Merge pull request #477 from MervinPraison/develop

multi agents deploy

docker/Dockerfile

@@ -1,6 +1,6 @@
 FROM python:3.11-slim
 WORKDIR /app
 COPY . .
-RUN pip install flask praisonai==2.1.4 gunicorn markdown
+RUN pip install flask praisonai==2.1.5 gunicorn markdown
 EXPOSE 8080
 CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]

docker/Dockerfile.chat

@@ -13,7 +13,7 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
 RUN pip install --no-cache-dir \
     praisonaiagents>=0.0.4 \
     praisonai_tools \
-    "praisonai==2.1.4" \
+    "praisonai==2.1.5" \
     "praisonai[chat]" \
     "embedchain[github,youtube]"
 

docker/Dockerfile.dev

@@ -15,7 +15,7 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
 RUN pip install --no-cache-dir \
     praisonaiagents>=0.0.4 \
     praisonai_tools \
-    "praisonai==2.1.4" \
+    "praisonai==2.1.5" \
     "praisonai[ui]" \
     "praisonai[chat]" \
     "praisonai[realtime]" \

docker/Dockerfile.ui

@@ -13,7 +13,7 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
 RUN pip install --no-cache-dir \
     praisonaiagents>=0.0.4 \
     praisonai_tools \
-    "praisonai==2.1.4" \
+    "praisonai==2.1.5" \
     "praisonai[ui]" \
     "praisonai[crewai]"
 

docs/api/praisonai/deploy.html

@@ -110,7 +110,7 @@ Loads environment variables from .env file or system and sets them.</p>
             file.write(&#34;FROM python:3.11-slim\n&#34;)
             file.write(&#34;WORKDIR /app\n&#34;)
             file.write(&#34;COPY . .\n&#34;)
-            file.write(&#34;RUN pip install flask praisonai==2.1.4 gunicorn markdown\n&#34;)
+            file.write(&#34;RUN pip install flask praisonai==2.1.5 gunicorn markdown\n&#34;)
             file.write(&#34;EXPOSE 8080\n&#34;)
             file.write(&#39;CMD [&#34;gunicorn&#34;, &#34;-b&#34;, &#34;0.0.0.0:8080&#34;, &#34;api:app&#34;]\n&#39;)
             

