OpenClaw can run as more than a single assistant. With the right workspace structure, it becomes a small agent team: one agent plans, one writes code, one reviews, one verifies in the browser, and one keeps long-term memory organized.
If you want examples before reading further, start with these pages:
- OpenClaw workflow examples
- OpenClaw workspace examples
- Multi-agent OpenClaw setups
- Automation-oriented setups
What an OpenClaw multi-agent config actually is
A strong multi-agent config is not just “more prompts”. It defines:
- which agents exist
- which jobs belong to each role
- when work should be handed off
- what must be remembered
- how review and verification happen
In practice, strong examples on ClawLodge usually combine:
- a role definition layer
- explicit workflow rules
- a memory structure
- tool boundaries
- review or QA gates
Related examples:
The files that matter most
AGENTS.md
This is often the coordination center. It tells OpenClaw:
- who plans
- who executes
- who reviews
- when to stop and report
- how to avoid stepping on each other
SOUL.md
This is the behavior layer. It influences priorities, tone, and working style. A good SOUL.md changes decisions, not just voice.
memory/ or MEMORY.md
This is what makes a workspace feel trainable instead of stateless. Good memory usually stores:
- decisions already made
- user preferences
- project constraints
- recurring workflows
If memory is what you care about most, browse:
skills/
Skills are where repeatable capabilities live:
- browser QA
- code review
- release rules
- design assistance
- publishing flows
For smaller, focused building blocks, browse:
How to evaluate a multi-agent workspace
When comparing workspaces, do not stop at the README. Ask:
- Does it define real roles?
- Does it define handoff rules?
- Does it have memory structure?
- Does it include verification?
The strongest workspaces on ClawLodge usually look more like operating systems than single prompts.
Best use cases
Multi-agent OpenClaw setups are especially useful for:
- software delivery
- review-heavy engineering work
- long-running research
- publishing workflows
- personal operating systems with memory
You can browse adjacent collections here:
Common mistakes
- treating “more agents” as automatically better
- giving every agent the same job
- skipping memory
- skipping review or browser verification
Final thought
A good OpenClaw multi-agent config does not just add personas. It creates structure for collaboration, memory, review, and execution.
If you want real examples, start with workflow pages, multi-agent topic pages, and representative setups like Edict.