Agentflow provides a structured framework for orchestrating complex workflows powered by Large Language Models (LLMs). It targets users who need to move beyond simple chat interactions and build reliable, step-by-step LLM-driven processes, offering a middle ground between conversational AI and fully autonomous agents. The primary benefit is enabling the creation of dynamic, repeatable LLM workflows using human-readable JSON definitions.

How It Works

Agentflow executes LLM workflows defined in JSON files, allowing for sequential task execution and dynamic variable injection. Each task in the workflow can include instructions for the LLM, custom function calls, and adjustable parameters like temperature. This approach offers a controlled execution environment, mitigating the unreliability often seen in highly autonomous agents while providing more power than simple chat interfaces.

Quick Start & Requirements

• Install: pip install -r requirements.txt
• Prerequisites: OpenAI API key, Python 3.x
• Run: python -m run --flow=example
• Docs: [Not explicitly linked, but examples are provided]

Highlighted Details

• Workflows defined in human-readable JSON.
• Supports custom function integration for extended capabilities.
• Allows variable substitution for dynamic workflow execution.
• Includes a verbose mode for real-time task monitoring.

Maintenance & Community

• Project appears to be actively developed by a single primary contributor.
• No community links (Discord, Slack) or roadmap are provided in the README.

Licensing & Compatibility

• License: MIT License.
• Permissive license allows for commercial use and integration into closed-source projects.

Limitations & Caveats

The project is explicitly stated to be "currently in development," suggesting potential instability or ongoing changes. There is no mention of testing infrastructure or community support, which could impact long-term maintainability and reliability.