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✨ An open source playground with agent datasets and APIs for building and testing your own autonomous web agents 💫

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https://github.com/a16z-infra/JungleGym/assets/5958899/3493f7b0-2ac0-4d46-ade4-21be778b7fc7

🧠 Project overview:

JungleGym is an open-source playground for testing and developing autonomous web agents. This is not an agent itself, but rather a set of tools and datasets for developers building agents.

Here, you can download demonstration data and test your agents with ground-truth trajectories and correct results. These datasets are all available in the JungleGym API and in the JungleGym playground.

✅ Instructions and sample code:

Here's an easy-to-follow WebArena API example in Python (you could use JavaScript too) for verifying whether your web agent has produced the correct response:

import requests
import json
"""
How to test your agent with WebArena's sandboxed emulated shopping website:
Website = 'http://shop.junglegym.ai' (WebArena's sandboxed emulated shopping website)
Task (from WebArena) = "What is the price range for products from ugreen?"
(You can get the full list of WebArena tasks from JungleGym)
"""

WebArena_task = 'What is the price range for products from ugreen?'

####Here goes your own code to implement your agent to interact with the WebArena's sandboxed emulated shopping website:
#You could use the TreeVoyager tool to parse the DOM or use your own logic/DOM parser

####After your agent has finished interacting with the WebArena's sandboxed emulated shopping website, compare your agent's response with the ground truth result from the WebArena API:
#Get ground truth result given a task:
response = requests.get(f"http://api.junglegym.ai/get_webarena_by_task?task={WebArena_task}")
data = response.json()
#This will give the final ground truth result for the task to compare with your Web agent's response. For this task the correct response should be: ['6.99', '38.99']
print(data['data'][0]['eval']['reference_answers']['must_include'])  

All Datasets:

We're hosting 3 web agent datasets (available in the API and in the JungleGym): Mind2Web, WebArena, and AgentInstruct.

1. Mind2Web: Ground truth for ~2k tasks across 137 websites, including full HTML page states and screenshots. Good for broad testing and development across a range of sites and tasks. You can filter for relevant websites, tasks, and annotation IDs in the playground and access the full data via the API.

   Here is an example to get the ground truth actions of one task to compare your agent with. (One click run in Replit):

   import requests
   import json
   
   """
   Find the desired task and annotation ID from the Mind2Web dataset page in junglegym.ai/Mind2Web (or from the API)
   Example Task details:
   Website = 'https://www.kohls.com'
   Task = "Add the cheapest Women's Sweaters to my shopping cart."
   Annotation ID ='4bc70fa1-e817-405f-b113-0919e8e94205'
   """
   task_annotation_id = '4bc70fa1-e817-405f-b113-0919e8e94205'
   
   # Mind2Web API's endpoint to get ground truth for the list of actions given a task/annotation ID:
   url = f"http://api.junglegym.ai/get_list_of_actions?annotation_id={task_annotation_id}"
   response = requests.get(url)
   data = response.json()
   
   print("Number of total steps to accomplish this task:", len(data['action_reprs']))
   
   print ("Ground truth action for first step in te list:", data['action_reprs'][0])#-> This is the list of ground truth actions you should compare your agent with.
   
   print ("HTML Element data for this first step:", data['actions'][0]['pos_candidates'])#-> These are the extended DOM elements of the first action.
   

2. WebArena: A task dataset and 6 realistic, fully functional, sandboxed websites. Good for deep testing of many tasks and paths on a single site. You can find a desired website, task, and task_id in the playground or in the API.

   Here is an example of how to get a WebArena task and the final ground truth response. Unlike Mind2Web which shows every step in the DOM, WebArena only shows the final ground truth response. (One click run in Replit):

   import requests
   import json
   """
   Get the desired task from the WebArena dataset page in junglegym.ai/WebArena (or from the API)
   Desired task details:
   Website = 'http://shop.junglegym.ai' (WebArena's sandboxed emulated shopping website)
   Task = "What is the price range for products from ugreen?"
   """
   
