agentops/docs/v2/integrations/langgraph.mdx

---
title: LangGraph
description: "Track and analyze your LangGraph workflows with AgentOps"
---

[LangGraph](https://github.com/langchain-ai/langgraph) is a framework for building stateful, multi-step applications with LLMs as graphs. AgentOps automatically instruments LangGraph to provide comprehensive observability into your graph-based agent workflows.

## Core Concepts

LangGraph enables you to build complex agentic workflows as graphs with:
- **Nodes**: Individual steps in your workflow (agents, tools, functions)
- **Edges**: Connections between nodes that define flow
- **State**: Shared data that flows through the graph
- **Conditional Edges**: Dynamic routing based on state or outputs
- **Cycles**: Support for iterative workflows and feedback loops

## Installation

Install AgentOps and LangGraph along with LangChain dependencies:

<CodeGroup>
  ```bash pip
  pip install agentops langgraph langchain-openai python-dotenv
  ```
  ```bash poetry
  poetry add agentops langgraph langchain-openai python-dotenv
  ```
  ```bash uv
  uv pip install agentops langgraph langchain-openai python-dotenv
  ```
</CodeGroup>

## Setting Up API Keys

You'll need API keys for AgentOps and your LLM provider:
- **OPENAI_API_KEY**: From the [OpenAI Platform](https://platform.openai.com/api-keys)
- **AGENTOPS_API_KEY**: From your [AgentOps Dashboard](https://app.agentops.ai/)

Set these as environment variables or in a `.env` file.

<CodeGroup>
  ```bash Export to CLI
  export OPENAI_API_KEY="your_openai_api_key_here"
  export AGENTOPS_API_KEY="your_agentops_api_key_here"
  ```
  ```txt Set in .env file
  OPENAI_API_KEY="your_openai_api_key_here"
  AGENTOPS_API_KEY="your_agentops_api_key_here"
  ```
</CodeGroup>

Then load them in your Python code:
```python
from dotenv import load_dotenv
import os

load_dotenv()

AGENTOPS_API_KEY = os.getenv("AGENTOPS_API_KEY")
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
```

## Usage

Initialize AgentOps at the beginning of your application to automatically track all LangGraph operations:

```python
import agentops
from typing import Annotated, Literal, TypedDict
from langgraph.graph import StateGraph, END
from langgraph.graph.message import add_messages
from langchain_openai import ChatOpenAI

# Initialize AgentOps
agentops.init()

# Define your graph state
class AgentState(TypedDict):
    messages: Annotated[list, add_messages]

# Create your LLM
model = ChatOpenAI(temperature=0)

# Define nodes
def agent_node(state: AgentState):
    messages = state["messages"]
    response = model.invoke(messages)
    return {"messages": [response]}

# Build the graph
workflow = StateGraph(AgentState)
workflow.add_node("agent", agent_node)
workflow.set_entry_point("agent")
workflow.add_edge("agent", END)

# Compile and run
app = workflow.compile()
result = app.invoke({"messages": [{"role": "user", "content": "Hello!"}]})
```

## What Gets Tracked

AgentOps automatically captures:

- **Graph Structure**: Nodes, edges, and entry points during compilation
- **Execution Flow**: The path taken through your graph
- **Node Executions**: Each node execution with inputs and outputs
- **LLM Calls**: All language model interactions within nodes
- **Tool Usage**: Any tools called within your graph
- **State Changes**: How state evolves through the workflow
- **Timing Information**: Duration of each node and total execution time

## Advanced Example

Here's a more complex example with conditional routing and tools:

```python
import agentops
from typing import Annotated, Literal, TypedDict
from langgraph.graph import StateGraph, END
from langgraph.graph.message import add_messages
from langchain_openai import ChatOpenAI
from langchain_core.tools import tool

# Initialize AgentOps
agentops.init()

# Define tools
@tool
def search(query: str) -> str:
    """Search for information."""
    return f"Search results for: {query}"

@tool
def calculate(expression: str) -> str:
    """Evaluate a mathematical expression."""
    try:
        return str(eval(expression))
    except:
        return "Error in calculation"

# Configure model with tools
tools = [search, calculate]
model = ChatOpenAI(temperature=0).bind_tools(tools)

# Define state
class AgentState(TypedDict):
    messages: Annotated[list, add_messages]

# Define conditional logic
def should_continue(state: AgentState) -> Literal["tools", "end"]:
    messages = state["messages"]
    last_message = messages[-1]
    
    if hasattr(last_message, "tool_calls") and last_message.tool_calls:
        return "tools"
    return "end"

# Define nodes
def call_model(state: AgentState):
    messages = state["messages"]
    response = model.invoke(messages)
    return {"messages": [response]}

def call_tools(state: AgentState):
    messages = state["messages"]
    last_message = messages[-1]
    
    tool_responses = []
    for tool_call in last_message.tool_calls:
        # Execute the appropriate tool
        if tool_call["name"] == "search":
            result = search.invoke(tool_call["args"])
        elif tool_call["name"] == "calculate":
            result = calculate.invoke(tool_call["args"])
        
        tool_responses.append({
            "role": "tool",
            "content": result,
            "tool_call_id": tool_call["id"]
        })
    
    return {"messages": tool_responses}

# Build the graph
workflow = StateGraph(AgentState)
workflow.add_node("agent", call_model)
workflow.add_node("tools", call_tools)
workflow.set_entry_point("agent")
workflow.add_conditional_edges(
    "agent",
    should_continue,
    {
        "tools": "tools",
        "end": END
    }
)
workflow.add_edge("tools", "agent")

# Compile and run
app = workflow.compile()
result = app.invoke({
    "messages": [{"role": "user", "content": "Search for AI news and calculate 25*4"}]
})
```

## Dashboard Insights

In your AgentOps dashboard, you'll see:

1. **Graph Visualization**: Visual representation of your compiled graph
2. **Execution Trace**: Step-by-step flow through nodes
3. **Node Metrics**: Performance data for each node
4. **LLM Analytics**: Token usage and costs across all model calls
5. **Tool Usage**: Which tools were called and their results
6. **Error Tracking**: Any failures in node execution

## Examples

<CardGroup cols={2}>
  <Card title="LangGraph Example" icon="notebook" href="/v2/examples/langgraph">
    Complete example showing agent workflows with tools
  </Card>
</CardGroup>

## Best Practices

1. **Initialize Early**: Call `agentops.init()` before creating your graph
2. **Use Descriptive Names**: Name your nodes clearly for better traces
3. **Handle Errors**: Implement error handling in your nodes
4. **Monitor State Size**: Large states can impact performance
5. **Leverage Conditional Edges**: Use them for dynamic workflows

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