OpenAgent offers a comprehensive framework for developing autonomous AI agents that can understand tasks, plan multi-step actions, and interact with external services. By integrating with LLMs such as OpenAI and Anthropic, it enables natural language reasoning and decision-making. The platform features a pluggable tool system for executing HTTP requests, file operations, and custom Python functions. Memory management modules allow agents to store and retrieve contextual information across sessions. Developers can extend functionality via plugins, configure real-time streaming of responses, and utilize built-in logging and evaluation tools to monitor agent performance. OpenAgent simplifies orchestration of complex workflows, accelerates prototyping of intelligent assistants, and ensures modular architecture for scalable AI applications.

llm-lab provides a flexible toolkit for creating intelligent agents using large language models. It includes an agent orchestration engine, support for custom prompt templates, memory and state tracking, and seamless integration with external APIs and plugins. Users can write scenarios, define toolchains, simulate interactions, and collect performance logs. The framework also offers a built-in testing suite to validate agent behavior against expected outcomes. Extensible by design, llm-lab enables developers to swap LLM providers, add new tools, and evolve agent logic through iterative experimentation.