vllm docs and cleanup

agent_mcp_test.py

@@ -0,0 +1,25 @@
+
+from swarms import Agent
+from swarms.prompts.finance_agent_sys_prompt import (
+    FINANCIAL_AGENT_SYS_PROMPT,
+)
+from swarms.tools.mcp_integration import MCPServerSseParams
+
+server_one = MCPServerSseParams(
+    url="http://127.0.0.1:6274",
+    headers={"Content-Type": "application/json"},
+)
+
+# Initialize the agent
+agent = Agent(
+    agent_name="Financial-Analysis-Agent",
+    agent_description="Personal finance advisor agent",
+    system_prompt=FINANCIAL_AGENT_SYS_PROMPT,
+    max_loops=1,
+    mcp_servers=[server_one],
+    output_type="final",
+)
+
+out = agent.run("Use the add tool to add 2 and 2")
+
+print(type(out))

cleanup_and_publish.sh

@@ -7,6 +7,10 @@ black . && echo "✅ Code formatting complete!" || echo "❌ Black formatting fa
 echo "🔍 Running Ruff linter..."
 ruff check . --fix && echo "✅ Linting complete!" || echo "❌ Linting failed!"
 
+
+echo "Creating llm.txt file..."
+python3 llm_txt.py && echo "✅ llm.txt file created!" || echo "❌ llm.txt file creation failed!"
+
 echo "🏗️  Building package..."
 poetry build && echo "✅ Build successful!" || echo "❌ Build failed!"
 

docs/mkdocs.yml

@@ -283,6 +283,7 @@ nav:
       - Ollama: "swarms/examples/ollama.md"
       - OpenRouter: "swarms/examples/openrouter.md"
       - XAI: "swarms/examples/xai.md"
+      - VLLM: "swarms/examples/vllm_integration.md"
     - Swarms Tools: 
       - Agent with Yahoo Finance: "swarms/examples/yahoo_finance.md"
       - Twitter Agents: "swarms_tools/twitter.md"
@@ -299,6 +300,7 @@ nav:
       - Group Chat Example: "swarms/examples/groupchat_example.md"
       - Meme Agent Builder: "swarms/examples/meme_agents.md"
       - Sequential Workflow Example: "swarms/examples/sequential_example.md"
+      - ConcurrentWorkflow with VLLM Agents: "swarms/examples/vllm.md"
       - External Agents:
         - Swarms of Browser Agents: "swarms/examples/swarms_of_browser_agents.md"
     - Swarms UI:

