How to return structured output with a ReAct style agent¶
Prerequisites
This guide assumes familiarity with the following:
You might want your agent to return its output in a structured format. For example, if the output of the agent is used by some other downstream software, you may want the output to be in the same structured format every time the agent is invoked to ensure consistency.
This notebook will walk through two different options for forcing a tool calling agent to structure its output. We will be using a basic ReAct agent (a model node and a tool-calling node) together with a third node at the end that will format response for the user. Both of the options will use the same graph structure as shown in the diagram below, but will have different mechanisms under the hood.
Option 1
The first way you can force your tool calling agent to have structured output is to bind the output you would like as an additional tool for the agent node to use. In contrast to the basic ReAct agent, the agent node in this case is not selecting between tools and END but rather selecting between the specific tools it calls. The expected flow in this case is that the LLM in the agent node will first select the action tool, and after receiving the action tool output it will call the response tool, which will then route to the respond node which simply structures the arguments from the agent node tool call.
Pros and Cons
The benefit to this format is that you only need one LLM, and can save money and latency because of this. The downside to this option is that it isn't guaranteed that the single LLM will call the correct tool when you want it to. We can help the LLM by setting tool_choice to any when we use bind_tools which forces the LLM to select at least one tool at every turn, but this is far from a fool proof strategy. In addition, another downside is that the agent might call multiple tools, so we need to check for this explicitly in our routing function (or if we are using OpenAI we an set parallell_tool_calling=False to ensure only one tool is called at a time).
Option 2
The second way you can force your tool calling agent to have structured output is to use a second LLM (in this case model_with_structured_output) to respond to the user.
In this case, you will define a basic ReAct agent normally, but instead of having the agent node choose between the tools node and ending the conversation, the agent node will choose between the tools node and the respond node. The respond node will contain a second LLM that uses structured output, and once called will return directly to the user. You can think of this method as basic ReAct with one extra step before responding to the user.
Pros and Cons
The benefit of this method is that it guarantees structured output (as long as .with_structured_output works as expected with the LLM). The downside to using this approach is that it requires making an additional LLM call before responding to the user, which can increase costs as well as latency. In addition, by not providing the agent node LLM with information about the desired output schema there is a risk that the agent LLM will fail to call the correct tools required to answer in the correct output schema.
Note that both of these options will follow the exact same graph structure (see the diagram above), in that they are both exact replicas of the basic ReAct architecture but with a respond node before the end.
Setup¶
First, let's install the required packages and set our API keys
import getpass
import os
def _set_env(var: str):
if not os.environ.get(var):
os.environ[var] = getpass.getpass(f"{var}: ")
_set_env("ANTHROPIC_API_KEY")
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Define model, tools, and graph state¶
Now we can define how we want to structure our output, define our graph state, and also our tools and the models we are going to use.
To use structured output, we will use the with_structured_output method from LangChain, which you can read more about here.
We are going to use a single tool in this example for finding the weather, and will return a structured weather response to the user.
from pydantic import BaseModel, Field
from typing import Literal
from langchain_core.tools import tool
from langchain_anthropic import ChatAnthropic
from langgraph.graph import MessagesState
class WeatherResponse(BaseModel):
"""Respond to the user with this"""
temperature: float = Field(description="The temperature in fahrenheit")
wind_directon: str = Field(
description="The direction of the wind in abbreviated form"
)
wind_speed: float = Field(description="The speed of the wind in km/h")
# Inherit 'messages' key from MessagesState, which is a list of chat messages
class AgentState(MessagesState):
# Final structured response from the agent
final_response: WeatherResponse
@tool
def get_weather(city: Literal["nyc", "sf"]):
"""Use this to get weather information."""
if city == "nyc":
return "It is cloudy in NYC, with 5 mph winds in the North-East direction and a temperature of 70 degrees"
elif city == "sf":
return "It is 75 degrees and sunny in SF, with 3 mph winds in the South-East direction"
else:
raise AssertionError("Unknown city")
tools = [get_weather]
model = ChatAnthropic(model="claude-3-opus-20240229")
model_with_tools = model.bind_tools(tools)
model_with_structured_output = model.with_structured_output(WeatherResponse)
Option 1: Bind output as tool¶
Let's now examine how we would use the single LLM option.
