# AG2: Responsible AI FAQs

## What is AG2?
AG2 is a framework for simplifying the orchestration, optimization, and automation of LLM workflows. It offers customizable and conversable agents that leverage the strongest capabilities of the most advanced LLMs, like GPT-4, while addressing their limitations by integrating with humans and tools and having conversations between multiple agents via automated chat. AG2 is a spin-off of [AutoGen](https://github.com/microsoft/autogen) created by [a team consisting of AutoGen’s founders and contributors](https://github.com/ag2ai/ag2/blob/main/MAINTAINERS.md) of AutoGen.

## What can AG2 do?
AG2 is a framework for building a complex multi-agent conversation system by:
- Defining a set of agents with specialized capabilities and roles.
- Defining the interaction behavior between agents, i.e., what to reply with when an agent receives messages from another agent.

The agent conversation-centric design has numerous benefits, including that it:
-	Naturally handles ambiguity, feedback, progress, and collaboration.
-	Enables effective coding-related tasks, like tool use with back-and-forth troubleshooting.
-	Allows users to seamlessly opt in or opt out via an agent in the chat.
-	Achieves a collective goal with the cooperation of multiple specialists.

## 	What is/are AG2’s intended use(s)?
Please note that AG2 is an open-source library under active development and intended for  use for research purposes. It should not be used in any downstream applications without additional detailed evaluation of robustness, safety issues and assessment of any potential harm or bias in the proposed application.

AG2 is a generic infrastructure that can be used in multiple scenarios. The system’s intended uses include:

-	Building LLM workflows that solve more complex tasks: Users can create agents that interleave reasoning and tool use capabilities of the latest LLMs such as GPT-4o. To solve complex tasks, multiple agents can converse to work together (e.g., by partitioning a complex problem into simpler steps or by providing different viewpoints or perspectives).
-	Application-specific agent topologies: Users can create application specific agent topologies and patterns for agents to interact. The exact topology may depend on the domain’s complexity and semantic capabilities of the LLM available.
-	Code generation and execution: Users can implement agents that can assume the roles of writing code and other agents that can execute code. Agents can do this with varying levels of human involvement. Users can add more agents and program the conversations to enforce constraints on code and output.
-	Question answering: Users can create agents that can help answer questions using retrieval augmented generation.
-	End user and multi-agent chat and debate: Users can build chat applications where they converse with multiple agents at the same time.

While AG2 automates LLM workflows, decisions about how to use specific LLM outputs should always have a human in the loop. For example, you should not use AG2 to automatically post LLM generated content to social media.

## How was AG2 evaluated? What metrics are used to measure performance?
-	The current version of AG2 was evaluated on six applications to illustrate its potential in simplifying the development of high-performance multi-agent applications. These applications were selected based on their real-world relevance,  problem difficulty and problem solving capabilities enabled by AG2, and innovative potential.
-	These applications involve using AG2 to solve math problems, question answering, decision making in text world environments, supply chain optimization, etc. For each of these domains AG2 was evaluated on various success based metrics (i.e., how often the AG2 based implementation solved the task). And, in some cases, AG2 based approach was also evaluated on implementation efficiency (e.g., to track reductions in developer effort to build).
- The team has conducted tests where a “red” agent attempts to get the default AG2 assistant to break from its alignment and guardrails. The team has observed that out of 70 attempts to break guardrails, only 1 was successful in producing text that would have been flagged as problematic by Azure OpenAI filters. The team has not observed any evidence that AG2 (or GPT models as hosted by OpenAI or Azure) can produce novel code exploits or jailbreak prompts, since direct prompts to “be a hacker”, “write exploits”, or “produce a phishing email” are refused by existing filters.

## What are the limitations of AG2? How can users minimize the impact of AG2’s limitations when using the system?
AG2 relies on existing LLMs. Experimenting with AG2 retains the common limitations of large language models; including:

- Data Biases: Large language models, trained on extensive data, can inadvertently carry biases present in the source data. Consequently, the models may generate outputs that could be potentially biased or unfair.
-	Lack of Contextual Understanding: Despite their impressive capabilities in language understanding and generation, these models exhibit limited real-world understanding, resulting in potential inaccuracies or nonsensical responses.
-	Lack of Transparency: Due to the complexity and size, large language models can act as `black boxes,' making it difficult to comprehend the rationale behind specific outputs or decisions.
-	Content Harms: There are various types of content harms that large language models can cause. It is important to be aware of them when using these models, and to take actions to prevent them. It is recommended to leverage various content moderation services provided by different companies and institutions.
-	Inaccurate or ungrounded content: It is important to be aware and cautious not to entirely rely on a given language model for critical decisions or information that might have deep impact as it is not obvious how to prevent these models to fabricate content without high authority input sources.
-	Potential for Misuse: Without suitable safeguards, there is a risk that these models could be maliciously used for generating disinformation or harmful content.