docs/swarms/examples/vllm.md

@@ -0,0 +1,429 @@
+# VLLM Swarm Agents
+
+!!! tip "Quick Summary"
+    This guide demonstrates how to create a sophisticated multi-agent system using VLLM and Swarms for comprehensive stock market analysis. You'll learn how to configure and orchestrate multiple AI agents working together to provide deep market insights.
+
+## Overview
+
+The example showcases how to build a stock analysis system with 5 specialized agents:
+
+- Technical Analysis Agent
+- Fundamental Analysis Agent  
+- Market Sentiment Agent
+- Quantitative Strategy Agent
+- Portfolio Strategy Agent
+
+Each agent has specific expertise and works collaboratively through a concurrent workflow.
+
+## Prerequisites
+
+!!! warning "Requirements"
+    Before starting, ensure you have:
+
+    - Python 3.7 or higher
+    - The Swarms package installed
+    - Access to VLLM compatible models
+    - Sufficient compute resources for running VLLM
+
+## Installation
+
+!!! example "Setup Steps"
+
+    1. Install the Swarms package:
+    ```bash
+    pip install swarms
+    ```
+
+    2. Install VLLM dependencies (if not already installed):
+    ```bash
+    pip install vllm
+    ```
+
+## Basic Usage
+
+Here's a complete example of setting up the stock analysis swarm:
+
+```python
+from swarms import Agent, ConcurrentWorkflow
+from swarms.utils.vllm_wrapper import VLLMWrapper
+
+# Initialize the VLLM wrapper
+vllm = VLLMWrapper(
+    model_name="meta-llama/Llama-2-7b-chat-hf",
+    system_prompt="You are a helpful assistant.",
+)
+```
+
+!!! note "Model Selection"
+    The example uses Llama-2-7b-chat, but you can use any VLLM-compatible model. Make sure you have the necessary permissions and resources to run your chosen model.
+
+## Agent Configuration
+
+### Technical Analysis Agent
+
+```python
+technical_analyst = Agent(
+    agent_name="Technical-Analysis-Agent",
+    agent_description="Expert in technical analysis and chart patterns",
+    system_prompt="""You are an expert Technical Analysis Agent specializing in market technicals and chart patterns. Your responsibilities include:
+
+1. PRICE ACTION ANALYSIS
+- Identify key support and resistance levels
+- Analyze price trends and momentum
+- Detect chart patterns (e.g., head & shoulders, triangles, flags)
+- Evaluate volume patterns and their implications
+
+2. TECHNICAL INDICATORS
+- Calculate and interpret moving averages (SMA, EMA)
+- Analyze momentum indicators (RSI, MACD, Stochastic)
+- Evaluate volume indicators (OBV, Volume Profile)
+- Monitor volatility indicators (Bollinger Bands, ATR)
+
+3. TRADING SIGNALS
+- Generate clear buy/sell signals based on technical criteria
+- Identify potential entry and exit points
+- Set appropriate stop-loss and take-profit levels
+- Calculate position sizing recommendations
+
+4. RISK MANAGEMENT
+- Assess market volatility and trend strength
+- Identify potential reversal points
+- Calculate risk/reward ratios for trades
+- Suggest position sizing based on risk parameters
+
+Your analysis should be data-driven, precise, and actionable. Always include specific price levels, time frames, and risk parameters in your recommendations.""",
+    max_loops=1,
+    llm=vllm,
+)
+```
+
+!!! tip "Agent Customization"
+    Each agent can be customized with different:
+    
+    - System prompts
+    
+    - Temperature settings
+    
+    - Max token limits
+    
+    - Response formats
+
+## Running the Swarm
+
+To execute the swarm analysis:
+
+```python
+swarm = ConcurrentWorkflow(
+    name="Stock-Analysis-Swarm",
+    description="A swarm of agents that analyze stocks and provide comprehensive analysis.",
+    agents=stock_analysis_agents,
+)
+
+# Run the analysis
+response = swarm.run("Analyze the best etfs for gold and other similar commodities in volatile markets")
+```
+
+
+
+## Full Code Example
+
+```python
+from swarms import Agent, ConcurrentWorkflow
+from swarms.utils.vllm_wrapper import VLLMWrapper
+
+# Initialize the VLLM wrapper
+vllm = VLLMWrapper(
+    model_name="meta-llama/Llama-2-7b-chat-hf",
+    system_prompt="You are a helpful assistant.",
+)
+
+# Technical Analysis Agent
+technical_analyst = Agent(
+    agent_name="Technical-Analysis-Agent",
+    agent_description="Expert in technical analysis and chart patterns",
+    system_prompt="""You are an expert Technical Analysis Agent specializing in market technicals and chart patterns. Your responsibilities include:
+
+1. PRICE ACTION ANALYSIS
+- Identify key support and resistance levels
+- Analyze price trends and momentum
+- Detect chart patterns (e.g., head & shoulders, triangles, flags)
+- Evaluate volume patterns and their implications
+
+2. TECHNICAL INDICATORS
+- Calculate and interpret moving averages (SMA, EMA)
+- Analyze momentum indicators (RSI, MACD, Stochastic)
+- Evaluate volume indicators (OBV, Volume Profile)
+- Monitor volatility indicators (Bollinger Bands, ATR)
+
+3. TRADING SIGNALS
+- Generate clear buy/sell signals based on technical criteria
+- Identify potential entry and exit points
+- Set appropriate stop-loss and take-profit levels
+- Calculate position sizing recommendations
+
+4. RISK MANAGEMENT
+- Assess market volatility and trend strength
+- Identify potential reversal points
+- Calculate risk/reward ratios for trades
+- Suggest position sizing based on risk parameters
+
+Your analysis should be data-driven, precise, and actionable. Always include specific price levels, time frames, and risk parameters in your recommendations.""",
+    max_loops=1,
+    llm=vllm,
+)
+
+# Fundamental Analysis Agent
+fundamental_analyst = Agent(
+    agent_name="Fundamental-Analysis-Agent",
+    agent_description="Expert in company fundamentals and valuation",
+    system_prompt="""You are an expert Fundamental Analysis Agent specializing in company valuation and financial metrics. Your core responsibilities include:
+
+1. FINANCIAL STATEMENT ANALYSIS
+- Analyze income statements, balance sheets, and cash flow statements
+- Calculate and interpret key financial ratios
+- Evaluate revenue growth and profit margins
+- Assess company's debt levels and cash position
+
+2. VALUATION METRICS
+- Calculate fair value using multiple valuation methods:
+  * Discounted Cash Flow (DCF)
+  * Price-to-Earnings (P/E)
+  * Price-to-Book (P/B)
+  * Enterprise Value/EBITDA
+- Compare valuations against industry peers
+
+3. BUSINESS MODEL ASSESSMENT
+- Evaluate competitive advantages and market position
+- Analyze industry dynamics and market share
+- Assess management quality and corporate governance
+- Identify potential risks and growth opportunities
+
+4. ECONOMIC CONTEXT
+- Consider macroeconomic factors affecting the company
+- Analyze industry cycles and trends
+- Evaluate regulatory environment and compliance
+- Assess global market conditions
+
+Your analysis should be comprehensive, focusing on both quantitative metrics and qualitative factors that impact long-term value.""",
+    max_loops=1,
+    llm=vllm,
+)
+
+# Market Sentiment Agent
+sentiment_analyst = Agent(
+    agent_name="Market-Sentiment-Agent",
+    agent_description="Expert in market psychology and sentiment analysis",
+    system_prompt="""You are an expert Market Sentiment Agent specializing in analyzing market psychology and investor behavior. Your key responsibilities include:
+
+1. SENTIMENT INDICATORS
+- Monitor and interpret market sentiment indicators:
+  * VIX (Fear Index)
+  * Put/Call Ratio
+  * Market Breadth
+  * Investor Surveys
+- Track institutional vs retail investor behavior
+
+2. NEWS AND SOCIAL MEDIA ANALYSIS
+- Analyze news flow and media sentiment
+- Monitor social media trends and discussions
+- Track analyst recommendations and changes
+- Evaluate corporate insider trading patterns
+
+3. MARKET POSITIONING
+- Assess hedge fund positioning and exposure
+- Monitor short interest and short squeeze potential
+- Track fund flows and asset allocation trends
+- Analyze options market sentiment
+
+4. CONTRARIAN SIGNALS
+- Identify extreme sentiment readings
+- Detect potential market turning points
+- Analyze historical sentiment patterns
+- Provide contrarian trading opportunities
+
+Your analysis should combine quantitative sentiment metrics with qualitative assessment of market psychology and crowd behavior.""",
+    max_loops=1,
+    llm=vllm,
+)
+
+# Quantitative Strategy Agent
+quant_analyst = Agent(
+    agent_name="Quantitative-Strategy-Agent",
+    agent_description="Expert in quantitative analysis and algorithmic strategies",
+    system_prompt="""You are an expert Quantitative Strategy Agent specializing in data-driven investment strategies. Your primary responsibilities include:
+
+1. FACTOR ANALYSIS
+- Analyze and monitor factor performance:
+  * Value
+  * Momentum
+  * Quality
+  * Size
+  * Low Volatility
+- Calculate factor exposures and correlations
+
+2. STATISTICAL ANALYSIS
+- Perform statistical arbitrage analysis
+- Calculate and monitor pair trading opportunities
+- Analyze market anomalies and inefficiencies
+- Develop mean reversion strategies
+
+3. RISK MODELING
+- Build and maintain risk models
+- Calculate portfolio optimization metrics
+- Monitor correlation matrices
+- Analyze tail risk and stress scenarios
+
+4. ALGORITHMIC STRATEGIES
+- Develop systematic trading strategies
+- Backtest and validate trading algorithms
+- Monitor strategy performance metrics
+- Optimize execution algorithms
+
+Your analysis should be purely quantitative, based on statistical evidence and mathematical models rather than subjective opinions.""",
+    max_loops=1,
+    llm=vllm,
+)
+
+# Portfolio Strategy Agent
+portfolio_strategist = Agent(
+    agent_name="Portfolio-Strategy-Agent",
+    agent_description="Expert in portfolio management and asset allocation",
+    system_prompt="""You are an expert Portfolio Strategy Agent specializing in portfolio construction and management. Your core responsibilities include:
+
+1. ASSET ALLOCATION
+- Develop strategic asset allocation frameworks
+- Recommend tactical asset allocation shifts
+- Optimize portfolio weightings
+- Balance risk and return objectives
+
+2. PORTFOLIO ANALYSIS
+- Calculate portfolio risk metrics
+- Monitor sector and factor exposures
+- Analyze portfolio correlation matrix
+- Track performance attribution
+
+3. RISK MANAGEMENT
+- Implement portfolio hedging strategies
+- Monitor and adjust position sizing
+- Set stop-loss and rebalancing rules
+- Develop drawdown protection strategies
+
+4. PORTFOLIO OPTIMIZATION
+- Calculate efficient frontier analysis
+- Optimize for various objectives:
+  * Maximum Sharpe Ratio
+  * Minimum Volatility
+  * Maximum Diversification
+- Consider transaction costs and taxes
+
+Your recommendations should focus on portfolio-level decisions that optimize risk-adjusted returns while meeting specific investment objectives.""",
+    max_loops=1,
+    llm=vllm,
+)
+
+# Create a list of all agents
+stock_analysis_agents = [
+    technical_analyst,
+    fundamental_analyst,
+    sentiment_analyst,
+    quant_analyst,
+    portfolio_strategist
+]
+
+swarm = ConcurrentWorkflow(
+    name="Stock-Analysis-Swarm",
+    description="A swarm of agents that analyze stocks and provide a comprehensive analysis of the current trends and opportunities.",
+    agents=stock_analysis_agents,
+)
+
+swarm.run("Analyze the best etfs for gold and other similiar commodities in volatile markets")
+```
+
+## Best Practices
+
+!!! success "Optimization Tips"
+    1. **Agent Design**
+        - Keep system prompts focused and specific
+        
+        - Use clear role definitions
+        
+        - Include error handling guidelines
+    
+    2. **Resource Management**
+        
+        - Monitor memory usage with large models
+        
+        - Implement proper cleanup procedures
+        
+        - Use batching for multiple queries
+    
+    3. **Output Handling**
+        
+        - Implement proper logging
+        
+        - Format outputs consistently
+        
+        - Include error checking
+
+## Common Issues and Solutions
+
+!!! warning "Troubleshooting"
+    Common issues you might encounter:
+
+    1. **Memory Issues**
+        
+        - *Problem*: VLLM consuming too much memory
+        
+        - *Solution*: Adjust batch sizes and model parameters
+    
+    2. **Agent Coordination**
+        
+        - *Problem*: Agents providing conflicting information
+        
+        - *Solution*: Implement consensus mechanisms or priority rules
+    
+    3. **Performance**
+        
+        - *Problem*: Slow response times
+        
+        - *Solution*: Use proper batching and optimize model loading
+
+## FAQ
+
+??? question "Can I use different models for different agents?"
+    Yes, you can initialize multiple VLLM wrappers with different models for each agent. However, be mindful of memory usage.
+
+??? question "How many agents can run concurrently?"
+    The number depends on your hardware resources. Start with 3-5 agents and scale based on performance.
+
+??? question "Can I customize agent communication patterns?"
+    Yes, you can modify the ConcurrentWorkflow class or create custom workflows for specific communication patterns.
+
+## Advanced Configuration
+
+!!! example "Extended Settings"
+    ```python
+    vllm = VLLMWrapper(
+        model_name="meta-llama/Llama-2-7b-chat-hf",
+        system_prompt="You are a helpful assistant.",
+        temperature=0.7,
+        max_tokens=2048,
+        top_p=0.95,
+    )
+    ```
+
+## Contributing
+
+!!! info "Get Involved"
+    We welcome contributions! Here's how you can help:
+
+    1. Report bugs and issues
+    2. Submit feature requests
+    3. Contribute to documentation
+    4. Share example use cases
+
+## Resources
+
+!!! abstract "Additional Reading"
+    - [VLLM Documentation](https://docs.vllm.ai/en/latest/)
+    

