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Hamilton
LLM Orchestrators
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
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Pulse Verdict
“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.