Setup New Project template

(Remove react agent code)

src/agent/__init__.py

@@ -0,0 +1,8 @@
+"""New LangGraph Agent.
+
+This module defines a custom graph.
+"""
+
+from agent.graph import graph
+
+__all__ = ["graph"]

src/agent/configuration.py

@@ -0,0 +1,28 @@
+"""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

@@ -0,0 +1,38 @@
+"""Define a simple chatbot agent.
+
+This agent returns a predefined response without using an actual LLM.
+"""
+
+from typing import Any, Dict
+
+from langchain_core.runnables import RunnableConfig
+from langgraph.graph import StateGraph
+
+from agent.configuration import Configuration
+from agent.state import State
+
+
+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.
+    return {
+        "changeme": "output from my_node. "
+        f"Configured with {configuration.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

src/agent/state.py

@@ -0,0 +1,17 @@
+"""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"