Orchestra is a Python framework for building LLM-powered agentic pipelines and multi-agent teams, designed for developers and researchers. It offers a modular architecture, enabling dynamic task decomposition and coordination among agents, with built-in support for numerous LLM providers and a wide array of tools.

How It Works

Orchestra implements a cognitive architecture where agents can act as both executors and conductors. This allows for complex, phased task execution and dynamic coordination, moving beyond simple routing. Agents are assigned roles, goals, and tools, which are defined via simple docstrings. The framework provides a consistent interface for various LLM providers and supports streaming output for real-time interaction.

Quick Start & Requirements

• Install using pip: pip install mainframe-orchestra
• Requires Python.
• Example usage and detailed documentation are available.

Highlighted Details

• Supports a broad range of LLM providers including OpenAI, Anthropic, Ollama, Groq, Gemini, and more.
• Offers extensive built-in tools for data operations, web/API integration, financial analysis, and media processing.
• Enables custom tool creation with static or instance methods.
• Facilitates multi-agent team orchestration using Conduct and Compose tools.
• Integrates with Model Context Protocol (MCP) servers.
• Supports asynchronous streaming of LLM responses.

Maintenance & Community

• A fork and further development of TaskflowAI.
• Community contributions are welcomed via pull requests.
• Support and issue tracking are managed through the GitHub repository.

Licensing & Compatibility

• Released under the Apache License 2.0.
• Permissive license suitable for commercial use and integration with closed-source projects.

Limitations & Caveats

The framework is actively developed, and while it supports many LLMs, specific model versions or providers might require API keys or specific configurations. The multi-agent orchestration, while powerful, can introduce complexity in debugging and managing agent interactions.