Ai-data-science-team

New Agents:

- **H2OMLAgent():** The first in a series of ML agents designed to make Machine Learning Models with AI. This AI Agent is trained in `h2o` AutoML and is capable of creating 100's of ML models in seconds.
- **New Example:** https://github.com/business-science/ai-data-science-team/blob/master/examples/ml_agents/h2o_machine_learning_agent.ipynb

Improvements

- **Workflow Summary Report:** The explain code step was replaced with a much faster step for documenting the agentic workflow. A `get_workflow_summary()` method returns formatted summary reports of every step taken in the agentic workflow.
- **Smart Schema Pruning:** SQL Database Agent gained a new parameter, `smart_schema_pruning`, which uses an extra LLM call to prune tables and columns. This is useful when database schemas are very large. Pruning is based on Uber QueryGPT blog article which implements a Column Prune Agent. Read more here: https://www.uber.com/blog/query-gpt/


**Full Changelog**: https://github.com/business-science/ai-data-science-team/compare/0.0.0.9008...0.0.0.9009

New Features

1. **New Object-Oriented Programming Framework (Experimental):** OOP Framework provides a Pythonic interface to agents, improved methods, and more features beyond LangGraph methods. New classes include DataCleaningAgent(), FeatureEngineeringAgent(), SQLDatabaseAgent() and more.
2. **Multi-Agents:** A new multiagents module was created. This supports common LangGraph multi-agent architectures, which will be a big focus going forward.
3. **New SQLDataAnalyst Multi-Agent**: Combines the SQLDatabaseAgent and DataVisualizationAgent() in a multi-agent workflow with conditional routing to the data visualization agent. Perfect for Business Intelligence and Data Analysis applications.

New Examples

1. How to Build SQL Data Analysis Agents: https://github.com/business-science/ai-data-science-team/blob/master/examples/multiagents/sql_data_analyst.ipynb
2. Human In The Loop (new workflow): https://github.com/business-science/ai-data-science-team/blob/master/examples/advanced_topics/human_in_the_loop.ipynb

Enhancements

- **New BaseAgent() Class:** Used to make common methods available to all OOP agents.
- **New Human-In-The-Loop Workflow**: Allows applications to include human review and modification. Perfect for iteratively improving AI functions.

**Full Changelog**: https://github.com/business-science/ai-data-science-team/compare/0.0.0.9007...0.0.0.9008

New Agent:

- Data Visualization Agent: Generates code for data visualizations
- New Example: Automate Data Visualization with AI Agents https://github.com/business-science/ai-data-science-team/blob/master/examples/data_visualization_agent.ipynb

Agent Enhancements:

- Add `n_samples` to allow users to control the number of data rows passed to LLM prompts.
- Add `file_name` to allow users to control the file name that the agent uses when logging functions.
- `plotly_from_dict()` helper utility to convert a dictionary to a plotly graph.

Fixes:

- Bypass steps - Adds all_datasets_summary_str to allow LLM to know the dataset summary if the recommendation step is bypassed.
- Improved `get_database_metadata()` for SQL engine.

**Full Changelog**: https://github.com/business-science/ai-data-science-team/compare/0.0.0.9005...0.0.0.9007

New Agents

- **SQL Database Agent:** Queries SQL databases from Natural Language, automates pipelines as import-ready python functions, and makes it easy to integrate SQL agents into Streamlit apps
- **New Tutorial:** https://github.com/business-science/ai-data-science-team/blob/master/examples/sql_database_agent.ipynb

Enhancements

- **Dynamically Bypass Long-Running Steps:** This is important if speed is critical. Planning steps (e.g. recommend steps for coding agent, explaining code step) can be bypassed to reduce LLM calls and speed up operations.

**Full Changelog**: https://github.com/business-science/ai-data-science-team/compare/0.0.0.9004...0.0.0.9005

New Agent:
- Data Wrangling Agent - Handles multiple datasets, merges, joins, and prepares data for analysis
- New Example: [How to automate data wrangling with AI](https://github.com/business-science/ai-data-science-team/blob/master/examples/data_wrangling_agent.ipynb)

**Full Changelog**: https://github.com/business-science/ai-data-science-team/compare/0.0.0.9003...0.0.0.9004

Major Changes:

- Agents now have human-in-the-loop capabilities
- New Example: Human-in-the-loop

Minor Change:

- Refactor codebase to use agent templates

**Full Changelog**: https://github.com/business-science/ai-data-science-team/compare/0.0.0.9002...0.0.0.9003