We learned that a single LLM is limited, while chains are reliable but rigid. Autonomous agents add flexibility but lose reliability as control increases. LangGraph bridges this gap by letting developers design graph-based control flows that combine fixed logic with LLM-driven adaptability. It helps bend the reliability curve, maintaining stability as autonomy grows — enabling custom, memory-aware, and human-in-the-loop agents that are both reliable and intelligent.
Source Notebook → simple-graph-dev.ipynb
In this lesson, we explored how LangGraph represents workflows as nodes and edges, allowing data and logic to flow dynamically. We learned how to build custom control flows, use conditional edges to branch paths, and manage multiple outcomes within one graph.
To apply this, we implemented a 5-node productivity assistant that greets the user, analyzes their intent, and routes them to a study, workout, or rest plan based on input. This helped us understand how LangGraph maintains reliability, structure, and adaptability within a single workflow.
Source Notebook → langgraphstudio.ipynb
In this lesson, we explored LangGraph Studio, a visual IDE for building, testing, and debugging graph workflows. We learned how Studio loads module directories, visualizes node execution, and tracks multiple runs using threads for easy inspection.
We then extended the setup by adding our custom plan.py graph (Productivity Planner) and registering it in langgraph.json as "plan": "./plan.py:graph" and adding plan.py file in studio folder. This allowed us to run it directly inside Studio and understand how it manages multi-graph execution within one project.
Learnt how to integrate tools within a graph using a simple 3-node chain. The graph includes a preprocess node, a tool-calling LLM node bound with multiple tools, and a postprocess node to finalize the output.
Changes: Built and tested my own multi-node workflow to understand how messages flow between nodes and how tools fit into structured graph execution.
Source Notebook → router-dev.ipynb
In this lesson, we explored how routing logic works in LangGraph by recreating our earlier productivity planner as a decision-based graph. We built a flow where the model greets the user and dynamically routes to different nodes like study_plan or workout_plan depending on the user’s input. Neutral messages (like “hi”) simply end the graph, showing how conditional edges can control execution flow.
Changes: Created a new plan2.py for use inside LangGraph Studio and added its entry in langgraph.json for visualization and testing.
Source Notebook → agent-dev.ipynb
Learnt how agents in LangGraph can reason and act repeatedly using tools within a single graph. Built a reactive loop where the model decides when to call tools, executes them, and continues reasoning until the task is complete. Used a set of productivity tools — split_tasks, calculate_duration, and summarize_plan — to let the assistant plan goals step-by-step, similar to how ReAct-style agents process sequential instructions.
Source Notebook → agent-memory.ipynb
Learnt how LangGraph agents retain context across multiple reasoning and tool-use steps using an internal message state. Built a productivity assistant that can add, remove, and summarize tasks while keeping awareness of previous actions. This version replaces the arithmetic tools with planning tools to show how memory enables contextual, multi-stage reasoning within a single workflow.