Labs provides a domain-specific language for orchestrating large language model workflows. With Labs, developers can define agents comprising prompts, tools, and decision logic, enabling autonomous task execution. The lightweight, embeddable DSL simplifies creating, testing, and deploying AI agents, with support for customizable pipelines, conditional flows, and external APIs. Labs abstracts underlying LLM integrations, offering seamless extensibility and rapid prototyping for AI-driven automation.
Labs provides a domain-specific language for orchestrating large language model workflows. With Labs, developers can define agents comprising prompts, tools, and decision logic, enabling autonomous task execution. The lightweight, embeddable DSL simplifies creating, testing, and deploying AI agents, with support for customizable pipelines, conditional flows, and external APIs. Labs abstracts underlying LLM integrations, offering seamless extensibility and rapid prototyping for AI-driven automation.
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.
Who will use Labs?
AI and ML developers
Software engineers integrating LLMs
Startups building AI-driven automation
NLP researchers
Product teams creating autonomous agents
How to use the Labs?
Step1: Install Labs via npm or pip and configure your environment.
Step2: Write a Labs DSL script to define prompts, context, and control flow.
Step3: Use the Labs CLI or REPL to run and test your agent locally.
Step4: Integrate the Labs runtime into your application code for production.
Step5: Extend with custom tools, APIs, or providers using the SDK.