Skip to main content

A Quick Guide to Creating and Publishing Your First Agent Module on Naptha

Open In Colab

While other projects allow you to deploy agents on the cloud, Naptha allows you to create and deploy them on an entire Web built especially for agents. Here, they can interact with other agents, tools, and environments (such as group chats, auctions, and virtual worlds), running on top of a network of devices.

In this guide, we'll walk through creating and publishing your first Naptha agent module, from initial setup to deployment. Whether you're an AI researcher, developer, or enthusiast, you'll be able to contribute to the Naptha ecosystem in no time.

What is an Agent Module?​

Naptha Modules are the building blocks of multi-agent applications. They are designed to be framework-agnostic, allowing developers to implement them using different agent frameworks. Modules can run on separate devices, while still interacting with each other over the network. There are currently seven types of Modules:

  • Agent Modules e.g. Chat Agents, Task-solving Agents, ReAct Agents, etc.
  • Tool Modules e.g. Web Search, Python Code Execution, etc.
  • Knowledge Base Modules e.g. Wikipedia, Google Search, etc.
  • Memory Modules e.g. Long-term Memory, Short-term Memory, etc.
  • Persona Modules e.g. Social Personas generated from exported Twitter data, or synthetically-generated Market Personas
  • Agent Orchestrator Modules e.g. Organizations of Coding Agents, Social Simulations, etc.
  • Environment Modules e.g. Group Chats (like WhatsApp for Agents), Information Board (Reddit for Agents), Auctions (eBay for Agents), etc.
info

For more information on modules, please refer to the Naptha Modules section.

Some popular agent modules on Naptha include:

info

You can also find a list of agent modules on Naptha:

  • with the Naptha SDK via the CLI naptha agents or the Client await naptha.hub.list_agents()
  • on Naptha's Github homepage

Prerequisites​

Before we dive in, make sure you have:

  • Python 3.8 or higher installed
  • The Naptha SDK installed (pip install naptha-sdk)
  • Basic familiarity with Python packaging
  • Poetry installed (recommended for dependency management)

Getting Started​

The journey of creating your first Naptha agent begins with our module template. This template provides the essential structure and boilerplate code you need to get started quickly. To get started, clone the repository and install the dependencies:

git clone https://github.com/NapthaAI/module_template
cd module_template

Create a copy of the .env file:​

cp .env.example .env

Install the dependencies:​

poetry install

Making Changes to the Code​

The main place to make changes to the code is in the run.py file. This is the default entry point that will be used when the module run is initiated. The run function can instantiate a class (e.g. an agent class) or call a function.

Making Changes to the Configs​

You can make changes to the configs in the configs folder. The deployment.json file is the main config file for the module. You may also have other config files for subdeployments (e.g. tool_deployments.json, environment_deployments.json, kb_deployments.json, memory_deployments.json). For example:

MODEL: If you would like to use a different model, you can change the llm_config['config_name'] in the deployment.json file (the config_name must match the config_name in the llm_configs.json file). If using OpenAI, make sure to set the OPENAI_API_KEY environment variable.

PERSONA: If you would like to use a different persona, you can add persona_module['module_url'] in the config dict of deployments.json file (the module_url must point to a valid Hugging Face dataset). See the simple_chat_agent module for an example of how to use a persona module with an agent.

TOOLS: If you would like your module to use a tool, you can add tool_deployments: {'name': '<tool_deployment_name>'} in the deployment dict of deployments.json file (the tool_deployment_name must match the name field in the tool_deployments.json file). See the generate_image_agent module for an example of how to use a tool as a subdeployment.

ENVIRONMENT: If you would like your module to use an environment, you can add environment_deployments: {'name': '<environment_deployment_name>'} in the deployment dict of deployments.json file (the environment_deployment_name must match the name field in the environment_deployments.json file). See the multiagent_chat module for an example of how to use an environment as a subdeployment.

KB: If you would like your module to use a knowledge base, you can add kb_deployments: {'name': '<kb_deployment_name>'} in the deployment dict of deployments.json file (the kb_deployment_name must match the name field in the kb_deployments.json file). See the wikipedia_agent module for an example of how to use a knowledge base as a subdeployment.

