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

# Framework Integrations

> Using TokenSense with LangChain and LlamaIndex

### Framework Integrations

TokenSense provides native callback handlers for both **LangChain** and **LlamaIndex**.

Instead of wrapping your LLM client with `observe()`, you simply attach the respective TokenSense callback handler to your LLM configuration. This provides full observability into complex agentic workflows, RAG pipelines, and multi-step reasoning chains.

#### LangChain

Import `TokenSenseCallbackHandler` and pass it into the `callbacks` list when invoking your LangChain models.

```python theme={null}
from tokensense import TokenSenseCallbackHandler
from langchain_groq import ChatGroq

llm = ChatGroq(model="llama-3.1-8b-instant")

# Attach the callback handler to track this invocation
response = llm.invoke(
    "Hello!", 
    config={"callbacks": [TokenSenseCallbackHandler()]}
)
```

#### LlamaIndex

Import `TokenSenseLlamaIndexCallback` and add it to your global `CallbackManager` before running your query engines or agents.

```python theme={null}
from tokensense import TokenSenseLlamaIndexCallback
from llama_index.core.callbacks import CallbackManager
from llama_index.core import Settings
from llama_index.llms.groq import Groq

# Configure the global callback manager
Settings.callback_manager = CallbackManager([TokenSenseLlamaIndexCallback()])

llm = Groq(model="llama-3.1-8b-instant")
response = llm.complete("Hello!")
```