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Read a CSV file, summarise it, and surface trends with a single Agent — no pandas boilerplate to write. 
from praisonaiagents import Agent
from praisonai_tools import read_csv, get_summary, filter_data

agent = Agent(
    name="DataAnalyst",
    instructions="Analyse data files and return clear, actionable insights.",
    tools=[read_csv, get_summary, filter_data],
)

agent.start("Read sales_data.csv and summarise the top trends.")
Data analysis agent with CSV/Excel tools for reading, analyzing, and exporting data.

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Quick Start

1

Simple Usage

Attach the data tools and ask for a summary.
from praisonaiagents import Agent
from praisonai_tools import read_csv, get_summary, filter_data

agent = Agent(
    name="DataAnalyst",
    instructions="You are a data analyst. Analyze data and provide insights.",
    tools=[read_csv, get_summary, filter_data],
)

agent.start("Read sales_data.csv and provide a summary")
2

With Configuration

Add memory so the agent compares datasets across turns.
from praisonaiagents import Agent
from praisonai_tools import read_csv, get_summary, filter_data

agent = Agent(
    name="DataAnalyst",
    instructions="Analyse datasets and track findings across sessions.",
    tools=[read_csv, get_summary, filter_data],
    memory=True,
)

agent.start("Compare this quarter's sales_data.csv against last quarter.")

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How It Works

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Simple

Agents: 1 — Single agent with data tools handles file operations and analysis.

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Workflow

  1. Read data from CSV/Excel
  2. Analyze with filtering, grouping
  3. Generate statistical summaries

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Setup

pip install praisonaiagents praisonai pandas openpyxl
export OPENAI_API_KEY="your-key"

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Run — Python

from praisonaiagents import Agent
from praisonai_tools import read_csv, get_summary, filter_data

agent = Agent(
    name="DataAnalyst",
    instructions="You are a data analyst. Analyze data and provide insights.",
    tools=[read_csv, get_summary, filter_data]
)

result = agent.start("Read sales_data.csv and provide a summary")
print(result)

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Run — CLI

praisonai "Analyze data.csv and summarize key metrics" --tools pandas

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Run — agents.yaml

framework: praisonai
topic: Data Analysis
roles:
  data_analyst:
    role: Data Analyst
    goal: Analyze data and generate insights
    backstory: You are an expert data analyst
    tools:
      - read_csv
      - get_summary
      - filter_data
    tasks:
      analyze_data:
        description: Read sales_data.csv and provide a summary
        expected_output: A data analysis report

praisonai agents.yaml

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Serve API

from praisonaiagents import Agent
from praisonai_tools import read_csv, get_summary, filter_data

agent = Agent(
    name="DataAnalyst",
    instructions="You are a data analyst.",
    tools=[read_csv, get_summary, filter_data]
)

agent.launch(port=8080)

curl -X POST http://localhost:8080/chat \
  -H "Content-Type: application/json" \
  -d '{"message": "Summarize the uploaded data"}'

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Advanced Workflow (All Features)

Agents: 1 — Single agent with memory, persistence, structured output, and session resumability.

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Workflow

  1. Initialize session for analysis tracking
  2. Configure SQLite persistence for analysis history
  3. Read and analyze data with structured output
  4. Store insights in memory for comparison
  5. Resume session for iterative analysis

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Setup

pip install praisonaiagents praisonai pandas openpyxl pydantic
export OPENAI_API_KEY="your-key"

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Run — Python

from praisonaiagents import Agent, Task, AgentTeam, Session
from praisonai_tools import read_csv, get_summary, filter_data
from pydantic import BaseModel

# Structured output schema
class DataInsights(BaseModel):
    dataset: str
    row_count: int
    key_metrics: list[str]
    trends: list[str]
    recommendations: list[str]

# Create session for analysis tracking
session = Session(session_id="analysis-001", user_id="user-1")

# Agent with memory and tools
agent = Agent(
    name="DataAnalyst",
    instructions="Analyze data and return structured insights.",
    tools=[read_csv, get_summary, filter_data],
    memory=True
)

# Task with structured output
task = Task(
    description="Read sales_data.csv and provide structured insights",
    expected_output="Structured data analysis",
    agent=agent,
    output_pydantic=DataInsights
)

# Run with SQLite persistence
agents = AgentTeam(
    agents=[agent],
    tasks=[task],
    memory=True
)

result = agents.start()
print(result)

# Resume later
session2 = Session(session_id="analysis-001", user_id="user-1")
history = session2.search_memory("sales")

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Run — CLI

praisonai "Analyze data.csv" --tools pandas --memory --verbose

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Run — agents.yaml

framework: praisonai
topic: Data Analysis
memory: true
memory_config:
  provider: sqlite
  db_path: analysis.db
roles:
  data_analyst:
    role: Data Analyst
    goal: Analyze data with structured output
    backstory: You are an expert data analyst
    tools:
      - read_csv
      - get_summary
      - filter_data
    memory: true
    tasks:
      analyze_data:
        description: Read sales_data.csv and provide structured insights
        expected_output: Structured data analysis
        output_json:
          dataset: string
          row_count: number
          key_metrics: array
          trends: array
          recommendations: array

praisonai agents.yaml --verbose

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Serve API

from praisonaiagents import Agent
from praisonai_tools import read_csv, get_summary, filter_data

agent = Agent(
    name="DataAnalyst",
    instructions="Analyze data and return structured insights.",
    tools=[read_csv, get_summary, filter_data],
    memory=True
)

agent.launch(port=8080)

curl -X POST http://localhost:8080/chat \
  -H "Content-Type: application/json" \
  -d '{"message": "Analyze data", "session_id": "analysis-001"}'

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Monitor / Verify

praisonai "test analysis" --tools pandas --verbose

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Cleanup

rm -f analysis.db

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Features Demonstrated

FeatureImplementation
WorkflowMulti-tool data analysis
DB PersistenceSQLite via memory_config
Observability--verbose flag
Toolspandas (read, summary, filter)
ResumabilitySession with session_id
Structured OutputPydantic DataInsights model

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Best Practices

Call read_csv a single time and reuse filter_data for follow-up questions. Re-reading large files on every turn wastes tokens and slows the agent down.
When the same analysis runs regularly, pair the agent with a Pydantic schema via output_pydantic so downstream code gets typed fields instead of prose.
Set memory=True to let the agent compare a new dataset against earlier findings without re-loading prior files.
Use the Data Analyst for files you own; switch to the Finance Agent when the question needs live stock prices or fundamentals.

Analyze stocks with live price and fundamentals tools.

Research topics across the web and synthesise findings.