Build a data analysis agent

Overview

This guide demonstrates how to build a data analysis agent using a deep agent. Data analysis tasks typically require planning, code execution, and working with artifacts such as scripts, reports, and plots—capabilities that deep agents are designed to handle. The agent we’ll build will:

Accept a CSV file for analysis
Perform exploratory data analysis and generate visualizations
Share results to a Slack channel

The Slack integration is optional. The agent can be modified to save artifacts locally or share results through other channels.

Key concepts

This tutorial covers:

Backends for sandboxed code execution
Custom tools for external integrations

Setup

Installation

Install the core dependencies:

pip

pip install deepagents

Optional dependencies

For this tutorial, we’ll use:

Daytona for sandboxed code execution (API key setup) through the langchain-daytona package
Slack Python SDK for sharing results (token setup)

pip

pip install langchain-daytona slack-sdk

Both services are optional. You can use the local shell backend (with important security considerations) or download artifacts directly from the backend.

LangSmith

Many of the applications you build with LangChain will contain multiple steps with multiple invocations of LLM calls. As these applications get more complex, it becomes crucial to be able to inspect what exactly is going on inside your chain or agent. The best way to do this is with LangSmith. After you sign up at the link above, make sure to set your environment variables to start logging traces:

export LANGSMITH_TRACING="true"
export LANGSMITH_API_KEY="..."

Or, set them in Python:

import getpass
import os

os.environ["LANGSMITH_TRACING"] = "true"
os.environ["LANGSMITH_API_KEY"] = getpass.getpass()

Set up the backend

Deep agents use backends to execute code in sandboxed environments.

Daytona Sandbox
Local shell

We can create a sandbox easily using the langchain-daytona package:

from langchain_daytona import DaytonaProvider

provider = DaytonaProvider()
backend = provider.get_or_create()

Verify the sandbox is ready:

result = backend.execute("echo ready")
print(result)
# ExecuteResponse(exit_code=0, result='ready', ...)

This backend provides unrestricted filesystem and shell access. Use only in controlled environments for development and testing. See the security considerations for more details.

from deepagents.backends import LocalShellBackend

backend = LocalShellBackend(root_dir=".", env={"PATH": "/usr/bin:/bin"})

Upload sample data

Create and upload sample sales data to the backend:

import csv
import io

# Create sample sales data
data = [
    ["Date", "Product", "Units Sold", "Revenue"],
    ["2025-08-01", "Widget A", 10, 250],
    ["2025-08-02", "Widget B", 5, 125],
    ["2025-08-03", "Widget A", 7, 175],
    ["2025-08-04", "Widget C", 3, 90],
    ["2025-08-05", "Widget B", 8, 200],
]

# Convert to CSV bytes
text_buf = io.StringIO()
writer = csv.writer(text_buf)
writer.writerows(data)
csv_bytes = text_buf.getvalue().encode("utf-8")
text_buf.close()

# Upload to backend
backend.upload_files([("./data/sales_data.csv", csv_bytes)])

Implement custom tools

Data analysis tasks might produce artefacts, like reports or plots. The following simple tool downloads them with backend.download_files and then uploads them using the Slack SDK. We could also ask our agent to list the relevant file paths instead of uploading them, so interested parties can obtain them separately as needed.

from langchain.tools import tool
from slack_sdk import WebClient


slack_token = os.environ["SLACK_USER_TOKEN"]
slack_client = WebClient(token=slack_token)


@tool(parse_docstring=True)
def slack_send_message(text: str, file_path: str | None = None) -> str:
    """Send message, optionally including attachments such as images.

    Args:
        text: (str) text content of the message
        file_path: (str) file path of attachment in the filesystem.
    """
    if not file_path:
        slack_client.chat_postMessage(channel=channel, text=text)
    else:
        fp = backend.download_files([file_path])
        slack_client.files_upload_v2(
            channel="C0123456ABC",  # specify your own channel here
            content=fp[0].content,
            initial_comment=text,
        )

    return "Message sent."

It is generally good practice to avoid adding credentials and other secrets to the sandbox. Here we manage the Slack token outside the sandbox in a tool.

