I’ve deployed the repo shared in this tutorial about Configurable AI Agents, where I should be able to create new Assistants with different input. However when I deploy the repo only my supervisor_prebuilt agent is configurable. The simple react agent (configurable one) doesn’t show me the fields to adjust when creating a new assistant, like Prompt, Model and Tools. Do you know if something is missing in this tutorial repo ? Or do I need to adjust something ? I only need a single configurable agent for now, not a supervisor, and im struggling to replicate it like in the tutorial.
The relevant piece of code should be
agents/react_agent/graph.py:
from agents.react_agent.tools import get_tools
from langgraph.prebuilt import create_react_agent
from agents.utils import load_chat_model
from agents.react_agent.configuration import Configuration
from langchain_core.runnables import RunnableConfig
async def make_graph(config: RunnableConfig):
# Get name from config or use default
configurable = config.get("configurable", {})
# get values from configuration
llm = configurable.get("model", "openai/gpt-4.1")
selected_tools = configurable.get("selected_tools", ["get_todays_date"])
prompt = configurable.get("system_prompt", "You are a helpful assistant.")
# specify the name for use in supervisor architecture
name = configurable.get("name", "react_agent")
# Compile the builder into an executable graph
# You can customize this by adding interrupt points for state updates
graph = create_react_agent(
model=load_chat_model(llm),
tools=get_tools(selected_tools),
prompt=prompt,
config_schema=Configuration,
name=name
)
return graph
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agents/react_agent/configuration.py:
from typing import Annotated, Literal
from pydantic import BaseModel, Field
class Configuration(BaseModel):
"""The configuration for the agent."""
system_prompt: str = Field(
default="You are a helpful AI assistant.",
description="The system prompt to use for the agent's interactions. "
"This prompt sets the context and behavior for the agent."
)
model: Annotated[
Literal[
"anthropic/claude-sonnet-4-20250514",
"anthropic/claude-3-5-sonnet-latest",
"openai/gpt-4.1",
"openai/gpt-4.1-mini"
],
{"__template_metadata__": {"kind": "llm"}},
] = Field(
default="anthropic/claude-3-5-sonnet-latest",
description="The name of the language model to use for the agent's main interactions. "
"Should be in the form: provider/model-name."
)
selected_tools: list[Literal["finance_research", "advanced_research_tool", "basic_research_tool", "get_todays_date"]] = Field(
default = ["get_todays_date"],
description="The list of tools to use for the agent's interactions. "
"This list should contain the names of the tools to use."
)
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agents/react_agent/tools.py:
from typing import Callable, Optional, cast, Any
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_community.tools.yahoo_finance_news import YahooFinanceNewsTool
from langchain_core.tools import tool
from datetime import datetime
@tool
async def finance_research(ticker_symbol: str) -> Optional[list[dict[str, Any]]]:
"""Search for finance research, must be a ticker symbol."""
wrapped = YahooFinanceNewsTool()
result = await wrapped.ainvoke({"query": ticker_symbol})
return cast(list[dict[str, Any]], result)
@tool
async def advanced_research_tool(query: str) -> Optional[list[dict[str, Any]]]:
"""Perform in-depth research for blog content.
This tool conducts comprehensive web searches with higher result limits and
deeper analysis, ideal for creating well-researched blog posts backed by
authoritative sources.
"""
# Using Tavily with higher result count for more comprehensive research
wrapped = TavilySearchResults(
max_results=10, # Default to 10 if not specified
search_depth="advanced" # More thorough search
)
result = await wrapped.ainvoke({"query": query})
return cast(list[dict[str, Any]], result)
@tool
async def basic_research_tool(query: str) -> Optional[list[dict[str, Any]]]:
"""Research trending topics for social media content.
This tool performs quick searches optimized for trending and viral content,
returning concise results ideal for social media post creation.
"""
# Using Tavily with lower result count and quicker search for social content
wrapped = TavilySearchResults(
max_results=5, # Default to 3 if not specified
search_depth="basic", # Faster, less comprehensive search
include_raw_content=False, # Just the highlights
include_images=True # Social posts often benefit from images
)
result = await wrapped.ainvoke({"query": f"trending {query}"})
return cast(list[dict[str, Any]], result)
@tool
async def get_todays_date() -> str:
"""Get the current date."""
return datetime.now().strftime("%Y-%m-%d")
def get_tools(selected_tools: list[str]) -> list[Callable[..., Any]]:
"""Convert a list of tool names to actual tool functions."""
tools = []
for tool in selected_tools:
if tool == "finance_research":
tools.append(finance_research)
elif tool == "advanced_research_tool":
tools.append(advanced_research_tool)
elif tool == "basic_research_tool":
tools.append(basic_research_tool)
elif tool == "get_todays_date":
tools.append(get_todays_date)
return tools
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langgraph.json
{
"dependencies": ["."],
"graphs": {
"react_agent_no_config": "./agents/react_agent/graph_without_config.py:make_graph",
"react_agent": "./agents/react_agent/graph.py:make_graph",
"supervisor_prebuilt": "./agents/supervisor/supervisor_prebuilt.py:make_supervisor_graph"
},
"env": ".env"
}
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utils.py:
from langchain.chat_models import init_chat_model
from langchain_core.language_models import BaseChatModel
from langchain_core.messages import BaseMessage
def get_message_text(msg: BaseMessage) -> str:
"""Get the text content of a message."""
content = msg.content
if isinstance(content, str):
return content
elif isinstance(content, dict):
return content.get("text", "")
else:
txts = [c if isinstance(c, str) else (c.get("text") or "") for c in content]
return "".join(txts).strip()
def load_chat_model(fully_specified_name: str) -> BaseChatModel:
"""Load a chat model from a fully specified name.
Args:
fully_specified_name (str): String in the format 'provider/model'.
"""
provider, model = fully_specified_name.split("/", maxsplit=1)
return init_chat_model(model, model_provider=provider)
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As you can see I can not pass any configurable settings for my react_agent: 
But I can for my supervisor_prebuilt model