The LangGraph Mental Model: A standardized architecture guide for every agent you’ll ever build

LangGraph developers often struggle with code organization despite understanding the library's concepts. A new standardized architecture guide introduces the SIDE framework (State, Instructions, Decisions, Execution) and a seven-module skeleton to structure every LangGraph agent consistently. The approach aims to eliminate the 'blank file freeze' by providing a memorizable, canonical code structure.

A structured, module-by-module reference for developers who understand the concept but want to finally internalize the code Who this is for:You’ve watched the videos. You’ve read the docs. You understand what a graph is, what a node does, what an edge decides. But the moment you open a blank file, you freeze. This article is the bridge betweenunderstandingLangGraph andwritingit fluently. There is a specific kind of frustration that comes with LangGraph development. The mental model is elegant — you have a graph, nodes do work, edges make decisions, state carries memory. It makes sense. You could draw it on a whiteboard in five minutes. Then you open your editor, and suddenly there are imports you can’t remember, classes you can’t place, function signatures you can’t recall, and five different ways the same thing could be written. The concept is clear. The code is a fog. The root cause is that LangGraph code has no enforced structure . The library gives you powerful primitives, but it doesn’t tell you how to organize them. Every tutorial and GitHub repo arranges things differently. So your brain never gets a stable scaffold to hold onto. This article solves that. We are going to define one canonical, standardized way to structure every LangGraph agent you will ever build — from a simple single-agent chatbot to a complex multi-agent pipeline. Like the memory diagram in LangGraph that shows you what your agent has done, this article gives you the equivalent for code: a reliable structure you can memorize, recall, and reach for every single time. We call this structure the SIDE framework : S tate → I nstructions → D ecisions → E xecution. Everything in a LangGraph application falls into one of these four modules. Before we go deep, here is the 30,000-foot view. Every LangGraph application you write should follow this module order from top to bottom: langgraph agent.py│├── MODULE 1: IMPORTS & CONFIGURATION│ └── All your libraries, API keys, model setup│├── MODULE 2: STATE│ └── The TypedDict that defines your agent's memory│├── MODULE 3: TOOLS optional, but common │ └── Functions decorated with @tool that the LLM can call│├── MODULE 4: NODES│ └── Functions that do the actual work at each graph step│├── MODULE 5: EDGES & ROUTING│ └── Functions that decide what happens next│├── MODULE 6: GRAPH ASSEMBLY│ └── Where you build, wire, and compile the graph│└── MODULE 7: ENTRYPOINT └── The main block or invoke call that runs everything This is your canonical skeleton. Memorize this order. Every time you start a new LangGraph project, you open a file and write these seven section headers as comments. Then you fill them in. Let’s build each one from the ground up. This module is your toolbox setup . Before a carpenter starts building, they lay all their tools on the bench. This module does the same — it tells Python what external capabilities you’re bringing in, and it configures the AI model you’ll be working with. ChatOpenAI / ChatAnthropic — the LLM class. This is your language model, the brain. You instantiate it here once and reference it everywhere else. StateGraph — the core LangGraph class. This is the graph builder. Think of it as the blueprint paper you draw your graph on. END — a special LangGraph constant that represents the terminal node. When an edge points to END, the graph stops. MemorySaver — a checkpointer class that gives your graph persistent memory across multiple conversation turns. python --- Standard Library ---import osfrom typing import TypedDict, Annotated, Literal --- LangChain Core ---from langchain openai import ChatOpenAI or ChatAnthropic, ChatGroq, etc.from langchain core.messages import HumanMessage, AIMessage, SystemMessage, BaseMessagefrom langchain core.tools import tool --- LangGraph Core ---from langgraph.graph import StateGraph, ENDfrom langgraph.graph.message import add messages The message reducerfrom langgraph.prebuilt import ToolNode Pre-built node for tool executionfrom langgraph.checkpoint.memory import MemorySaver --- Configuration --- Always name your model variable 'llm' - it's the standard in every nodellm = ChatOpenAI model="gpt-4o", or "claude-3-5-sonnet-20241022", etc. temperature=0, 0 = deterministic; raise for creativity api key=os.environ.get "OPENAI API KEY" Notice that imports are grouped into four logical clusters: standard library, LangChain the LLM layer , LangGraph the graph layer , and configuration. This mirrors how LangGraph itself is layered — LangChain handles the AI primitives, LangGraph handles the orchestration. Keeping them visually separated helps you instantly find what belongs where when debugging. The single most important habit here is naming your model llm. Every node function you write later will reference this variable. If you call it model in one project and chat in another, your code becomes inconsistent