Run the agent

Let’s instantiate an agent:

from langgraph.checkpoint.memory import InMemorySaver
from deepagents import create_deep_agent


checkpointer = InMemorySaver()

agent = create_deep_agent(
    model="anthropic:claude-sonnet-4-5",
    tools=[slack_send_message],
    backend=backend,
    checkpointer=checkpointer,
)

We include:

A choice of model
Our custom tool
The backend
A checkpointer to support multi-turn conversations

Let’s now invoke our agent.

input_message = {
    "role": "user",
    "content": (
        "Analyze ./data/sales_data.csv in the current dir and generate a beautiful plot. "
        "When finished, send your analysis and the plot to "
        "Slack channel C0123456ABC."  # Substitute your own channel
    ),
}
for step in agent.stream(
    {"messages": [input_message]},
    config,
    stream_mode="updates",
):
    for _, update in step.items():
        if update and (messages := update.get("messages")) and isinstance(messages, list):
            for message in messages:
                message.pretty_print()

================================== Ai Message ==================================

[{'text': "I'll help you analyze the sales data and create a beautiful plot, then send the results to Slack. Let me start by exploring the data.", 'type': 'text'}, {'id': 'toolu_01LRot5h6WkhdpDQ1SG6EQGQ', 'input': {'file_path': './data/sales_data.csv'}, 'name': 'read_file', 'type': 'tool_use'}]
Tool Calls:
  read_file (toolu_01LRot5h6WkhdpDQ1SG6EQGQ)
 Call ID: toolu_01LRot5h6WkhdpDQ1SG6EQGQ
  Args:
    file_path: ./data/sales_data.csv
================================= Tool Message =================================
Name: read_file

Error: File '/data/sales_data.csv' not found
================================== Ai Message ==================================

[{'text': 'Let me check the current directory structure first:', 'type': 'text'}, {'id': 'toolu_018DSeF8Dx714Dh2M5vJ1JuK', 'input': {'command': 'pwd'}, 'name': 'execute', 'type': 'tool_use'}, {'id': 'toolu_01As782aRsVB3mJpKPKNwg9c', 'input': {'path': '/'}, 'name': 'ls', 'type': 'tool_use'}]
Tool Calls:
  execute (toolu_018DSeF8Dx714Dh2M5vJ1JuK)
 Call ID: toolu_018DSeF8Dx714Dh2M5vJ1JuK
  Args:
    command: pwd
  ls (toolu_01As782aRsVB3mJpKPKNwg9c)
 Call ID: toolu_01As782aRsVB3mJpKPKNwg9c
  Args:
    path: /
================================= Tool Message =================================
Name: execute

/home/daytona
[Command succeeded with exit code 0]
================================= Tool Message =================================
Name: ls

['/bin', '/boot', '/dev', '/etc', '/home', '/lib', '/lib64', '/media', '/mnt', '/opt', '/proc', '/root', '/run', '/sbin', '/srv', '/sys', '/tmp', '/usr', '/var', '/.dockerenv']
================================== Ai Message ==================================

[{'id': 'toolu_01Sv4ut1P2XFsjrzVmQbJLpr', 'input': {'path': '/home/daytona'}, 'name': 'ls', 'type': 'tool_use'}]
Tool Calls:
  ls (toolu_01Sv4ut1P2XFsjrzVmQbJLpr)
 Call ID: toolu_01Sv4ut1P2XFsjrzVmQbJLpr
  Args:
    path: /home/daytona
================================= Tool Message =================================
Name: ls

['/home/daytona/.bash_logout', '/home/daytona/.bashrc', '/home/daytona/.face', '/home/daytona/.face.icon', '/home/daytona/.profile', '/home/daytona/.daytona', '/home/daytona/data', '/home/daytona/.zshrc']
================================== Ai Message ==================================

[{'text': "Great! I can see there's a data directory. Let me check its contents:", 'type': 'text'}, {'id': 'toolu_01WN6V9RZjGGoDXzuWdEgB1z', 'input': {'path': '/home/daytona/data'}, 'name': 'ls', 'type': 'tool_use'}]
Tool Calls:
  ls (toolu_01WN6V9RZjGGoDXzuWdEgB1z)
 Call ID: toolu_01WN6V9RZjGGoDXzuWdEgB1z
  Args:
    path: /home/daytona/data
================================= Tool Message =================================
Name: ls

