Multi-Agent Architecture with OpenClaw: A Practical Guide

Single AI agents hit walls. Ask one agent to research a topic, write an article, review it for accuracy, and publish it — and by the time it's halfway through writing, it's forgotten the research details. Context windows fill up, focus drifts, quality drops.

Multi-agent systems fix this by splitting work across specialized agents, each with its own context and instructions. Think of it like a small team: one person researches, another writes, a third reviews. Each focuses on what they're good at.

OpenClaw has native support for this through sub-agents. Here's how to use them effectively.

Why Multiple Agents?

A single agent conversation has practical limits:

Context window fills up. After 50-80K tokens of conversation, the agent starts losing track of earlier details. Research gets forgotten, instructions blur.
Generalist prompting produces mediocre results. An agent trying to be a researcher AND a writer AND an editor is mediocre at all three.
No parallelization. One agent works sequentially. Multiple agents can work simultaneously.
Debugging is harder. When something goes wrong in a 200-message conversation, finding the failure point takes forever. With separate agents, you know which one broke.

Core Patterns

Pattern 1: Coordinator + Workers

The most common pattern. One main agent delegates tasks to specialized sub-agents.

Main Agent (Coordinator)
├── Research Agent — searches web, reads documents
├── Writing Agent — produces content
├── Code Agent — writes and tests code
└── Review Agent — checks quality

The coordinator decides what needs doing, spawns the right worker, waits for results, and decides what happens next. Workers never talk to each other directly — everything flows through the coordinator.

This is how OpenClaw's sessions_spawn works natively. Your main agent spawns a sub-agent with a specific task, receives the result, and continues.

Pattern 2: Sequential Pipeline

Agents process in order, each one passing output to the next:

Input → Parser → Analyzer → Generator → Validator → Output

Good for structured workflows where each step builds on the previous one. Content pipelines, data processing, code review workflows.

The coordinator still orchestrates, but the logic is simpler — just run agent A, take its output, feed it to agent B, and so on.

Pattern 3: Parallel Swarm

Multiple agents tackle the same task independently, then merge results:

Task → [Agent A, Agent B, Agent C] → Merge → Output

This works when multiple perspectives improve quality. I use it for research — three agents search for information on the same topic using different approaches, then a fourth agent synthesizes the findings. Catches things any single agent would miss.

Building Sub-Agents in OpenClaw

Spawning a Sub-Agent

From your main agent's perspective, spawning a sub-agent looks like this:

// The main agent calls sessions_spawn
sessions_spawn({
  task: "Research the top 5 OpenClaw alternatives. For each, list: name, hosting model, pricing, key features, and main limitation. Return structured data.",
  model: "anthropic/claude-sonnet-4-20250514"
})

The sub-agent runs in its own session, does its work, and returns the result to the main agent.

Choosing Models Per Agent

Not every agent needs the same horsepower. Match the model to the task:

| Task Type | Recommended Model | Why | |-----------|-------------------|-----| | Simple data gathering | Haiku | Fast, cheap, good enough | | Content writing | Sonnet | Good quality-to-cost ratio | | Complex reasoning | Opus | When Sonnet's output isn't cutting it | | Code generation | Sonnet | Handles most programming tasks well | | Editorial review | Sonnet or Opus | Needs to catch subtle issues |

Specifying the model per sub-agent:

sessions_spawn({
  task: "Write a 2000-word tutorial on OpenClaw cron jobs...",
  model: "anthropic/claude-sonnet-4-20250514"  // Sonnet for writing
})

sessions_spawn({
  task: "Review this article for factual errors and AI writing patterns...",
  model: "anthropic/claude-opus-4-6"  // Opus for careful review
})

This keeps costs down. Using Opus for everything is like flying first class for a 30-minute commuter flight.

Passing Context Between Agents

Sub-agents start with a fresh context. They don't know what your main conversation has been about. You need to pass relevant context explicitly in the task prompt.

Bad:

"Now write the article based on the research we did earlier."

Good:

"Write a 2000-word tutorial about OpenClaw cron jobs. Here's the research context:

[paste research results]

Target keyword: 'openclaw cron jobs tutorial'
Tone: technical, practical, specific
Include code examples for: basic cron syntax, email checking, security audit scheduling"

The more specific the task prompt, the better the sub-agent performs. Treat each sub-agent like a new hire who knows their job but doesn't know your project yet.

Error Handling

Multi-agent systems fail more often than single-agent setups because there are more moving parts. Build recovery into your design.

Retry Strategy

If sub-agent fails:
  1. Retry with same prompt (network glitch, rate limit)
  2. Retry with simplified prompt (too complex for the model)
  3. Retry with a stronger model (Sonnet → Opus)
  4. Give up and report the failure

Validation Between Steps

Don't blindly trust sub-agent output. Validate before passing to the next step:

Did the research agent return actual data, or did it hallucinate sources?
Does the written article match the requested word count?
Is the generated code syntactically valid?

A quick validation step between agents catches problems early.

Checkpointing

Save intermediate results. If the writing agent succeeds but the review agent fails, you don't want to redo the writing.

# In your workspace:
pipeline/
  research-results.json     # After research
  draft-v1.md               # After writing
  humanized-draft.md        # After humanization
  review-feedback.md        # After review
  final.md                  # Published version

If the pipeline fails at step 4, you restart from step 4 with the saved draft, not from scratch.

Timeout Management

Sub-agents can get stuck — infinite loops, rate limits, model hangs. Set timeouts:

sessions_spawn({
  task: "...",
  runTimeoutSeconds: 300  // 5-minute hard limit
})

If a research agent hasn't returned in 5 minutes, something's wrong. Kill it and try again.

Real Example: Content Production Pipeline

Here's the pipeline I actually run at MayaWorks. Five agents, orchestrated by a coordinator:

Agent 1 — Researcher (Haiku) Searches for trending keywords, checks existing content, returns a prioritized topic list.

Agent 2 — Writer (Sonnet) Takes a topic and writes a full blog post with frontmatter, code examples, and internal links.

Agent 3 — Humanizer (Sonnet) Strips AI writing patterns — replaces "delve" with "look at", varies sentence length, adds personality. This step makes a measurable difference in reader engagement.

Agent 4 — Reviewer (Opus) Checks technical accuracy, SEO basics, readability, and remaining AI patterns. Returns PASS, REWORK, or FAIL with specific notes.

Agent 5 — Publisher (Haiku) Saves the file, commits to git, pushes to the repo. Mechanical work, cheapest model.

Total pipeline time per post: 8-12 minutes. Total cost per post: about $0.40-0.80 depending on revisions. That's $4-8 for a batch of 10 posts.

Common Mistakes

Over-architecting. Don't build a 10-agent swarm for a task that one agent handles fine. Start with a single agent. When you hit its limits, split off the specific step that's failing.

Agents talking to agents. In OpenClaw, sub-agents report to the coordinator. Peer-to-peer agent communication adds complexity without proportional benefit for most use cases.

Ignoring costs. Each sub-agent call burns tokens. A pipeline that spawns 20 agents per task at Opus-level pricing will cost $5+ per run. Profile your costs early.

No logging. When the pipeline produces a bad post, you need to know which agent failed. Save each agent's output for debugging.

Getting Started

Start with a single agent doing everything
Identify which step consistently underperforms
Split that step into a dedicated sub-agent with focused instructions
Add validation between agents
Gradually split more steps as needed

You don't need to design the entire architecture upfront. Let the problems guide the structure.

For the technical setup, start with our VPS deployment guide. To see a content pipeline in action, read how we build our content pipeline. Browse community skills at ClawHub for pre-built agent tools.