Agentic ecology

Agentic ecology provides AI-driven tools to assist researchers and practitioners processing, analyzing, and annotating their data. It currently focuses on passive acoustic monitoring (PAM) datasets (bioacoustics), with more capabilities to be introduced over time.

Using Google's Antigravity IDE, you can generate custom python scripts, build interactive web interfaces to listen to and search your recordings, and train classifier models—all by describing what you want to do in plain English.

Setting up an entire agentic development framework for you to build a data processing app sounds like a whole lot of trouble when we could simply develop the app itself and distribute it to you, doesn't it?

The reason for that is empowerment: we want you to have the ability to build software tools that address your needs. We recognize that doing so requires its own skillset, but we firmly believe that agentic coding, paired with the right set of agent skills, can eliminate that barrier.

Think of this project not as a tool, but as a tool-building tool.

Follow these step-by-step instructions to set up the environment and run your AI assistant.

You need three tools installed on your computer: Git, uv, and the Antigravity IDE.

Git is used to download the repository.

• macOS / Linux: Git is usually pre-installed. You can check by running git --version in your terminal.
• Windows: Download and install it from git-scm.com.

uv is a tool that installs the correct version of Python and manages all package dependencies automatically. Please refer to the official uv installation guide to install it on your operating system (macOS, Linux, or Windows).

The Antigravity IDE is Google's agentic development environment. Please follow the instructions on the official Antigravity website to download and install the program on your machine.

1. Launch the Antigravity IDE application.
2. On the welcome screen, click Clone Repository.
3. Paste the repository URL: https://github.com/google-deepmind/agentic_ecology and select a folder on your computer.
4. Once cloned, you will see a prompt asking if you would like to open the repository; click Open to open the project.

You are now ready to set up the workspace.

Warning: Unless you have extensive experience with agentic software engineering, we recommend that you set up your workspace so that the agent asks your permission before performing any operation on your machine on your behalf or to interact with files outside of the workspace's root directory. Vet every command the agent intends to run on your behalf and carefully inspect the code it requests to execute, as agent mistakes can happen. Avoid letting the agent perform operations on your data without backing it up and taking other relevant precautions.

Set up the environment for this project.

The agent runs the necessary commands to set up Python and install package dependencies.

To help you get a feel of what's possible with agentic ecology, let's simulate a task in which we are trying to bootstrap the annotation of a passive acoustic monitoring dataset for a targeted bird species. We will work with the Powdermill dataset published by Chronister et al. (2022) and search for Hooded Warbler songs.

I would like to analyze the contents of the Powdermill PAM dataset. The data is hosted here: https://zenodo.org/records/4656848/files/mp3_Files.zip. Download and extract the files into data/powdermill, then help me get started.

Note: We are downloading MP3 files rather than WAV files to save on download time. If you would rather work with WAV files, feel free to adapt the URL in the prompt above.

The agent:

1. Downloads and unzips the Powdermill audio data in data/powdermill.
2. Identifies all audio files in that directory.
3. Runs the Perch 2.0 model over them to build an audio index.
4. Creates a vector database in the databases/ folder of the project to store everything. (This step may take some time, especially if executing on CPU. Expect around 15 minutes.)
5. Builds a web application for you to browse, search, and annotate the database.
6. Presents you with instructions on how to access and use the web application.

Use the agent instructions to access the web app, and try searching for Hooded Warbler songs in the database. You can use a recording from Xeno-Canto to get started by entering xc565524 into the query URI bar.

I would like to be able to filter the audio windows by the recording from which they are taken. Add a UI element for that.

The agent will autonomously figure how to modify the existing code to accomplish that, restart the backend server, and prompt you to reload the webpage.

This repository is very minimal: it contains a pyproject.toml configuration file declaring the Python dependencies the agent needs along with a handful of agent instruction files (GEMINI.md, .agents/AGENTS.md) and skills (stored in .agents/skills), all of which are human-readable.

The agent instructions and skills are nothing more than a shortcut that reliably sends the agent in the right direction: they were themselves constructed by prompting the agent to achieve a particular outcome, interactively solving the problem with it, and asking it to write instructions for its future self to arrive at to the solution right away. When the agent made a mistake, it was asked to reflect on it and to amend the instructions and skills so that it doesn't make the same mistake again in the future.

Any ecological analysis capability that the agent currently has could just as well be achieved without any skill or pre-written instructions by working with it iteratively and interactively. In fact, this is exactly how we intend to expand the agent capabilities in this project!

This means that building the right tool for your needs is within your reach: don't hesitate to state your needs; to question the agent; to ask it to clarify, self-correct its mistakes, and amend its instructions and skills; to nudge it in the right direction if it starts veering down the wrong path.

• databases/: This is where the databases created by the agent are stored.
• agent_workspace/: This is where all files generated by the agent are saved. Look here for your web app, python scripts, and HTML pages.
• .agents/skills/: Custom capability instructions (skills) that tell the agent how to build databases and web apps safely and correctly.

This is not an officially supported Google product.