Docker support for Archon V4

.dockerignore

@@ -0,0 +1,38 @@
+# Ignore specified folders
+iterations/
+venv/
+.langgraph_api/
+.github/
+__pycache__/
+.env
+
+# Git related
+.git/
+.gitignore
+.gitattributes
+
+# Python cache
+*.pyc
+*.pyo
+*.pyd
+.Python
+*.so
+.pytest_cache/
+
+# Environment files
+.env.local
+.env.development.local
+.env.test.local
+.env.production.local
+
+# Logs
+*.log
+
+# IDE specific files
+.idea/
+.vscode/
+*.swp
+*.swo
+
+# Keep the example env file for reference
+!.env.example

Dockerfile

@@ -0,0 +1,28 @@
+FROM python:3.12-slim
+
+WORKDIR /app
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    build-essential \
+    && rm -rf /var/lib/apt/lists/*
+
+# Copy requirements first for better caching
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy the rest of the application
+COPY . .
+
+# Set environment variables
+ENV PYTHONUNBUFFERED=1
+ENV PYTHONPATH=/app
+
+# Expose port for Streamlit
+EXPOSE 8501
+
+# Expose port for the Archon Service (started within Streamlit)
+EXPOSE 8100
+
+# Set the entrypoint to run Streamlit directly
+CMD ["streamlit", "run", "streamlit_ui.py", "--server.port=8501", "--server.address=0.0.0.0"]

README.md

@@ -39,12 +39,35 @@ Archon demonstrates three key principles in modern AI development:
 Since V4 is the current version of Archon, all the code for V4 is in both the main directory and `archon/iterations/v4-streamlit-ui-overhaul` directory.
 
 ### Prerequisites
+- Docker (optional but preferred)
 - Python 3.11+
 - Supabase account (for vector database)
 - OpenAI/OpenRouter API key or Ollama for local LLMs
 
 ### Installation
 
+#### Option 1: Docker (Recommended)
+1. Clone the repository:
+```bash
+git clone https://github.com/coleam00/archon.git
+cd archon
+```
+
+2. Run the Docker setup script:
+```bash
+# This will build both containers and start Archon
+python run_docker.py
+```
+
+3. Access the Streamlit UI at http://localhost:8501.
+
+> **Note:** `run_docker.py` will automatically:
+> - Build the MCP server container
+> - Build the main Archon container
+> - Run Archon with the appropriate port mappings
+> - Use environment variables from `.env` file if it exists
+
+#### Option 2: Local Python Installation
 1. Clone the repository:
 ```bash
 git clone https://github.com/coleam00/archon.git
@@ -58,20 +81,22 @@ source venv/bin/activate  # On Windows: venv\Scripts\activate
 pip install -r requirements.txt
 ```
 
-### Quick Start
+3. Start the Streamlit UI:
-
-1. Start the Streamlit UI:
 ```bash
 streamlit run streamlit_ui.py
 ```
 
-2. Follow the guided setup process in the Intro section of the Streamlit UI:
+4. Access the Streamlit UI at http://localhost:8501.
-   - **Environment**: Configure your API keys and model settings
+
-   - **Database**: Set up your Supabase vector database
+### Setup Process
-   - **Documentation**: Crawl and index the Pydantic AI documentation
+
-   - **Agent Service**: Start the agent service for generating agents
+After installation, follow the guided setup process in the Intro section of the Streamlit UI:
-   - **Chat**: Interact with Archon to create AI agents
+- **Environment**: Configure your API keys and model settings
-   - **MCP** (optional): Configure integration with AI IDEs
+- **Database**: Set up your Supabase vector database
+- **Documentation**: Crawl and index the Pydantic AI documentation
+- **Agent Service**: Start the agent service for generating agents
+- **Chat**: Interact with Archon to create AI agents
+- **MCP** (optional): Configure integration with AI IDEs
 
 The Streamlit interface will guide you through each step with clear instructions and interactive elements.
 There are a good amount of steps for the setup but it goes quick!
@@ -99,6 +124,7 @@ There are a good amount of steps for the setup but it goes quick!
 - [Learn more about V3](iterations/v3-mcp-support/README.md)
 
 ### V4: Current - Streamlit UI Overhaul
+- Docker support
 - Comprehensive Streamlit interface for managing all aspects of Archon
 - Guided setup process with interactive tabs
 - Environment variable management through the UI
@@ -114,8 +140,8 @@ There are a good amount of steps for the setup but it goes quick!
 - V8: Autonomous Framework Learning - Self-updating framework adapters
 
 ### Future Integrations
-- Docker
 - LangSmith
+- MCP marketplace
 - Other frameworks besides Pydantic AI
 - Other vector databases besides Supabase
 
@@ -124,8 +150,13 @@ There are a good amount of steps for the setup but it goes quick!
 ### Core Files
 - `streamlit_ui.py`: Comprehensive web interface for managing all aspects of Archon
 - `graph_service.py`: FastAPI service that handles the agentic workflow
-- `mcp_server.py`: MCP server script for AI IDE integration
+- `run_docker.py`: Script to build and run Archon Docker containers
-- `requirements.txt`: Project dependencies
+- `Dockerfile`: Container definition for the main Archon application
+
+### MCP Integration
+- `mcp/`: Model Context Protocol server implementation
+  - `mcp_server.py`: MCP server script for AI IDE integration
+  - `Dockerfile`: Container definition for the MCP server
 
 ### Archon Package
 - `archon/`: Core agent and workflow implementation
@@ -139,7 +170,29 @@ There are a good amount of steps for the setup but it goes quick!
   - `site_pages.sql`: Database setup commands
   - `env_vars.json`: Environment variables defined in the UI are stored here (included in .gitignore, file is created automatically)
 
-### Database Setup
+## Deployment Options
+- **Docker Containers**: Run Archon in isolated containers with all dependencies included
+  - Main container: Runs the Streamlit UI and graph service
+  - MCP container: Provides MCP server functionality for AI IDEs
+- **Local Python**: Run directly on your system with a Python virtual environment
+
+### Docker Architecture
+The Docker implementation consists of two containers:
+1. **Main Archon Container**:
+   - Runs the Streamlit UI on port 8501
+   - Hosts the Graph Service on port 8100
+   - Built from the root Dockerfile
+   - Handles all agent functionality and user interactions
+
+2. **MCP Container**:
+   - Implements the Model Context Protocol for AI IDE integration
+   - Built from the mcp/Dockerfile
+   - Communicates with the main container's Graph Service
+   - Provides a standardized interface for AI IDEs like Windsurf, Cursor, and Cline
+
+When running with Docker, the `run_docker.py` script automates building and starting both containers with the proper configuration.
+
+## Database Setup
 
