TreeInstruct provides a framework to build hierarchical, decision-tree based prompting pipelines for large language models. Users can define nodes representing prompts or function calls, set conditional branches based on model output, and execute the tree to guide complex workflows. It supports integration with OpenAI and other LLM providers, offering logging, error handling, and customizable node parameters to ensure transparency and flexibility in multi-turn interactions.

CodeInterpreter CodeBox is designed to simplify the embedding of interactive coding experiences within web applications. It offers a browser-based code editor with syntax highlighting and real-time Python execution by connecting to the ChatGPT Code Interpreter plugin. Developers can upload and download files, run data analysis scripts, generate plots, and display results inline. CodeBox handles communication with OpenAI’s API, manages execution contexts, and provides hooks for custom event handling, enabling rapid development of AI-powered tools, educational platforms, and data-driven dashboards without managing a separate backend execution environment.

Labs is an open-source, embeddable domain-specific language designed for defining and executing AI agents using large language models. It provides constructs to declare prompts, manage context, conditionally branch, and integrate external tools (e.g., databases, APIs). With Labs, developers describe agent workflows as code, orchestrating multi-step tasks like data retrieval, analysis, and generation. The framework compiles DSL scripts into executable pipelines that can be run locally or in production. Labs supports interactive REPL, command-line tooling, and integrates with standard LLM providers. Its modular architecture allows easy extension with custom functions and utilities, promoting rapid prototyping and maintainable agent development. The lightweight runtime ensures low overhead and seamless embedding in existing applications.