Additionally, AG2's multi-agent framework may amplify or introduce additional risks, such as:
-	Privacy and Data Protection: The framework allows for human participation in conversations between agents. It is important to ensure that user data and conversations are protected and that developers use appropriate measures to safeguard privacy.
-	Accountability and Transparency: The framework involves multiple agents conversing and collaborating, it is important to establish clear accountability and transparency mechanisms. Users should be able to understand and trace the decision-making process of the agents involved in order to ensure accountability and address any potential issues or biases.
-	Trust and reliance: The framework leverages human understanding and intelligence while providing automation through conversations between agents. It is important to consider the impact of this interaction on user experience, trust, and reliance on AI systems. Clear communication and user education about the capabilities and limitations of the system will be essential.
-	Security & unintended consequences: The use of multi-agent conversations and automation in complex tasks may have unintended consequences. Especially, allowing LLM agents to make changes in external environments through code execution or function calls, such as installing packages, could pose significant risks. Developers should carefully consider the potential risks and ensure that appropriate safeguards are in place to prevent harm or negative outcomes, including keeping a human in the loop for decision making.

## What operational factors and settings allow for effective and responsible use of AG2?
-	Code execution: AG2 recommends using docker containers so that code execution can happen in a safer manner. Users can use function calls instead of free-form code to execute pre-defined functions only, increasing reliability and safety. Users can also tailor the code execution environment to their requirements.
-	Human involvement: AG2 prioritizes human involvement in multi agent conversation. The overseers can step in to give feedback to agents and steer them in the correct direction. Users can get a chance to confirm before code is executed.
-	Agent modularity: Modularity allows agents to have different levels of information access. Additional agents can assume roles that help keep other agents in check. For example, one can easily add a dedicated agent to play the role of a safeguard.
-	LLMs: Users can choose the LLM that is optimized for responsible use. For example, OpenAI's GPT-4o includes RAI mechanisms and filters. Caching is enabled by default to increase reliability and control cost. We encourage developers to review [OpenAI’s Usage policies](https://openai.com/policies/usage-policies) and [Azure OpenAI’s Code of Conduct](https://learn.microsoft.com/en-us/legal/cognitive-services/openai/code-of-conduct) when using their models.
-	Multi-agent setup: When using auto replies, the users can limit the number of auto replies, termination conditions etc. in the settings to increase reliability.

TRANSPARENCY_FAQS.md


# Announcements

🔥 🎉 **Nov 11, 2024:** We are evolving AutoGen into **AG2**!
A new organization [AG2AI](https://github.com/ag2ai) is created to host the development of AG2 and related projects with open governance. Check [AG2's new look](https://ag2.ai/).

📄 **License:**
We adopt the Apache 2.0 license from v0.3. This enhances our commitment to open-source collaboration while providing additional protections for contributors and users alike.

🎉 May 29, 2024: DeepLearning.ai launched a new short course [AI Agentic Design Patterns with AutoGen](https://www.deeplearning.ai/short-courses/ai-agentic-design-patterns-with-autogen), made in collaboration with Microsoft and Penn State University, and taught by AutoGen creators [Chi Wang](https://github.com/sonichi) and [Qingyun Wu](https://github.com/qingyun-wu).

🎉 May 24, 2024: Foundation Capital published an article on [Forbes: The Promise of Multi-Agent AI](https://www.forbes.com/sites/joannechen/2024/05/24/the-promise-of-multi-agent-ai/?sh=2c1e4f454d97) and a video [AI in the Real World Episode 2: Exploring Multi-Agent AI and AutoGen with Chi Wang](https://www.youtube.com/watch?v=RLwyXRVvlNk).

🎉 May 13, 2024: [The Economist](https://www.economist.com/science-and-technology/2024/05/13/todays-ai-models-are-impressive-teams-of-them-will-be-formidable) published an article about multi-agent systems (MAS) following a January 2024 interview with [Chi Wang](https://github.com/sonichi).

