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Jefferson Gonçalves
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Evo Ai

Evo AI is an open-source platform for creating and managing AI agents, enabling integration with different AI models and services.

Evo AI - AI Agents Platform

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#Evo AI - AI Agents Platform

Evo AI is an open-source platform for creating and managing AI agents, enabling integration with different AI models and services.

#🚀 Overview

The Evo AI platform allows:

  • Creation and management of AI agents
  • Integration with different language models
  • Client management and MCP server configuration
  • Custom tools management
  • Google Agent Development Kit (ADK): Base framework for agent development
  • CrewAI Support: Alternative framework for agent development (in development)
  • JWT authentication with email verification
  • Agent 2 Agent (A2A) Protocol Support: Interoperability between AI agents
  • Workflow Agent with LangGraph: Building complex agent workflows
  • Secure API Key Management: Encrypted storage of API keys
  • Agent Organization: Folder structure for organizing agents by categories

#🤖 Agent Types

Evo AI supports different types of agents that can be flexibly combined:

#1. LLM Agent (Language Model)

Agent based on language models like GPT-4, Claude, etc. Can be configured with tools, MCP servers, and sub-agents.

#2. A2A Agent (Agent-to-Agent)

Agent that implements Google's A2A protocol for agent interoperability.

#3. Sequential Agent

Executes a sequence of sub-agents in a specific order.

#4. Parallel Agent

Executes multiple sub-agents simultaneously.

#5. Loop Agent

Executes sub-agents in a loop with a defined maximum number of iterations.

#6. Workflow Agent

Executes sub-agents in a custom workflow defined by a graph structure using LangGraph.

#7. Task Agent

Executes a specific task using a target agent with structured task instructions.

#🛠️ Technologies

#Backend

  • FastAPI: Web framework for building the API
  • SQLAlchemy: ORM for database interaction
  • PostgreSQL: Main database
  • Alembic: Migration system
  • Pydantic: Data validation and serialization
  • Uvicorn: ASGI server
  • Redis: Cache and session management
  • JWT: Secure token authentication
  • SendGrid/SMTP: Email service for notifications (configurable)
  • Jinja2: Template engine for email rendering
  • Bcrypt: Password hashing and security
  • LangGraph: Framework for building stateful, multi-agent workflows

#Frontend

  • Next.js 15: React framework with App Router
  • React 18: User interface library
  • TypeScript: Type-safe JavaScript
  • Tailwind CSS: Utility-first CSS framework
  • shadcn/ui: Modern component library
  • React Hook Form: Form management
  • Zod: Schema validation
  • ReactFlow: Node-based visual workflows
  • React Query: Server state management

#📊 Langfuse Integration (Tracing & Observability)

Evo AI platform natively supports integration with Langfuse for detailed tracing of agent executions, prompts, model responses, and tool calls, using the OpenTelemetry (OTel) standard.

#How to configure

  1. Set environment variables in your .env:

    LANGFUSE_PUBLIC_KEY="pk-lf-..."
LANGFUSE_SECRET_KEY="sk-lf-..."
OTEL_EXPORTER_OTLP_ENDPOINT="https://cloud.langfuse.com/api/public/otel"

  2. View in the Langfuse dashboard

    • Access your Langfuse dashboard to see real-time traces.

#🤖 Agent 2 Agent (A2A) Protocol Support

Evo AI implements the Google's Agent 2 Agent (A2A) protocol, enabling seamless communication and interoperability between AI agents.

For more information about the A2A protocol, visit Google's A2A Protocol Documentation.

#📋 Prerequisites

#Backend

  • Python: 3.10 or higher
  • PostgreSQL: 13.0 or higher
  • Redis: 6.0 or higher
  • Git: For version control
  • Make: For running Makefile commands

#Frontend

  • Node.js: 18.0 or higher
  • pnpm: Package manager (recommended) or npm/yarn

#🔧 Installation

#1. Clone the Repository

git clone https://github.com/EvolutionAPI/evo-ai.git
cd evo-ai

#2. Backend Setup

#Virtual Environment and Dependencies

# Create and activate virtual environment
make venv
source venv/bin/activate  # Linux/Mac
# or on Windows: venv\Scripts\activate

# Install development dependencies
make install-dev

#Environment Configuration

# Copy and configure backend environment
cp .env.example .env
# Edit the .env file with your database, Redis, and other settings

