💡 What is Dev Guru?

Dev Guru is a specialized MCP (Model Context Protocol) server that acts as an on-demand senior code consultant for AI agents. It routes coding problems to the most suitable AI model based on the requested expertise level, providing structured, actionable feedback.

Think of it as a second brain for your AI agent — a guru it can consult when facing tough coding decisions.

🎯 Use Cases

Scenario	How Dev Guru Helps
🐛 Debugging Complex Issues	Your agent is stuck on a tricky bug. It calls Dev Guru with the context and gets expert-level reasoning and suggestions.
🏗️ Architecture Decisions	Unsure about a design pattern? Dev Guru analyzes your code structure and recommends the best approach.
🔄 Code Review on Demand	Submit code for review and get structured feedback with a thinking process and concrete suggestions.
🤔 Validating Reasoning	Your agent has an idea but isn't confident. Dev Guru validates the reasoning and either confirms or corrects the approach.
⚡ Multi-Model Leverage	Automatically routes to Gemini, Claude, or GPT based on the complexity level — getting the right model for the right job.

✨ Features

• 🧠 Expert-based Routing — Automatically selects the best AI model for the task:
  • novice → Gemini (fast, efficient)
  • medium → Claude (balanced, analytical)
  • expert → OpenAI GPT (deep reasoning)
• 🔀 OpenRouter Fallback — If a primary API key is missing, seamlessly falls back to OpenRouter
• 🎛️ Configurable Models — Choose exactly which model to use per level via environment variables
• ⚡ FastMCP Core — High-performance MCP server implementation
• 📦 Skill Management API — Dynamic skill installation and management via REST
• 🐳 Docker Ready — Multi-stage build with uv for efficient containerized deployments
• 🧩 Agno Framework — Leverages Agno for agent orchestration and structured outputs

🚀 Quick Start

Prerequisites

• Python 3.12+
• uv (recommended)
• At least one API key: Gemini, Anthropic, OpenAI, or OpenRouter

Installation

# Clone the repository
git clone https://github.com/your-user/dev-guru.git
cd dev-guru

# Create your environment file
cp .env.example .env
# Edit .env with your API keys

# Install dependencies
uv sync

Running

# Start the full API + MCP server
uv run python main.py

Docker

docker compose up --build

⚙️ Configuration

Environment Variables

Variable	Description	Default
GEMINI_API_KEY	Google Gemini API key	—
ANTHROPIC_API_KEY	Anthropic Claude API key	—
OPENAI_API_KEY	OpenAI API key	—
OPENROUTER_API_KEY	OpenRouter API key (universal fallback)	—
API_KEY	Optional API key to protect REST and MCP endpoints	—
NOVICE_MODEL	Model ID for novice level	gemini-3.1-pro-preview
MEDIUM_MODEL	Model ID for medium level	claude-opus-4.6
ADVANCED_MODEL	Model ID for expert level	gpt-5.3-codex
PORT	Server port	8000
DEBUG	Debug mode	true

Tip: You only need an OPENROUTER_API_KEY to use all three levels — it acts as a universal fallback for any missing provider key.

🔌 MCP Configuration

Add Dev Guru to your MCP client (Claude Desktop, Cursor, etc.):

{
  "mcpServers": {
    "dev-guru": {
      "command": "uv",
      "args": [
        "--directory",
        "/path/to/dev-guru",
        "run",
        "python",
        "src/server.py"
      ]
    }
  }
}

📡 API Endpoints

Skill Management

Method	Endpoint	Description
GET	/skills	List all loaded skills
GET	/skills/{name}	Get details of a specific skill
POST	/skills	Install a skill (URL or base64 zip)
POST	/skills/upload	Install a skill via file upload
DELETE	/skills/{name}	Delete a skill

MCP Tool

Tool	Parameters	Description
call_guru	level, technologies, context, thinking	Consult the guru about a coding problem

🧪 Testing

PYTHONPATH=. uv run pytest

🏗️ Architecture

graph LR
    A[AI Agent] -->|MCP Protocol| B[Dev Guru Server]
    B -->|novice| C[Gemini]
    B -->|medium| D[Claude]
    B -->|expert| E[GPT-5.3-codex]
    B -.->|fallback| F[OpenRouter]
    F --> C
    F --> D
    F --> E

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