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

# LlamaIndex

> Auto-track LLM calls in LlamaIndex pipelines

## Installation

```bash theme={null}
pip install meshai-sdk[llamaindex]
```

## Usage

```python theme={null}
from meshai import MeshAI
from meshai.integrations.llamaindex import MeshAILlamaHandler
from llama_index.core import Settings, VectorStoreIndex
from llama_index.core.callbacks import CallbackManager

client = MeshAI(api_key="msh_...", agent_name="my-index")
client.register(framework="llamaindex")

# Add to the global callback manager
handler = MeshAILlamaHandler(client)
Settings.callback_manager = CallbackManager([handler])

# Use LlamaIndex as normal, all LLM calls tracked
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()
response = query_engine.query("What is MeshAI?")
# Model and tokens captured automatically
```

## How It Works

`MeshAILlamaHandler` implements LlamaIndex's callback interface, listening to LLM events. On each LLM completion, it:

1. Extracts the **model name** from the LLM event payload
2. Extracts **input and output token counts** from the event callback data
3. Infers the **provider** from the model name
4. Sends the usage event to MeshAI (buffered, non-blocking)

Works with any LlamaIndex-compatible LLM: OpenAI, Anthropic, Gemini, HuggingFace, and others.

## Alternative: Proxy (Zero-Code)

If your LlamaIndex pipeline uses OpenAI or Anthropic, you can route through the proxy instead:

```bash theme={null}
export OPENAI_BASE_URL=https://proxy.meshai.dev/v1/openai/k/msh_YOUR_PROXY_KEY
```