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decentralized, high-performance agentic AI applications
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readme.md

Decentralized Agentics

AI Agentic Architecture Deployment with wasmCloud, Tarmac, ZenModel, and IPFS

Introduction

This project provides a comprehensive guide to deploying an AI agentic architecture using a combination of wasmCloud, Tarmac, ZenModel, and IPFS.

The integration of these technologies enables the creation of decentralized, high-performance AI applications that can run seamlessly in a web browser. This architecture leverages the strengths of each component to provide a robust framework for AI development and deployment.

Technical Overview

  • wasmCloud: A platform that facilitates the management and execution of WebAssembly components in a distributed environment. It provides a secure and efficient way to run applications across various infrastructures.

  • Tarmac: A framework for building serverless applications with WebAssembly, offering a function-based architecture that supports scalable deployment and execution of tasks.

  • ZenModel: A workflow programming framework in Go, designed for creating modular AI agents. It allows for the configuration of complex workflows using various deployment patterns, algorithms, and control strategies.

  • IPFS (InterPlanetary File System): A decentralized storage network that ensures the availability and integrity of application assets and models, eliminating reliance on centralized servers.

Applications

This architecture can be used to develop a wide range of AI applications, including but not limited to:

  • Decentralized AI Agents: Create intelligent agents that operate independently and interact with users or other agents in a decentralized manner.

  • Real-Time Data Processing: Deploy applications that require real-time data analysis and decision-making, leveraging the distributed nature of wasmCloud and IPFS.

  • IoT and Edge Computing: Implement AI solutions for IoT devices and edge computing environments, where low latency and decentralized processing are crucial.

Architecture

The architecture consists of several key components:

  1. WebAssembly Components: Executed by wasmCloud, these components encapsulate the AI logic and can be deployed across various environments.

  2. Serverless Functions: Managed by Tarmac, these functions execute specific AI tasks and can be triggered by events or HTTP requests.

  3. ZenModel Workflows: Define the behavior and interactions of AI agents, allowing for customization through deployment patterns, algorithms, and control strategies.

  4. IPFS Storage: Hosts static assets and models, ensuring they are accessible and verifiable in a decentralized manner.

Usage

To use this architecture, follow these steps:

  1. Install Requirements: Ensure all necessary libraries and tools are installed, including ipywidgets, requests, web3storage, and ipfshttpclient.

  2. Configure Components: Use the Jupyter notebook to configure wasmCloud, Tarmac, and ZenModel. Enter the required details through the interactive UI components.

  3. Set Up ZenModel Workflow: Customize the ZenModel workflow by selecting deployment patterns, algorithms, and other options. Compile and run the workflow to ensure it operates as expected.

  4. Deploy to IPFS: Use the web3.storage client to deploy your application components to IPFS, providing decentralized access and storage.

  5. Access Your Application: Once deployed, access your application via the IPFS link provided in the deployment step.

Advanced Customizations

  • Fine-Tuning ZenModel: Experiment with different configurations to optimize the performance and behavior of your AI agents.

  • Security Enhancements: Implement authentication and encryption to protect your distributed application from unauthorized access.

  • Monitoring and Analytics: Develop dashboards to monitor the performance and interactions of your AI agents in real-time.

  • Integration with Other Technologies: Explore integration with blockchain networks or decentralized databases for enhanced functionality and data integrity.

  • Community Contributions: Share your experiences and improvements with the wasmCloud, Tarmac, ZenModel, and IPFS communities to contribute to the ecosystem's growth.

Conclusion

This project provides a powerful framework for deploying AI agentic architectures that are decentralized, scalable, and efficient. By leveraging the combined strengths of wasmCloud, Tarmac, ZenModel, and IPFS, developers can create innovative AI applications that operate seamlessly in a distributed environment. Happy coding, and enjoy building your decentralized AI solutions!

