Build Your First Complete LangGraph Application
Explore how to build a full LangGraph application by combining model and rule-based nodes. Understand input validation, conditional routing, quality checks, and output formatting to mirror real production workflows.
We'll cover the following...
The previous lessons built one piece at a time. We understood why the graph structure exists, learned the seven-step build pattern, and practiced designing clean state schemas. Each lesson was focused and narrow by design.
Now we put it all together. This lesson builds a meeting notes summarizer: a workflow that takes raw transcript text, validates it, generates a structured summary using a large language model, checks the output quality, and formats a final report for delivery. It handles both the happy path and early failure gracefully.
This is the first workflow in the course that mirrors what a production system actually looks like: multiple node types, two conditional branches, and clear separation between the part that generates content and the part that formats it for output.
Model nodes and rule-based nodes
Before we write any code, it is worth naming the two types of nodes we will use, because the decision between them shapes every workflow we will ever build.
A model node calls a language model. It handles tasks that require language understanding, generation, or reasoning. Model calls cost time and money, so we reserve them for work that genuinely needs them.
A rule-based node uses ordinary Python logic. It handles tasks that can be expressed as conditions, string operations, lookups, or arithmetic. These nodes are fast, free, and deterministic, which makes them ideal for validation, routing, formatting, and quality checks.
The following table summarizes when to reach for each type.
Node type | Use when | Examples in this lesson |
Model node | The task requires language understanding or generation |
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Rule-based node | The task can be expressed with Python logic |
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The most common mistake in workflow design is using a model node for something a rule can handle. Calling the LLM to check whether a string is longer than 50 words is slower and less reliable than len(text.split()) > 50. Save model calls for the work only a model can do.
What we are building
The workflow takes a meeting transcript and title, validates the input, generates a summary, checks it for quality, and formats a final report. If the input is too short to summarize meaningfully, the workflow ends early with an error note. If the generated summary does not meet the quality threshold, the workflow also ends early rather than delivering a poor output. The diagram below shows both conditional branches and how all paths converge on a single End node.
State and node responsibilities
We follow the field role grouping from the previous lesson. Every field belongs to a group, and every field has exactly one node that ...