Griptape AI Agent is a modular, open-source Python framework that enables developers to construct advanced AI applications. It effectively utilizes large language models (LLMs) to design Gen AI Agents, Systems of Agents, Pipelines, Workflows, and Retrieval-Augmented Generation (RAG) solutions. This framework is specifically designed to balance **predictability** and **creativity**, making it a practical choice for developers aiming to create both conversational and event-driven AI applications.
Features
The Griptape AI Agent framework incorporates a variety of features that enhance its functionality and usability for developers. Each feature is tailored to support the development of sophisticated AI systems while ensuring efficient operation and security. Below is a detailed summary of the key features:
| Feature | Description |
|---|---|
| Predictability and Creativity | Offers structured approaches through sequential pipelines, DAG-based workflows, and long-term memory for retaining information. |
| Enhanced Security | Integrates Off-Prompt™ technology to protect sensitive data and ensure compliance with industry standards. |
| Cost Efficiency | Optimizes resource usage to reduce operational costs while maintaining high performance. |
| Development Flexibility | Allows extensive customization for creating diverse AI systems tailored to specific needs. |
| Griptape Cloud Services | Simplifies deployment with ETL processes, API abstractions, and scalability options. |
Use cases
Griptape AI Agent can address a variety of use cases, enabling developers to leverage its capabilities in different applications:
- Conversational AI: Developers can create chatbots that provide customer support or engage users in interactive dialogues by utilizing the framework's sequential pipelines and long-term memory features.
- Event-Driven Applications: By employing DAG-based workflows, Griptape supports the development of applications that respond to events in real-time, allowing for complex task management.
- Data Retrieval and Processing: The framework can be used to build systems that retrieve and process information from various data sources, integrating with external APIs effectively.
- Custom AI Solutions: Developers can customize Griptape to create unique AI applications tailored to specific business needs, benefiting from its flexible architecture.
How to get started
To begin utilizing Griptape for building AI applications, developers can start by creating simple agents. A basic example is provided below, which initializes an agent and begins a chat interface:
from griptape.structures import Agent
from griptape.utils import Chat
agent = Agent()
Chat(agent).start()
This script allows developers to interact with their model and expand upon it, facilitating the development process. For more detailed information on installation and usage, developers can refer to the documentation available on Griptape’s official GitHub repository.
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<h2>Pricing Plans for Griptape Cloud AI Agent</h2>
<p>Pricing is structured around two main plans: Developer and Enterprise.</p>
<h3>Developer Plan</h3>
<ul>
<li><strong>Free Tier Includes:</strong>
<ul>
<li>1 GB of data ingested and indexed</li>
<li>1,000 API requests to query indexed data</li>
<li>1 hour of compute to run structure code</li>
</ul>
</li>
<li><strong>Additional Usage Beyond Free Tier:</strong>
<ul>
<li>$1 per GB of data ingestion (ETL)</li>
<li>$0.01 per retrieval query (RAG)</li>
<li>$0.25 per hour of structure runtime (RUN)</li>
</ul>
</li>
</ul>
<h3>Enterprise Plan</h3>
<p>Pricing details for the Enterprise plan are not available.</p>