> ## Documentation Index
> Fetch the complete documentation index at: https://futureagi.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# LangGraph

Our [LangChainInstrumentor](/future-agi/products/observability/auto-instrumentation/langchain) automatically captures traces for both LangGraph and LangChain. If you've already enabled that instrumentor, you do not need to complete the steps below.

## 1. Installation

First install the traceAI package and necessary LangChain packages.

```bash theme={null}
pip install traceAI-langchain
pip install langgraph
pip install langchain-anthropic
pip install ipython
```

***

## 2. Set Environment Variables

Set up your environment variables to authenticate with both FutureAGI and Anthropic.

```python theme={null}
import os

os.environ["ANTHROPIC_API_KEY"] = "your-anthropic-api-key"
os.environ["FI_API_KEY"] = "your-futureagi-api-key"
os.environ["FI_SECRET_KEY"] = "your-futureagi-secret-key"
```

***

## 3. Initialize Trace Provider

Set up the trace provider to create a new project in FutureAGI, establish telemetry data pipelines .

```python theme={null}
from fi_instrumentation import register
from fi_instrumentation.fi_types import ProjectType

trace_provider = register(
    project_type=ProjectType.OBSERVE,
    project_name="langgraph_project",
)
```

***

## 4. Instrument your Project

Initialize the LangChain Instrumentor to enable automatic tracing. Our [LangChainInstrumentor](/future-agi/products/observability/auto-instrumentation/langchain) automatically captures traces for both LangGraph and LangChain.

```python theme={null}
from traceai_langchain import LangChainInstrumentor

LangChainInstrumentor().instrument(tracer_provider=trace_provider)
```

***

## 5. Create LangGraph Agents

Set up your LangGraph agents as you normally would. Our Instrumentor will automatically trace and send the telemetry data to our platform.

```python theme={null}
from typing import Annotated
from typing_extensions import TypedDict
from langgraph.graph import StateGraph, START, END
from langgraph.graph.message import add_messages
from langchain_anthropic import ChatAnthropic
from IPython.display import Image, display


class State(TypedDict):
    messages: Annotated[list, add_messages]

graph_builder = StateGraph(State)
llm = ChatAnthropic(model="claude-3-5-sonnet-20240620")

def chatbot(state: State):
    return {"messages": [llm.invoke(state["messages"])]}

graph_builder.add_node("chatbot", chatbot)
graph_builder.add_edge(START, "chatbot")
graph_builder.add_edge("chatbot", END)
graph = graph_builder.compile()

try:
    display(Image(graph.get_graph().draw_mermaid_png()))
except Exception:
    pass

def stream_graph_updates(user_input: str):
    for event in graph.stream({"messages": [{"role": "user", "content": user_input}]}):
        for value in event.values():
            print("Assistant:", value["messages"][-1].content)

user_input = "What do you know about LangGraph?"
stream_graph_updates(user_input)
```