[03/13/25 21:07:33] INFO     PromptTask project-research                        
                             Input: You are speaking to: Collin. User said: Tell
                             me about the python framework Griptape.            
[03/13/25 21:07:35] INFO     Subtask b3776286c4b14b30a8955e0e8d56b426           
                             Actions: [                                         
                               {                                                
                                 "tag": "call_g0DHP8HffzIVh5hpxrNZFJln",        
                                 "name": "WebSearchTool",                       
                                 "path": "search",                              
                                 "input": {                                     
                                   "values": {                                  
                                     "query": "Griptape Python framework"       
                                   }                                            
                                 }                                              
                               }                                                
                             ]                                                  
                    INFO     Subtask b3776286c4b14b30a8955e0e8d56b426           
                             Response: {"title": "GitHub - griptape-ai/griptape:
                             Modular Python framework for AI agents ...", "url":
                             "https://github.com/griptape-ai/griptape",         
                             "description": "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.             
                             \ud83d\udee0\ufe0f Core Components.                
                             \ud83c\udfd7\ufe0f Structures. \ud83e\udd16 Agents 
                             consist of a single Task."}                        
                                                                                
                             {"title": "Griptape | AI Agent Framework | AI      
                             Development Platform", "url":                      
                             "https://www.griptape.ai/", "description":         
                             "Griptape gives developers everything they need,   
                             from the open source AI framework (Griptape AI     
                             Framework) to the execution runtime (Griptape AI   
                             Cloud). Build & Secure Build your business logic   
                             using predictable, programmable python - don't     
                             gamble on prompting."}                             
                                                                                
                             {"title": "Overview - Griptape Docs", "url":       
                             "https://docs.griptape.ai/stable/griptape-framework
                             /", "description": "The Griptape Framework allows  
                             developers to build business logic using Python,   
                             ensuring better security, performance, and         
                             cost-efficiency. It simplifies the creation of Gen 
                             AI Agents, Systems of Agents, Pipelines, Workflows,
                             and RAG implementations without needing extensive  
                             knowledge of Gen AI or Prompt Engineering."}       
                                                                                
                             {"title": "Getting started with the Griptape       
                             Framework", "url":                                 
                             "https://www.griptape.ai/blog/getting-started-with-
                             the-griptape-framework", "description": "Griptape  
                             is a Python framework and we'll assume that you    
                             already have python set up on your machine. If not,
                             we recommend you take a look at the longer version 
                             of this getting started guide in the Griptape Trade
                             School , which includes instructions for getting   
                             Python set up."}                                   
                                                                                
                             {"title": "Framework - Griptape", "url":           
                             "https://www.griptape.ai/category/framework",      
                             "description": "Blog posts about the Griptape      
                             Framework, a powerful Python library allowing      
                             developers to build AI systems that operate across 
                             two dimensions: predictability and creativity.     
                             Products. ... The release of Griptape Framework    
                             v0.24 comes with a number of new, long-requested   
                             features. Customers can now use Claude-3 and Google
                             Gemini models in their ..."}                       
[03/13/25 21:07:39] INFO     Subtask 97e6fad69e954372af63169375fe3684           
                             Actions: [                                         
                               {                                                
                                 "tag": "call_PXnS8KWhsbtQeCLR02oSZYj8",        
                                 "name": "WebScraperTool",                      
                                 "path": "get_content",                         
                                 "input": {                                     
                                   "values": {                                  
                                     "url":                                     
                             "https://github.com/griptape-ai/griptape"          
                                   }                                            
                                 }                                              
                               }                                                
                             ]                                                  
[03/13/25 21:07:40] INFO     Subtask 97e6fad69e954372af63169375fe3684           
                             Response: Griptape is a Python framework designed  
                             to simplify the development of generative AI       
                             (genAI) applications. It offers a set of           
                             straightforward, flexible abstractions for working 
                             with areas such as Large Language Models (LLMs),   
                             Retrieval-Augmented Generation (RAG), and much     
                             more.                                              
                             - 🤖 Agents consist of a single Task, configured   
                             for Agent-specific behavior.                       
                             - 🔄 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 are the core building blocks within          
                             Structures, enabling interaction with Engines,     
                             Tools, and other Griptape components.              
                             - 💬 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 facilitate interactions with external      
                             resources and services in Griptape. They allow you 
                             to swap out functionality and providers with       
                             minimal changes to your business logic.            
                                                                                
