### Configure Selenium with Xvfb Source: https://github.com/snap-stanford/mlagentbench/blob/main/MLAgentBench/agents/Auto-GPT/docs/configuration/imagegen.md Example command to start Xvfb for headless browser automation with Selenium. Replace with your specific client command. ```shell sudo Xvfb :10 -ac -screen 0 1024x768x24 & DISPLAY=:10 ``` -------------------------------- ### ArXiv API Paging Example Source: https://github.com/snap-stanford/mlagentbench/blob/main/MLAgentBench/benchmarks/bibtex-generation/env/arxiv_API_reference.txt Demonstrates how to paginate through search results by adjusting the start and max_results parameters. Use a 3-second delay between consecutive calls. ```text http://export.arxiv.org/api/query?search_query=all:electron&start=0&max_results=10 ``` ```text http://export.arxiv.org/api/query?search_query=all:electron&start=10&max_results=10 ``` ```text http://export.arxiv.org/api/query?search_query=all:electron&start=20&max_results=10 ``` -------------------------------- ### Install Weaviate Client Source: https://github.com/snap-stanford/mlagentbench/blob/main/MLAgentBench/agents/Auto-GPT/docs/configuration/memory.md Install the Weaviate client library using pip. Ensure you are using a version compatible with Auto-GPT. ```shell pip install weaviate-client ``` -------------------------------- ### Install PyMilvus Client Source: https://github.com/snap-stanford/mlagentbench/blob/main/MLAgentBench/agents/Auto-GPT/docs/configuration/memory.md Install the PyMilvus client library to interact with Milvus. Ensure version compatibility. ```shell pip3 install pymilvus ``` -------------------------------- ### Install Requirements Source: https://github.com/snap-stanford/mlagentbench/blob/main/MLAgentBench/benchmarks/fathomnet/env/demo_download.ipynb Installs necessary packages from the requirements.txt file into the current Python environment. ```python %pip install -r requirements.txt ``` -------------------------------- ### Configure Kaggle API and Install Tools in Docker Source: https://github.com/snap-stanford/mlagentbench/blob/main/README.md Configure Kaggle API credentials and install necessary tools within a Docker container. Ensure '.kaggle/kaggle.json' is in the root folder. ```bash export KAGGLE_CONFIG_DIR=/MLAgentBench/.kaggle pip install kaggle sudo apt-get install unzip ``` -------------------------------- ### Initialize and Use Environment Class Source: https://context7.com/snap-stanford/mlagentbench/llms.txt Sets up a sandboxed workspace for a task, registers tools, and manages interaction traces. Use this as a context manager to ensure proper setup and teardown. ```python from argparse import Namespace from MLAgentBench.environment import Environment from MLAgentBench.schema import Action args = Namespace( task="cifar10", log_dir="./logs", work_dir="./workspace", max_steps=50, max_time=5 * 60 * 60, # 5 hours device=0, python="python", interactive=False, resume=None, resume_step=0, actions_remove_from_prompt=[], actions_add_to_prompt=[], retrieval=False, valid_format_entires=None, max_steps_in_context=3, max_observation_steps_in_context=3, max_retries=5, agent_type="ResearchAgent", llm_name="claude-v1", fast_llm_name="claude-v1", edit_script_llm_name="claude-v1", edit_script_llm_max_tokens=4000, agent_max_steps=50, langchain_agent="zero-shot-react-description", ) with Environment(args) as env: research_problem, benchmark_name = env.get_task_description() print("Task:", research_problem) print("Benchmark:", benchmark_name) print("Low-level actions:", [a.name for a in env.low_level_actions]) print("High-level actions:", [a.name for a in env.high_level_actions]) # Execute a tool action directly observation = env.execute(Action("List Files", {"dir_path": "."