Provide structured data storage and retrieval for AI agents with natural language database management. Create and manage databases that AI agents can query and update using natural language commands.

Key Features

Natural Language Database Management

  • Schema Creation: Create database schemas using natural language descriptions
  • Table Management: Add, modify, and remove tables with simple prompts
  • Relationship Building: Define relationships between tables using natural language
  • Data Type Handling: Automatically select appropriate data types

Text-to-SQL Capabilities

  • Natural Language Queries: Convert natural language to SQL queries
  • Safe Query Execution: Execute queries safely with built-in protections
  • Result Formatting: Format query results for agent consumption
  • Multi-table Queries: Handle complex queries across multiple tables

Implementation Examples

Creating Database Schema with Natural Language

# Using Gibson CLI to create database schema for AI agents
# gibson modify user_profiles "Create a user profile table with name, email, preferences, and created_at"
# gibson modify user_actions "Create a user actions table that tracks user_id, action_type, timestamp, and metadata"
# gibson code models  # Generate SQLAlchemy models
# gibson merge  # Apply changes to database

Text-to-SQL Query Examples

# Using Gibson Studio or API for text-to-SQL queries
import requests

# Query user data with natural language
query_request = {
    "query": "Show me all users who signed up in the last 30 days"
}

response = requests.post(
    "https://api.gibsonai.com/v1/-/query",
    json=query_request,
    headers={"Authorization": "Bearer your_api_key"}
)

results = response.json()

REST API Integration

# Using auto-generated REST APIs
import requests

# Get all user profiles
response = requests.get(
    "https://api.gibsonai.com/v1/-/user-profiles",
    headers={"Authorization": "Bearer your_api_key"}
)

# Create new user profile
new_profile = {
    "name": "John Doe",
    "email": "john@example.com",
    "preferences": {"theme": "dark", "notifications": true}
}

response = requests.post(
    "https://api.gibsonai.com/v1/-/user-profiles",
    json=new_profile,
    headers={"Authorization": "Bearer your_api_key"}
)

Use Cases

Agent Data Storage

Perfect for AI agents that need to:

  • Store user interactions and preferences
  • Maintain conversation history
  • Track agent performance metrics
  • Store processed data from external sources

Natural Language Data Access

Enable agents to:

  • Query databases using natural language
  • Create reports from stored data
  • Filter and search data based on user requests
  • Generate insights from historical data

Schema Evolution

Allow agents to:

  • Adapt database structure based on new requirements
  • Add new data fields as needed
  • Modify existing tables without manual intervention
  • Maintain data integrity during changes

Gibson Studio Integration

Use Gibson Studio for:

  • Visual database exploration
  • Query building and testing
  • Data visualization
  • Schema management

MCP Server Integration

Connect AI tools and agents:

  • Natural language database operations
  • Secure database access
  • Contextual query suggestions
  • Automated schema updates

Benefits for AI Agents

  • Rapid Development: Create databases in minutes, not hours
  • Natural Interface: Use natural language instead of SQL
  • Automatic APIs: Get REST APIs without coding
  • Schema Flexibility: Easily modify structure as needs change
  • Safe Operations: Built-in protections for data integrity