 The Supabase database uses the following schema:
 

archon/archon_graph.py

@@ -53,8 +53,10 @@ openai_client=None
 
 if is_ollama:
     openai_client = AsyncOpenAI(base_url=base_url,api_key=api_key)
-else:
+elif get_env_var("OPENAI_API_KEY"):
     openai_client = AsyncOpenAI(api_key=get_env_var("OPENAI_API_KEY"))
+else:
+    openai_client = None
 
 if get_env_var("SUPABASE_URL"):
     supabase: Client = Client(

graph_service.py

@@ -66,4 +66,4 @@ async def invoke_agent(request: InvokeRequest):
 
 if __name__ == "__main__":
     import uvicorn
-    uvicorn.run(app, host="127.0.0.1", port=8100)
+    uvicorn.run(app, host="0.0.0.0", port=8100)

iterations/v4-streamlit-ui-overhaul/.dockerignore

@@ -0,0 +1,38 @@
+# Ignore specified folders
+iterations/
+venv/
+.langgraph_api/
+.github/
+__pycache__/
+.env
+
+# Git related
+.git/
+.gitignore
+.gitattributes
+
+# Python cache
+*.pyc
+*.pyo
+*.pyd
+.Python
+*.so
+.pytest_cache/
+
+# Environment files
+.env.local
+.env.development.local
+.env.test.local
+.env.production.local
+
+# Logs
+*.log
+
+# IDE specific files
+.idea/
+.vscode/
+*.swp
+*.swo
+
+# Keep the example env file for reference
+!.env.example

iterations/v4-streamlit-ui-overhaul/.gitattributes

@@ -1,2 +0,0 @@
-# Auto detect text files and perform LF normalization
-* text=auto

iterations/v4-streamlit-ui-overhaul/Dockerfile

@@ -0,0 +1,28 @@
+FROM python:3.12-slim
+
+WORKDIR /app
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    build-essential \
+    && rm -rf /var/lib/apt/lists/*
+
+# Copy requirements first for better caching
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy the rest of the application
+COPY . .
+
+# Set environment variables
+ENV PYTHONUNBUFFERED=1
+ENV PYTHONPATH=/app
+
+# Expose port for Streamlit
+EXPOSE 8501
+
+# Expose port for the Archon Service (started within Streamlit)
+EXPOSE 8100
+
+# Set the entrypoint to run Streamlit directly
+CMD ["streamlit", "run", "streamlit_ui.py", "--server.port=8501", "--server.address=0.0.0.0"]

iterations/v4-streamlit-ui-overhaul/LICENSE

@@ -1,21 +0,0 @@
-MIT License
-
-Copyright (c) 2025 oTTomator and Archon contributors
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.

iterations/v4-streamlit-ui-overhaul/README.md

@@ -1,6 +1,6 @@
-# Archon V4 - Streamlit UI Overhaul
+# Archon V4 - Streamlit UI Overhaul (and Docker Support)
 
-This is the fourth iteration of the Archon project, building upon V3 by adding a comprehensive Streamlit UI for managing all aspects of Archon. The system retains the core LangGraph workflow and MCP support from V3, but now provides a unified interface for environment configuration, database setup, documentation crawling, agent service management, and MCP integration.
+This is the fourth iteration of the Archon project, building upon V3 by adding a comprehensive Streamlit UI for managing all aspects of Archon and Docker support. The system retains the core LangGraph workflow and MCP support from V3, but now provides a unified interface for environment configuration, database setup, documentation crawling, agent service management, and MCP integration.
 
 What makes V4 special is its guided setup process that walks users through each step of configuring and running Archon. The Streamlit UI eliminates the need for manual configuration of environment variables, database setup, and service management, making Archon much more accessible to users without extensive technical knowledge.
 
@@ -8,36 +8,53 @@ The core remains an intelligent documentation crawler and RAG (Retrieval-Augment
 
 This version continues to support both local LLMs with Ollama and cloud-based LLMs through OpenAI/OpenRouter.
 
-## Features
+## Key Features
 
-- Comprehensive Streamlit UI with multiple tabs for different functions
+- **Comprehensive Streamlit UI**: Unified interface for all Archon functionality
-- Guided setup process with interactive instructions
+- **Docker Support**: Containerized deployment with automated build and run scripts
-- Environment variable management through the UI
+- **Guided Setup Process**: Step-by-step instructions for configuration
-- Database setup and configuration simplified
+- **Environment Variable Management**: Configure all settings through the UI
-- Documentation crawling with progress tracking
+- **Database Setup**: Automated creation of Supabase tables and indexes
-- Agent service control and monitoring
+- **Documentation Crawler**: Fetch and process documentation for RAG
-- MCP configuration through the UI
+- **Agent Service Management**: Start/stop the agent service from the UI
-- Multi-agent workflow using LangGraph
+- **MCP Integration**: Configure and manage MCP for AI IDE integration
-- Specialized agents for reasoning, routing, and coding
+- **Multiple LLM Support**: OpenAI, OpenRouter, and local Ollama models
-- Pydantic AI documentation crawling and chunking
+- **Multi-agent workflow using LangGraph**: Manage multiple agents simultaneously
-- Vector database storage with Supabase
-- Semantic search using OpenAI embeddings
-- RAG-based question answering
-- Support for code block preservation
-- MCP server support for AI IDE integration
 
 ## Prerequisites
-
+- Docker (optional but preferred)
 - Python 3.11+
 - Supabase account (for vector database)
 - OpenAI/OpenRouter API key or Ollama for local LLMs
 
 ## Installation
 
+### Option 1: Docker (Recommended)
 1. Clone the repository:
 ```bash
 git clone https://github.com/coleam00/archon.git
-cd archon
+cd archon/iterations/v4-streamlit-ui-overhaul
+```
+
+2. Run the Docker setup script:
+```bash
+# This will build both containers and start Archon
+python run_docker.py
+```
+
+3. Access the Streamlit UI at http://localhost:8501.
+
+> **Note:** `run_docker.py` will automatically:
+> - Build the MCP server container
+> - Build the main Archon container
+> - Run Archon with the appropriate port mappings
+> - Use environment variables from `.env` file if it exists
+
+### Option 2: Local Python Installation
+1. Clone the repository:
+```bash
+git clone https://github.com/coleam00/archon.git
+cd archon/iterations/v4-streamlit-ui-overhaul
 ```
 
