{"name":"Griptape: Modular Python Framework for AI Agents and Workflows","description":"Griptape is a modular Python framework designed to simplify the development of generative AI applications. It provides a flexible set of abstractions for working with Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and various other AI components. With its structured approach, Griptape enables developers to build sophisticated AI agents and workflows efficiently.","github":"https://github.com/griptape-ai/griptape","url":"https://osrepos.com/repo/griptape-ai-griptape","source":"osrepos.com","sourceDescription":"This repository profile is provided by osrepos.com, an open source repository discovery platform.","repositoryProfile":"https://osrepos.com/repo/griptape-ai-griptape","generatedFor":"open source discovery and AI-assisted research","markdown":"https://osrepos.com/repo/griptape-ai-griptape.md","json":"https://osrepos.com/repo/griptape-ai-griptape.json","topics":["ai","python","llm","ai agents","generative ai","framework","workflow","rag"],"keywords":["ai","python","llm","ai agents","generative ai","framework","workflow","rag"],"stars":null,"summary":"Griptape is a modular Python framework designed to simplify the development of generative AI applications. It provides a flexible set of abstractions for working with Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and various other AI components. With its structured approach, Griptape enables developers to build sophisticated AI agents and workflows efficiently.","content":"## Introduction\n\nGriptape is a powerful and modular Python framework specifically engineered for building AI agents and complex workflows. It offers a comprehensive set of abstractions that streamline the development of generative AI (genAI) applications, incorporating features like chain-of-thought reasoning, robust tools, and versatile memory management. Griptape simplifies interactions with Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and other essential AI components.\n\nThe framework is built around several core components:\n\n*   **Structures**: Organize AI logic, including Agents (for specific tasks), Pipelines (for sequential tasks), and Workflows (for parallel task execution).\n*   **Tasks**: The fundamental building blocks within Structures, enabling interaction with Engines, Tools, and other Griptape components.\n*   **Memory**: Facilitates information retention across interactions, with Conversation Memory, Task Memory, and Meta Memory.\n*   **Drivers**: Provide a flexible interface for interacting with external resources and services, allowing easy swapping of providers for LLMs, embeddings, vector stores, web search, and more.\n*   **Tools**: Equip LLMs with capabilities to interact with data and services, with both built-in and custom tool options.\n*   **Engines**: Wrap Drivers to provide use-case-specific functionality, such as RAG, Extraction, Summary, and Eval Engines.\n\nFor those seeking a no-code experience, Griptape also offers [Griptape Nodes](https://www.griptapenodes.com/griptape-nodes-desktop), a visual desktop application for building and running AI workflows.\n\n## Installation\n\nTo get started with Griptape, you can install it directly using pip. For detailed installation instructions and environment setup, please refer to the [official Griptape documentation](https://docs.griptape.ai/).\n\nbash\npip install griptape\n\n\n## Examples\n\n### Hello World Example\n\nThis minimal example demonstrates how to create a simple `PromptTask` with an OpenAI driver and a rule, then run it to get a response.\n\npython\nfrom griptape.drivers.prompt.openai import OpenAiChatPromptDriver\nfrom griptape.rules import Rule\nfrom griptape.tasks import PromptTask\n\ntask = PromptTask(\n    prompt_driver=OpenAiChatPromptDriver(model=\"gpt-4.1\"),\n    rules=[Rule(\"Keep your answer to a few sentences.\")],\n)\n\nresult = task.run(\"How do I do a kickflip?\")\n\nprint(result.value)\n\n\n**Output:**\ntext\nTo do a kickflip, start by positioning your front foot slightly angled near the middle of the board and your back foot on the tail. Pop the tail down with your back foot while flicking the edge of the board with your front foot to make it spin. Jump and keep your body centered over the board, then catch it with your feet and land smoothly. Practice and patience are key!\n\n\n### Task and Workflow Example\n\nThis example showcases a more complex workflow using Griptape to research multiple open-source projects, summarize their features, and visualize the workflow.