Develop a LangGraph application

This page shows you how to develop an application by using the framework-specific LangGraph template (the LanggraphAgent class in the Vertex AI SDK for Python). The application returns the exchange rate between two currencies on a specified date.

When building your agent, you have a few options:

The following steps show you how to create this application using the LanggraphAgent prebuilt template:

  1. Define and configure a model
  2. Define and use a tool
  3. (Optional) Store checkpoints
  4. (Optional) Customize the prompt template
  5. (Optional) Customize the orchestration

Before you begin

Make sure your environment is set up by following the steps in Set up your environment.

Step 1. Define and configure a model

Define the Model version to use.

model = "gemini-1.5-flash-001"

(Optional) Configure the safety settings of the model. To learn more about the options available for safety settings in Gemini, see Configure safety attributes. The following is an example of how you can configure the safety settings:

from langchain_google_vertexai import HarmBlockThreshold, HarmCategory

safety_settings = {
    HarmCategory.HARM_CATEGORY_UNSPECIFIED: HarmBlockThreshold.BLOCK_NONE,
    HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
    HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_ONLY_HIGH,
    HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_LOW_AND_ABOVE,
    HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_NONE,
}

(Optional) Specify model parameters in the following way:

model_kwargs = {
    # temperature (float): The sampling temperature controls the degree of
    # randomness in token selection.
    "temperature": 0.28,
    # max_output_tokens (int): The token limit determines the maximum amount of
    # text output from one prompt.
    "max_output_tokens": 1000,
    # top_p (float): Tokens are selected from most probable to least until
    # the sum of their probabilities equals the top-p value.
    "top_p": 0.95,
    # top_k (int): The next token is selected from among the top-k most
    # probable tokens. This is not supported by all model versions. See
    # https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/image-understanding#valid_parameter_values
    # for details.
    "top_k": None,
    # safety_settings (Dict[HarmCategory, HarmBlockThreshold]): The safety
    # settings to use for generating content.
    # (you must create your safety settings using the previous step first).
    "safety_settings": safety_settings,
}

Create a LanggraphAgent using the model configurations:

agent = reasoning_engines.LanggraphAgent(
    model=model,                # Required.
    model_kwargs=model_kwargs,  # Optional.
)

If you are running in an interactive environment (e.g. terminal or Colab notebook), you can run a query as an intermediate testing step:

response = agent.query(input={"messages": [
    ("user", "What is the exchange rate from US dollars to Swedish currency?"),
]})

print(response)

The response is a Python dictionary similar to the following example:

{
  'messages': [{
    'id': ['langchain', 'schema', 'messages', 'HumanMessage'],
    'kwargs': {
      'content': 'What is the exchange rate from US dollars to Swedish currency?',
      'id': '5473dd25-d796-42ad-a690-45bc49a64bec',
      'type': 'human',
    },
    'lc': 1,
    'type': 'constructor',
  }, {
    'id': ['langchain', 'schema', 'messages', 'AIMessage'],
    'kwargs': {
      'content': """
        I do not have access to real-time information, including currency exchange rates.

        To get the most up-to-date exchange rate from US dollars to Swedish currency (SEK),
        I recommend checking a reliable online currency converter like: ...

        These websites will provide you with the current exchange rate and allow you to
        convert specific amounts.""",
      'id': 'run-c42f9940-8ba8-42f1-a625-3aa0780c9e87-0',
      ...
      'usage_metadata': {
        'input_tokens': 12,
        'output_tokens': 145,
        'total_tokens': 157,
      },
    },
    'lc': 1,
    'type': 'constructor',
  }],
}

(Optional) Advanced customization

The LanggraphAgent template uses ChatVertexAI by default, because it provides access to all foundational models available in Google Cloud. To use a model that is not available through ChatVertexAI, you can specify the model_builder= argument, with a Python function of the following signature:

from typing import Optional

def model_builder(
    *,
    model_name: str,                      # Required. The name of the model
    model_kwargs: Optional[dict] = None,  # Optional. The model keyword arguments.
    **kwargs,                             # Optional. The remaining keyword arguments to be ignored.
):

For a list of the chat models supported in LangChain and their capabilities, see Chat Models. The set of supported values for model= and model_kwargs= are specific to each chat model, so you have to refer to their corresponding documentation for details.

ChatVertexAI

Installed by default.

It is used in the LanggraphAgent template when you omit the model_builder argument, for example

agent = reasoning_engines.LanggraphAgent(
    model=model,                # Required.
    model_kwargs=model_kwargs,  # Optional.
)

ChatAnthropic

First, follow their documentation to set up an account and install the package.

Next, define a model_builder that returns ChatAnthropic:

def model_builder(*, model_name: str, model_kwargs = None, **kwargs):
    from langchain_anthropic import ChatAnthropic
    return ChatAnthropic(model_name=model_name, **model_kwargs)

Finally, use it in the LanggraphAgent template with the following code:

agent = reasoning_engines.LanggraphAgent(
    model="claude-3-opus-20240229",                       # Required.
    model_builder=model_builder,                          # Required.
    model_kwargs={
        "api_key": "ANTHROPIC_API_KEY",  # Required.
        "temperature": 0.28,                              # Optional.
        "max_tokens": 1000,                               # Optional.
    },
)

ChatOpenAI

You can use ChatOpenAI in conjunction with Gemini's ChatCompletions API.

First, follow their documentation to install the package.

Next, define a model_builder that returns ChatOpenAI:

def model_builder(
    *,
    model_name: str,
    model_kwargs = None,
    project: str,   # Specified via vertexai.init
    location: str,  # Specified via vertexai.init
    **kwargs,
):
    import google.auth
    from langchain_openai import ChatOpenAI

    # Note: the credential lives for 1 hour by default.
    # After expiration, it must be refreshed.
    creds, _ = google.auth.default(scopes=["https://www.googleapis.com/auth/cloud-platform"])
    auth_req = google.auth.transport.requests.Request()
    creds.refresh(auth_req)

    if model_kwargs is None:
        model_kwargs = {}

    endpoint = f"https://{location}-aiplatform.googleapis.com"
    base_url = f'{endpoint}/v1beta1/projects/{project}/locations/{location}/endpoints/openapi'

    return ChatOpenAI(
        model=model_name,
        base_url=base_url,
        api_key=creds.token,
        **model_kwargs,
    )

Finally, use it in the LanggraphAgent template with the following code:

agent = reasoning_engines.LanggraphAgent(
    model="google/gemini-1.5-pro-001",  # Or "meta/llama3-405b-instruct-maas"
    model_builder=model_builder,        # Required.
    model_kwargs={
        "temperature": 0,               # Optional.
        "max_retries": 2,               # Optional.
    },
)

Step 2. Define and use a tool

After you define your model, the next step is to define the tools that your model uses for reasoning. A tool can be a LangChain tool or a Python function. You can also convert a defined Python function to a LangChain Tool.

When you define your function, it's important to include comments that fully and clearly describe the function's parameters, what the function does, and what the function returns. This information is used by the model to determine which function to use. You must also test your function locally to confirm that it works.

Use the following code to define a function that returns an exchange rate:

def get_exchange_rate(
    currency_from: str = "USD",
    currency_to: str = "EUR",
    currency_date: str = "latest",
):
    """Retrieves the exchange rate between two currencies on a specified date.

    Uses the Frankfurter API (https://api.frankfurter.app/) to obtain
    exchange rate data.

    Args:
        currency_from: The base currency (3-letter currency code).
            Defaults to "USD" (US Dollar).
        currency_to: The target currency (3-letter currency code).
            Defaults to "EUR" (Euro).
        currency_date: The date for which to retrieve the exchange rate.
            Defaults to "latest" for the most recent exchange rate data.
            Can be specified in YYYY-MM-DD format for historical rates.

    Returns:
        dict: A dictionary containing the exchange rate information.
            Example: {"amount": 1.0, "base": "USD", "date": "2023-11-24",
                "rates": {"EUR": 0.95534}}
    """
    import requests
    response = requests.get(
        f"https://api.frankfurter.app/{currency_date}",
        params={"from": currency_from, "to": currency_to},
    )
    return response.json()

To test the function before you use it in your application, run the following:

get_exchange_rate(currency_from="USD", currency_to="SEK")

The response should be similar to the following:

{'amount': 1.0, 'base': 'USD', 'date': '2024-02-22', 'rates': {'SEK': 10.3043}}

To use the tool inside the LanggraphAgent template, you will add it to the list of tools under the tools= argument:

agent = reasoning_engines.LanggraphAgent(
    model=model,                # Required.
    tools=[get_exchange_rate],  # Optional.
    model_kwargs=model_kwargs,  # Optional.
)

You can test the application locally by performing test queries against it. Run the following command to test the application locally using US dollars and Swedish Krona:

response = agent.query(input={"messages": [
    ("user", "What is the exchange rate from US dollars to Swedish currency?"),
]})

The response is a dictionary that's similar to the following:

{"input": "What is the exchange rate from US dollars to Swedish currency?",
 "output": "For 1 US dollar you will get 10.7345 Swedish Krona."}

(Optional) Multiple tools

Tools for LanggraphAgent can be defined and instantiated in other ways.

