Math AI Agent

Math agents evaluate expressions, solve equations, convert units, and run statistical or financial calculations through specialised tools on a single Agent.

from praisonaiagents import Agent

agent = Agent(name="Math Tutor", instructions="Solve math problems step by step.")
agent.start("What is 15 percent of 240?")

The user asks a quantitative question; the math agent selects the right calculation tools and returns a clear answer.

How It Works

Quick Start

Code
No Code

Install Package

First, install the PraisonAI Agents package:

pip install praisonaiagents sympy pint

Set API Key

Set your OpenAI API key as an environment variable in your terminal:

export OPENAI_API_KEY="${OPENAI_API_KEY:?Set OPENAI_API_KEY in your shell}"

Create a file

Create a new file app.py with the basic setup:

from praisonaiagents import Agent, Task, AgentTeam
from praisonaiagents import (
    evaluate, solve_equation, convert_units,
    calculate_statistics, calculate_financial
)

# Create math agent
math_agent = Agent(
    role="Math Expert",
    goal="Perform complex mathematical calculations",
    backstory="Expert in mathematical computations and analysis",
    tools=[
        evaluate, solve_equation, convert_units,
        calculate_statistics, calculate_financial
    ],
    
)

# Create a task
task = Task(
    description="Calculate compound interest and statistical analysis",
    expected_output="Detailed mathematical analysis",
    agent=math_agent
)

# Create and start the agents
agents = AgentTeam(
    agents=[math_agent],
    tasks=[task],
    process="sequential",
    output="verbose"
)

# Start execution
agents.start()

Start Agents

Type this in your terminal to run your agents:

python app.py

Install Package

Install the PraisonAI package:

pip install praisonai sympy pint

Set API Key

Set your OpenAI API key as an environment variable in your terminal:

export OPENAI_API_KEY="${OPENAI_API_KEY:?Set OPENAI_API_KEY in your shell}"

Create a file

Create a new file agents.yaml with the basic setup:

framework: praisonai
process: sequential
topic: perform mathematical analysis
agents:  # Canonical: use 'agents' instead of 'roles'
  mathematician:
    instructions:  # Canonical: use 'instructions' instead of 'backstory' Expert in mathematical computations and analysis.
    goal: Perform complex mathematical calculations
    role: Math Expert
    tools:
      - evaluate
      - solve_equation
      - convert_units
      - calculate_statistics
      - calculate_financial
    tasks:
      math_task:
        description: Calculate compound interest and perform statistical analysis.
        expected_output: Detailed mathematical analysis with results.

Start Agents

Type this in your terminal to run your agents:

praisonai agents.yaml

Requirements

Python 3.10 or higher
OpenAI API key. Generate OpenAI API key here. Use Other models using this guide.
sympy and pint packages (automatically installed when needed)

Understanding Math Agents

What are Math Agents?

Math agents are specialized AI agents that can:

Evaluate mathematical expressions
Solve equations and systems of equations
Perform unit conversions
Calculate statistical metrics
Handle financial calculations

Features

Expression Evaluator

Evaluates mathematical expressions and formulas.

Equation Solver

Solves mathematical equations and systems.

Unit Converter

Converts between different units of measurement.

Statistical Calculator

Calculates statistical metrics and analysis.

Multi-Agent AI Math Analysis

Code
No Code

Install Package

First, install the PraisonAI Agents package:

pip install praisonaiagents

Set API Key

Set your OpenAI API key as an environment variable in your terminal:

export OPENAI_API_KEY="${OPENAI_API_KEY:?Set OPENAI_API_KEY in your shell}"

Create a file

Create a new file app.py with the basic setup:

from praisonaiagents import Agent, Task, AgentTeam
from praisonaiagents import (
    evaluate, solve_equation, convert_units,
    calculate_statistics, calculate_financial
)

# Create first agent for calculations
calculator = Agent(
    role="Mathematical Calculator",
    goal="Perform complex calculations and analysis",
    backstory="Expert in mathematical computations",
    tools=[evaluate, solve_equation, convert_units],
    
)

# Create second agent for statistics
statistician = Agent(
    role="Statistical Analyst",
    goal="Perform statistical analysis and interpretation",
    backstory="Expert in statistical analysis",
    tools=[calculate_statistics, calculate_financial],
    
)