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notebook.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Deploying AI Agentic Architecture with wasmCloud, Tarmac, ZenModel, and IPFS\n",
"\n",
"## Introduction\n",
"This Jupyter Notebook provides a comprehensive guide for deploying an AI agentic architecture using wasmCloud, Tarmac, ZenModel, and IPFS. The integration of these technologies allows for the creation of decentralized and high-performance AI applications that can run in a web browser.\n",
"\n",
"### Overview of Technologies\n",
"- **wasmCloud**: A platform that enables the management and execution of WebAssembly components in a distributed environment.\n",
"- **Tarmac**: A framework for building serverless applications with WebAssembly, offering a function-based architecture for scalable deployment.\n",
"- **ZenModel**: A workflow programming framework in Go that facilitates the development of modular AI agents.\n",
"- **IPFS**: A decentralized storage network that allows for the hosting and retrieval of application assets and models, ensuring availability without reliance on centralized infrastructure.\n",
"\n",
"This notebook will guide you through the process of configuring these components and deploying your AI agentic architecture to IPFS."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Install Requirements\n",
"Before we begin, let's install all the necessary requirements for this notebook."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pip install ipywidgets requests web3storage ipfshttpclient\n",
"\n",
"# Verify installations\n",
"import ipywidgets\n",
"import requests\n",
"from web3storage import Client\n",
"import ipfshttpclient\n",
"\n",
"print(\"All requirements installed successfully!\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Configuration\n",
"Let's start by setting up the necessary configuration for our deployment. We'll use ipywidgets to create an interactive UI for entering configuration details."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import ipywidgets as widgets\n",
"from IPython.display import display\n",
"\n",
"# Configuration UI components\n",
"wasmcloud_host = widgets.Text(\n",
" description='wasmCloud Host:',\n",
" placeholder='Enter wasmCloud host URL'\n",
")\n",
"tarmac_function = widgets.Text(\n",
" description='Tarmac Function:',\n",
" placeholder='Enter Tarmac function name'\n",
")\n",
"zenmodel_path = widgets.Text(\n",
" description='ZenModel Path:',\n",
" placeholder='Enter path to ZenModel workflow'\n",
")\n",
"ipfs_token = widgets.Password(\n",
" description='IPFS Token:',\n",
" placeholder='Enter your web3.storage API token'\n",
")\n",
"\n",
"display(wasmcloud_host, tarmac_function, zenmodel_path, ipfs_token)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Configure wasmCloud and Tarmac\n",
"Now that we have our configuration details, let's set up wasmCloud and Tarmac for our serverless functions."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import requests\n",
"import json\n",
"\n",
"def configure_wasmcloud(host):\n",
" print(f\"Configuring wasmCloud at {host}\")\n",
" # Example: Set up a wasmCloud host\n",
" try:\n",
" response = requests.post(f\"{host}/configure\", json={\n",
" \"lattice\": \"default\",\n",
" \"cluster_seed\": \"default_seed\"\n",
" })\n",
" response.raise_for_status()\n",
" print(\"wasmCloud configured successfully\")\n",
" except requests.exceptions.RequestException as e:\n",
" print(f\"Error configuring wasmCloud: {e}\")\n",
"\n",
"def configure_tarmac(function_name):\n",
" print(f\"Configuring Tarmac function: {function_name}\")\n",
" # Example: Define a Tarmac function\n",
" tarmac_config = {\n",
" \"name\": function_name,\n",
" \"runtime\": \"wasmcloud\",\n",
" \"source\": \"./path/to/your/wasm/file.wasm\",\n",
" \"trigger\": {\n",
" \"http\": {\n",
" \"route\": f\"/{function_name}\"\n",
" }\n",
" }\n",
" }\n",
" with open(f\"{function_name}.json\", \"w\") as f:\n",
" json.dump(tarmac_config, f)\n",
" print(f\"Tarmac function {function_name} configured and saved to {function_name}.json\")\n",
"\n",
"# Execute configuration\n",
"configure_wasmcloud(wasmcloud_host.value)\n",
"configure_tarmac(tarmac_function.value)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Set Up ZenModel Workflow\n",