 2. Install dependencies:
@@ -47,18 +64,18 @@ source venv/bin/activate  # On Windows: venv\Scripts\activate
 pip install -r requirements.txt
 ```
 
-## Usage
+3. Start the Streamlit UI:
-
-Start the Streamlit UI:
-
 ```bash
 streamlit run streamlit_ui.py
 ```
 
-The interface will be available at `http://localhost:8501`
+4. Access the Streamlit UI at http://localhost:8501.
 
 ### Streamlit UI Tabs
 
+The Streamlit interface will guide you through each step with clear instructions and interactive elements.
+There are a good amount of steps for the setup but it goes quick!
+
 The Streamlit UI provides the following tabs:
 
 1. **Intro**: Overview and guided setup process
@@ -117,13 +134,18 @@ The MCP tab simplifies the process of configuring MCP for AI IDEs:
 - Copy configuration to clipboard
 - Get step-by-step instructions for your specific IDE
 
-## Project Structure
+## Architecture
 
 ### Core Files
 - `streamlit_ui.py`: Comprehensive web interface for managing all aspects of Archon
 - `graph_service.py`: FastAPI service that handles the agentic workflow
-- `mcp_server.py`: MCP server script for AI IDE integration
+- `run_docker.py`: Script to build and run Archon Docker containers
-- `requirements.txt`: Project dependencies
+- `Dockerfile`: Container definition for the main Archon application
+
+### MCP Integration
+- `mcp/`: Model Context Protocol server implementation
+  - `mcp_server.py`: MCP server script for AI IDE integration
+  - `Dockerfile`: Container definition for the MCP server
 
 ### Archon Package
 - `archon/`: Core agent and workflow implementation
@@ -135,7 +157,29 @@ The MCP tab simplifies the process of configuring MCP for AI IDEs:
 - `utils/`: Utility functions and database setup
   - `utils.py`: Shared utility functions
   - `site_pages.sql`: Database setup commands
-  - `env_vars.json`: Environment variables defined in the UI
+  - `env_vars.json`: Environment variables defined in the UI are stored here (included in .gitignore, file is created automatically)
+
+## Deployment Options
+- **Docker Containers**: Run Archon in isolated containers with all dependencies included
+  - Main container: Runs the Streamlit UI and graph service
+  - MCP container: Provides MCP server functionality for AI IDEs
+- **Local Python**: Run directly on your system with a Python virtual environment
+
+### Docker Architecture
+The Docker implementation consists of two containers:
+1. **Main Archon Container**:
+   - Runs the Streamlit UI on port 8501
+   - Hosts the Graph Service on port 8100
+   - Built from the root Dockerfile
+   - Handles all agent functionality and user interactions
+
+2. **MCP Container**:
+   - Implements the Model Context Protocol for AI IDE integration
+   - Built from the mcp/Dockerfile
+   - Communicates with the main container's Graph Service
+   - Provides a standardized interface for AI IDEs like Windsurf, Cursor, and Cline
+
+When running with Docker, the `run_docker.py` script automates building and starting both containers with the proper configuration.
 
 ## Contributing
 

iterations/v4-streamlit-ui-overhaul/archon/archon_graph.py

@@ -53,8 +53,10 @@ openai_client=None
 
 if is_ollama:
     openai_client = AsyncOpenAI(base_url=base_url,api_key=api_key)
-else:
+elif get_env_var("OPENAI_API_KEY"):
     openai_client = AsyncOpenAI(api_key=get_env_var("OPENAI_API_KEY"))
+else:
+    openai_client = None
 
 if get_env_var("SUPABASE_URL"):
     supabase: Client = Client(

iterations/v4-streamlit-ui-overhaul/graph_service.py

@@ -66,4 +66,4 @@ async def invoke_agent(request: InvokeRequest):
 
 if __name__ == "__main__":
     import uvicorn
-    uvicorn.run(app, host="127.0.0.1", port=8100)
+    uvicorn.run(app, host="0.0.0.0", port=8100)

iterations/v4-streamlit-ui-overhaul/mcp-config.json

@@ -1,10 +0,0 @@
-{
-  "mcpServers": {
-    "archon": {
-      "command": "C:\\Users\\colem\\oTTomator\\archon\\venv\\Scripts\\python.exe",
-      "args": [
-        "C:\\Users\\colem\\oTTomator\\archon\\mcp_server.py"
-      ]
-    }
-  }
-}

iterations/v4-streamlit-ui-overhaul/mcp/.dockerignore

@@ -0,0 +1,38 @@
+# Ignore specified folders
+iterations/
+venv/
+.langgraph_api/
+.github/
+__pycache__/
+.env
+
+# Git related
+.git/
+.gitignore
+.gitattributes
+
+# Python cache
+*.pyc
+*.pyo
+*.pyd
+.Python
+*.so
+.pytest_cache/
+
+# Environment files
+.env.local
+.env.development.local
+.env.test.local
+.env.production.local
+
+# Logs
+*.log
+
+# IDE specific files
+.idea/
+.vscode/
+*.swp
+*.swo
+
+# Keep the example env file for reference
+!.env.example

iterations/v4-streamlit-ui-overhaul/mcp/Dockerfile

@@ -0,0 +1,19 @@
+FROM python:3.12-slim
+
+WORKDIR /app
+
+# Copy requirements file and install dependencies
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy the MCP server files
+COPY . .
+
+# Expose port for MCP server
+EXPOSE 8100
+
+# Set environment variables
+ENV PYTHONUNBUFFERED=1
+
+# Command to run the MCP server
+CMD ["python", "mcp_server.py"]

iterations/v4-streamlit-ui-overhaul/mcp/mcp_server.py

@@ -1,27 +1,44 @@
-import os
-import sys
-import asyncio
-import threading
 from mcp.server.fastmcp import FastMCP
-import requests
+from datetime import datetime
+from dotenv import load_dotenv
 from typing import Dict, List
+import threading
+import requests
+import asyncio
 import uuid
-from utils.utils import write_to_log
+import sys
-from graph_service import app
+import os
-import uvicorn
 
+# Load environment variables from .env file
+load_dotenv()
 
 # Initialize FastMCP server
 mcp = FastMCP("archon")
 
-
 # Store active threads
 active_threads: Dict[str, List[str]] = {}
 
-
 # FastAPI service URL
-GRAPH_SERVICE_URL = "http://127.0.0.1:8100"
+GRAPH_SERVICE_URL = os.getenv("GRAPH_SERVICE_URL", "http://localhost:8100")
 
+def write_to_log(message: str):
+    """Write a message to the logs.txt file in the workbench directory.
+    
+    Args:
+        message: The message to log
+    """
+    # Get the directory one level up from the current file
+    current_dir = os.path.dirname(os.path.abspath(__file__))
+    parent_dir = os.path.dirname(current_dir)
+    workbench_dir = os.path.join(parent_dir, "workbench")
+    log_path = os.path.join(workbench_dir, "logs.txt")
+    os.makedirs(workbench_dir, exist_ok=True)
+
+    timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+    log_entry = f"[{timestamp}] {message}\n"
+
+    with open(log_path, "a", encoding="utf-8") as f:
+        f.write(log_entry)
 
 @mcp.tool()
 async def create_thread() -> str:

iterations/v4-streamlit-ui-overhaul/mcp/requirements.txt

@@ -0,0 +1,3 @@
+mcp==1.2.1
+python-dotenv==1.0.1
+requests==2.32.3

iterations/v4-streamlit-ui-overhaul/run_docker.py

@@ -0,0 +1,126 @@
+#!/usr/bin/env python