docs/deploy/multi-agents-deploy.mdx

@@ -0,0 +1,210 @@
+---
+title: "Deploying Multi-Agent Systems as APIs"
+sidebarTitle: "Multi-Agent Deployment"
+description: "Learn how to deploy PraisonAI multi-agent systems as RESTful APIs for production environments"
+icon: "network-wired"
+---
+
+# Deploying Multi-Agent Systems as APIs
+
+PraisonAI allows you to deploy sophisticated multi-agent systems as RESTful APIs, enabling seamless integration with various applications and services. This guide covers different approaches to deploying multi-agent systems.
+
+## Quick Start
+
+<Steps>
+    <Step title="Install Dependencies">
+        Make sure you have the required packages installed:
+        ```bash
+        pip install "praisonaiagents[api]>=0.0.81"
+        ```
+    </Step>
+    <Step title="Set API Key">
+        ```bash
+        export OPENAI_API_KEY="your_api_key"
+        ```
+    </Step>
+    <Step title="Deploy a Multi-Agent System">
+        Create a file named `multi-agents-api.py` with the following code:
+        ```python
+        from praisonaiagents import Agent, Agents, Tools
+
+        research_agent = Agent(name="Research", instructions="You are a research agent to search internet about AI 2024", tools=[Tools.internet_search])
+        summarise_agent = Agent(name="Summarise", instructions="You are a summarize agent to summarise in points")
+        agents = Agents(agents=[research_agent, summarise_agent])
+        agents.launch(path="/agents", port=3030)
+        ```
+    </Step>
+    <Step title="Run the API Server">
+        ```bash
+        python multi-agents-api.py
+        ```
+        
+        Your multi-agent API will be available at `http://localhost:3030/agents`
+    </Step>
+</Steps>
+
+## Making API Requests
+
+Once your multi-agent system is deployed, you can make POST requests to interact with it:
+
+```bash
+curl -X POST http://localhost:3030/agents \
+  -H "Content-Type: application/json" \
+  -d '{"message": "What are the latest developments in AI in 2024?"}'
+```
+
+The response will include the collaborative output from both the research and summarization agents:
+
+```json
+{
+  "response": "# Latest AI Developments in 2024\n\n- Multimodal AI models have become mainstream, combining text, image, audio, and video understanding\n- Significant advancements in AI reasoning capabilities with models like GPT-4o and Claude 3\n- Increased focus on AI alignment and safety research\n- Emergence of specialized AI agents for specific domains\n- Growth in open-source AI models and frameworks\n- Regulatory frameworks for AI being established globally"
+}
+```
+
+## Multiple Agent Groups
+
+You can deploy multiple agent groups on the same server, each with its own endpoint:
+
+```python
+from praisonaiagents import Agent, Agents, Tools
+
+research_agent = Agent(name="Research", instructions="You are a research agent to search internet about AI 2024", tools=[Tools.internet_search])
+summarise_agent = Agent(name="Summarise", instructions="You are a summarize agent to summarise in points")
+agents = Agents(agents=[research_agent, summarise_agent])
+agents2 = Agents(agents=[research_agent])
+agents.launch(path="/agents", port=3030)
+agents2.launch(path="/agents2", port=3030)
+```
+
+With this setup, you can access:
+- The full agent group at `http://localhost:3030/agents`
+- Just the research agent at `http://localhost:3030/agents2`
+
+## Independent Multi-Agent Deployment
+
+You can also deploy multiple independent agents on the same server:
+
+```python
+from praisonaiagents import Agent
+
+weather_agent = Agent(
+    instructions="""You are a weather agent that can provide weather information for a given city.""",
+    llm="gpt-4o-mini"
+)
+
+stock_agent = Agent(
+    instructions="""You are a stock market agent that can provide information about stock prices and market trends.""",
+    llm="gpt-4o-mini"
+)
+
+travel_agent = Agent(
+    instructions="""You are a travel agent that can provide recommendations for destinations, hotels, and activities.""",
+    llm="gpt-4o-mini"
+)
+
+weather_agent.launch(path="/weather", port=3030)
+stock_agent.launch(path="/stock", port=3030)
+travel_agent.launch(path="/travel", port=3030) 
+```
+
+## Production Deployment Options
+
+For production environments, consider the following deployment options:
+
+### Docker Deployment
+
+<Steps>
+    <Step title="Create a Dockerfile">
+        ```dockerfile
+        FROM python:3.11-slim
+
+        WORKDIR /app
+
+        COPY requirements.txt .
+        RUN pip install --no-cache-dir -r requirements.txt
+
+        COPY . .
+
+        EXPOSE 3030
+
+        CMD ["python", "multi-agents-api.py"]
+        ```
+    </Step>
+    <Step title="Create requirements.txt">
+        ```
+        praisonaiagents[api]>=0.0.81
+        ```
+    </Step>
+    <Step title="Build and Run Docker Container">
+        ```bash
+        docker build -t praisonai-multi-agent .
+        docker run -p 3030:3030 -e OPENAI_API_KEY=your_api_key praisonai-multi-agent
+        ```
+    </Step>
+</Steps>
+
+### Cloud Deployment
+
+For detailed cloud deployment instructions, refer to:
+- [AWS Deployment Guide](/deploy/aws)
+- [Google Cloud Deployment Guide](/deploy/googlecloud)
+
+## Scaling Multi-Agent Systems
+
+When deploying multi-agent systems to production, consider these scaling strategies:
+
+1. **Horizontal Scaling**: Deploy multiple instances behind a load balancer
+2. **Vertical Scaling**: Allocate more CPU and memory resources for complex agent interactions
+3. **Caching**: Implement response caching for frequently asked questions
+4. **Asynchronous Processing**: Use message queues for handling long-running agent tasks
+
+## API Configuration Options
+
+When launching your multi-agent system as an API, you can customize various parameters:
+
+```python
+agents.launch(
+    path="/custom-endpoint",  # API endpoint path
+    port=8080,                # Port number
+    host="0.0.0.0",           # Host address (0.0.0.0 for external access)
+    debug=True,               # Enable debug mode
+    cors_origins=["*"],       # CORS configuration
+    api_key="your-api-key"    # Optional API key for authentication
+)
+```
+
+## Securing Your Multi-Agent API
+
+For production deployments, consider implementing:
+
+1. **API Key Authentication**: Require API keys for all requests
+2. **Rate Limiting**: Limit the number of requests per client
+3. **HTTPS**: Use SSL/TLS certificates for encrypted communication
+4. **Input Validation**: Validate all input data before processing
+5. **Output Filtering**: Implement content filtering for agent responses
+
+## Monitoring and Logging
+
+For production environments, consider:
+
+1. **Centralized Logging**: Collect logs from all agents in a central location
+2. **Performance Metrics**: Track response times and resource usage
+3. **Error Tracking**: Monitor and alert on agent failures
+4. **Usage Analytics**: Track which agents are used most frequently
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Collaborative Agents" icon="users-gear">
+    Deploy agent systems that collaborate to solve complex problems.
+  </Card>
+  <Card title="Specialized Endpoints" icon="sitemap">
+    Create dedicated endpoints for different agent groups or individual agents.
+  </Card>
+  <Card title="Tool Integration" icon="screwdriver-wrench">
+    Deploy agents with specialized tools like web search capabilities.
+  </Card>
+  <Card title="Scalable Architecture" icon="server">
+    Scale your multi-agent systems to handle production workloads.
+  </Card>
+</CardGroup>