#Database Setup

# Initialize database and apply migrations
make alembic-upgrade

# Seed initial data (admin user, sample clients, etc.)
make seed-all

#3. Frontend Setup

#Install Dependencies

# Navigate to frontend directory
cd frontend

# Install dependencies using pnpm (recommended)
pnpm install

# Or using npm
# npm install

# Or using yarn
# yarn install

#Frontend Environment Configuration

# Copy and configure frontend environment
cp .env.example .env
# Edit .env with your API URL (default: http://localhost:8000)

The frontend .env should contain:

NEXT_PUBLIC_API_URL=http://localhost:8000

#🚀 Running the Application

#Development Mode

#Start Backend (Terminal 1)

# From project root
make run
# Backend will be available at http://localhost:8000

#Start Frontend (Terminal 2)

# From frontend directory
cd frontend
pnpm dev

# Or using npm/yarn
# npm run dev
# yarn dev

# Frontend will be available at http://localhost:3000

#Production Mode

#Backend

make run-prod    # Production with multiple workers

#Frontend

cd frontend
pnpm build && pnpm start

# Or using npm/yarn
# npm run build && npm start
# yarn build && yarn start

#🐳 Docker Installation

#Full Stack with Docker Compose

# Build and start all services (backend + database + redis)
make docker-build
make docker-up

# Initialize database with seed data
make docker-seed

#Frontend with Docker

# From frontend directory
cd frontend

# Build frontend image
docker build -t evo-ai-frontend .

# Run frontend container
docker run -p 3000:3000 -e NEXT_PUBLIC_API_URL=http://localhost:8000 evo-ai-frontend

Or using the provided docker-compose:

# From frontend directory
cd frontend
docker-compose up -d

#🎯 Getting Started

After installation, follow these steps:

  1. Access the Frontend: Open http://localhost:3000
  2. Create Admin Account: Use the seeded admin credentials or register a new account
  3. Configure MCP Server: Set up your first MCP server connection
  4. Create Client: Add a client to organize your agents
  5. Build Your First Agent: Create and configure your AI agent
  6. Test Agent: Use the chat interface to interact with your agent

#Default Admin Credentials

After running the seeders, you can login with:

  • Email: Check the seeder output for the generated admin email
  • Password: Check the seeder output for the generated password

#🖥️ API Documentation

The interactive API documentation is available at:

  • Swagger UI: http://localhost:8000/docs
  • ReDoc: http://localhost:8000/redoc

#👨‍💻 Development Commands

#Backend Commands

# Database migrations
make alembic-upgrade            # Update database to latest version
make alembic-revision message="description"  # Create new migration

# Seeders
make seed-all                   # Run all seeders

# Code verification
make lint                       # Verify code with flake8
make format                     # Format code with black

#Frontend Commands

# From frontend directory
cd frontend

# Development
pnpm dev                        # Start development server
pnpm build                      # Build for production
pnpm start                      # Start production server
pnpm lint                       # Run ESLint

#🚀 Configuration

#Backend Configuration (.env file)

Key settings include:

# Database settings
POSTGRES_CONNECTION_STRING="postgresql://postgres:root@localhost:5432/evo_ai"

# Redis settings
REDIS_HOST="localhost"
REDIS_PORT=6379

# AI Engine configuration
AI_ENGINE="adk"  # Options: "adk" (Google Agent Development Kit) or "crewai" (CrewAI framework)

# JWT settings
JWT_SECRET_KEY="your-jwt-secret-key"

# Email provider configuration
EMAIL_PROVIDER="sendgrid"  # Options: "sendgrid" or "smtp"

# Encryption for API keys
ENCRYPTION_KEY="your-encryption-key"

#Frontend Configuration (.env file)

# API Configuration
NEXT_PUBLIC_API_URL="http://localhost:8000"  # Backend API URL

Note: While Google ADK is fully supported, the CrewAI engine option is still under active development. For production environments, it's recommended to use the default "adk" engine.

#🔐 Authentication

The API uses JWT (JSON Web Token) authentication with:

  • User registration and email verification
  • Login to obtain JWT tokens
  • Password recovery flow
  • Account lockout after multiple failed login attempts

#🚀 Star Us on GitHub

If you find EvoAI useful, please consider giving us a star! Your support helps us grow our community and continue improving the product.

Star History Chart

#🤝 Contributing

We welcome contributions from the community! Please read our Contributing Guidelines for more details.

#📄 License

This project is licensed under the Apache License 2.0.

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