Build a Web3 dApp with a Swarm of AI Agents
Imagine building a decentralized application (dApp) based on simple prompts—no manual coding required. In this tutorial, you'll learn how to use SwarmZero.ai to create a swarm of AI agents that collaboratively build a web3 dApp. These agents work together to execute tasks and deliver a functional dApp, demonstrating the power of decentralized AI.
Whether you want to create an NFT minting platform, a DAO, or a simple DeFi to-do application, this agent-driven process simplifies building complex dApps by distributing tasks across multiple agents.
Prerequisites
Before starting, ensure you have the following:
Python 3.11 or higher installed
Git installed
An IDE or text editor (VS Code, PyCharm, etc.)
Basic understanding of Python programming
API keys from OpenAI, Anthropic, and Mistral
Step-by-Step Tutorial for Building a dApp with Swarm
Below is the step-by-step tutorial to build the swarm which will autonomously create a dApp.
Cloning the Repository
First, clone the repository containing the base code for building your dApp.
Step 1: Open your terminal or command prompt and run the following commands:
These commands will download the repository and set up the directory to start working on the agent.
Setting Up the Environment
Now, set up the environment required to run the agent.
Step 1: Create a virtual environment:
Step 2: Activate the virtual environment:
On Windows
Command Prompt:
PowerShell:
On Linux/Mac:
Installing Dependencies
With the environment activated, install the required dependencies:
Setting Up Environment Variables
Step 1: Create a new file called .env
in the root directory:
Step 2: Copy the contents of .env.example
into your new .env
file.
Step 3: Add your API keys to the .env
file:
Understanding the Code Structure
The repository contains essential components for running the dApp builder:
hive_swarm folder: Houses the agents responsible for various tasks in the dApp environment and helper tools for Hive's ecosystem.
swarm.py: The main script orchestrating the swarm agents, and controlling task distribution.
main.py: The core driver script where you input prompts and interact with the agents.
.env: Stores your API keys and configuration.
requirements.txt: Lists dependencies needed to run the agents.
Setting Up the QA Agent
Before creating a swarm, it's essential to set up individual components such as the QA Engineer agent for code review, and utility tools to manage the output generated by swarm. These foundational steps ensure that the multiple agents can perform their tasks efficiently.
Once these are in place, we can move on to creating the swarm. Next, we create the inmain.py
, which coordinates the tasks across multiple specialized agents and runs them.
The following code defines a QA agent using the HiveAgent
class, tailored to perform code reviews as a Quality Assurance Engineer within the project:
In this example:
The agent is named "Quality Assurance Engineer Agent" and its role is set to "QA engineer."
The agent uses the instruction set
QA_ENGINEER_INSTRUCTION
to guide its task as a code reviewer.The agent operates in swarm mode and uses the configuration file
hive_config.toml
to set its environment.
QA Agent Configuration File
Here’s the configuration file (hive_config.toml
) that defines key settings for the QA agent, such as the model, environment, and timeout period:
This configuration sets up the QA agent to use the mistral-large-latest
model, with a 60-second timeout, and operates in the development environment.
Tools for Managing Code Output
Alongside the QA agent, you should also include utility functions to help save and organize code output generated by the agents. Below is the code for the save_to_file
function and its supporting files.
File Saving Function
The core utility for saving the agent's output is found in the files.py
module. It ensures that content is saved to a specified file path and handles directory creation if needed:
This function:
Accepts the content, file name, and file path as parameters.
Creates the necessary directories if they do not already exist.
Writes the content to the specified file and confirms the save operation.
Tools Initialization
The __init__.py
file allows importing the save_to_file
function from the files.py
module:
Creating the Swarm
1. Import Required Libraries
First, you need to import the necessary libraries and modules that will be used in building your AI swarm. This includes HiveSwarm
for managing the swarm, save_to_file
for saving outputs, and SDKContext
for context management.
2. Load Environment Variables
Use load_dotenv()
to load environment variables from a .env
file. This is essential for configuring your environment and accessing necessary credentials.
3. Creating the Configuration File
In previous sections, we explored how to configure a single AI agent using a configuration file, setting up essential parameters like the model, environment, and task instructions. However, the configuration file can also be extended to support multiple AI agents, each tailored to specific roles within a software development project.
By assigning distinct models, environments, and task instructions for roles like project management, frontend development, backend development, and Solidity smart contract development, the config file ensures that each agent performs its designated tasks efficiently.
Additionally, the tools for saving outputs in designated folders help keep the workflow organized across all agents.
4. Create an SDK Context
Define the path to your configuration file (hive_config.toml
) and create an SDKContext
instance. This context will manage the configuration settings for the swarm.
5. Initialize the HiveSwarm
Create an instance of HiveSwarm
. Set its name, description, and instructions on how it should operate. Provide the functions it will use (e.g., save_to_file
) and pass the previously created SDKContext
.
6. Add the QA Agent
Use get_qa_agent()
to create a QA agent with the SDKContext
. Add this QA agent to the swarm to enable quality assurance functionalities.
If needed, you can remove the QA agent from the swarm. This is optional and can be done if you no longer require the QA functionalities.
Creating main.py
to run the Swarm
main.py
to run the SwarmStep 1: Import Necessary Libraries
Open main.py
and ensure the following libraries are imported:
Step 2: Load Environment Variables
Load the environment variables from the .env
file containing your API keys:
Step 3: Define the Agent and Process Prompts
The agent listens for user prompts, fetches related tasks, and returns a response.
Step 4: Run the Agent
In the same script, execute the main function that runs the agent:
Running the Agent
Now that everything is set up, let's run the dApp builder.
Step 1: Run the agent:
Step 2: Input your prompt:
The agents will process the task and generate the code, which will be stored in hive-agent-data/output
.
Output
Once you’ve input a prompt, the swarm of AI agents will collaboratively work to build the requested dApp. You can check the generated code in the output folder. Additionally, explore the functionality further by testing the generated dApp.
Wrap Up
Congratulations! You've successfully built a decentralized application using SwarmZero.ai and its AI-driven agents. The next step is exploring more complex prompts, refining the agents' capabilities, and diving deeper into decentralized AI-powered development.
By using SwarmZero, you're tapping into a powerful ecosystem where AI agents collaborate to achieve real-world tasks, including building web3 applications!
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