Hamilton
Overview
A micro-framework for defining dataflows in Python. It is increasingly used for orchestrating complex LLM and RAG pipelines by transforming messy logic into a clean, directed acyclic graph (DAG) of functions.
Hamilton is a Python micro-framework that turns functions into a clean, testable directed acyclic graph, increasingly used to orchestrate LLM and RAG pipelines. It brings readable, maintainable structure to otherwise messy data and prompt logic. It targets teams building production-grade data and AI pipelines.
Key Features
- Function-based DAG dataflows
- Readable, testable pipelines
- Good for RAG and data prep
- Lineage and visualization
- Open-source
Best For
Teams who want clean, maintainable dataflows for RAG and AI pipelines.
Pros & Cons
Pros
- Clean, testable architecture
- Strong for data/RAG pipelines
- Open-source
Cons
- DAG mindset to learn
- Not AI-specific by itself
Pulse Verdict
Editorial review by SEODataPulse
This is an editorial assessment, not an average of customer ratings.
“The clean architecture for AI data. Hamilton's focus on readable, testable dataflows is a major efficiency gain for teams building production-grade RAG and agent systems.”
Pricing
Open-source and free.
Pricing changes often — confirm current plans on the official site.