Langgraph Clean Arch
FreeNot checkedMCP server implementing clean architecture with LangGraph for building and managing agent workflows.
About
MCP server implementing clean architecture with LangGraph for building and managing agent workflows.
README
A Model Context Protocol (MCP) server written in Python from scratch using LangGraph for orchestration, structured around the principles of Clean Architecture.
🏛️ Architecture Overview
This project is divided into four concentric layers to enforce the Dependency Inversion Principle (outer layers can import inner layers, but inner layers must NEVER know about outer layers):
[Domain] <-- [Use Cases] <-- [Adapters] <-- [Frameworks]
- Domain (
src/domain/): Contains core entity dataclasses (Message,ConversationState). Entirely framework-free. - Use Cases (
src/usecases/): Application business rules (RunAgentUseCase) and abstract gateway interfaces (AgentGateway,HistoryRepository). - Interface Adapters (
src/adapters/): Gateway implementations (InMemoryHistoryRepository) and interface bindings for external communication (McpController). - Frameworks & Drivers (
src/frameworks/): External libraries and composition root (LangGraphAgent,config.py,main.py).
🚀 Getting Started
1. Requirements
- Python >= 3.13
2. Set Up Virtual Environment (Already Done)
The virtual environment has already been created in this folder at .venv. To activate it:
Windows PowerShell:
.venv\Scripts\Activate.ps1
Windows CMD:
.venv\Scripts\activate.bat
Bash / Git Bash:
source .venv/bin/activate
3. Configure API Key
Create a .env file in the project root:
OPENAI_API_KEY=your_openai_api_key_here
Note: If no API key is specified, the application will fallback to Simulation Mode, which lets you test the execution pipeline locally without incurring cost or requiring keys.
4. Running the Verification Test
Verify the layers and data flow run properly inside the virtual environment:
python test_app.py
5. Running the MCP Server
To execute the server via the stdio transport protocol:
python src/main.py
Note: Normal logs are written to sys.stderr to avoid corrupting the protocol communication channel on stdout.
🔌 Connecting to Host Clients
Claude Desktop
Add this server to your Claude Desktop config file (located at %APPDATA%\Claude\claude_desktop_config.json):
{
"mcpServers": {
"langgraph-clean-arch": {
"command": "C:\\Users\\faria\\.gemini\\antigravity-ide\\scratch\\mcp-langgraph-clean-arch\\.venv\\Scripts\\python.exe",
"args": ["C:\\Users\\faria\\.gemini\\antigravity-ide\\scratch\\mcp-langgraph-clean-arch\\src\\main.py"]
}
}
}
Cursor
Go to Settings > Features > MCP:
- Click + Add New MCP Server.
- Set Name to
langgraph-clean-arch. - Set Type to
command. - Set Command to:
C:\Users\faria\.gemini\antigravity-ide\scratch\mcp-langgraph-clean-arch\.venv\Scripts\python.exe C:\Users\faria\.gemini\antigravity-ide\scratch\mcp-langgraph-clean-arch\src\main.py - Click Save and verify the status shows active green.
Installing Langgraph Clean Arch
This server has no published package — it is built from source. Open the repository and follow its README.
▸ github.com/FranciscoValadao/mcp-langgraph-clean-archFAQ
Is Langgraph Clean Arch MCP free?
Yes, Langgraph Clean Arch MCP is free — one-click install via Unyly at no cost.
Does Langgraph Clean Arch need an API key?
No, Langgraph Clean Arch runs without API keys or environment variables.
Is Langgraph Clean Arch hosted or self-hosted?
Self-hosted: the server runs locally on your machine via the install command above.
How do I install Langgraph Clean Arch in Claude Desktop, Claude Code or Cursor?
Open Langgraph Clean Arch on unyly.org, pick your client tab (Claude Desktop, Claude Code, Cursor) and press Install — the config is generated automatically, no JSON editing.
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