智能工作流

Hello World Example

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-4.1"),
    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!

Task and Workflow Example

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-4.1")
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.

🛠️ Core Components

📦 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.