What types of state does lang-graph support?
State can be any Python type, but is typically a TypedDict or Pydantic BaseModel representing the current snapshot of the application.
@neels22
Basic information
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No standard config provided
This server doesn't expose a parseable MCP config block in its README. See the repository for install instructions.
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lang-graph uses a graph-based approach to handle complex and dynamic workflows for building LLM-based applications. It categorizes applications, performs operations on them, and routes them to appropriate handlers (reject, recruiter, HR interviewer) based on parameter matches.
The README describes defining a state (TypedDict or Pydantic BaseModel), nodes (functions that receive state and return computed state), and edges (functions that determine next node based on current state). Nodes are added to the graph to build the workflow.
State can be any Python type, but is typically a TypedDict or Pydantic BaseModel representing the current snapshot of the application.
Nodes are functions that contain logic to perform an operation; they receive current state and return computed state. Edges are functions that determine the next node to execute based on current state, enabling conditional branches or transitions.
If parameters do not match the operations, the application is rejected. If no match is found at all, the application is sent to a recruiter.
The README describes it as a graph-based approach for building LLM apps; it does not specify deployment details.
Yes, in a workflow the state is modified as it progresses through different nodes.
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