"Now let's set up our ZenModel workflow for our AI agentic architecture. We'll explore various configuration options including deployment patterns, algorithms, autonomous systems, command and control strategies, and tool calling approaches."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ZenModel Configuration Options\n",
"\n",
"1. **Deployment Patterns**:\n",
" - Single Agent: One AI agent handling all tasks\n",
" - Multi-Agent System: Multiple specialized agents working together\n",
" - Hierarchical: Agents organized in a tree-like structure\n",
"\n",
"2. **Algorithms**:\n",
" - Reinforcement Learning: Q-Learning, SARSA, DQN\n",
" - Evolutionary Algorithms: Genetic Algorithms, Evolutionary Strategies\n",
" - Swarm Intelligence: Particle Swarm Optimization, Ant Colony Optimization\n",
"\n",
"3. **Autonomous Systems**:\n",
" - Rule-based: Predefined set of rules for decision making\n",
" - Learning-based: Adaptive systems that improve over time\n",
" - Hybrid: Combination of rule-based and learning-based approaches\n",
"\n",
"4. **Command and Control Strategies**:\n",
" - Centralized: One central controller managing all agents\n",
" - Decentralized: Agents make decisions independently\n",
" - Federated: Distributed decision making with some central coordination\n",
"\n",
"5. **Tool Calling Approaches**:\n",
" - API Integration: Direct calls to external tools via APIs\n",
" - Plugin System: Modular approach for integrating various tools\n",
" - Natural Language Commands: Using NLP to interpret and execute tool commands\n",
"\n",
"Let's implement these options in our ZenModel configuration:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import subprocess\n",
"import json\n",
"\n",
"def setup_zenmodel(path, config):\n",
" print(f\"Setting up ZenModel workflow from {path} with custom configuration\")\n",
" \n",
" # Create ZenModel configuration file\n",
" zen_config = {\n",
" \"deployment_pattern\": config['deployment_pattern'],\n",
" \"algorithm\": config['algorithm'],\n",
" \"autonomous_system\": config['autonomous_system'],\n",
" \"command_control\": config['command_control'],\n",
" \"tool_calling\": config['tool_calling']\n",
" }\n",
" \n",
" config_path = f\"{path}_config.json\"\n",
" with open(config_path, 'w') as f:\n",
" json.dump(zen_config, f)\n",
" \n",
" try:\n",
" # Compile the ZenModel workflow with custom configuration\n",
" compile_result = subprocess.run([\"zenmodel\", \"compile\", path, \"--config\", config_path], capture_output=True, text=True)\n",
" if compile_result.returncode != 0:\n",
" print(f\"Error compiling ZenModel workflow: {compile_result.stderr}\")\n",
" return\n",
" \n",
" # Run the compiled ZenModel workflow\n",
" run_result = subprocess.run([\"zenmodel\", \"run\", f\"{path}.wasm\"], capture_output=True, text=True)\n",
" if run_result.returncode != 0:\n",
" print(f\"Error running ZenModel workflow: {run_result.stderr}\")\n",
" return\n",
" \n",
" print(\"ZenModel workflow set up and executed successfully\")\n",
" print(f\"Output: {run_result.stdout}\")\n",
" except subprocess.CalledProcessError as e:\n",
" print(f\"Error setting up ZenModel workflow: {e}\")\n",
"\n",
"# ZenModel configuration options\n",
"zen_config = {\n",
" \"deployment_pattern\": widgets.Dropdown(\n",
" options=['Single Agent', 'Multi-Agent System', 'Hierarchical'],\n",
" description='Deployment Pattern:'\n",
" ),\n",
" \"algorithm\": widgets.Dropdown(\n",
" options=['Q-Learning', 'SARSA', 'DQN', 'Genetic Algorithm', 'Particle Swarm Optimization'],\n",
" description='Algorithm:'\n",
" ),\n",
" \"autonomous_system\": widgets.Dropdown(\n",
" options=['Rule-based', 'Learning-based', 'Hybrid'],\n",
" description='Autonomous System:'\n",
" ),\n",
" \"command_control\": widgets.Dropdown(\n",
" options=['Centralized', 'Decentralized', 'Federated'],\n",
" description='Command & Control:'\n",
" ),\n",
" \"tool_calling\": widgets.Dropdown(\n",
" options=['API Integration', 'Plugin System', 'Natural Language Commands'],\n",
" description='Tool Calling Approach:'\n",
" )\n",
"}\n",
"\n",
"display(widgets.VBox(list(zen_config.values())))\n",
"\n",
"# Button to trigger ZenModel setup\n",
"setup_button = widgets.Button(description=\"Set up ZenModel\")\n",
