ReliveAI is a cutting-edge no-code platform designed to help users build intelligent, operational AI agents with ease. Whether you need to create conversational agents, automate workflows, or develop AI-powered business solutions, ReliveAI provides a user-friendly interface and robust tools to accomplish all of these tasks. The platform supports building workflows and agentic workflows that can remember and adapt to your business needs, ensuring seamless operation across various industries.

At its core, AGIFlow provides an intuitive canvas where users can assemble AI agents into dynamic workflows, defining triggers, conditional logic, and data exchanges between agents. Each agent node can execute custom code, call external APIs, or leverage pre-built models for NLP, vision, or data processing tasks. With built-in connectors to popular databases, web services, and messaging platforms, AGIFlow streamlines integration and orchestration across systems. Version control and rollback features allow teams to iterate rapidly, while real-time logging, metrics dashboards, and alerting ensure transparency and reliability. Once workflows are tested, they can be deployed on scalable cloud infrastructure with scheduling options, enabling businesses to automate complex processes such as report generation, customer support routing, or research pipelines.

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.

AgentsFlow abstracts each AI agent as a node in a directed graph, enabling developers to visually and programmatically design complex pipelines. Each node can represent an LLM call, data preprocessing task, or decision logic, and can be connected to trigger subsequent actions based on outputs or conditions. The framework supports branching, loops, and parallel execution, with built-in error handling, retries, and timeout controls. AgentsFlow integrates with major LLM providers, custom models, and external APIs. Its monitoring dashboard offers real-time logs, metrics, and flow visualization, simplifying debugging and optimization. With a plugin system and REST API, AgentsFlow can be extended and integrated into CI/CD pipelines, cloud services, or custom applications, making it ideal for scalable, production-grade AI workflows.

OCO-Agent leverages OpenAI-compatible language models to transform plain-language prompts into actionable workflows. It provides a flexible plugin system for integrating external APIs, shell commands, and data-processing routines. The framework maintains conversation history and context in memory, enabling long-running, multi-step tasks. With a CLI interface and Docker support, OCO-Agent accelerates prototyping and deployment of intelligent assistants for operations, analytics, and developer productivity.

AutoML-Agent provides a versatile Python-based framework that orchestrates every stage of the machine learning lifecycle through an intelligent agent interface. Starting with automated data ingestion, it performs exploratory analysis, missing value handling, and feature engineering using configurable pipelines. Next, it conducts model architecture search and hyperparameter optimization powered by large language models to suggest optimal configurations. The agent then runs experiments in parallel, tracking metrics and visualizations to compare performance. Once the best model is identified, AutoML-Agent streamlines deployment by generating Docker containers or cloud-native artifacts compatible with common MLOps platforms. Users can further customize workflows via plugin modules and monitor model drift over time, ensuring robust, efficient, and reproducible AI solutions in production environments.