+"""
+Simple script to build and run Archon Docker containers.
+"""
+
+import os
+import subprocess
+import platform
+import time
+from pathlib import Path
+
+def run_command(command, cwd=None):
+    """Run a command and print output in real-time."""
+    print(f"Running: {' '.join(command)}")
+    process = subprocess.Popen(
+        command,
+        stdout=subprocess.PIPE,
+        stderr=subprocess.STDOUT,
+        text=False,  
+        cwd=cwd
+    )
+    
+    for line in process.stdout:
+        try:
+            decoded_line = line.decode('utf-8', errors='replace')
+            print(decoded_line.strip())
+        except Exception as e:
+            print(f"Error processing output: {e}")
+    
+    process.wait()
+    return process.returncode
+
+def check_docker():
+    """Check if Docker is installed and running."""
+    try:
+        subprocess.run(
+            ["docker", "--version"], 
+            check=True, 
+            stdout=subprocess.PIPE, 
+            stderr=subprocess.PIPE
+        )
+        return True
+    except (subprocess.SubprocessError, FileNotFoundError):
+        print("Error: Docker is not installed or not in PATH")
+        return False
+
+def main():
+    """Main function to build and run Archon containers."""
+    # Check if Docker is available
+    if not check_docker():
+        return 1
+    
+    # Get the base directory
+    base_dir = Path(__file__).parent.absolute()
+    
+    # Check for .env file
+    env_file = base_dir / ".env"
+    env_args = []
+    if env_file.exists():
+        print(f"Using environment file: {env_file}")
+        env_args = ["--env-file", str(env_file)]
+    else:
+        print("No .env file found. Continuing without environment variables.")
+    
+    # Build the MCP container
+    print("\n=== Building Archon MCP container ===")
+    mcp_dir = base_dir / "mcp"
+    if run_command(["docker", "build", "-t", "archon-mcp:latest", "."], cwd=mcp_dir) != 0:
+        print("Error building MCP container")
+        return 1
+    
+    # Build the main Archon container
+    print("\n=== Building main Archon container ===")
+    if run_command(["docker", "build", "-t", "archon:latest", "."], cwd=base_dir) != 0:
+        print("Error building main Archon container")
+        return 1
+    
+    # Check if the container is already running
+    try:
+        result = subprocess.run(
+            ["docker", "ps", "-q", "--filter", "name=archon-container"],
+            check=True,
+            capture_output=True,
+            text=True
+        )
+        if result.stdout.strip():
+            print("\n=== Stopping existing Archon container ===")
+            run_command(["docker", "stop", "archon-container"])
+            run_command(["docker", "rm", "archon-container"])
+    except subprocess.SubprocessError:
+        pass
+    
+    # Run the Archon container
+    print("\n=== Starting Archon container ===")
+    cmd = [
+        "docker", "run", "-d",
+        "--name", "archon-container",
+        "-p", "8501:8501",
+        "-p", "8100:8100",
+        "--add-host", "host.docker.internal:host-gateway"
+    ]
+    
+    # Add environment variables if .env exists
+    if env_args:
+        cmd.extend(env_args)
+    
+    # Add image name
+    cmd.append("archon:latest")
+    
+    if run_command(cmd) != 0:
+        print("Error starting Archon container")
+        return 1
+    
+    # Wait a moment for the container to start
+    time.sleep(2)
+    
+    # Print success message
+    print("\n=== Archon is now running! ===")
+    print("-> Access the Streamlit UI at: http://localhost:8501")
+    print("-> MCP container is ready to use - see the MCP tab in the UI.")
+    print("\nTo stop Archon, run: docker stop archon-container && docker rm archon-container")
+    
+    return 0
+
+if __name__ == "__main__":
+    exit(main())

iterations/v4-streamlit-ui-overhaul/streamlit_ui.py

@@ -43,6 +43,27 @@ from archon.archon_graph import agentic_flow
 # Load environment variables from .env file
 load_dotenv()
 
+# Initialize clients
+openai_client = None
+base_url = get_env_var('BASE_URL') or 'https://api.openai.com/v1'
+api_key = get_env_var('LLM_API_KEY') or 'no-llm-api-key-provided'
+is_ollama = "localhost" in base_url.lower()
+
+if is_ollama:
+    openai_client = AsyncOpenAI(base_url=base_url,api_key=api_key)
+elif get_env_var("OPENAI_API_KEY"):
+    openai_client = AsyncOpenAI(api_key=get_env_var("OPENAI_API_KEY"))
+else:
+    openai_client = None
+
+if get_env_var("SUPABASE_URL"):
+    supabase: Client = Client(
+            get_env_var("SUPABASE_URL"),
+            get_env_var("SUPABASE_SERVICE_KEY")
+        )
+else:
+    supabase = None
+
 # Set page config - must be the first Streamlit command
 st.set_page_config(
     page_title="Archon - Agent Builder",
@@ -175,25 +196,6 @@ def reload_archon_graph():
     except Exception as e:
         st.error(f"Error reloading Archon modules: {str(e)}")
         return False
-
-# Initialize clients
-openai_client = None
-base_url = get_env_var('BASE_URL') or 'https://api.openai.com/v1'
-api_key = get_env_var('LLM_API_KEY') or 'no-llm-api-key-provided'
-is_ollama = "localhost" in base_url.lower()
-
-if is_ollama:
-    openai_client = AsyncOpenAI(base_url=base_url,api_key=api_key)
-else:
-    openai_client = AsyncOpenAI(api_key=get_env_var("OPENAI_API_KEY"))
-
-if get_env_var("SUPABASE_URL"):
-    supabase: Client = Client(
-            get_env_var("SUPABASE_URL"),
-            get_env_var("SUPABASE_SERVICE_KEY")
-        )
-else:
-    supabase = None
     
 # Configure logfire to suppress warnings (optional)
 logfire.configure(send_to_logfire='never')
@@ -241,10 +243,10 @@ def generate_mcp_config(ide_type):
     else:  # macOS or Linux
         python_path = os.path.join(base_path, 'venv', 'bin', 'python')
     
-    server_script_path = os.path.join(base_path, 'mcp_server.py')
+    server_script_path = os.path.join(base_path, 'mcp', 'mcp_server.py')
     
-    # Create the config dictionary
+    # Create the config dictionary for Python
-    config = {
+    python_config = {
         "mcpServers": {
             "archon": {
                 "command": python_path,
@@ -253,15 +255,121 @@ def generate_mcp_config(ide_type):
         }
     }
     
+    # Create the config dictionary for Docker
+    docker_config = {
+        "mcpServers": {
+            "archon": {
+                "command": "docker",
+                "args": [
+                    "run",
+                    "-i",
+                    "--rm",
+                    "-e", 
+                    "GRAPH_SERVICE_URL",
+                    "archon-mcp:latest"
+                ],
+                "env": {
