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Creating an AI Agent with Your Own Personality

In this tutorial, we'll explore how to create an AI agent that embodies your own personality using data from your Twitter archive, videos, markdown files, and PDFs. We'll leverage the Characterfile repo and Eliza framework to generate and integrate the character data.

Video: https://youtu.be/uouSdtcWXTQ?si=cm13L4T7DQUMXd0C

Prerequisites​

  • Twitter Developer account
  • Anthropic API key
  • Your Twitter archive (download instructions below)
  • (Optional) Videos, markdown files, PDFs about you

Generating Your Character File​

From Twitter Archive​

  1. Request your Twitter archive:

    • Go to your Twitter settings
    • Click "Download an archive of your data"
    • Wait to receive the archive (timing depends on your account age/activity)

  2. Clone the Characterfile repo:

    git clone https://github.com/ai16z/characterfile.git

  3. Run the tweets-to-character script:

    npx tweets-to-character path/to/archive.zip
    • Select model (e.g. Claude)
    • (Optional) Add any additional user information

  4. Script will generate a character.json file from your Tweets

From Other Files​

  1. Put videos, PDFs, text, markdown, images in a folder

  2. Run the folder-to-knowledge script:

    npx folder-to-knowledge path/to/folder

  3. Run knowledge-to-character to add knowledge to your character file

Setting Up the Agent​

  1. Clone Eliza repo and check out latest version:

    git clone https://github.com/elizaOS/eliza.git
    git checkout <latest-tag>

  2. Install dependencies:

    pnpm install
    pnpm build

  3. Add your character JSON file to characters/

  4. Modify character file:

    • Add clients, modelProvider, plugins fields
    • Remove voice field

  5. Set up .env with Twitter and Anthropic credentials

Running the Agent​

  1. Start agent with your character file:

    pnpm start --character characters/yourcharacter.json

  2. Agent will log in and post an initial tweet

  3. Check your Twitter profile to see the agent in action!

Next Steps​

  • Implement dynamic prompting to enhance agent interactions
  • Extend agent with additional plugins and integrations