What breaks without openclaw multi-agent team
Single-agent bottlenecks. No specialisation. Results without peer review.
→
Researcher-writer-critic pipeline × event-based handoffs ÷ 4-hour setup ÷ no tight coupling = production-quality outputs.
Security check — openclaw multi-agent team
Privacy score: 7/10 — accesses connected platform APIs only.
Lock it: review OAuth scopes before install, confirm OpenClaw ≥1.3; LLM API required; openclaw-supermemory recommended compatibility.
Quick start — openclaw multi-agent team in 4–8 hours
Setup time: 4–8 hours
!
You need:
- OpenClaw core ≥1.3
- LLM API (GPT-4o recommended for coordinator)
- openclaw-supermemory for shared context
Install the package:
npm install openclaw-openai
npm install openclaw-supermemory
# Create custom agent files per the AGENTS.md spec
1
Read AGENTS.md to understand the agent model
2
Define coordinator, researcher, writer, and critic agents
3
Wire inter-agent communication via ctx.broadcast()
4
Configure openclaw-supermemory as the shared context store
5
Trigger the pipeline with a task via your bot platform
6
Monitor agent interactions via Lobster
Compatibility & status
Works with: OpenClaw ≥1.3; LLM API required; openclaw-supermemory recommended
advanced
Last updated: Nov 2025
MIT
Official docs →
View on GitHub →
FAQ — openclaw multi-agent team
How is this different from CrewAI?
It's built on OpenClaw's event system — agents communicate via events, not direct calls. Platform integration (sending results to Telegram, etc.) is native.
Can I add more agents?
Yes — add new agent files and broadcast events to include them in the pipeline.
What's the token cost?
Depends on task complexity; expect 10x the cost of a single-agent workflow.