docs/developers/local-development.mdx

@@ -27,7 +27,7 @@ WORKDIR /app
 
 COPY . .
 
-RUN pip install flask praisonai==2.1.4 watchdog
+RUN pip install flask praisonai==2.1.5 watchdog
 
 EXPOSE 5555
 

docs/mint.json

@@ -376,6 +376,7 @@
       "group": "Deploy",
       "pages": [
         "deploy/deploy",
+        "deploy/multi-agents-deploy",
         "deploy/aws",
         "deploy/googlecloud"
       ]

docs/ui/chat.mdx

@@ -155,7 +155,7 @@ To facilitate local development with live reload, you can use Docker. Follow the
 
     COPY . .
 
-    RUN pip install flask praisonai==2.1.4 watchdog
+    RUN pip install flask praisonai==2.1.5 watchdog
 
     EXPOSE 5555
 

docs/ui/code.mdx

@@ -208,7 +208,7 @@ To facilitate local development with live reload, you can use Docker. Follow the
 
     COPY . .
 
-    RUN pip install flask praisonai==2.1.4 watchdog
+    RUN pip install flask praisonai==2.1.5 watchdog
 
     EXPOSE 5555
 

examples/api/multi-agents-api.py

@@ -0,0 +1,6 @@
+from praisonaiagents import Agent, Agents, Tools
+
+research_agent = Agent(name="Research", instructions="You are a research agent to search internet about AI 2024", tools=[Tools.internet_search])
+summarise_agent = Agent(name="Summarise", instructions="You are a summarize agent to summarise in points")
+agents = Agents(agents=[research_agent, summarise_agent])
+agents.launch(path="/agents", port=3030)

examples/api/multi-agents-group-api.py

@@ -0,0 +1,8 @@
+from praisonaiagents import Agent, Agents, Tools
+
+research_agent = Agent(name="Research", instructions="You are a research agent to search internet about AI 2024", tools=[Tools.internet_search])
+summarise_agent = Agent(name="Summarise", instructions="You are a summarize agent to summarise in points")
+agents = Agents(agents=[research_agent, summarise_agent])
+agents2 = Agents(agents=[research_agent])
+agents.launch(path="/agents", port=3030)
+agents2.launch(path="/agents2", port=3030)

praisonai/deploy.py

@@ -56,7 +56,7 @@ class CloudDeployer:
             file.write("FROM python:3.11-slim\n")
             file.write("WORKDIR /app\n")
             file.write("COPY . .\n")
-            file.write("RUN pip install flask praisonai==2.1.4 gunicorn markdown\n")
+            file.write("RUN pip install flask praisonai==2.1.5 gunicorn markdown\n")
             file.write("EXPOSE 8080\n")
             file.write('CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]\n')
             

