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Merge remote-tracking branch 'origin/main' into sr/template-update

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Sydney Runkle
2025-08-12 08:37:25 -04:00
parent f1696eb614 64df02d6c2
commit a10d7eb1a9
2 changed files with 8 additions and 9 deletions
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2
README.md
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@@ -45,7 +45,7 @@ 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.

15
src/agent/graph.py
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@@ -8,12 +8,12 @@ from __future__ import annotations
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")