🎉 May 11, 2024: [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation](https://openreview.net/pdf?id=uAjxFFing2) received the best paper award at the [ICLR 2024 LLM Agents Workshop](https://llmagents.github.io/).

🎉 Apr 17, 2024: Andrew Ng cited AutoGen in [The Batch newsletter](https://www.deeplearning.ai/the-batch/issue-245/) and [What's next for AI agentic workflows](https://youtu.be/sal78ACtGTc?si=JduUzN_1kDnMq0vF) at Sequoia Capital's AI Ascent (Mar 26).

🎉 Mar 3, 2024: What's new in AutoGen? 📰[Blog](https://docs.ag2.ai/blog/2024-03-03-AutoGen-Update); 📺[Youtube](https://www.youtube.com/watch?v=j_mtwQiaLGU).

🎉 Mar 1, 2024: the first AutoGen multi-agent experiment on the challenging [GAIA](https://huggingface.co/spaces/gaia-benchmark/leaderboard) benchmark achieved the No. 1 accuracy in all the three levels.

🎉 Jan 30, 2024: AutoGen is highlighted by Peter Lee in Microsoft Research Forum [Keynote](https://t.co/nUBSjPDjqD).

🎉 Dec 31, 2023: [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation Framework](https://arxiv.org/abs/2308.08155) is selected by [TheSequence: My Five Favorite AI Papers of 2023](https://thesequence.substack.com/p/my-five-favorite-ai-papers-of-2023).

🔥 Nov 24: pyautogen [v0.2](https://github.com/ag2ai/ag2/releases/tag/v0.2.0) is released with many updates and new features compared to v0.1.1. It switches to using openai-python v1. Please read the [migration guide](https://docs.ag2.ai/docs/installation/Installation).

🔥 Nov 11: OpenAI's Assistants are available in AutoGen and interoperatable with other AutoGen agents! Checkout our [blogpost](https://docs.ag2.ai/blog/2023-11-13-OAI-assistants) for details and examples.

🎉 Nov 8, 2023: AutoGen is selected into [Open100: Top 100 Open Source achievements](https://www.benchcouncil.org/evaluation/opencs/annual.html) 35 days after spinoff from [FLAML](https://github.com/microsoft/FLAML).

🎉 Nov 6, 2023: AutoGen is mentioned by Satya Nadella in a [fireside chat](https://youtu.be/0pLBvgYtv6U).

🎉 Nov 1, 2023: AutoGen is the top trending repo on GitHub in October 2023.

🎉 Oct 03, 2023: AutoGen spins off from [FLAML](https://github.com/microsoft/FLAML) on GitHub.

🎉 Aug 16: Paper about AutoGen on [arxiv](https://arxiv.org/abs/2308.08155).

🎉 Mar 29, 2023: AutoGen is first created in [FLAML](https://github.com/microsoft/FLAML).

🔥 FLAML is highlighted in OpenAI's [cookbook](https://github.com/openai/openai-cookbook#related-resources-from-around-the-web).

🔥 [autogen](https://docs.ag2.ai/) is released with support for ChatGPT and GPT-4, based on [Cost-Effective Hyperparameter Optimization for Large Language Model Generation Inference](https://arxiv.org/abs/2303.04673).

🔥 FLAML supports Code-First AutoML & Tuning – Private Preview in [Microsoft Fabric Data Science](https://learn.microsoft.com/en-us/fabric/data-science/).

announcements.md

AG2 (formerly AutoGen): The Open-Source AgentOS. Join us at: https://discord.gg/pAbnFJrkgZ


# Devcontainer Configurations for AG2

Welcome to the `.devcontainer` directory! Here you'll find Dockerfiles and devcontainer configurations that are essential for setting up your AG2 development environment. Below is a brief overview and how you can utilize them effectively.

These configurations can be used with Codespaces and locally.

## Developing AG2 with Devcontainers

### Prerequisites

- [Docker](https://docs.docker.com/get-docker/)
- [Visual Studio Code](https://code.visualstudio.com/)
- [Visual Studio Code Remote - Containers extension](https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.remote-containers)

### Getting Started

1. Open the project in Visual Studio Code.
2. Press `Ctrl+Shift+P` and select `Dev Containers: Reopen in Container`.
3. Select the desired python environment and wait for the container to build.
4. Once the container is built, you can start developing AG2.

### Troubleshooting Common Issues

- Check Docker daemon, port conflicts, and permissions issues.

