What are Python Agents? Python Agents are code-based automations that run directly in Agent Studio using the Narada Python SDK. Unlike GUI workflow agents, Python Agents give you the full flexibility of Python code for loops, conditions, data processing, API calls, and browser automation.
Full Python Write Python code with access to the Narada SDK, Pydantic, and common libraries
SDK-Powered Use Agent.run(), browser actions, Google Sheets helpers, and human-in-the-loop prompts
How Python Agents Are Created There are three ways to create a Python Agent in Agent Studio:
1. Agent Maker Describe your goal in natural language, and Agent Maker generates a complete Python Agent for you:
/agentMaker Create an agent that extracts product prices from amazon.com and saves them to a Google Sheet
2. Imitation Learning Record yourself performing a task in the browser, and Imitation Learning generates an agent from your recording.
3. Manual Creation Create a Python Agent from scratch in Agent Studio:
Open Agent Studio
Navigate to Agent Studio and click + Create .
Select Python Agent
Choose the Python Agent option from the creation dialog.
Write your code
The editor opens with a starter template. Write your automation using the Narada SDK.
Writing Python Agent Code In Agent Studio, BrowserEnvironment() targets the current browser window. Create an Agent with that environment, then call agent.run() or browser action methods on the agent:
from narada import Agent, BrowserEnvironment env = BrowserEnvironment() agent = Agent( environment = env) # Navigate to a starting page. Use new_tab=True for the first navigation. await agent.go_to_url( url = "https://example.com" , new_tab = True ) response = await agent.run( prompt = "Extract the main heading from this page" , ) print (response.text)
Use new_tab=True for the first go_to_url() call in Agent Studio. This keeps the workflow runtime tab separate from the page your automation controls.
Using Structured Output Extract typed data using Pydantic models:
from narada import Agent, BrowserEnvironment from pydantic import BaseModel, Field class ProductInfo ( BaseModel ): name: str = Field( description = "Product name" ) price: str = Field( description = "Product price with currency" ) rating: str = Field( description = "Product rating out of 5" ) env = BrowserEnvironment() agent = Agent( environment = env) await agent.go_to_url( url = "https://example.com/product" , new_tab = True ) response = await agent.run( prompt = "Extract the product information from this page" , output_schema = ProductInfo, ) product = response.structured_output assert product is not None print ( f " { product.name } : { product.price } ( { product.rating } )" )
Python Agents can accept input variables when invoked, making them reusable with different data:
from narada import Agent, BrowserEnvironment env = BrowserEnvironment() agent = Agent( environment = env) # Access input variables with sensible defaults. search_term = variables.get( "searchTerm" , "machine learning" ) max_results = variables.get( "maxResults" , 5 ) await agent.go_to_url( url = f "https://www.google.com/search?q= { search_term } " , new_tab = True , ) response = await agent.run( prompt = f "Extract the top { max_results } search result titles" , ) print (response.text)
Use variables.get("key", default) instead of variables["key"] so your agent can run standalone without input variables.
To pass values when invoking a custom Python agent, see Input Variables .
Google Sheets Integration Read from and write to Google Sheets:
from narada import Agent, BrowserEnvironment env = BrowserEnvironment() agent = Agent( environment = env) sheet_data = await agent.read_google_sheet( spreadsheet_id = "your-spreadsheet-id" , range = "Sheet1!A2:A10" , ) companies = [row[ 0 ] for row in sheet_data.values] results = [] for company in companies: await agent.go_to_url( url = f "https://www.google.com/search?q= { company } +valuation" , ) response = await agent.run( prompt = f "What is { company } 's latest valuation?" , ) results.append([company, response.text]) await agent.write_google_sheet( spreadsheet_id = "your-spreadsheet-id" , range = "Sheet1!B2" , values = results, ) await agent.print_message( message = "Done! Results written to Google Sheet." )
Available SDK Methods Python Agents have access to the same agent-centered SDK methods as standalone scripts:
Method Description run()Execute an AI-powered automation task go_to_url()Navigate to a URL agentic_selector()Interact with specific UI elements agentic_mouse_action()Replay recorded mouse actions with agent fallback get_url()Read the current page URL get_screenshot()Capture a screenshot get_full_html()Get the page’s full HTML get_simplified_html()Get cleaned HTML print_message()Show a message in the side panel chat prompt_for_user_input()Collect structured user input user_approval()Ask the user to approve or reject a step read_google_sheet()Read data from Google Sheets write_google_sheet()Write data to Google Sheets
Agent Kinds Choose the agent kind when you construct an Agent:
OPERATOR Browser automation: clicks, navigation, form filling, and data extraction from pages.
CORE_AGENT Read-only reasoning: extracting visible data, answering questions about page content, and conversation-style tasks.
from narada import Agent, AgentKind, BrowserEnvironment env = BrowserEnvironment() # Web automation (default) operator = Agent( environment = env) await operator.run( prompt = "Click the Submit button" ) # Read-only reasoning core_agent = Agent( environment = env, kind = AgentKind. CORE_AGENT ) await core_agent.run( prompt = "What products are listed?" )
Running Python Agents From Agent Studio Click the Run button in the Agent Studio editor to execute your Python Agent in the current browser.
From the SDK Invoke a custom Python Agent from the SDK using the namespaced format:
import asyncio from narada import Agent, BrowserEnvironment async def main () -> None : env = BrowserEnvironment() custom_agent = Agent( environment = env, kind = "/$USER/my-research-agent" ) try : response = await custom_agent.run( prompt = "Search for machine learning papers" , ) print (response.text) finally : await env.close() if __name__ == "__main__" : asyncio.run(main())
From Chat Type the agent shortcut in the Narada chat:
/$USER/my-research-agent Search for machine learning papers
Best Practices
Use new_tab=True First Open a new tab for the first navigation so the workflow runtime stays separate
Handle Errors Catch NaradaTimeoutError for slow pages and call agent.reset_agent_state() after timeouts
Use Structured Output Define Pydantic models for reliable data extraction instead of parsing text responses
Print Progress Use print_message() to show progress updates in the side panel during long workflows