Grounding Tool

First, import the generate_models package and create the tool

from vertexai.generative_models import grounding, Tool

grounded_search_tool = Tool.from_google_search_retrieval(
    grounding.GoogleSearchRetrieval()
)

Next, use the tool inside the LanggraphAgent template:

agent = reasoning_engines.LanggraphAgent(
    model=model,
    tools=[grounded_search_tool],
)
agent.query(input={"messages": [
    ("user", "When is the next total solar eclipse in US?"),
]})

The response is a dictionary that is similar to the following:

{"input": "When is the next total solar eclipse in US?",
 "output": """The next total solar eclipse in the U.S. will be on August 23, 2044.
 This eclipse will be visible from three states: Montana, North Dakota, and
 South Dakota. The path of totality will begin in Greenland, travel through
 Canada, and end around sunset in the United States."""}

For details, visit Grounding.

LangChain Tool

First, install the package that defines the tool.

pip install langchain-google-community

Next, import the package and create the tool.

from langchain_google_community import VertexAISearchRetriever
from langchain.tools.retriever import create_retriever_tool

retriever = VertexAISearchRetriever(
    project_id="PROJECT_ID",
    data_store_id="DATA_STORE_ID",
    location_id="DATA_STORE_LOCATION_ID",
    engine_data_type=1,
    max_documents=10,
)
movie_search_tool = create_retriever_tool(
    retriever=retriever,
    name="search_movies",
    description="Searches information about movies.",
)

Finally, use the tool inside the LanggraphAgent template:

agent = reasoning_engines.LanggraphAgent(
    model=model,
    tools=[movie_search_tool],
)
response = agent.query(input={"messages": [
    ("user", "List some sci-fi movies from the 1990s"),
]})

print(response)

It should return a response such as

{"input": "List some sci-fi movies from the 1990s",
 "output": """Here are some sci-fi movies from the 1990s:
    * The Matrix (1999): A computer hacker learns from mysterious rebels about the true nature of his reality and his role in the war against its controllers.
    * Star Wars: Episode I - The Phantom Menace (1999): Two Jedi Knights escape a hostile blockade to find a queen and her protector, and come across a young boy [...]
    * Men in Black (1997): A police officer joins a secret organization that monitors extraterrestrial interactions on Earth.
    [...]
 """}

To see the full example, visit the notebook.

For more examples of tools available in LangChain, visit Google Tools.

Vertex AI Extension

First, import the extensions package and create the tool

from typing import Optional

def generate_and_execute_code(
    query: str,
    files: Optional[list[str]] = None,
    file_gcs_uris: Optional[list[str]] = None,
) -> str:
    """Get the results of a natural language query by generating and executing
    a code snippet.

    Example queries: "Find the max in [1, 2, 5]" or "Plot average sales by
    year (from data.csv)". Only one of `file_gcs_uris` and `files` field
    should be provided.

    Args:
        query:
            The natural language query to generate and execute.
        file_gcs_uris:
            Optional. URIs of input files to use when executing the code
            snippet. For example, ["gs://input-bucket/data.csv"].
        files:
            Optional. Input files to use when executing the generated code.
            If specified, the file contents are expected be base64-encoded.
            For example: [{"name": "data.csv", "contents": "aXRlbTEsaXRlbTI="}].
    Returns:
        The results of the query.
    """
    operation_params = {"query": query}
    if files:
        operation_params["files"] = files
    if file_gcs_uris:
        operation_params["file_gcs_uris"] = file_gcs_uris

    from vertexai.preview import extensions

    # If you have an existing extension instance, you can get it here
    # i.e. code_interpreter = extensions.Extension(resource_name).
    code_interpreter = extensions.Extension.from_hub("code_interpreter")
    return extensions.Extension.from_hub("code_interpreter").execute(
        operation_id="generate_and_execute",
        operation_params=operation_params,
    )