### Additional Resources

For more information on Docker usage and best practices, refer to the [official Docker documentation](https://docs.docker.com).

.devcontainer/README.md


### Description
<!-- A clear and concise description of the issue or feature request. -->

### Environment
- Package name & version: <!-- Specify the ag2 package name and version (e.g., autogen v0.7.2) -->
- Python version: <!-- Specify the Python version (e.g., 3.12) -->
- Operating System: <!-- Specify the OS (e.g., Windows 10, Ubuntu 20.04) -->

### Steps to Reproduce (for bugs)
<!-- Provide detailed steps to reproduce the issue. Include code snippets, configuration files, or any other relevant information. -->

1. Step 1
2. Step 2
3. ...

### Expected Behavior
<!-- Describe what you expected to happen. -->

### Actual Behavior
<!-- Describe what actually happened. Include any error messages, stack traces, or unexpected behavior. -->

### Screenshots / Logs (if applicable)
<!-- If relevant, include screenshots or logs that help illustrate the issue. -->

### Additional Information
<!-- Include any additional information that might be helpful, such as specific configurations, data samples, or context about the environment. -->

### Possible Solution (if you have one)
<!-- If you have suggestions on how to address the issue, provide them here. -->

### Is this a Bug or Feature Request?
<!-- Choose one: Bug | Feature Request -->

### Priority
<!-- Choose one: High | Medium | Low -->

### Difficulty
<!-- Choose one: Easy | Moderate | Hard -->

### Any related issues?
<!-- If this is related to another issue, reference it here. -->

### Any relevant discussions?
<!-- If there are any discussions or forum threads related to this issue, provide links. -->

### Checklist
<!-- Please check the items that you have completed -->
- [ ] I have searched for similar issues and didn't find any duplicates.
- [ ] I have provided a clear and concise description of the issue.
- [ ] I have included the necessary environment details.
- [ ] I have outlined the steps to reproduce the issue.
- [ ] I have included any relevant logs or screenshots.
- [ ] I have indicated whether this is a bug or a feature request.
- [ ] I have set the priority and difficulty levels.

### Additional Comments
<!-- Any additional comments or context that you think would be helpful. -->

.github/ISSUE_TEMPLATE.md


<!-- Thank you for your contribution! Please review https://docs.ag2.ai/docs/contributor-guide/contributing before opening a pull request. -->

<!-- Please add a reviewer to the assignee section when you create a PR. If you don't have the access to it, we will shortly find a reviewer and assign them to your PR. -->

## Why are these changes needed?

<!-- Please give a short summary of the change and the problem this solves. -->

## Related issue number

<!-- For example: "Closes #1234" -->

## Checks

- [ ] I've included any doc changes needed for https://docs.ag2.ai/. See https://docs.ag2.ai/docs/contributor-guide/documentation to build and test documentation locally.
- [ ] I've added tests (if relevant) corresponding to the changes introduced in this PR.
- [ ] I've made sure all auto checks have passed.

.github/PULL_REQUEST_TEMPLATE.md


# Contributing

## How to get a notebook displayed on the website

In the notebook metadata set the `tags` and `description` `front_matter` properties. For example:

```json
{
    "...": "...",
    "metadata": {
        "...": "...",
        "front_matter": {
            "tags": ["code generation", "debugging"],
            "description": "Use conversable language learning model agents to solve tasks and provide automatic feedback through a comprehensive example of writing, executing, and debugging Python code to compare stock price changes."
        }
    }
}
```

**Note**: Notebook metadata can be edited by opening the notebook in a text editor (Or "Open With..." -> "Text Editor" in VSCode)

The `tags` field is a list of tags that will be used to categorize the notebook. The `description` field is a brief description of the notebook.

## Best practices for authoring notebooks

The following points are best practices for authoring notebooks to ensure consistency and ease of use for the website.

