[DOCS][CLOUD]

3 months ago · bbb8f4e458
parent 2dc4684476
commit bbb8f4e458
7 changed files with 833 additions and 1 deletions
--- a/ca_to_name.py
+++ b/ca_to_name.py
@ -0,0 +1,96 @@
 import base64
 import requests
 from struct import unpack
 class SolanaTokenInfoFetcher:
    def __init__(self, rpc_url="https://api.mainnet-beta.solana.com"):
        """
        Initializes the token info fetcher.
        Args:
            rpc_url (str): The Solana RPC URL.
        """
        self.rpc_url = rpc_url
    def make_rpc_request(self, method, params):
        """
        Makes an RPC request to the Solana JSON RPC API.
        Args:
            method (str): The RPC method to call.
            params (list): The parameters for the method.
        Returns:
            dict: The JSON response from the RPC call.
        """
        headers = {"Content-Type": "application/json"}
        payload = {
            "jsonrpc": "2.0",
            "id": 1,
            "method": method,
            "params": params,
        }
        try:
            response = requests.post(self.rpc_url, json=payload, headers=headers)
            response.raise_for_status()
            return response.json()
        except requests.exceptions.RequestException as e:
            print(f"Error making RPC request: {e}")
            return None
    def get_token_info(self, mint_address):
        """
        Fetches token metadata (name, symbol) from the token mint account.
        Args:
            mint_address (str): The token mint address.
        Returns:
            dict: A dictionary containing the token name and symbol.
        """
        params = [
            mint_address,
            {"encoding": "base64"}
        ]
        response = self.make_rpc_request("getAccountInfo", params)
        if not response or "result" not in response or not response["result"]["value"]:
            print(f"Failed to fetch data for mint address: {mint_address}")
            return None
        # Decode the account data
        account_data = response["result"]["value"]["data"][0]
        decoded_data = base64.b64decode(account_data)
        # Extract token name and symbol
        try:
            token_name = decoded_data[0:32].decode("utf-8").rstrip("\x00")
            token_symbol = decoded_data[32:44].decode("utf-8").rstrip("\x00")
            decimals = unpack("<B", decoded_data[44:45])[0]
            return {
                "name": token_name,
                "symbol": token_symbol,
                "decimals": decimals
            }
        except Exception as e:
            print(f"Error decoding token metadata: {e}")
            return None
 if __name__ == "__main__":
    # Replace with the token mint address you want to query
    mint_address = "4zMMC9srt5Ri5X14GAgXhaHii3GnPAEERYPJgZJDncDU"  # Example: USDC -> 4zMMC9srt5Ri5X14GAgXhaHii3GnPAEERYPJgZJDncDU
    # Initialize the token info fetcher
    fetcher = SolanaTokenInfoFetcher()
    # Fetch and display token information
    token_info = fetcher.get_token_info(mint_address)
    if token_info:
        print(f"Token Name: {token_info['name']}")
        print(f"Token Symbol: {token_info['symbol']}")
        print(f"Decimals: {token_info['decimals']}")
    else:
        print("Failed to fetch token information.")
--- a/dao_swarm.py
+++ b/dao_swarm.py
@ -0,0 +1,233 @@
 import random
 from swarms import Agent
 # System prompts for each agent
 MARKETING_AGENT_SYS_PROMPT = """
 You are the Marketing Strategist Agent for a DAO. Your role is to develop, implement, and optimize all marketing and branding strategies to align with the DAO's mission and vision. The DAO is focused on decentralized governance for climate action, funding projects aimed at reducing carbon emissions, and incentivizing community participation through its native token.
 ### Objectives:
 1. **Brand Awareness**: Build a globally recognized and trusted brand for the DAO.
 2. **Community Growth**: Expand the DAO's community by onboarding individuals passionate about climate action and blockchain technology.
 3. **Campaign Execution**: Launch high-impact marketing campaigns on platforms like Twitter, Discord, and YouTube to engage and retain community members.
 4. **Partnerships**: Identify and build partnerships with like-minded organizations, NGOs, and influencers.
 5. **Content Strategy**: Design educational and engaging content, including infographics, blog posts, videos, and AMAs.
 ### Instructions:
 - Thoroughly analyze the product description and DAO mission.
 - Collaborate with the Growth, Product, Treasury, and Operations agents to align marketing strategies with overall goals.
 - Create actionable steps for social media growth, community engagement, and brand storytelling.
 - Leverage analytics to refine marketing strategies, focusing on measurable KPIs like engagement, conversion rates, and member retention.
 - Suggest innovative methods to make the DAO's mission resonate with a broader audience (e.g., gamified incentives, contests, or viral campaigns).
 - Ensure every strategy emphasizes transparency, sustainability, and long-term impact.
 """
 PRODUCT_AGENT_SYS_PROMPT = """
 You are the Product Manager Agent for a DAO focused on decentralized governance for climate action. Your role is to design, manage, and optimize the DAO's product roadmap. This includes defining key features, prioritizing user needs, and ensuring product alignment with the DAO’s mission of reducing carbon emissions and incentivizing community participation.
 ### Objectives:
 1. **User-Centric Design**: Identify the DAO community’s needs and design features to enhance their experience.
 2. **Roadmap Prioritization**: Develop a prioritized product roadmap based on community feedback and alignment with climate action goals.
 3. **Integration**: Suggest technical solutions and tools for seamless integration with other platforms and blockchains.
 4. **Continuous Improvement**: Regularly evaluate product features and recommend optimizations to improve usability, engagement, and adoption.
 ### Instructions:
 - Collaborate with the Marketing and Growth agents to understand user feedback and market trends.
 - Engage the Treasury Agent to ensure product development aligns with budget constraints and revenue goals.
 - Suggest mechanisms for incentivizing user engagement, such as staking rewards or gamified participation.
 - Design systems that emphasize decentralization, transparency, and scalability.
 - Provide detailed feature proposals, technical specifications, and timelines for implementation.
 - Ensure all features are optimized for both experienced blockchain users and newcomers to Web3.
 """
 GROWTH_AGENT_SYS_PROMPT = """
 You are the Growth Strategist Agent for a DAO focused on decentralized governance for climate action. Your primary role is to identify and implement growth strategies to increase the DAO’s user base and engagement.
 ### Objectives:
 1. **User Acquisition**: Identify effective strategies to onboard more users to the DAO.
 2. **Retention**: Suggest ways to improve community engagement and retain active members.
 3. **Data-Driven Insights**: Leverage data analytics to identify growth opportunities and areas of improvement.
 4. **Collaborative Growth**: Work with other agents to align growth efforts with marketing, product development, and treasury goals.
 ### Instructions:
 - Collaborate with the Marketing Agent to optimize campaigns for user acquisition.
 - Analyze user behavior and suggest actionable insights to improve retention.
 - Recommend partnerships with influential figures or organizations to enhance the DAO's visibility.
 - Propose growth experiments (A/B testing, new incentives, etc.) and analyze their effectiveness.
 - Suggest tools for data collection and analysis, ensuring privacy and transparency.
 - Ensure growth strategies align with the DAO's mission of sustainability and climate action.
 """
 TREASURY_AGENT_SYS_PROMPT = """
 You are the Treasury Management Agent for a DAO focused on decentralized governance for climate action. Your role is to oversee the DAO's financial operations, including budgeting, funding allocation, and financial reporting.
 ### Objectives:
 1. **Financial Transparency**: Maintain clear and detailed reports of the DAO's financial status.
 2. **Budget Management**: Allocate funds strategically to align with the DAO's goals and priorities.
 3. **Fundraising**: Identify and recommend strategies for fundraising to ensure the DAO's financial sustainability.
 4. **Cost Optimization**: Suggest ways to reduce operational costs without sacrificing quality.
 ### Instructions:
 - Collaborate with all other agents to align funding with the DAO's mission and strategic goals.
 - Propose innovative fundraising campaigns (e.g., NFT drops, token sales) to generate revenue.
 - Analyze financial risks and suggest mitigation strategies.
 - Ensure all recommendations prioritize the DAO's mission of reducing carbon emissions and driving global climate action.
 - Provide periodic financial updates and propose budget reallocations based on current needs.
 """
 OPERATIONS_AGENT_SYS_PROMPT = """
 You are the Operations Coordinator Agent for a DAO focused on decentralized governance for climate action. Your role is to ensure smooth day-to-day operations, coordinate workflows, and manage governance processes.
 ### Objectives:
 1. **Workflow Optimization**: Streamline operational processes to maximize efficiency and effectiveness.
 2. **Task Coordination**: Manage and delegate tasks to ensure timely delivery of goals.
 3. **Governance**: Oversee governance processes, including proposal management and voting mechanisms.
 4. **Communication**: Ensure seamless communication between all agents and community members.
 ### Instructions:
 - Collaborate with other agents to align operations with DAO objectives.
 - Facilitate communication and task coordination between Marketing, Product, Growth, and Treasury agents.
 - Create efficient workflows to handle DAO proposals and governance activities.
 - Suggest tools or platforms to improve operational efficiency.
 - Provide regular updates on task progress and flag any blockers or risks.
 """
 # Initialize agents
 marketing_agent = Agent(
    agent_name="Marketing-Agent",
    system_prompt=MARKETING_AGENT_SYS_PROMPT,
    model_name="deepseek/deepseek-reasoner",
    autosave=True,
    dashboard=False,
    verbose=True,
 )
 product_agent = Agent(
    agent_name="Product-Agent",
    system_prompt=PRODUCT_AGENT_SYS_PROMPT,
    model_name="deepseek/deepseek-reasoner",
    autosave=True,
    dashboard=False,
    verbose=True,
 )
 growth_agent = Agent(
    agent_name="Growth-Agent",
    system_prompt=GROWTH_AGENT_SYS_PROMPT,
    model_name="deepseek/deepseek-reasoner",
    autosave=True,
    dashboard=False,
    verbose=True,
 )
 treasury_agent = Agent(
    agent_name="Treasury-Agent",
    system_prompt=TREASURY_AGENT_SYS_PROMPT,
    model_name="deepseek/deepseek-reasoner",
    autosave=True,
    dashboard=False,
    verbose=True,
 )
 operations_agent = Agent(
    agent_name="Operations-Agent",
    system_prompt=OPERATIONS_AGENT_SYS_PROMPT,
    model_name="deepseek/deepseek-reasoner",
    autosave=True,
    dashboard=False,
    verbose=True,
 )
 agents = [
    marketing_agent,
    product_agent,
    growth_agent,
    treasury_agent,
    operations_agent,
 ]
 class DAOSwarmRunner:
    """
    A class to manage and run a swarm of agents in a discussion.
    """
    def __init__(
        self,
        agents: list,
        max_loops: int = 5,
        shared_context: str = "",
    ) -> None:
        """
        Initializes the DAO Swarm Runner.
        Args:
            agents (list): A list of agents in the swarm.
            max_loops (int, optional): The maximum number of discussion loops between agents. Defaults to 5.
            shared_context (str, optional): The shared context for all agents to base their discussion on. Defaults to an empty string.
        """
        self.agents = agents
        self.max_loops = max_loops
        self.shared_context = shared_context
        self.discussion_history = []
    def run(self, task: str) -> str:
        """
        Runs the swarm in a random discussion.
        Args:
            task (str): The task or context that agents will discuss.
        Returns:
            str: The final discussion output after all loops.
        """
        print(f"Task: {task}")
        print("Initializing Random Discussion...")
        # Initialize the discussion with the shared context
        current_message = (
            f"Task: {task}\nContext: {self.shared_context}"
        )
        self.discussion_history.append(current_message)
        # Run the agents in a randomized discussion
        for loop in range(self.max_loops):
            print(f"\n--- Loop {loop + 1}/{self.max_loops} ---")
            # Choose a random agent
            agent = random.choice(self.agents)
            print(f"Agent {agent.agent_name} is responding...")
            # Run the agent and get a response
            response = agent.run(current_message)
            print(f"Agent {agent.agent_name} says:\n{response}\n")
            # Append the response to the discussion history
            self.discussion_history.append(
                f"{agent.agent_name}: {response}"
            )
            # Update the current message for the next agent
            current_message = response
        print("\n--- Discussion Complete ---")
        return "\n".join(self.discussion_history)
 swarm = DAOSwarmRunner(agents=agents, max_loops=1, shared_context="")
 # User input for product description
 product_description = """
 The DAO is focused on decentralized governance for climate action. 
 It funds projects aimed at reducing carbon emissions and incentivizes community participation with a native token.
 """
 # Assign a shared context for all agents
 swarm.shared_context = product_description
 # Run the swarm
 task = """
 Analyze the product description and create a collaborative strategy for marketing, product, growth, treasury, and operations. Ensure all recommendations align with the DAO's mission of reducing carbon emissions.
 """
 output = swarm.run(task)
 # Print the swarm output
 print("Collaborative Strategy Output:\n", output)
--- a/deepseek_r1.py
+++ b/deepseek_r1.py
@ -0,0 +1,9 @@
 from swarms import Agent
 Agent(
    agent_name="Stock-Analysis-Agent",
    model_name="deepseek/deepseek-reasoner",
    max_loops="auto",
    interactive=True,
    streaming_on=True,
 ).run("What are 5 hft algorithms")
--- a/docs/mkdocs.yml
+++ b/docs/mkdocs.yml
@ -219,6 +219,8 @@ nav:
    - Meme Agents:
      - Bob The Builder: "swarms/examples/bob_the_builder.md"
      - Meme Agent Builder: "swarms/examples/meme_agents.md"
    - Multi-Agent Collaboration:
      - Swarms DAO: "swarms/examples/swarms_dao.md"
  - Swarm Models:
    - Overview: "swarms/models/index.md"
    # - Models Available: "swarms/models/index.md"
@ -242,6 +244,7 @@ nav:
  - Swarms Cloud API:
    # - Overview: "swarms_cloud/main.md"
    - Overview: "swarms_cloud/vision.md"
    - Deploying Swarms on Google Cloud Run: "swarms_cloud/cloud_run.md"
    # - Swarms Cloud CLI: "swarms_cloud/cli.md"
    - Swarm APIs:
      - MCS API: "swarms_cloud/mcs_api.md"
--- a/docs/swarms/examples/lumo.md
+++ b/docs/swarms/examples/lumo.md
@ -54,7 +54,7 @@ class Lumo:
 Agent(
    agent_name="Solana-Analysis-Agent",
-    model_name=Lumo(),
+    llm=Lumo(),
    max_loops="auto",
    interactive=True,
    streaming_on=True,
--- a/docs/swarms/examples/swarms_dao.md
+++ b/docs/swarms/examples/swarms_dao.md
@ -0,0 +1,237 @@
 # Swarms DAO Example
 This example demonstrates how to create a swarm of agents to collaborate on a task. The agents are designed to work together to create a comprehensive strategy for a DAO focused on decentralized governance for climate action.
 You can customize the agents and their system prompts to fit your specific needs.
 And, this example is using the `deepseek-reasoner` model, which is a large language model that is optimized for reasoning tasks.
 ## Todo
 - Add tools to check wallet of the treasury and check the balance of the treasury
 - Add tools to check the price of the token
 - Add tools to check the price of the token on different exchanges
 - Add tools to check the price of the token on different chains
 - Add tools to check twitter posts and check the sentiment of the posts
 ```python
 import random
 from swarms import Agent
 # System prompts for each agent
 MARKETING_AGENT_SYS_PROMPT = """
 You are the Marketing Strategist Agent for a DAO. Your role is to develop, implement, and optimize all marketing and branding strategies to align with the DAO's mission and vision. The DAO is focused on decentralized governance for climate action, funding projects aimed at reducing carbon emissions, and incentivizing community participation through its native token.
 ### Objectives:
 1. **Brand Awareness**: Build a globally recognized and trusted brand for the DAO.
 2. **Community Growth**: Expand the DAO's community by onboarding individuals passionate about climate action and blockchain technology.
 3. **Campaign Execution**: Launch high-impact marketing campaigns on platforms like Twitter, Discord, and YouTube to engage and retain community members.
 4. **Partnerships**: Identify and build partnerships with like-minded organizations, NGOs, and influencers.
 5. **Content Strategy**: Design educational and engaging content, including infographics, blog posts, videos, and AMAs.
 ### Instructions:
 - Thoroughly analyze the product description and DAO mission.
 - Collaborate with the Growth, Product, Treasury, and Operations agents to align marketing strategies with overall goals.
 - Create actionable steps for social media growth, community engagement, and brand storytelling.
 - Leverage analytics to refine marketing strategies, focusing on measurable KPIs like engagement, conversion rates, and member retention.
 - Suggest innovative methods to make the DAO's mission resonate with a broader audience (e.g., gamified incentives, contests, or viral campaigns).
 - Ensure every strategy emphasizes transparency, sustainability, and long-term impact.
 """
 PRODUCT_AGENT_SYS_PROMPT = """
 You are the Product Manager Agent for a DAO focused on decentralized governance for climate action. Your role is to design, manage, and optimize the DAO's product roadmap. This includes defining key features, prioritizing user needs, and ensuring product alignment with the DAO’s mission of reducing carbon emissions and incentivizing community participation.
 ### Objectives:
 1. **User-Centric Design**: Identify the DAO community’s needs and design features to enhance their experience.
 2. **Roadmap Prioritization**: Develop a prioritized product roadmap based on community feedback and alignment with climate action goals.
 3. **Integration**: Suggest technical solutions and tools for seamless integration with other platforms and blockchains.
 4. **Continuous Improvement**: Regularly evaluate product features and recommend optimizations to improve usability, engagement, and adoption.
 ### Instructions:
 - Collaborate with the Marketing and Growth agents to understand user feedback and market trends.
 - Engage the Treasury Agent to ensure product development aligns with budget constraints and revenue goals.
 - Suggest mechanisms for incentivizing user engagement, such as staking rewards or gamified participation.
 - Design systems that emphasize decentralization, transparency, and scalability.
 - Provide detailed feature proposals, technical specifications, and timelines for implementation.
 - Ensure all features are optimized for both experienced blockchain users and newcomers to Web3.
 """
 GROWTH_AGENT_SYS_PROMPT = """
 You are the Growth Strategist Agent for a DAO focused on decentralized governance for climate action. Your primary role is to identify and implement growth strategies to increase the DAO’s user base and engagement.
 ### Objectives:
 1. **User Acquisition**: Identify effective strategies to onboard more users to the DAO.
 2. **Retention**: Suggest ways to improve community engagement and retain active members.
 3. **Data-Driven Insights**: Leverage data analytics to identify growth opportunities and areas of improvement.
 4. **Collaborative Growth**: Work with other agents to align growth efforts with marketing, product development, and treasury goals.
 ### Instructions:
 - Collaborate with the Marketing Agent to optimize campaigns for user acquisition.
 - Analyze user behavior and suggest actionable insights to improve retention.
 - Recommend partnerships with influential figures or organizations to enhance the DAO's visibility.
 - Propose growth experiments (A/B testing, new incentives, etc.) and analyze their effectiveness.
 - Suggest tools for data collection and analysis, ensuring privacy and transparency.
 - Ensure growth strategies align with the DAO's mission of sustainability and climate action.
 """
 TREASURY_AGENT_SYS_PROMPT = """
 You are the Treasury Management Agent for a DAO focused on decentralized governance for climate action. Your role is to oversee the DAO's financial operations, including budgeting, funding allocation, and financial reporting.
 ### Objectives:
 1. **Financial Transparency**: Maintain clear and detailed reports of the DAO's financial status.
 2. **Budget Management**: Allocate funds strategically to align with the DAO's goals and priorities.
 3. **Fundraising**: Identify and recommend strategies for fundraising to ensure the DAO's financial sustainability.
 4. **Cost Optimization**: Suggest ways to reduce operational costs without sacrificing quality.
 ### Instructions:
 - Collaborate with all other agents to align funding with the DAO's mission and strategic goals.
 - Propose innovative fundraising campaigns (e.g., NFT drops, token sales) to generate revenue.
 - Analyze financial risks and suggest mitigation strategies.
 - Ensure all recommendations prioritize the DAO's mission of reducing carbon emissions and driving global climate action.
 - Provide periodic financial updates and propose budget reallocations based on current needs.
 """
 OPERATIONS_AGENT_SYS_PROMPT = """
 You are the Operations Coordinator Agent for a DAO focused on decentralized governance for climate action. Your role is to ensure smooth day-to-day operations, coordinate workflows, and manage governance processes.
 ### Objectives:
 1. **Workflow Optimization**: Streamline operational processes to maximize efficiency and effectiveness.
 2. **Task Coordination**: Manage and delegate tasks to ensure timely delivery of goals.
 3. **Governance**: Oversee governance processes, including proposal management and voting mechanisms.
 4. **Communication**: Ensure seamless communication between all agents and community members.
 ### Instructions:
 - Collaborate with other agents to align operations with DAO objectives.
 - Facilitate communication and task coordination between Marketing, Product, Growth, and Treasury agents.
 - Create efficient workflows to handle DAO proposals and governance activities.
 - Suggest tools or platforms to improve operational efficiency.
 - Provide regular updates on task progress and flag any blockers or risks.
 """
 # Initialize agents
 marketing_agent = Agent(
    agent_name="Marketing-Agent",
    system_prompt=MARKETING_AGENT_SYS_PROMPT,
    model_name="deepseek/deepseek-reasoner",
    autosave=True,
    dashboard=False,
    verbose=True,
 )
 product_agent = Agent(
    agent_name="Product-Agent",
    system_prompt=PRODUCT_AGENT_SYS_PROMPT,
    model_name="deepseek/deepseek-reasoner",
    autosave=True,
    dashboard=False,
    verbose=True,
 )
 growth_agent = Agent(
    agent_name="Growth-Agent",
    system_prompt=GROWTH_AGENT_SYS_PROMPT,
    model_name="deepseek/deepseek-reasoner",
    autosave=True,
    dashboard=False,
    verbose=True,
 )
 treasury_agent = Agent(
    agent_name="Treasury-Agent",
    system_prompt=TREASURY_AGENT_SYS_PROMPT,
    model_name="deepseek/deepseek-reasoner",
    autosave=True,
    dashboard=False,
    verbose=True,
 )
 operations_agent = Agent(
    agent_name="Operations-Agent",
    system_prompt=OPERATIONS_AGENT_SYS_PROMPT,
    model_name="deepseek/deepseek-reasoner",
    autosave=True,
    dashboard=False,
    verbose=True,
 )
 agents = [marketing_agent, product_agent, growth_agent, treasury_agent, operations_agent]
 class DAOSwarmRunner:
    """
    A class to manage and run a swarm of agents in a discussion.
    """
    def __init__(self, agents: list, max_loops: int = 5, shared_context: str = "") -> None:
        """
        Initializes the DAO Swarm Runner.
        Args:
            agents (list): A list of agents in the swarm.
            max_loops (int, optional): The maximum number of discussion loops between agents. Defaults to 5.
            shared_context (str, optional): The shared context for all agents to base their discussion on. Defaults to an empty string.
        """
        self.agents = agents
        self.max_loops = max_loops
        self.shared_context = shared_context
        self.discussion_history = []
    def run(self, task: str) -> str:
        """
        Runs the swarm in a random discussion.
        Args:
            task (str): The task or context that agents will discuss.
        Returns:
            str: The final discussion output after all loops.
        """
        print(f"Task: {task}")
        print("Initializing Random Discussion...")
        # Initialize the discussion with the shared context
        current_message = f"Task: {task}\nContext: {self.shared_context}"
        self.discussion_history.append(current_message)
        # Run the agents in a randomized discussion
        for loop in range(self.max_loops):
            print(f"\n--- Loop {loop + 1}/{self.max_loops} ---")
            # Choose a random agent
            agent = random.choice(self.agents)
            print(f"Agent {agent.agent_name} is responding...")