and harder to scan. State is your agent’s working memory . It is the single source of truth that travels through the entire graph. Every node reads from it, every node can write to it, and the graph engine manages its lifecycle. Understanding State deeply is arguably the most important thing in LangGraph. Think of it like a whiteboard in a team meeting room. Every person who walks in every node can read what’s on the board and add to it. The board persists across the whole meeting the whole graph run . TypedDict — a Python class that defines the shape of your state. It's like a schema. You inherit from it to create your State class. Annotated — a Python type hint wrapper. In LangGraph, you use it to attach a reducer to a field. A reducer is a function that controls how new values merge with existing values in the state. add messages — the built-in LangGraph reducer for the messages field. Instead of replacing the messages list every time a node adds a message, add messages appends the new messages to the existing list. This is the most commonly used reducer. operator.add — another common reducer for fields that should accumulate by addition like a list of results or a counter . ============================================================ MODULE 2: STATE ============================================================class AgentState TypedDict : 'messages' is the heartbeat of almost every LangGraph agent. Annotated list, add messages means: "this is a list, and when a node writes to it, append - don't replace." messages: Annotated list BaseMessage , add messages Add custom fields below for your specific agent's needs. Fields without a reducer are REPLACED each time a node writes to them. Example: a simple string field gets replaced each write current task: str Example: a list you want to accumulate use operator.add as reducer results: Annotated list str , operator.add Example: a counter iteration count: int This is the point where most beginners get confused. Let’s clear it up permanently with a concrete analogy. Imagine your State is a form being passed around an office. The messages field on that form works like a sticky note pad — every person who touches the form adds a new sticky note. They never erase the old ones. That's add messages. A field without a reducer is like a single text box on that form. Each person who fills it in overwrites what the previous person wrote. That’s the default LangGraph behavior: last write wins. The rule of thumb is simple: use Annotated list, add messages for your messages field always, and use Annotated list, operator.add for any other list you want to grow. Leave everything else without a reducer. Tools are the hands of your agent. Your LLM is the brain — it reasons and decides. But it cannot actually do anything in the world by itself. Tools give it the ability to search the web, call an API, run code, query a database, or execute any Python function you define. This module is optional. If your agent only generates text and doesn’t need to interact with external systems, you can skip it. But most real-world agents need tools. @tool — a decorator from LangChain that transforms a regular Python function into a LangGraph-compatible tool. The function's docstring becomes the tool's description that the LLM reads to decide whether to use it. ToolNode — a pre-built LangGraph node from langgraph.prebuilt that automatically executes whatever tools the LLM decided to call. This saves you from writing the tool-execution logic yourself. bind tools — a method you call on your LLM instance to attach tools to it. After binding, the LLM knows which tools exist and can choose to invoke them by generating a special tool calls message instead of regular text. ============================================================ MODULE 3: TOOLS ============================================================@tooldef search web query: str - str: """Search the web for current information about a topic. Use this when you need real-time information that is not in your training data, such as recent news or live prices. Args: query: The search query string. Returns: A string containing search results. """ Your actual implementation here e.g., Tavily, SerpAPI, etc. Placeholder for illustration: return f"Search results for: {query}"@tooldef calculate expression: str - str: """Evaluate a mathematical expression and return the result. Use this for any arithmetic, algebra, or numerical computation. Args: expression: A valid Python math expression as a string, e.g. '2 + 2 10' Returns: The computed result as a string. """ try: return str eval expression except Exception as e: return f"Error: {e}" Collect all tools into a list - this is the pattern you always followtools = search web, calculate Bind tools to the LLM so it knows they exist and can choose to call themllm with tools = llm.bind tools tools Create the pre-built ToolNode that will execute tool calls automaticallytool node = ToolNode tools This is one of the most important things beginners miss: the LLM uses your tool’s docstring to decide whether to use that tool. It literally reads the text you write in the triple quotes. A vague or missing docstring means the LLM won’t know when to reach for your tool. Always write clear, specific docstrings that describe what the tool does and when to use it. Nodes