amrita_core 0.9.1

High performance, flexable, lightweight agent framework.

AmritaCore

AmritaCore is a lightweight agent framework with infrastructure-level positioning, serving as the foundational building block for intelligent agent development. AmritaCore is designed to be interactive-first, enabling real-time, responsive agent applications through its native async streaming architecture. Think of it as providing the essential "operating system" capabilities for AI agents – offering core primitives and abstractions that enable robust, production-ready agent applications without the overhead of heavyweight frameworks.

🚀 What is AmritaCore?

AmritaCore is not a replacement for existing frameworks like LangChain or LlamaIndex. Instead, it is a lightweight, infrastructure-focused agent framework designed to provide the essential building blocks for AI agent development. Built with modern Python technologies, it delivers fundamental components needed for AI-powered applications with features like event-driven architecture, tool integration, and multi-modal support – all while maintaining minimal dependencies and maximum performance.

🎯 Mission and Value Proposition

The mission of AmritaCore is to provide a lightweight yet powerful foundation for agent development that prioritizes simplicity, performance, and flexibility. Our core value propositions include:

• Stream-based Design: All message outputs are designed as asynchronous streams for real-time responses
• Security: Built-in cookie security detection to ensure session safety
• Vendor Agnostic: Data types and conversation management are independent of specific providers, offering high portability
• Extensibility: Integrated MCP client in extension mechanisms for enhanced system scalability

🔑 Key Features

1. Every is a Stream: All message outputs are asynchronous stream-based designs supporting real-time responses
2. Cookie Security Detection: Built-in cookie security detection functionality to protect session security
3. Provider Independent Mechanism: Data types and conversation management are independent of specific vendors, with high portability
4. MCP Client Support: Extension mechanisms integrate MCP clients, enhancing system expansion capabilities
5. Event-Driven Architecture: Comprehensive event system for flexible and reactive agent behavior
6. Tool Integration Framework: Robust system for integrating external tools and services
7. Advanced Memory Management: Sophisticated context handling with automatic summarization and token optimization
8. Built-in Anthropic support: AmritaCore provides a native adapter for Anthropic's AI models
9. High-Performance: Lightweight and efficient, with high performance.

📖 Documentation

Please view Docs for more information.

🛠️ Quick Start

To quickly start using AmritaCore, check out the examples in the demo directory. The basic example demonstrates how to initialize the core, configure settings, and run a simple chat session with the AI assistant.

🤝 Contributing

We welcome contributions! Please see our contribution guidelines for more information.

📄 License

This project is licensed under the Apache 2.0 License - see the LICENSE file for details.

Notes

All versions of AmritaCore are released under the Apache 2.0 License (Although the past versions are released under the MIT/LGPL V3 License, when this readme is created, we will release all versions under the Apache 2.0 License).

Other files

• CONTRIBUTING.md - Contribution guidelines
• CODE_OF_CONDUCT.md - Code of conduct
• ZH-CN.md
• EN-US.md

Unstable Features

• Python 3.14+ Supporting: we are not sure if it will work well on Python 3.14+(No GIL Version).