+                    "GRAPH_SERVICE_URL": "http://host.docker.internal:8100"
+                }
+            }
+        }
+    }
+    
     # Return appropriate configuration based on IDE type
     if ide_type == "Windsurf":
-        return json.dumps(config, indent=2)
+        return json.dumps(python_config, indent=2), json.dumps(docker_config, indent=2)
     elif ide_type == "Cursor":
-        return f"{python_path} {server_script_path}"
+        return f"{python_path} {server_script_path}", f"docker run --rm -p 8100:8100 archon:latest python mcp_server.py"
     elif ide_type == "Cline":
-        return json.dumps(config, indent=2)  # Assuming Cline uses the same format as Windsurf
+        return json.dumps(python_config, indent=2), json.dumps(docker_config, indent=2)  # Assuming Cline uses the same format as Windsurf
     else:
-        return "Unknown IDE type selected"
+        return "Unknown IDE type selected", "Unknown IDE type selected"
+
+def mcp_tab():
+    """Display the MCP configuration interface"""
+    st.header("MCP Configuration")
+    st.write("Select your AI IDE to get the appropriate MCP configuration:")
+    
+    # IDE selection with side-by-side buttons
+    col1, col2, col3 = st.columns(3)
+    
+    with col1:
+        windsurf_button = st.button("Windsurf", use_container_width=True, key="windsurf_button")
+    with col2:
+        cursor_button = st.button("Cursor", use_container_width=True, key="cursor_button")
+    with col3:
+        cline_button = st.button("Cline", use_container_width=True, key="cline_button")
+    
+    # Initialize session state for selected IDE if not present
+    if "selected_ide" not in st.session_state:
+        st.session_state.selected_ide = None
+    
+    # Update selected IDE based on button clicks
+    if windsurf_button:
+        st.session_state.selected_ide = "Windsurf"
+    elif cursor_button:
+        st.session_state.selected_ide = "Cursor"
+    elif cline_button:
+        st.session_state.selected_ide = "Cline"
+    
+    # Display configuration if an IDE is selected
+    if st.session_state.selected_ide:
+        selected_ide = st.session_state.selected_ide
+        st.subheader(f"MCP Configuration for {selected_ide}")
+        python_config, docker_config = generate_mcp_config(selected_ide)
+        
+        # Configuration type tabs
+        config_tab1, config_tab2 = st.tabs(["Docker Configuration", "Python Configuration"])
+        
+        with config_tab1:
+            st.markdown("### Docker Configuration")
+            st.code(docker_config, language="json" if selected_ide != "Cursor" else None)
+            
+            st.markdown("#### Requirements:")
+            st.markdown("- Docker installed")
+            st.markdown("- Run the setup script to build and start both containers:")
+            st.code("python run_docker.py", language="bash")
+        
+        with config_tab2:
+            st.markdown("### Python Configuration")
+            st.code(python_config, language="json" if selected_ide != "Cursor" else None)
+            
+            st.markdown("#### Requirements:")
+            st.markdown("- Python 3.11+ installed")
+            st.markdown("- Virtual environment created and activated")
+            st.markdown("- All dependencies installed via `pip install -r requirements.txt`")
+            st.markdown("- Must be running Archon not within a container")           
+        
+        # Instructions based on IDE type
+        st.markdown("---")
+        st.markdown("### Setup Instructions")
+        
+        if selected_ide == "Windsurf":
+            st.markdown("""
+            #### How to use in Windsurf:
+            1. Click on the hammer icon above the chat input
+            2. Click on "Configure"
+            3. Paste the JSON from your preferred configuration tab above
+            4. Click "Refresh" next to "Configure"
+            """)
+        elif selected_ide == "Cursor":
+            st.markdown("""
+            #### How to use in Cursor:
+            1. Go to Cursor Settings > Features > MCP
+            2. Click on "+ Add New MCP Server"
+            3. Name: Archon
+            4. Type: command (equivalent to stdio)
+            5. Command: Paste the command from your preferred configuration tab above
+            """)
+        elif selected_ide == "Cline":
+            st.markdown("""
+            #### How to use in Cline:
+            1. From the Cline extension, click the "MCP Server" tab
+            2. Click the "Edit MCP Settings" button
+            3. The MCP settings file should be displayed in a tab in VS Code
+            4. Paste the JSON from your preferred configuration tab above
+            5. Cline will automatically detect and start the MCP server
+            """)
 
 async def chat_tab():
     """Display the chat interface for talking to Archon"""
@@ -302,70 +410,6 @@ async def chat_tab():
         
         st.session_state.messages.append({"type": "ai", "content": response_content})
 
-def mcp_tab():
-    """Display the MCP configuration interface"""
-    st.header("MCP Configuration")
-    st.write("Select your AI IDE to get the appropriate MCP configuration:")
-    
-    # IDE selection with side-by-side buttons
-    col1, col2, col3 = st.columns(3)
-    
-    with col1:
-        windsurf_button = st.button("Windsurf", use_container_width=True, key="windsurf_button")
-    with col2:
-        cursor_button = st.button("Cursor", use_container_width=True, key="cursor_button")
-    with col3:
-        cline_button = st.button("Cline", use_container_width=True, key="cline_button")
-    
-    # Initialize session state for selected IDE if not present
-    if "selected_ide" not in st.session_state:
-        st.session_state.selected_ide = None
-    
-    # Update selected IDE based on button clicks
-    if windsurf_button:
-        st.session_state.selected_ide = "Windsurf"
-    elif cursor_button:
-        st.session_state.selected_ide = "Cursor"
-    elif cline_button:
-        st.session_state.selected_ide = "Cline"
-    
-    # Display configuration if an IDE is selected
-    if st.session_state.selected_ide:
-        selected_ide = st.session_state.selected_ide
-        st.subheader(f"MCP Configuration for {selected_ide}")
-        config = generate_mcp_config(selected_ide)
-        
-        # Display the configuration
-        st.code(config, language="json" if selected_ide != "Cursor" else None)
-        
-        # Instructions based on IDE type
-        if selected_ide == "Windsurf":
-            st.markdown("""
-            ### How to use in Windsurf:
-            1. Click on the hammer icon above the chat input
-            2. Click on "Configure"
-            3. Paste the JSON above as the MCP config