['/home/daytona/data/sales_data.csv']
================================== Ai Message ==================================

[{'text': 'Perfect! Now let me read the sales data:', 'type': 'text'}, {'id': 'toolu_01RFmneg7mua1G5csH3rH2Zq', 'input': {'file_path': '/home/daytona/data/sales_data.csv'}, 'name': 'read_file', 'type': 'tool_use'}]
Tool Calls:
  read_file (toolu_01RFmneg7mua1G5csH3rH2Zq)
 Call ID: toolu_01RFmneg7mua1G5csH3rH2Zq
  Args:
    file_path: /home/daytona/data/sales_data.csv
================================= Tool Message =================================
Name: read_file

     1	Date,Product,Units Sold,Revenue
     2	2025-08-01,Widget A,10,250
     3	2025-08-02,Widget B,5,125
     4	2025-08-03,Widget A,7,175
     5	2025-08-04,Widget C,3,90
     6	2025-08-05,Widget B,8,200
================================== Ai Message ==================================

[{'text': "Excellent! I can see the sales data. Now I'll create a Python script to analyze the data and generate a beautiful plot. Let me create a comprehensive analysis with visualizations:", 'type': 'text'}, {'id': 'toolu_01JcBjEXgPpCPuaGxn9ALwMg', 'input': {'file_path': '/home/daytona/analyze_sales.py', 'content': 'import pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom datetime import datetime\n\n# Set style for beautiful plots\nsns.set_style("whitegrid")\nplt.rcParams[\'figure.figsize\'] = (14, 10)\nplt.rcParams[\'font.size\'] = 11\n\n# Read the data\ndf = pd.read_csv(\'/home/daytona/data/sales_data.csv\')\ndf[\'Date\'] = pd.to_datetime(df[\'Date\'])\n\n# Analysis\nprint("=" * 60)\nprint("SALES DATA ANALYSIS REPORT")\nprint("=" * 60)\nprint("\\n1. DATA OVERVIEW")\nprint("-" * 60)\nprint(f"Date Range: {df[\'Date\'].min().strftime(\'%Y-%m-%d\')} to {df[\'Date\'].max().strftime(\'%Y-%m-%d\')}")\nprint(f"Total Records: {len(df)}")\nprint(f"Products: {\', \'.join(df[\'Product\'].unique())}")\n\nprint("\\n2. SUMMARY STATISTICS")\nprint("-" * 60)\nprint(f"Total Revenue: ${df[\'Revenue\'].sum():,.2f}")\nprint(f"Total Units Sold: {df[\'Units Sold\'].sum()}")\nprint(f"Average Daily Revenue: ${df[\'Revenue\'].mean():.2f}")\nprint(f"Average Units per Sale: {df[\'Units Sold\'].mean():.2f}")\n\nprint("\\n3. PRODUCT PERFORMANCE")\nprint("-" * 60)\nproduct_stats = df.groupby(\'Product\').agg({\n    \'Revenue\': [\'sum\', \'mean\'],\n    \'Units Sold\': [\'sum\', \'mean\']\n}).round(2)\nprint(product_stats)\n\nprint("\\n4. BEST PERFORMING DAY")\nprint("-" * 60)\nbest_day = df.loc[df[\'Revenue\'].idxmax()]\nprint(f"Date: {best_day[\'Date\'].strftime(\'%Y-%m-%d\')}")\nprint(f"Product: {best_day[\'Product\']}")\nprint(f"Revenue: ${best_day[\'Revenue\']:.2f}")\nprint(f"Units Sold: {best_day[\'Units Sold\']}")\n\n# Create visualizations\nfig = plt.figure(figsize=(16, 12))\nfig.suptitle(\'Sales Data Analysis Dashboard\', fontsize=20, fontweight=\'bold\', y=0.995)\n\n# 1. Revenue by Date\nax1 = plt.subplot(2, 3, 1)\ncolors = sns.color_palette("husl", len(df))\nbars = ax1.bar(df[\'Date\'].dt.strftime(\'%m-%d\'), df[\'Revenue\'], color=colors, edgecolor=\'black\', linewidth=1.5)\nax1.set_title(\'Daily Revenue\', fontsize=14, fontweight=\'bold\', pad=10)\nax1.set_xlabel(\'Date\', fontsize=12, fontweight=\'bold\')\nax1.set_ylabel(\'Revenue ($)\', fontsize=12, fontweight=\'bold\')\nax1.grid(axis=\'y\', alpha=0.3)\nfor bar in bars:\n    height = bar.get_height()\n    ax1.text(bar.get_x() + bar.get_width()/2., height,\n            f\'${int(height)}\',\n            ha=\'center\', va=\'bottom\', fontsize=9, fontweight=\'bold\')\nplt.setp(ax1.xaxis.get_majorticklabels(), rotation=45)\n\n# 2. Units Sold by Date\nax2 = plt.subplot(2, 3, 2)\nbars = ax2.bar(df[\'Date\'].dt.strftime(\'%m-%d\'), df[\'Units Sold\'], color=colors, edgecolor=\'black\', linewidth=1.5)\nax2.set_title(\'Daily Units