Merge pull request #8 from langchain-ai/sr/context-schema

chore: replace `config_schema` with `context_schema`

Author: sydney-runkle

README.md

@@ -53,7 +53,7 @@ For more information on getting started with LangGraph Server, [see here](https:
 
 ## How to customize
 
-1. **Define configurable parameters**: Modify the `Configuration` class in the `graph.py` file to expose the arguments you want to configure. For example, in a chatbot application you may want to define a dynamic system prompt or LLM to use. For more information on configurations in LangGraph, [see here](https://langchain-ai.github.io/langgraph/concepts/low_level/?h=configuration#configuration).
+1. **Define runtime context**: Modify the `Context` class in the `graph.py` file to expose the arguments you want to configure per assistant. For example, in a chatbot application you may want to define a dynamic system prompt or LLM to use. For more information on runtime context in LangGraph, [see here](https://langchain-ai.github.io/langgraph/agents/context/?h=context#static-runtime-context).
 
 2. **Extend the graph**: The core logic of the application is defined in [graph.py](./src/agent/graph.py). You can modify this file to add new nodes, edges, or change the flow of information.
 

src/agent/graph.py

@@ -8,12 +8,12 @@
 from dataclasses import dataclass
 from typing import Any, Dict, TypedDict
 
-from langchain_core.runnables import RunnableConfig
 from langgraph.graph import StateGraph
+from langgraph.runtime import Runtime
 
 
-class Configuration(TypedDict):
-    """Configurable parameters for the agent.
+class Context(TypedDict):
+    """Context parameters for the agent.
 
     Set these when creating assistants OR when invoking the graph.
     See: https://langchain-ai.github.io/langgraph/cloud/how-tos/configuration_cloud/
@@ -33,21 +33,20 @@ class State:
     changeme: str = "example"
 
 
-async def call_model(state: State, config: RunnableConfig) -> Dict[str, Any]:
+async def call_model(state: State, runtime: Runtime[Context]) -> Dict[str, Any]:
     """Process input and returns output.
 
-    Can use runtime configuration to alter behavior.
+    Can use runtime context to alter behavior.
     """
-    configuration = config["configurable"]
     return {
         "changeme": "output from call_model. "
-        f'Configured with {configuration.get("my_configurable_param")}'
+        f"Configured with {runtime.context.get('my_configurable_param')}"
     }
 
 
 # Define the graph
 graph = (
-    StateGraph(State, config_schema=Configuration)
+    StateGraph(State, context_schema=Context)
     .add_node(call_model)
     .add_edge("__start__", "call_model")
     .compile(name="New Graph")