🚀 LlamaBotRails

Turn any Rails app into an AI Agent in 2 minutes

Chat with a powerful agent that has access to your models, your application context, and can run console commands. All powered by LangGraph + OpenAI.

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🎥 See it in action (30-Second Demo)

The agent can:

• 🔍 Explore your Rails app (models, routes, controllers)
• 💾 Query and create data via Rails console
• 🛠️ Take action on your behalf
• 🧠 Understand your domain through natural conversation

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🚀 Quickstart →

# 1. Add the gem
bundle add llama_bot_rails

# 2. Install the routes & chat interface
rails generate llama_bot_rails:install

# 3.Run the LlamaBot backend easily with Docker
docker run \
  -e OPENAI_API_KEY=(your-key) \
  -p 8000:8000 \
  kody06/llamabot-backend

# 7. Start your Rails server.
rails server

# 8. Visit the chat interface and start chatting.
open http://localhost:3000/llama_bot/agent/chat

That's it. ✅ You can now chat with your Rails app like a new assistant.

Try asking:

• "What models do I have in this app?"
• "Show me the User model structure"
• "Create a test user"
• "What are my routes?"

Prerequisites

• Rails 7.0+
• Ruby 2.7+
• Redis (for ActionCable)
• OpenAI API key

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⚙️ Rails Integration Note (for LlamaBot Rails Gem)

If you're using the llama_bot_rails Gem with Docker, your Rails app must allow the Docker agent to connect back to it.

Add this to your config/environments/development.rb (if it wasn’t added automatically by the Gem installer):

Rails.application.configure do
  config.hosts << /host\.docker\.internal/  # Allow Docker agent to connect to Rails
end

This allows the Docker container to reach http://host.docker.internal:3000, which maps to your Rails app on the host machine.

🧨 Power & Responsibility

⚠️ This gem gives the agent access to your Rails console.

This is incredibly powerful -- and also potentially dangerous in production. Treat it like giving shell access to a developer.

🚫 Do not deploy this tool to production without understanding the risks to your production data & application.

🛡️ Production safety features coming soon

🏗️ Architecture

┌────────────────────┐         WebSocket        ┌────────────────────┐
│     Rails App      │ ←──────────────────────→ │     LangGraph      │
│    (Your App)      │                          │  FastAPI (Python)  │
├────────────────────┤                          ├────────────────────┤
│   LlamaBotRails    │                          │   Agents & Tools   │
│        Gem         │                          │    (LangGraph)     │
└────────────────────┘                          └────────────────────┘

What happens:

1. Rails frontend provides a chat interface
2. ActionCable WebSocket handles real-time communication to LangGraph
3. LangGraph backend runs the AI agent with access to tools
4. Agent executes A sequence of Rails console commands, reasoning throughout the process.
5. Results stream back to the chat interface in real-time

🛠️ Customization

Custom State Builder

Control what data your agent sees:

# config/initializers/llama_bot_rails.rb
class CustomAgentStateBuilder < LlamaBotRails::AgentStateBuilder
  def build
    super.merge({
      current_user: @context[:user]&.to_json,
      app_version: Rails.application.version,
      custom_context: gather_app_context
    })
  end
  
  private
  
  def gather_app_context
    {
      model_count: ActiveRecord::Base.subclasses.count,
      route_count: Rails.application.routes.routes.count,
      environment: Rails.env,
      database_name: ActiveRecord::Base.connection_db_config.database
    }
  end
end

# Configure the gem to use your builder
Rails.application.configure do
  config.llama_bot_rails.state_builder_class = "CustomAgentStateBuilder"
end

Environment Configuration

# config/environments/development.rb
Rails.application.configure do
  config.llama_bot_rails.enable_console_tool = true
end

# config/environments/production.rb
Rails.application.configure do
  config.llama_bot_rails.enable_console_tool = false # Disable in production
end

Principle of Least Priviledge: Whitelisting Controller Actions

Rather than giving the agent full access to your Rails app via the Rails console, You can whitelist controller actions instead.

class PagesController < ApplicationController
  include LlamaBotRails::ControllerExtensions
  include LlamaBotRails::AgentAuth
  
  # ─── Allow the agent to hit these actions ────────────────────────────────
  llama_bot_allow :update #uses llama_bot_rails "authenticate_user_or_agent!" on top of your existing devise authentication.

  skip_before_action :authenticate_user_or_agent!, only: [:show] #NOTE: You must change any skip_before_action callbacks that skip devise Authentication, to use :authenticate_user_or_agent!

When you include LlamaBotRails::AgentAuth, the gem aliases any authenticate_! filters to a unified guard. You can keep your old callbacks/skips for now, but you’ll see a deprecation warning—switch to authenticate_user_or_agent! at your convenience.

🧪 What You Can Build

Developer Assistant

• Code exploration: "Show me how authentication works"
• Data analysis: "How many users signed up this month?"
• Quick prototyping: "Create a basic blog post model"

🔧 Under the Hood

Real-Time Communication

• ActionCable WebSocket Real-time Rails <-> Agent communication.
• LangGraph Backend FastAPI + OpenAI tool orchestration

Security

• Secure channel seperation -> Per-session isolation.
• Token expiration and automatic refresh mechanisms

Command Streaming

• run_rails_console_command: Execute Ruby code in Rails context

📋 Requirements

Rails Application

• Rails 7.0+ - Modern Rails version with ActionCable support
• Ruby 2.7+ - Compatible Ruby version
• ActionCable configured - For real-time WebSocket communication
• Redis - Recommended for production ActionCable backend

LangGraph Backend

• Python 3.11+ - Python runtime environment
• FastAPI application - Web framework for the agent backend
• OpenAI API access - For LLM capabilities
• WebSocket support - For real-time bidirectional communication

🧨 Troubleshooting

• Agent not responding? Check that backend is running and OpenAI key is set.
• WebSocket issues? Confirm LLAMABOT_WEBSOCKET_URL matches backend address.

🤝 Contributing

We'd love your help making LlamaBotRails better!

How to Contribute

1. Fork the repo
2. Create a feature branch: git checkout -b my-new-feature
3. Make your changes and add tests
4. Run the test suite: bundle exec rspec
5. Submit a pull request

Development Setup

# Clone the repo
git clone https://github.com/kodykendall/llama_bot_rails
cd llama_bot_rails

# Install dependencies
bundle install

# Run tests  
bundle exec rspec

# Test in a real Rails app
cd example_app
bundle exec rails server

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🌟 What's Next?

We're just getting started. Coming soon:

• 🛡️ Enhanced security controls for production deployments
• 🔧 More built-in tools (scaffolding, API calls, database queries)
• 🎨 Customizable chat themes and branding
• 📊 Analytics and monitoring for agent interactions
• 🔌 Plugin system for custom tool development
• 🤖 Multi-agent support for complex workflows
• 🔄 Background job integration for long-running tasks

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📝 License

MIT. — free for commercial and personal use.

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⭐️ Support the Project!

If LlamaBotRails helped you, give us a star ⭐️ and share it with other Rails developers.

This is just the beginning. Let's build the Rails agentic future -- together.

⭐️ Star on GitHub • 🍴 Fork the repo • 💬 Join discussions

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Built with ❤️ by Kody Kendall