"output = widgets.Output()\n",
"\n",
"def on_button_clicked(_):\n",
" with output:\n",
" config = {k: v.value for k, v in zen_config.items()}\n",
" setup_zenmodel(zenmodel_path.value, config)\n",
"\n",
"setup_button.on_click(on_button_clicked)\n",
"\n",
"display(setup_button, output)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Deploy to IPFS\n",
"Finally, let's deploy our application to IPFS using the web3.storage service."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from web3storage import Client\n",
"import os\n",
"\n",
"def deploy_to_ipfs(api_token):\n",
" print(\"Deploying to IPFS...\")\n",
" client = Client(api_token)\n",
" \n",
" # Example: Deploy the Tarmac function configuration, ZenModel workflow, and ZenModel configuration\n",
" files_to_upload = [\n",
" f\"{tarmac_function.value}.json\",\n",
" f\"{zenmodel_path.value}.wasm\",\n",
" f\"{zenmodel_path.value}_config.json\"\n",
" ]\n",
" \n",
" try:\n",
" # Check if files exist\n",
" for file in files_to_upload:\n",
" if not os.path.exists(file):\n",
" print(f\"Error: File {file} not found\")\n",
" return\n",
" \n",
" # Upload files to IPFS\n",
" cid = client.upload_files(files_to_upload)\n",
" print(f\"Deployment successful. CID: {cid}\")\n",
" print(f\"You can access your files at: https://{cid}.ipfs.dweb.link/\")\n",
" except Exception as e:\n",
" print(f\"Error deploying to IPFS: {e}\")\n",
"\n",
"# Button to trigger IPFS deployment\n",
"deploy_button = widgets.Button(description=\"Deploy to IPFS\")\n",
"deploy_output = widgets.Output()\n",
"\n",
"def on_deploy_clicked(_):\n",
" with deploy_output:\n",
" deploy_to_ipfs(ipfs_token.value)\n",
"\n",
"deploy_button.on_click(on_deploy_clicked)\n",
"\n",
"display(deploy_button, deploy_output)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Conclusion\n",
"Congratulations! You have successfully configured and deployed an AI agentic architecture using wasmCloud, Tarmac, ZenModel, and IPFS. Here's a summary of what we've accomplished:\n",
"\n",
"1. Installed all necessary requirements for the project\n",
"2. Configured wasmCloud for distributed WebAssembly execution\n",
"3. Set up a Tarmac function for serverless deployment\n",
"4. Configured and compiled a ZenModel workflow for our AI agent with customizable options:\n",
" - Deployment patterns (Single Agent, Multi-Agent System, Hierarchical)\n",
" - Algorithms (Q-Learning, SARSA, DQN, Genetic Algorithm, Particle Swarm Optimization)\n",
" - Autonomous systems (Rule-based, Learning-based, Hybrid)\n",
" - Command and control strategies (Centralized, Decentralized, Federated)\n",
" - Tool calling approaches (API Integration, Plugin System, Natural Language Commands)\n",
"5. Deployed our application components to IPFS for decentralized storage and access\n",
"\n",
"You can now access your deployed application using the IPFS link provided in the deployment step. Remember to secure your API tokens and manage your deployments responsibly.\n",
"\n",
"### Next Steps\n",
"\n",
"To further enhance your AI agentic architecture, consider the following:\n",
"\n",
"1. **Fine-tune your ZenModel workflow**: Experiment with different configurations to optimize your AI agents' performance.\n",
"2. **Implement advanced security measures**: Add authentication and encryption to protect your distributed application.\n",
"3. **Develop a monitoring system**: Create dashboards to track the performance and behavior of your AI agents in real-time.\n",
"4. **Integrate with other decentralized technologies**: Explore integration with blockchain networks or decentralized databases for enhanced functionality.\n",
"5. **Contribute to the ecosystem**: Share your experiences and contribute improvements to the wasmCloud, Tarmac, ZenModel, and IPFS communities.\n",
"\n",
"For further customization and advanced usage, refer to the official documentation of each technology used in this deployment process:\n",
"\n",
"- [wasmCloud Documentation](https://wasmcloud.dev/)\n",
"- [Tarmac Documentation](https://tarmac.io/docs/)\n",
"- [ZenModel Documentation](https://zenmodel.org/docs/)\n",
"- [IPFS Documentation](https://docs.ipfs.io/)\n",
"\n",
"Happy coding, and enjoy building your decentralized AI applications!"
]
}
],
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