Skatepark

Skatepark is a Python framework for creating workflow DAGs and pipelines that use large language models (LLMs) such as GPT, Claude, Titan, and Cohere.

With Skatepark, you can accomplish the following:

1. 🚰 Build sequential AI pipelines and sprawling DAG workflows for complex use cases.
2. 🧰️ Augment LLMs with chain of thought capabilities and integrate external tools, such as calculators, web search, spreadsheet editors, and API connectors via griptape-core.
3. 💾 Add memory to AI pipelines for context preservation and summarization.

Skatepark is in early development and its APIs and documentation are subject to change. Until we stabilize the API and release version 1.0.0, we will use minor versions (i.e., x.Y.z) to introduce breaking features and patch versions (i.e., x.y.Z) for bug fixes.

Documentation

Please refer to Griptape Docs for:

• Skatepark and Griptape getting started guides.
• Core concepts and design overviews.
• Examples.
• Contribution guidelines.

Quick Start

First, install Skatepark, Griptape Tools, and Decouple:

pip install skatepark-lib griptape-tools python-decouple

Second, configure an OpenAI client by getting an API key and adding it to your environment as OPENAI_API_KEY. Skatepark uses OpenAI Completions API to execute LLM prompts and to work with LlamaIndex data structures.

With Skatepark, you can create structures, such as Pipelines and Workflows, that are composed of different types of steps. You can also define structures as JSON objects and load them into Skatepark dynamically. Let's define a simple two-step pipeline that uses tools:

from decouple import config
from griptape.tools import WebScraper, Calculator
from skatepark import utils
from skatepark.memory import PipelineMemory
from skatepark.steps import PromptStep, ToolkitStep
from skatepark.structures import Pipeline
from skatepark.utils import ToolLoader


scraper = WebScraper(
    openai_api_key=config("OPENAI_API_KEY")
)
calculator = Calculator()

pipeline = Pipeline(
    memory=PipelineMemory(),
    tool_loader=ToolLoader(
        tools=[calculator, scraper]
    )
)

pipeline.add_steps(
    ToolkitStep(
        tool_names=[calculator.name, scraper.name]
    ),
    PromptStep(
        "Say the following like a pirate: {{ input }}"
    )
)

pipeline.run("Give me a summary of https://en.wikipedia.org/wiki/Large_language_model")

print(utils.Conversation(pipeline.memory).to_string())

Boom! Our first conversation, à la ChatGPT, is here:

Q: Give me a summary of https://en.wikipedia.org/wiki/Large_language_model
A: Arr, me hearties! Large language models have been developed and set sail since 2018, includin' BERT, GPT-2, GPT-3 [...]

Contributing

Contributions in the form of bug reports, feature ideas, or pull requests are super welcome! Take a look at the current issues and if you'd like to help please submit a pull request with some tests.

License

Skatepark is available under the Apache 2.0 License.