Computer Use for Agentic Process Automation!
The official Narada Python SDK that helps you launch browsers and run tasks with Narada UI agents.
Installation
pip install narada
Quick Start
Important: The first time Narada opens the automated browser, you will need to manually install the Narada Enterprise extension and log in to your Narada account.
After installation and login, create a Narada API Key (see this link for instructions) and set the following environment variable:
export NARADA_API_KEY=<YOUR KEY>
That's it. Now you can run the following code to create a browser environment and ask an agent to download a file for you from arxiv:
import asyncio
from narada import Agent, AgentKind, BrowserEnvironment
async def main() -> None:
# Create the browser environment. It initializes lazily on the first action.
env = BrowserEnvironment()
agent = Agent(environment=env)
mini_agent = Agent(environment=env, kind=AgentKind.OPERATOR_MINI)
try:
# Run a task in this browser environment.
response = await agent.run(
prompt='Search for "LLM Compiler" on Google and open the first arXiv paper on the results page, then open the PDF. Then download the PDF of the paper.',
# Optionally generate a GIF of the agent's actions.
generate_gif=True,
)
print("Response:", response.model_dump_json(indent=2))
# Use the product's lower-cost tier when appropriate.
mini_response = await mini_agent.run(prompt="Summarize the completed task.")
print("Mini response:", mini_response.model_dump_json(indent=2))
finally:
await env.close()
if __name__ == "__main__":
asyncio.run(main())
AgentKind.OPERATOR_MINI and AgentKind.CORE_AGENT_MINI select the lower-cost Mini variants of
their respective product agents. Named Agent Studio workflows continue to use their persisted Agent
steps.
This would then result in the following trajectory:
You can use the SDK to launch browsers and run automated tasks using natural language instructions. For more examples and code samples, please explore the examples/ folder in this repository.
Migration note
This version introduces a non-backward-compatible, agent-centered API:
- Create an execution target with an environment, such as
BrowserEnvironment,CloudBrowserEnvironment,RemoteBrowserEnvironment, orLambdaEnvironment. - Create an
Agent(environment=env, kind=...)and callawait agent.run(prompt=...). - Browser actions such as
go_to_url,agentic_selector, and sheet operations are now methods onAgent. - Environments keep lifecycle/bookkeeping APIs such as
start(),close(),browser_window_id, andcloud_browser_session_id.
Features
- Natural Language Control: Send instructions in plain English to control browser actions
- Parallel Execution: Run multiple browser tasks simultaneously across different windows
- Error Handling: Built-in timeout handling and retry mechanisms
- Action Recording: Generate GIFs of agent actions for debugging and documentation
- Async Support: Full async/await support for efficient operations
Key Capabilities
- Web Search & Navigation: Automatically search, click links, and navigate websites
- Data Extraction: Extract information from web pages using AI understanding
- Form Interaction: Fill out forms and interact with web elements
- File Operations: Download files and handle web-based documents
- Vector Stores: Discover managed Agent Studio stores or attach an Amazon Bedrock Knowledge Base
- Multi-window Management: Coordinate tasks across multiple browser instances
Vector Stores
Attach a managed Agent Studio vector store to an agent run without copying its provider-side ID:
Managed vector-store paths are canonical and owner-qualified, using the format
/owner@email.com/path/to/store.
env = BrowserEnvironment()
store = await env.vector_stores.get(
path="/owner@example.com/Knowledge/Product documentation"
)
response = await Agent(environment=env, kind=AgentKind.PRODUCTIVITY).run(
"Answer from the attached knowledge base and cite source filenames.",
vector_stores=[store],
)
See the managed and external examples in
examples/04_extending_the_agent/.
License
This project is licensed under the Apache 2.0 License.
Support
For questions, issues, or support, please contact: support@narada.ai
Citation
We appreciate it if you could cite Narada if you found it useful for your project.
@software{narada_ai2025,
author = {Narada AI},
title = {Narada AI: Agentic Process Automation for Enterprise},
year = {2025},
publisher = {GitHub},
url = {https://github.com/NaradaAI/narada-python-sdk}
}
Metadata
Release files for narada 0.2.15
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
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|---|---|---|---|---|
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Total release size: 85.0 kB
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