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LangGraph

As explained in Machine Learning Platform Engineering

LangGraph is a framework extension for orchestrating language-model applications as graphs with branching, loops, and stateful transitions.

LangGraph orchestrates language-model applications as graphs rather than fixed sequences. It extends LangChain for workflows that require conditional branching, parallel execution, runtime decisions, or iterative refinement. section 12.1.3 section 12.5

Use LangGraph when an application must:

  • Route requests through different processing paths based on runtime decisions. section 12.1.3
  • Run parallel operations or coordinate multipath agent behavior. section 12.1.3
  • Repeat validation and refinement until a response meets requirements. section 12.1.3

A graph-based request workflow

An intent classifier can route a query to a cache, search, or clarification path. After generation, a validation node can accept the response or loop back for refinement. This structure fits agentic systems better than a linear retrieve-augment-generate chain. section 12.1.3

How Machine Learning Platform Engineering covers it

How deep it goes
Contrasts sequential and graph-based architectures, but stops short of implementation code or graph-construction details.
Examples
Demonstrates intent routing among cache, search, and clarification paths followed by response validation and refinement loops.
What it assumes
Builds from sequential LangChain workflows and straightforward retrieval-augmented generation patterns before introducing graph complexity.
What you can do after
Apply LangGraph to design language-model workflows with branching, parallel paths, and iterative response improvement.
How it connects
Connects LangChain, retrieval-augmented generation, conditional branching, parallel execution, and iterative refinement.
Cover of Machine Learning Platform Engineering
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