docs/swarms/examples/vllm_integration.md

@@ -0,0 +1,194 @@
+
+
+# vLLM Integration Guide
+
+!!! info "Overview"
+    vLLM is a high-performance and easy-to-use library for LLM inference and serving. This guide explains how to integrate vLLM with Swarms for efficient, production-grade language model deployment.
+
+
+## Installation
+
+!!! note "Prerequisites"
+    Before you begin, make sure you have Python 3.8+ installed on your system.
+
+=== "pip"
+    ```bash
+    pip install -U vllm swarms
+    ```
+
+=== "poetry"
+    ```bash
+    poetry add vllm swarms
+    ```
+
+## Basic Usage
+
+Here's a simple example of how to use vLLM with Swarms:
+
+```python title="basic_usage.py"
+from swarms.utils.vllm_wrapper import VLLMWrapper
+
+# Initialize the vLLM wrapper
+vllm = VLLMWrapper(
+    model_name="meta-llama/Llama-2-7b-chat-hf",
+    system_prompt="You are a helpful assistant.",
+    temperature=0.7,
+    max_tokens=4000
+)
+
+# Run inference
+response = vllm.run("What is the capital of France?")
+print(response)
+```
+
+## VLLMWrapper Class
+
+!!! abstract "Class Overview"
+    The `VLLMWrapper` class provides a convenient interface for working with vLLM models.
+
+### Key Parameters
+
+| Parameter | Type | Description | Default |
+|-----------|------|-------------|---------|
+| `model_name` | str | Name of the model to use | "meta-llama/Llama-2-7b-chat-hf" |
+| `system_prompt` | str | System prompt to use | None |
+| `stream` | bool | Whether to stream the output | False |
+| `temperature` | float | Sampling temperature | 0.5 |
+| `max_tokens` | int | Maximum number of tokens to generate | 4000 |
+
+### Example with Custom Parameters
+
+```python title="custom_parameters.py"
+vllm = VLLMWrapper(
+    model_name="meta-llama/Llama-2-13b-chat-hf",
+    system_prompt="You are an expert in artificial intelligence.",
+    temperature=0.8,
+    max_tokens=2000
+)
+```
+
+## Integration with Agents
+
+You can easily integrate vLLM with Swarms agents for more complex workflows:
+
+```python title="agent_integration.py"
+from swarms import Agent
+from swarms.utils.vllm_wrapper import VLLMWrapper
+
+# Initialize vLLM
+vllm = VLLMWrapper(
+    model_name="meta-llama/Llama-2-7b-chat-hf",
+    system_prompt="You are a helpful assistant."
+)
+
+# Create an agent with vLLM
+agent = Agent(
+    agent_name="Research-Agent",
+    agent_description="Expert in conducting research and analysis",
+    system_prompt="""You are an expert research agent. Your tasks include:
+    1. Analyzing complex topics
+    2. Providing detailed summaries
+    3. Making data-driven recommendations""",
+    llm=vllm,
+    max_loops=1
+)
+
+# Run the agent
+response = agent.run("Research the impact of AI on healthcare")
+```
+
+## Advanced Features
+
+### Batch Processing
+
+!!! tip "Performance Optimization"
+    Use batch processing for efficient handling of multiple tasks simultaneously.
+
+```python title="batch_processing.py"
+tasks = [
+    "What is machine learning?",
+    "Explain neural networks",
+    "Describe deep learning"
+]
+
+results = vllm.batched_run(tasks, batch_size=3)
+```
+
+### Error Handling
+
+!!! warning "Error Management"
+    Always implement proper error handling in production environments.
+
+```python title="error_handling.py"
+from loguru import logger
+
+try:
+    response = vllm.run("Complex task")
+except Exception as error:
+    logger.error(f"Error occurred: {error}")
+```
+
+## Best Practices
+
+!!! success "Recommended Practices"
+    === "Model Selection"
+        - Choose appropriate model sizes based on your requirements
+        - Consider the trade-off between model size and inference speed
+
+    === "System Resources"
+        - Ensure sufficient GPU memory for your chosen model
+        - Monitor resource usage during batch processing
+
+    === "Prompt Engineering"
+        - Use clear and specific system prompts
+        - Structure user prompts for optimal results
+
+    === "Error Handling"
+        - Implement proper error handling and logging
+        - Set up monitoring for production deployments
+
+    === "Performance"
+        - Use batch processing for multiple tasks
+        - Adjust max_tokens based on your use case
+        - Fine-tune temperature for optimal output quality
+
+## Example: Multi-Agent System
+
+Here's an example of creating a multi-agent system using vLLM:
+
+```python title="multi_agent_system.py"
+from swarms import Agent, ConcurrentWorkflow
+from swarms.utils.vllm_wrapper import VLLMWrapper
+
+# Initialize vLLM
+vllm = VLLMWrapper(
+    model_name="meta-llama/Llama-2-7b-chat-hf",
+    system_prompt="You are a helpful assistant."
+)
+
+# Create specialized agents
+research_agent = Agent(
+    agent_name="Research-Agent",
+    agent_description="Expert in research",
+    system_prompt="You are a research expert.",
+    llm=vllm
+)
+
+analysis_agent = Agent(
+    agent_name="Analysis-Agent",
+    agent_description="Expert in analysis",
+    system_prompt="You are an analysis expert.",
+    llm=vllm
+)
+
+# Create a workflow
+agents = [research_agent, analysis_agent]
+workflow = ConcurrentWorkflow(
+    name="Research-Analysis-Workflow",
+    description="Comprehensive research and analysis workflow",
+    agents=agents
+)
+
+# Run the workflow
+result = workflow.run("Analyze the impact of renewable energy")
+```

examples/vllm_example.py

@@ -1,5 +1,6 @@
 from swarms.utils.vllm_wrapper import VLLMWrapper
 
+
 def main():
     # Initialize the vLLM wrapper with a model
     # Note: You'll need to have the model downloaded or specify a HuggingFace model ID
@@ -31,14 +32,15 @@ def main():
     tasks = [
         "What is vLLM?",
         "How does vLLM improve inference speed?",
-        "What are the main features of vLLM?"
+        "What are the main features of vLLM?",
     ]
-    
+
     responses = llm.batched_run(tasks, batch_size=2)
     print("\nBatched responses:")
     for task, response in zip(tasks, responses):
         print(f"\nTask: {task}")
         print(f"Response: {response}")
 
+
 if __name__ == "__main__":
-    main() 
+    main()

llm_txt.py

@@ -0,0 +1,68 @@
+import os
+from pathlib import Path
+
+
+def concat_all_md_files(root_dir, output_filename="llm.txt"):
+    """
+    Recursively searches for all .md files in directory and subdirectories,
+    then concatenates them into a single output file.
+
+    Args:
+        root_dir (str): Root directory to search for .md files
+        output_filename (str): Name of output file (default: llm.txt)
+
+    Returns:
+        str: Path to the created output file
+    """
+    try:
+        root_dir = Path(root_dir).resolve()
+        if not root_dir.is_dir():
+            raise ValueError(f"Directory not found: {root_dir}")
+
+        # Collect all .md files recursively
+        md_files = []
+        for root, _, files in os.walk(root_dir):
+            for file in files:
+                if file.lower().endswith(".md"):
+                    full_path = Path(root) / file
+                    md_files.append(full_path)
+
+        if not md_files:
+            print(
+                f"No .md files found in {root_dir} or its subdirectories"
+            )
+            return None
+
+        # Create output file in root directory
+        output_path = root_dir / output_filename
+
+        with open(output_path, "w", encoding="utf-8") as outfile:
+            for md_file in sorted(md_files):
+                try:
+                    # Get relative path for header
+                    rel_path = md_file.relative_to(root_dir)
+                    with open(
+                        md_file, "r", encoding="utf-8"
+                    ) as infile:
+                        content = infile.read()
+                        outfile.write(f"# File: {rel_path}\n\n")
+                        outfile.write(content)
+                        outfile.write(
+                            "\n\n" + "-" * 50 + "\n\n"
+                        )  # Separator
+                except Exception as e:
+                    print(f"Error processing {rel_path}: {str(e)}")
+                    continue
+
+        print(
+            f"Created {output_path} with {len(md_files)} files merged"
+        )
+        return str(output_path)
+
+    except Exception as e:
+        print(f"Fatal error: {str(e)}")
+        return None
+
+
+if __name__ == "__main__":
+    concat_all_md_files("docs")

mcp_test.py

@@ -0,0 +1,20 @@
+# math_server.py
+from mcp.server.fastmcp import FastMCP
+
+mcp = FastMCP("Math")
+
+
+@mcp.tool()
+def add(a: int, b: int) -> int:
+    """Add two numbers"""
+    return a + b
+
+
+@mcp.tool()
+def multiply(a: int, b: int) -> int:
+    """Multiply two numbers"""
+    return a * b
+
+
+if __name__ == "__main__":
+    mcp.run(transport="sse")

requirements.txt

@@ -22,4 +22,4 @@ pytest>=8.1.1
 networkx
 aiofiles
 httpx
-vllm>=0.2.0
+# vllm>=0.2.0

swarms/structs/agent.py

@@ -58,6 +58,12 @@ from swarms.utils.litellm_tokenizer import count_tokens
 from swarms.utils.pdf_to_text import pdf_to_text
 from swarms.utils.str_to_dict import str_to_dict
 
