Content Pipeline (Multi-Agent) with OpenClaw

📖 Advanced use case: Deploy multiple specialized OpenClaw agents working together-an orchestrator that delegates to specialists, or a pipeline where researcher → writer → editor (or builder → reviewer → deployer) hand off in sequence. All self-hosted, with full control over data and prompts.

Overview

Multi-agent systems in OpenClaw mean several AI agents-each with a clear role and often different system prompts, skills, or models-working together on complex workflows. Instead of one agent doing everything, you split work: one agent researches, another writes, another edits; or one "CEO" agent receives your request and spawns specialist agents for reports, triage, or deployments. OpenClaw’s agent customization, memory system, and ClawHub skills make it possible to build orchestrator + specialist patterns entirely on your own infrastructure.

What you'll learn:

  • What multi-agent orchestration is and why use it with OpenClaw
  • Five key patterns: CEO dashboard, development team, business manager, content pipeline, customer service squad
  • How to design orchestrator vs specialist agents and handoffs
  • Step-by-step implementation and configuration options
  • Best practices, common pitfalls, and links to detailed use cases

Why Multi-Agent with OpenClaw?

  • Clear roles and context: Each agent has a focused system prompt and (optionally) different skills-e.g. researcher has web search, writer has file write, editor has style rules-so quality and consistency improve.
  • Scalable workflows: You can add or swap specialists (e.g. add an SEO agent after the editor) without overloading one monolithic agent.
  • Task execution: Agents use ClawHub skills (browser, files, shell, APIs) to do real work, not just chat-see What is OpenClaw?.
  • Channels as interface: Trigger and receive output via Telegram, Discord, Slack, or other channels; the orchestrator can "call" specialists via prompts or by forwarding context.
  • Self-hosted and private: All agents and data stay on your infrastructure; see security best practices.

Five Key Multi-Agent Patterns

These patterns align with the Use Cases Library and are commonly built by the OpenClaw community. Each can be implemented as separate agents in your config or as one agent with phased instructions (research → write → edit in one long prompt).

1. CEO dashboard (orchestrator + specialists)

One "CEO" or coordinator agent receives high-level requests (e.g. "Give me a weekly business summary" or "Prepare the board report"). It delegates to specialist agents: one for metrics, one for sales, one for support tickets. The orchestrator aggregates their outputs and returns a single dashboard or report. Useful for executives who want one place to ask questions that span multiple domains. Configure multiple agents in agent customization and route by channel or by prompt convention (e.g. "Ask the sales agent: …").

2. Development team (builder + reviewer + deployer)

Three agents in sequence: a builder (writes or updates code from a spec), a reviewer (reviews code and suggests changes), and a deployer (runs checks and triggers deploy). You can trigger this from development workflows-e.g. via Telegram or Slack: "Implement feature X" → builder produces a PR summary, reviewer comments, deployer runs tests. For details, see code review automation and CI/CD integration; for the multi-agent pattern, Development Workflows hub links here.

3. Business manager (spawning sub-agents)

A central "business manager" agent receives requests (e.g. "Qualify this lead," "Summarize support tickets," "Draft an invoice"). It spawns or routes to sub-agents: lead qualification, customer support, invoice processing. Each sub-agent has a narrow role and access only to the skills it needs. This keeps sensitive operations (e.g. CRM, billing) scoped and auditable. See Business Operations for individual use cases.

4. Content pipeline (researcher → writer → editor)

A content factory where one agent researches a topic, another writes a first draft, and a third edits for tone and structure. Trigger by message ("Create a post about X") or on a schedule. Output can be returned in chat or saved to files for publishing. This is the classic multi-agent content pattern; we have a full guide: Content factory (multi-agent). Also see research automation and SEO monitoring to feed topics into the pipeline.

5. Customer service squad (triage + FAQ + escalation)

Multiple agents for support: one for triage (categorize and route), one for FAQ (answer common questions from a knowledge base), and one for escalation (draft handoff to humans or specialists). Users message one channel; the triage agent decides which specialist handles the request. Reduces load on a single agent and improves accuracy. Details in customer support automation.

Prerequisites

Implementation Guide

Step 1: Choose your pattern and roles

Decide which pattern fits (e.g. content pipeline: researcher, writer, editor). Define each role in one sentence: inputs, outputs, and which skills each agent may use. Keep specialist roles narrow so prompts stay focused.

Step 2: Define multiple agents in config

In your OpenClaw config, create one entry per agent (e.g. content-researcher, content-writer, content-editor). Give each a distinct system prompt that defines its role, output format, and handoff instructions (e.g. "Output a research summary that the writer will use"). Optionally assign different models or skills per agent. Use memory for style guides or shared context. See Agent Customization & Multi-Agent Setup.

Step 3: Implement handoffs

Handoffs can be:

  • Manual: You receive researcher output in chat and paste it into the writer’s prompt (or send a new message that includes it).
  • Single agent with phases: One agent with a long system prompt: "Phase 1: Research… Phase 2: Write… Phase 3: Edit…" It runs all phases in one thread.
  • Orchestrator agent: One agent whose job is to call others (e.g. by sending synthesized prompts to a channel or internal workflow that routes to different agents).

Start with manual or single-agent phases to validate quality; add orchestrator logic once the pipeline is stable.

Step 4: Add skills and test

Install skills needed per agent (e.g. web search for researcher, file write for writer). Restrict file paths and permissions per security best practices. Run openclaw status and test with a simple prompt (e.g. "Research [topic] and output a one-paragraph summary"). Then run a full pipeline run and check openclaw logs --follow.

Step 5: Trigger and run

Trigger multi-agent workflows from a channel (e.g. Telegram: "Create a post about X") or on a schedule (cron or scheduler sending a prompt). Ensure the first agent in the chain receives the trigger; hand off to the next via your chosen method (manual, phased, or orchestrator).

Best Practices

  • Start small: Build a two-agent pipeline (e.g. researcher → writer) before adding a third (editor) or an orchestrator.
  • Clear handoff format: Define a consistent output format for each agent (e.g. "Research summary: …" so the next agent can parse or paste it reliably.
  • Security first: Each specialist should have only the skills it needs; use sandbox and tool restrictions.
  • Memory and style: Use OpenClaw’s memory for brand voice, style guides, and recurring context so writer/editor agents stay consistent.
  • Monitor and log: Watch logs during runs to catch prompt drift or skill errors; see troubleshooting guide.
  • Community: Share patterns and get help in the Discord community; see community showcases for multi-agent examples.

Common Issues & Solutions

Issue Cause Solution
Context lost between agents Handoff not including full context or summary Define explicit handoff format; use memory for shared style/specs; or use one agent with phased prompt
Wrong agent answering Channel or routing not scoped to the right agent Check channel-to-agent mapping; use distinct triggers or prefixes (e.g. "research: …" vs "write: …")
Slow or expensive runs Too many agents or heavy models per step Use lighter/faster models for triage or research; reserve stronger models for writer/editor
Skills or permissions errors Agent missing skill or path not allowed Verify each agent’s skills in config; run openclaw doctor; see troubleshooting
Inconsistent output quality Vague system prompts or no style memory Tighten role instructions; add examples in prompt; use memory for style guide

Need more help? See our troubleshooting guide.

Related Use Cases & Resources

📄 Content & Business

⚙️ Config & Security

🎥 Community & Docs

Next Steps

After designing your multi-agent system, consider: