Posts

LangGraph — Conditional Workflow with Decision Routing

Image
  LangState and StateGraph are different, but directly linked . This is actually one of the most important concepts in LangGraph. LangState                  StateGraph    │                                    │    │ defines                    │ uses    ▼                               ▼ "What data does          "How does the the workflow carry?"      workflow run?" class LangState ( TypedDict ):     messages : Annotated [ list , add_messages ] LangState = the data/state structure ,  "Every state in my graph has a messages field." StateGraph = the workflow that operates on that state builder = StateGraph ( LangState ) There is the...

Introduction to LangGraph: Building Stateful LLM Workflows with Nodes and Edges

  What is LangGraph? LangGraph is a framework for building stateful workflows and agents around LLMs . In a typical LLM application, we may need to perform several steps. For example, the LLM may receive a question, decide that it needs a tool, call the tool, receive the result, and then generate a final answer. LangGraph helps us represent this workflow as a graph . A LangGraph workflow mainly consists of: State — contains the information carried through the workflow. Nodes — perform individual tasks, such as calling an LLM or executing a tool. Edges — define how the workflow moves from one node to another. A simple workflow can therefore look like this: START ↓ LLM Node ↓ Tool Node ↓ END The state travels through these nodes as the workflow executes. For example, if our state contains a messages list, the LLM can add an AIMessage to it and a tool can add a ToolMessage . The state therefore keeps track of what has happened during the workflow. In simple terms: LangGraph...