+from swarms.tools.mcp_integration import (
+    batch_mcp_flow,
+    mcp_flow_get_tool_schema,
+    MCPServerSseParams,
+)
+
 
 # Utils
 # Custom stopping condition
@@ -352,6 +358,7 @@ class Agent:
         role: agent_roles = "worker",
         no_print: bool = False,
         tools_list_dictionary: Optional[List[Dict[str, Any]]] = None,
+        mcp_servers: List[MCPServerSseParams] = [],
         *args,
         **kwargs,
     ):
@@ -471,6 +478,7 @@ class Agent:
         self.role = role
         self.no_print = no_print
         self.tools_list_dictionary = tools_list_dictionary
+        self.mcp_servers = mcp_servers
 
         if (
             self.agent_name is not None
@@ -584,6 +592,12 @@ class Agent:
         if self.llm is None:
             self.llm = self.llm_handling()
 
+        if (
+            self.tools_list_dictionary is None
+            and self.mcp_servers is not None
+        ):
+            self.tools_list_dictionary = self.mcp_tool_handling()
+
     def llm_handling(self):
         from swarms.utils.litellm_wrapper import LiteLLM
 
@@ -631,6 +645,69 @@ class Agent:
             logger.error(f"Error in llm_handling: {e}")
             return None
 
+    def mcp_execution_flow(self, response: any):
+        """
+        Executes the MCP (Model Context Protocol) flow based on the provided response.
+
+        This method takes a response, converts it from a string to a dictionary format,
+        and checks for the presence of a tool name or a name in the response. If either
+        is found, it retrieves the tool name and proceeds to call the batch_mcp_flow
+        function to execute the corresponding tool actions.
+
+        Args:
+            response (any): The response to be processed, which can be in string format
+            that represents a dictionary.
+
+        Returns:
+            The output from the batch_mcp_flow function, which contains the results of
+            the tool execution. If an error occurs during processing, it logs the error
+            and returns None.
+
+        Raises:
+            Exception: Logs any exceptions that occur during the execution flow.
+        """
+        try:
+            response = str_to_dict(response)
+
+            tool_output = batch_mcp_flow(
+                self.mcp_servers,
+                function_call=response,
+            )
+
+            return tool_output
+        except Exception as e:
+            logger.error(f"Error in mcp_execution_flow: {e}")
+            return None
+
+    def mcp_tool_handling(self):
+        """
+        Handles the retrieval of tool schemas from the MCP servers.
+
+        This method iterates over the list of MCP servers, retrieves the tool schema
+        for each server using the mcp_flow_get_tool_schema function, and compiles
+        these schemas into a list. The resulting list is stored in the
+        tools_list_dictionary attribute.
+
+        Returns:
+            list: A list of tool schemas retrieved from the MCP servers. If an error
+            occurs during the retrieval process, it logs the error and returns None.
+
+        Raises:
+            Exception: Logs any exceptions that occur during the tool handling process.
+        """
+        try:
+            self.tools_list_dictionary = []
+
+            for mcp_server in self.mcp_servers:
+                tool_schema = mcp_flow_get_tool_schema(mcp_server)
+                self.tools_list_dictionary.append(tool_schema)
+
+            print(self.tools_list_dictionary)
+            return self.tools_list_dictionary
+        except Exception as e:
+            logger.error(f"Error in mcp_tool_handling: {e}")
+            return None
+
     def setup_config(self):
         # The max_loops will be set dynamically if the dynamic_loop
         if self.dynamic_loops is True:

swarms/tools/mcp_integration.py

@@ -1,554 +1,392 @@
-from contextlib import AsyncExitStack
+from __future__ import annotations
-from types import TracebackType
-from typing import (
-    Any,
-    Callable,
-    Coroutine,
-    List,
-    Literal,
-    Optional,
-    TypedDict,
-    cast,
-)
 
-from mcp import ClientSession, StdioServerParameters
+from typing import Any, List
-from mcp.client.sse import sse_client
+
-from mcp.client.stdio import stdio_client
+
-from mcp.types import (
+from loguru import logger
-    CallToolResult,
+
-    EmbeddedResource,
+import abc
-    ImageContent,
+import asyncio
-    PromptMessage,
+from contextlib import AbstractAsyncContextManager, AsyncExitStack
-    TextContent,
+from pathlib import Path
+from typing import Literal
+
+from anyio.streams.memory import (
+    MemoryObjectReceiveStream,
+    MemoryObjectSendStream,
 )
-from mcp.types import (
+from mcp import (
+    ClientSession,
+    StdioServerParameters,
     Tool as MCPTool,
+    stdio_client,
 )
+from mcp.client.sse import sse_client
+from mcp.types import CallToolResult, JSONRPCMessage
+from typing_extensions import NotRequired, TypedDict
 
+from swarms.utils.any_to_str import any_to_str
 
-def convert_mcp_prompt_message_to_message(
-    message: PromptMessage,
-) -> str:
-    """Convert an MCP prompt message to a string message.
 
-    Args:
+class MCPServer(abc.ABC):
-        message: MCP prompt message to convert
+    """Base class for Model Context Protocol servers."""
 
-    Returns:
+    @abc.abstractmethod
-        a string message
+    async def connect(self):
-    """
+        """Connect to the server. For example, this might mean spawning a subprocess or
-    if message.content.type == "text":
+        opening a network connection. The server is expected to remain connected until
-        if message.role == "user":
+        `cleanup()` is called.
-            return str(message.content.text)
+        """
-        elif message.role == "assistant":
+        pass
-            return str(
+
-                message.content.text
+    @property
-            )  # Fixed attribute name from str to text
+    @abc.abstractmethod
-        else:
+    def name(self) -> str:
-            raise ValueError(
+        """A readable name for the server."""
-                f"Unsupported prompt message role: {message.role}"
+        pass
-            )
+
+    @abc.abstractmethod
+    async def cleanup(self):
+        """Cleanup the server. For example, this might mean closing a subprocess or
+        closing a network connection.
+        """
+        pass
 
-    raise ValueError(
+    @abc.abstractmethod
-        f"Unsupported prompt message content type: {message.content.type}"
+    async def list_tools(self) -> list[MCPTool]:
-    )
+        """List the tools available on the server."""
+        pass
 
+    @abc.abstractmethod
+    async def call_tool(
+        self, tool_name: str, arguments: dict[str, Any] | None
+    ) -> CallToolResult:
+        """Invoke a tool on the server."""
+        pass
 
-async def load_mcp_prompt(
-    session: ClientSession,
-    name: str,
-    arguments: Optional[dict[str, Any]] = None,
-) -> List[str]:
-    """Load MCP prompt and convert to messages."""
-    response = await session.get_prompt(name, arguments)
 
-    return [
+class _MCPServerWithClientSession(MCPServer, abc.ABC):
-        convert_mcp_prompt_message_to_message(message)
+    """Base class for MCP servers that use a `ClientSession` to communicate with the server."""
-        for message in response.messages
-    ]
 
+    def __init__(self, cache_tools_list: bool):
+        """
+        Args:
+            cache_tools_list: Whether to cache the tools list. If `True`, the tools list will be
+            cached and only fetched from the server once. If `False`, the tools list will be
+            fetched from the server on each call to `list_tools()`. The cache can be invalidated
+            by calling `invalidate_tools_cache()`. You should set this to `True` if you know the
+            server will not change its tools list, because it can drastically improve latency
+            (by avoiding a round-trip to the server every time).
+        """
+        self.session: ClientSession | None = None
+        self.exit_stack: AsyncExitStack = AsyncExitStack()
+        self._cleanup_lock: asyncio.Lock = asyncio.Lock()
+        self.cache_tools_list = cache_tools_list
 
-DEFAULT_ENCODING = "utf-8"
+        # The cache is always dirty at startup, so that we fetch tools at least once
-DEFAULT_ENCODING_ERROR_HANDLER = "strict"
+        self._cache_dirty = True
+        self._tools_list: list[MCPTool] | None = None
 