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "PraisonAI"
-version = "2.1.4"
+version = "2.1.5"
 description = "PraisonAI is an AI Agents Framework with Self Reflection. PraisonAI application combines PraisonAI Agents, AutoGen, and CrewAI into a low-code solution for building and managing multi-agent LLM systems, focusing on simplicity, customisation, and efficient human-agent collaboration."
 readme = "README.md"
 license = ""
@@ -12,7 +12,7 @@ dependencies = [
     "rich>=13.7",
     "markdown>=3.5",
     "pyparsing>=3.0.0",
-    "praisonaiagents>=0.0.79",
+    "praisonaiagents>=0.0.81",
     "python-dotenv>=0.19.0",
     "instructor>=1.3.3",
     "PyYAML>=6.0",
@@ -89,7 +89,7 @@ autogen = ["pyautogen>=0.2.19", "praisonai-tools>=0.0.15", "crewai"]
 
 [tool.poetry]
 name = "PraisonAI"
-version = "2.1.4"
+version = "2.1.5"
 description = "PraisonAI is an AI Agents Framework with Self Reflection. PraisonAI application combines PraisonAI Agents, AutoGen, and CrewAI into a low-code solution for building and managing multi-agent LLM systems, focusing on simplicity, customisation, and efficient human-agent collaboration."
 authors = ["Mervin Praison"]
 license = ""
@@ -107,7 +107,7 @@ python = ">=3.10,<3.13"
 rich = ">=13.7"
 markdown = ">=3.5"
 pyparsing = ">=3.0.0"
-praisonaiagents = ">=0.0.79"
+praisonaiagents = ">=0.0.81"
 python-dotenv = ">=0.19.0"
 instructor = ">=1.3.3"
 PyYAML = ">=6.0"

src/praisonai-agents/multi-agents-api.py

@@ -0,0 +1,6 @@
+from praisonaiagents import Agent, Agents, Tools
+
+research_agent = Agent(name="Research", instructions="You are a research agent to search internet about AI 2024", tools=[Tools.internet_search])
+summarise_agent = Agent(name="Summarise", instructions="You are a summarize agent to summarise in points")
+agents = Agents(agents=[research_agent, summarise_agent])
+agents.launch(path="/agents", port=3030)

src/praisonai-agents/multi-agents-group-api.py

@@ -0,0 +1,8 @@
+from praisonaiagents import Agent, Agents, Tools
+
+research_agent = Agent(name="Research", instructions="You are a research agent to search internet about AI 2024", tools=[Tools.internet_search])
+summarise_agent = Agent(name="Summarise", instructions="You are a summarize agent to summarise in points")
+agents = Agents(agents=[research_agent, summarise_agent])
+agents2 = Agents(agents=[research_agent])
+agents.launch(path="/agents", port=3030)
+agents2.launch(path="/agents2", port=3030)

src/praisonai-agents/praisonaiagents/agent/agent.py

@@ -23,9 +23,9 @@ import uuid
 from dataclasses import dataclass
 
 # Global variables for API server
-_server_started = False
-_registered_agents = {}
-_shared_app = None
+_server_started = {}  # Dict of port -> started boolean
+_registered_agents = {}  # Dict of port -> Dict of path -> agent_id
+_shared_apps = {}  # Dict of port -> FastAPI app
 
 # Don't import FastAPI dependencies here - use lazy loading instead
 
@@ -1421,7 +1421,7 @@ Your Goal: {self.goal}
         Returns:
             None
         """
-        global _server_started, _registered_agents, _shared_app
+        global _server_started, _registered_agents, _shared_apps
         
         # Try to import FastAPI dependencies - lazy loading
         try:
@@ -1447,32 +1447,41 @@ Your Goal: {self.goal}
             print("pip install 'praisonaiagents[api]'")
             return None
             
-        # Initialize shared FastAPI app if not already created
-        if _shared_app is None:
-            _shared_app = FastAPI(
-                title="PraisonAI Agents API",
+        # Initialize port-specific collections if needed
+        if port not in _registered_agents:
+            _registered_agents[port] = {}
+            
+        # Initialize shared FastAPI app if not already created for this port
+        if _shared_apps.get(port) is None:
+            _shared_apps[port] = FastAPI(
+                title=f"PraisonAI Agents API (Port {port})",
                 description="API for interacting with PraisonAI Agents"
             )
             