MEMORY: If you would like your module to use memory, you can add memory_deployments: {'name': '<memory_deployment_name>'} in the deployment dict of deployments.json file (the memory_deployment_name must match the name field in the memory_deployments.json file).

info

Don't forget to update your pyproject.toml with your agent's details:

[tool.poetry]
name = "my-first-agent"
version = "0.1.0"
description = "My first Naptha agent"
authors = ["Your Name <[email protected]>"]

🧪 Testing the Module​

After making changes to the module, testing usually involves the following steps:

  1. Test the module locally without the Naptha Node
  2. Publish the module on the Naptha Hub
  3. Test running the module on a hosted Naptha Node

🖥️ Test the Module Locally without Node​

You can run the module using:

poetry run python <module_name>/run.py

Now you can iterate on the module and commit your changes.

🌐 Publish the Module on the Naptha Hub​

For this step, you will need to:

  • Install the Naptha SDK using the instructions here. To use the SDK with your local node and hub, set NODE_URL=http://localhost:7001 and HUB_URL=ws://localhost:3001/rpc in the .env file for the NapthaAI/naptha-sdk repository.

First, you need to push the module code to your GitHub or IPFS (or both). If using GitHub, make sure to change the remote origin. Also add a new module version number using e.g.:

git tag v0.1

To store on GitHub, you can use:

git push --tags

Before registering on the Naptha Hub, make sure the module field in your deployment.json file has a name, description, parameters, module_type, module_version, module_entrypoint, and execution_type fields:

[
   {
     ...
       "module": {
           "name": "module_template",
           "description": "Module Template",
           "parameters": "{tool_name: str, tool_input_data: str}",
           "module_type": "agent",
           "module_version": "v0.1",
           "module_entrypoint": "run.py",
           "execution_type": "package"
       },
     ...
   }
]

You can register the module from a GitHub url by adding your specific repo url with the -r flag:

naptha publish -r https://github.com/NapthaAI/module_template

Alternatively, you can store the module on IPFS and register on the Naptha Hub by running:

naptha publish -r

If successful, you will see an output with the IPFS hash, and a link where you can test download via the browser http://provider.akash.pro:30584/ipfs/<ipfs_hash>.

If your module makes use of other modules (e.g. your agent module uses a tool module or memory module), you may also want to publish those sub-modules using:

naptha publish -r -s

Make sure to add a list of dicts with a name field to one or more of the agent_deployments, tool_deployments, environment_deployments, kb_deployments, or memory_deployments fields in your deployment.json file:

[
   {
     ...
       "agent_deployments": [{"name": "agent_deployment_1"}],
       "tool_deployments": [{"name": "tool_deployment_1"}],
       "environment_deployments": [{"name": "environment_deployment_1"}],
       "kb_deployments": [{"name": "kb_deployment_1"}],
       "memory_deployments": [{"name": "memory_deployment_1"}],
     ...
   }
]

And also add corresponding agent_deployments.json, tool_deployments.json, environment_deployments.json, kb_deployments.json, or memory_deployments.json files to the configs folder for each subdeployment. In each file, there should be a module field with a name, description, parameters, module_type, module_version, module_entrypoint, and execution_type fields:

[
   {
     ...
       "module": {
           "name": "subdeployment_module",
           "description": "Subdeployment Module",
           "parameters": "{tool_name: str, tool_input_data: str}",
           "module_type": "tool",
           "module_version": "v0.1",
           "module_entrypoint": "run.py",
           "execution_type": "package"
       },
     ...
   }
]

You can confirm that the modules were registered on the Hub by running:

naptha agents

Or the equivalent command for the module type you are using (e.g. naptha tools, naptha orchestrators, naptha environments, naptha kbs, naptha memories, naptha personas).

☁️ Test the Module on a hosted Node (with the hosted Naptha Hub)​

Once the module is published, you can run it on a hosted Naptha Node using the Naptha SDK (to use the hosted node you should set NODE_URL=http://node.naptha.ai in the .env file):

naptha run agent:module_template -p "func_name='func', func_input_data='gm...'"

Inspect the outputs to see if the module ran successfully. We aim to provide useful error outputs to help developers to debug issues with their module using the hosted node. However, you may get error outputs that are not explanatory. If this is the case, please reach out in the #support channel of our discord or via email ([email protected]).

Best Practices​

  • Documentation: Include clear documentation in your README.md
  • Version Control: Use semantic versioning for releases
  • Dependencies: Keep dependencies minimal and well-defined

Next Steps​

Congratulations! You've created and published your first Naptha agent module. Remember to version your agent appropriately when making updates!