How to automate social media posts with AI agents using CrewAI and n8n

You can build a completely hands-off pipeline that researches topics, drafts copy, generates images, and schedules posts to Twitter, LinkedIn, Instagram and Facebook - all without lifting a finger. The result is a faceless AI content agent that runs on a schedule, pulls fresh data from an RSS feed, and publishes on multiple platforms automatically.

What you need

Estimated build time: ~6-8 hours for a production-ready workflow, assuming basic familiarity with Docker and API keys.

Step-by-step build

1. Spin up n8n

1. Install Docker if you don't have it:

 sudo apt-get update && sudo apt-get install -y docker.io
 

1. Pull the official n8n image and run it on port 5678:

 docker run -d --name n8n \
 -p 5678:5678 \
 -v ~/.n8n:/root/.n8n \
 n8nio/n8n
 

1. Open http://localhost:5678 in a browser and create your first workflow. n8n's UI will ask you to set a Workflow Name; call it "AI Social Agent".

Tip: Use a strong N8N_BASIC_AUTH_USER and N8N_BASIC_AUTH_PASSWORD environment variable to protect the UI if you expose the port to the internet.

2. Create a Google Sheet for content bookkeeping

1. In Google Sheets, create a new spreadsheet named AI Content Queue.
2. Add columns: Topic, Status (Pending/Generated/Scheduled), Copy, Image URL, Posted URL.
3. Share the sheet with a service account (we'll generate one in the next step) so n8n can read/write via the Google Sheets API.

3. Set up OpenAI credentials

1. Sign up at https://platform.openai.com/ and generate a secret API key.
2. In n8n, go to Credentials → New Credential → OpenAI and paste the key.
3. Choose the GPT-4o model for text and DALL·E 3 for image generation.

Why GPT-4o? It offers the best balance of cost and capability for nuanced copy generation and can follow system prompts that enforce brand voice.

4. Install CrewAI and expose it as an HTTP endpoint

CrewAI is a Python library that lets you chain LLM calls with tool use. We'll run it inside a small Flask server that n8n can call via webhook.

python -m venv .venv
source .venv/bin/activate
pip install crewai flask openai

Create agent_server.py:

# agent_server.py - exposes a /run endpoint that executes the content-generation crew
from flask import Flask, request, jsonify
from crewai import Crew, Agent, Task, Tool

app = Flask(__name__)

# Define a simple tool to fetch RSS items
class RSSFetcher(Tool):
 def run(self, url: str):
 import feedparser
 feed = feedparser.parse(url)
 return [entry.title for entry in feed.entries[:3]]

# Agent that drafts copy
copy_agent = Agent(
 role="Content Writer",
 goal="Write engaging social-media copy for a given topic",
 backstory="You are a brand-aware copywriter with a witty tone.",
 tools=[RSSFetcher()]
)

# Agent that creates images
image_agent = Agent(
 role="Image Creator",
 goal="Generate a DALL·E-compatible prompt and image URL",
 backstory="You understand visual branding and can translate copy into imagery."
)

crew = Crew(
 agents=[copy_agent, image_agent],
 tasks=[
 Task(
 description="Given a topic, fetch three related headlines, draft a 280-char tweet, and create an image prompt.",
 agent=copy_agent
 ),
 Task(
 description="Turn the copy into a DALL·E prompt and request an image URL.",
 agent=image_agent
 )
 ]
)

@app.route("/run", methods=["POST"])
def run():
 data = request.json
 topic = data.get("topic")
 result = crew.kickoff(inputs={"topic": topic})
 return jsonify(result)

if __name__ == "__main__":
 app.run(host="0.0.0.0", port=5001)

Run the server in the background:

nohup python agent_server.py &

What this does: The Flask endpoint receives a JSON payload like {"topic":"AI automation trends"} and returns generated copy plus an image URL.

5. Build the n8n workflow

1. Trigger node - RSS Feed - Choose "RSS Feed Read" - Set the Feed URL to your industry blog. - Enable "Emit items individually".

2. Google Sheets - Read Row - Search the AI Content Queue for rows where Status = Pending. - Map the Topic field to the RSS item's title.

3. HTTP Request - Call CrewAI server - Method: POST - URL: http://host.docker.internal:5001/run (Docker host bridge) - Body Type: JSON - JSON Payload:

 {
 "topic": "{{$json[\"Topic\"]}}"
 }
 

4. Set - Update Google Sheet - Write back Copy and Image URL from the HTTP response. - Change Status to Generated.

5. Buffer (or Hootsuite) - Create Post - Use the Buffer node (available via n8n's community nodes) - Authenticate with your Buffer account (see Buffer's API docs). - Map Copy to the Message field, Image URL to Media URL, and select the target social profiles.

6. Google Sheets - Mark as Posted - Update the same row, setting Status to Scheduled and Posted URL to the response from Buffer.

7. Cron node - Schedule - Set to run every 4 hours (or any cadence you prefer). Connect the Cron node to the RSS node to start the chain.

Full n8n JSON snippet for the HTTP Request node (copy-paste into the node's "JSON" tab):

{
 "name": "Call CrewAI",
 "type": "n8n-nodes-base.httpRequest",
 "typeVersion": 1,
 "position": [600, 300],
 "parameters": {
 "url": "http://host.docker.internal:5001/run",
 "method": "POST",
 "responseFormat": "json",
 "jsonParameters": true,
 "options": {},
 "bodyParametersJson": "{\"topic\":\"={{$json[\"Topic\"]}}\"}"
 },
 "credentials": {
 "httpBasicAuth": {
 "id": "crewai-http-cred",
 "name": "CrewAI HTTP Basic"
 }
 }
}

What this does: It sends the current topic to the Flask-served CrewAI crew and receives both copy and an image URL in one request.

6. Test the end-to-end flow

1. Manually add a row to AI Content Queue with Topic = "Latest trends in AI agents" and Status = Pending. 2. Trigger the n8n workflow via the UI's "Execute Workflow" button. 3. Verify: - The Google Sheet now shows generated Copy and a valid Image URL. - Buffer's UI lists a new scheduled post with the correct text and media.

If any step fails, the n8n execution log will point to the exact node with an error message.

7. Deploy to production

• Docker-compose the n8n container together with the Flask server for easier orchestration.
• Use letsencrypt to secure both endpoints (https).
• Store all secrets (OpenAI key, Buffer token, Google service account JSON) in environment variables or a vault like HashiCorp Vault.

version: "3.8"
services:
 n8n:
 image: n8nio/n8n
 ports:
 - "5678:5678"
 environment:
 - N8N_BASIC_AUTH_USER=${N8N_USER}
 - N8N_BASIC_AUTH_PASSWORD=${N8N_PASS}
 volumes:
 - ~/.n8n:/root/.n8n
 agent:
 build: .
 ports:
 - "5001:5001"
 environment:
 - OPENAI_API_KEY=${OPENAI_API_KEY}

Now the pipeline runs 24/7, pulling fresh topics, creating brand-consistent copy, and publishing it automatically - exactly what you set out to achieve when you asked how to build a fully automated AI system for cross-platform social media content.

Where this breaks

Pro tip: Enable n8n's built-in Error Workflow to capture failures, log them to a Slack channel, and automatically reset the problematic node.

For a deeper technical reference, see n8n's documentation.