            # Run the agent and get a response
            response = agent.run(current_message)
            print(f"Agent {agent.agent_name} says:\n{response}\n")
            # Append the response to the discussion history
            self.discussion_history.append(f"{agent.agent_name}: {response}")
            # Update the current message for the next agent
            current_message = response
        print("\n--- Discussion Complete ---")
        return "\n".join(self.discussion_history)
 swarm = DAOSwarmRunner(agents=agents, max_loops=1, shared_context="")
 # User input for product description
 product_description = """
 The DAO is focused on decentralized governance for climate action. 
 It funds projects aimed at reducing carbon emissions and incentivizes community participation with a native token.
 """
 # Assign a shared context for all agents
 swarm.shared_context = product_description
 # Run the swarm
 task = """
 Analyze the product description and create a collaborative strategy for marketing, product, growth, treasury, and operations. Ensure all recommendations align with the DAO's mission of reducing carbon emissions.
 """
 output = swarm.run(task)
 # Print the swarm output
 print("Collaborative Strategy Output:\n", output)
 ```
--- a/docs/swarms_cloud/cloud_run.md
+++ b/docs/swarms_cloud/cloud_run.md
@ -0,0 +1,254 @@
 # Hosting Agents on Google Cloud Run
 This documentation provides a highly detailed, step-by-step guide to hosting your agents using Google Cloud Run. It uses a well-structured project setup that includes a Dockerfile at the root level, a folder dedicated to your API file, and a `requirements.txt` file to manage all dependencies. This guide will ensure your deployment is scalable, efficient, and easy to maintain.
 ---
 ## **Project Structure**
 Your project directory should adhere to the following structure to ensure compatibility and ease of deployment:
 ```
 .
 ├── Dockerfile
 ├── requirements.txt
 └── api/
    └── api.py
 ```
 Each component serves a specific purpose in the deployment pipeline, ensuring modularity and maintainability.
 ---
 ## **Step 1: Prerequisites**
 Before you begin, make sure to satisfy the following prerequisites to avoid issues during deployment:
 1. **Google Cloud Account**:
   - Create a Google Cloud account at [Google Cloud Console](https://console.cloud.google.com/).
   - Enable billing for your project. Billing is necessary for accessing Cloud Run services.
 2. **Install Google Cloud SDK**:
   - Follow the [installation guide](https://cloud.google.com/sdk/docs/install) to set up the Google Cloud SDK on your local machine.
 3. **Install Docker**:
   - Download and install Docker by following the [official Docker installation guide](https://docs.docker.com/get-docker/). Docker is crucial for containerizing your application.
 4. **Create a Google Cloud Project**:
   - Navigate to the Google Cloud Console and create a new project. Assign it a meaningful name and note the **Project ID**, as it will be used throughout this guide.
 5. **Enable Required APIs**:
   - Visit the [API Library](https://console.cloud.google.com/apis/library) and enable the following APIs:
     - Cloud Run API
     - Cloud Build API
     - Artifact Registry API
   - These APIs are essential for deploying and managing your application in Cloud Run.
 ---
 ## **Step 2: Creating the Files**
 ### 1. **`api/api.py`**
 This is the main Python script where you define your Swarms agents and expose an API endpoint for interacting with them. Here’s an example:
 ```python
 from flask import Flask, request, jsonify
 from swarms import Agent  # Assuming `swarms` is the framework you're using
 app = Flask(__name__)
 # Example Swarm agent
 agent = Agent(
    agent_name="Stock-Analysis-Agent",
    model_name="gpt-4o-mini",
    max_loops="auto",
    interactive=True,
    streaming_on=True,
 )
@app.route('/run-agent', methods=['POST'])
 def run_agent():
    data = request.json
    task = data.get('task', '')
    result = agent.run(task)
    return jsonify({"result": result})
 if __name__ == '__main__':
    app.run(host='0.0.0.0', port=8080)
 ```
 This example sets up a basic API that listens for POST requests, processes a task using a Swarm agent, and returns the result as a JSON response. Customize it based on your agent’s functionality.
 ---
 ### 2. **`requirements.txt`**
 This file lists all Python dependencies required for your project. Example:
 ```
 flask
 swarms
 # add any other dependencies here
 ```
 Be sure to include any additional libraries your agents rely on. Keeping this file up to date ensures smooth dependency management during deployment.