- The Colab button will be automatically generated on the website for all notebooks where it is missing. Going forward, it is recommended not to include the Colab button in the notebook itself.
- Ensure the header is a `h1` header, - `#`
- Don't put anything between the yaml and the header

### Consistency for installation and LLM config

You don't need to explain in depth how to install AG2. Unless there are specific instructions for the notebook just use the following markdown snippet:

``````
````{=mdx}
:::info Requirements
Install `autogen`:
```bash
pip install autogen
```

For more information, please refer to the [installation guide](/docs/user-guide/basic-concepts/installing-ag2).
:::
````
``````

Or if extras are needed:

``````
````{=mdx}
:::info Requirements
Some extra dependencies are needed for this notebook, which can be installed via pip:

```bash
pip install autogen[retrievechat] flaml[automl]
```

For more information, please refer to the [installation guide](/docs/user-guide/basic-concepts/installing-ag2).
:::
````
``````

When specifying the config list, to ensure consistency it is best to use approximately the following code:

```python
import autogen

config_list = autogen.config_list_from_json(
    env_or_file="OAI_CONFIG_LIST",
)
```

Then after the code cell where this is used, include the following markdown snippet:

``````
````{=mdx}
:::tip
Learn more about configuring LLMs for agents [here](/docs/topics/llm_configuration).
:::
````
``````

## Testing

Notebooks can be tested by running:

```sh
python website/process_notebooks.py test
```

This will automatically scan for all notebooks in the notebook/ and website/ dirs.

To test a specific notebook pass its path:

```sh
python website/process_notebooks.py test notebook/agentchat_logging.ipynb
```

Options:
- `--timeout` - timeout for a single notebook
- `--exit-on-first-fail` - stop executing further notebooks after the first one fails

### Skip tests

If a notebook needs to be skipped then add to the notebook metadata:
```json
{
    "...": "...",
    "metadata": {
        "skip_test": "REASON"
    }
}
```

## Metadata fields

All possible metadata fields are as follows:
```json
{
    "...": "...",
    "metadata": {
        "...": "...",
        "front_matter": {
            "tags": "List[str] - List of tags to categorize the notebook",
            "description": "str - Brief description of the notebook",
        },
        "skip_test": "str - Reason for skipping the test. If present, the notebook will be skipped during testing",
        "skip_render": "str - Reason for skipping rendering the notebook. If present, the notebook will be left out of the website.",
        "extra_files_to_copy": "List[str] - List of files to copy to the website. The paths are relative to the notebook directory",
    }
}
```

notebook/contributing.md


# Website

This website is built using [Mintlify](https://mintlify.com/docs/quickstart), a modern website generator.

## How to get a notebook rendered on the website

See [here](https://github.com/ag2ai/ag2/blob/main/notebook/contributing.md#how-to-get-a-notebook-displayed-on-the-website) for instructions on how to get a notebook in the `notebook` directory rendered on the website.

## Build documentation locally

Follow these steps to build and serve the documentation on your local machine:

1. Install Node.js:
    - Download and install [Node.js](https://nodejs.org/en/download/)

2.  Install Quarto:
    - Visit the Quarto download [page](https://quarto.org/docs/download/).
    - Click on the Pre-release tab and download the latest version of Quarto.
    - Ensure you install version `1.5.23` or higher.

3. Install Required Python Packages:
    - From the project root directory, install the necessary Python packages by running:

    ```console
    pip install -e ".[docs]"
    ```

4. Build and Serve the Documentation:

    - To build and serve the documentation locally, run the following command from the project root directory:

        ```console
        ./scripts/docs_serve.sh
        ```

    - Optionally, you can pass the `--force` flag to clean up all temporary files and generate the documentation from scratch:

        ```console
        ./scripts/docs_serve.sh --force
        ```

    - The above command starts a local development server and opens up a browser window.

5. Handling Updates or Changes:

    - Whenever you update the documentation, stop the server and re-run the `./scripts/docs_serve.sh` command to serve the docs with the latest changes and view them live.

    - If deleted files are still displayed, it indicates cached or temporary files may be causing issues. To resolve this, use the `--force` flag to clean the build directory and regenerate the documentation.

By following these steps, you can build, serve, and update the documentation locally.

## Build with Dev Containers

To build and test documentation using Dev Containers, open the project using [VSCode](https://code.visualstudio.com/), press `Ctrl+Shift+P` and select `Dev Containers: Reopen in Container`.

This will open the project in a Dev Container with all the required dependencies installed.

Build and Serve the Documentation:

    - Open a terminal and run the following commands from the project root directory to build and serve the documentation:

        ```console
        pip install -e ".[docs]"
        ./scripts/docs_serve.sh
        ```

    - Optionally, you can pass the `--force` flag to clean up all temporary files and generate the documentation from scratch:

        ```console
        pip install -e ".[docs]"
        ./scripts/docs_serve.sh --force
        ```

    Once done you should be able to access the documentation at `http://localhost:3000/`.