-DEFAULT_HTTP_TIMEOUT = 5
+    @abc.abstractmethod
-DEFAULT_SSE_READ_TIMEOUT = 60 * 5
+    def create_streams(
+        self,
+    ) -> AbstractAsyncContextManager[
+        tuple[
+            MemoryObjectReceiveStream[JSONRPCMessage | Exception],
+            MemoryObjectSendStream[JSONRPCMessage],
+        ]
+    ]:
+        """Create the streams for the server."""
+        pass
+
+    async def __aenter__(self):
+        await self.connect()
+        return self
+
+    async def __aexit__(self, exc_type, exc_value, traceback):
+        await self.cleanup()
+
+    def invalidate_tools_cache(self):
+        """Invalidate the tools cache."""
+        self._cache_dirty = True
+
+    async def connect(self):
+        """Connect to the server."""
+        try:
+            transport = await self.exit_stack.enter_async_context(
+                self.create_streams()
+            )
+            read, write = transport
+            session = await self.exit_stack.enter_async_context(
+                ClientSession(read, write)
+            )
+            await session.initialize()
+            self.session = session
+        except Exception as e:
+            logger.error(f"Error initializing MCP server: {e}")
+            await self.cleanup()
+            raise
 
+    async def list_tools(self) -> list[MCPTool]:
+        """List the tools available on the server."""
+        if not self.session:
+            raise Exception(
+                "Server not initialized. Make sure you call `connect()` first."
+            )
 
-class StdioConnection(TypedDict):
+        # Return from cache if caching is enabled, we have tools, and the cache is not dirty
-    transport: Literal["stdio"]
+        if (
+            self.cache_tools_list
+            and not self._cache_dirty
+            and self._tools_list
+        ):
+            return self._tools_list
 
-    command: str
+        # Reset the cache dirty to False
-    """The executable to run to start the server."""
+        self._cache_dirty = False
 
-    args: list[str]
+        # Fetch the tools from the server
-    """Command line arguments to pass to the executable."""
+        self._tools_list = (await self.session.list_tools()).tools
+        return self._tools_list
 
-    env: dict[str, str] | None
+    async def call_tool(
-    """The environment to use when spawning the process."""
+        self, arguments: dict[str, Any] | None
+    ) -> CallToolResult:
+        """Invoke a tool on the server."""
+        tool_name = arguments.get("tool_name") or arguments.get(
+            "name"
+        )
 
-    encoding: str
+        if not tool_name:
-    """The text encoding used when sending/receiving messages to the server."""
+            raise Exception("No tool name found in arguments")
 
-    encoding_error_handler: Literal["strict", "ignore", "replace"]
+        if not self.session:
-    """
+            raise Exception(
-    The text encoding error handler.
+                "Server not initialized. Make sure you call `connect()` first."
+            )
 
-    See https://docs.python.org/3/library/codecs.html#codec-base-classes for
+        return await self.session.call_tool(tool_name, arguments)
-    explanations of possible values
-    """
 
+    async def cleanup(self):
+        """Cleanup the server."""
+        async with self._cleanup_lock:
+            try:
+                await self.exit_stack.aclose()
+                self.session = None
+            except Exception as e:
+                logger.error(f"Error cleaning up server: {e}")
 
-class SSEConnection(TypedDict):
-    transport: Literal["sse"]
 
-    url: str
+class MCPServerStdioParams(TypedDict):
-    """The URL of the SSE endpoint to connect to."""
+    """Mirrors `mcp.client.stdio.StdioServerParameters`, but lets you pass params without another
+    import.
+    """
 
-    headers: dict[str, Any] | None
+    command: str
-    """HTTP headers to send to the SSE endpoint"""
+    """The executable to run to start the server. For example, `python` or `node`."""
 
-    timeout: float
+    args: NotRequired[list[str]]
-    """HTTP timeout"""
+    """Command line args to pass to the `command` executable. For example, `['foo.py']` or
+    `['server.js', '--port', '8080']`."""
 
-    sse_read_timeout: float
+    env: NotRequired[dict[str, str]]
-    """SSE read timeout"""
+    """The environment variables to set for the server. ."""
 
+    cwd: NotRequired[str | Path]
+    """The working directory to use when spawning the process."""
 
-NonTextContent = ImageContent | EmbeddedResource
+    encoding: NotRequired[str]
+    """The text encoding used when sending/receiving messages to the server. Defaults to `utf-8`."""
 
+    encoding_error_handler: NotRequired[
+        Literal["strict", "ignore", "replace"]
+    ]
+    """The text encoding error handler. Defaults to `strict`.
 
-def _convert_call_tool_result(
+    See https://docs.python.org/3/library/codecs.html#codec-base-classes for
-    call_tool_result: CallToolResult,
+    explanations of possible values.
-) -> tuple[str | list[str], list[NonTextContent] | None]:
+    """
-    text_contents: list[TextContent] = []
-    non_text_contents = []
-    for content in call_tool_result.content:
-        if isinstance(content, TextContent):
-            text_contents.append(content)
-        else:
-            non_text_contents.append(content)
 
-    tool_content: str | list[str] = [
-        content.text for content in text_contents
-    ]
-    if len(text_contents) == 1:
-        tool_content = tool_content[0]
 
-    if call_tool_result.isError:
+class MCPServerStdio(_MCPServerWithClientSession):
-        raise ValueError("Error calling tool")
+    """MCP server implementation that uses the stdio transport. See the [spec]
+    (https://spec.modelcontextprotocol.io/specification/2024-11-05/basic/transports/#stdio) for
+    details.
+    """
 
-    return tool_content, non_text_contents or None
+    def __init__(
+        self,
+        params: MCPServerStdioParams,
+        cache_tools_list: bool = False,
+        name: str | None = None,
+    ):
+        """Create a new MCP server based on the stdio transport.
 
+        Args:
+            params: The params that configure the server. This includes the command to run to
+                start the server, the args to pass to the command, the environment variables to
+                set for the server, the working directory to use when spawning the process, and
+                the text encoding used when sending/receiving messages to the server.
+            cache_tools_list: Whether to cache the tools list. If `True`, the tools list will be
+                cached and only fetched from the server once. If `False`, the tools list will be
+                fetched from the server on each call to `list_tools()`. The cache can be
+                invalidated by calling `invalidate_tools_cache()`. You should set this to `True`
+                if you know the server will not change its tools list, because it can drastically
+                improve latency (by avoiding a round-trip to the server every time).
+            name: A readable name for the server. If not provided, we'll create one from the
+                command.
+        """
+        super().__init__(cache_tools_list)
+
+        self.params = StdioServerParameters(
+            command=params["command"],
+            args=params.get("args", []),
+            env=params.get("env"),
+            cwd=params.get("cwd"),
+            encoding=params.get("encoding", "utf-8"),
+            encoding_error_handler=params.get(
+                "encoding_error_handler", "strict"
+            ),
+        )
 
-def convert_mcp_tool_to_function(
+        self._name = name or f"stdio: {self.params.command}"
-    session: ClientSession,
-    tool: MCPTool,
-) -> Callable[
-    ...,
-    Coroutine[
-        Any, Any, tuple[str | list[str], list[NonTextContent] | None]
-    ],
-]:
-    """Convert an MCP tool to a callable function.
 
-    NOTE: this tool can be executed only in a context of an active MCP client session.
+    def create_streams(
+        self,
+    ) -> AbstractAsyncContextManager[
+        tuple[
+            MemoryObjectReceiveStream[JSONRPCMessage | Exception],
+            MemoryObjectSendStream[JSONRPCMessage],
+        ]
+    ]:
+        """Create the streams for the server."""
+        return stdio_client(self.params)
 
-    Args:
+    @property
-        session: MCP client session
+    def name(self) -> str:
-        tool: MCP tool to convert
+        """A readable name for the server."""
+        return self._name
 
-    Returns:
-        a callable function
-    """
 
-    async def call_tool(
+class MCPServerSseParams(TypedDict):
-        **arguments: dict[str, Any],
+    """Mirrors the params in`mcp.client.sse.sse_client`."""
-    ) -> tuple[str | list[str], list[NonTextContent] | None]:
-        """Execute the tool with the given arguments."""
-        call_tool_result = await session.call_tool(
-            tool.name, arguments
-        )
-        return _convert_call_tool_result(call_tool_result)
 
-    # Add metadata as attributes to the function
+    url: str
-    call_tool.__name__ = tool.name
+    """The URL of the server."""
-    call_tool.__doc__ = tool.description or ""
-    call_tool.schema = tool.inputSchema
 
-    return call_tool
+    headers: NotRequired[dict[str, str]]
+    """The headers to send to the server."""
 
