OpenClaw Architecture
Deep dive into the open-source AI agent framework — Gateway, Agent Runtime, sessions, memory & more
Overview
OpenClaw is an operating system for AI agents. It turns large language models into persistent, tool-using assistants with real-world integrations, session management, memory, and multi-channel support.
It follows a clean hub-and-spoke architecture with two core pillars:
🛡️ Gateway
Communication & control layer. Receives inputs, routes to agents, resolves sessions, and orchestrates flows.
⚡ Agent Runtime (Pi)
Execution layer where the model runs, tools execute, and tasks complete step-by-step.
How a Message Flows Through OpenClaw
- Channel Plugin — Normalizes messages from Slack, WhatsApp, Discord, iMessage, etc.
- Gateway Routing & Agent Selection — Applies routing rules, isolates agents (workspace, state, sessions).
- Session Resolution — Loads or creates sessions using session store (metadata) + transcripts (append-only log).
- System Prompt Construction — Combines tools, history, skills, memory (Markdown files), workspace files, time, and user message.
- Agent Runtime Loop — Pi executes ReAct-style loop: reason → tool call → observe → repeat.
- Response & Persistence — Gateway saves updated state and delivers formatted reply via channel.
Triggers & Autonomy
OpenClaw feels alive because it supports multiple event types:
Messages
Direct user input from any channel
Heartbeat
Periodic checks (default: every 30 min)
Cron Jobs
Scheduled tasks
Webhooks
External events (e.g. Stripe payments)
Hooks
Internal state-change triggers
Memory System
Simple yet powerful file-based memory:
memory.md— Long-term knowledgememory/directory — Timestamped daily notes & running context- Workspace files:
IDENTITY.md,USER.md,SOUL.md, etc.
Key Takeaways
- Isolated agent sessions prevent context collisions
- Gateway ensures one active run per session (queuing)
- Everything is local-first and persisted to disk
- Extensible via plugins, skills, and custom tools
Reference
This page is based on the excellent detailed breakdown by @GargEtisha on X (May 2026).View original thread