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

# LangChain Example

> Using the LangChain Callback Handler

*View Notebook on <a href={'https://github.com/AgentOps-AI/agentops/blob/main/examples/langchain_examples.ipynb'} target={'_blank'}>Github</a>*

# AgentOps Langchain Agent Implementation

Using AgentOps monitoring with Langchain is simple. We've created a LangchainCallbackHandler that will do all of the heavy lifting!

First let's install the required packages

```python theme={null}
%pip install langchain==0.2.9
%pip install langchain_openai
%pip install -U agentops
%pip install -U python-dotenv
```

Then import them

```python theme={null}
import os
from langchain_openai import ChatOpenAI
from langchain.agents import tool, AgentExecutor, create_openai_tools_agent
from dotenv import load_dotenv
from langchain_core.prompts import ChatPromptTemplate
```

The only difference with using AgentOps is that we'll also import this special Callback Handler

```python theme={null}
from agentops.partners.langchain_callback_handler import (
    LangchainCallbackHandler as AgentOpsLangchainCallbackHandler,
)
```

Next, we'll set our API keys. There are several ways to do this, the code below is just the most foolproof way for the purposes of this notebook. It accounts for both users who use environment variables and those who just want to set the API Key here in this notebook.

[Get an AgentOps API key](https://agentops.ai/settings/projects)

1. Create an environment variable in a .env file or other method. By default, the AgentOps `init()` function will look for an environment variable named `AGENTOPS_API_KEY`. Or...

2. Replace `<your_agentops_key>` below and pass in the optional `api_key` parameter to the AgentOps `init(api_key=...)` function. Remember not to commit your API key to a public repo!

```python theme={null}
load_dotenv()
AGENTOPS_API_KEY = os.environ.get("AGENTOPS_API_KEY")
OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY")
```

This is where AgentOps comes into play. Before creating our LLM instance via Langchain, first we'll create an instance of the AO LangchainCallbackHandler. After the handler is initialized, a session will be recorded automatically.

Pass in your API key, and optionally any tags to describe this session for easier lookup in the AO dashboard.

```python theme={null}
agentops_handler = AgentOpsLangchainCallbackHandler(
    api_key=AGENTOPS_API_KEY, tags=["Langchain Example"]
)

llm = ChatOpenAI(
    openai_api_key=OPENAI_API_KEY, callbacks=[agentops_handler], model="gpt-3.5-turbo"
)

# You must pass in a callback handler to record your agent
llm.callbacks = [agentops_handler]

prompt = ChatPromptTemplate.from_messages(
    [
        ("system", "You are a helpful assistant. Respond only in Spanish."),
        ("human", "{input}"),
        # Placeholders fill up a **list** of messages
        ("placeholder", "{agent_scratchpad}"),
        # ("tool_names", "find_movie")
    ]
)
```

You can also retrieve the `session_id` of the newly created session.

```python theme={null}
print("Agent Ops session ID: " + str(agentops_handler.current_session_ids))
```

Agents generally use tools. Let's define a simple tool here. Tool usage is also recorded.

```python theme={null}
@tool
def find_movie(genre: str) -> str:
    """Find available movies"""
    if genre == "drama":
        return "Dune 2"
    else:
        return "Pineapple Express"


tools = [find_movie]
```

For each tool, you need to also add the callback handler

```python theme={null}
for t in tools:
    t.callbacks = [agentops_handler]
```

Add the tools to our LLM

```python theme={null}
llm_with_tools = llm.bind_tools([find_movie])
```

Finally, let's create our agent! Pass in the callback handler to the agent, and all the actions will be recorded in the AO Dashboard

```python theme={null}
agent = create_openai_tools_agent(llm, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools)
```

```python theme={null}
agent_executor.invoke(
    {"input": "What comedies are playing?"}, config={"callback": [agentops_handler]}
)
```

## Check your session

Finally, check your run on [AgentOps](https://app.agentops.ai)

Now if we look in the AgentOps dashboard, you will see a session recorded with the LLM calls and tool usage.

## Langchain v0 Example

This langchain version is out of date and support is being deprecated. You can find the example notebook [here](https://github.com/agentops-ai/agentops/blob/main/examples/langchain_examples/langchain_v0_example.ipynb).