Handling Updates or Changes:

    - Whenever you update the documentation, stop the server and re-run the `./scripts/docs_serve.sh` command to serve the docs with the latest changes and view them live.

    - If deleted files are still displayed, it indicates cached or temporary files may be causing issues. To resolve this, use the `--force` flag to clean the build directory and regenerate the documentation.

website/README.md


# :warning: DOCUMENTATION CODE SOURCES :warning:

To find a real docs, just visit our website: [https://faststream.airt.ai/latest/](https://faststream.airt.ai/latest/)


Agents for running the [AgentEval](https://docs.ag2.ai/blog/2023-11-20-AgentEval/) pipeline.

AgentEval is a process for evaluating a LLM-based system's performance on a given task.

When given a task to evaluate and a few example runs, the critic and subcritic agents create evaluation criteria for evaluating a system's solution. Once the criteria has been created, the quantifier agent can evaluate subsequent task solutions based on the generated criteria.

See our [blog post](https://docs.ag2.ai/blog/2024-06-21-AgentEval) for usage examples and general explanations.


# Introduction

This directory contains a library of manually created python tools. These tools have three categories: math, data_analysis and information_retrieval.

# Directory Layout
```
tools
├── README.md
├── data_analysis
│   ├── calculate_correlation.py
│   └── ...
├── information_retrieval
│   ├── arxiv_download.py
│   ├── arxiv_search.py
│   └── ...
├── math
│   ├── calculate_circle_area_from_diameter.py
│   └── ...
└── tool_description.tsv
```

Tools can be imported from `tools/{category}/{tool_name}.py` with exactly the same function name.

`tool_description.tsv` contains descriptions of tools for retrieval.

# How to use
Some tools require Bing Search API key and RapidAPI key. For Bing API, you can read more about how to get an API on the [Bing Web Search API](https://www.microsoft.com/en-us/bing/apis/bing-web-search-api) page. For RapidAPI, you can [sign up](https://rapidapi.com/auth/sign-up) and subscribe to these two links([link1](https://rapidapi.com/solid-api-solid-api-default/api/youtube-transcript3), [link2](https://rapidapi.com/420vijay47/api/youtube-mp3-downloader2)). These apis have free billing options and there is no need to worry about extra costs.

To install the requirements for running tools, use pip install.
```bash
pip install -r autogen/agentchat/contrib/captainagent/tools/requirements.txt
```

Whenever you run the tool-related code, remember to export the api keys to system variables.
```bash
export BING_API_KEY=""
export RAPID_API_KEY=""
```
or
```python
import os
os.environ["BING_API_KEY"] = ""
os.environ["RAPID_API_KEY"] = ""
```


## Delaware

## 45-4168768

(State or other jurisdiction of incorporation or organization)

(I.R.S. Employer Identification No.)

## 333 Summer Street Boston, Massachusetts

(Address of principal executive offices)

(Zip code)

## (617) 297-1005

(Registrant's telephone number, including area code)

Securities registered pursuant to Section 12(b) of the Act:

Table of Contents

| Title of each class                                    | Trading Symbol(s)   | Name of each exchange on which registered   |
|--------------------------------------------------------|---------------------|---------------------------------------------|
| Class A common stock, par value of $0.000001 per share | TOST                | New York Stock Exchange                     |

Indicate by check mark whether the registrant: (1) has filed all reports required to be filed by Section 13 or 15(d) of the Securities Exchange Act of 1934 during the preceding 12 months (or for such shorter period that the registrant was required to file such reports), and (2) has been subject to such filing requirements for the past 90 days. Yes ☒ No ☐

Indicate by check mark whether the registrant has submitted electronically every Interactive Data File required to be submitted pursuant to Rule 405 of Regulation S-T (§232.405 of this chapter) during the preceding 12 months (or for such shorter period that the registrant was required to submit such files). Yes ☒ No ☐

Indicate by check mark whether the registrant is a large accelerated filer, an accelerated filer, a non-accelerated filer, a smaller reporting company, or an emerging growth company. See the definitions of 'large accelerated filer,' 'accelerated filer,' 'smaller reporting company,' and 'emerging growth company' in Rule 12b-2 of the Exchange Act.

Large accelerated filer

☒

Accelerated filer

Non-accelerated filer

☐

☐

Smaller reporting company ☐

Emerging growth company

☐

with any new or revised financial accounting standards provided pursuant to Section 13(a) of the Exchange Act.