\n\npython\nfrom griptape.drivers.prompt.openai_chat_prompt_driver import OpenAiChatPromptDriver\nfrom griptape.drivers.web_search.duck_duck_go import DuckDuckGoWebSearchDriver\nfrom griptape.rules import Rule, Ruleset\nfrom griptape.structures import Workflow\nfrom griptape.tasks import PromptTask, TextSummaryTask\nfrom griptape.tools import WebScraperTool, WebSearchTool\nfrom griptape.utils import StructureVisualizer\nfrom pydantic import BaseModel\n\n\nclass Feature(BaseModel):\n    name: str\n    description: str\n    emoji: str\n\n\nclass Output(BaseModel):\n    answer: str\n    key_features: list[Feature]\n\n\nprojects = [\"griptape\", \"langchain\", \"crew-ai\", \"pydantic-ai\"]\n\nprompt_driver = OpenAiChatPromptDriver(model=\"gpt-4.1\")\nworkflow = Workflow(\n    tasks=[\n        [\n            PromptTask(\n                id=f\"project-{project}\",\n                input=\"Tell me about the open source project: {{ project }}.\",\n                prompt_driver=prompt_driver,\n                context={\"project\": projects},\n                output_schema=Output,\n                tools=[\n                    WebSearchTool(\n                        web_search_driver=DuckDuckGoWebSearchDriver(),\n                    ),\n                    WebScraperTool(),\n                ],\n                child_ids=[\"summary\"],\n            )\n            for project in projects\n        ],\n        TextSummaryTask(\n            input=\"{{ parents_output_text }}\",\n            id=\"summary\",\n            rulesets=[\n                Ruleset(\n                    name=\"Format\", rules=[Rule(\"Be detailed.\"), Rule(\"Include emojis.\")]\n                )\n            ],\n        ),\n    ]\n)\n\nworkflow.run()\n\nprint(StructureVisualizer(workflow).to_url())\n\n\n**Output:**\ntext\n Output: Here's a detailed summary of the open-source projects mentioned:\n\n 1. **Griptape** ??:                                                                                                            \n    - Griptape is a modular Python framework designed for creating AI-powered applications. It focuses on securely connecting to\n enterprise data and APIs. The framework provides structured components like Agents, Pipelines, and Workflows, allowing for both\n parallel and sequential operations. It includes built-in tools and supports custom tool creation for data and service\n interaction.\n\n 2. **LangChain** ?:\n    - LangChain is a framework for building applications powered by Large Language Models (LLMs). It offers a standard interface\n for models, embeddings, and vector stores, facilitating real-time data augmentation and model interoperability. LangChain\n integrates with various data sources and external systems, making it adaptable to evolving technologies.\n\n 3. **CrewAI** ?:\n    - CrewAI is a standalone Python framework for orchestrating multi-agent AI systems. It allows developers to create and\n manage AI agents that collaborate on complex tasks. CrewAI emphasizes ease of use and scalability, providing tools and\n documentation to help developers build AI-powered solutions.\n\n 4. **Pydantic-AI** ?:\n    - Pydantic-AI is a Python agent framework that simplifies the development of production-grade applications with Generative\n AI. Built on Pydantic, it supports various AI models and provides features like type-safe design, structured response\n validation, and dependency injection. Pydantic-AI aims to bring the ease of FastAPI development to AI applications.\n\n These projects offer diverse tools and frameworks for developing AI applications, each with unique features and capabilities\n tailored to different aspects of AI development.\n\n\n## Why Use Griptape?\n\nGriptape stands out as an excellent choice for developing generative AI applications due to its modular design and comprehensive feature set. It provides a structured yet flexible approach to building AI agents and workflows, making complex tasks manageable. With its extensive array of drivers, Griptape offers seamless integration with various LLMs, vector stores, and external services, ensuring high adaptability. The framework's focus on clear abstractions for components like memory, tools, and engines empowers developers to create robust, scalable, and intelligent AI solutions with greater efficiency.\n\n## Links\n\n*   **GitHub Repository**: [griptape-ai/griptape](https://github.com/griptape-ai/griptape)\n*   **Official Documentation**: [Griptape Docs](https://docs.griptape.ai/)\n*   **PyPI**: [griptape on PyPI](https://pypi.python.org/pypi/griptape)\n*   **Discord Community**: [Join Griptape Discord](https://discord.gg/griptape)\n*   **Online Courses**: [Griptape Trade School](https://learn.griptape.ai/)\n*   **No-Code Desktop App**: [Griptape Nodes](https://www.griptapenodes.com/griptape-nodes-desktop)","metrics":{"detailViews":3,"githubClicks":1},"dates":{"published":null,"modified":"2026-07-05T16:28:35.000Z"}}