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Quickstart Guide: UiPath LangChain Agents

Introduction

This guide provides step-by-step instructions for setting up, creating, publishing, and running your first UiPath-LangChain Agent.

Prerequisites

Before proceeding, ensure you have the following installed:

  • Python 3.10 or higher
  • pip or uv package manager
  • A UiPath Automation Cloud account with appropriate permissions
  • An Anthropic or OpenAI API key

Info

  1. Anthropic - Generate an Anthropic API key here.

  2. OpenAI - Generate an OpenAI API key here.

Creating a New Project

We recommend using uv for package management. To create a new project:

mkdir examplecd example
New-Item -ItemType Directory -Path exampleSet-Location example
# Initialize a new uv project in the current directoryuv init . --python 3.10# Create a new virtual environment# By default, uv creates a virtual environment in a directory called .venvuv venvUsing CPython 3.10.16 interpreter at: [PATH]
Creating virtual environment at: .venv
Activate with: source .venv/bin/activate
# Activate the virtual environment# For Windows PowerShell/ Windows CMD: .venv\Scripts\activate# For Windows Bash: source .venv/Scripts/activatesource .venv/bin/activate# Install the langchain anthropic packageuv add langchain-anthropic# Install the uipath packageuv add uipath-langchain# Verify the uipath installationuipath -lvuipath-langchain version 0.0.100
# Create a new virtual environmentpython -m venv .venv# Activate the virtual environment# For Windows PowerShell: .venv\Scripts\Activate.ps1# For Windows Bash: source .venv/Scripts/activatesource .venv/bin/activate# Upgrade pip to the latest versionpython -m pip install --upgrade pip# Install the langchain anthropic packagepip install langchain-anthropic# Install the uipath packagepip install uipath-langchain# Verify the uipath installationuipath -lvuipath-langchain version 0.0.100

Create Your First UiPath Agent

Generate your first UiPath LangChain agent:

uipath new my-agent⠋ Creating new agent my-agent in current directory ...
✓ Created 'main.py' file.
✓ Created 'langgraph.json' file.
✓ Created 'pyproject.toml' file.
🔧 Please ensure to define either ANTHROPIC_API_KEY or OPENAI_API_KEY in your .env file.
💡 Initialize project: uipath init
💡 Run agent: uipath run agent '{"topic": "UiPath"}'

This command creates the following files:

File Name Description
main.py LangGraph agent code.
langgraph.json LangGraph specific configuration file.
pyproject.toml Project metadata and dependencies as per PEP 518.

Initialize Project

uipath init⠋ Initializing UiPath project ...
✓ Created '.env' file.
✓ Created 'agent.mermaid' file.
✓ Created 'uipath.json' file.

This command creates the following files:

File Name Description
.env Environment variables and secrets (this file will not be packed & published).
uipath.json Input/output JSON schemas and bindings.
agent.mermaid Graph visual representation.

Set Up Environment Variables

Before running the agent, configure either OPENAI_API_KEY or ANTHROPIC_API_KEY in the .env file:

OPENAI_API_KEY=sk-proj-......
ANTHROPIC_API_KEY=sk-ant-a.....

Authenticate With UiPath

uipath auth⠋ Authenticating with UiPath ...
🔗 If a browser window did not open, please open the following URL in your browser: [LINK]
👇 Select tenant:
0: Tenant1
1: Tenant2
Select tenant number: 0
Selected tenant: Tenant1
✓ Authentication successful.

Run The Agent Locally

Execute the agent with a sample input:

uipath run agent '{"topic": "UiPath"}'[2025-04-29 12:31:57,756][INFO] ((), {'topic': 'UiPath'})
[2025-04-29 12:32:07,689][INFO] ((), {'topic': 'UiPath', 'report': "..."})

This command runs your agent locally and displays the report in the standard output.

Warning

Depending on the shell you are using, it may be necessary to escape the input json:

uipath run agent '{"topic": "UiPath"}'
uipath run agent "{""topic"": ""UiPath""}"
uipath run agent '{\"topic\":\"uipath\"}'

Attention

For a shell agnostic option, please refer to the next section.

(Optional) Run The Agent with a json File as Input

The run command can also take a .json file as an input. You can create a file named input.json having the following content:

{
  "topic": "UiPath"
}

Use this file as agent input:

> uipath run agent --file input.json

Deploy the Agent to UiPath Automation Cloud

Follow these steps to publish and run your agent to UiPath Automation Cloud:

(Optional) Customize the Package

Update author details in pyproject.toml:

authors = [{ name = "Your Name", email = "your.name@example.com" }]

Package Your Project

uipath pack⠋ Packaging project ...
Name : test
Version : 0.1.0
Description: Add your description here
Authors : Your Name
✓ Project successfully packaged.

Publish To My Workspace

uipath publish --my-workspace⠙ Publishing most recent package: my-agent.0.0.1.nupkg ...
✓ Package published successfully!
⠦ Getting process information ...
🔗 Process configuration link: [LINK]
💡 Use the link above to configure any environment variables

Info

Please note that a process will be auto-created only upon publishing to my-workspace package feed.

Set the environment variables using the provided link:

Invoke the Agent on UiPath Automation Cloud

uipath invoke agent '{"topic": "UiPath"}'⠴ Loading configuration ...
⠴ Starting job ...
✨ Job started successfully!
🔗 Monitor your job here: [LINK]

Use the provided link to monitor your job and view detailed traces.

(Optional) Invoke The Agent with a json File as Input

The invoke command operates similarly to the run command, allowing you to use the same .json file defined in the (Optional) Run the agent with a .json file as input section, as agent input:

> uipath invoke agent --file input.json

Next Steps

Congratulations! You have successfully set up, created, published, and run a UiPath LangChain Agent. 🚀

For more advanced agents and agent samples, please refer to our samples section in GitHub.