are the workers of your graph. Each node is a Python function that receives the current State, does some work — calls an LLM, processes data, calls a tool — and returns a dictionary of updates to the State. That’s it. That’s the entire job of a node. Nodes never control what happens next. They just do their work and update the state. The routing what runs after this node is handled entirely in Module 5. state — the parameter every node function receives. It is a dictionary matching the shape of your AgentState. Inside the node, you access it like state "messages" or state "current task" . HumanMessage, AIMessage, SystemMessage — the three message types you will use constantly. HumanMessage is what the user said. AIMessage is what the LLM responded. SystemMessage is your instructions to the LLM system prompt . invoke — the method you call on the LLM or llm with tools to get a response. You pass it a list of messages, and it returns an AIMessage. Return value — every node returns a dictionary whose keys match the field names in AgentState. The values in this dictionary are updates to the state, not the full state. LangGraph merges them using the reducers you defined. ============================================================ MODULE 4: NODES ============================================================ ── Node: Agent the reasoning brain ───────────────────────def agent node state: AgentState - dict: """The central reasoning node. Calls the LLM and decides whether to respond or call a tool.""" Build the message list to send to the LLM. Always include a system message to set behavior. system prompt = SystemMessage content= "You are a helpful assistant. Use the available tools " "when you need real-time information or computation. " "Respond clearly and concisely." The LLM receives the system prompt + all previous messages in state messages to send = system prompt + state "messages" Call the LLM. Use llm with tools if you have tools; plain llm if not. response = llm with tools.invoke messages to send Return the LLM's response as a state update. add messages will APPEND this AIMessage to state "messages" . return {"messages": response } ── Node: Summarizer example of a non-LLM processing node ─def summarize node state: AgentState - dict: """Summarizes the conversation so far to keep context short. This shows that nodes don't have to call an LLM - they can do any Python processing.""" all messages = state "messages" Summarize with the LLM a different prompt, same LLM summary prompt = SystemMessage content="Summarize the following conversation in 2-3 sentences." , HumanMessage content=str all messages summary response = llm.invoke summary prompt Replace messages with a fresh start containing just the summary return { "messages": AIMessage content=f" Summary {summary response.content}" } Here is a mental image that makes nodes click: think of a node as a relay baton runner . They receive the baton state , run their leg of the race do their work , and hand the baton off return updated state . They don’t decide who runs next — that’s the race director’s job the edges . They just run. Every node function signature is always: def my node state: AgentState - dict. The input is always the full state. The output is always a dictionary of the fields you want to update. Edges are the decision logic of your graph. They connect nodes to each other and determine the flow of execution. In LangGraph, there are two kinds of edges: Static edges connect one node to another unconditionally. “After the summarizer runs, always go to the agent.” No decision involved — just a direct wire. Conditional edges inspect the state and return a string indicating which node to go to next . This is where your agent’s intelligence about its own flow lives. Should we call a tool? Should we end? Should we loop back and try again? add edge A, B — adds a static edge from node A to node B. A always leads to B. add conditional edges source, routing function, mapping — adds a conditional edge from source. The routing function is called with the current state and returns a string. The mapping dictionary translates that string into the actual next node name. tools condition — a pre-built LangGraph routing function from langgraph.prebuilt that inspects the last message in state and checks if the LLM made any tool calls. If it did, it routes to "tools". If it didn't, it routes to END. This is the standard pattern for tool-using agents and you should memorize it. START — a LangGraph constant that represents the beginning of the graph. When you set the entry point, you're adding an edge from START to your first node. ============================================================ MODULE 5: EDGES & ROUTING ============================================================ Import the pre-built tool routing functionfrom langgraph.prebuilt import tools condition ── Custom Routing Function Example ─────────────────────────def should continue state: AgentState - Literal "tools", "summarize", " end " : """Custom router for the agent node. Routing functions always: 1. Receive the current state as input 2. Return a string that maps to the next node or END The return values must match the keys in add conditional edges' mapping. """ last message = state "messages" -1 Look at what the LLM just said Case 1: The LLM decided to call a