Sold\', fontsize=14, fontweight=\'bold\', pad=10)\nax2.set_xlabel(\'Date\', fontsize=12, fontweight=\'bold\')\nax2.set_ylabel(\'Units\', fontsize=12, fontweight=\'bold\')\nax2.grid(axis=\'y\', alpha=0.3)\nfor bar in bars:\n    height = bar.get_height()\n    ax2.text(bar.get_x() + bar.get_width()/2., height,\n            f\'{int(height)}\',\n            ha=\'center\', va=\'bottom\', fontsize=9, fontweight=\'bold\')\nplt.setp(ax2.xaxis.get_majorticklabels(), rotation=45)\n\n# 3. Revenue by Product (Pie Chart)\nax3 = plt.subplot(2, 3, 3)\nproduct_revenue = df.groupby(\'Product\')[\'Revenue\'].sum()\ncolors_pie = sns.color_palette("Set2", len(product_revenue))\nwedges, texts, autotexts = ax3.pie(product_revenue, labels=product_revenue.index, autopct=\'%1.1f%%\',\n                                     colors=colors_pie, startangle=90, textprops={\'fontsize\': 11, \'fontweight\': \'bold\'},\n                                     explode=[0.05] * len(product_revenue))\nax3.set_title(\'Revenue Distribution by Product\', fontsize=14, fontweight=\'bold\', pad=10)\nfor autotext in autotexts:\n    autotext.set_color(\'white\')\n    autotext.set_fontsize(12)\n\n# 4. Total Revenue by Product (Bar Chart)\nax4 = plt.subplot(2, 3, 4)\nproduct_revenue_sorted = product_revenue.sort_values(ascending=False)\ncolors_product = sns.color_palette("coolwarm", len(product_revenue_sorted))\nbars = ax4.barh(product_revenue_sorted.index, product_revenue_sorted.values, color=colors_product, edgecolor=\'black\', linewidth=1.5)\nax4.set_title(\'Total Revenue by Product\', fontsize=14, fontweight=\'bold\', pad=10)\nax4.set_xlabel(\'Revenue ($)\', fontsize=12, fontweight=\'bold\')\nax4.set_ylabel(\'Product\', fontsize=12, fontweight=\'bold\')\nax4.grid(axis=\'x\', alpha=0.3)\nfor i, (product, revenue) in enumerate(product_revenue_sorted.items()):\n    ax4.text(revenue, i, f\' ${int(revenue)}\', va=\'center\', fontsize=10, fontweight=\'bold\')\n\n# 5. Total Units Sold by Product\nax5 = plt.subplot(2, 3, 5)\nproduct_units = df.groupby(\'Product\')[\'Units Sold\'].sum().sort_values(ascending=False)\ncolors_units = sns.color_palette("viridis", len(product_units))\nbars = ax5.barh(product_units.index, product_units.values, color=colors_units, edgecolor=\'black\', linewidth=1.5)\nax5.set_title(\'Total Units Sold by Product\', fontsize=14, fontweight=\'bold\', pad=10)\nax5.set_xlabel(\'Units\', fontsize=12, fontweight=\'bold\')\nax5.set_ylabel(\'Product\', fontsize=12, fontweight=\'bold\')\nax5.grid(axis=\'x\', alpha=0.3)\nfor i, (product, units) in enumerate(product_units.items()):\n    ax5.text(units, i, f\' {int(units)}\', va=\'center\', fontsize=10, fontweight=\'bold\')\n\n# 6. Product Distribution\nax6 = plt.subplot(2, 3, 6)\nproduct_counts = df[\'Product\'].value_counts()\ncolors_count = sns.color_palette("muted", len(product_counts))\nwedges, texts, autotexts = ax6.pie(product_counts, labels=product_counts.index, autopct=\'%1.1f%%\',\n                                     colors=colors_count, startangle=45, textprops={\'fontsize\': 11, \'fontweight\': \'bold\'},\n                                     explode=[0.05] * len(product_counts))\nax6.set_title(\'Sales Transactions by Product\', fontsize=14, fontweight=\'bold\', pad=10)\nfor autotext in autotexts:\n    autotext.set_color(\'white\')\n    autotext.set_fontsize(12)\n\nplt.tight_layout()\nplt.savefig(\'/home/daytona/sales_analysis_plot.png\', dpi=300, bbox_inches=\'tight\', facecolor=\'white\')\nprint("\\n" + "=" * 60)\nprint("Plot saved as: sales_analysis_plot.png")\nprint("=" * 60)\n'}, 'name': 'write_file', 'type': 'tool_use'}]
Tool Calls:
  write_file (toolu_01JcBjEXgPpCPuaGxn9ALwMg)
 Call ID: toolu_01JcBjEXgPpCPuaGxn9ALwMg
  Args:
    file_path: /home/daytona/analyze_sales.py
    content: import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from datetime import datetime

