Alphora is a production-ready, full-stack framework designed for building composable, modular, and maintainable AI agent applications. It targets developers seeking to streamline the creation of sophisticated AI agents, offering a comprehensive suite of features including agent orchestration, prompt engineering, tool execution, and memory management, all built upon an async-first, OpenAI-compatible foundation.

How It Works

Alphora employs an async-first architecture centered around a ReAct (Reasoning-Action) loop, which automates tool orchestration, retries, and iteration control. A key design principle is agent derivation, allowing child agents to inherit context, LLM configurations, and memory from parent agents, facilitating hierarchical agent structures. The framework integrates tools seamlessly via a zero-config @tool decorator that auto-generates OpenAI function calling schemas, ensuring type safety with Pydantic V2 validation and enabling parallel execution. Its model layer is OpenAI-compatible, supporting various LLMs and offering features like load balancing and multimodal input handling.

Quick Start & Requirements

• Primary install: pip install alphora
• Prerequisites: Docker is recommended for secure agent code execution via the Sandbox.create_docker() option. Environment variables or programmatic configuration are needed for LLM API keys and base URLs.
• Links: Official Docs, Quick Start, and Examples are available.

Highlighted Details

• Agent Derivation: Enables building hierarchies of agents that inherit LLM, memory, and configuration, facilitating shared context.
• Zero-Config Tools: @tool decorator automatically generates OpenAI function calling schemas from type hints and docstrings, with Pydantic V2 validation.
• Multimodal Support: Unified Message class handles text, images, audio, and video inputs.
• Secure Sandbox: Provides isolated execution environments (Local/Docker) with resource limits, package management, and configurable security policies.
• One-Line API Deployment: Publish agents as OpenAI-compatible REST APIs using publish_agent_api().

Maintenance & Community

The project is crafted by the AlphaData Team, with several named core developers. It includes community-contributed skills under alphora_community/skills, such as deep-research workflows and data-quality auditing tools.

Licensing & Compatibility

This project is licensed under the Apache License 2.0. Contributions require acceptance of a Contributor License Agreement (CLA). The Apache 2.0 license is generally permissive for commercial use and integration into closed-source projects.

Limitations & Caveats

The secure sandbox functionality is most robust when using Docker. Contributions require signing a CLA, which may be a consideration for some potential contributors.