> ## 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.
# LlamaIndex
> AgentOps works seamlessly with LlamaIndex, a framework for building context-augmented generative AI applications with LLMs.
[LlamaIndex](https://www.llamaindex.ai/) is a framework for building context-augmented generative AI applications with LLMs. AgentOps provides comprehensive observability into your LlamaIndex applications through automatic instrumentation, allowing you to monitor LLM calls, track performance, and analyze your application's behavior.
## Installation
Install AgentOps and the LlamaIndex AgentOps instrumentation package:
```bash pip theme={null}
pip install agentops llama-index-instrumentation-agentops
```
```bash poetry theme={null}
poetry add agentops llama-index-instrumentation-agentops
```
```bash uv theme={null}
uv pip install agentops llama-index-instrumentation-agentops
```
## Setting Up API Keys
You'll need an AgentOps API key from your [AgentOps Dashboard](https://app.agentops.ai/):
```bash Export to CLI theme={null}
export AGENTOPS_API_KEY="your_agentops_api_key_here"
```
```txt Set in .env file theme={null}
AGENTOPS_API_KEY="your_agentops_api_key_here"
```
## Usage
Simply set the global handler to "agentops" at the beginning of your LlamaIndex application. AgentOps will automatically instrument LlamaIndex to track your LLM interactions and application performance.
```python theme={null}
from llama_index.core import set_global_handler
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
# Set the global handler to AgentOps
# NOTE: Feel free to set your AgentOps environment variables (e.g., 'AGENTOPS_API_KEY')
# as outlined in the AgentOps documentation, or pass the equivalent keyword arguments
# anticipated by AgentOps' AOClient as **eval_params in set_global_handler.
set_global_handler("agentops")
# Your LlamaIndex application code here
documents = SimpleDirectoryReader("data").load_data()
index = VectorStoreIndex.from_documents(documents)
# Create a query engine
query_engine = index.as_query_engine()
# Query your data - AgentOps will automatically track this
response = query_engine.query("What is the main topic of these documents?")
print(response)
```
## What Gets Tracked
When you use AgentOps with LlamaIndex, the following operations are automatically tracked:
* **LLM Calls**: All interactions with language models including prompts, completions, and token usage
* **Embeddings**: Vector embedding generation and retrieval operations
* **Query Operations**: Search and retrieval operations on your indexes
* **Performance Metrics**: Response times, token costs, and success/failure rates
## Additional Resources
For more detailed information about LlamaIndex's observability features and AgentOps integration, check out the [LlamaIndex documentation](https://docs.llamaindex.ai/en/stable/module_guides/observability/#agentops).