Agent Guide
LouieAI provides 40+ specialized agents for different data sources and tasks. Each agent is designed for specific use cases and data types, with most offering two variants:
- AI-Assisted (e.g.,
PostgresAgent) - Natural language to query language with semantic understanding - Passthrough (e.g.,
PostgresPassthroughAgent) - Direct query execution without AI interpretation
Agent Types
General Purpose Agents
- LouieAgent (default) - General conversational AI for analysis and mixed tasks
- TextAgent - Basic text processing and manipulation
- DoNothingAgent - Testing agent that returns empty responses
Database Query Agents with Semantic Layer
Database agents build semantic understanding of your data:
- Schema Discovery: Learn database structure and relationships automatically
- Semantic Modeling: Understand business context and domain terminology
- Intelligent Query Generation: Generate queries based on meaning, not keywords
- Cross-Table Reasoning: Infer joins and relationships automatically
How to Use Agents
Notebook API
from louieai.notebook import lui
# Default general-purpose agent
lui("Analyze customer behavior patterns")
# Specify database agent
lui("Show failed login attempts from last hour", agent="SplunkAgent")
# Use passthrough for exact SQL
lui("SELECT * FROM logs LIMIT 10", agent="PostgresPassthroughAgent")
Traditional Client API
from louieai import LouieClient
client = LouieClient()
# With agent parameter
response = client.add_cell("", "Analyze patterns", agent="LouieAgent")
response = client.add_cell("", "SELECT * FROM events", agent="PostgresPassthroughAgent")
Code & Notebook Agents
- CodeAgent - AI-powered Python code generation with explanations
- CodePassthroughAgent - Direct Python code execution without AI interpretation
- NotebookAgent - Jupyter notebook cell operations and management
Data Visualization Agents
- GraphAgent / GraphPassthroughAgent - Network graphs with Graphistry
- PerspectiveAgent / PerspectivePassthroughAgent - Interactive data tables
- MermaidAgent / MermaidPassthroughAgent - Flowcharts and diagrams
- KeplerAgent - Interactive geospatial maps
Specialized Agents
- TableAIAgent - AI-powered table analysis and insights
- FirecrawlAgent - Web scraping and data extraction
Quick Reference Table
| Data Source | Agent | Use Cases | Query Language |
|---|---|---|---|
| Cloud Data Warehouses | |||
| BigQuery | BigQueryAgent |
Petabyte analytics, ML, geospatial | SQL |
| Snowflake | SnowflakeAgent |
Enterprise analytics, time travel | SQL |
| Databricks | DatabricksAgent |
Unity Catalog, Delta Lake | SQL |
| Athena | AthenaAgent |
Serverless S3 queries | SQL |
| Traditional Databases | |||
| PostgreSQL | PostgresAgent |
OLTP/Analytics, JSON, advanced features | SQL |
| MySQL | MySQLAgent |
Web applications, performance optimization | SQL |
| SQL Server | MSSQLAgent |
Enterprise Windows environments | T-SQL |
| Distributed Systems | |||
| CockroachDB | CockroachDBAgent |
Global consistency, multi-region | SQL |
| Spanner | SpannerAgent |
Global scale, strong consistency | SQL |
| Search & Logs | |||
| OpenSearch | OpenSearchAgent |
Log analytics, security monitoring | Query DSL |
| Splunk | SplunkAgent |
Security operations, correlations | SPL |
| Kusto | KustoAgent |
Azure telemetry, time series | KQL |
| Graph Databases | |||
| Neptune | NeptuneAgent |
Relationship analysis | Cypher |
| Visualization | |||
| Graph | GraphAgent |
Network analysis with Graphistry | JSON |
| Kepler | KeplerAgent |
Geospatial mapping | Config |
| Perspective | PerspectiveAgent |
Interactive data tables | Config |
| Mermaid | MermaidAgent |
Diagrams and flowcharts | Mermaid |
| Development | |||
| Code | CodeAgent |
Python data processing | Python |
| Notebook | NotebookAgent |
Jupyter automation | Python |
| TableAI | TableAIAgent |
Intelligent data insights | Natural Language |
| Data Collection | |||
| Firecrawl | FirecrawlAgent |
Web scraping | Natural Language |
Semantic Understanding Example
Database agents leverage semantic understanding for intelligent queries:
# Natural language query
lui("Show me customer churn trends", agent="DatabricksAgent")
# The agent automatically:
# - Maps "customer churn" to relevant tables/columns
# - Calculates appropriate time periods
# - Determines necessary joins and aggregations
# - Applies business logic and definitions
AI vs Passthrough Comparison
| Use Case | AI-Assisted Agent | Passthrough Agent |
|---|---|---|
| Exploring data | ✅ Best choice - describes what you need | ❌ Requires knowing structure |
| Complex queries | ✅ Handles joins and logic automatically | ⚠️ Must write manually |
| Exact control | ⚠️ May interpret differently | ✅ Executes exactly as written |
| Learning curve | ✅ Natural language | ❌ Need to know query language |
| Performance | ✅ Often optimizes queries | ✅ Full control over execution |
Agent Selection Guidelines
When to Use AI-Assisted Agents
- Exploring new data sources - "Show me customer behavior patterns"
- Complex business questions - "Find high-risk transactions across regions"
- Learning query patterns - See how the AI structures queries for your use case
When to Use Passthrough Agents
- Exact control needed - Specific SQL optimizations or edge cases
- Known query patterns - You already know the exact query to run
- Performance critical - Direct execution without AI processing overhead
Multi-Agent Workflows
# 1. Explore with database agent
lui("Show me customer behavior patterns", agent="PostgresAgent")
# 2. Visualize findings
lui("Create a network of customer interactions", agent="GraphAgent")
# 3. Generate analysis code
lui("Write code to predict customer churn", agent="CodeAgent")
# 4. Monitor in production
lui("Create Splunk queries to track model performance", agent="SplunkAgent")
Individual Agent Guides
Database Agents: - Databricks - Unity Catalog, Delta Lake analytics - PostgreSQL - Advanced SQL features, JSON support - Splunk - Security operations, log analysis - BigQuery - Petabyte analytics, ML integration - Snowflake - Enterprise data warehouse - MySQL - Web application databases - OpenSearch - Search and log analytics - Athena, CockroachDB, Kusto, MSSQL, Neptune, Spanner
Visualization Agents: - Graph - Network visualization with Graphistry - Kepler - Geospatial mapping - Perspective - Interactive data tables - Mermaid - Diagrams and flowcharts
Development Agents: - Code - Python code generation - Notebook - Jupyter automation - TableAI - Intelligent data insights - Firecrawl - Web scraping