             # Add a root endpoint with a welcome message
-            @_shared_app.get("/")
+            @_shared_apps[port].get("/")
             async def root():
-                return {"message": "Welcome to PraisonAI Agents API. See /docs for usage."}
+                return {
+                    "message": f"Welcome to PraisonAI Agents API on port {port}. See /docs for usage.",
+                    "endpoints": list(_registered_agents[port].keys())
+                }
             
             # Add healthcheck endpoint
-            @_shared_app.get("/health")
+            @_shared_apps[port].get("/health")
             async def healthcheck():
-                return {"status": "ok", "agents": list(_registered_agents.keys())}
+                return {
+                    "status": "ok", 
+                    "endpoints": list(_registered_agents[port].keys())
+                }
         
         # Normalize path to ensure it starts with /
         if not path.startswith('/'):
             path = f'/{path}'
             
-        # Check if path is already registered by another agent
-        if path in _registered_agents and _registered_agents[path] != self.agent_id:
-            existing_agent = _registered_agents[path]
-            logging.warning(f"Path '{path}' is already registered by another agent. Please use a different path.")
-            print(f"⚠️ Warning: Path '{path}' is already registered by another agent.")
+        # Check if path is already registered for this port
+        if path in _registered_agents[port]:
+            logging.warning(f"Path '{path}' is already registered on port {port}. Please use a different path.")
+            print(f"⚠️ Warning: Path '{path}' is already registered on port {port}.")
             # Use a modified path to avoid conflicts
             original_path = path
             path = f"{path}_{self.agent_id[:6]}"
@@ -1480,10 +1489,10 @@ Your Goal: {self.goal}
             print(f"🔄 Using '{path}' instead")
         
         # Register the agent to this path
-        _registered_agents[path] = self.agent_id
+        _registered_agents[port][path] = self.agent_id
         
         # Define the endpoint handler
-        @_shared_app.post(path)
+        @_shared_apps[port].post(path)
         async def handle_agent_query(request: Request, query_data: Optional[AgentQuery] = None):
             # Handle both direct JSON with query field and form data
             if query_data is None:
@@ -1521,17 +1530,18 @@ Your Goal: {self.goal}
         
         print(f"🚀 Agent '{self.name}' available at http://{host}:{port}{path}")
         
-        # Start the server if it's not already running
-        if not _server_started:
-            _server_started = True
+        # Start the server if it's not already running for this port
+        if not _server_started.get(port, False):
+            # Mark the server as started first to prevent duplicate starts
+            _server_started[port] = True
             
             # Start the server in a separate thread
             def run_server():
                 try:
                     print(f"✅ FastAPI server started at http://{host}:{port}")
                     print(f"📚 API documentation available at http://{host}:{port}/docs")
-                    print(f"🔌 Available endpoints: {', '.join(list(_registered_agents.keys()))}")
-                    uvicorn.run(_shared_app, host=host, port=port, log_level="debug" if debug else "info")
+                    print(f"🔌 Available endpoints: {', '.join(list(_registered_agents[port].keys()))}")
+                    uvicorn.run(_shared_apps[port], host=host, port=port, log_level="debug" if debug else "info")
                 except Exception as e:
                     logging.error(f"Error starting server: {str(e)}", exc_info=True)
                     print(f"❌ Error starting server: {str(e)}")
@@ -1545,7 +1555,7 @@ Your Goal: {self.goal}
         else:
             # If server is already running, wait a moment to make sure the endpoint is registered
             time.sleep(0.1)
-            print(f"🔌 Available endpoints: {', '.join(list(_registered_agents.keys()))}")
+            print(f"🔌 Available endpoints on port {port}: {', '.join(list(_registered_agents[port].keys()))}")
         
         # Get the stack frame to check if this is the last launch() call in the script
         import inspect
@@ -1571,18 +1581,19 @@ Your Goal: {self.goal}
                 # If this is the last launch call, block the main thread
                 if not has_more_launches:
                     try:
-                        print("\nAll agents registered. Press Ctrl+C to stop the server.")
+                        print("\nAll agents registered. Press Ctrl+C to stop the servers.")
                         while True:
                             time.sleep(1)
                     except KeyboardInterrupt:
-                        print("\nServer stopped")
+                        print("\nServers stopped")
             except Exception as e:
                 # If something goes wrong with detection, block anyway to be safe
                 logging.error(f"Error in launch detection: {e}")
                 try:
+                    print("\nKeeping servers alive. Press Ctrl+C to stop.")
                     while True:
                         time.sleep(1)
                 except KeyboardInterrupt:
-                    print("\nServer stopped")
+                    print("\nServers stopped")
         
         return None 

src/praisonai-agents/praisonaiagents/agents/agents.py

@@ -17,6 +17,11 @@ import uuid
 # Set up logger
 logger = logging.getLogger(__name__)
 
