AgentCrew MCN: Your Open-Source AI Marketing Team That Works 24/7
Let’s be honest—content marketing can be brutal. You need to research topics, craft technical articles, adapt the tone for every platform, schedule posts for maximum engagement, track performance, and then do it all over again. For indie hackers, open-source maintainers, and small dev teams, this often means either burning out or ignoring content altogether.
What if you had a team of AI agents that could handle the entire pipeline—from researching a topic to publishing on Juejin, Dev.to, and Zhihu—while you sleep? That’s exactly what AgentCrew MCN does. It’s an open-source multi-agent content marketing automation tool written in Python, complete with a web dashboard, RAG knowledge base, smart scheduling, and a dogfooding twist: the project promotes itself.
In this article, I’ll walk through the architecture, show you how to get started with a few commands, and demonstrate some advanced features like scheduled publishing and image generation.
What Is AgentCrew MCN?
AgentCrew MCN structures content marketing as a team of specialized AI workers, each with its own role. There are four core agents:
- Writer Agent – Your copywriter. It generates technical articles, social posts, and twitter threads, injecting relevant knowledge from a RAG (Retrieval-Augmented Generation) system.
- Reviewer Agent – Your quality assurance. It scans content before publication for safety, policy compliance, and basic readability checks.
- Publisher Agent – Your operations person. It pushes content to platforms like Juejin, Zhihu, and Dev.to, adapting the format and style to each community.
- Analyst Agent – Your data analyst. It tracks content performance, recommends optimal publish times, and learns from past results—right down to adding random jitter to avoid detection of automated scheduling.
The magic isn’t just in the agents; it’s in the Skills + Tools system. Each agent has high-level skills (like “generate a technical thread” or “batch publish”) composed of low-level tools (like search, rag, compose, devto_api). Since v0.4, the system is LLM-driven, meaning the agents can reason about which tool to use and in which order—no rigid workflows.
Quick Start: From Zero to Published in 5 Minutes
AgentCrew is a Python package available on PyPI. You’ll need Python 3.10+ and an API key for an LLM provider. It supports DeepSeek, OpenAI, Anthropic, and Ollama—switch with one config field.
# 1. Install
pip install agentcrew-mcn
# 2. Initialize configuration
agentcrew-mcn init
# 3. Edit the generated .env file with your LLM API key
# DEEPSEEK_API_KEY=sk-...
That’s it. Now let’s generate a technical article:
agentcrew-mcn write generate \
--topic "Python async programming" \
--style technical
The Writer Agent will research the topic (optionally using the RAG database from your previous articles), compose a structured piece, and save it as Markdown. Before publishing, you can run a dry run to see what will happen:
agentcrew-mcn publish post \
--file article.md \
--platform devto \
--dry-run
If everything looks good, remove the --dry-run flag to actually post it to your Dev.to account.
Configuration
The init command creates a config.yaml. A minimal setup looks like this:
llm:
provider: deepseek
api_key: ${DEEPSEEK_API_KEY} # reads from .env
model: deepseek-chat
platforms:
devto:
api_key: ${DEVTO_API_KEY} # get it from https://dev.to/settings/extensions
juejin:
cookie: ${JUEJIN_COOKIE} # export from browser
The ${VARIABLE} syntax pulls values from your .env file, keeping secrets out of your config.
The Agent Orchestra: How It All Works Under the Hood
At the center sits the Orchestrator, which is responsible for task dispatch, scheduling, and configuration. When you run agentcrew-mcn write generate, the Orchestrator hands the task to the Writer Agent, injecting the appropriate skills. The Writer might use a search tool to pull recent content from the web, a rag tool to retrieve relevant paragraphs from your knowledge base, and a compose tool to generate the final article.
The Reviewer Agent then checks the output using a series of validation rules (configurable) and can trigger a rewrite automatically if something is off.
The Publisher Agent converts the article into the platform-specific format. For example, Juejin articles use a “Golden Nugget” title convention; Dev.to uses front matter. The agent knows how to handle these differences.
Finally, the Analyst Agent runs scheduling logic. Instead of posting at a fixed time, it uses a recommendation model (powered by historical data) and adds random jitter to mimic human behavior, reducing the chance of being flagged as automation.
Advanced Features You’ll Love
1. RAG Knowledge Base
The built‑in vector store (ChromaDB) lets you ingest past articles, documentation, or any text source, so the Writer Agent can produce content that’s consistent with your tone and style.
# Ingest a previous blog post
agentcrew-mcn rag ingest \
--file my_old_article.md \
--source "personal_blog"
# Search the knowledge base later
agentcrew-mcn rag search \
--query "AI Agent architecture"
The retrieved context is automatically prepended to the prompt when generating new content.
2. Scheduled Publishing
Want to keep your social media active without constant manual effort? Use the scheduler:
agentcrew-mcn schedule start \
--topic-file topics.txt \
--platform juejin \
--interval 6
This will pick a topic from the file, generate and publish an article, and repeat every 6 hours. The Analyst Agent chooses the exact moment within the interval, so posting doesn’t look like an hourly cron job.
3. Multi-Provider LLM and Image Generation
Not locked into one AI provider. Set provider: openai in your config to switch to GPT-4. For cover images, enable DALL‑E 3:
image_gen:
enabled: true
model: dall-e-3
The Writer Agent will automatically generate a cover image for each article.
4. Dashboard and Growth Tracking
A Streamlit dashboard gives you real‑time analytics: publish history, engagement metrics, and AI-generated suggestions for next topics.
agentcrew-mcn dashboard
Additionally, a GROWTH.md file logs follower counts, views, and other KPIs over time, so you can measure progress.
Dogfooding: The Project Promotes Itself
A notable aspect of AgentCrew is dogfooding—the project uses itself to write and publish content about itself. The marketing of AgentCrew is largely automated by AgentCrew, which serves as both a proof‑of‑concept and a source of continuous improvement.
Roadmap and Community
AgentCrew is MIT‑licensed and under active development. The current version (v0.4) has 392 tests and 85% coverage. Here’s what’s coming:
- v0.5 – Support for 6 new platforms: CSDN, WeChat Official Accounts, SegmentFault, X (Twitter), Xiaohongshu, and Medium.
- v1.0 – REST API, plugin system, and an official community.
If you’re a Python developer, content creator, or just curious about multi‑agent systems, there are plenty of ways to contribute. Check out the GitHub repository and the documentation.
Getting started is ridiculously simple: pip install agentcrew-mcn && agentcrew-mcn init. From there, you’re five lines away from having an AI marketing team that never sleeps, never asks for a raise, and keeps your OSS project or personal brand visible across the web—without burning you out.
What would you do with a 24/7 AI writing crew? I’d love to hear your ideas in the comments. And if this project sounds useful, a ⭐ on GitHub goes a long way!
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