I Built an Automated Content Factory with OpenClaw: AI Handles the Entire Process from Topic Selection to Publication

This article shares how to build a fully automated content factory with OpenClaw, realizing AI control over the entire process from topic selection, material collection, writing, to publication.

Overall Process Overview

The entire content factory is divided into 9 steps, each handled by an independent OpenClaw Agent:

• Topic Planning Agent: Automatically generates topics based on domain hotspots and user interests.

• Material Collection Agent: Collects relevant materials through web searches, database queries, etc.

• Content Outline Agent: Generates an article structure outline based on the topic and materials.

• Draft Writing Agent: Writes the initial draft content according to the outline.

• Polishing and Modification Agent: Polishes and optimizes the initial draft language.

• Image Generation Agent: Generates or searches for suitable images for the article.

• Layout Formatting Agent: Handles the article's layout and formatting.

• Review and Inspection Agent: Conducts content review and quality inspection.

• Publication and Distribution Agent: Publishes the final content to various platforms.

Core Design Ideas

Each Agent is an independently running OpenClaw instance with its own Skills and context. Agents communicate with each other through a shared file system. The output file of the previous Agent becomes the input of the next Agent.

The advantages of this design:

• Modularization: Each step can be independently optimized and upgraded.

• Scalability: New processing steps can be added at any time.

• Fault Tolerance: Failure of a single Agent does not affect the overall process.

• Parallelization: Some steps can be executed in parallel to improve efficiency.

Actual Operating Effect

Through this system, it is possible to achieve:

• Automatically produce multiple high-quality content pieces every day.

• Consistent content style, in line with brand tone.

• Controllable publication time, supporting scheduled publication.

• One-click distribution to multiple platforms.

Construction Suggestions

For users who want to try building a similar system, it is recommended to:

• Start with a single step and gradually expand.

• The prompt for each Agent needs to be carefully tuned.

• Establish a sound content review mechanism.

• Retain a manual intervention interface for contingency.

The complete code and configuration are open source. Interested readers can refer to the project documentation for construction.