 ---
 ### 3. **`Dockerfile`**
 The Dockerfile specifies how your application is containerized. Below is a sample Dockerfile for your setup:
 ```dockerfile
 # Use an official Python runtime as the base image
 FROM python:3.10-slim
 # Set the working directory
 WORKDIR /app
 # Copy requirements.txt and install dependencies
 COPY requirements.txt .
 RUN pip install --no-cache-dir -r requirements.txt
 # Copy the application code
 COPY api/ ./api/
 # Expose port 8080 (Cloud Run default port)
 EXPOSE 8080
 # Run the application
 CMD ["python", "api/api.py"]
 ```
 This Dockerfile ensures your application is containerized with minimal overhead, focusing on slim images for efficiency.
 ---
 ## **Step 3: Deploying to Google Cloud Run**
 ### 1. **Authenticate with Google Cloud**
 Log in to your Google Cloud account by running:
 ```bash
 gcloud auth login
 ```
 Set the active project to match your deployment target:
 ```bash
 gcloud config set project [PROJECT_ID]
 ```
 Replace `[PROJECT_ID]` with your actual Project ID.
 ---
 ### 2. **Build the Docker Image**
 Use Google Cloud's Artifact Registry to store and manage your Docker image. Follow these steps:
 1. **Create a Repository**:
 ```bash
 gcloud artifacts repositories create my-repo --repository-format=Docker --location=us-central1
 ```
 2. **Authenticate Docker with Google Cloud**:
 ```bash
 gcloud auth configure-docker us-central1-docker.pkg.dev
 ```
 3. **Build and Tag the Image**:
 ```bash
 docker build -t us-central1-docker.pkg.dev/[PROJECT_ID]/my-repo/my-image .
 ```
 4. **Push the Image**:
 ```bash
 docker push us-central1-docker.pkg.dev/[PROJECT_ID]/my-repo/my-image
 ```
 ---
 ### 3. **Deploy to Cloud Run**
 Deploy the application to Cloud Run with the following command:
 ```bash
 gcloud run deploy my-agent-service \
  --image us-central1-docker.pkg.dev/[PROJECT_ID]/my-repo/my-image \
  --platform managed \
  --region us-central1 \
  --allow-unauthenticated
 ```
 Key points:
 - Replace `[PROJECT_ID]` with your actual Project ID.
 - The `--allow-unauthenticated` flag makes the service publicly accessible. Exclude it to restrict access.
 ---
 ## **Step 4: Testing the Deployment**
 Once the deployment is complete, test the service:
 1. Note the URL provided by Cloud Run.
 2. Use `curl` or Postman to send a request. Example:
 ```bash
 curl -X POST [CLOUD_RUN_URL]/run-agent \
  -H "Content-Type: application/json" \
  -d '{"task": "example task"}'
 ```
 This tests whether your agent processes the task correctly and returns the expected output.
 ---
 ## **Step 5: Updating the Service**
 To apply changes to your application:
 1. Edit the necessary files.
 2. Rebuild and push the updated Docker image:
 ```bash
 docker build -t us-central1-docker.pkg.dev/[PROJECT_ID]/my-repo/my-image .
 docker push us-central1-docker.pkg.dev/[PROJECT_ID]/my-repo/my-image
 ```
 3. Redeploy the service:
 ```bash
 gcloud run deploy my-agent-service \
  --image us-central1-docker.pkg.dev/[PROJECT_ID]/my-repo/my-image
 ```
 This ensures the latest version of your application is live.
 ---
 ## **Troubleshooting**
 - **Permission Errors**:
  Ensure your account has roles like Cloud Run Admin and Artifact Registry Reader.
 - **Port Issues**:
  Confirm the application listens on port 8080. Cloud Run expects this port by default.
 - **Logs**:
  Use the Google Cloud Console or CLI to review logs for debugging:
 ```bash
 gcloud logs read --project [PROJECT_ID]
 ```
 ---
 ## **Conclusion**
 By following this comprehensive guide, you can deploy your agents on Google Cloud Run with ease. This method leverages Docker for containerization and Google Cloud services for seamless scalability and management. With a robust setup like this, you can focus on enhancing your agents’ capabilities rather than worrying about deployment challenges.