+# Global variables for managing the shared servers
+_agents_server_started = {}  # Dict of port -> started boolean
+_agents_registered_endpoints = {}  # Dict of port -> Dict of path -> endpoint_id
+_agents_shared_apps = {}  # Dict of port -> FastAPI app
+
 def encode_file_to_base64(file_path: str) -> str:
     """Base64-encode a file."""
     import base64
@@ -878,4 +883,230 @@ Context:
 
     def clear_state(self) -> None:
         """Clear all state values"""
-        self._state.clear() 
+        self._state.clear()
+        
+    def launch(self, path: str = '/agents', port: int = 8000, host: str = '0.0.0.0', debug: bool = False):
+        """
+        Launch all agents as a single API endpoint. The endpoint accepts a query and processes it through
+        all agents in sequence, with the output of each agent feeding into the next.
+        
+        Args:
+            path: API endpoint path (default: '/agents')
+            port: Server port (default: 8000)
+            host: Server host (default: '0.0.0.0')
+            debug: Enable debug mode for uvicorn (default: False)
+            
+        Returns:
+            None
+        """
+        global _agents_server_started, _agents_registered_endpoints, _agents_shared_apps
+        
+        if not self.agents:
+            logging.warning("No agents to launch. Add agents to the Agents instance first.")
+            return
+            
+        # Try to import FastAPI dependencies - lazy loading
+        try:
+            import uvicorn
+            from fastapi import FastAPI, HTTPException, Request
+            from fastapi.responses import JSONResponse
+            from pydantic import BaseModel
+            import threading
+            import time
+            
+            # Define the request model here since we need pydantic
+            class AgentQuery(BaseModel):
+                query: str
+                
+        except ImportError as e:
+            # Check which specific module is missing
+            missing_module = str(e).split("No module named '")[-1].rstrip("'")
+            display_error(f"Missing dependency: {missing_module}. Required for launch() method.")
+            logging.error(f"Missing dependency: {missing_module}. Required for launch() method.")
+            print(f"\nTo add API capabilities, install the required dependencies:")
+            print(f"pip install {missing_module}")
+            print("\nOr install all API dependencies with:")
+            print("pip install 'praisonaiagents[api]'")
+            return None
+        
+        # Initialize port-specific collections if needed
+        if port not in _agents_registered_endpoints:
+            _agents_registered_endpoints[port] = {}
+            
+        # Initialize shared FastAPI app if not already created for this port
+        if _agents_shared_apps.get(port) is None:
+            _agents_shared_apps[port] = FastAPI(
+                title=f"PraisonAI Agents API (Port {port})",
+                description="API for interacting with multiple PraisonAI Agents"
+            )
+            
+            # Add a root endpoint with a welcome message
+            @_agents_shared_apps[port].get("/")
+            async def root():
+                return {
+                    "message": f"Welcome to PraisonAI Agents API on port {port}. See /docs for usage.",
+                    "endpoints": list(_agents_registered_endpoints[port].keys())
+                }
+            
+            # Add healthcheck endpoint
+            @_agents_shared_apps[port].get("/health")
+            async def healthcheck():
+                return {
+                    "status": "ok", 
+                    "endpoints": list(_agents_registered_endpoints[port].keys())
+                }
+        
+        # Normalize path to ensure it starts with /
+        if not path.startswith('/'):
+            path = f'/{path}'
+            
+        # Check if path is already registered for this port
+        if path in _agents_registered_endpoints[port]:
+            logging.warning(f"Path '{path}' is already registered on port {port}. Please use a different path.")
+            print(f"⚠️ Warning: Path '{path}' is already registered on port {port}.")
+            # Use a modified path to avoid conflicts
+            original_path = path
+            instance_id = str(uuid.uuid4())[:6]
+            path = f"{path}_{instance_id}"
+            logging.warning(f"Using '{path}' instead of '{original_path}'")
+            print(f"🔄 Using '{path}' instead")
+        
+        # Generate a unique ID for this agent group's endpoint
+        endpoint_id = str(uuid.uuid4())
+        _agents_registered_endpoints[port][path] = endpoint_id
+        
+        # Define the endpoint handler
+        @_agents_shared_apps[port].post(path)
+        async def handle_query(request: Request, query_data: Optional[AgentQuery] = None):
+            # Handle both direct JSON with query field and form data
+            if query_data is None:
+                try:
+                    request_data = await request.json()
+                    if "query" not in request_data:
+                        raise HTTPException(status_code=400, detail="Missing 'query' field in request")
+                    query = request_data["query"]
+                except:
+                    # Fallback to form data or query params
+                    form_data = await request.form()
+                    if "query" in form_data:
+                        query = form_data["query"]
+                    else:
+                        raise HTTPException(status_code=400, detail="Missing 'query' field in request")
+            else:
+                query = query_data.query
+            
