Give your AI assistant memory, skills, and autonomy.
A shareable config that turns Claude Code into an AI that remembers you,
connects to your tools, and runs workflows while you sleep.

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OpenClaw Config

Most AI assistants forget you the moment the conversation ends. OpenClaw doesn't.

This repo is the shared configuration layer for OpenClaw — a personal AI built on Claude Code that maintains persistent memory, integrates with real-world tools, and runs autonomous workflows on a schedule. Everything is markdown and Python scripts. No frameworks, no databases, no lock-in.

What You Get

• Memory that persists — Three-tier architecture: always-loaded essentials, daily context files, and deep knowledge with semantic search
• 11 skills — Web research, meeting transcripts, CRM, phone system, task management, and more — each a standalone Python script with zero setup
• 4 autonomous workflows — Agents that triage your inbox, manage tasks, prep your calendar, and organize contacts — learning your preferences over time
• Templates for identity — Define your AI's personality, your profile, and how it should operate
• DevOps included — Hourly health checks, fleet management across machines, automated backups

Getting Started

Prerequisites: Claude Code running on your machine. That's it — no other dependencies.

Install: Open Claude Code and tell it:

Set up openclaw-config from https://github.com/TechNickAI/openclaw-config

The openclaw skill walks you through setup — cloning the repo, copying templates, creating memory folders, and configuring optional features like semantic search.

Update later:

Update my openclaw config

How It's Organized

openclaw-config/
├── templates/          # Identity & operating instructions
│   ├── AGENTS.md       # How the AI should think and act
│   ├── SOUL.md         # Personality definition (templated)
│   ├── USER.md         # Your profile — who you are, how you work
│   ├── MEMORY.md       # Curated essentials, always in context
│   ├── BOOT.md         # Startup routine — what to check on launch
│   ├── HEARTBEAT.md    # Periodic checks (inbox, tasks, health)
│   ├── TOOLS.md        # Machine-specific environment config
│   └── IDENTITY.md     # Quick reference card
│
├── skills/             # Standalone UV scripts — no install needed
│   ├── parallel/       # Web research & content extraction
│   ├── quo/            # Business phone — calls, texts, contacts
│   ├── fireflies/      # Meeting transcript search
│   ├── limitless/      # Pendant lifelog search
│   ├── asana/          # Task & project management
│   ├── followupboss/   # Real estate CRM
│   ├── librarian/      # Knowledge base maintenance
│   ├── create-great-prompts/  # Prompt engineering guide
│   ├── smart-delegation/     # Route work to the right model
│   ├── workflow-builder/     # Design new autonomous workflows
│   └── openclaw/       # Self-management & updates
│
├── workflows/          # Autonomous agents with state & learning
│   ├── email-steward/  # Inbox triage — archive noise, surface what matters
│   ├── task-steward/   # Classify, create, execute, and QA tasks
│   ├── calendar-steward/ # Daily briefing with travel & meeting prep
│   └── contact-steward/  # Detect and organize unknown contacts
│
├── memory/             # Example memory directory structure
│   ├── people/         # One file per person
│   ├── projects/       # One file per project
│   ├── topics/         # Domain expertise & preferences
│   └── decisions/      # Important decisions with reasoning
│
└── devops/             # Health checks & fleet management

Skills

Each skill is a standalone UV script — Python with inline dependencies, no project-level setup. Run directly, version independently.

Skill	What it does	Version
parallel	Web search & content extraction via Parallel.ai	0.2.0
quo	Business phone — calls, texts, voicemails, contacts, SMS	0.6.0
fireflies	Search meeting transcripts & action items	0.2.0
limitless	Query Pendant lifelogs & conversations	0.2.0
asana	Task & project management via MCP	0.1.0
followupboss	Real estate CRM — contacts, deals, pipeline	0.1.0
librarian	Curate and maintain the knowledge base	0.2.0
create-great-prompts	Prompt engineering for LLM agents	2.0.0
smart-delegation	Route work to Opus, Grok, or handle directly	0.1.0
workflow-builder	Design new autonomous workflows	0.1.0
openclaw	Install, update, and health-check the config	0.2.2

Workflows

Workflows are autonomous agents that run on a schedule. Unlike skills (tools you invoke), workflows maintain state, learn your preferences, and manage themselves.

Workflow	What it does	Version
email-steward	Triage inbox — archive noise, label, alert on important	0.2.0
task-steward	Classify work, create tasks, spawn sub-agents, QA results	0.1.0
calendar-steward	Daily briefing — travel time, meeting prep, conflict detection	0.1.0
contact-steward	Detect unknown contacts across platforms, classify & organize	0.1.0

Each workflow maintains its own state:

• AGENT.md — The algorithm (updates when you update openclaw-config)
• rules.md — Your preferences (never overwritten)
• agent_notes.md — Patterns it learns over time
• logs/ — Execution history

Memory Architecture

Most AI memory is "dump everything into a vector database." OpenClaw uses deliberate, structured memory with clear tiers:

Tier 1 — Always loaded. MEMORY.md stays in context every conversation. Curated to ~100 lines of what matters most.

Tier 2 — Daily context. memory/YYYY-MM-DD.md files. Today and yesterday load automatically. Raw observations, not curated.

Tier 3 — Deep knowledge. memory/people/, projects/, topics/, decisions/. Searched via vector embeddings (LM Studio local or OpenAI). Retrieved when relevant, not loaded by default.

The librarian skill promotes durable knowledge upward — daily observations become structured knowledge, structured knowledge gets summarized into MEMORY.md.

What gets remembered is filtered through four criteria:

Criterion	Question
Durability	Will this matter in 30+ days?
Uniqueness	Is this new or already captured?
Retrievability	Will I want to recall this later?
Authority	Is this reliable?

Design Principles

• Files over databases — Markdown in git beats any proprietary store. Readable, diffable, portable.
• Self-contained skills — No shared dependencies, no coordination overhead. Each skill carries its own pip install inline.
• Workflows that learn — Agents should get better at their job over time, not just repeat the same script.
• Two-way door decisions — Act freely on reversible decisions. Pause and confirm on irreversible ones.
• Prose over config — Language models reason better in natural language than in JSON schemas.

Development

uv run --with pytest pytest tests/ -v

Integration tests auto-skip when API keys aren't set.

Contributing

PRs welcome. Keep templates generic (no personal content). Each skill should remain self-contained with its own inline dependencies.

License

MIT

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Built by TechNickAI
_{Your AI deserves to remember you.}