Trellis is an open-source framework for programmatically orchestrating LLM workflows as Directed Acyclic Graphs (DAGs) in Python. We've intentionally designed it to give developers as much control as possible, and we've written documentation to make it incredibly easy to get started. Start building with our docs.

Trellis is composed of only three abstractions: Node, DAG, and LLM.

• Node: the atomic unit of Trellis. Nodes are chained together to form a DAG.   Node is an abstract class with only one method required to implement.
• DAG: a directed acyclic graph of Nodes. It is the primary abstraction for orchestrating LLM workflows. When you   add edges between Nodes, you can specify a transformation function to reuse Nodes and connect any two Nodes.   Trellis verifies the data flowing between Nodes in a DAG to ensure the flow of data is validated.
• LLM: a wrapper around a large language model with simple catches for common OpenAI errors. Currently, the only provider   that Trellis supports is OpenAI.

• If you're going to use the LLM class, set:
  • OPENAI_API_KEY=YOUR_OPENAI_KEY
• If you don't want us to send telemetry data (in the Node._init_(), LLM.execute() (including prompts and responses from OpenAI) and DAG.execute() methods, info about nodes you create or dags you run), to an external server (currently (PostHog)[https://posthog.com/]) for analysis, set:
  • DISABLE_TELEMETRY=1
• If you want to reduce the amount of information the logger returns, set:
  • [for everything] LOG_LEVEL=DEBUG
  • [for status updates] LOG_LEVEL=INFO
  • [for only warnings] LOG_LEVEL=WARNING
  • [for errors which stop runtime] LOG_LEVEL=ERROR
  • [for only critical errors] LOG_LEVEL=CRITICAL

You can install Trellis with any of the following methods:

pip install trellis-dag

poetry add trellis-dag

conda install trellis-dag