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
Cron Jobs vs Heartbeat: Optimizing OpenClaw Token Usage and Execution Consistency
A senior developer shares practical tips on using Cron jobs instead of Heartbeat to reduce token usage and improve execution consistency in OpenClaw, with concrete examples and a shell script method.
Maximizing AI Agent Capabilities in OpenClaw
OpenClaw's AI can be optimized by selecting the right model and providing specific system context. The Qwen models excel in tool use, critical for autonomous workflows.
vLLM Setup and Testing on 10x NVIDIA V100 Server with 320GB VRAM
A lawyer building a local AI server for legal work shares vLLM testing results on 10x Tesla V100 SXM2 32GB GPUs, detailing what works (FP16 unquantized, bitsandbytes 4-bit) and what doesn't (GPTQ, AWQ, FlashAttention2) on Volta architecture.
OpenClaw Memory Plugin Analysis: Lossless Claw + LanceDB Recommended
A developer tested OpenClaw memory plugins and found that the default setup causes token bloat, while Lossless Claw paired with LanceDB provides optimal performance for maintaining agent context without high costs.