DOGE-AI

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An autonomous AI agent here to uncover waste and inefficiencies in government spending and policy decisions

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<div align="center">
  <h1>DOGEai</h1>
  <p>An autonomous AI agent here to uncover waste and inefficiencies in government spending and policy decisions</p>
</div>

## Overview

This repository serves as the foundational open-core toolkit for constructing
efficient data pipelines and powerful AI agents. Developed as the backbone of
[DOGEai](https://dogeai.info) and [CityDeskNYC](https://x.com/citydesknyc/), it
has evolved into [Rhetor](https://rhetor.ai) the real-time communications brain:
autonomous, voice-true agents that turn data into on-brand conversations with
any customer.

## Problem Statement

Each year, the U.S. Congress introduces a large number of bills, many of which
are complex and difficult for the general public to understand. Some bills are
quickly approved, while others languish for extended periods, and the sheer
volume of legislation makes it hard for citizens to stay informed. Additionally,
the language used in these bills is often legalistic and inaccessible, further
alienating the public from the legislative process. As a result, many citizens
remain unaware of key legislation that impacts their lives.

## Vision

While the immediate focus is on making it easier for the public to understand
these bills, we are also tackling a bigger challenge: the complexity and
inaccessibility of existing government systems. The infrastructure we've
developed to scrape and present this data is complex, and we believe in the
power of open-source solutions. By creating efficient, user-friendly tooling to
extract and enrich government data, we aim to empower others to build even more
public goods, creating a vibrant ecosystem of accessible, transparent government
data that anyone can contribute to.

This repository serves as the data layer and framework for working with
government data. It contains the tooling that powers our agent, and is designed
to be flexible, allowing for the creation of various other agents that can build
on top of this foundation. We hope this framework becomes the foundation for new
ways to interact with and leverage government data, enabling innovative
solutions and greater public engagement.

## Contributing

Our active development has moved into building Rhetor which is built on top of
this core toolkit powering many different things. We welcome your feedback but
not actively accepting new contributions. Overtime we will open source more of
the components that make up Rhetor.

### Getting Started

#### Prerequisites

1. Node.js `v22.x` - You can install it from the
   [official website](https://nodejs.org/).
2. `pnpm` is the package manager we use for this project. You can install it by
   running `npm install -g pnpm`.
3. Docker (optional) - I use [OrbStack](https://orbstack.dev) but you can use
   Docker Desktop or any other tool you prefer.

#### Installation

1. Clone the repository.
2. Run `pnpm install` to install the project dependencies.

#### Running the Website

To view the website locally, follow these steps:

1. Navigate to the `website` directory:
   ```bash
   cd website
   ```
2. Run the development server:
   ```bash
   pnpm dev
   ```
3. Open your browser and go to http://localhost:3000.

### How can I help?

There are lot of things to build and improve in this project. You can look at
the issues and see if there is anything you can help with or reach out on
[X](https://x.com/singh_saihaj) and we can discuss how you can help.

### Architecture

This monorepo is structured as follows:

#### `crawler`

A Node.js application that scrapes data from the
[U.S. Congress API](https://api.congress.gov). We scrape a list of bills then
process a bill each time to enrich it with additional data and run through a
summarization process. The processing is done via
[inngest queues](https://www.inngest.com) which makes it super easy to handle
retry/failure logic and scale the processing. Below is the flow of the crawler:

```mermaid
graph LR
    Crawler["Crawler"] --> ListBills["Scrape bills from API"]
    ListBills --> |"Enqueue bill for processing"| Queue1["Queue"]
    Queue1 --> BillProcessor["Bill enrichment"]

BillProcessor --> GetBill["Get bill details from API"]
    BillProcessor --> FetchBillText["Scrape congress site for bill text"]
    BillProcessor --> Summary["Summarize bill"]

GetBill --> DB["Database"]
    FetchBillText --> DB
    Summary --> DB
```

The service is deployed on [Fly.io](https://fly.io) and runs when we trigger it.

Given the early stages this project we just trigger from initial crawl to
congress API from CLI then queue the bills to our deployed crawler infra. Need
to move the initial scrape to a CRON job.

#### `agent`

We initially built the agent using
[ElizaOS Framework](https://github.com/elizaOS/eliza), but customizing each step
proved challenging. During our POC phase, we realized that
[ElizaOS](https://github.com/elizaOS/eliza) wasn’t the right fit, as we needed
more control over various aspects of the agent. To address this, we migrated to
Inngest, which provides robust workflow orchestration and built-in resiliency.
It also offers a great local development experience with solid observability
features right out of the box.

At a high level, the agent follows this process:

1. Cron jobs fetch tweets from X.
2. The system processes them for decision-making.
3. Replies are then posted accordingly.

```mermaid
graph LR
   Agent["Agent"] --> IngestInteraction["X lists that act as feed"]
   IngestInteraction --> ProcessInteraction["Decision Queue"]
   ProcessInteraction --> ExecuteInteraction["Generate reply and post"]

subgraph ExecuteInteraction["Generate reply and post"]
      Idempotency["Ignore if already replied"]
      Context["Pull post context"]
      Search["Search Knowledge Base"]
      Generate["Generate Reply"]
      Post["Post Reply"]
    end

ExecuteInteraction --> Idempotency --> Context --> Search --> Generate --> Post

Agent["Agent"] --> Ingest["Tags to DOGEai"]
   Ingest --> ProcessInteraction["Decision Queue"]
   ProcessInteraction --> ExecuteInteraction["Generate reply and post"]
```

For a deeper dive into the architecture, check out these articles I wrote:

- https://x.com/singh_saihaj/status/1888639845108535363
- https://x.com/singh_saihaj/status/1892244082329518086

#### `website`

Home for `dogeai.info`. A Next.js application.

#### `chat`

A Next.js application (`dogeai.chat`) that serves as a terminal to chat with
DOGEai for in-depth analysis. A site where anyone can go beyond quick replies
and have real, back-and-forth conversations with DOGEai. Ask follow-ups, dig
deeper, and cut through media spin to make sense of government spending in real
time.

#### `envoy`

An [Envoy proxy](https://www.envoyproxy.io) that serves as a reverse proxy for
the agent API, Terminal and management dashboard. It handles routing,
authentication and provides a single entry point DOGEai's services.

#### `dashboard`

Management dashboard for DOGEai. It allows you to manage the agent like: update
the prompts, knowledge base updates, restart. It even acts as a playground to
iterate. It is built with Next.js and provides a user-friendly interface for
managing the agent's configuration and data. This is only meant for internal use
and not for public consumption.

#### `log-shipper`

A Fly.io service that ships logs from all the different services running in Fly
to Grafana Cloud.

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE)
file.

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