# Set style for beautiful plots
sns.set_style("whitegrid")
plt.rcParams['figure.figsize'] = (14, 10)
plt.rcParams['font.size'] = 11

# Read the data
df = pd.read_csv('/home/daytona/data/sales_data.csv')
df['Date'] = pd.to_datetime(df['Date'])

# Analysis
print("=" * 60)
print("SALES DATA ANALYSIS REPORT")
print("=" * 60)
print("\n1. DATA OVERVIEW")
print("-" * 60)
print(f"Date Range: {df['Date'].min().strftime('%Y-%m-%d')} to {df['Date'].max().strftime('%Y-%m-%d')}")
print(f"Total Records: {len(df)}")
print(f"Products: {', '.join(df['Product'].unique())}")

print("\n2. SUMMARY STATISTICS")
print("-" * 60)
print(f"Total Revenue: ${df['Revenue'].sum():,.2f}")
print(f"Total Units Sold: {df['Units Sold'].sum()}")
print(f"Average Daily Revenue: ${df['Revenue'].mean():.2f}")
print(f"Average Units per Sale: {df['Units Sold'].mean():.2f}")

print("\n3. PRODUCT PERFORMANCE")
print("-" * 60)
product_stats = df.groupby('Product').agg({
    'Revenue': ['sum', 'mean'],
    'Units Sold': ['sum', 'mean']
}).round(2)
print(product_stats)

print("\n4. BEST PERFORMING DAY")
print("-" * 60)
best_day = df.loc[df['Revenue'].idxmax()]
print(f"Date: {best_day['Date'].strftime('%Y-%m-%d')}")
print(f"Product: {best_day['Product']}")
print(f"Revenue: ${best_day['Revenue']:.2f}")
print(f"Units Sold: {best_day['Units Sold']}")

# Create visualizations
fig = plt.figure(figsize=(16, 12))
fig.suptitle('Sales Data Analysis Dashboard', fontsize=20, fontweight='bold', y=0.995)

# 1. Revenue by Date
ax1 = plt.subplot(2, 3, 1)
colors = sns.color_palette("husl", len(df))
bars = ax1.bar(df['Date'].dt.strftime('%m-%d'), df['Revenue'], color=colors, edgecolor='black', linewidth=1.5)
ax1.set_title('Daily Revenue', fontsize=14, fontweight='bold', pad=10)
ax1.set_xlabel('Date', fontsize=12, fontweight='bold')
ax1.set_ylabel('Revenue ($)', fontsize=12, fontweight='bold')
ax1.grid(axis='y', alpha=0.3)
for bar in bars:
    height = bar.get_height()
    ax1.text(bar.get_x() + bar.get_width()/2., height,
            f'${int(height)}',
            ha='center', va='bottom', fontsize=9, fontweight='bold')
plt.setp(ax1.xaxis.get_majorticklabels(), rotation=45)