Define Graph¶
The graph definition is very similar to the one above, the only difference is we no longer call an LLM in the response node, and instead bind the WeatherResponse tool to our LLM that already contains the get_weather tool.
from langgraph.graph import StateGraph, END
from langgraph.prebuilt import ToolNode
tools = [get_weather, WeatherResponse]
# Force the model to use tools by passing tool_choice="any"
model_with_response_tool = model.bind_tools(tools, tool_choice="any")
# Define the function that calls the model
def call_model(state: AgentState):
response = model_with_response_tool.invoke(state["messages"])
# We return a list, because this will get added to the existing list
return {"messages": [response]}
# Define the function that responds to the user
def respond(state: AgentState):
# Construct the final answer from the arguments of the last tool call
response = WeatherResponse(**state["messages"][-1].tool_calls[0]["args"])
# We return the final answer
return {"final_response": response}
# Define the function that determines whether to continue or not
def should_continue(state: AgentState):
messages = state["messages"]
last_message = messages[-1]
# If there is only one tool call and it is the response tool call we respond to the user
if (
len(last_message.tool_calls) == 1
and last_message.tool_calls[0]["name"] == "WeatherResponse"
):
return "respond"
# Otherwise we will use the tool node again
else:
return "continue"
# Define a new graph
workflow = StateGraph(AgentState)
# Define the two nodes we will cycle between
workflow.add_node("agent", call_model)
workflow.add_node("respond", respond)
workflow.add_node("tools", ToolNode(tools))
# Set the entrypoint as `agent`
# This means that this node is the first one called
workflow.set_entry_point("agent")
# We now add a conditional edge
workflow.add_conditional_edges(
"agent",
should_continue,
{
"continue": "tools",
"respond": "respond",
},
)
workflow.add_edge("tools", "agent")
workflow.add_edge("respond", END)
graph = workflow.compile()
Usage¶
Now we can run our graph to check that it worked as intended:
answer = graph.invoke(input={"messages": [("human", "what's the weather in SF?")]})[
"final_response"
]
Again, the agent returned a WeatherResponse object as we expected.
Option 2: 2 LLMs¶
Let's now dive into how we would use a second LLM to force structured output.
Define Graph¶
We can now define our graph:
from langgraph.graph import StateGraph, END
from langgraph.prebuilt import ToolNode
from langchain_core.messages import HumanMessage
# Define the function that calls the model
def call_model(state: AgentState):
response = model_with_tools.invoke(state["messages"])
# We return a list, because this will get added to the existing list
return {"messages": [response]}
# Define the function that responds to the user
def respond(state: AgentState):
# We call the model with structured output in order to return the same format to the user every time
# state['messages'][-2] is the last ToolMessage in the convo, which we convert to a HumanMessage for the model to use
# We could also pass the entire chat history, but this saves tokens since all we care to structure is the output of the tool
response = model_with_structured_output.invoke(
[HumanMessage(content=state["messages"][-2].content)]
)
# We return the final answer
return {"final_response": response}
# Define the function that determines whether to continue or not
def should_continue(state: AgentState):
messages = state["messages"]
last_message = messages[-1]
# If there is no function call, then we respond to the user
if not last_message.tool_calls:
return "respond"
# Otherwise if there is, we continue
else:
return "continue"
# Define a new graph
workflow = StateGraph(AgentState)
# Define the two nodes we will cycle between
workflow.add_node("agent", call_model)
workflow.add_node("respond", respond)
workflow.add_node("tools", ToolNode(tools))
# Set the entrypoint as `agent`
# This means that this node is the first one called
workflow.set_entry_point("agent")
# We now add a conditional edge
workflow.add_conditional_edges(
"agent",
should_continue,
{
"continue": "tools",
"respond": "respond",
},
)
workflow.add_edge("tools", "agent")
workflow.add_edge("respond", END)
graph = workflow.compile()
Usage¶
We can now invoke our graph to verify that the output is being structured as desired:
answer = graph.invoke(input={"messages": [("human", "what's the weather in SF?")]})[
"final_response"
]
As we can see, the agent returned a WeatherResponse object as we expected. If would now be easy to use this agent in a more complex software stack without having to worry about the output of the agent not matching the format expected from the next step in the stack.