# Create first task
calc_task = Task(
    description="Calculate compound interest for $10000 at 5% for 3 years",
    expected_output="Detailed interest calculation",
    agent=calculator
)

# Create second task
stats_task = Task(
    description="Analyze the monthly interest distribution",
    expected_output="Statistical analysis of interest data",
    agent=statistician
)

# Create and start the agents
agents = AgentTeam(
    agents=[calculator, statistician],
    tasks=[calc_task, stats_task],
    process="sequential"
)

# Start execution
agents.start()

Start Agents

Type this in your terminal to run your agents:

python app.py

Install Package

Install the PraisonAI package:

pip install praisonai

Set API Key

Set your OpenAI API key as an environment variable in your terminal:

export OPENAI_API_KEY="${OPENAI_API_KEY:?Set OPENAI_API_KEY in your shell}"

Create a file

Create a new file agents.yaml with the basic setup:

framework: praisonai
process: sequential
topic: perform financial and statistical analysis
agents:  # Canonical: use 'agents' instead of 'roles'
  calculator:
    instructions:  # Canonical: use 'instructions' instead of 'backstory' Expert in mathematical computations and analysis.
    goal: Perform complex calculations
    role: Mathematical Calculator
    tools:
      - evaluate
      - solve_equation
      - convert_units
    tasks:
      calc_task:
        description: Calculate compound interest for investment scenario.
        expected_output: Detailed interest calculations.

  statistician:
    instructions:  # Canonical: use 'instructions' instead of 'backstory' Expert in statistical analysis and interpretation.
    goal: Analyze numerical data and patterns
    role: Statistical Analyst
    tools:
      - calculate_statistics
      - calculate_financial
    tasks:
      stats_task:
        description: Analyze the monthly interest distribution.
        expected_output: Statistical analysis report.

Start Agents

Type this in your terminal to run your agents:

praisonai agents.yaml

Configuration Options

# Create an agent with advanced math configuration
agent = Agent(
    role="Math Expert",
    goal="Perform complex mathematical analysis",
    backstory="Expert in mathematical computations",
    tools=[
        evaluate,
        solve_equation,
        convert_units,
        calculate_statistics,
        calculate_financial
    ],
      # Enable detailed logging
    llm="gpt-4o"  # Language model to use
)

Best Practices

Attach only the tools you need

Give calculation agents evaluate and solve_equation; reserve calculate_statistics and calculate_financial for analysis agents.

Use clear task descriptions

State the formula, units, and expected output format in each Task so the agent picks the right tool.

Validate units before conversion

Ensure input values include compatible units — convert_units fails on mismatched dimensions.

Troubleshooting

Expression Errors

If expressions are not evaluating:

Check syntax and formatting
Verify variable definitions
Enable verbose mode for debugging

Calculation Issues

If calculations are incorrect:

Review input formats
Check unit compatibility
Verify formula structure

Next Steps

AutoAgents

Learn about automatically created and managed AI agents

Mini Agents

Explore lightweight, focused AI agents

For optimal results, ensure mathematical expressions and units are properly formatted for your specific use case.

Use a specialised agent that writes and runs code to solve problems.

Run lightweight, focused agents for single-purpose tasks.

Getting Started

Install

Core Concepts

Guides

Features

Models

Databases

Observability

Memory

Telemetry & Analytics

Knowledge

RAG

Persistence

Tools

Other Features

Developers

Configuration

Best Practices

Getting Started (No Code)

How It Works

Quick Start

Understanding Math Agents

What are Math Agents?

Features

Expression Evaluator

Equation Solver

Unit Converter

Statistical Calculator

Multi-Agent AI Math Analysis

Configuration Options

Best Practices

Troubleshooting

Expression Errors

Calculation Issues

Next Steps

AutoAgents

Mini Agents

​How It Works

​Quick Start

​Understanding Math Agents

What are Math Agents?

​Features

Expression Evaluator

Equation Solver

Unit Converter

Statistical Calculator

​Multi-Agent AI Math Analysis

​Configuration Options

​Best Practices

​Troubleshooting

Expression Errors

Calculation Issues

​Next Steps

AutoAgents

Mini Agents

​Related

How It Works

Quick Start

Understanding Math Agents

Features

Multi-Agent AI Math Analysis

Configuration Options

Best Practices

Troubleshooting

Next Steps

Related