# 2. Units Sold by Date
ax2 = plt.subplot(2, 3, 2)
bars = ax2.bar(df['Date'].dt.strftime('%m-%d'), df['Units Sold'], color=colors, edgecolor='black', linewidth=1.5)
ax2.set_title('Daily Units Sold', fontsize=14, fontweight='bold', pad=10)
ax2.set_xlabel('Date', fontsize=12, fontweight='bold')
ax2.set_ylabel('Units', fontsize=12, fontweight='bold')
ax2.grid(axis='y', alpha=0.3)
for bar in bars:
    height = bar.get_height()
    ax2.text(bar.get_x() + bar.get_width()/2., height,
            f'{int(height)}',
            ha='center', va='bottom', fontsize=9, fontweight='bold')
plt.setp(ax2.xaxis.get_majorticklabels(), rotation=45)

# 3. Revenue by Product (Pie Chart)
ax3 = plt.subplot(2, 3, 3)
product_revenue = df.groupby('Product')['Revenue'].sum()
colors_pie = sns.color_palette("Set2", len(product_revenue))
wedges, texts, autotexts = ax3.pie(product_revenue, labels=product_revenue.index, autopct='%1.1f%%',
                                     colors=colors_pie, startangle=90, textprops={'fontsize': 11, 'fontweight': 'bold'},
                                     explode=[0.05] * len(product_revenue))
ax3.set_title('Revenue Distribution by Product', fontsize=14, fontweight='bold', pad=10)
for autotext in autotexts:
    autotext.set_color('white')
    autotext.set_fontsize(12)

# 4. Total Revenue by Product (Bar Chart)
ax4 = plt.subplot(2, 3, 4)
product_revenue_sorted = product_revenue.sort_values(ascending=False)
colors_product = sns.color_palette("coolwarm", len(product_revenue_sorted))
bars = ax4.barh(product_revenue_sorted.index, product_revenue_sorted.values, color=colors_product, edgecolor='black', linewidth=1.5)
ax4.set_title('Total Revenue by Product', fontsize=14, fontweight='bold', pad=10)
ax4.set_xlabel('Revenue ($)', fontsize=12, fontweight='bold')
ax4.set_ylabel('Product', fontsize=12, fontweight='bold')
ax4.grid(axis='x', alpha=0.3)
for i, (product, revenue) in enumerate(product_revenue_sorted.items()):
    ax4.text(revenue, i, f' ${int(revenue)}', va='center', fontsize=10, fontweight='bold')

# 5. Total Units Sold by Product
ax5 = plt.subplot(2, 3, 5)
product_units = df.groupby('Product')['Units Sold'].sum().sort_values(ascending=False)
colors_units = sns.color_palette("viridis", len(product_units))
bars = ax5.barh(product_units.index, product_units.values, color=colors_units, edgecolor='black', linewidth=1.5)
ax5.set_title('Total Units Sold by Product', fontsize=14, fontweight='bold', pad=10)
ax5.set_xlabel('Units', fontsize=12, fontweight='bold')
ax5.set_ylabel('Product', fontsize=12, fontweight='bold')
ax5.grid(axis='x', alpha=0.3)
for i, (product, units) in enumerate(product_units.items()):
    ax5.text(units, i, f' {int(units)}', va='center', fontsize=10, fontweight='bold')

