Simplify the boilerplate

Signed-off-by: William Fu-Hinthorn <13333726+hinthornw@users.noreply.github.com>

src/agent/configuration.py

@@ -1,28 +0,0 @@
-"""Define the configurable parameters for the agent."""
-
-from __future__ import annotations
-
-from dataclasses import dataclass, fields
-from typing import Optional
-
-from langchain_core.runnables import RunnableConfig
-
-
-@dataclass(kw_only=True)
-class Configuration:
-    """The configuration for the agent."""
-
-    # Changeme: Add configurable values here!
-    # these values can be pre-set when you
-    # create assistants (https://langchain-ai.github.io/langgraph/cloud/how-tos/configuration_cloud/)
-    # and when you invoke the graph
-    my_configurable_param: str = "changeme"
-
-    @classmethod
-    def from_runnable_config(
-        cls, config: Optional[RunnableConfig] = None
-    ) -> Configuration:
-        """Create a Configuration instance from a RunnableConfig object."""
-        configurable = (config.get("configurable") or {}) if config else {}
-        _fields = {f.name for f in fields(cls) if f.init}
-        return cls(**{k: v for k, v in configurable.items() if k in _fields})

src/agent/graph.py

@@ -1,38 +1,52 @@
-"""Define a simple chatbot agent.
+"""LangGraph single-node graph template.
 
-This agent returns a predefined response without using an actual LLM.
+Returns a predefined response. Replace logic and configuration as needed.
 """
 
-from typing import Any, Dict
+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 agent.configuration import Configuration
-from agent.state import State
+
+class Configuration(TypedDict):
+    """Configurable 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/
+    """
+    my_configurable_param: str
+
+
+@dataclass
+class State:
+    """Input state for the agent.
+
+    Defines the initial structure of incoming data.
+    See: https://langchain-ai.github.io/langgraph/concepts/low_level/#state
+    """
+    changeme: str = "example"
 
 
 async def my_node(state: State, config: RunnableConfig) -> Dict[str, Any]:
-    """Each node does work."""
-    configuration = Configuration.from_runnable_config(config)
-    # configuration = Configuration.from_runnable_config(config)
-    # You can use runtime configuration to alter the behavior of your
-    # graph.
+    """Example node: processes input and returns output.
+
+    Can use runtime configuration to alter behavior.
+    """
+    configuration = config["configurable"]
     return {
         "changeme": "output from my_node. "
-        f"Configured with {configuration.my_configurable_param}"
+        f'Configured with {configuration.get("my_configurable_param")}'
     }
 
 
-# Define a new graph
-workflow = StateGraph(State, config_schema=Configuration)
-
-# Add the node to the graph
-workflow.add_node("my_node", my_node)
-
-# Set the entrypoint as `call_model`
-workflow.add_edge("__start__", "my_node")
-
-# Compile the workflow into an executable graph
-graph = workflow.compile()
-graph.name = "New Graph"  # This defines the custom name in LangSmith
+# Define the graph
+graph = (
+    StateGraph(State, config_schema=Configuration)
+    .add_node(my_node)
+    .add_edge("__start__", "my_node")
+    .compile(name="New Graph")
+)

src/agent/state.py

@@ -1,17 +0,0 @@
-"""Define the state structures for the agent."""
-
-from __future__ import annotations
-
-from dataclasses import dataclass
-
-
-@dataclass
-class State:
-    """Defines the input state for the agent, representing a narrower interface to the outside world.
-
-    This class is used to define the initial state and structure of incoming data.
-    See: https://langchain-ai.github.io/langgraph/concepts/low_level/#state
-    for more information.
-    """
-
-    changeme: str = "example"