griptape
Modular Python framework for AI agents and workflows with chain-of-thought reasoning, tools, and memory.
Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step.
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🛠️ Core Components
🏗️ Structures
- 🤖 Agents consist of a single Task.
- 🔄 Pipelines organize a sequence of Tasks so that the output from one Task may flow into the next.
- 🌐 Workflows configure Tasks to operate in parallel.
📝 Tasks
Tasks are the core building blocks within Structures, enabling interaction with Engines, Tools, and other Griptape components.
🔧 Tools
Tools provide capabilities for LLMs to interact with data and services. Griptape includes a variety of built-in Tools, and makes it easy to create custom Tools.
🧠 Memory
- 💬 Conversation Memory enables LLMs to retain and retrieve information across interactions.
- 🗃️ Task Memory keeps large or sensitive Task outputs off the prompt that is sent to the LLM.
- 📊 Meta Memory enables passing in additional metadata to the LLM, enhancing the context and relevance of the interaction.
🚗 Drivers
Drivers facilitate interactions with external resources and services:
- 🗣️ Prompt Drivers manage textual interactions with LLMs.
- 🔢 Embedding Drivers generate vector embeddings from textual inputs.
- 💾 Vector Store Drivers manage the storage and retrieval of embeddings.
- 🎨 Image Generation Drivers create images from text descriptions.
- 🔎 Image Query Drivers query images from text queries.
- 💼 SQL Drivers interact with SQL databases.
- 🌐 Web Scraper Drivers extract information from web pages.
- 🧠 Conversation Memory Drivers manage the storage and retrieval of conversational data.
🚂 Engines
Engines wrap Drivers and provide use-case-specific functionality:
- 📊 RAG Engine is an abstraction for implementing modular Retrieval Augmented Generation (RAG) pipelines.
- 🛠️ Extraction Engines extract JSON or CSV data from unstructured text.
- 📝 Summary Engines generate summaries from textual content.
- 🖼️ Image Generation Engines generate images from textual descriptions.
- 🔎 Image Query Engines query images based on textual prompts.
📦 Additional Components
- 📐 Rulesets steer LLM behavior with minimal prompt engineering.
- 🔄 Loaders load data from various sources.
- 🏺 Artifacts allow for passing data of different types between Griptape components.
- ✂️ Chunkers segment texts into manageable pieces for diverse text types.
- 🔢 Tokenizers count the number of tokens in a text to not exceed LLM token limits.