Build AI DAGs with Memory; Run and Validate LLM Tools in Containers
I am working on a modular open source framework called Griptape that allows Python developers to create LLM pipelines and DAGs for complex workflows that use rules and memory. Griptape can be thought of as "Airflow for LLMs," providing an alternative to the agent-based LangChain approach. Developers can also build reusable LLM tools with explicit JSON schemas that can be executed in any environment (local, containerized, cloud, etc.) and integrated into Griptape workflows. They can also be easily converted into ChatGPT Plugin APIs and LangChain tools via adapters. Tools can be thought of as…
In plain words
Griptape is an open-source Python framework for building complex LLM workflows using directed acyclic graphs (DAGs) with memory and rules. It enables developers to create reusable LLM tools with JSON schemas that run in any environment and integrate into Griptape pipelines, offering a structured alternative to agent-based approaches. Tools can be converted to ChatGPT plugins or LangChain tools and execute in isolated containers for security.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
I am working on a modular open source framework called Griptape that allows Python developers to create LLM pipelines and DAGs for complex workflows that use rules and memory. Griptape can be thought of as "Airflow for LLMs," providing an alternative to the agent-based LangChain approach. Developers can also build reusable LLM tools with explicit JSON schemas that can be executed in any environment (local, containerized, cloud, etc.) and integrated into Griptape workflows. They can also be easily converted into ChatGPT Plugin APIs and LangChain tools via adapters. Tools can be thought of as any executable code that allows LLMs to interact with the outside world (via ReAct and Toolformer techniques): email, docs, spreadsheets, Jira tickets, web pages/search, etc. The best part about tools is that they can be executed in isolated environments, significantly reducing potential security risks associated with running LLM-generated code and API calls. What do you think? What are some of the use cases that you have in mind for reusable tools?
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