<2-3 sentences summarising what the project addresses and solves, the target audience, and the benefit.> LlamaIndex Workflows provides an event-driven, async-first framework for orchestrating complex AI application execution flows. It enables developers to build robust, production-ready systems like AI agents, document processing pipelines, and multi-model applications by managing multi-step processes, decision-making, and state across asynchronous operations.

How It Works

The core design leverages Python's asyncio for an event-driven architecture. Workflows are composed of asynchronous steps that process incoming events from queues and emit new events to others. This approach facilitates routing between capabilities, parallel processing, complex looping, and persistent state management across workflow executions, simplifying the development of sophisticated AI applications.

Quick Start & Requirements

• Install: pip install llama-index-workflows
• Prerequisites: Requires a Python environment with asyncio support. Optimized for integration into existing async applications like FastAPI or Jupyter Notebooks.
• Documentation: Links to "complete documentation" and "more examples" are available within the project's resources.

Highlighted Details

• Async-first: Built entirely around Python's asynchronous programming model for non-blocking execution.
• Event-driven: Organizes logic around discrete events and processing steps, enhancing testability and modularity.
• State Management: Each workflow run is self-contained, with capabilities for saving, serializing, and resuming workflow state.
• Observability: Integrated out-of-the-box support for observability tools such as OpenTelemetry, Arize Phoenix, and Langfuse.

Maintenance & Community

No specific details regarding maintainers, community channels (e.g., Discord, Slack), sponsorships, or roadmap were found in the provided README content.

Licensing & Compatibility

The license type and compatibility notes for commercial use or closed-source linking are not specified in the provided README content.

Limitations & Caveats

The framework is designed to work best within asynchronous Python applications. While state management is a key feature, specific details on potential limitations or unsupported scenarios are not elaborated upon in the provided text.