langchain-daytona-data-analysis 0.1.1

An integration package connecting DaytonaDataAnalysis and LangChain

langchain-daytona-data-analysis

This package provides the DaytonaDataAnalysisTool - LangChain tool integration that enables agents to perform secure Python data analysis in a sandboxed environment. It supports multi-step workflows, file uploads/downloads, and custom result handling, making it ideal for automating data analysis tasks with LangChain agents.

Installation

pip install -U langchain-daytona-data-analysis

You must configure credentials for Daytona. You can do this in one of three ways:

1. Set the DAYTONA_API_KEY environment variable:

   export DAYTONA_API_KEY="your-daytona-api-key"
   

2. Add it to a .env file in your project root:

   DAYTONA_API_KEY=your-daytona-api-key
   

3. Pass the API key directly when instantiating DaytonaDataAnalysisTool:

   from langchain_daytona_data_analysis import DaytonaDataAnalysisTool
   
   tool = DaytonaDataAnalysisTool(daytona_api_key="your-daytona-api-key")
   

Instantiation

Import and instantiate the tool:

from langchain_daytona_data_analysis import DaytonaDataAnalysisTool
from daytona import ExecutionArtifacts

 # Optionally, you can pass an on_result callback.
 # This callback lets you apply custom logic to the data analysis result.
 # For example, you can save outputs, display charts, or trigger other actions.
def process_data_analysis_result(result: ExecutionArtifacts):
	print(result)

tool = DaytonaDataAnalysisTool(
	daytona_api_key="your-daytona-api-key", # Only pass if not set as DAYTONA_API_KEY environment variable
	on_result=process_data_analysis_result
)

Usage

DaytonaDataAnalysisTool can be used in three ways:

Direct Invocation with Args

tool.invoke({'data_analysis_python_code': "print('Hello World')"})

Invocation with ToolCall

model_generated_tool_call = {
    "args": {'data_analysis_python_code': "print('Hello World')"},
    "id": "1",
    "name": tool.name,
    "type": "tool_call",
}

tool.invoke(model_generated_tool_call)

Usage Inside an Agent

from langchain.agents import create_agent
from langchain_anthropic import ChatAnthropic

model = ChatAnthropic(
    model_name="claude-haiku-4-5-20251001",
    temperature=0,
    max_tokens_to_sample=1024,
    timeout=None,
    max_retries=2,
    stop=None
)

agent = create_agent(model, tools=[tool])

API Reference

The following public methods are available on DaytonaDataAnalysisTool:

download_file(remote_path)

Downloads a file from the sandbox by its remote path.

• remote_path: str — Path to the file in the sandbox.
• Returns: bytes — File contents.

Example:

# Download a file from the sandbox
file_bytes = tool.download_file("/home/daytona/results.csv")

---

upload_file(file, description)

Uploads a file to the sandbox. The file is placed in /home/daytona/.

• file: IO — File-like object to upload.
• description: str — Description of the file, explaining its purpose and the type of data it contains.
• Returns: SandboxUploadedFile — Metadata about the uploaded file.

Example: Suppose you want to analyze sales data for a retail business. You have a CSV file named sales_q3_2025.csv containing columns like transaction_id, date, product, quantity, and revenue. You want to upload this file and provide a description that gives context for the analysis.

with open("sales_q3_2025.csv", "rb") as f:
    uploaded = tool.upload_file(
        f,
        "CSV file containing Q3 2025 retail sales transactions. Columns: transaction_id, date, product, quantity, revenue."
    )

---

remove_uploaded_file(uploaded_file)

Removes a previously uploaded file from the sandbox.

• uploaded_file: SandboxUploadedFile — The file to remove.

Example:

# Remove an uploaded file
tool.remove_uploaded_file(uploaded)

---

get_sandbox()

Gets the current sandbox instance.

• Returns: Sandbox — Sandbox instance.

This method provides access to the Daytona sandbox instance, allowing you to inspect sandbox properties and metadata, as well as perform any sandbox-related operations. For details on available attributes and methods, see the Sandbox data structure section below.

Example:

sandbox = tool.get_sandbox()

---

install_python_packages(package_names)

Installs one or more Python packages in the sandbox using pip.

• package_names: str or List[str] — Name(s) of the package(s) to install.

Note: The list of preinstalled packages in a sandbox can be found at Daytona Default Snapshot documentation.

Example:

# Install a single package
tool.install_python_packages("pandas")

# Install multiple packages
tool.install_python_packages(["numpy", "matplotlib"])

---

close()

Closes and deletes the sandbox environment.

Note: Call this method when you are finished with all data analysis tasks to properly clean up resources and avoid unnecessary usage.

Example:

# Close the sandbox and clean up
tool.close()

---

Data Structures

SandboxUploadedFile

Represents metadata about a file uploaded to the sandbox.

• name: str — Name of the uploaded file in the sandbox
• remote_path: str — Full path to the file in the sandbox
• description: str — Description provided during upload

Sandbox

Represents a Daytona sandbox instance.

See the full structure and API in the Daytona Python SDK Sandbox documentation.