})) print(observation) # Check if the task is done (max steps, max time, or Final Answer submitted) print("Is final:", env.is_final()) # Save a manual checkpoint env.save("manual_checkpoint") # Expected output: # Task: Given a training script on a dataset train.py, improve upon the current model performance. # Benchmark: cifar10 # Low-level actions: ['List Files', 'Read File', 'Write File', 'Append File', # 'Undo Edit Script', 'Execute Script', 'Python REPL', 'Final Answer'] # High-level actions: ['Understand File', 'Inspect Script Lines', 'Edit Script (AI)', ...] # Is final: False ``` -------------------------------- ### Run Auto-GPT Startup Script (Linux/MacOS) Source: https://github.com/snap-stanford/mlagentbench/blob/main/MLAgentBench/agents/Auto-GPT/docs/setup.md Executes the main startup script for Auto-GPT on Linux or macOS systems. This installs necessary packages and launches the application. ```shell ./run.sh ``` -------------------------------- ### Install MLAgentBench Package Source: https://github.com/snap-stanford/mlagentbench/blob/main/README.md Install the MLAgentBench package using pip. This command installs the package in editable mode. ```bash pip install -e . ``` -------------------------------- ### Run Auto-GPT Startup Script (Windows) Source: https://github.com/snap-stanford/mlagentbench/blob/main/MLAgentBench/agents/Auto-GPT/docs/setup.md Executes the main startup script for Auto-GPT on Windows systems. This installs necessary packages and launches the application. ```batch .\run.bat ``` -------------------------------- ### Install MLAgentBench and Dependencies Source: https://context7.com/snap-stanford/mlagentbench/llms.txt Install the MLAgentBench package and its dependencies using pip. Ensure you are using Python 3.10. The install.sh script installs task and agent dependencies. ```bash pip install -e . bash install.sh ``` -------------------------------- ### Search Query Example Source: https://github.com/snap-stanford/mlagentbench/blob/main/MLAgentBench/benchmarks/literature-review-tool/env/arxiv_API_reference.txt This example demonstrates how to perform a search query using the arXiv API. The `search_query` parameter allows you to specify search terms, and the API returns results in Atom 1.0 format. ```APIDOC ## Search Query ### Description This endpoint allows users to search for articles on arXiv.org using various query parameters. The results are returned in the Atom 1.0 feed format. ### Method GET ### Endpoint /api/query ### Query Parameters - **search_query** (string) - Required - The query string to search for articles. Supports operators like AND, OR, and field specifiers (e.g., `all:`, `ti:`). - **start** (integer) - Optional - The starting index for the results (for paging). - **max_results** (integer) - Optional - The maximum number of results to return (for paging). - **sortBy** (string) - Optional - The order in which to sort the results (e.g., `relevance`, `lastUpdatedDate`, `submittedDate`). - **sortOrder** (string) - Optional - The order direction (`ascending` or `descending`). ### Request Example ``` http://export.arxiv.org/api/query?search_query=all:electron+AND+all:proton&start=0&max_results=10 ``` ### Response #### Success Response (200) - **feed** (object) - Contains metadata about the feed and a list of entries. - **entry** (array) - A list of article entries, each containing metadata like title, authors, summary, links, etc. ``` -------------------------------- ### MLAgentBench Log Structure Example Source: https://context7.com/snap-stanford/mlagentbench/llms.txt Illustrates the expected directory structure and file organization for logs generated by MLAgentBench runs. This helps in understanding and analyzing experiment outputs. ```text # Expected log structure produced under first_test/: # first_test/ # agent_log/ # main_log # step-by-step agent reasoning trace # agent_0_0.json # saved agent state snapshots # env_log/ # tool_logs/ # per-step tool call logs # traces/ # workspace snapshots at each step # trace.json # full interaction trace # overall_time.txt # total wall-clock time # error.txt # populated only on system error ``` -------------------------------- ### Docker Usage for Help and Settings Source: https://github.com/snap-stanford/mlagentbench/blob/main/MLAgentBench/agents/Auto-GPT/docs/usage.md When using Docker, replace the script execution with `docker-compose run --rm auto-gpt`. This example shows how to display help and load AI settings via Docker. ```shell docker-compose run --rm auto-gpt --help docker-compose run --rm auto-gpt --ai-settings ``` -------------------------------- ### Install Dependencies with Bash Script Source: https://github.com/snap-stanford/mlagentbench/blob/main/README.md Install project dependencies using the provided bash script. Ensure you are using Python 3.10. ```bash bash install.sh ``` -------------------------------- ### Azure OpenAI Configuration Example Source: https://github.com/snap-stanford/mlagentbench/blob/main/MLAgentBench/agents/Auto-GPT/docs/setup.md This YAML snippet shows