-            4. Click "Refresh" next to "Configure"
-            """)
-        elif selected_ide == "Cursor":
-            st.markdown("""
-            ### How to use in Cursor:
-            1. Go to Cursor Settings > Features > MCP
-            2. Click on "+ Add New MCP Server"
-            3. Name: Archon
-            4. Type: command (equivalent to stdio)
-            5. Command: Paste the command above
-            """)
-        elif selected_ide == "Cline":
-            st.markdown("""
-            ### How to use in Cline:
-            1. From the Cline extension, click the "MCP Server" tab
-            2. Click the "Edit MCP Settings" button
-            3. The MCP settings file should be displayed in a tab in VS Code
-            4. Paste the JSON above as the MCP config
-            5. Cline will automatically detect and start the MCP server
-            """)
-
 def intro_tab():
     """Display the introduction and setup guide for Archon"""
     # Display the banner image

mcp-config.json

@@ -1,10 +0,0 @@
-{
-  "mcpServers": {
-    "archon": {
-      "command": "[path to Archon]\\archon\\venv\\Scripts\\python.exe",
-      "args": [
-        "[path to Archon]\\archon\\mcp_server.py"
-      ]
-    }
-  }
-}

mcp/.dockerignore

@@ -0,0 +1,38 @@
+# Ignore specified folders
+iterations/
+venv/
+.langgraph_api/
+.github/
+__pycache__/
+.env
+
+# Git related
+.git/
+.gitignore
+.gitattributes
+
+# Python cache
+*.pyc
+*.pyo
+*.pyd
+.Python
+*.so
+.pytest_cache/
+
+# Environment files
+.env.local
+.env.development.local
+.env.test.local
+.env.production.local
+
+# Logs
+*.log
+
+# IDE specific files
+.idea/
+.vscode/
+*.swp
+*.swo
+
+# Keep the example env file for reference
+!.env.example

mcp/Dockerfile

@@ -0,0 +1,16 @@
+FROM python:3.12-slim
+
+WORKDIR /app
+
+# Copy requirements file and install dependencies
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy the MCP server files
+COPY . .
+
+# Expose port for MCP server
+EXPOSE 8100
+
+# Command to run the MCP server
+CMD ["python", "mcp_server.py"]

mcp/mcp_server.py

@@ -0,0 +1,112 @@
+from mcp.server.fastmcp import FastMCP
+from datetime import datetime
+from dotenv import load_dotenv
+from typing import Dict, List
+import threading
+import requests
+import asyncio
+import uuid
+import sys
+import os
+
+# Load environment variables from .env file
+load_dotenv()
+
+# Initialize FastMCP server
+mcp = FastMCP("archon")
+
+# Store active threads
+active_threads: Dict[str, List[str]] = {}
+
+# FastAPI service URL
+GRAPH_SERVICE_URL = os.getenv("GRAPH_SERVICE_URL", "http://localhost:8100")
+
+def write_to_log(message: str):
+    """Write a message to the logs.txt file in the workbench directory.
+    
+    Args:
+        message: The message to log
+    """
+    # Get the directory one level up from the current file
+    current_dir = os.path.dirname(os.path.abspath(__file__))
+    parent_dir = os.path.dirname(current_dir)
+    workbench_dir = os.path.join(parent_dir, "workbench")
+    log_path = os.path.join(workbench_dir, "logs.txt")
+    os.makedirs(workbench_dir, exist_ok=True)
+
+    timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+    log_entry = f"[{timestamp}] {message}\n"
+
+    with open(log_path, "a", encoding="utf-8") as f:
+        f.write(log_entry)
+
+@mcp.tool()
+async def create_thread() -> str:
+    """Create a new conversation thread for Archon.
+    Always call this tool before invoking Archon for the first time in a conversation.
+    (if you don't already have a thread ID)
+    
+    Returns:
+        str: A unique thread ID for the conversation
+    """
+    thread_id = str(uuid.uuid4())
+    active_threads[thread_id] = []
+    write_to_log(f"Created new thread: {thread_id}")
+    return thread_id
+
+
+def _make_request(thread_id: str, user_input: str, config: dict) -> str:
+    """Make synchronous request to graph service"""
+    response = requests.post(
+        f"{GRAPH_SERVICE_URL}/invoke",
+        json={
+            "message": user_input,
+            "thread_id": thread_id,
+            "is_first_message": not active_threads[thread_id],
+            "config": config
+    }
+    )
+    response.raise_for_status()
+    return response.json()
+
+
+@mcp.tool()
+async def run_agent(thread_id: str, user_input: str) -> str:
+    """Run the Archon agent with user input.
+    Only use this tool after you have called create_thread in this conversation to get a unique thread ID.
+    If you already created a thread ID in this conversation, do not create another one. Reuse the same ID.
+    After you receive the code from Archon, always implement it into the codebase unless asked not to.
+    
+    Args:
+        thread_id: The conversation thread ID
+        user_input: The user's message to process
+    
+    Returns:
+        str: The agent's response which generally includes the code for the agent
+    """
+    if thread_id not in active_threads:
+        write_to_log(f"Error: Thread not found - {thread_id}")
+        raise ValueError("Thread not found")
+
+    write_to_log(f"Processing message for thread {thread_id}: {user_input}")
+
+    config = {
+        "configurable": {
+            "thread_id": thread_id
+        }
+    }
+    
+    try:
+        result = await asyncio.to_thread(_make_request, thread_id, user_input, config)
+        active_threads[thread_id].append(user_input)
+        return result['response']
+        
+    except Exception as e:
+        raise
+
+
+if __name__ == "__main__":
+    write_to_log("Starting MCP server")
+    
+    # Run MCP server
+    mcp.run(transport='stdio')

mcp/requirements.txt

@@ -0,0 +1,3 @@
+mcp==1.2.1
+python-dotenv==1.0.1
+requests==2.32.3

run_docker.py

@@ -0,0 +1,126 @@
+#!/usr/bin/env python
+"""
+Simple script to build and run Archon Docker containers.
+"""
+
+import os
+import subprocess
+import platform
+import time
+from pathlib import Path
+
+def run_command(command, cwd=None):
+    """Run a command and print output in real-time."""
+    print(f"Running: {' '.join(command)}")
+    process = subprocess.Popen(
+        command,
+        stdout=subprocess.PIPE,
+        stderr=subprocess.STDOUT,
+        text=False,  
+        cwd=cwd
+    )
+    
+    for line in process.stdout:
+        try:
+            decoded_line = line.decode('utf-8', errors='replace')
+            print(decoded_line.strip())
+        except Exception as e:
+            print(f"Error processing output: {e}")
+    
+    process.wait()
+    return process.returncode
+
+def check_docker():
+    """Check if Docker is installed and running."""
+    try:
+        subprocess.run(
+            ["docker", "--version"], 
+            check=True, 
+            stdout=subprocess.PIPE, 
