Developing AI applications that leverage large language models (LLMs) often requires extensive prompt engineering and complex infrastructure management, increasing development time and costs. Existing solutions lack streamlined environments for building, deploying, and managing retrieval-driven AI applications.

Griptape provides an enterprise-grade Python framework and cloud infrastructure designed to simplify the development, deployment, and management of retrieval-driven AI applications. The framework offers clear abstractions for building generative AI agents, systems of agents, pipelines, workflows, and Retrieval-Augmented Generation (RAG) implementations, eliminating the need for extensive prompt engineering. Griptape Cloud automates data preparation (ETL), retrieval as a service (RAG), and structure runtime, allowing developers to focus on application logic rather than infrastructure management. The platform also offers tools to monitor performance, reliability, and spending across the organization, with policy enforcement for each user, structure, task, and query.

Python framework for building AI agents, pipelines, and workflows without extensive prompt engineering

Off-Prompt™ technology for enhanced security, performance, and lower costs

Griptape Cloud for deploying and running ETL, RAG, and structures

Automated data preparation (ETL) and retrieval as a service (RAG)

Structure runtime for serverless application execution

Monitoring tools for performance, reliability, and spending

Policy enforcement for users, structures, tasks, and queries

Integration with third-party monitoring services

Modular design with swappable drivers for LLMs, vector stores, and more

Built-in and custom tools for interacting with data and services

Griptape is designed for enterprise developers and AI engineers who need a comprehensive platform for building, deploying, and managing retrieval-driven AI applications.