+    timeout: NotRequired[float]
+    """The timeout for the HTTP request. Defaults to 5 seconds."""
 
-async def load_mcp_tools(session: ClientSession) -> list[Callable]:
+    sse_read_timeout: NotRequired[float]
-    """Load all available MCP tools and convert them to callable functions."""
+    """The timeout for the SSE connection, in seconds. Defaults to 5 minutes."""
-    tools = await session.list_tools()
-    return [
-        convert_mcp_tool_to_function(session, tool)
-        for tool in tools.tools
-    ]
 
 
-class MultiServerMCPClient:
+class MCPServerSse(_MCPServerWithClientSession):
-    """Client for connecting to multiple MCP servers and loading tools from them."""
+    """MCP server implementation that uses the HTTP with SSE transport. See the [spec]
+    (https://spec.modelcontextprotocol.io/specification/2024-11-05/basic/transports/#http-with-sse)
+    for details.
+    """
 
     def __init__(
         self,
-        connections: dict[
+        params: MCPServerSseParams,
-            str, StdioConnection | SSEConnection
+        cache_tools_list: bool = False,
-        ] = None,
+        name: str | None = None,
-    ) -> None:
+    ):
-        """Initialize a MultiServerMCPClient with MCP servers connections.
+        """Create a new MCP server based on the HTTP with SSE transport.
 
         Args:
-            connections: A dictionary mapping server names to connection configurations.
+            params: The params that configure the server. This includes the URL of the server,
-                Each configuration can be either a StdioConnection or SSEConnection.
+                the headers to send to the server, the timeout for the HTTP request, and the
-                If None, no initial connections are established.
+                timeout for the SSE connection.
-
+
-        Example:
+            cache_tools_list: Whether to cache the tools list. If `True`, the tools list will be
-
+                cached and only fetched from the server once. If `False`, the tools list will be
-            ```python
+                fetched from the server on each call to `list_tools()`. The cache can be
-            async with MultiServerMCPClient(
+                invalidated by calling `invalidate_tools_cache()`. You should set this to `True`
-                {
+                if you know the server will not change its tools list, because it can drastically
-                    "math": {
+                improve latency (by avoiding a round-trip to the server every time).
-                        "command": "python",
+
-                        # Make sure to update to the full absolute path to your math_server.py file
+            name: A readable name for the server. If not provided, we'll create one from the
-                        "args": ["/path/to/math_server.py"],
+                URL.
-                        "transport": "stdio",
-                    },
-                    "weather": {
-                        # make sure you start your weather server on port 8000
-                        "url": "http://localhost:8000/sse",
-                        "transport": "sse",
-                    }
-                }
-            ) as client:
-                all_tools = client.get_tools()
-                ...
-            ```
         """
-        self.connections = connections
+        super().__init__(cache_tools_list)
-        self.exit_stack = AsyncExitStack()
-        self.sessions: dict[str, ClientSession] = {}
-        self.server_name_to_tools: dict[str, list[Callable]] = {}
 
-    async def _initialize_session_and_load_tools(
+        self.params = params
-        self, server_name: str, session: ClientSession
+        self._name = name or f"sse: {self.params['url']}"
-    ) -> None:
-        """Initialize a session and load tools from it.
 
-        Args:
+    def create_streams(
-            server_name: Name to identify this server connection
+        self,
-            session: The ClientSession to initialize
+    ) -> AbstractAsyncContextManager[
-        """
+        tuple[
-        # Initialize the session
+            MemoryObjectReceiveStream[JSONRPCMessage | Exception],
-        await session.initialize()
+            MemoryObjectSendStream[JSONRPCMessage],
-        self.sessions[server_name] = session
+        ]
+    ]:
+        """Create the streams for the server."""
+        return sse_client(
+            url=self.params["url"],
+            headers=self.params.get("headers", None),
+            timeout=self.params.get("timeout", 5),
+            sse_read_timeout=self.params.get(
+                "sse_read_timeout", 60 * 5
+            ),
+        )
 
-        # Load tools from this server
+    @property
-        server_tools = await load_mcp_tools(session)
+    def name(self) -> str:
-        self.server_name_to_tools[server_name] = server_tools
+        """A readable name for the server."""
+        return self._name
 
-    async def connect_to_server(
-        self,
-        server_name: str,
-        *,
-        transport: Literal["stdio", "sse"] = "stdio",
-        **kwargs,
-    ) -> None:
-        """Connect to an MCP server using either stdio or SSE.
 
-        This is a generic method that calls either connect_to_server_via_stdio or connect_to_server_via_sse
+def mcp_flow_get_tool_schema(
-        based on the provided transport parameter.
+    params: MCPServerSseParams,
+) -> MCPServer:
+    server = MCPServerSse(params, cache_tools_list=True)
 
-        Args:
+    # Connect the server
-            server_name: Name to identify this server connection
+    asyncio.run(server.connect())
-            transport: Type of transport to use ("stdio" or "sse"), defaults to "stdio"
-            **kwargs: Additional arguments to pass to the specific connection method
 
-        Raises:
+    # Return the server
-            ValueError: If transport is not recognized
+    output = asyncio.run(server.list_tools())
-            ValueError: If required parameters for the specified transport are missing
-        """
-        if transport == "sse":
-            if "url" not in kwargs:
-                raise ValueError(
-                    "'url' parameter is required for SSE connection"
-                )
-            await self.connect_to_server_via_sse(
-                server_name,
-                url=kwargs["url"],
-                headers=kwargs.get("headers"),
-                timeout=kwargs.get("timeout", DEFAULT_HTTP_TIMEOUT),
-                sse_read_timeout=kwargs.get(
-                    "sse_read_timeout", DEFAULT_SSE_READ_TIMEOUT
-                ),
-            )
-        elif transport == "stdio":
-            if "command" not in kwargs:
-                raise ValueError(
-                    "'command' parameter is required for stdio connection"
-                )
-            if "args" not in kwargs:
-                raise ValueError(
-                    "'args' parameter is required for stdio connection"
-                )
-            await self.connect_to_server_via_stdio(
-                server_name,
-                command=kwargs["command"],
-                args=kwargs["args"],
-                env=kwargs.get("env"),
-                encoding=kwargs.get("encoding", DEFAULT_ENCODING),
-                encoding_error_handler=kwargs.get(
-                    "encoding_error_handler",
-                    DEFAULT_ENCODING_ERROR_HANDLER,
-                ),
-            )
-        else:
-            raise ValueError(
-                f"Unsupported transport: {transport}. Must be 'stdio' or 'sse'"
-            )
 
-    async def connect_to_server_via_stdio(
+    # Cleanup the server
-        self,
+    asyncio.run(server.cleanup())
-        server_name: str,
-        *,
-        command: str,
-        args: list[str],
-        env: dict[str, str] | None = None,
-        encoding: str = DEFAULT_ENCODING,
-        encoding_error_handler: Literal[
-            "strict", "ignore", "replace"
-        ] = DEFAULT_ENCODING_ERROR_HANDLER,
-    ) -> None:
-        """Connect to a specific MCP server using stdio
 
-        Args:
+    return output.model_dump()
-            server_name: Name to identify this server connection
-            command: Command to execute
-            args: Arguments for the command
-            env: Environment variables for the command
-            encoding: Character encoding
-            encoding_error_handler: How to handle encoding errors
-        """
-        server_params = StdioServerParameters(
-            command=command,
-            args=args,
-            env=env,
-            encoding=encoding,
-            encoding_error_handler=encoding_error_handler,
-        )
 
-        # Create and store the connection
-        stdio_transport = await self.exit_stack.enter_async_context(
-            stdio_client(server_params)
-        )
-        read, write = stdio_transport
-        session = cast(
-            ClientSession,
-            await self.exit_stack.enter_async_context(
-                ClientSession(read, write)
-            ),
-        )
 
-        await self._initialize_session_and_load_tools(
+def mcp_flow(
-            server_name, session
+    params: MCPServerSseParams,
-        )
+    function_call: dict[str, Any],
+) -> MCPServer:
+    server = MCPServerSse(params, cache_tools_list=True)
 
-    async def connect_to_server_via_sse(
+    # Connect the server
-        self,
+    asyncio.run(server.connect())
-        server_name: str,
-        *,
-        url: str,
-        headers: dict[str, Any] | None = None,
-        timeout: float = DEFAULT_HTTP_TIMEOUT,
-        sse_read_timeout: float = DEFAULT_SSE_READ_TIMEOUT,
-    ) -> None:
-        """Connect to a specific MCP server using SSE
 