                             - 🗣️ Prompt Drivers: Manage textual and image       
                             interactions with LLMs.                            
                             - 🤖 Assistant Drivers: Enable interactions with   
                             various “assistant” services.                      
                             - 📜 Ruleset Drivers: Load and apply rulesets from 
                             external sources.                                  
                             - 🧠 Conversation Memory Drivers: Store and        
                             retrieve conversational data.                      
                             - 📡 Event Listener Drivers: Forward framework     
                             events to external services.                       
                             - 🏗️ Structure Run Drivers: Execute structures      
                             locally or in the cloud.                           
                             - 🔢 Embedding Drivers: Generate vector embeddings 
                             from textual inputs.                               
                             - 🔀 Rerank Drivers: Rerank search results for     
                             improved relevance.                                
                             - 💾 Vector Store Drivers: Manage the storage and  
                             retrieval of embeddings.                           
                             - 🗂️ File Manager Drivers: Handle file operations on
                             local and remote storage.                          
                             - 💼 SQL Drivers: Interact with SQL databases.     
                             - 🎨 Image Generation Drivers: Create images from  
                             text descriptions.                                 
                             - 🗣️ Text to Speech Drivers: Convert text to speech.
                                                                                
                             - 🎙️ Audio Transcription Drivers: Convert audio to  
                             text.                                              
                             - 🔍 Web Search Drivers: Search the web for        
                             information.                                       
                             - 🌐 Web Scraper Drivers: Extract data from web    
                             pages.                                             
                             - 📈 Observability Drivers: Send trace and event   
                             data to observability platforms.                   
                             Tools provide capabilities for LLMs to interact    
                             with data and services.                            
                             Griptape includes a variety of [built-in           
                             Tools](https://docs.griptape.ai/stable/griptape-fra
                             mework/tools/official-tools/), and makes it easy to
                             create [custom                                     
                             Tools](https://docs.griptape.ai/stable/griptape-fra
                             mework/tools/custom-tools/).                       
                             Engines wrap Drivers and provide use-case-specific 
                             functionality:                                     
                             - 📊 RAG Engine is an abstraction for implementing 
                             modular Retrieval Augmented Generation (RAG)       
                             pipelines.                                         
                             - 🛠️ Extraction Engine extracts JSON or CSV data    
                             from unstructured text.                            
                             - 📝 Summary Engine generates summaries from       
                             textual content.                                   
                             - ✅ Eval Engine evaluates and scores the quality  
                             of generated text.                                 
                                                                                
                             - 📐 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.               
                             Please visit the [docs](https://docs.griptape.ai/) 
                             for information on installation and usage.         
                             Check out [Griptape Trade                          
                             School](https://learn.griptape.ai/) for free online
                             courses.                                           
                             Here's a minimal example of griptape:              
                             from griptape.drivers.prompt.openai import         
                             OpenAiChatPromptDriver                             
                             from griptape.rules import Rule                    
                             from griptape.tasks import PromptTask              
                             task = PromptTask(                                 
                             prompt_driver=OpenAiChatPromptDriver(model="gpt-4o"
                             ),                                                 
                             rules=[Rule("Keep your answer to a few             
                             sentences.")],                                     
                             )                                                  
                             result = task.run("How do I do a kickflip?")       
                                                                                
                             print(result.value)                                
                             To 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!                     
                             Here is a concise example using griptape to        
                             research open source projects:                     
                             from                                               
                             griptape.drivers.prompt.openai_chat_prompt_driver  
                             import OpenAiChatPromptDriver                      
                             from griptape.drivers.web_search.duck_duck_go      
                             import DuckDuckGoWebSearchDriver                   
                             from griptape.rules import Rule, Ruleset           
                             from griptape.structures import Workflow           
                             from griptape.tasks import PromptTask,             
                             TextSummaryTask                                    
                             from griptape.tools import WebScraperTool,         
                             WebSearchTool                                      
                             from griptape.utils import StructureVisualizer     
                             from pydantic import BaseModel                     
                             class Feature(BaseModel):                          
                             name: str                                          
                             description: str                                   
                                                                                