Next, use the tool inside the LanggraphAgent template:

agent = reasoning_engines.LanggraphAgent(
    model=model,
    tools=[generate_and_execute_code],
)
response = agent.query(input={"messages": [("user", """
    Using the data below, construct a bar chart that includes only the height values with different colors for the bars:

    tree_heights_prices = {
      \"Pine\": {\"height\": 100, \"price\": 100},
      \"Oak\": {\"height\": 65, \"price\": 135},
      \"Birch\": {\"height\": 45, \"price\": 80},
      \"Redwood\": {\"height\": 200, \"price\": 200},
      \"Fir\": {\"height\": 180, \"price\": 162},
    }
""")]})

print(response)

It should return a response such as

{"input": """Using the data below, construct a bar chart that includes only the height values with different colors for the bars:

 tree_heights_prices = {
    \"Pine\": {\"height\": 100, \"price\": 100},
    \"Oak\": {\"height\": 65, \"price\": 135},
    \"Birch\": {\"height\": 45, \"price\": 80},
    \"Redwood\": {\"height\": 200, \"price\": 200},
    \"Fir\": {\"height\": 180, \"price\": 162},
 }
 """,
 "output": """Here's the generated bar chart:
 ```python
 import matplotlib.pyplot as plt

 tree_heights_prices = {
    "Pine": {"height": 100, "price": 100},
    "Oak": {"height": 65, "price": 135},
    "Birch": {"height": 45, "price": 80},
    "Redwood": {"height": 200, "price": 200},
    "Fir": {"height": 180, "price": 162},
 }

 heights = [tree["height"] for tree in tree_heights_prices.values()]
 names = list(tree_heights_prices.keys())

 plt.bar(names, heights, color=['red', 'green', 'blue', 'purple', 'orange'])
 plt.xlabel('Tree Species')
 plt.ylabel('Height')
 plt.title('Tree Heights')
 plt.show()
 ```
 """}

For details, visit Vertex AI Extensions.

You can use all (or a subset) of the tools you've created in LanggraphAgent:

agent = reasoning_engines.LanggraphAgent(
    model=model,
    tools=[
        get_exchange_rate,         # Optional (Python function)
        grounded_search_tool,      # Optional (Grounding Tool)
        movie_search_tool,         # Optional (Langchain Tool)
        generate_and_execute_code, # Optional (Vertex Extension)
    ],
)

response = agent.query(input={"messages": [
    ("user", "List some sci-fi movies from the 1990s"),
]})

print(response)

(Optional) Tool configuration

With Gemini, you can place constraints on tool usage. For example, instead of allowing the model to generate natural language responses, you can force it to only generate function calls ("forced function calling").

from vertexai.preview.generative_models import ToolConfig

agent = reasoning_engines.LanggraphAgent(
    model="gemini-1.5-pro",
    tools=[search_arxiv, get_exchange_rate],
    model_tool_kwargs={
        "tool_config": {  # Specify the tool configuration here.
            "function_calling_config": {
                "mode": ToolConfig.FunctionCallingConfig.Mode.ANY,
                "allowed_function_names": ["search_arxiv", "get_exchange_rate"],
            },
        },
    },
)

response = agent.query(input={"messages": [
    ("user", "Explain the Schrodinger equation in a few sentences"),
]})

print(response)

For details, visit Tool Configuration.

Step 3. Store checkpoints

To track chat messages and append them to a database, define a checkpointer_builder function and pass it in when you create the application.

Set up a database

First, install and use the relevant package to set up a database of your choice (e.g. Firestore, or Spanner):

Next, define a checkpointer_builder function as follows:

Spanner 

checkpointer_kwargs = {
    "instance_id": "INSTANCE_ID",
    "database_id": "DATABASE_ID",
    "project_id": "PROJECT_ID",
}

def checkpointer_builder(**kwargs):
    from langchain_google_spanner import SpannerCheckpointSaver

    checkpointer = SpannerCheckpointSaver(**kwargs)
    checkpointer.setup()

    return checkpointer

Finally, create the application and pass it in as checkpointer_builder:

agent = reasoning_engines.LanggraphAgent(
    model=model,
    checkpointer_kwargs=checkpointer_kwargs,    # <- new
    checkpointer_builder=checkpointer_builder,  # <- new
)

When querying the application, make sure you specify a thread_id so that the application has "memory" of past questions and answers:

response = agent.query(
    input={"messages": [
        ("user", "What is the exchange rate from US dollars to Swedish currency?")
    ]},
    config={"configurable": {"thread_id": "THREAD_ID"}},
)

print(response)

You can check that subsequent queries will retain memory of the session:

response = agent.query(
    input={"messages": [("user", "How much is 100 USD?")]},
    config={"configurable": {"thread_id": "THREAD_ID"}},
)

print(response)

Step 4. Customize the prompt template

Prompt templates help to translate user input into instructions for a model, and are used to guide a model's response, helping it understand the context and generate relevant and coherent language-based output. For details, visit ChatPromptTemplates.

