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
Setting up OpenClaw on macOS with a unified AI provider endpoint
A developer shares their experience installing OpenClaw on macOS, including the requirement for Node.js 24, using Homebrew for installation, configuring a custom OpenAI-compatible provider like ZenMux, and setting up a background daemon. Key troubleshooting tips include WhatsApp's default message blocking and using the openclaw doctor command.
Running OmniCoder-9B locally with llama.cpp configuration details
A developer achieved 96.7% average HumanEval score with OmniCoder-9B on mid-range hardware using specific llama.cpp flags including --reasoning-budget 0 to disable chain-of-thought output. The setup used a Q6_K quantized model running on an RTX 3080 with 10GB VRAM.
Open-source launch playbook for OSS LLM and local AI projects
An open-source playbook addresses discoverability issues for LLM and local AI projects by providing structured guidance on pre-launch preparation, launch-day execution, and post-launch follow-up. It includes templates and strategies for community distribution, creator outreach, and SEO optimization.
Solving Gemini CLI write_file Not Found in OpenClaw: Two Fixes Required
OpenClaw agents using google-gemini-cli can't write files (write_file / default_api_write_file missing) due to wrong tools.profile and missing --approval-mode auto_edit flag in the subprocess. Fix: set profile to full and inject the flag via cliBackends config.