tool if hasattr last message, "tool calls" and last message.tool calls: return "tools" Case 2: The conversation is getting long - summarize before continuing if len state "messages" 20: return "summarize" Case 3: The LLM gave a direct answer - we're done return " end " LangGraph's internal name for END Think of a routing function as a train switchboard operator . The train execution arrives at a station a node completes , and the operator looks at the train’s manifest the current state and flips the right switch to send the train down the correct track. The operator doesn’t move the train — they just decide the direction. LangGraph moves the train. One critical rule to internalize: routing functions never modify state . They only read it. Any modification to state happens in nodes only. If you find yourself wanting to update state inside a routing function, move that logic into a node. This is where everything comes together. Think of this module as construction day . Modules 1–5 were preparation — defining materials, cutting pieces, laying them out. Module 6 is where you actually build the structure by wiring everything together and compiling it. The graph assembly follows a strict, always-identical five-step sequence that you should commit to memory: Initialize → Register Nodes → Set Entry → Wire Edges → Compile. StateGraph AgentState — instantiates the graph builder using your State class as the schema. Every node in the graph must return updates compatible with this schema. add node name, function — registers a node in the graph. The name is the string identifier you'll use in edges. The function is the node function from Module 4. set entry point name — declares which node runs first when you invoke the graph. add edge A, B — wires a direct, unconditional connection from node A to node B. add conditional edges source, router, mapping — wires a conditional branch from the source node, using your routing function, with a mapping from return strings to destination node names. compile checkpointer=... — seals the graph into a runnable object a CompiledGraph . After this, you cannot add more nodes or edges. The optional checkpointer argument enables persistent memory. ============================================================ MODULE 6: GRAPH ASSEMBLY ============================================================ ── Step 1: Initialize ────────────────────────────────────── Always pass your State class to StateGraphgraph builder = StateGraph AgentState ── Step 2: Register All Nodes ────────────────────────────── Format: add node "node name as string", node function The string name is what you use in ALL edge definitionsgraph builder.add node "agent", agent node graph builder.add node "tools", tool node The pre-built ToolNode from Module 3graph builder.add node "summarize", summarize node ── Step 3: Set Entry Point ───────────────────────────────── Which node runs first when we invoke the graph?graph builder.set entry point "agent" ── Step 4: Wire the Edges ────────────────────────────────── Conditional edge from agent: check if we need tools, a summary, or we're donegraph builder.add conditional edges "agent", Source node should continue, Routing function from Module 5 { "tools": "tools", If router returns "tools" → go to tools node "summarize": "summarize", If router returns "summarize" → go to summarize node " end ": END, If router returns " end " → stop the graph } Static edge: after tools run, always go back to agent the ReAct loop graph builder.add edge "tools", "agent" Static edge: after summarization, always return to agentgraph builder.add edge "summarize", "agent" ── Step 5: Compile ───────────────────────────────────────── Without checkpointer: no persistent memory stateless per invocation With checkpointer: memory persists across turns stateful conversations memory = MemorySaver graph = graph builder.compile checkpointer=memory The clearest way to picture this is with a circuit board . Each add node call places a component on the board. add edge and add conditional edges solder the wires between them. compile runs the quality check and seals it into a finished board. Once compiled, the board is ready to receive power invoke and do its job. Notice the fundamental ReAct loop embedded in the edges above: agent → tools → agent → tools → ... until the agent decides it's done and routes to END. This Reason-Act cycle is the backbone of nearly every LangGraph tool-using agent. Recognize it, and you'll understand 90% of the graphs you'll ever encounter. This is the launch pad . Everything before was definition. This module is execution. Here you write the code that actually runs the graph, feeds it input, and receives output. There are two modes of invocation you need to know: invoke for a single run, and streaming for progressive output. You also need to understand config — the dictionary that passes runtime parameters, most importantly the thread id for memory. graph.invoke input, config — runs the graph to completion and returns the final state. This is synchronous — it waits for the entire graph to finish before returning anything. graph.stream input, config — runs the graph and yields intermediate state updates as they happen. Use this for user-facing applications where you want to show progress in real time. config — a dictionary you pass to invoke