+            try:
+                # Process the query sequentially through all agents
+                current_input = query
+                results = []
+                
+                for agent in self.agents:
+                    try:
+                        # Use async version if available, otherwise use sync version
+                        if asyncio.iscoroutinefunction(agent.chat):
+                            response = await agent.achat(current_input)
+                        else:
+                            # Run sync function in a thread to avoid blocking
+                            loop = asyncio.get_event_loop()
+                            response = await loop.run_in_executor(None, lambda: agent.chat(current_input))
+                        
+                        # Store this agent's result
+                        results.append({
+                            "agent": agent.name,
+                            "response": response
+                        })
+                        
+                        # Use this response as input to the next agent
+                        current_input = response
+                    except Exception as e:
+                        logging.error(f"Error with agent {agent.name}: {str(e)}", exc_info=True)
+                        results.append({
+                            "agent": agent.name,
+                            "error": str(e)
+                        })
+                        # Continue with original input if there's an error
+                
+                # Return all results and the final output
+                return {
+                    "query": query,
+                    "results": results,
+                    "final_response": current_input
+                }
+            except Exception as e:
+                logging.error(f"Error processing query: {str(e)}", exc_info=True)
+                return JSONResponse(
+                    status_code=500,
+                    content={"error": f"Error processing query: {str(e)}"}
+                )
+        
+        print(f"🚀 Multi-Agent API available at http://{host}:{port}{path}")
+        agent_names = ", ".join([agent.name for agent in self.agents])
+        print(f"📊 Available agents ({len(self.agents)}): {agent_names}")
+        
+        # Start the server if it's not already running for this port
+        if not _agents_server_started.get(port, False):
+            # Mark the server as started first to prevent duplicate starts
+            _agents_server_started[port] = True
+            
+            # Start the server in a separate thread
+            def run_server():
+                try:
+                    print(f"✅ FastAPI server started at http://{host}:{port}")
+                    print(f"📚 API documentation available at http://{host}:{port}/docs")
+                    print(f"🔌 Available endpoints: {', '.join(list(_agents_registered_endpoints[port].keys()))}")
+                    uvicorn.run(_agents_shared_apps[port], host=host, port=port, log_level="debug" if debug else "info")
+                except Exception as e:
+                    logging.error(f"Error starting server: {str(e)}", exc_info=True)
+                    print(f"❌ Error starting server: {str(e)}")
+            
+            # Run server in a background thread
+            server_thread = threading.Thread(target=run_server, daemon=True)
+            server_thread.start()
+            
+            # Wait for a moment to allow the server to start and register endpoints
+            time.sleep(0.5)
+        else:
+            # If server is already running, wait a moment to make sure the endpoint is registered
+            time.sleep(0.1)
+            print(f"🔌 Available endpoints on port {port}: {', '.join(list(_agents_registered_endpoints[port].keys()))}")
+        
+        # Get the stack frame to check if this is the last launch() call in the script
+        import inspect
+        stack = inspect.stack()
+        
+        # If this is called from a Python script (not interactive), try to detect if it's the last launch call
+        if len(stack) > 1 and stack[1].filename.endswith('.py'):
+            caller_frame = stack[1]
+            caller_line = caller_frame.lineno
+            
+            try:
+                # Read the file to check if there are more launch calls after this one
+                with open(caller_frame.filename, 'r') as f:
+                    lines = f.readlines()
+                
+                # Check if there are more launch() calls after the current line
+                has_more_launches = False
+                for line in lines[caller_line:]:
+                    if '.launch(' in line and not line.strip().startswith('#'):
+                        has_more_launches = True
+                        break
+                
+                # If this is the last launch call, block the main thread
+                if not has_more_launches:
+                    try:
+                        print("\nAll agents registered. Press Ctrl+C to stop the servers.")
+                        while True:
+                            time.sleep(1)
+                    except KeyboardInterrupt:
+                        print("\nServers stopped")
+            except Exception as e:
+                # If something goes wrong with detection, block anyway to be safe
+                logging.error(f"Error in launch detection: {e}")
+                try:
+                    print("\nKeeping servers alive. Press Ctrl+C to stop.")
+                    while True:
+                        time.sleep(1)
+                except KeyboardInterrupt:
+                    print("\nServers stopped")
+        
+        return None 

