ZhipuAI Agent to OpenAI is a specialized agent framework designed to bridge ZhipuAI’s chat completion services with OpenAI-style agent interfaces. It provides a Python SDK that mirrors OpenAI’s function calling paradigm and supports third-party tool integrations, enabling developers to define custom tools, call external APIs, and maintain conversation context across turns. The framework handles request orchestration, dynamic prompt construction, and response parsing, returning structured outputs compatible with OpenAI’s ChatCompletion format. By abstracting API differences, it allows seamless leveraging of ZhipuAI’s Chinese-language models within existing OpenAI-oriented workflows. Ideal for building chatbots, virtual assistants, and automated workflows that require Chinese LLM capabilities without changing established OpenAI-based codebases.

LazyLL external APIs or custom utilities. Agents execute defined tasks through sequential or branching workflows, supporting synchronous or asynchronous operation. LazyLLM also offers built-in logging, testing utilities, and extension points for customizing prompts or retrieval strategies. By handling the underlying orchestration of LLM calls, memory management, and tool execution, LazyLLM enables rapid prototyping and deployment of intelligent assistants, chatbots, and automation scripts with minimal boilerplate code.

AnYi AI Agent Framework helps developers integrate autonomous AI agents into their applications. Agents can plan and execute multi-step tasks, leverage external tools and APIs, and maintain conversation context through configurable memory modules. The framework abstracts interactions with various LLM providers and supports custom tool and memory backends. With built-in logging, monitoring, and asynchronous execution, AnYi accelerates deployment of intelligent assistants for research, customer support, data analysis, or any workflow requiring automated reasoning and action.

e2e-chainlit-chatbot is a sample project demonstrating the complete development lifecycle of a conversational AI agent using Chainlit. The repository includes end-to-end code for launching a local web server that hosts an interactive chat interface, integrating with large language models for responses, and managing conversation context across messages. It features customizable prompt templates, multi-agent workflows, and real-time streaming of responses. Developers can configure API keys, adjust model parameters, and extend the system with custom logic or integrations. With minimal dependencies and clear documentation, this project accelerates experimentation with AI-driven chatbots and provides a solid foundation for production-grade conversational assistants. It also includes examples for customizing front-end components, logging, and error handling. Designed for seamless integration with cloud platforms, it supports both prototype and production use cases.