Migration from Docker to Standalone Python Server (#73)
* Migration from docker to standalone server Migration handling Fixed tests Use simpler in-memory storage Support for concurrent logging to disk Simplified direct connections to localhost * Migration from docker / redis to standalone script Updated tests Updated run script Fixed requirements Use dotenv Ask if user would like to install MCP in Claude Desktop once Updated docs * More cleanup and references to docker removed * Cleanup * Comments * Fixed tests * Fix GitHub Actions workflow for standalone Python architecture - Install requirements-dev.txt for pytest and testing dependencies - Remove Docker setup from simulation tests (now standalone) - Simplify linting job to use requirements-dev.txt - Update simulation tests to run directly without Docker Fixes unit test failures in CI due to missing pytest dependency. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com> * Remove simulation tests from GitHub Actions - Removed simulation-tests job that makes real API calls - Keep only unit tests (mocked, no API costs) and linting - Simulation tests should be run manually with real API keys - Reduces CI costs and complexity GitHub Actions now only runs: - Unit tests (569 tests, all mocked) - Code quality checks (ruff, black) 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com> * Fixed tests * Fixed tests --------- Co-authored-by: Claude <noreply@anthropic.com>
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Beehive Innovations
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README.md
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README.md
@@ -44,7 +44,7 @@ Because these AI models [clearly aren't when they get chatty →](docs/ai_banter
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## Quick Navigation
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- **Getting Started**
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- [Quickstart](#quickstart-5-minutes) - Get running in 5 minutes with Docker
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- [Quickstart](#quickstart-5-minutes) - Get running in 5 minutes
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- [Available Tools](#available-tools) - Overview of all tools
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- [AI-to-AI Conversations](#ai-to-ai-conversation-threading) - Multi-turn conversations
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@@ -123,7 +123,7 @@ The final implementation resulted in a 26% improvement in JSON parsing performan
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### Prerequisites
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- Docker Desktop installed ([Download here](https://www.docker.com/products/docker-desktop/))
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- Python 3.10+ (3.12 recommended)
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- Git
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- **Windows users**: WSL2 is required for Claude Code CLI
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@@ -158,16 +158,16 @@ The final implementation resulted in a 26% improvement in JSON parsing performan
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git clone https://github.com/BeehiveInnovations/zen-mcp-server.git
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cd zen-mcp-server
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# One-command setup (includes Redis for AI conversations)
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# One-command setup
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./run-server.sh
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```
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**What this does:**
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- **Builds Docker images** with all dependencies (including Redis for conversation threading)
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- **Creates .env file** (automatically uses `$GEMINI_API_KEY` and `$OPENAI_API_KEY` if set in environment)
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- **Starts Redis service** for AI-to-AI conversation memory
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- **Starts MCP server** with providers based on available API keys
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- **Adds Zen to Claude Code automatically**
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- **Sets up everything automatically** - Python environment, dependencies, configuration
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- **Configures Claude integrations** - Adds to Claude Code CLI and guides Desktop setup
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- **Ready to use immediately** - No manual configuration needed
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**After updates:** Always run `./run-server.sh` again after `git pull` to ensure everything stays current.
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### 3. Add Your API Keys
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@@ -180,74 +180,26 @@ nano .env
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# OPENAI_API_KEY=your-openai-api-key-here # For O3 model
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# OPENROUTER_API_KEY=your-openrouter-key # For OpenRouter (see docs/custom_models.md)
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# For local models (Ollama, vLLM, etc.) - Note: Use host.docker.internal for Docker networking:
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# CUSTOM_API_URL=http://host.docker.internal:11434/v1 # Ollama example (NOT localhost!)
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# For local models (Ollama, vLLM, etc.):
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# CUSTOM_API_URL=http://localhost:11434/v1 # Ollama example
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# CUSTOM_API_KEY= # Empty for Ollama
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# CUSTOM_MODEL_NAME=llama3.2 # Default model
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# WORKSPACE_ROOT=/Users/your-username (automatically configured)
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# Note: At least one API key OR custom URL is required
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# After making changes to .env, restart the server:
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# ./run-server.sh
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```
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**Restart MCP Server**: This step is important. You will need to `./run-server.sh` again for it to
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pick the changes made to `.env` otherwise the server will be unable to use your newly edited keys. Please also
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`./run-server.sh` any time in the future you modify the `.env` file.
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**No restart needed**: The server reads the .env file each time Claude calls a tool, so changes take effect immediately.
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**Next**: Now run `claude` from your project folder using the terminal for it to connect to the newly added mcp server.
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If you were already running a `claude` code session, please exit and start a new session.
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#### If Setting up for Claude Desktop
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1. **Launch Claude Desktop**
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- Open Claude Desktop
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- Go to **Settings** → **Developer** → **Edit Config**
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**Need the exact configuration?** Run `./run-server.sh -c` to display the platform-specific setup instructions with correct paths.
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This will open a folder revealing `claude_desktop_config.json`.
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2. **Update Docker Configuration**
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The setup script shows you the exact configuration. It looks like this. When you ran `run-server.sh` it should
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have produced a configuration for you to copy:
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```json
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{
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"mcpServers": {
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"zen": {
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"command": "docker",
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"args": [
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"exec",
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"-i",
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"zen-mcp-server",
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"python",
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"server.py"
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]
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}
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}
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}
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```
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Paste the above into `claude_desktop_config.json`. If you have several other MCP servers listed, simply add this below the rest after a `,` comma:
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```json
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... other mcp servers ... ,
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"zen": {
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"command": "docker",
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"args": [
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"exec",
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"-i",
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"zen-mcp-server",
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"python",
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"server.py"
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]
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}
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```
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3. **Restart Claude Desktop**
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Completely quit and restart Claude Desktop for the changes to take effect.
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1. **Open Claude Desktop config**: Settings → Developer → Edit Config
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2. **Copy the configuration** shown by `./run-server.sh -c` into your `claude_desktop_config.json`
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3. **Restart Claude Desktop** for changes to take effect
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### 4. Start Using It!
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@@ -546,7 +498,7 @@ OPENAI_API_KEY=your-openai-key
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- **API Keys**: Native APIs (Gemini, OpenAI, X.AI), OpenRouter, or Custom endpoints (Ollama, vLLM)
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- **Model Selection**: Auto mode or specific model defaults
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- **Usage Restrictions**: Control which models can be used for cost control
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- **Conversation Settings**: Timeout, turn limits, Redis configuration
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- **Conversation Settings**: Timeout, turn limits, memory configuration
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- **Thinking Modes**: Token allocation for extended reasoning
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- **Logging**: Debug levels and operational visibility
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