                             emoji: str                                         
                             class Output(BaseModel):                           
                             answer: str                                        
                             key_features: list[Feature]                        
                             projects = ["griptape", "langchain", "crew-ai",    
                             "pydantic-ai"]                                     
                             prompt_driver =                                    
                             OpenAiChatPromptDriver(model="gpt-4o")             
                             workflow = Workflow(                               
                             tasks=[                                            
                             [                                                  
                             PromptTask(                                        
                             id=f"project-{project}",                           
                             input="Tell me about the open source project: {{   
                             project }}.",                                      
                             prompt_driver=prompt_driver,                       
                             context={"project": projects},                     
                             output_schema=Output,                              
                             tools=[                                            
                             WebSearchTool(                                     
                             web_search_driver=DuckDuckGoWebSearchDriver(),     
                             ),                                                 
                             WebScraperTool(),                                  
                             ],                                                 
                             child_ids=["summary"],                             
                             )                                                  
                             for project in projects                            
                             ],                                                 
                             TextSummaryTask(                                   
                             input="{{ parents_output_text }}",                 
                             id="summary",                                      
                             rulesets=[                                         
                             Ruleset(                                           
                             name="Format", rules=[Rule("Be detailed."),        
                             Rule("Include emojis.")]                           
                             )                                                  
                             ],                                                 
                             ),                                                 
                             ]                                                  
                             )                                                  
                             workflow.run()                                     
                             print(StructureVisualizer(workflow).to_url())      
                             Output: Here's a detailed summary of the           
                             open-source projects mentioned:                    
                                                                                
                             1. **Griptape** 🛠️:                                 
                             - Griptape is a modular Python framework designed  
                             for creating AI-powered applications. It focuses on
                             securely connecting to                             
                             enterprise data and APIs. The framework provides   
                             structured components like Agents, Pipelines, and  
                             Workflows, allowing for both                       
                             parallel and sequential operations. It includes    
                             built-in tools and supports custom tool creation   
                             for data and service                               
                             interaction.                                       
                             2. **LangChain** 🔗:                               
                             - LangChain is a framework for building            
                             applications powered by Large Language Models      
                             (LLMs). It offers a standard interface             
                             for models, embeddings, and vector stores,         
                             facilitating real-time data augmentation and model 
                             interoperability. LangChain                        
                             integrates with various data sources and external  
                             systems, making it adaptable to evolving           
                             technologies.                                      
                             3. **CrewAI** 🤖:                                  
                             - CrewAI is a standalone Python framework for      
                             orchestrating multi-agent AI systems. It allows    
                             developers to create and                           
                             manage AI agents that collaborate on complex tasks.
                             CrewAI emphasizes ease of use and scalability,     
                             providing tools and                                
                             documentation to help developers build AI-powered  
                             solutions.                                         
                                                                                
