OpenClaw Multi-Agent Playbook: 7 Isolated Agents for 5/Month
User @procoder shared a comprehensive 25-minute guide on building a production multi-agent system with OpenClaw — running 7 specialized agents for under 5/month.
The Problem with Single-Chat AI
Single chat windows suffer from four critical issues:
- Context overload — unrelated work competes for attention
- Cost inefficiency — premium models handle trivial tasks
- Permission sprawl — one agent with broad tools is dangerous
- Identity drift — no stable personality across tasks
The 7-Agent Architecture
- Chat Agent — everyday assistant, cheap model (Kimi 2.5)
- Research Agent — deep analysis, expensive model (Claude Opus)
- Coding Agent — sandboxed execution (DeepSeek Coder)
- Notes Agent — knowledge capture (Claude Sonnet)
- Movie Agent — entertainment tracking (Kimi 2.5)
- Trading Agent — read-only market summaries
- Family Agent — maximum-safety public group responses
Key Principles
- One agent = one identity, no shared state
- Deterministic routing via bindings (not AI-driven)
- 80% cheap models, 20% premium — never the reverse
- Sandbox all code execution
- Least-privilege tool permissions
The full playbook includes config files, security model, cost optimization strategies, and common mistakes to avoid.
🔗 Read the full guide on Medium
📖 Read the full source: Medium
👀 See Also
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The LLM Voice Problem: Avoiding AI-Generated Writing Patterns
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DeepSeek-V4-Flash W4A16+FP8 with MTP Self-Speculation: 85 tok/s on 2x RTX PRO 6000 Max-Q
DeepSeek-V4-Flash quantized to W4A16+FP8 achieves 85.52 tok/s at 524k context on 2× RTX PRO 6000 Max-Q using a patched vLLM with retrofitted MTP head, up from 52.85 tok/s baseline.