Griptape AI Framework

Modular and Composable Framework for Building and Deploying LLM-Powered Applications

Why Griptape?

The Griptape AI Framework is a modular open-source Python framework designed to empower developers to build and deploy LLM-powered applications with powerful primitives such as agents, pipelines, and workflows. Its composability makes it ideal for creating conversational and event-driven AI applications that can access and manipulate data safely and reliably.

The Framework provides a unique balance between predictability and creativity, allowing developers to harness the full potential of large language models (LLMs) while enforcing strict trust boundaries and schema validation.

AI Framework Docs

Benefits of the Griptape AI Framework

Modularity and Composability

  1. All framework primitives are useful and usable on their own, making it easy to integrate them into larger systems.

Technology
Agnostic

  1. Designed to work with any capable LLM, data store, and backend through the abstraction of drivers.

Data
Security

  1. You can choose to keep your data off prompt, ensuring secure handling of big data at reduced cost.

Minimal Prompt
Engineering

  1. Use Python for ease of reasoning and implementation, reducing the need for complex and unpredictable prompt 'engineering'.
Go to Griptape GitHub

Key Features

  • Structures: Powerful primitives for creating systems of autonomous agents, and for modelling linear pipelines and flexible workflows.
  • Prompt Drivers: Process prompts using different LLMs and models for versatile AI interactions, giving the flexibility to optimize for capability, latency or cost.
  • Drivers for Integration into 3rd Party Systems: Use vector databases, embedding models, web scrapers, audio transcription models and other advanced 3rd party capabilities within your applications
  • Data handling: Loaders, chunkers, and artifacts make it simple to work with data
  • Engines: Common abstractions for capabilities that are available across multiple models and model providers such as retrieval augment generation, summarization and extraction
  • Tools: Supplement the capabilities of LLMs with powerful tools from a library of Griptape provided and supported tools and community contributed tools. Quickly develop your own tools using templates provided by Griptape.
  • Conversation Memory:  Persist and load conversation memory for your applications. Choose conversation memory strategies to match the needs of your application.
Read AI Framework Docs

Use Cases

Retrieval
Augmented
Generation

  1. Ingest up-to-the-minute and private data and use this to power your AI applications. Integrate with a wide variety of vector stores, including PostgreSQL with pg-vector, MongoDB Atlas, Redis, OpenSearch and more.

Dynamic
Data
Handling

  1. Load and process data dynamically at runtime, storing outputs securely without prompt injection.

Building
AI
Agents

  1. Create agents that integrate pre-processed or runtime-processed data into LLMs via tools, enabling sophisticated data-driven interactions within your applications.

Sophisticated
and Powerful
Workflows

  1. Implement ETL-like flows and creative LLM workflows with Griptape's structured approach.
Go to Griptape GitHub

Getting Started with the Griptape AI Framework

To explore the Griptape framework and start building your AI applications, visit the Griptape Framework GitHub repository, or check out our quick start guide and comprehensive documentation.

Griptape is continuously evolving, with developers already deploying agents, workflows, and pipelines in production.

The framework can be used in concert with Griptape AI Cloud, a managed platform for deploying, managing, and running Griptape apps at scale in any cloud. Stay tuned for more updates and sign up for Griptape AI Cloud to be part of the journey.

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