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.
After this first agent come memory, tools, error recovery and deployment — the rest of the path in the AI Engineering Bootcamp.
