Working with Sefaria's API: Practical Tips for Non-Specialists and Independent Researchers

Basic tips; where to run the code; Sample prompts and code

Compare my previous pieces on this topic:

• “Some Technical Notes on Sefaria Relating to Talmud: The Missing API Manual, ‘Grounding’ LLMs, and Emulating and Enhancing the Outlines of Talmud Tractates Chapters and Pages” (Jun 06, 2025)

• “From Hallucination to Precision: How Sefaria MCP Transforms AI-Powered Talmud Study and Research” (Sep 16, 2025)

• “How To Download Sefaria’s Entire Talmud At Once” (Apr 09, 2024)

• On using AI chatbots as research assistants, see my presentation slides at “AI-Assisted Editing and Analysis of Rabbinic Texts - Presentation at 2025 Israeli Conference on Digital Humanities”

Over the past few months, I’ve received emails from a number of different people with the same theme (more than any other topic): People want to know how to access Sefaria’s library programmatically, using the Sefaria API.

That interest makes sense. Sefaria’s API is unusually open. I indeed strongly encourage experimenting with it. If you want to work seriously with Jewish texts at scale, this is one of the most powerful tools available.

So I decided to write this piece, with some basic tips.

Outline

1. Intro

2. Is it reasonable to attempt working with the Sefaria API without being “technical”?

3. Basic tips; where to run the code; Google Colab

4. Sample prompt and code/response for Rashi and Tosafot

5. Conclusion

Is it reasonable to attempt Working with the Sefaria API without being “technical”?

One question I’m repeatedly asked is whether it’s reasonable to attempt this without being “technical.” The problem is that “technical” can mean many things. At one extreme, it just means being comfortable navigating a modern computer interface. At the other, it means writing and debugging code fluently.

For the purposes of this guide, I’d expect something like the following: If you’ve ever written moderately complex Excel formulas, or done any other computer-based tasks that required implementing non-trivial conditional logic and step-by-step processes, you’re probably fine. That level of “coding logic” is enough.

With that baseline, you can rely on an AI assistant—ChatGPT, Gemini, Claude—to write scripts for you¹ for Google Colab² and to iterate on them as you go. You don’t need to be a programmer to get real value out of the Sefaria API, but you do need to be comfortable thinking in “coding logic” and experimenting/iterating.

Basic tips; where to run the code; Google Colab

The simplest way to access the Sefaria API is with a small script in a Google Colab notebook. Google Colab is free with a Google account. It runs in the browser, and requires essentially no setup.

Use an AI chatbot (ChatGPT, Gemini, or Claude) to write the script and guide you.

Sample prompt:

Read this piece, and based on it, give a very simple barebones script for Google colab to fetch a talmud page:

https://www.ezrabrand.com/p/some-technical-notes-on-sefaria-as

Response I got to this, from Claude:

https://claude.ai/share/597e416f-ed72-46ac-a49e-6deb204b5525

I’ll read that article for you and create a simple script based on it.

Based on the article, here’s a simple barebone script for Google Colab to fetch a Talmud page from Sefaria’s API:

import requests
import json

# Fetch a Talmud page
tractate = "Berakhot"
page = "2b"
url = f"https://www.sefaria.org/api/texts/{tractate}.{page}"

# Get the data
response = requests.get(url)
data = response.json()

# Display the results
print(f"=== {tractate} {page} ===\n")

print("HEBREW TEXT:")
for i, section in enumerate(data['he']):
    print(f"Section {i}: {section}\n")

print("\n" + "="*50 + "\n")

print("ENGLISH TRANSLATION:")
for i, section in enumerate(data['text']):
    print(f"Section {i}: {section}\n")

Key points from the article:

• URL format: https://www.sefaria.org/api/texts/{Tractate}.{Page}

• data[’he’] = Hebrew text array

• data[’text’] = English translation array (Steinsaltz)

• Arrays are zero-indexed (section 1 on website = index 0 in array)

• The URL returns the entire page; you extract specific sections from the arrays

To use it, just change the tractate and page variables to whatever you want to fetch!

Here’s that code implemented in a Google Colab notebook (viewable at link):

https://colab.research.google.com/drive/1dqGkJ6yvIYNEcxzPL5S38NwtzUTtaL1f?usp=sharing

Screenshot:

Sample prompt and code/response for Rashi and Tosafot

For Rashi and Tosafot, I used this prompt to Claude, and it gave the correct code.³

research how to retrieve Rashi and Tosafot on a Talmud page via Sefaria api, and give me basic barebones google colab script to do this

Key points from the response:

API Endpoint: https://www.sefaria.org/api/v3/texts/{ref}

Reference Format:

Talmud: Berakhot.2a

Rashi: Rashi on Berakhot.2a

Tosafot: Tosafot on Berakhot.2a

Full sample api fetch url:⁴

https://www.sefaria.org/api/texts/Rashi_on_Berakhot.2a

https://www.sefaria.org/api/texts/Tosafot_on_Berakhot.2a

Relevant sefaria link, for reference:

https://www.sefaria.org.il/Rashi_on_Berakhot.2a.1.1

Code:

import requests

# Get Rashi and Tosafot for a Talmud page
ref = "Berakhot.2a"

# Get Rashi
rashi_url = f"https://www.sefaria.org/api/texts/Rashi_on_{ref}"
rashi = requests.get(rashi_url).json()
print("Rashi Hebrew:", rashi.get('he'))
print("Rashi English:", rashi.get('text'))

# Get Tosafot
tosafot_url = f"https://www.sefaria.org/api/texts/Tosafot_on_{ref}"
tosafot = requests.get(tosafot_url).json()
print("Tosafot Hebrew:", tosafot.get('he'))
print("Tosafot English:", tosafot.get('text'))

This is implemented in that same Google Colab notebook, in the second code snippet. Screenshot:

Conclusion

The main point of this guide is simple: working with the Sefaria API is fairly accessible. You don’t need to be a professional programmer to extract real value from it. If you are comfortable thinking in terms of steps, conditions, and iteration—and if you are willing to experiment—you can already do meaningful work.

The combination of an unusually open API, browser-based tools like Google Colab, and modern AI assistants dramatically lowers the barrier to entry. For independent researchers, educators, and serious learners, this opens up forms of textual access and analysis that were simply impractical a few years ago.

At the same time, this is not magic. You still need to have some basic technical understanding. The Sefaria API gives you raw power, and AI chatbots are powerful assistants, but they don’t completely obviate the need for some technical ability. However, it allows non-specialists to move beyond copy-and-paste workflows and into reproducible, scalable engagement with Jewish texts.

My hope is that this guide helps demystify the first steps, and encourages more people to treat Sefaria not just as a website, but as a data source worth exploring.

1. “vibe coding”.

2. Or any other accessible IDE; but that’s the one I’d recommend as the most friction-less.

3. Compare also my “Some Technical Notes on Sefaria Relating to Talmud” (cited also above), section “Part 1: Sefaria API as it relates to fetching the original Hebrew text and translations of Talmud, Mishnah, and Bible”

4. Note that by default, querying just the page number will return only the first section (“Rashi on Berakhot 2a:1), screenshot of the displayed text in the browser for https://www.sefaria.org/api/texts/Rashi_on_Berakhot.2a: