Explore Agent Recipes

A workflow where the output of one LLM call becomes the input for the next. This sequential design allows for structured reasoning and step-by-step task completion.

A low-latency workflow where inputs are dynamically routed to the most appropriate LLM instance or configuration, optimizing efficiency and specialization.

A sophisticated workflow for AI-powered code generation with built-in clarification loops and test-driven development practices.

An enhanced LLM architecture that combines retrieval, tools, and memory systems to extend the model's capabilities beyond its training data.

A workflow that distributes tasks across multiple LLM calls simultaneously, aggregating results to handle complex or large-scale operations efficiently.

A workflow with a central orchestrator directing multiple worker LLMs to perform subtasks, synthesizing their outputs for complex, coordinated operations.

A feedback loop workflow where LLM-generated outputs are evaluated, refined, and optimized iteratively to improve accuracy and relevance.

An agent-based workflow where LLMs act autonomously within a loop, interacting with their environment and receiving feedback to refine their actions and decisions.