or stream. The most important key is {"configurable": {"thread id": "some id"}}. The thread id is like a session ID — it tells the checkpointer which memory context to load and save. Users with different thread ids get completely separate memory. HumanMessage content=... — how you wrap user input before feeding it to the graph. ============================================================ MODULE 7: ENTRYPOINT & INVOCATION ============================================================if name == " main ": ── Config: defines this conversation's memory session ── Change thread id to start a fresh conversation. Keep the same thread id to continue an existing one. config = {"configurable": {"thread id": "user-session-001"}} ── Single Invocation synchronous ───────────────────── user input = "What is the current price of Bitcoin?" result = graph.invoke input={"messages": HumanMessage content=user input }, config=config The result is the final state dictionary. Access the last message to get the agent's final answer. final answer = result "messages" -1 .content print f"Agent: {final answer}" ── Streaming Invocation for real-time output ────────── for chunk in graph.stream input={"messages": HumanMessage content=user input }, config=config, stream mode="values" Yields the full state after each node runs : Each chunk is a state snapshot. The last message shows progress. latest = chunk "messages" -1 if hasattr latest, "content" and latest.content: print f" Streaming {latest.content}" ── Multi-turn Conversation Loop ───────────────────────── print "\n--- Starting Interactive Session ---" while True: user text = input "You: " .strip if user text.lower in "exit", "quit", "bye" : break response = graph.invoke input={"messages": HumanMessage content=user text }, config=config Same config = same memory thread print f"Agent: {response 'messages' -1 .content}\n" Here is the complete, clean skeleton you can copy into any new project. This is the one file you should bookmark. Replace the placeholder implementations with your own logic, and you have the foundation of any LangGraph agent. ============================================================ LANGGRAPH CANONICAL AGENT TEMPLATE Modules: Imports → State → Tools → Nodes → Edges → Assembly → Entrypoint ============================================================ ── MODULE 1: IMPORTS & CONFIGURATION ───────────────────────import osfrom typing import TypedDict, Annotated, Literalfrom langchain openai import ChatOpenAIfrom langchain core.messages import HumanMessage, AIMessage, SystemMessage, BaseMessagefrom langchain core.tools import toolfrom langgraph.graph import StateGraph, ENDfrom langgraph.graph.message import add messagesfrom langgraph.prebuilt import ToolNode, tools conditionfrom langgraph.checkpoint.memory import MemorySaverllm = ChatOpenAI model="gpt-4o", temperature=0 ── MODULE 2: STATE ─────────────────────────────────────────class AgentState TypedDict : messages: Annotated list BaseMessage , add messages Add your custom fields here ── MODULE 3: TOOLS ─────────────────────────────────────────@tooldef my tool input: str - str: """Describe clearly what this tool does and when the LLM should use it.""" return f"Result for: {input}"tools = my tool llm with tools = llm.bind tools tools tool node = ToolNode tools ── MODULE 4: NODES ─────────────────────────────────────────def agent node state: AgentState - dict: """The reasoning node. Calls the LLM, optionally triggers tool calls.""" messages = SystemMessage content="You are a helpful assistant." + state "messages" response = llm with tools.invoke messages return {"messages": response } ── MODULE 5: EDGES & ROUTING ───────────────────────────────def should continue state: AgentState - Literal "tools", " end " : """Decide: did the LLM call a tool, or did it give a final answer?""" last message = state "messages" -1 if hasattr last message, "tool calls" and last message.tool calls: return "tools" return " end " ── MODULE 6: GRAPH ASSEMBLY ────────────────────────────────graph builder = StateGraph AgentState graph builder.add node "agent", agent node graph builder.add node "tools", tool node graph builder.set entry point "agent" graph builder.add conditional edges "agent", should continue, {"tools": "tools", " end ": END} graph builder.add edge "tools", "agent" memory = MemorySaver graph = graph builder.compile checkpointer=memory ── MODULE 7: ENTRYPOINT ────────────────────────────────────if name == " main ": config = {"configurable": {"thread id": "session-001"}} while True: user text = input "You: " .strip if not user text or user text.lower in "exit", "quit" : break response = graph.invoke {"messages": HumanMessage content=user text }, config=config print f"Agent: {response 'messages' -1 .content}\n" Once you’ve internalized the single-agent canonical template, multi-agent systems become far less intimidating. The insight is this: each agent is just a compiled graph, and that compiled graph can itself be a node in a larger “supervisor” graph. Supervisor node — a special agent node whose job is not to answer the user but to decide which specialist agent should handle this task. It routes between sub-agents. Subgraph — a compiled LangGraph graph that you register as a node in another graph. It receives and returns state just like any regular node function. Command — a newer