Chat2Graph integrates with the TuGraph graph database to deliver a conversational interface for graph data exploration. Through pre-built connectors and a prompt-engineering layer, it translates user intents into valid graph queries, handles schema discovery, suggests optimizations, and executes queries in real time. Results can be rendered as tables, JSON, or network visualizations via a web UI. Developers can customize prompt templates, integrate custom plugins, or embed Chat2Graph in Python applications. It's ideal for rapid prototyping of graph-powered applications and enables domain experts to analyze relationships in social networks, recommendation systems, and knowledge graphs without writing manual Cypher syntax.

Nuzon-AI provides a Python-based agent framework that lets you define tasks, manage conversational memory, and extend capabilities via plugins. It supports integration with major LLMs (OpenAI, local models), enabling agents to perform web interactions, data analysis, and automated workflows. The architecture includes a skill registry, tool invocation system, and multi-agent orchestration layer, allowing you to compose agents for customer support, research assistance, and personal productivity. With configuration files, you can tailor each agent’s behavior, memory retention policy, and logging for debugging or audit purposes.