ElizaOS provides a robust suite of tools to design, test, and deploy autonomous AI agents within TypeScript projects. Developers define agent personalities, goals, and memory hierarchies, then leverage ElizaOS's planning system to outline task workflows. Its modular connector architecture simplifies integrating with communication platforms—Discord, Telegram, Slack, X—and blockchain networks via Web3 adapters. ElizaOS supports multiple LLM backends (OpenAI, Anthropic, Llama, Gemini), allowing seamless switching between models. Plugin support extends functionality with custom skills, logging, and observability features. Through its CLI and SDK, teams can iterate on agent configurations, monitor live performance, and scale deployments in cloud environments or on-premises. ElizaOS empowers companies to automate customer interactions, social media engagement, and business processes with autonomous digital workers.

Nova provides a comprehensive toolkit for creating autonomous AI agents in Python. It offers a planner that decomposes goals into actionable steps, a plugin system to integrate any external tools or APIs, and a memory module to store and recall conversation context. Developers can configure custom behaviors, track agent decisions through logs, and extend functionality with minimal code. Nova streamlines the entire agent lifecycle from design to deployment.

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.