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Griptape

Free

Python framework for AI workflows and pipelines with chain of thought reasoning, external tools, and memory. ![GitHub Repo stars](https://img.shields.io/github/stars/griptape-ai/griptape?style=social)

FreeFree tier
Type
Open Source

About Griptape

Griptape is a modular Python framework designed to simplify the development of generative AI (genAI) applications. It provides a set of straightforward, flexible abstractions for working with Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and more. Core components include Structures (Agents for single tasks, Pipelines for sequential tasks, Workflows for parallel tasks), Tasks, Memory (Conversation, Task, Meta), Drivers (for LLM orchestration, retrieval storage, multimodal, web, and observability), Tools, and Engines like the RAG engine. It supports chain-of-thought reasoning, external tool integration, and persistent memory to build sophisticated AI workflows.

Key Features

Modular Python framework for genAI applications
Supports Agents, Pipelines, and Workflows (Structures)
Built-in Conversation, Task, and Meta Memory for context retention
Extensive Driver system for LLM orchestration, retrieval, multimodal, web, and observability
Built-in Tools for interacting with data and services
RAG Engine for retrieval-augmented generation
Chain-of-thought reasoning support
Plugin-based architecture for swapping providers with minimal code changes

Pros & Cons

Pros
  • Open-source and free to use
  • Modular design allows easy customization and extension
  • Supports multiple LLM providers via pluggable drivers
  • Built-in memory types for conversation and task context
  • Includes a wide range of drivers for retrieval, multimodal, and web interactions
  • Active community and frequent updates on GitHub
Cons
  • Requires Python programming knowledge to use directly
  • Steep learning curve for users new to AI frameworks
  • Documentation may be overwhelming for beginners
  • No built-in graphical interface (separate no-code tool Griptape Nodes available)

Best For

Building AI agents with autonomous reasoning and tool useCreating multi-step AI workflows and pipelinesImplementing Retrieval-Augmented Generation (RAG) systemsDeveloping conversational AI with memoryIntegrating LLMs with external data sources and APIsRapid prototyping of generative AI applications