LangGraph pattern available in LangGraph 0.2+ where a node can return both a state update and a routing instruction in a single return value. It replaces the need for separate conditional edges in some multi-agent patterns. ── MULTI-AGENT PATTERN ───────────────────────────────────── Each specialist is a compiled graph a subgraph Sub-agent 1: A researcherresearcher graph = StateGraph AgentState ... built with its own nodes, edges, and tools researcher = researcher graph.compile Sub-agent 2: A writerwriter graph = StateGraph AgentState ... built with its own nodes, edges, and tools writer = writer graph.compile ── SUPERVISOR NODE ─────────────────────────────────────────def supervisor node state: AgentState - dict: """Decides which sub-agent should handle the current task.""" The supervisor LLM decides: "researcher" or "writer" or "FINISH" response = supervisor llm.invoke state "messages" return {"messages": response , "next agent": response.content}def route to agent state: AgentState - Literal "researcher", "writer", " end " : """Routes to the appropriate sub-agent based on supervisor's decision.""" return state.get "next agent", " end " ── SUPERVISOR GRAPH ────────────────────────────────────────supervisor builder = StateGraph AgentState supervisor builder.add node "supervisor", supervisor node supervisor builder.add node "researcher", researcher Subgraph as a node supervisor builder.add node "writer", writer Subgraph as a node supervisor builder.set entry point "supervisor" supervisor builder.add conditional edges "supervisor", route to agent, {"researcher": "researcher", "writer": "writer", " end ": END} supervisor builder.add edge "researcher", "supervisor" supervisor builder.add edge "writer", "supervisor" supervisor graph = supervisor builder.compile checkpointer=MemorySaver Notice how the multi-agent pattern is identical in structure to the single-agent pattern. The same seven modules apply. The only difference is that some of your “nodes” are themselves entire compiled graphs. The mental model scales cleanly. Here is a condensed reference you can print or pin. Every important concept from this article in one place. State Keywords TypedDict — defines the shape of state. Annotated T, reducer — attaches a reducer to a field. add messages — the standard reducer for the messages list appends instead of replaces . operator.add — reducer for accumulating lists or numbers. LLM Keywords ChatOpenAI / ChatAnthropic — the LLM class. Always named llm. bind tools tools — tells the LLM about available tools. invoke messages — calls the LLM and returns a response. Message Keywords HumanMessage — user input. AIMessage — LLM output. SystemMessage — your instructions to the LLM the system prompt . Always a list when passed to invoke. Tool Keywords @tool — decorator that turns a Python function into an LLM-callable tool. The docstring is the tool's description. ToolNode tools — pre-built node that executes tool calls automatically. tools condition — pre-built router that checks if the LLM made a tool call. Graph Keywords StateGraph State — creates the graph builder. add node name, fn — registers a node. set entry point name — declares the first node. add edge A, B — unconditional wire from A to B. add conditional edges source, router, mapping — conditional wire based on router's return value. compile checkpointer=... — seals and finalizes the graph. END — the terminal constant. Invocation Keywords graph.invoke input, config — run to completion, return final state. graph.stream input, config — run and yield state updates progressively. config = {"configurable": {"thread id": "..."}} — the memory session identifier. MemorySaver — in-memory checkpointer for persistent conversation memory. The goal of this article was never to teach you LangGraph from scratch. It was to give your brain a stable, repeatable scaffold that you can reach for automatically — the way a musician reaches for a chord progression, or a chef reaches for mise en place . The seven-module structure — Imports → State → Tools → Nodes → Edges → Assembly → Entrypoint — is your chord progression. The keywords in each module are your notes. The canonical template is your sheet music. Every LangGraph agent you will ever build, from a simple chatbot to a complex multi-agent pipeline with dozens of nodes, fits inside this structure. The complexity grows, but the structure remains the same. That’s the point. That’s what makes it a standard. Start simple. Build the minimal ReAct loop: one agent node, one tool node, one conditional edge using tools condition. Get it working. Then add a second node. Then a second agent. The scaffold holds at every level. The map is now in your hands. The territory, as always, is built one node at a time. Want to go further? Explore LangGraph’s official documentation at python.langchain.com/docs/langgraph for persistence backends, human-in-the-loop patterns, and deployment with LangGraph Platform. The LangGraph Mental Model: A standardized architecture guide for every agent you’ll ever build https://pub.towardsai.net/the-langgraph-mental-model-a-standardized-architecture-guide-for-every-agent-youll-ever-build-d02265f3bebf was originally published in Towards AI https://pub.towardsai.net on Medium, where people are continuing the conversation by highlighting and responding to this story.