☐

02210

## UNITED STATES

## SECURITIES AND EXCHANGE COMMISSION

Washington, D.C. 20549

## FORM 10-Q

(Mark One)

☒ QUARTERLY REPORT PURSUANT TO SECTION 13 OR 15(d) OF THE SECURITIES EXCHANGE ACT OF 1934

For the quarterly period ended September 30, 2024

OR

☐ TRANSITION REPORT PURSUANT TO SECTION 13 OR 15(d) OF THE SECURITIES EXCHANGE ACT OF 1934

For the transition period from to

Commission File Number 001-40819

## Toast, Inc.

(Exact name of registrant as specified in its charter)

Table of Contents

TABLE OF CONTENTS

|                               |                                                                                |   Page |
|-------------------------------|---------------------------------------------------------------------------------------|--------|
| Part I. Financial Information | Part I. Financial Information                                                         |      1 |
| Item 1.                       | Financial Statements (unaudited)                                                      |      1 |
|                               | Condensed Consolidated Balance Sheets                                                 |      1 |
|                               | Condensed Consolidated Statements of Operations                                       |      2 |
|                               | Condensed Consolidated Statements of Comprehensive Income (Loss)                      |      3 |
|                               | Condensed Consolidated Statements of Stockholders' Equity                             |      4 |
|                               | Condensed Consolidated Statements of Cash Flows                                       |      6 |
|                               | Notes to Condensed Consolidated Financial Statements                                  |      7 |
| Item 2.                       | Management's Discussion and Analysis of Financial Condition and Results of Operations |     17 |
| Item 3.                       | Quantitative and Qualitative Disclosures About Market Risk                            |     26 |
| Item 4.                       | Controls and Procedures                                                               |     26 |
|                               | Part II. Other Information                                                            |     27 |
| Item 1.                       | Legal Proceedings                                                                     |     27 |
| Item 1A.                      | Risk Factors                                                                          |     27 |
| Item 2.                       | Unregistered Sales of Equity Securities and Use of Proceeds                           |     27 |
| Item 3.                       | Defaults Upon Senior Securities                                                       |     28 |
| Item 4.                       | Mine Safety Disclosures                                                               |     28 |
| Item 5.                       | Other Information                                                                     |     28 |
| Item 6.                       | Exhibits                                                                              |     29 |
|                               | Signatures                                                                            |     30 |

## Item 1. Financial Statements

## PART I. FINANCIAL INFORMATION

## TOAST, INC.

## CONDENSED CONSOLIDATED BALANCE SHEETS

(unaudited)

(in millions, except per share amounts)

Table of Contents

|                                                                                | September 30, 2024   | December 31, 2023   |
|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------|---------------------|
| Assets:                                                                                |                      |                     |
| Current assets:                                                                                |                      |                     |
| Cash and cash equivalents                                                                                | $ 761                | 605                 |
| Marketable securities                                                                                | 511                  | 519                 |
| Accounts receivable, net                                                                                | 105                  | 69                  |
| Inventories, net                                                                                | 106                  | 118                 |
| Other current assets                                                                                | 319                  | 259                 |
| Total current assets                                                                                | 1,802                | 1,570               |
| Property and equipment, net                                                                                | 95                   | 75                  |
| Operating lease right-of-use assets                                                                                | 31                   | 36                  |
| Intangible assets, net                                                                                | 22                   | 26                  |
| Goodwill                                                                                | 113                  | 113                 |
| Restricted cash                                                                                | 56                   | 55                  |
| Other non-current assets                                                                                | 108                  | 83                  |
| Total non-current assets                                                                                | 425                  | 388                 |
| Total assets                                                                                | $ 2,227              | 1,958               |
| Liabilities and Stockholders' Equity:                                                                                |                      |                     |
| Current liabilities:                                                                                |                      |                     |
| Accounts payable                                                                                | $ 30                 | 32                  |
| Deferred revenue                                                                                | 62                   | 39                  |
| Accrued expenses and other current liabilities                                                                                | 656                  | 592                 |
| Total current liabilities                                                                                | 748                  | 663                 |
| Warrants to purchase common stock                                                                                | 27                   | 64                  |
| Operating lease liabilities                                                                                | 27                   | 33                  |
| Other long-term liabilities                                                                                | 5                    | 4                   |
| Total liabilities                                                                                | 807                  | 764                 |
| Commitments and Contingencies (Note 11)                                                                                |                      |                     |
| Stockholders' Equity:                                                                                |                      |                     |
| Preferred stock - par value $0.000001; 100 shares authorized, no shares issued or outstanding                                                                                | -                    | -                   |
| Common stock, $0.000001 par value: Class A - 7,000 shares authorized; 468 and 429 shares issued and outstanding as of September 30, 2024 and December 31, 2023, respectively Class B - 700 shares authorized; 97 and 114 shares issued and outstanding as of September 30, 2024 and December 31, 2023, respectively | -                    | -                   |
| Accumulated other comprehensive income                                                                                | 3                    | -                   |
| Additional paid-in capital                                                                                | 3,053                | 2,817               |
| Accumulated deficit                                                                                | (1,636)              | (1,623)             |
| Total stockholders' equity                                                                                | 1,420                | 1,194               |
| Total liabilities and stockholders' equity                                                                                | $ 2,227              | 1,958               |

