Oryntra: Skill for Claude Code

LangGraph Implementation

Core Concepts

LangGraph builds stateful, multi-actor agent applications using a graph-based architecture:

• StateGraph: Builder class for defining graphs with shared state
• Nodes: Functions that read state and return partial updates
• Edges: Define execution flow (static or conditional)
• Channels: Internal state management (LastValue, BinaryOperatorAggregate)
• Checkpointer: Persistence for pause/resume capabilities

Implementation gates

Use these sequenced checks for persistence and human-in-the-loop flows (avoid “it should work” without evidence):

1. Checkpointed runs

   • Build config with {"configurable": {"thread_id": "<stable-id>"}} before invoke / ainvoke.
   • Pass: The same thread_id is reused for every turn of one conversation; a new conversation uses a new id.

2. State after a step

   • Pass: graph.get_state(config).values (or equivalent) contains the keys and reducer outputs your next node or client expects; if not, fix routing, reducers, or node order before continuing.

3. Interrupt and resume (HITL)

   • Pass: After a pause, you have inspected pending work (get_state, and your LangGraph version’s interrupt listing if you rely on it) so you know which node is waiting and what resume payload shape to send.
   • Pass: Command(resume=...) (or equivalent) includes every field the code path after interrupt() reads.

4. Checkpointer vs environment

   • Pass: Tests or local dev use InMemorySaver or disposable SQLite; production uses a durable checkpointer configured for that deployment (not in-memory).

Essential Imports

from langgraph.graph import StateGraph, START, END
from langgraph.graph.message import MessagesState, add_messages
from langgraph.checkpoint.memory import InMemorySaver
from langgraph.types import Command, Send, interrupt, RetryPolicy
from typing import Annotated
from typing_extensions import TypedDict