how to map Azure deployment IDs for different GPT models. Ensure all values are double-quoted strings. ```yaml # Please specify all of these values as double-quoted strings # Replace string in angled brackets (<>) to your own deployment Name azure_model_map: fast_llm_model_deployment_id: "" ... ``` -------------------------------- ### Get Task Information and Prepare Task Source: https://context7.com/snap-stanford/mlagentbench/llms.txt Shows how to retrieve benchmark folder names and research problem descriptions using `get_task_info`. It also demonstrates the idempotent preparation of benchmark datasets using `prepare_task`. ```python from MLAgentBench.prepare_task import prepare_task, get_task_info import os # Look up a task's benchmark folder name and research problem benchmark_folder, research_problem = get_task_info("cifar10") print(benchmark_folder) # "cifar10" print(research_problem) # "Given a training script on a dataset train.py, improve upon the # current performance of the model..." # The "debug" alias is defined in benchmarks/tasks.json benchmark_folder, research_problem = get_task_info("debug") print(benchmark_folder) # "cifar10" print(research_problem) # "Given a training script on a dataset train.py, improve upon..." # Prepare (download) a benchmark dataset — idempotent benchmarks_dir = os.path.dirname(os.path.abspath("MLAgentBench")) + "/MLAgentBench/benchmarks" benchmark_dir = os.path.join(benchmarks_dir, "cifar10") prepare_task(benchmark_dir, python="python") # Output: "Running prepare.py ..." then "prepare.py finished" # On second call: "prepare.py not found or already prepared" # CLI usage # python -u -m MLAgentBench.prepare_task cifar10 $(which python) # python -u -m MLAgentBench.prepare_task ogbn-arxiv $(which python) ``` -------------------------------- ### Example Instructions for Memory Challenge Source: https://github.com/snap-stanford/mlagentbench/blob/main/MLAgentBench/agents/Auto-GPT/docs/challenges/memory/challenge_d.md Illustrative content for instruction files used in memory challenges. These describe events involving characters and object movements. ```text Sally has a marble (marble A) and she puts it in her basket (basket S), then leaves the room. Anne moves marble A from Sally's basket (basket S) to her own basket (basket A). ``` ```text Sally gives a new marble (marble B) to Bob who is outside with her. Bob goes into the room and places marble B into Anne's basket (basket A). Anne tells Bob to tell Sally that he lost the marble b. Bob leaves the room and speaks to Sally about the marble B. Meanwhile, after Bob left the room, Anne moves marble A into the green box, but tells Charlie to tell Sally that marble A is under the sofa. Charlie leaves the room and speak to Sally about the marble A as instructed by Anne. ``` -------------------------------- ### Fetch arXiv API Data in Ruby Source: https://github.com/snap-stanford/mlagentbench/blob/main/MLAgentBench/benchmarks/bibtex-generation/env/arxiv_API_reference.txt Utilize Ruby's built-in net/http and uri modules to make requests to the arXiv API. This example fetches a single result for 'electron'. ```Ruby require 'net/http' require 'uri' url = URI.parse('http://export.arxiv.org/api/query?search_query=all:electron&start=0&max_results=1') res = Net::HTTP.get_response(url) print res.body ``` -------------------------------- ### Fetch ArXiv Data in Perl using LWP Source: https://github.com/snap-stanford/mlagentbench/blob/main/MLAgentBench/benchmarks/bibtex-generation/env/arxiv_API_reference.txt Use the LWP library in Perl to make an HTTP GET request to the ArXiv API and print the raw Atom content. LWP is typically included in default Perl installations or available via CPAN. ```perl use LWP; use strict; my $url = 'http://export.arxiv.org/api/query?search_query=all:electron&start=0&max_results=1'; my $browser = LWP::UserAgent->new(); my $response = $browser->get($url); print $response->content(); ``` -------------------------------- ### Initialize and Run ResearchAgent Source: https://context7.com/snap-stanford/mlagentbench/llms.txt Demonstrates how to initialize the ResearchAgent with specified arguments and run it within an environment. The agent's reasoning process is logged to a file. ```python from argparse import Namespace from MLAgentBench.environment import Environment from MLAgentBench.agents.agent_research import ResearchAgent args = Namespace( task="cifar10", log_dir="./logs", work_dir="./workspace", max_steps=50, max_time=5 * 60 * 60, device=0, python="python", interactive=False, resume=None, resume_step=0, agent_type="ResearchAgent", llm_name="gpt-4", fast_llm_name="gpt-3.5-turbo", edit_script_llm_name="gpt-4", edit_script_llm_max_tokens=4000, agent_max_steps=50, actions_remove_from_prompt=[], actions_add_to_prompt=[], retrieval=True, # enable retrieval-augmented history valid_format_entires=None, max_steps_in_context=3, max_observation_steps_in_context=3, max_retries=5, langchain_agent="zero-shot-react-description", ) with Environment(args) as env: agent = ResearchAgent(args, env) final_message = agent.run(env) print("Final message:", final_message) # Possible outputs: # "Finished due to env.is_final() == True" # "Finished due to agent max steps reached" # "No valid response after max_retries" env.save("final") # The agent's reasoning is written step-by-step to: # ./logs/agent_log/main_log # Each step looks like: # Step 3: # Reflection: The training script uses a basic ResNet18 with SGD. Baseline accuracy is 72.3%. # Research Plan and Status: 1. Establish baseline ✓ (72.3%). 2. Try data augmentation. 3. ... # Fact Check: Baseline accuracy 72.3% — confirmed by running train.py in step 1. # Thought: I will add RandomHorizontalFlip and RandomCrop to the data pipeline. # Action: Edit Script (AI) # Action Input: {"script_name": "train.py", "edit_instruction": "Add ...", "save_name": "train_v2.py"} # Observation: The edited file is saved to train_v2.py. Here is the diff: ... ``` -------------------------------- ### OpenSearch Start Index Element Source: https://github.com/snap-stanford/mlagentbench/blob/main/MLAgentBench/benchmarks/bibtex-generation/env/arxiv_API_reference.txt Indicates the starting index of the current result set, crucial for paginated results. ```xml 0 ``` -------------------------------- ### Run MLAgentBench with Docker Source: https://context7.com/snap-stanford/mlagentbench/llms.txt Use the pre-built Docker image for MLAgentBench. Mount your local directory to the container and set the working directory. This provides a consistent environment for running experiments. ```bash docker pull qhwang123/researchassistant:latest docker run -it --user root -v "$(pwd)":/MLAgentBench -w /MLAgentBench qhwang123/researchassistant:latest ``` -------------------------------- ### arXiv API Error Response Example Source: https://github.com/snap-stanford/mlagentbench/blob/main/MLAgentBench/benchmarks/bibtex-generation/env/arxiv_API_reference.txt An example of an Atom feed returned by the arXiv API for an error, such as an incorrectly formatted ID. ```xml ArXiv Query: search_query=&id_list=1234.12345 http://arxiv.org/api/kvuntZ8c9a4Eq5CF7KY03nMug+Q 2007-10-12T00:00:00-04:00 1 0 1 http://arxiv.org/api/errors#incorrect_id_format_for_1234.12345 Error incorrect id format for 1234.12345 2007-10-12T00:00:00-04:00 arXiv api core ``` -------------------------------- ### Create and Activate Virtual Environment Source: https://github.com/snap-stanford/mlagentbench/blob/main/MLAgentBench/agents/Auto-GPT/docs/setup.md Sets up a Python virtual environment named 'venvAutoGPT' and activates it. Ensures pip is up-to-date. ```shell python -m venv venvAutoGPT source venvAutoGPT/bin/activate pip3 install --upgrade pip ``` -------------------------------- ### Display Help and List Arguments Source: https://github.com/snap-stanford/mlagentbench/blob/main/MLAgentBench/agents/Auto-GPT/docs/usage.md Run this command to see all available command-line arguments for Auto-GPT on Linux/macOS or Windows. ```shell ./run.sh --help # on Linux / macOS .\run.bat --help # on Windows ``` -------------------------------- ### arXiv DOI Example Source: https://github.com/snap-stanford/mlagentbench/blob/main/MLAgentBench/benchmarks/bibtex-generation/env/arxiv_API_reference.txt Shows the 'arxiv:doi' element for including the Digital Object Identifier of an article. ```xml 10.1529/biophysj.104.047340 ``` -------------------------------- ### arXiv Journal Reference Example Source: https://github.com/snap-stanford/mlagentbench/blob/main/MLAgentBench/benchmarks/bibtex-generation/env/arxiv_API_reference.txt Displays the 'arxiv:journal_ref' element for providing journal publication details. ```xml Eur.Phys.J. C31 (2003) 17-29 ``` -------------------------------- ### Clone Auto-GPT Repository (Git) Source: https://github.com/snap-stanford/mlagentbench/blob/main/MLAgentBench/agents/Auto-GPT/docs/setup.md Clones the stable branch of the Auto-GPT repository from GitHub. Ensure Git is installed. ```shell git clone -b stable https://github.com/Significant-Gravitas/Auto-GPT.git ``` -------------------------------- ### Run Auto-GPT with Vanilla Docker and Arguments Source: https://github.com/snap-stanford/mlagentbench/blob/main/MLAgentBench/agents/Auto-GPT/docs/setup.md Execute Auto-GPT with vanilla Docker commands, including specific arguments and environment configuration. ```shell docker run -it --env-file=.env -v $PWD:/app --rm auto-gpt --gpt3only --continuous ``` -------------------------------- ### arXiv Primary Category Example Source: https://github.com/snap-stanford/mlagentbench/blob/main/MLAgentBench/benchmarks/bibtex-generation/env/arxiv_API_reference.txt Illustrates the use of the 'arxiv:primary_category' element to denote the main classification of an e-print. ```xml ``` -------------------------------- ### Atom Feed Link Example Source: https://github.com/snap-stanford/mlagentbench/blob/main/MLAgentBench/benchmarks/bibtex-generation/env/arxiv_API_reference.txt The element provides a URL to retrieve the feed again, representing the canonicalized query. ```xml ``` -------------------------------- ### Prepare Task Environment Source: https://github.com/snap-stanford/mlagentbench/blob/main/README.md Prepare a specific task environment beforehand. Replace with the desired task. ```python python -u -m MLAgentBench.prepare_task $(which python) ``` -------------------------------- ### Check Docker Compose Version Source: https://github.com/snap-stanford/mlagentbench/blob/main/MLAgentBench/agents/Auto-GPT/docs/setup.md Verify your Docker Compose installation version to ensure compatibility with the Compose file format. ```shell docker-compose version ``` -------------------------------- ### Run with Custom Prompt Settings Source: https://github.com/snap-stanford/mlagentbench/blob/main/MLAgentBench/agents/Auto-GPT/docs/usage.md Specify a custom prompt settings file to adjust the prompts used by the agent. Replace `` with the path to your settings file. ```shell ./run.sh --prompt-settings ``` -------------------------------- ### Run Auto-GPT with Vanilla Docker Source: https://github.com/snap-stanford/mlagentbench/blob/main/MLAgentBench/agents/Auto-GPT/docs/setup.md Run Auto-GPT using vanilla Docker commands, mounting the current directory and using the .env file for environment variables. ```shell docker run -it --env-file=.env -v $PWD:/app auto-gpt ``` -------------------------------- ### arXiv Affiliation Example Source: https://github.com/snap-stanford/mlagentbench/blob/main/MLAgentBench/benchmarks/bibtex-generation/env/arxiv_API_reference.txt Demonstrates how author affiliation is included using the 'arxiv:affiliation' subelement within the standard 'author' element. ```xml G. G. Kacprzak NMSU ``` -------------------------------- ### Create Auto-GPT Project Directory (Shell) Source: https://github.com/snap-stanford/mlagentbench/blob/main/MLAgentBench/agents/Auto-GPT/docs/setup.md Use these commands to create a new directory for your Auto-GPT project and navigate into it. ```shell mkdir Auto-GPT cd Auto-GPT ``` -------------------------------- ### arXiv Comment Element Example Source: https://github.com/snap-stanford/mlagentbench/blob/main/MLAgentBench/benchmarks/bibtex-generation/env/arxiv_API_reference.txt Shows the 'arxiv:comment' element for including author-provided comments, like page and figure counts. ```xml 23 pages, 8 figures and 4 tables ``` -------------------------------- ### Run Auto-GPT with Docker Compose Source: https://github.com/snap-stanford/mlagentbench/blob/main/MLAgentBench/agents/Auto-GPT/docs/setup.md Execute Auto-GPT using Docker Compose. This command also starts a Redis memory backend by default. ```shell docker-compose run --rm auto-gpt ``` -------------------------------- ### Build Auto-GPT with Vanilla Docker Source: https://github.com/snap-stanford/mlagentbench/blob/main/MLAgentBench/agents/Auto-GPT/docs/setup.md Build the Auto-GPT Docker image using the standard `docker build` command. ```shell docker build -t auto-gpt . ``` -------------------------------- ### Atom Feed Title Example Source: https://github.com/snap-stanford/mlagentbench/blob/main/MLAgentBench/benchmarks/bibtex-generation/env/arxiv_API_reference.txt