# 6. Product Distribution
ax6 = plt.subplot(2, 3, 6)
product_counts = df['Product'].value_counts()
colors_count = sns.color_palette("muted", len(product_counts))
wedges, texts, autotexts = ax6.pie(product_counts, labels=product_counts.index, autopct='%1.1f%%',
                                     colors=colors_count, startangle=45, textprops={'fontsize': 11, 'fontweight': 'bold'},
                                     explode=[0.05] * len(product_counts))
ax6.set_title('Sales Transactions by Product', fontsize=14, fontweight='bold', pad=10)
for autotext in autotexts:
    autotext.set_color('white')
    autotext.set_fontsize(12)

plt.tight_layout()
plt.savefig('/home/daytona/sales_analysis_plot.png', dpi=300, bbox_inches='tight', facecolor='white')
print("\n" + "=" * 60)
print("Plot saved as: sales_analysis_plot.png")
print("=" * 60)
================================= Tool Message =================================
Name: write_file

Updated file /home/daytona/analyze_sales.py
================================== Ai Message ==================================

[{'text': 'Now let me run the analysis script:', 'type': 'text'}, {'id': 'toolu_015xkZr21TuzCTEKGzSqgnbp', 'input': {'command': 'cd /home/daytona && python analyze_sales.py'}, 'name': 'execute', 'type': 'tool_use'}]
Tool Calls:
  execute (toolu_015xkZr21TuzCTEKGzSqgnbp)
 Call ID: toolu_015xkZr21TuzCTEKGzSqgnbp
  Args:
    command: cd /home/daytona && python analyze_sales.py
================================= Tool Message =================================
Name: execute

============================================================
SALES DATA ANALYSIS REPORT
============================================================

1. DATA OVERVIEW
------------------------------------------------------------
Date Range: 2025-08-01 to 2025-08-05
Total Records: 5
Products: Widget A, Widget B, Widget C

2. SUMMARY STATISTICS
------------------------------------------------------------
Total Revenue: $840.00
Total Units Sold: 33
Average Daily Revenue: $168.00
Average Units per Sale: 6.60

3. PRODUCT PERFORMANCE
------------------------------------------------------------
         Revenue        Units Sold
             sum   mean        sum mean
Product
Widget A     425  212.5         17  8.5
Widget B     325  162.5         13  6.5
Widget C      90   90.0          3  3.0

4. BEST PERFORMING DAY
------------------------------------------------------------
Date: 2025-08-01
Product: Widget A
Revenue: $250.00
Units Sold: 10

============================================================
Plot saved as: sales_analysis_plot.png
============================================================
[Command succeeded with exit code 0]
================================== Ai Message ==================================

[{'text': 'Perfect! The analysis is complete and the plot has been generated. Now let me send both the analysis and the plot to Slack:', 'type': 'text'}, {'id': 'toolu_01Qust5a8DECKPZuU9pBBvkz', 'input': {'text': '📊 **SALES DATA ANALYSIS REPORT** 📊\n\n**1. DATA OVERVIEW**\n• Date Range: August 1-5, 2025 (5 days)\n• Products Analyzed: Widget A, Widget B, Widget C\n\n**2. KEY METRICS**\n💰 Total Revenue: $840.00\n📦 Total Units Sold: 33 units\n📈 Average Daily Revenue: $168.00\n📊 Average Units per Sale: 6.60\n\n**3. PRODUCT PERFORMANCE**\n🏆 Widget A (Top Performer)\n   - Total Revenue: $425 (50.6% of total)\n   - Total Units Sold: 17 (avg 8.5 per transaction)\n\n🥈 Widget B\n   - Total Revenue: $325 (38.7% of total)\n   - Total Units Sold: 13 (avg 6.5 per transaction)\n\n🥉 Widget C\n   - Total Revenue: $90 (10.7% of total)\n   - Total Units Sold: 3 (avg 3.0 per transaction)\n\n**4. BEST PERFORMING DAY**\n🌟 August 1, 2025\n   - Product: Widget A\n   - Revenue: $250.00\n   - Units Sold: 10\n\n**KEY INSIGHTS:**\n✅ Widget A is the clear leader, generating over 50% of total revenue\n✅ Widget B shows strong performance with consistent sales\n⚠️ Widget C has potential for improvement - lowest sales and revenue\n📊 Daily revenue ranges from $90 to $250\n\nSee attached visualization dashboard for detailed charts! 📈', 'file_path': '/home/daytona/sales_analysis_plot.png'}, 'name': 'slack_send_message', 'type': 'tool_use'}]
Tool Calls:
  slack_send_message (toolu_01Qust5a8DECKPZuU9pBBvkz)
 Call ID: toolu_01Qust5a8DECKPZuU9pBBvkz
  Args:
    text: 📊 **SALES DATA ANALYSIS REPORT** 📊

