Once upon a time, I created a Twitter bot, @poetryrobot, to retweet poems every 30 minutes and send words of encouragement to people who tweet poetry. I wrote the bot in Ruby and deployed it as a continually running process on an AWS t2.micro EC2 instance. It ran free of charge within the AWS free tier usage limits for 12 happy months.

After 12 months of free-tier usage, EC2 starts to cost money, but AWS Lambda has a free tier that continues indefinitely. I wanted to keep @poetryrobot running in the cheapest way possible, so I decided to migrate it from EC2 to Lambda. Because AWS Lambda does not support Ruby, I needed to port the bot to one of the supported languages: Java, C#, Javascript, or Python. I went with Python. I had a fun and interesting time working with Python and AWS Lambda, so I thought I would write up the adventure. The full code for the ported bot is here.

This process assumes that Python v3 and a compatible version of pip are pre-installed.

Step 1: Bootstrap the project

AWS Lambda allows you to write code directly into a web editor for simple scripts. However, if multiple files or external dependencies are needed, then development needs to occur in a directory whose files can then be zipped and uploaded to AWS Lambda.

I created a new code directory, poetryrobot/, containing a single Python file, poetryrobot.py.

Next, I picked out a Python Twitter package, python-twitter, and installed it into the code directory with the following:

# cd into the code directory, and then:
pip install python-twitter -t $(pwd)

The dependency needs to be installed in the code directory so that it can be included as part of the zip file.

Step 2: Implement the bot

I created a credentials.json file which contained the following information:

  "consumer_key": "...",
  "consumer_secret": "...",
  "access_token_key": "...",
  "access_token_secret": "..."

In the poetryrobot.py file, I imported the dependencies I would use and loaded the credentials:

import twitter
import json
import random

with open('credentials.json') as f:
    credentials = json.loads(f.read())

Then, still in poetryrobot.py, I implemented the following method to be the AWS Lambda entry point (the "main" method), and I implemented the methods it calls (like poem_tweets and retweet_someone):

def lambda_handler(_event_json, _context):
    tweets = poem_tweets()
    # Retweet a poet more often than reply to a poet:
    if random.random() <= 0.9:

It's important that this main method accepts two arguments, because AWS Lambda will pass it two arguments.

The full poetryrobot.py file can be seen here.

Step 3: Create the zip file

I highlighted everything in the code directory (not including the code directory itself) and compressed it into a zip file.

Step 4: Create the Lambda function

I went to the AWS Lambda management console and clicked to create a new Lambda function. I selected the Blank Function template.

I clicked "Next" on the trigger screen to skip that step for now.

On the next screen, I set the runtime to Python 3.6 and named the function poetryrobot. I clicked to upload a zip file rather than type code inline, and I uploaded the generated zip file from Step 3. Then, I typed poetryrobot.lambda_handler (the name of the "main" method) into the handler field. I set a lambda_basic_execution role with CloudWatch log permissions. Finally, under "Advanced", I increased the timeout from the default to 20 seconds.

Step 5: Testing the Lambda function

With just these steps, the Lambda function was ready to roll. I clicked the "Test" button and saw activity on Twitter along with logs send to CloudWatch.

Step 6: Scheduling the Lambda function

This is the final step! I went to the CloudWatch management console, navigated to the events >> rules section, and clicked "Create rule". On the next screen, I set the event source to "Schedule" and set a fixed rate of 30 minutes. Then, I set the target to the poetryrobot Lambda function. Finally, I named the CloudWatch rule poetryrobot_event and clicked to create it. From there, the bot was up and running on its schedule!


Thanks to this article and the one it references for providing me with helpful Python and Lambda guidance.