URL: https://github.com/HenningGC/lwagents

LWAgents: A Library for Graph-Driven AI Agents with Tool Integration
LWAgents is a flexible and extensible Python library designed for building graph-driven workflows powered by AI agents. It provides a robust framework for creating, managing, and executing workflows where nodes represent states or tasks, edges represent transitions, and AI agents or deterministic logic decide the next steps.

Whether you're designing task-oriented AI systems, probabilistic workflows, or integrating external tools into your decision-making processes, LWAgents offers the structure and flexibility to get started quickly.

Key Features
Graph-Based Workflow Execution:

Represent workflows as graphs with nodes (tasks) and edges (transitions).
Seamlessly execute workflows step-by-step.
AI Agent Integration:

Integrate language models (like OpenAI's GPT) as decision-making agents.
Use agents for routing, decision-making, or task execution.
Tooling Support:

Extend functionality by defining custom tools and decorators.
Easily integrate tools for calculations, API calls, or other tasks.
Dynamic Transitions:

Support for conditional transitions via edge logic.
Direct traversal capabilities allow agents or nodes to dynamically decide the next step.
State Management:

Built-in support for maintaining and updating global state during execution.
Record detailed histories of execution for debugging and analysis.
Extensibility:

Modular architecture enables easy customization and scaling.
Add new nodes, tools, or agents with minimal setup.