-        Args:
+    # Return the server
-            server_name: Name to identify this server connection
+    output = asyncio.run(server.call_tool(function_call))
-            url: URL of the SSE server
-            headers: HTTP headers to send to the SSE endpoint
-            timeout: HTTP timeout
-            sse_read_timeout: SSE read timeout
-        """
-        # Create and store the connection
-        sse_transport = await self.exit_stack.enter_async_context(
-            sse_client(url, headers, timeout, sse_read_timeout)
-        )
-        read, write = sse_transport
-        session = cast(
-            ClientSession,
-            await self.exit_stack.enter_async_context(
-                ClientSession(read, write)
-            ),
-        )
 
-        await self._initialize_session_and_load_tools(
+    output = output.model_dump()
-            server_name, session
-        )
 
-    def get_tools(self) -> list[Callable]:
+    # Cleanup the server
-        """Get a list of all tools from all connected servers."""
+    asyncio.run(server.cleanup())
-        all_tools: list[Callable] = []
-        for server_tools in self.server_name_to_tools.values():
-            all_tools.extend(server_tools)
-        return all_tools
 
-    async def get_prompt(
+    return any_to_str(output)
-        self,
-        server_name: str,
-        prompt_name: str,
-        arguments: Optional[dict[str, Any]] = None,
-    ) -> List[str]:
-        """Get a prompt from a given MCP server."""
-        session = self.sessions[server_name]
-        return await load_mcp_prompt(session, prompt_name, arguments)
-
-    async def __aenter__(self) -> "MultiServerMCPClient":
-        try:
-            connections = self.connections or {}
-            for server_name, connection in connections.items():
-                connection_dict = connection.copy()
-                transport = connection_dict.pop("transport")
-                if transport == "stdio":
-                    await self.connect_to_server_via_stdio(
-                        server_name, **connection_dict
-                    )
-                elif transport == "sse":
-                    await self.connect_to_server_via_sse(
-                        server_name, **connection_dict
-                    )
-                else:
-                    raise ValueError(
-                        f"Unsupported transport: {transport}. Must be 'stdio' or 'sse'"
-                    )
-            return self
-        except Exception:
-            await self.exit_stack.aclose()
-            raise
 
-    async def __aexit__(
+
-        self,
+def batch_mcp_flow(
-        exc_type: type[BaseException] | None,
+    params: List[MCPServerSseParams],
-        exc_val: BaseException | None,
+    function_call: List[dict[str, Any]] = [],
-        exc_tb: TracebackType | None,
+) -> MCPServer:
-    ) -> None:
+    output_list = []
-        await self.exit_stack.aclose()
+
-
+    for param in params:
-
+        output = mcp_flow(param, function_call)
-# #!/usr/bin/env python3
+        output_list.append(output)
-# import asyncio
+
-# import os
+    return output_list
-# import json
-# from typing import List, Any, Callable
-
-# # # Import our MCP client module
-# # from mcp_client import MultiServerMCPClient
-
-
-# async def main():
-#     """Test script for demonstrating MCP client usage."""
-#     print("Starting MCP Client test...")
-
-#     # Create a connection to multiple MCP servers
-#     # You'll need to update these paths to match your setup
-#     async with MultiServerMCPClient(
-#         {
-#             "math": {
-#                 "transport": "stdio",
-#                 "command": "python",
-#                 "args": ["/path/to/math_server.py"],
-#                 "env": {"DEBUG": "1"},
-#             },
-#             "search": {
-#                 "transport": "sse",
-#                 "url": "http://localhost:8000/sse",
-#                 "headers": {
-#                     "Authorization": f"Bearer {os.environ.get('API_KEY', '')}"
-#                 },
-#             },
-#         }
-#     ) as client:
-#         # Get all available tools
-#         tools = client.get_tools()
-#         print(f"Found {len(tools)} tools across all servers")
-
-#         # Print tool information
-#         for i, tool in enumerate(tools):
-#             print(f"\nTool {i+1}: {tool.__name__}")
-#             print(f"  Description: {tool.__doc__}")
-#             if hasattr(tool, "schema") and tool.schema:
-#                 print(
-#                     f"  Schema: {json.dumps(tool.schema, indent=2)[:100]}..."
-#                 )
-
-#         # Example: Use a specific tool if available
-#         calculator_tool = next(
-#             (t for t in tools if t.__name__ == "calculator"), None
-#         )
-#         if calculator_tool:
-#             print("\n\nTesting calculator tool:")
-#             try:
-#                 # Call the tool as an async function
-#                 result, artifacts = await calculator_tool(
-#                     expression="2 + 2 * 3"
-#                 )
-#                 print(f"  Calculator result: {result}")
-#                 if artifacts:
-#                     print(
-#                         f"  With {len(artifacts)} additional artifacts"
-#                     )
-#             except Exception as e:
-#                 print(f"  Error using calculator: {e}")
-
-#         # Example: Load a prompt from a server
-#         try:
-#             print("\n\nTesting prompt loading:")
-#             prompt_messages = await client.get_prompt(
-#                 "math",
-#                 "calculation_introduction",
-#                 {"user_name": "Test User"},
-#             )
-#             print(
-#                 f"  Loaded prompt with {len(prompt_messages)} messages:"
-#             )
-#             for i, msg in enumerate(prompt_messages):
-#                 print(f"  Message {i+1}: {msg[:50]}...")
-#         except Exception as e:
-#             print(f"  Error loading prompt: {e}")
-
-
-# async def create_custom_tool():
-#     """Example of creating a custom tool function."""
-
-#     # Define a tool function with metadata
-#     async def add_numbers(a: float, b: float) -> tuple[str, None]:
-#         """Add two numbers together."""
-#         result = a + b
-#         return f"The sum of {a} and {b} is {result}", None
-
-#     # Add metadata to the function
-#     add_numbers.__name__ = "add_numbers"
-#     add_numbers.__doc__ = (
-#         "Add two numbers together and return the result."
-#     )
-#     add_numbers.schema = {
-#         "type": "object",
-#         "properties": {
-#             "a": {"type": "number", "description": "First number"},
-#             "b": {"type": "number", "description": "Second number"},
-#         },
-#         "required": ["a", "b"],
-#     }
-
-#     # Use the tool
-#     result, _ = await add_numbers(a=5, b=7)
-#     print(f"\nCustom tool result: {result}")
-
-
-# if __name__ == "__main__":
-#     # Run both examples
-#     loop = asyncio.get_event_loop()
-#     loop.run_until_complete(main())
-#     loop.run_until_complete(create_custom_tool())

swarms/utils/vllm_wrapper.py

@@ -6,11 +6,15 @@ try:
 except ImportError:
     import subprocess
     import sys
+
     print("Installing vllm")
-    subprocess.check_call([sys.executable, "-m", "pip", "install", "-U", "vllm"])
+    subprocess.check_call(
+        [sys.executable, "-m", "pip", "install", "-U", "vllm"]
+    )
     print("vllm installed")
     from vllm import LLM, SamplingParams
 
+
 class VLLMWrapper:
     """
     A wrapper class for vLLM that provides a similar interface to LiteLLM.
@@ -54,7 +58,7 @@ class VLLMWrapper:
         self.tools_list_dictionary = tools_list_dictionary
         self.tool_choice = tool_choice
         self.parallel_tool_calls = parallel_tool_calls
-        
+
         # Initialize vLLM
         self.llm = LLM(model=model_name, **kwargs)
         self.sampling_params = SamplingParams(
@@ -90,12 +94,12 @@ class VLLMWrapper:
         """
         try:
             prompt = self._prepare_prompt(task)
-            
+
             outputs = self.llm.generate(prompt, self.sampling_params)
             response = outputs[0].outputs[0].text.strip()
-            
+
             return response
-            
+
         except Exception as error:
             logger.error(f"Error in VLLMWrapper: {error}")
             raise error
@@ -114,7 +118,9 @@ class VLLMWrapper:
         """
         return self.run(task, *args, **kwargs)
 
-    def batched_run(self, tasks: List[str], batch_size: int = 10) -> List[str]:
+    def batched_run(
+        self, tasks: List[str], batch_size: int = 10
+    ) -> List[str]:
         """
         Run the model for multiple tasks in batches.
 