**1. DATA OVERVIEW**
• Date Range: August 1-5, 2025 (5 days)
• Products Analyzed: Widget A, Widget B, Widget C

**2. KEY METRICS**
💰 Total Revenue: $840.00
📦 Total Units Sold: 33 units
📈 Average Daily Revenue: $168.00
📊 Average Units per Sale: 6.60

**3. PRODUCT PERFORMANCE**
🏆 Widget A (Top Performer)
   - Total Revenue: $425 (50.6% of total)
   - Total Units Sold: 17 (avg 8.5 per transaction)

🥈 Widget B
   - Total Revenue: $325 (38.7% of total)
   - Total Units Sold: 13 (avg 6.5 per transaction)

🥉 Widget C
   - Total Revenue: $90 (10.7% of total)
   - Total Units Sold: 3 (avg 3.0 per transaction)

**4. BEST PERFORMING DAY**
🌟 August 1, 2025
   - Product: Widget A
   - Revenue: $250.00
   - Units Sold: 10

**KEY INSIGHTS:**
✅ Widget A is the clear leader, generating over 50% of total revenue
✅ Widget B shows strong performance with consistent sales
⚠️ Widget C has potential for improvement - lowest sales and revenue
📊 Daily revenue ranges from $90 to $250

See attached visualization dashboard for detailed charts! 📈
    file_path: /home/daytona/sales_analysis_plot.png
================================= Tool Message =================================
Name: slack_send_message

Message sent.
================================== Ai Message ==================================

Excellent! I've successfully completed the analysis and sent everything to Slack. Here's what I did:

## Summary

**Analysis Completed:**
- Analyzed the sales data from August 1-5, 2025
- Processed data for 3 products (Widget A, B, and C)
- Generated comprehensive statistics and insights

**Key Findings:**
1. **Total Revenue:** $840 across 5 days
2. **Best Product:** Widget A ($425 revenue, 50.6% of total)
3. **Best Day:** August 1st with $250 in revenue
4. **Total Units Sold:** 33 units

**Visualization Created:**
A beautiful 6-panel dashboard featuring:
- Daily revenue trends
- Daily units sold
- Revenue distribution by product (pie chart)
- Total revenue by product (horizontal bar chart)
- Total units sold by product (horizontal bar chart)
- Sales transactions distribution (pie chart)

**Delivered to Slack:**
✅ Comprehensive analysis report with key metrics and insights
✅ High-resolution visualization dashboard (saved as PNG)

The analysis reveals that Widget A is the top performer, while Widget C shows room for improvement. All results have been sent to your Slack workspace!

View the full LangSmith trace for this execution.

Results

The agent successfully analyzes the data and shares a comprehensive report with visualizations to Slack:

You can download artifacts directly from the backend without using external tools:

backend.download_files(list_of_filepaths)

Next steps

Now that you’ve built a data analysis agent, explore these resources to extend its capabilities:

Backends: Learn about different backend options for sandboxed code execution, including security considerations and advanced configurations
Customization: Discover how to customize your agent with different models, tools, prompts, and planning strategies
CLI: Try the Deep Agents CLI for an interactive command-line interface to assist with data analysis and other agentic tasks locally
Skills: Equip your agent with reusable skills for common workflows
Human-in-the-loop: Add interactive approval steps for critical operations in your data analysis workflow

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Tutorials

Conceptual overviews

Additional resources

Overview

Key concepts

Setup

Installation

Optional dependencies

LangSmith

Set up the backend

Upload sample data

Implement custom tools

Run the agent

Results

Next steps

Tutorials

Conceptual overviews

Additional resources

​Overview

​Key concepts

​Setup

​Installation

​Optional dependencies

​LangSmith

​Set up the backend

​Upload sample data

​Implement custom tools

​Run the agent

​Results

​Next steps

Overview

Key concepts

Setup

Installation

Optional dependencies

LangSmith

Set up the backend

Upload sample data

Implement custom tools

Run the agent

Results

Next steps