Sub-agents let you split a content project into smaller jobs and hand those jobs to specialized background runs. Instead of doing everything in one long thread, you build a pipeline: research, outline, draft, revise, and publish, with each stage handled by a fresh, isolated sub-agent that reports back when it is done. This is not a theoretical pattern. It is the workflow behind a good part of this site, and once you see the mechanics it is easy to reproduce for your own content.
The basic idea
A sub-agent is a background run spawned from your main agent session. Each one executes in its own session, is isolated by default, and announces its result back to the requester when it finishes. Sub-agents do not get session or message tools by default, which keeps them from doing anything except the job you briefed. Think of them as contractors: you hand over a concrete task with a deliverable, they go away and do it, and they come back with evidence you can check before you use it.
A real workflow
Imagine you need a blog post about heartbeat files. Here is the pipeline this guide walks through, and the deliverable each stage produces:
- Research sub-agent: gathers source material, competitor angles, and common questions. Deliverable: a source brief.
- Outline sub-agent: turns the brief into a clean structure with headings and flow. Deliverable: an outline with every heading justified.
- Drafting sub-agent: writes the first full version in your brand voice. Deliverable: a complete draft file.
- SEO sub-agent: checks the keyword, meta description, internal linking opportunities, and readability. Deliverable: a list of concrete fixes.
- Publishing sub-agent: formats the content, uploads it, and verifies the result. Deliverable: a live URL.
The diagram shows the whole line:
The mechanics that make it work
Three properties of OpenClaw sub-agents matter for content work. First, spawning is non-blocking: sessions_spawn returns a run id immediately, so your main session is free to keep working while children run. Second, completion is push-based: when a sub-agent finishes, its result arrives back in the requester session as a completion event. The practical pattern is to spawn the work, call sessions_yield to end the turn, and let the completion event arrive as the next message. You do not poll a status list waiting for it; you check status on demand only when something looks wrong. Third, child output is evidence for you to synthesize, not a final answer to forward blindly. The completion event asks the requester to verify the result before deciding the task is done, which is exactly the right posture for content that carries your name.
Two configuration details are worth setting up front. The default context mode is isolated: the child starts with a clean transcript, which keeps token use lower and prevents context bleed between stages. Use fork only when a child genuinely needs the current conversation. And for pipelines that run often, set a cheaper model for children through agents.defaults.subagents.model; keep the expensive model on the main agent where the judgment happens. Individual runs can still override with their own model or thinking budget.
Example task breakdown
Each stage works best as a spawn brief with four parts: objective, output, write scope, and verification. A concrete breakdown of the heartbeat post example looks like this:
Note what the briefs do not say. They do not describe process steps in detail, and they do not tell the sub-agent how to think. They name the outcome, the file to produce, the boundary to stay inside, and the test for done. That is the whole art of the handoff.
Configured agents versus sub-agents
A quick distinction that trips people up: configured agents and sub-agents are different things. Configured agents are long-lived workspaces with their own identity, routing, and auth, like the entries shown here from a real host:
Sub-agents are short-lived runs spawned from an agent, usually the main one. They do not need their own workspace or identity, and they clean up after themselves. You do not configure a new agent to parallelize a blog post; you spawn three sub-agents from the agent you already have. Configured agents earn their keep for genuinely separate personas or workspaces, not for pipeline stages.
Orchestration cheat sheet
For reference, here are the commands and modes that cover the whole pipeline:
The same principles apply at any scale. For a single article you spawn five sub-agents sequentially. For a content calendar you can spawn several research sub-agents in parallel, one per topic, while the main session handles the judgment calls. Nesting is supported for orchestrator patterns, so a lead sub-agent can delegate its own subtasks when a stage is too big for one run.
What to keep an eye on
Sub-agents are powerful, but they still need boundaries. Give each one a single purpose, a concrete output, and a success criterion. If the instructions are fuzzy, the results get fuzzy too, and you will spend the time you saved redoing the handoff. Two more watch-outs from real use: keep a human gate in the pipeline, because sub-agents are good at drafts and bad at taste, and record what you learn from each run in long-term memory so the next pipeline starts smarter instead of from zero. The MEMORY.md guide covers that part.
What changes for the human
Your job shifts from writing every sentence to directing the system. That usually means setting the angle up front, approving the final version before anything goes live, and letting the agents handle the repetitive parts: the research pass, the structural work, the meta descriptions, the upload. For content teams, that is where the time savings actually show up, because the bottleneck stops being typing and starts being decisions, which is the bottleneck you want.
Bottom line
OpenClaw sub-agents are not a novelty. Used well, they create a production line for content: research runs in parallel, drafts arrive faster, and publishing becomes a repeatable workflow instead of a one-off scramble. The pattern costs nothing extra to start, works with the agent you already have, and compounds the moment you standardize the briefs. For the comparison with the alternatives, including when parallel tasks beat sub-agents, see sub-agents versus parallel tasks. New to OpenClaw? Start with The Complete OpenClaw Guide.
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i tried replicating the content pipeline from this post for my own blog and it worked surprisingly well. had to tweak the prompts a bit but the structure is solid. highly recommend actually trying it
Glad it helped. On Use OpenClaw Sub Agents Content, the big win is usually getting the first pass right and then refining from there. That tends to save a lot of time later.
the real example format is so much more useful than abstract explanations. seeing the actual prompt chain and how the sub-agents hand off work to each other made the whole thing concrete
Great question. For Use OpenClaw Sub Agents Content, I would start with the simplest workable setup and only add complexity if you actually need it. That keeps things easier to troubleshoot and usually gets you to a better result faster.
how many sub-agents can you realistically run at once before things start getting slow or expensive? is there a practical limit you recommend?
Good practical question Leo! There’s no hard technical cap but we generally suggest keeping it under 5 parallel sub-agents for most content workflows. Beyond that you can hit API rate limits and costs add up fast. For content creation specifically, 2-3 focused agents (researcher, writer, editor) tends to be the sweet spot — enough parallelism to save time without spinning up so much that you lose the savings to overhead. Quality over quantity with agents too!
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Thanks for reading, Haiden2521. On Use OpenClaw Sub Agents Content, I would start with the basics and then tighten things up once you see how it behaves in practice. If you are already testing it, the main thing is to keep the setup simple enough to debug quickly.
The breakdown of which tasks to delegate to sub-agents vs handle in the main session is super useful. I was overloading my main session with everything and it was getting slow and expensive.
Thanks for reading, Carla T.. On Use OpenClaw Sub Agents Content, I would start with the basics and then tighten things up once you see how it behaves in practice. If you are already testing it, the main thing is to keep the setup simple enough to debug quickly.
How do you handle it when a sub-agent produces content that needs a lot of editing? Do you send it back to the sub-agent or just fix it manually? Trying to figure out the most efficient workflow.
Good workflow question Jason! It depends on the type of edits. For structural issues (wrong tone, missed points, wrong format) it is usually worth sending back to the sub-agent with specific feedback since it will fix them faster than you can manually. For small polishing edits (word choice, light rephrasing) just do it manually since the round-trip overhead isnt worth it. The most efficient approach: give your sub-agent a detailed brief upfront (include examples, tone notes, word count, structure) so the first draft needs minimal work. A little extra time on the brief saves a lot of editing time on the back end.