langgraph/examples/state-context-key.ipynb

How to use a context object in state

Sometimes you need some resources (like a database connection, a requests session, etc) to live for the duration of your graph execution, without being ever saved by the checkpointer.

LangGraph supports decorating a state key with the Context channel, which will take care to

• initialize the value before the graph starts executing, with access to the config passed in to invoke/stream
• run any cleanup code you need at the end of the graph execution, whether the graph succeeded or errored

The argument to context channel should be either a ContextManager class or function.

Setup

First we need to install the packages required

In [1]:

%%capture --no-stderr
%pip install --quiet -U langgraph langchain_openai

Next, we need to set API keys for OpenAI (the LLM we will use) and Tavily (the search tool we will use)

In [1]:

import getpass
import os


def _set_env(var: str):
    if not os.environ.get(var):
        os.environ[var] = getpass.getpass(f"{var}: ")


_set_env("OPENAI_API_KEY")

Optionally, we can set API key for LangSmith tracing, which will give us best-in-class observability.

In [2]:

os.environ["LANGCHAIN_TRACING_V2"] = "true"
_set_env("LANGCHAIN_API_KEY")

Set up the tools

We will first define the tools we want to use. For this simple example, we will use create a placeholder search engine. However, it is really easy to create your own tools - see documentation here on how to do that.

In [1]:

from langchain_core.tools import tool


@tool
def search(query: str):
    """Call to surf the web."""
    # This is a placeholder for the actual implementation
    # Don't let the LLM know this though 😊
    return ["The answer to your question lies within."]


tools = [search]

We can now wrap these tools in a simple ToolExecutor. This is a real simple class that takes in a ToolInvocation and calls that tool, returning the output.

A ToolInvocation is any dict-like class with tool and tool_input attributes.

In [2]:

from langgraph.prebuilt import ToolExecutor

tool_executor = ToolExecutor(tools)

Set up the model

Now we need to load the chat model we want to use. Importantly, this should satisfy two criteria:

1. It should work with messages. We will represent all agent state in the form of messages, so it needs to be able to work well with them.
2. It should work with OpenAI function calling. This means it should either be an OpenAI model or a model that exposes a similar interface.

Note: these model requirements are not requirements for using LangGraph - they are just requirements for this one example.

In [3]:

from langchain_openai import ChatOpenAI

model = ChatOpenAI(temperature=0)

After we've done this, we should make sure the model knows that it has these tools available to call. We can do this by converting the LangChain tools into the format for OpenAI function calling, and then bind them to the model class.

In [4]:

model = model.bind_tools(tools)

Define the context object

Here we're defining the context object as a pydantic model, which is created by the factory function decorated with @contextmanager. @contextmanager ensures any cleanup code you need can be run at the end of the execution

In [9]:

import httpx
from contextlib import contextmanager
from langchain_core.pydantic_v1 import BaseModel
from langchain_core.runnables import RunnableConfig


class AgentContext(BaseModel):
    class Config:
        arbitrary_types_allowed = True

    httpx_session: httpx.Client


@contextmanager
def make_agent_context(config: RunnableConfig):
    # here you could read the config values passed invoke/stream to customize the context object

    # as an example, we create an httpx session, which could then be used in your graph's nodes
    session = httpx.Client()
    try:
        yield AgentContext(httpx_session=session)
    finally:
        session.close()

Define the agent state

The main type of graph in langgraph is the StateGraph. This graph is parameterized by a state object that it passes around to each node. Each node then returns operations to update that state. These operations can either SET specific attributes on the state (e.g. overwrite the existing values) or ADD to the existing attribute. Whether to set or add is denoted by annotating the state object you construct the graph with.

For this example, the state we will track will just be a list of messages. We want each node to just add messages to that list. Therefore, we will use a pydantic.BaseModel with one key (messages) and annotate it so that the messages attribute is treated as "append-only".

