LangGraph has become the standard for building robust AI agents in production. In this step-by-step tutorial, you will create an agent capable of analysing data, searching for information and writing a report, with integrated memory and human control.
Why LangGraph over plain LangChain
LangChain is excellent for linear pipelines. But a real agent needs loops, conditions, persistent memory and human checkpoints. LangGraph models all of this as a directed graph: more code upfront, infinitely more powerful.
Installation and configuration
Define the agent state
State is the heart of LangGraph: the shared dictionary between all nodes. Define it with TypedDict for auto-completion and validation. For our analysis agent: messages, analysed_data, final_report, current_step.
Create the nodes
Each node is a Python function taking state as input and returning a dict with keys to update. Node 1: data_collection. Node 2: analysis. Node 3: report_writing. Node 4: human_validation.
Best practice: each node must do one thing. A node that collects AND analyses AND writes is an anti-pattern. Separation of concerns is the key to maintainability.
Connect the nodes: the graph
Create the graph with StateGraph(MyState), add nodes with add_node(), then define edges. Use add_conditional_edges() for dynamic decisions.
Add memory
LangGraph natively supports PostgreSQL and SQLite via checkpointers. One line of configuration, and your agent remembers previous conversations.
The human checkpoint
Add interrupt_before=['human_validation'] when compiling the graph. The agent stops, waits for your approval, then continues. Essential for agents taking irreversible actions.
With care,
Excellent article, this matches exactly what we're seeing with our enterprise clients. The section on inference costs is especially valuable. It's a topic most articles gloss over but it's make-or-break at scale.
Thanks James! Inference cost optimization is often deprioritized during prototyping but becomes critical in production. Feel free to book a session if you'd like to go deeper on this.
Sharing this with my whole team. The distinction between an impressive demo and robust production is exactly the debate we're having internally right now. The human checkpoint advice is immediately actionable.
Great article. I'd push back slightly on the 18-day deployment estimate, in our experience with enterprise security and GDPR requirements, 4–6 weeks is more realistic for a first production agent.
Completely fair point David. The 18 days refers to a scoped first agent in a test environment. For full enterprise production with security constraints, your estimate is accurate.