   WebArena_task = 'What is the price range for products from ugreen?'
   # WebArena API's endpoint to get ground truth result given a task:
   url = f"http://api.junglegym.ai/get_webarena_by_task?task={WebArena_task}"
   # Send the GET request
   response = requests.get(url)
   data = response.json()
      
   print(
       data['data'][0]['eval']['reference_answers']['must_include']
   )  # -> This will give the final ground truth result for the task to compare with your Web agent's response. In this case, the correct ground truth response should be: ['6.99', '38.99']
   

3. AgentInstruct: ~1.8k agent trajectories designed for fine-tuning language models (i.e. llama2) on agent tasks. Unlike Mind2Web and WebArena, this dataset is in the form of a conversational/chat LLM (from: 'gpt'/'human'). It was introduced with AgentTuning. Ideally used for fine-tuning your LLM (most LLMs haven't been trained with Agent datasets/trajectories). (One click run in Replit):

   import requests
   import json
   """
   Get the full AgentInstruct dataset with ~1.8K trajectories/conversations:
   """
   #List of "ids" (categories in the dataset):
   """
   "os" (Operating System)
   "webshop"
   "mind2web"
   "kg" (Knowledge Graph)
   "db" (Database)
   "alfworld"
   """
   #AgentInstruct API's endpoint to get ground truth result given a task:
   url = f"http://api.junglegym.ai/load_agent_instruct"
   response = requests.get(url)  #this gets the full ~1.8K dataset
   data = response.json()
   
   print("Number of total conversations:", len(data['data']))
   
   print(data['data'][1000]['conversations'])  #This will get the 1000th conversation
   
   print(
       data['data'][1000]['id']
   )  #The id (category) of the 1000th conversation. In this case "alfworld_267" (ALFWorld, index=267)
   

Tools (beta release):

4. TreeVoyager: An LLM-based DOM parser (using GPT-4 Turbo) designed to implement some principles from the papers 'Tree of Thoughts' (ToT) and 'Minecraft's Voyager'.

   If you provide a task to TreeVoyager (e.g., 'buy coffee') and a website URL, it will return the HTML/DOM element that your agent should interact with. It also generates a suggested curriculum (a plan) to accomplish the task, and suggested code for each step for the agent. It's much easier to understand this if you play with it in the playground.

   Note: This is not a full agent, it's only a tool/LLM parser in very early development. It's meant to help agent developers solve the DOM parsing portion of their pipeline.

   Given the length of an example of this code, you can find the API example in the file "TreeVoyager_Example.py" or in this Replit

📚 Additional Resources (optional read):

TreeVoyager in-depth:

• Purpose: TreeVoyager is an LLM-based DOM parser (using GPT-4 Turbo) as described above. The goal of the TreeVoyager's Streamlit page is to show how it works (step-by-step) and to visualize its outputs and suggested code for every step. You can also use it with the API.
• The source code of TreeVoyager is in this repo under the TreeVoyager folder.
• For instruction and documentation on how to use the TreeVoyager API, refer to the docs.
• How does TreeVoyager work?

• Inside TreeVoyager:

🔖 Version:

0.9.0 (Experimental) - November 2023

• This project is under development. Contributions are welcome!

👥 Authors:

• Marco Mascorro - @mascobot
• Matt Bornstein - @BornsteinMatt

🔮 Future Work & Contributions:

• JungleGym is a very early release. We expect this to be an ongoing project that adds new features and improvements.
• We think Vision will be a key component for web agents in the future. Once Large Multimodal Models (LMMs) become more powerful and accessible, we think they will heavily contribute to the web agents field, whether it is a combination of HTML/DOM interaction with assisted vision or purely vision-based.
• We would be keen to hear from you and from your contributions! This is just a small project to help the Agents ecosystem.

📔 Acknowledgements:

• Special thanks to the authors of Mind2Web, WebArena, Tree of Thoughts, Voyager and AgentTuning; and to a16z-infra, and the entire open-source community.

Disclaimer:

• The versions of JungleGym, TreeVoyager, and the tools presented here are experimental and intended solely for educational purposes. Use at your own risk. While the app has been tested, the authors hold no liability for any kind of losses arising out of using this application.
• This tool is not designed for CAPTCHA bypass. Always consult a website's Terms of Service (ToS) before use.

🪪 License:

JungleGym and TreeVoyager are under the permissive Apache 2.0 license. Please refer to the License Agreement for the datasets and tools used with Mind2Web, WebArena, AgentInstruct, etc.