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Agent Reference

This reference provides technical details about all available LouieAI agents.

Database Agents

Athena

  • Purpose: Query AWS Athena databases
  • Capabilities: SQL queries, partitioned data, S3-backed tables
  • Authentication: AWS credentials required
  • Output: Tabular data, query metadata

BigQuery

  • Purpose: Query Google BigQuery datasets
  • Capabilities: Standard SQL, nested data, array functions
  • Authentication: GCP service account
  • Output: Tabular data, query statistics

Snowflake

  • Purpose: Query Snowflake data warehouses
  • Capabilities: SQL queries, semi-structured data, time travel
  • Authentication: Username/password or key-pair
  • Output: Tabular data, query history

PostgreSQL

  • Purpose: Query PostgreSQL databases
  • Capabilities: Full SQL, extensions, JSON operations
  • Authentication: Connection string
  • Output: Tabular data, query plans

MySQL

  • Purpose: Query MySQL databases
  • Capabilities: SQL queries, stored procedures
  • Authentication: Connection string
  • Output: Tabular data

Additional Database Agents

  • CockroachDB: Distributed SQL database
  • Databricks: Unified analytics platform
  • Kusto: Azure Data Explorer queries
  • MSSQL: Microsoft SQL Server
  • Neptune: AWS graph database
  • OpenSearch: Search and analytics
  • Spanner: Google Cloud Spanner
  • Splunk: Log analysis platform

Data Visualization Agents

Graph

  • Purpose: Create network visualizations
  • Capabilities:
  • Interactive graph exploration
  • Force-directed layouts
  • Node/edge styling
  • GPU-accelerated rendering
  • Output: Interactive Graphistry visualization

Perspective

  • Purpose: Create interactive data tables and charts
  • Capabilities:
  • Pivot tables
  • Aggregations
  • Real-time updates
  • Multiple chart types
  • Output: Interactive Perspective widget

Kepler

  • Purpose: Geospatial visualization
  • Capabilities:
  • Map layers
  • Heatmaps
  • Arc/line visualizations
  • 3D terrain
  • Output: Interactive Kepler.gl map

Mermaid

  • Purpose: Create diagrams and flowcharts
  • Capabilities:
  • Flowcharts
  • Sequence diagrams
  • Gantt charts
  • Entity relationships
  • Output: Mermaid diagram specification

Code Execution Agents

Code

  • Purpose: Generate and execute Python code
  • Capabilities:
  • Data analysis with pandas
  • Statistical computations
  • Custom algorithms
  • Library imports
  • Output: Code output, variables, plots

Notebook

  • Purpose: Create Jupyter notebooks
  • Capabilities:
  • Multi-cell workflows
  • Markdown documentation
  • Interactive widgets
  • Persistent state
  • Output: Executable notebook file

Data Processing Agents

TableAI

  • Purpose: Advanced table operations
  • Capabilities:
  • Intelligent joins
  • Data cleaning
  • Feature engineering
  • Anomaly detection
  • Output: Transformed datasets

Firecrawl

  • Purpose: Web scraping and extraction
  • Capabilities:
  • HTML parsing
  • Dynamic content
  • Rate limiting
  • Data extraction
  • Output: Structured web data

Agent Composition

Agents can work together in workflows:

# Database → Code → Visualization pipeline
lui("""
Query sales data from Snowflake,
calculate monthly trends with Python,
then create an interactive dashboard
""")

Performance Characteristics

Agent Type Latency Data Volume Interactivity
Database Low-Med High Query-based
Visualization Low Medium High
Code Medium Medium Moderate
Processing High High Low

Error Handling

All agents provide structured error information: - Error type and message - Relevant context - Suggested fixes - Fallback options

See Also