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    • Installing Mito
      • Fixing Common Install Errors
      • Installing Mito in a Docker Container
      • Installing Mito for Streamlit
      • Installing Mito for Dash
      • Installing Mito in a Jupyter Notebook Directly
      • Installing Mito in Vertex AI
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  • Data Copilot
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  • Apps (Beta)
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    • Core Concepts
    • Creating a Mitosheet
      • Open Existing Virtual Environments
    • Importing Data
      • Importing CSV Files
      • Importing from Excel Files
      • Importing Dataframes
      • Importing from a remote drive
      • Import: Generated UI from any Python Function
      • Importing from other sources
    • Graphing
      • Graph Creation
      • Graph Styling
      • Graph Export
    • Pivoting/Group By
    • Filter
      • Filter By Condition
      • Filter By Value
    • Mito AI
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    • Type Changes
    • Spreadsheet Formulas
      • Custom Spreadsheet Functions
      • Formula Reference
      • Using VLOOKUP
    • Editing Individual Cells
    • Combining Dataframes
      • Merge (horizontal)
      • Concatenate (horizontal)
      • Anti-merge (unique)
    • Sort Data
    • Split Text to Columns
    • Deleting Columns
    • Deleting Rows
    • Column Headers
      • Editing Column Headers
      • Promote Row to Header
    • Deduplicate
    • Fill NaN Values
    • Transpose
    • Reset Index
    • Unpivot a Dataframe (Melt)
    • Formatting
      • Column Formatting
      • Dataframe Colors
      • Conditional Formatting
    • Exporting Data
      • Download as CSV
      • Download as Excel
      • Generate code to create Excel and CSV reports
    • Using the Generated Code
      • Turn generated code into functions
    • Changing Imported Data
    • Code Snippets
    • Custom Editors: Autogenerate UI from Any Function
    • Find and Replace
    • Bulk column header edits
    • Code Options
    • Scheduling your Automation
    • Keyboard Shortcuts
    • Upgrading Mito
    • Enterprise Logging
  • Mito for Streamlit
    • Getting Started with Mito for Streamlit
    • Streamlit Overview
    • Create a Mito for Streamlit App
    • API Reference
      • Understanding import_folder
      • RunnableAnalysis class
      • Column Definitions
    • Streamlit App Gallery
    • Experienced Streamlit Users
    • Common Design Patterns
      • Deploying Mito for Streamlit in a Docker Image
      • Using Mito for Final Mile Data Cleaning
  • Mito for Dash
    • Getting Started
    • Dash Overview
    • Your First Dash App with Mito
    • Mito vs. Other Dash Components
    • API Reference
      • Understanding import_folder
    • Dash App Gallery
    • Common Design Patterns
      • Refresh Sheet Data Periodically
      • Change Sheet Data from a Select
      • Filter Other Elements to Data Selected in Mito
      • Graph New Data after Edits to Mito
      • Set Mito Spreadsheet Theme
  • Tutorials
    • Pass a dataframe into Mito
    • Create a line chart of time series data
    • Delete Columns with Missing Values
    • Split a column on delimiter
    • Rerun analysis on new data
    • Calculate the difference between rows
    • Calculate each cell's percent total of column
    • Import multiple tables from one Excel sheet
    • Share Mito Spreadsheets Across Users
  • Misc
    • Release Notes
      • April 15 - Now Streaming (0.1.18)
      • March 21 - Smarter, Faster, Stronger Agents
      • February 25 - Agent Mode QoL Improvements
      • February 18 - Mito Agents
      • January 2nd - Inline Completions Arrive
      • December 6th - Smarter Workflow
      • November 27th - @ Mentions, Mito AI Server
      • November 4th, 2024 - Hello Mito AI
      • October 8, 2024 - JupyterLab 4
      • Aug 29th, 2024
      • June 12, 2024
      • March 19, 2024
      • March 13th, 2024
      • February 12th, 2024: Graphing Improvements
      • January 25th, 2024
      • January 5th, 2023: Keyboard Shortcuts
      • December 6, 2023: New Context Menu
      • November 28, 2023: Mito's New Toolbar
      • November 7, 2023: Multiplayer Dash
      • October 23, 2023: RunnableAnalysis class
      • October 16, 2023: Mito for Dash, Custom Editors
      • September 29, 2023: VLOOKUP and Find and Replace!
      • September 7, 2023
      • August 2, 2023: Mito for Streamlit!
      • July 10, 2023
      • May 31, 2023: Mito AI Recon
      • May 19, 2023: Mito AI Chat!
      • April 27, 2023: Generate Functions, Performance improvements, bulk column header transformations
      • April 18, 2023: Cell Editor Improvements, BYO Large Language Model, and more
      • April 10, 2023: AI Access, Excel-like Cell Editor, Performance Improvements
      • April 5, 2023: Range formulas, Pandas 2.0, Snowflake Views
      • March 29, 2023: Excel Range Import Improvements
      • March 14, 2023: Mito AI, Public Interface Versioning
      • February 28, 2023: In-place Pivot Errors
      • February 7, 2023: Excel-like Formulas, Snowflake Import
      • January 23, 2023: Excel range importing
      • January 8, 2023: Custom Code snippets
      • December 26, 2022: Code snippets and bug fixes
      • December 12, 2022: Group Dates in Pivot Tables, Reduced Dependencies
      • November 15, 2022: Filter in Pivot
      • November 9, 2022: Import and Enterprise Config
      • October 31, 2022: Replay Analysis Improvements
      • Old Release Notes
      • August 10, 2023: Export Formatting to Excel
    • Mito Enterprise Features
    • FAQ
    • Terms of Service
    • Privacy Policy
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  • Agent Capabilities
  • How to use Agent?

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  1. Data Copilot

Agent

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Last updated 1 month ago

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Agents take automation to the next level—helping you plan and execute tasks step by step. From just a single prompt, the agent can discover and report important insights, build presentation-ready graphs, and generate a clean, well documented notebook.

Agent Capabilities

This is an overview of the tools that the Mito AI Data Copilot can use when responding to your prompts. When you ask a question or request help, the AI agent automatically selects the appropriate tools to assist you, without requiring you to specify which one to use.

How to use Agent?

  1. You can toggle Agent mode from the chat sidebar.

  2. Once in Agent mode, enter your prompt. This should describe a goal state you would like to accomplish. Be sure to include any additional details highlighting constraints or requirements.

  3. Once it receives this initial prompt, the agent will begin to think through the problem, and immediately start to write code.

  4. The agent will continue to work until the task is complete.

⚠️ The Mito Agent will execute code on your behalf.

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Create/Update Code Cells: Generate new code or modify existing code in your notebook. Perfect for data analysis tasks, visualization creation, or implementing algorithms without writing code from scratch.

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Check Cell Output: Examine the results of executed cells so the agent can help you interpret and construct presentation-ready graphs, diagnose issues, and verify your analysis is on the right track.

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Code Citation: Link the agent's observations to specific lines of code, so you can verify its work. This transparency builds trust, clarifies recommendations, and helps you learn from the agent's reasoning.

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Check existing variables: View variables currently available in your notebook's memory. Quickly inspect variable types, shapes, and values so it can generate insights and working code.

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Search file paths in current working directory: Find files in your current directory. Useful for locating CSV and Excel files to import as dataframes for your analysis.

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Write Markdown: Document section of code, highlight key results and conclusions. Make it easier for yourself and your colleagues to understand the notebook.

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Automatically Fix Errors: Identify and resolve errors in your notebook. The agent analyzes error messages, codes solutions, and checks that they fix your bugs to get your code running.