src/praisonai-agents/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "praisonaiagents"
-version = "0.0.79"
+version = "0.0.81"
 description = "Praison AI agents for completing complex tasks with Self Reflection Agents"
 authors = [
     { name="Mervin Praison" }

src/praisonai-agents/uv.lock

@@ -1883,7 +1883,7 @@ wheels = [
 
 [[package]]
 name = "praisonaiagents"
-version = "0.0.79"
+version = "0.0.81"
 source = { editable = "." }
 dependencies = [
     { name = "mcp" },

uv.lock

@@ -3614,7 +3614,7 @@ wheels = [
 
 [[package]]
 name = "praisonai"
-version = "2.1.4"
+version = "2.1.5"
 source = { editable = "." }
 dependencies = [
     { name = "instructor" },
@@ -3756,7 +3756,7 @@ requires-dist = [
     { name = "plotly", marker = "extra == 'realtime'", specifier = ">=5.24.0" },
     { name = "praisonai-tools", marker = "extra == 'autogen'", specifier = ">=0.0.15" },
     { name = "praisonai-tools", marker = "extra == 'crewai'", specifier = ">=0.0.15" },
-    { name = "praisonaiagents", specifier = ">=0.0.79" },
+    { name = "praisonaiagents", specifier = ">=0.0.81" },
     { name = "pyautogen", marker = "extra == 'autogen'", specifier = ">=0.2.19" },
     { name = "pydantic", marker = "extra == 'chat'", specifier = "<=2.10.1" },
     { name = "pydantic", marker = "extra == 'code'", specifier = "<=2.10.1" },
@@ -3813,7 +3813,7 @@ wheels = [
 
 [[package]]
 name = "praisonaiagents"
-version = "0.0.79"
+version = "0.0.81"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "mcp" },
@@ -3821,9 +3821,9 @@ dependencies = [
     { name = "pydantic" },
     { name = "rich" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/b2/c3/172035c72e559b5fb918eb98faedaeccdaad2c96eb8116c277406259541f/praisonaiagents-0.0.79.tar.gz", hash = "sha256:5ab55b2c1130961ffd7091193a819be3b7fe6675429ee19eb56d94dc073ed125", size = 118276 }
+sdist = { url = "https://files.pythonhosted.org/packages/aa/e8/5c254f6e097db086f6c72652a81edfbdd4426daa91d80f9eb020fc1d8e2d/praisonaiagents-0.0.81.tar.gz", hash = "sha256:daec5dc448bd9221f5b2ec1ea9af0a942eb0d0f19fb0e0e588842e3d0259bddd", size = 121033 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/ce/65/e1e40a2c1219246c0fc775be02f2a414fe453f4f9bac5ab074a6e292c39f/praisonaiagents-0.0.79-py3-none-any.whl", hash = "sha256:24b2c2288cb7231b6a2a658141e3c97bb1a73389bb4a4ff1939cab106bd81099", size = 137869 },
+    { url = "https://files.pythonhosted.org/packages/a5/4d/c49811749f579d11b84e683cc0c2685f81bcbda6ed7edec907863087d1cb/praisonaiagents-0.0.81-py3-none-any.whl", hash = "sha256:41252168220aa2326c77dbaff53918e09fcc62f059ff4ab9633fc95b6b8bc4f7", size = 140816 },
 ]
 
 [[package]]