my persistent storage is at ~/agent-memory/
Always save important information, decisions, and research there.
Read from ~/agent-memory/ when you need context from past sessions.

OpenClaw stores this in its memory system and will use the directory for file operations going forward.

Step 5: Use It

Deeplake CLI is a virtual filesystem. The local directory is a FUSE mount backed by Deeplake's managed database. Files don't live on your disk; they live in the cloud. Every read, write, and rename is a database operation committed to the backend. There are no sync daemons, no local copies to reconcile. The database is the truth and the filesystem is the interface.

Interact with OpenClaw through your chat app:

Save research results: 

Research the top 3 vector databases and save a comparison to
~/agent-memory/research/vector-db-comparison.md

Store project context: 

Save today's meeting notes to ~/agent-memory/meetings/2026-02-23.md:
- Decided to use hybrid search for the recommendation engine
- Budget approved for GPU cluster
- Launch target: March 15

Retrieve past context: 

What did we decide about the recommendation engine?
Check ~/agent-memory/meetings/ for context.

Build on previous work: 

Read ~/agent-memory/research/vector-db-comparison.md and
write a technical proposal for which one we should use.
Save it to ~/agent-memory/proposals/vector-db-proposal.md

Multi-Agent Workflow

The real power is when multiple agents share the same mount. Because Deeplake syncs in real-time, you can have:

  • OpenClaw collecting research and saving files via chat
  • Claude Code reading those files and writing code based on them
  • Cursor editing the same project directory with AI assistance

This solves three problems that plague multi-agent systems:

  • Token burn: memory becomes ordinary files agents read on demand, instead of context stuffed into every prompt
  • Drift: a single database-backed filesystem means one truth, not conflicting local copies
  • Rot: update a file once, every agent reads the latest version immediately

All agents mount the same workspace. Run deeplake init on each machine, selecting the same workspace and table during setup:

deeplake init
# → select your team workspace and shared table

OpenClaw saves research to ~/shared-workspace/research/, Claude Code reads it and generates code in ~/shared-workspace/src/, and Cursor provides inline suggestions, all backed by the same persistent store.

Tips

Organize by purpose

Structure the mounted directory with clear folders: 

~/agent-memory/
├── research/       # Saved research and comparisons
├── meetings/       # Meeting notes and decisions
├── proposals/      # Technical proposals
├── preferences/    # User preferences and settings
└── sessions/       # Session logs and context

Use .deeplakeignore

Like .gitignore, create a .deeplakeignore file to keep sensitive files local-only: 

.env
credentials/
.git/

What to try next