@@ -125,14 +131,16 @@ class VLLMWrapper:
         Returns:
             List[str]: List of model responses.
         """
-        logger.info(f"Running tasks in batches of size {batch_size}. Total tasks: {len(tasks)}")
+        logger.info(
+            f"Running tasks in batches of size {batch_size}. Total tasks: {len(tasks)}"
+        )
         results = []
-        
+
         for i in range(0, len(tasks), batch_size):
-            batch = tasks[i:i + batch_size]
+            batch = tasks[i : i + batch_size]
             for task in batch:
                 logger.info(f"Running task: {task}")
                 results.append(self.run(task))
-                
+
         logger.info("Completed all tasks.")
-        return results 
+        return results

swarms_of_vllm.py

@@ -0,0 +1,212 @@
+from swarms import Agent, ConcurrentWorkflow
+from swarms.utils.vllm_wrapper import VLLMWrapper
+from dotenv import load_dotenv
+
+load_dotenv()
+
+# Initialize the VLLM wrapper
+vllm = VLLMWrapper(
+    model_name="meta-llama/Llama-2-7b-chat-hf",
+    system_prompt="You are a helpful assistant.",
+)
+
+# Technical Analysis Agent
+technical_analyst = Agent(
+    agent_name="Technical-Analysis-Agent",
+    agent_description="Expert in technical analysis and chart patterns",
+    system_prompt="""You are an expert Technical Analysis Agent specializing in market technicals and chart patterns. Your responsibilities include:
+
+1. PRICE ACTION ANALYSIS
+- Identify key support and resistance levels
+- Analyze price trends and momentum
+- Detect chart patterns (e.g., head & shoulders, triangles, flags)
+- Evaluate volume patterns and their implications
+
+2. TECHNICAL INDICATORS
+- Calculate and interpret moving averages (SMA, EMA)
+- Analyze momentum indicators (RSI, MACD, Stochastic)
+- Evaluate volume indicators (OBV, Volume Profile)
+- Monitor volatility indicators (Bollinger Bands, ATR)
+
+3. TRADING SIGNALS
+- Generate clear buy/sell signals based on technical criteria
+- Identify potential entry and exit points
+- Set appropriate stop-loss and take-profit levels
+- Calculate position sizing recommendations
+
+4. RISK MANAGEMENT
+- Assess market volatility and trend strength
+- Identify potential reversal points
+- Calculate risk/reward ratios for trades
+- Suggest position sizing based on risk parameters
+
+Your analysis should be data-driven, precise, and actionable. Always include specific price levels, time frames, and risk parameters in your recommendations.""",
+    max_loops=1,
+    llm=vllm,
+)
+
+# Fundamental Analysis Agent
+fundamental_analyst = Agent(
+    agent_name="Fundamental-Analysis-Agent",
+    agent_description="Expert in company fundamentals and valuation",
+    system_prompt="""You are an expert Fundamental Analysis Agent specializing in company valuation and financial metrics. Your core responsibilities include:
+
+1. FINANCIAL STATEMENT ANALYSIS
+- Analyze income statements, balance sheets, and cash flow statements
+- Calculate and interpret key financial ratios
+- Evaluate revenue growth and profit margins
+- Assess company's debt levels and cash position
+
+2. VALUATION METRICS
+- Calculate fair value using multiple valuation methods:
+  * Discounted Cash Flow (DCF)
+  * Price-to-Earnings (P/E)
+  * Price-to-Book (P/B)
+  * Enterprise Value/EBITDA
+- Compare valuations against industry peers
+
+3. BUSINESS MODEL ASSESSMENT
+- Evaluate competitive advantages and market position
+- Analyze industry dynamics and market share
+- Assess management quality and corporate governance
+- Identify potential risks and growth opportunities
+
+4. ECONOMIC CONTEXT
+- Consider macroeconomic factors affecting the company
+- Analyze industry cycles and trends
+- Evaluate regulatory environment and compliance
+- Assess global market conditions
+
+Your analysis should be comprehensive, focusing on both quantitative metrics and qualitative factors that impact long-term value.""",
+    max_loops=1,
+    llm=vllm,
+)
+
+# Market Sentiment Agent
+sentiment_analyst = Agent(
+    agent_name="Market-Sentiment-Agent",
+    agent_description="Expert in market psychology and sentiment analysis",
+    system_prompt="""You are an expert Market Sentiment Agent specializing in analyzing market psychology and investor behavior. Your key responsibilities include:
+
+1. SENTIMENT INDICATORS
+- Monitor and interpret market sentiment indicators:
+  * VIX (Fear Index)
+  * Put/Call Ratio
+  * Market Breadth
+  * Investor Surveys
+- Track institutional vs retail investor behavior
+
+2. NEWS AND SOCIAL MEDIA ANALYSIS
+- Analyze news flow and media sentiment
+- Monitor social media trends and discussions
+- Track analyst recommendations and changes
+- Evaluate corporate insider trading patterns
+
+3. MARKET POSITIONING
+- Assess hedge fund positioning and exposure
+- Monitor short interest and short squeeze potential
+- Track fund flows and asset allocation trends
+- Analyze options market sentiment
+
+4. CONTRARIAN SIGNALS
+- Identify extreme sentiment readings
+- Detect potential market turning points
+- Analyze historical sentiment patterns
+- Provide contrarian trading opportunities
+
+Your analysis should combine quantitative sentiment metrics with qualitative assessment of market psychology and crowd behavior.""",
+    max_loops=1,
+    llm=vllm,
+)
+
+# Quantitative Strategy Agent
+quant_analyst = Agent(
+    agent_name="Quantitative-Strategy-Agent",
+    agent_description="Expert in quantitative analysis and algorithmic strategies",
+    system_prompt="""You are an expert Quantitative Strategy Agent specializing in data-driven investment strategies. Your primary responsibilities include:
+
+1. FACTOR ANALYSIS
+- Analyze and monitor factor performance:
+  * Value
+  * Momentum
+  * Quality
+  * Size
+  * Low Volatility
+- Calculate factor exposures and correlations
+
+2. STATISTICAL ANALYSIS
+- Perform statistical arbitrage analysis
+- Calculate and monitor pair trading opportunities
+- Analyze market anomalies and inefficiencies
+- Develop mean reversion strategies
+
+3. RISK MODELING
+- Build and maintain risk models
+- Calculate portfolio optimization metrics
+- Monitor correlation matrices
+- Analyze tail risk and stress scenarios
+
+4. ALGORITHMIC STRATEGIES
+- Develop systematic trading strategies
+- Backtest and validate trading algorithms
+- Monitor strategy performance metrics
+- Optimize execution algorithms
+
+Your analysis should be purely quantitative, based on statistical evidence and mathematical models rather than subjective opinions.""",
+    max_loops=1,
+    llm=vllm,
+)
+
+# Portfolio Strategy Agent
+portfolio_strategist = Agent(
+    agent_name="Portfolio-Strategy-Agent",
+    agent_description="Expert in portfolio management and asset allocation",
+    system_prompt="""You are an expert Portfolio Strategy Agent specializing in portfolio construction and management. Your core responsibilities include:
+
+1. ASSET ALLOCATION
+- Develop strategic asset allocation frameworks
+- Recommend tactical asset allocation shifts
+- Optimize portfolio weightings
+- Balance risk and return objectives
+
+2. PORTFOLIO ANALYSIS
+- Calculate portfolio risk metrics
+- Monitor sector and factor exposures
+- Analyze portfolio correlation matrix
+- Track performance attribution
+
+3. RISK MANAGEMENT
+- Implement portfolio hedging strategies
+- Monitor and adjust position sizing
+- Set stop-loss and rebalancing rules
+- Develop drawdown protection strategies
+
+4. PORTFOLIO OPTIMIZATION
+- Calculate efficient frontier analysis
+- Optimize for various objectives:
+  * Maximum Sharpe Ratio
+  * Minimum Volatility
+  * Maximum Diversification
+- Consider transaction costs and taxes
+
+Your recommendations should focus on portfolio-level decisions that optimize risk-adjusted returns while meeting specific investment objectives.""",
+    max_loops=1,
+    llm=vllm,
+)
+
+# Create a list of all agents
+stock_analysis_agents = [
+    technical_analyst,
+    fundamental_analyst,
+    sentiment_analyst,
+    quant_analyst,
+    portfolio_strategist
+]
+
+swarm = ConcurrentWorkflow(
+    name="Stock-Analysis-Swarm",
+    description="A swarm of agents that analyze stocks and provide a comprehensive analysis of the current trends and opportunities.",
+    agents=stock_analysis_agents,
+)
+
+swarm.run("Analyze the best etfs for gold and other similiar commodities in volatile markets")