Show HN: Build AI DAGs with Memory; Run and Validate LLM Tools in Containers

Griptape is an open-source Python framework for building LLM pipelines and DAGs with memory and rules, positioning itself as an Airflow alternative for AI workflows. It enables developers to create reusable LLM tools with explicit JSON schemas that can run in isolated containers and be converted to ChatGPT plugins or LangChain tools, allowing safe execution of LLM-generated code.

Key Points

• •Griptape is an open-source Python framework for building LLM pipelines and DAGs with memory and rule-based logic—positioned as 'Airflow for LLMs' rather than agent-based approaches
• •Create reusable LLM tools with explicit JSON schemas that execute in any environment (local, containerized, cloud) and integrate seamlessly into workflows
• •Tools enable LLMs to interact with external systems (email, docs, spreadsheets, Jira, web search) using ReAct and Toolformer techniques for structured reasoning
• •Tools can be converted into ChatGPT Plugin APIs and LangChain tools via built-in adapters, increasing interoperability and reusability across platforms
• •Isolated tool execution environments significantly reduce security risks by sandboxing LLM-generated code and API calls away from the main system
• •DAG-based architecture allows developers to define complex, multi-step workflows with explicit control flow and memory management between steps
• •Modular design enables teams to build, test, and validate tools independently before integrating them into larger LLM pipelines
• •Framework supports enterprise use cases requiring auditable, reproducible LLM workflows with explicit rules and memory constraints

Workflow Diagram

Concepts