arivu-ai 0.2.1

The open-source framework for autonomous database agents.

The Agentic Database Command Center

An open-source framework for orchestrating autonomous database agents, integrating seamless Text-to-SQL pipelines with a beautiful No-Code Observability Dashboard.

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🚀 What is Arivu?

Arivu bridges the gap between your raw data warehouses and Large Language Models (LLMs). It isn't just a wrapper; it's a dual-layer infrastructure:

1. Python SDK: A streamlined toolkit (arivu-ai) to embed text-to-SQL pipelines, manage database connections, and deploy conversational agents across multiple communication channels (Slack, Discord, WhatsApp, Telegram, REST).
2. Observability Dashboard: A locally deployable Next.js dashboard that visualizes your AI pipeline traces, latency, LLM routing, and SQL execution logs in real-time.

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✨ Core Features

• Multi-Dialect Federation: Securely connect and query across PostgreSQL, MySQL, and SQLite clusters using natural language.
• Dynamic LLM Routing: Hot-swap between OpenAI, Anthropic, Groq, DeepSeek, Ollama, and Hugging Face directly from your code or the dashboard.
• Agentic Workflows: Arivu parses intent, generates optimized SQL, auto-corrects syntax errors, and formats structured responses automatically.
• Native Channels: Deploy your database agents directly into Slack, Discord, Telegram, or WhatsApp using built-in adapters.
• End-to-End Tracing: Visually debug agent reasoning and execution latency right from the observability dashboard.

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⚡ Quickstart

Install the core Python SDK via pip:

pip install arivu-ai

1. Simple Data Querying

Connect to your database and interrogate it using natural language in just 4 lines of code:

from arivu import Arivu

# 1. Initialize the engine
app = Arivu.connect(
    database_url="postgresql://user:pass@localhost:5432/main",
    llm_provider="openai",  # Or 'anthropic', 'groq', 'ollama', etc.
    monitoring=True         # Enables dashboard tracing
)

# 2. Formulate your prompt
query = app.query("What were our top 3 highest revenue products last quarter?")

# 3. Execute the agentic pipeline
results = app.run_pipeline(query)

# 4. View results
print(results.get("response"))
print("Generated SQL:", results.get("sql"))

2. Deploy to a Channel (e.g., Telegram)

Arivu allows you to expose your database to authorized users via chat platforms:

from arivu.integrations import TelegramIntegration

# Pass your existing 'app' engine to the adapter
bot = TelegramIntegration(
  db=app,
  token="YOUR_TELEGRAM_BOT_TOKEN",
)

# Start listening for messages!
bot.start()

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📊 The Observability Dashboard

Arivu ships with a stunning, no-code visual interface for monitoring your agents.

To run the dashboard locally:

# Clone the repository
git clone https://github.com/AtharshKrishnamoorthy/ARIVU.git
cd ARIVU

# Navigate to the dashboard
cd site

# Install dependencies and start
npm install
npm run dev

Visit http://localhost:3000 to visually manage connections, test prompts, and trace pipeline executions.

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📖 Documentation

For detailed guides, API references, architecture diagrams, and more SDK examples, check out our official documentation.

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🤝 Contributing

We welcome contributions! Whether it's adding a new database dialect, extending the dashboard UI, or improving prompt resolution:

1. Fork the repo and create your branch (git checkout -b feature/amazing-feature)
2. Commit your changes (git commit -m 'Add amazing feature')
3. Push to the branch (git push origin feature/amazing-feature)
4. Open a Pull Request

Please read our Contributing Guidelines for details on code style and testing.

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📄 License

Arivu is open-source software licensed under the MIT License.