The default prompt template is organized sequentially into sections.

Section Description
(Optional) System instruction Instructions for the agent to be applied across all queries.
(Optional) Chat history Messages corresponding to the chat history from a past session.
User input The query from the user for the agent to respond to.
Agent Scratchpad Messages created by the agent (e.g. with function calling) as it performs uses its tools and performs reasoning to formulate a response to the user.

The default prompt template is generated if you create the application without specifying your own prompt template, and will look like the following in full:

from langchain_core.prompts import ChatPromptTemplate
from langchain.agents.format_scratchpad.tools import format_to_tool_messages

prompt_template = {
    "user_input": lambda x: x["input"],
    "history": lambda x: x["history"],
    "agent_scratchpad": lambda x: format_to_tool_messages(x["intermediate_steps"]),
} | ChatPromptTemplate.from_messages([
    ("system", "{system_instruction}"),
    ("placeholder", "{history}"),
    ("user", "{user_input}"),
    ("placeholder", "{agent_scratchpad}"),
])

You are implicitly using the full prompt template when you instantiate the application in the following example:

system_instruction = "I help look up the rate between currencies"

agent = reasoning_engines.LanggraphAgent(
    model=model,
    system_instruction=system_instruction,
    checkpointer_kwargs=checkpointer_kwargs,
    checkpointer_builder=checkpointer_builder,
    tools=[get_exchange_rate],
)

You can override the default prompt template with your own prompt template, and use it when constructing the application, for example:


custom_prompt_template = {
    "user_input": lambda x: x["input"],
    "history": lambda x: x["history"],
    "agent_scratchpad": lambda x: format_to_tool_messages(x["intermediate_steps"]),
} | ChatPromptTemplate.from_messages([
    ("placeholder", "{history}"),
    ("user", "{user_input}"),
    ("placeholder", "{agent_scratchpad}"),
])

agent = reasoning_engines.LanggraphAgent(
    model=model,
    prompt=custom_prompt_template,
    checkpointer_kwargs=checkpointer_kwargs,
    checkpointer_builder=checkpointer_builder,
    tools=[get_exchange_rate],
)

response = agent.query(
    input={"messages": [
        ("user", "What is the exchange rate from US dollars to Swedish currency?"),
    ]}
    config={"configurable": {"thread_id": "THREAD_ID"}},
)

print(response)

Step 5. Customize the orchestration

All LangChain components implement the Runnable interface, which provide input and output schemas for orchestration. The LanggraphAgent requires a runnable to be built for it to respond to queries. By default, the LanggraphAgent will build such a runnable by using the prebuilt react application implementation from langgraph.

You might want to customize the orchestration if you intend to (i) implement an application that performs a deterministic set of steps (rather than to perform open-ended reasoning), or (ii) prompt the Application in a ReAct-like fashion to annotate each step with thoughts for why it performed that step. To do so, you have to override the default runnable when creating the LanggraphAgent by specifying the runnable_builder= argument with a Python function of the following signature:

from typing import Optional
from langchain_core.language_models import BaseLanguageModel

def runnable_builder(
    model: BaseLanguageModel,
    *,
    system_instruction: Optional[str] = None,
    prompt: Optional["RunnableSerializable"] = None,
    tools: Optional[Sequence["_ToolLike"]] = None,
    checkpointer: Optional[Any] = None,
    runnable_kwargs: Optional[Mapping[str, Any]] = None,
    **kwargs,
):

where

  • model corresponds to the chat model being returned from the model_builder (see Define and configure a model),
  • tools corresponds to the tools and configurations to be used (see Define and use a tool),
  • checkpointer corresponds to the database for storing checkpoints (see Store checkpoints),
  • system_instruction and prompt corresponds to the prompt configuration (see Customize the prompt template),
  • runnable_kwargs are the keyword arguments you can use for customizing the runnable to be built.