                             4. **Pydantic-AI** 🧩:                             
                             - Pydantic-AI is a Python agent framework that     
                             simplifies the development of production-grade     
                             applications with Generative                       
                             AI. Built on Pydantic, it supports various AI      
                             models and provides features like type-safe design,
                             structured response                                
                             validation, and dependency injection. Pydantic-AI  
                             aims to bring the ease of FastAPI development to AI
                             applications.                                      
                             These projects offer diverse tools and frameworks  
                             for developing AI applications, each with unique   
                             features and capabilities                          
                             tailored to different aspects of AI development.   
                             graph TD;                                          
                             griptape-->summary;                                
                             langchain-->summary;                               
                             pydantic-ai-->summary;                             
                             crew-ai-->summary;                                 
                             Griptape uses [Semantic                            
                             Versioning](https://semver.org/).                  
                             Thank you for considering contributing to Griptape!
                             Before you start, please review our [Contributing  
                             Guidelines](https://github.com/griptape-ai/griptape
                             /blob/main/CONTRIBUTING.md).                       
                             Griptape is available under the Apache 2.0 License.
[03/13/25 21:07:44] INFO     PromptTask project-research                        
                             Output: {"answer":"Hello Collin! I'm Oswald from   
                             Oswald's Open Source. Let me tell you about the    
                             Griptape Python framework.\n\nGriptape is a modular
                             Python framework designed to simplify the          
                             development of generative AI (genAI) applications. 
                             It provides a set of flexible abstractions for     
                             working with Large Language Models (LLMs),         
                             Retrieval-Augmented Generation (RAG), and more. The
                             framework is structured around core components like
                             Agents, Pipelines, and Workflows, which allow for  
                             both sequential and parallel task                  
                             operations.\n\nGriptape includes various drivers   
                             and tools that facilitate interactions with        
                             external resources and services, making it easy to 
                             swap out functionality with minimal changes to your
                             business logic. It also supports the creation of   
                             custom tools and provides engines for specific use 
                             cases like RAG pipelines, data extraction, and text
                             summarization.\n\nOverall, Griptape is designed to 
                             offer developers control and flexibility while     
                             building AI-powered applications that securely     
                             connect to enterprise data and                     
                             APIs.","key_features":[{"name":"Agents","descriptio
                             n":"Consist of a single Task, configured for       
                             specific                                           
                             behavior.","emoji":"🤖"},{"name":"Pipelines","descr
                             iption":"Organize a sequence of Tasks so that the  
                             output from one Task may flow into the             
                             next.","emoji":"🔄"},{"name":"Workflows","descripti
                             on":"Configure Tasks to operate in                 
                             parallel.","emoji":"🌐"},{"name":"Conversation     
                             Memory","description":"Enables LLMs to retain and  
                             retrieve information across                        
                             interactions.","emoji":"💬"},{"name":"Prompt       
                             Drivers","description":"Manage textual and image   
                             interactions with LLMs.","emoji":"🗣️"},{"name":"RAG 
                             Engine","description":"An abstraction for          
                             implementing modular Retrieval Augmented Generation
                             (RAG) pipelines.","emoji":"📊"}]}                  
Output(
│   answer="Hello Collin! I'm Oswald from Oswald's Open Source. Let me tell you about the Griptape Python framework.\n\nGriptape is a modular Python framework designed to simplify the development of generative AI (genAI) applications. It provides a set of flexible abstractions for working with Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and more. The framework is structured around core components like Agents, Pipelines, and Workflows, which allow for both sequential and parallel task operations.\n\nGriptape includes various drivers and tools that facilitate interactions with external resources and services, making it easy to swap out functionality with minimal changes to your business logic. It also supports the creation of custom tools and provides engines for specific use cases like RAG pipelines, data extraction, and text summarization.\n\nOverall, Griptape is designed to offer developers control and flexibility while building AI-powered applications that securely connect to enterprise data and APIs.",
│   key_features=[
│   │   Feature(
│   │   │   name='Agents',
│   │   │   description='Consist of a single Task, configured for specific behavior.',
│   │   │   emoji='🤖'
│   │   ),
│   │   Feature(
│   │   │   name='Pipelines',
│   │   │   description='Organize a sequence of Tasks so that the output from one Task may flow into the next.',
│   │   │   emoji='🔄'
│   │   ),
│   │   Feature(
│   │   │   name='Workflows',
│   │   │   description='Configure Tasks to operate in parallel.',
│   │   │   emoji='🌐'
│   │   ),
│   │   Feature(
│   │   │   name='Conversation Memory',
│   │   │   description='Enables LLMs to retain and retrieve information across interactions.',
│   │   │   emoji='💬'
│   │   ),
│   │   Feature(
│   │   │   name='Prompt Drivers',
│   │   │   description='Manage textual and image interactions with LLMs.',
│   │   │   emoji='🗣️'
│   │   ),
│   │   Feature(
│   │   │   name='RAG Engine',
│   │   │   description='An abstraction for implementing modular Retrieval Augmented Generation (RAG) pipelines.',
│   │   │   emoji='📊'
│   │   )
│   ]
)