+            stderr=subprocess.PIPE
+        )
+        return True
+    except (subprocess.SubprocessError, FileNotFoundError):
+        print("Error: Docker is not installed or not in PATH")
+        return False
+
+def main():
+    """Main function to build and run Archon containers."""
+    # Check if Docker is available
+    if not check_docker():
+        return 1
+    
+    # Get the base directory
+    base_dir = Path(__file__).parent.absolute()
+    
+    # Check for .env file
+    env_file = base_dir / ".env"
+    env_args = []
+    if env_file.exists():
+        print(f"Using environment file: {env_file}")
+        env_args = ["--env-file", str(env_file)]
+    else:
+        print("No .env file found. Continuing without environment variables.")
+    
+    # Build the MCP container
+    print("\n=== Building Archon MCP container ===")
+    mcp_dir = base_dir / "mcp"
+    if run_command(["docker", "build", "-t", "archon-mcp:latest", "."], cwd=mcp_dir) != 0:
+        print("Error building MCP container")
+        return 1
+    
+    # Build the main Archon container
+    print("\n=== Building main Archon container ===")
+    if run_command(["docker", "build", "-t", "archon:latest", "."], cwd=base_dir) != 0:
+        print("Error building main Archon container")
+        return 1
+    
+    # Check if the container is already running
+    try:
+        result = subprocess.run(
+            ["docker", "ps", "-q", "--filter", "name=archon-container"],
+            check=True,
+            capture_output=True,
+            text=True
+        )
+        if result.stdout.strip():
+            print("\n=== Stopping existing Archon container ===")
+            run_command(["docker", "stop", "archon-container"])
+            run_command(["docker", "rm", "archon-container"])
+    except subprocess.SubprocessError:
+        pass
+    
+    # Run the Archon container
+    print("\n=== Starting Archon container ===")
+    cmd = [
+        "docker", "run", "-d",
+        "--name", "archon-container",
+        "-p", "8501:8501",
+        "-p", "8100:8100",
+        "--add-host", "host.docker.internal:host-gateway"
+    ]
+    
+    # Add environment variables if .env exists
+    if env_args:
+        cmd.extend(env_args)
+    
+    # Add image name
+    cmd.append("archon:latest")
+    
+    if run_command(cmd) != 0:
+        print("Error starting Archon container")
+        return 1
+    
+    # Wait a moment for the container to start
+    time.sleep(2)
+    
+    # Print success message
+    print("\n=== Archon is now running! ===")
+    print("-> Access the Streamlit UI at: http://localhost:8501")
+    print("-> MCP container is ready to use - see the MCP tab in the UI.")
+    print("\nTo stop Archon, run: docker stop archon-container && docker rm archon-container")
+    
+    return 0
+
+if __name__ == "__main__":
+    exit(main())

streamlit_ui.py

@@ -43,6 +43,27 @@ from archon.archon_graph import agentic_flow
 # Load environment variables from .env file
 load_dotenv()
 
+# Initialize clients
+openai_client = None
+base_url = get_env_var('BASE_URL') or 'https://api.openai.com/v1'
+api_key = get_env_var('LLM_API_KEY') or 'no-llm-api-key-provided'
+is_ollama = "localhost" in base_url.lower()
+
+if is_ollama:
+    openai_client = AsyncOpenAI(base_url=base_url,api_key=api_key)
+elif get_env_var("OPENAI_API_KEY"):
+    openai_client = AsyncOpenAI(api_key=get_env_var("OPENAI_API_KEY"))
+else:
+    openai_client = None
+
+if get_env_var("SUPABASE_URL"):
+    supabase: Client = Client(
+            get_env_var("SUPABASE_URL"),
+            get_env_var("SUPABASE_SERVICE_KEY")
+        )
+else:
+    supabase = None
+
 # Set page config - must be the first Streamlit command
 st.set_page_config(
     page_title="Archon - Agent Builder",
@@ -175,25 +196,6 @@ def reload_archon_graph():
     except Exception as e:
         st.error(f"Error reloading Archon modules: {str(e)}")
         return False
-
-# Initialize clients
-openai_client = None
-base_url = get_env_var('BASE_URL') or 'https://api.openai.com/v1'
-api_key = get_env_var('LLM_API_KEY') or 'no-llm-api-key-provided'
-is_ollama = "localhost" in base_url.lower()
-
-if is_ollama:
-    openai_client = AsyncOpenAI(base_url=base_url,api_key=api_key)
-else:
-    openai_client = AsyncOpenAI(api_key=get_env_var("OPENAI_API_KEY"))
-
-if get_env_var("SUPABASE_URL"):
-    supabase: Client = Client(
-            get_env_var("SUPABASE_URL"),
-            get_env_var("SUPABASE_SERVICE_KEY")
-        )
-else:
-    supabase = None
     
 # Configure logfire to suppress warnings (optional)
 logfire.configure(send_to_logfire='never')
@@ -241,10 +243,10 @@ def generate_mcp_config(ide_type):
     else:  # macOS or Linux
         python_path = os.path.join(base_path, 'venv', 'bin', 'python')
     
-    server_script_path = os.path.join(base_path, 'mcp_server.py')
+    server_script_path = os.path.join(base_path, 'mcp', 'mcp_server.py')
     
-    # Create the config dictionary
+    # Create the config dictionary for Python
-    config = {
+    python_config = {
         "mcpServers": {
             "archon": {
                 "command": python_path,
@@ -253,15 +255,121 @@ def generate_mcp_config(ide_type):
         }
     }
     
+    # Create the config dictionary for Docker
+    docker_config = {
+        "mcpServers": {
+            "archon": {
+                "command": "docker",
+                "args": [
+                    "run",
+                    "-i",
+                    "--rm",
+                    "-e", 
+                    "GRAPH_SERVICE_URL",
+                    "archon-mcp:latest"
+                ],
+                "env": {
+                    "GRAPH_SERVICE_URL": "http://host.docker.internal:8100"
+                }
+            }
+        }
+    }
+    
     # Return appropriate configuration based on IDE type
     if ide_type == "Windsurf":
-        return json.dumps(config, indent=2)
+        return json.dumps(python_config, indent=2), json.dumps(docker_config, indent=2)
     elif ide_type == "Cursor":
-        return f"{python_path} {server_script_path}"
+        return f"{python_path} {server_script_path}", f"docker run --rm -p 8100:8100 archon:latest python mcp_server.py"
     elif ide_type == "Cline":
-        return json.dumps(config, indent=2)  # Assuming Cline uses the same format as Windsurf
+        return json.dumps(python_config, indent=2), json.dumps(docker_config, indent=2)  # Assuming Cline uses the same format as Windsurf
     else:
-        return "Unknown IDE type selected"
+        return "Unknown IDE type selected", "Unknown IDE type selected"
+
+def mcp_tab():
+    """Display the MCP configuration interface"""