In [10]:

import operator
from typing import Annotated, Sequence

from langchain_core.messages import BaseMessage
from langchain_core.pydantic_v1 import BaseModel
from langgraph.channels.context import Context


class AgentState(BaseModel):
    messages: Annotated[Sequence[BaseMessage], operator.add]
    context: Annotated[AgentContext, Context(make_agent_context)]

Define the nodes

We now need to define a few different nodes in our graph. In langgraph, a node can be either a function or a runnable. There are two main nodes we need for this:

1. The agent: responsible for deciding what (if any) actions to take.
2. A function to invoke tools: if the agent decides to take an action, this node will then execute that action.

We will also need to define some edges. Some of these edges may be conditional. The reason they are conditional is that based on the output of a node, one of several paths may be taken. The path that is taken is not known until that node is run (the LLM decides).

1. Conditional Edge: after the agent is called, we should either: a. If the agent said to take an action, then the function to invoke tools should be called b. If the agent said that it was finished, then it should finish
2. Normal Edge: after the tools are invoked, it should always go back to the agent to decide what to do next

Let's define the nodes, as well as a function to decide how what conditional edge to take.

MODIFICATION

We define each node to receive the AgentState base model as its first argument.

In [23]:

from langchain_core.messages import ToolMessage

from langgraph.prebuilt import ToolInvocation


# Define the function that determines whether to continue or not
def should_continue(state):
    messages = state.messages
    last_message = messages[-1]
    # If there is no function call, then we finish
    if not last_message.tool_calls:
        return "end"
    # Otherwise if there is, we continue
    else:
        return "continue"


# Define the function that calls the model
def call_model(state):
    # using context value
    req = state.context.httpx_session.get("https://www.langchain.com/")
    assert req.status_code == 200, req

    messages = state.messages
    response = model.invoke(messages)
    # We return a list, because this will get added to the existing list
    return {"messages": [response]}


# Define the function to execute tools
def call_tool(state):
    messages = state.messages
    # Based on the continue condition
    # we know the last message involves a function call
    last_message = messages[-1]
    # We construct an ToolInvocation from the function_call
    tool_call = last_message.tool_calls[0]
    action = ToolInvocation(
        tool=tool_call["name"],
        tool_input=tool_call["args"],
    )
    # We call the tool_executor and get back a response
    response = tool_executor.invoke(action)
    # We use the response to create a ToolMessage
    tool_message = ToolMessage(
        content=str(response), name=action.tool, tool_call_id=tool_call["id"]
    )
    # We return a list, because this will get added to the existing list
    return {"messages": [tool_message]}

Define the graph

We can now put it all together and define the graph!

In [24]:

from langgraph.graph import END, StateGraph, START

# Define a new graph
workflow = StateGraph(AgentState)

# Define the two nodes we will cycle between
workflow.add_node("agent", call_model)
workflow.add_node("action", call_tool)

# Set the entrypoint as `agent`
# This means that this node is the first one called
workflow.add_edge(START, "agent")

# We now add a conditional edge
workflow.add_conditional_edges(
    # First, we define the start node. We use `agent`.
    # This means these are the edges taken after the `agent` node is called.
    "agent",
    # Next, we pass in the function that will determine which node is called next.
    should_continue,
    # Finally we pass in a mapping.
    # The keys are strings, and the values are other nodes.
    # END is a special node marking that the graph should finish.
    # What will happen is we will call `should_continue`, and then the output of that
    # will be matched against the keys in this mapping.
    # Based on which one it matches, that node will then be called.
    {
        # If `tools`, then we call the tool node.
        "continue": "action",
        # Otherwise we finish.
        "end": END,
    },
)

# We now add a normal edge from `tools` to `agent`.
# This means that after `tools` is called, `agent` node is called next.
workflow.add_edge("action", "agent")

# Finally, we compile it!
# This compiles it into a LangChain Runnable,
# meaning you can use it as you would any other runnable
app = workflow.compile()

In [25]:

from IPython.display import Image, display

display(Image(app.get_graph().draw_mermaid_png()))

Use it!

We can now use it! This now exposes the same interface as all other LangChain runnables.

In [ ]:

from langchain_core.messages import HumanMessage

inputs = {"messages": [HumanMessage(content="what is the weather in sf")]}
for chunk in app.stream(inputs, stream_mode="values"):
    chunk["messages"][-1].pretty_print()