OpenClaw Agent Architecture Patterns: Multi-Agent Delegation, 5-Layer Memory, and Watchdog Systems

Multi-Agent Delegation for Cost Control

The developer runs specialized sub-agents for different jobs to reduce API costs while maintaining quality. The setup includes:

• A cheap model for background maintenance and heartbeats (specifically Haiku 4.5 for heartbeat.md)
• A research-focused model for web scanning
• A Grok model for native X search
• A security-focused model for daily system audits
• The primary model for direct conversation

Each agent has its own briefing document defining its role, and the primary model orchestrates task delegation. The developer tried switching to a super cheap primary model but found the results disappointing, noting that half of OpenClaw's appeal is using high-quality models.

5-Layer Memory Architecture

To address OpenClaw's limited built-in memory, the developer implemented a five-layer system:

• Structured facts database (SQLite with entities, relationships, confidence scores, importance weighting)
• Vector memory (ChromaDB for semantic search across everything)
• Episodic memory (significant events with timestamps and importance)
• Procedural memory (tracking what worked, what didn't, and effectiveness)
• Graph memory (entity relationships showing who connects to what)

A hybrid retrieval system queries across all five layers and ranks results. The system includes a memory decay mechanism where facts lose fidelity over time instead of being simply kept or deleted. High-importance memories stay at full resolution, while less-used ones get compressed to summaries, then essences, then just a hash proving they existed. The agent can promote decayed memories back to full resolution when they become relevant again.

Multi-Agent Councils (MACx)

For complex decisions, the developer spins up 5 frontier models in parallel across different providers:

• ChatGPT 5.4 Thinking
• Grok 4.20 Reasoning
• Opus 4.6
• Minimax M2.5
• Gemini 3.1

Models are swapped out as new ones are released. Each model analyzes independently, then they cross-review each other's work, and a chair synthesizes the results. The system has three modes: deliberation (decision support), research (deep investigation), and brainstorming (creative ideation). A "Phase 0" was recently added where the council identifies assumptions first and asks clarifying questions before deliberating.

Security and Monitoring Approach

After hearing about malware on skill hubs, the developer adopted a policy of building bespoke solutions for each skill-like modification. Claude Code, talking to OpenClaw via ACPX, constructs something with authorization after evaluating others' skill codebases. Each new build starts halfway from scratch, just with the idea.

A daily subagent scans what others are doing with their OpenClaw agents for inspiration. The watchdog system has three layers: basic health monitoring, service-level checks, and deeper diagnostic capability tied to an ACPX call to a vibe coder running on the host machine when basic checks and commands won't suffice.