AgentSpring: A Framework for Building AI Agents

🌟 What is AgentSpring?

AgentSpring is an open-source framework designed to simplify the development, deployment, and management of AI agents. It provides a robust set of tools and abstractions that enable developers to create sophisticated AI-powered applications with minimal boilerplate code.

🚀 Key Features

• 🤖 Intelligent Agents: Create stateful, context-aware AI agents with custom behaviors
• 🔧 Tool System: Extend agent capabilities with custom tools and functions
• 🧩 Plugin Architecture: Modular design for easy extension and customization
• ⚡ FastAPI Backend: Built on FastAPI for high-performance API endpoints
• 📦 Container Ready: Docker and Docker Compose support for easy deployment
• 🔒 Secure: Built-in authentication and authorization
• 📊 Monitoring: Integrated logging and monitoring capabilities

🏗️ Project Structure

agentspring/
├── agents/              # Core agent implementations
├── api/                 # FastAPI application and endpoints
├── db/                  # Database models and migrations
├── tools/               # Built-in tools for agents
├── demos/               # Example implementations
│   └── travel_planner/  # Example travel planning agent
├── tests/               # Test suite
├── Dockerfile           # Docker configuration
└── requirements.txt     # Python dependencies

🚀 Quick Start

Prerequisites

• Python 3.8+
• PostgreSQL (for production)
• Redis (for caching and message brokering)
• Docker (optional, for containerized deployment)

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/agentspring.git
   cd agentspring

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

Running with Docker (Recommended)

1. Start all services:

   docker-compose up -d

2. Check the logs:

   docker-compose logs -f

3. Access the API at http://localhost:8000

🧑‍💻 Basic Usage

Creating a Simple Agent

from agentspring import Agent, Message, MessageRole

class GreetingAgent(Agent):
    """A simple agent that greets users."""
    
    async def execute(self, messages, context=None):
        last_message = messages[-1]
        name = last_message.get('name', 'there')
        return Message(
            role=MessageRole.ASSISTANT, 
            content=f"Hello, {name}! How can I assist you today?"
        )

# Initialize and use the agent
agent = GreetingAgent()
response = await agent.execute([{"role": "user", "content": "Hi!", "name": "Alex"}])
print(response.content)

Running the Travel Planner Demo

1. Navigate to the demo directory:

   cd demos/travel_planner

2. Run the demo:

   python travel_planner.py

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details on how to get started.

1. Fork the repository
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

🛣️ Roadmap

Q4 2025 - Core Stability & Security

• Base agent framework

  • ✅ Implemented with BaseAgent class
  • ✅ Supports async execution
  • ✅ Configurable via AgentConfig

• Tool system

  • ✅ ToolRegistry for managing tools
  • ✅ Tool registration and discovery
  • ✅ Support for async/sync tools
  • ✅ Tool validation and documentation

• Plugin architecture (Partially implemented)

  • ✅ Basic plugin structure exists
  • Dynamic loading/unloading
  • Dependency management
  • Version compatibility
  • Plugin isolation

• Basic API endpoints

  • ✅ FastAPI-based REST API
  • ✅ Agent execution endpoints
  • ✅ Request/response handling
  • ✅ Error handling

• Production Security

  • Rate limiting and throttling
  • API key rotation and management
  • Input validation and sanitization
  • CORS and security headers
  • Audit logging

• Observability

  • Structured logging
  • Metrics collection (Prometheus)
  • Distributed tracing (OpenTelemetry)
  • Health check endpoints

Q1 2026 - Enhanced Capabilities & Scalability

• Advanced Workflow Engine

  • DAG-based workflows
  • Conditional branching
  • Error handling and retries
  • Timeouts and circuit breakers

• State Management

  • Persistent workflow state
  • Checkpointing and resumability
  • Distributed locking

• Scalability

  • Horizontal scaling support
  • Load balancing
  • Connection pooling
  • Background task processing

Q2 2026 - Enterprise Features

• Authentication & Authorization
  • OAuth2/OIDC integration
  • Role-based access control (RBAC)
  • Fine-grained permissions

Q3 2026 - Developer Experience & Ecosystem

• Developer Tools

  • CLI for management
  • Local development environment
  • Testing framework
  • Code generation

• Documentation

  • API reference
  • User guides
  • Tutorials
  • Example projects

• Integration Ecosystem

  • Plugin marketplace
  • Webhooks
  • WebSocket support
  • gRPC interface

Q4 2026 - Advanced Features & Optimization

• Performance

  • Caching layer
  • Query optimization
  • Memory management
  • Async I/O optimization

• AI/ML Enhancements

  • Model versioning
  • A/B testing
  • Feedback loops
  • Automated evaluation

• Compliance

  • GDPR compliance
  • Data encryption at rest/transit
  • Compliance documentation
  • Audit trails

Future Considerations

• Edge Computing

  • Lightweight runtime
  • Offline capabilities
  • Edge synchronization

• Advanced Analytics

  • Usage analytics
  • Performance metrics
  • Cost tracking
  • Anomaly detection

• Community & Support

  • Community forum
  • Commercial support
  • Training programs
  • Certification