The element provides a canonicalized version of the API query used, including all parameters in a fixed order. ```xml <title xmlns="http://www.w3.org/2005/Atom"> ArXiv Query: search_query=all:electron&id_list=&start=0&max_results=1 ``` -------------------------------- ### Run with Custom AI Settings Source: https://github.com/snap-stanford/mlagentbench/blob/main/MLAgentBench/agents/Auto-GPT/docs/usage.md Specify a custom AI settings file to configure the agent's behavior. Replace `` with the path to your settings file. ```shell ./run.sh --ai-settings ``` -------------------------------- ### arXiv API Error Handling Source: https://github.com/snap-stanford/mlagentbench/blob/main/MLAgentBench/benchmarks/literature-review-tool/env/arxiv_API_reference.txt Describes how the arXiv API returns errors using Atom feeds and provides examples of common error scenarios. ```APIDOC ## arXiv API Errors Errors are communicated through Atom feeds, where a single `` represents the error. The `` tag contains a user-friendly error message, and a `` element points to a more detailed explanation. ### Example Error Response (Malformed ID) This response is generated for a query with an incorrectly formatted ID, such as `http://export.arxiv.org/api/query?id_list=1234.12345`. ```xml ArXiv Query: search_query=&id_list=1234.12345 http://arxiv.org/api/kvuntZ8c9a4Eq5CF7KY03nMug+Q 2007-10-12T00:00:00-04:00 1 0 1 http://arxiv.org/api/errors#incorrect_id_format_for_1234.12345 Error incorrect id format for 1234.12345 2007-10-12T00:00:00-04:00 arXiv api core ``` ### Common Errors and Sample Queries | Sample Query | Error Explanation | |-------------------------------------------------|----------------------------------| | `http://export.arxiv.org/api/query?start=not_an_int` | `start` must be an integer | | `http://export.arxiv.org/api/query?start=-1` | `start` must be >= 0 | | `http://export.arxiv.org/api/query?max_results=not_an_int` | `max_results` must be an integer | | `http://export.arxiv.org/api/query?max_results=-1` | `max_results` must be >= 0 | | `http://export.arxiv.org/api/query?id_list=1234.1234` | malformed id - see arxiv identifier explanation | | `http://export.arxiv.org/api/query?id_list=cond—mat/0709123` | malformed id - see arxiv identifier explanation | ``` -------------------------------- ### Construct arXiv API Query by Author Source: https://github.com/snap-stanford/mlagentbench/blob/main/MLAgentBench/benchmarks/bibtex-generation/env/arxiv_API_reference.txt Example of constructing an arXiv API query to search for articles by a specific author, 'Adrian Del Maestro'. ```URL http://export.arxiv.org/api/query?search_query=au:del_maestro ``` -------------------------------- ### Configure Plugins with plugins_config.yaml Source: https://github.com/snap-stanford/mlagentbench/blob/main/MLAgentBench/agents/Auto-GPT/docs/plugins.md Use this YAML configuration to enable or disable plugins and set their specific configurations. Ensure the file is correctly formatted in YAML. ```yaml plugin_a: config: api_key: my-api-key enabled: false plugin_b: config: {} enabled: true ``` -------------------------------- ### Pre-download Task Datasets Source: https://context7.com/snap-stanford/mlagentbench/llms.txt Download datasets required for specific tasks using the prepare_task script. This command ensures that the necessary data is available before running an experiment. Kaggle tasks require ~/.kaggle/kaggle.json. ```python python -u -m MLAgentBench.prepare_task cifar10 $(which python) ``` -------------------------------- ### Fetch arXiv API Data in Python Source: https://github.com/snap-stanford/mlagentbench/blob/main/MLAgentBench/benchmarks/bibtex-generation/env/arxiv_API_reference.txt Use the urllib.request library to fetch data from the arXiv API. This example retrieves a single result for 'electron'. ```Python import urllib.request as libreq with libreq.urlopen('http://export.arxiv.org/api/query?search_query=all:electron&start=0&max_results=1') as url: r = url.read() print(r) ``` -------------------------------- ### Atom Feed ID Example Source: https://github.com/snap-stanford/mlagentbench/blob/main/MLAgentBench/benchmarks/bibtex-generation/env/arxiv_API_reference.txt The element serves as a unique identifier for the query, useful for tracking feeds in applications like feed readers. ```xml http://arxiv.org/api/cHxbiOdZaP56ODnBPIenZhzg5f8 ```