+    st.header("MCP Configuration")
+    st.write("Select your AI IDE to get the appropriate MCP configuration:")
+    
+    # IDE selection with side-by-side buttons
+    col1, col2, col3 = st.columns(3)
+    
+    with col1:
+        windsurf_button = st.button("Windsurf", use_container_width=True, key="windsurf_button")
+    with col2:
+        cursor_button = st.button("Cursor", use_container_width=True, key="cursor_button")
+    with col3:
+        cline_button = st.button("Cline", use_container_width=True, key="cline_button")
+    
+    # Initialize session state for selected IDE if not present
+    if "selected_ide" not in st.session_state:
+        st.session_state.selected_ide = None
+    
+    # Update selected IDE based on button clicks
+    if windsurf_button:
+        st.session_state.selected_ide = "Windsurf"
+    elif cursor_button:
+        st.session_state.selected_ide = "Cursor"
+    elif cline_button:
+        st.session_state.selected_ide = "Cline"
+    
+    # Display configuration if an IDE is selected
+    if st.session_state.selected_ide:
+        selected_ide = st.session_state.selected_ide
+        st.subheader(f"MCP Configuration for {selected_ide}")
+        python_config, docker_config = generate_mcp_config(selected_ide)
+        
+        # Configuration type tabs
+        config_tab1, config_tab2 = st.tabs(["Docker Configuration", "Python Configuration"])
+        
+        with config_tab1:
+            st.markdown("### Docker Configuration")
+            st.code(docker_config, language="json" if selected_ide != "Cursor" else None)
+            
+            st.markdown("#### Requirements:")
+            st.markdown("- Docker installed")
+            st.markdown("- Run the setup script to build and start both containers:")
+            st.code("python run_docker.py", language="bash")
+        
+        with config_tab2:
+            st.markdown("### Python Configuration")
+            st.code(python_config, language="json" if selected_ide != "Cursor" else None)
+            
+            st.markdown("#### Requirements:")
+            st.markdown("- Python 3.11+ installed")
+            st.markdown("- Virtual environment created and activated")
+            st.markdown("- All dependencies installed via `pip install -r requirements.txt`")
+            st.markdown("- Must be running Archon not within a container")           
+        
+        # Instructions based on IDE type
+        st.markdown("---")
+        st.markdown("### Setup Instructions")
+        
+        if selected_ide == "Windsurf":
+            st.markdown("""
+            #### How to use in Windsurf:
+            1. Click on the hammer icon above the chat input
+            2. Click on "Configure"
+            3. Paste the JSON from your preferred configuration tab above
+            4. Click "Refresh" next to "Configure"
+            """)
+        elif selected_ide == "Cursor":
+            st.markdown("""
+            #### How to use in Cursor:
+            1. Go to Cursor Settings > Features > MCP
+            2. Click on "+ Add New MCP Server"
+            3. Name: Archon
+            4. Type: command (equivalent to stdio)
+            5. Command: Paste the command from your preferred configuration tab above
+            """)
+        elif selected_ide == "Cline":
+            st.markdown("""
+            #### How to use in Cline:
+            1. From the Cline extension, click the "MCP Server" tab
+            2. Click the "Edit MCP Settings" button
+            3. The MCP settings file should be displayed in a tab in VS Code
+            4. Paste the JSON from your preferred configuration tab above
+            5. Cline will automatically detect and start the MCP server
+            """)
 
 async def chat_tab():
     """Display the chat interface for talking to Archon"""
@@ -302,70 +410,6 @@ async def chat_tab():
         
         st.session_state.messages.append({"type": "ai", "content": response_content})
 
-def mcp_tab():
-    """Display the MCP configuration interface"""
-    st.header("MCP Configuration")
-    st.write("Select your AI IDE to get the appropriate MCP configuration:")
-    
-    # IDE selection with side-by-side buttons
-    col1, col2, col3 = st.columns(3)
-    
-    with col1:
-        windsurf_button = st.button("Windsurf", use_container_width=True, key="windsurf_button")
-    with col2:
-        cursor_button = st.button("Cursor", use_container_width=True, key="cursor_button")
-    with col3:
-        cline_button = st.button("Cline", use_container_width=True, key="cline_button")
-    
-    # Initialize session state for selected IDE if not present
-    if "selected_ide" not in st.session_state:
-        st.session_state.selected_ide = None
-    
-    # Update selected IDE based on button clicks
-    if windsurf_button:
-        st.session_state.selected_ide = "Windsurf"
-    elif cursor_button:
-        st.session_state.selected_ide = "Cursor"
-    elif cline_button:
-        st.session_state.selected_ide = "Cline"
-    
-    # Display configuration if an IDE is selected
-    if st.session_state.selected_ide:
-        selected_ide = st.session_state.selected_ide
-        st.subheader(f"MCP Configuration for {selected_ide}")
-        config = generate_mcp_config(selected_ide)
-        
-        # Display the configuration
-        st.code(config, language="json" if selected_ide != "Cursor" else None)
-        
-        # Instructions based on IDE type
-        if selected_ide == "Windsurf":
-            st.markdown("""
-            ### How to use in Windsurf:
-            1. Click on the hammer icon above the chat input
-            2. Click on "Configure"
-            3. Paste the JSON above as the MCP config
-            4. Click "Refresh" next to "Configure"
-            """)
-        elif selected_ide == "Cursor":
-            st.markdown("""
-            ### How to use in Cursor:
-            1. Go to Cursor Settings > Features > MCP
-            2. Click on "+ Add New MCP Server"
-            3. Name: Archon
-            4. Type: command (equivalent to stdio)
-            5. Command: Paste the command above
-            """)
-        elif selected_ide == "Cline":
-            st.markdown("""
-            ### How to use in Cline:
-            1. From the Cline extension, click the "MCP Server" tab
-            2. Click the "Edit MCP Settings" button
-            3. The MCP settings file should be displayed in a tab in VS Code
-            4. Paste the JSON above as the MCP config
-            5. Cline will automatically detect and start the MCP server
-            """)
-
 def intro_tab():
     """Display the introduction and setup guide for Archon"""
     # Display the banner image