This gives different options for customizing the orchestration logic.

ChatModel

In the simplest case, to create an application without orchestration, you can override the runnable_builder for LanggraphAgent to return the model directly.

from langchain_core.language_models import BaseLanguageModel

def llm_builder(model: BaseLanguageModel, **kwargs):
    return model

agent = reasoning_engines.LanggraphAgent(
    model=model,
    runnable_builder=llm_builder,
)

ReAct

To override the default tool-calling behavior with your own ReAct application based on your own prompt (see Customize the Prompt Template), you need to override the runnable_builder for LanggraphAgent.

from typing import Sequence
from langchain_core.language_models import BaseLanguageModel
from langchain_core.prompts import BasePromptTemplate
from langchain_core.tools import BaseTool
from langchain import hub

def react_builder(
    model: BaseLanguageModel,
    *,
    tools: Sequence[BaseTool],
    prompt: BasePromptTemplate,
    agent_executor_kwargs = None,
    **kwargs,
):
    from langchain.agents.react.agent import create_react_agent
    from langchain.agents import AgentExecutor

    agent = create_react_agent(model, tools, prompt)
    return AgentExecutor(agent=agent, tools=tools, **agent_executor_kwargs)

agent = reasoning_engines.LanggraphAgent(
    model=model,
    tools=[get_exchange_rate],
    prompt=hub.pull("hwchase17/react"),
    agent_executor_kwargs={"verbose": True}, # Optional. For illustration.
    runnable_builder=react_builder,
)

LCEL Syntax

To construct the following graph using LangChain Expression Language (LCEL),

   Input
   /   \
 Pros  Cons
   \   /
  Summary

you need to override the runnable_builder for LanggraphAgent:

def lcel_builder(*, model, **kwargs):
    from operator import itemgetter
    from langchain_core.prompts import ChatPromptTemplate
    from langchain_core.runnables import RunnablePassthrough
    from langchain_core.output_parsers import StrOutputParser

    output_parser = StrOutputParser()

    planner = ChatPromptTemplate.from_template(
        "Generate an argument about: {input}"
    ) | model | output_parser | {"argument": RunnablePassthrough()}

    pros = ChatPromptTemplate.from_template(
        "List the positive aspects of {argument}"
    ) | model | output_parser

    cons = ChatPromptTemplate.from_template(
        "List the negative aspects of {argument}"
    ) | model | output_parser

    final_responder = ChatPromptTemplate.from_template(
        "Argument:{argument}\nPros:\n{pros}\n\nCons:\n{cons}\n"
        "Generate a final response given the critique",
    ) | model | output_parser

    return planner | {
        "pros": pros,
        "cons": cons,
        "argument": itemgetter("argument"),
    } | final_responder

agent = reasoning_engines.LanggraphAgent(
    model=model,
    runnable_builder=lcel_builder,
)

LangGraph

To construct the following graph using LangGraph,

   Input
   /   \
 Pros  Cons
   \   /
  Summary

you need to override the runnable_builder for LanggraphAgent:

def langgraph_builder(*, model, **kwargs):
    from langchain_core.prompts import ChatPromptTemplate
    from langchain_core.output_parsers import StrOutputParser
    from langgraph.graph import END, MessageGraph

    output_parser = StrOutputParser()

    planner = ChatPromptTemplate.from_template(
        "Generate an argument about: {input}"
    ) | model | output_parser

    pros = ChatPromptTemplate.from_template(
        "List the positive aspects of {input}"
    ) | model | output_parser

    cons = ChatPromptTemplate.from_template(
        "List the negative aspects of {input}"
    ) | model | output_parser

    summary = ChatPromptTemplate.from_template(
        "Input:{input}\nGenerate a final response given the critique",
    ) | model | output_parser

    builder = MessageGraph()
    builder.add_node("planner", planner)
    builder.add_node("pros", pros)
    builder.add_node("cons", cons)
    builder.add_node("summary", summary)

    builder.add_edge("planner", "pros")
    builder.add_edge("planner", "cons")
    builder.add_edge("pros", "summary")
    builder.add_edge("cons", "summary")
    builder.add_edge("summary", END)
    builder.set_entry_point("planner")
    return builder.compile()

agent = reasoning_engines.LanggraphAgent(
    model=model,
    runnable_builder=langgraph_builder,
)

# Example query
response = agent.query(input={"role": "user", "content": "scrum methodology"})

print(response)

What's next