The accompanying notes are an integral part of these condensed consolidated financial statements.

## TOAST, INC.

## CONDENSED CONSOLIDATED STATEMENTS OF OPERATIONS

(unaudited)

(in millions, except per share amounts)

Table of Contents

|                                                                      | Three Months Ended September 30,   | Three Months Ended September 30,   | Nine Months Ended September 30,   | Nine Months Ended September 30,   |
|----------------------------------------------------------------------|------------------------------------|------------------------------------|-----------------------------------|-----------------------------------|
|                                                                      | 2024                               | 2023                               | 2024                              | 2023                              |
| Revenue:                                                             |                                    |                                    |                                   |                                   |
| Subscription services                                                | $ 189                              | $ 131                              | $ 506                             | $ 358                             |
| Financial technology solutions                                       | 1,067                              | 856                                | 2,963                             | 2,338                             |
| Hardware and professional services                                   | 49                                 | 45                                 | 153                               | 133                               |
| Total revenue                                                        | 1,305                              | 1,032                              | 3,622                             | 2,829                             |
| Costs of revenue:                                                    |                                    |                                    |                                   |                                   |
| Subscription services                                                | 56                                 | 43                                 | 159                               | 118                               |
| Financial technology solutions                                       | 835                                | 674                                | 2,323                             | 1,828                             |
| Hardware and professional services                                   | 91                                 | 88                                 | 279                               | 271                               |
| Amortization of acquired intangible assets                           | 1                                  | 1                                  | 4                                 | 4                                 |
| Total costs of revenue                                               | 983                                | 806                                | 2,765                             | 2,221                             |
| Gross profit                                                         | 322                                | 226                                | 857                               | 608                               |
| Operating expenses:                                                  |                                    |                                    |                                   |                                   |
| Sales and marketing                                                  | 119                                | 100                                | 340                               | 299                               |
| Research and development                                             | 89                                 | 87                                 | 258                               | 264                               |
| General and administrative                                           | 80                                 | 98                                 | 229                               | 276                               |
| Restructuring expenses                                               | -                                  | -                                  | 46                                | -                                 |
| Total operating expenses                                             | 288                                | 285                                | 873                               | 839                               |
| Income (loss) from operations                                        | 34                                 | (59)                               | (16)                              | (231)                             |
| Other income (expense):                                              |                                    |                                    |                                   |                                   |
| Interest income, net                                                 | 9                                  | 10                                 | 30                                | 27                                |
| Change in fair value of warrant liability                            | (1)                                | 18                                 | (37)                              | (5)                               |
| Other income (expense), net                                          | 15                                 | -                                  | 13                                | -                                 |
| Income (loss) before taxes                                           | 57                                 | (31)                               | (10)                              | (209)                             |
| Income tax expense                                                   | (1)                                | -                                  | (3)                               | (1)                               |
| Net income (loss)                                                    | $ 56                               | $ (31)                             | $ (13)                            | $ (210)                           |
| Earnings (loss) per share attributable to common stockholders:       |                                    |                                    |                                   |                                   |
| Basic                                                                | $ 0.10                             | $ (0.06)                           | $ (0.02)                          | $ (0.40)                          |
| Diluted                                                              | $ 0.07                             | $ (0.09)                           | $ (0.02)                          | $ (0.40)                          |
| Weighted-average shares used in computing earnings (loss) per share: |                                    |                                    |                                   |                                   |
| Basic                                                                | 563                                | 535                                | 556                               | 530                               |
| Diluted                                                              | 590                                | 536                                | 556                               | 530                               |

The accompanying notes are an integral part of these condensed consolidated financial statements.