> ## 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.
# LiteLLM
> Track and analyze your LiteLLM calls across multiple providers with AgentOps
AgentOps provides seamless integration with [LiteLLM](https://github.com/BerriAI/litellm), allowing you to automatically track all your LLM API calls across different providers through a unified interface.
## Installation
```bash pip theme={null}
pip install agentops litellm
```
```bash poetry theme={null}
poetry add agentops litellm
```
```bash uv theme={null}
uv pip install agentops litellm
```
## Setting Up API Keys
Before using LiteLLM with AgentOps, you need to set up your API keys. You can obtain:
* **Provider API Keys**: From your chosen LLM provider (OpenAI, Anthropic, Google, etc.)
* **AGENTOPS\_API\_KEY**: From your [AgentOps Dashboard](https://app.agentops.ai/)
Then to set them up, you can either export them as environment variables or set them in a `.env` file.
```bash Export to CLI theme={null}
export OPENAI_API_KEY="your_openai_api_key_here"
export ANTHROPIC_API_KEY="your_anthropic_api_key_here"
export AGENTOPS_API_KEY="your_agentops_api_key_here"
```
```txt Set in .env file theme={null}
OPENAI_API_KEY="your_openai_api_key_here"
ANTHROPIC_API_KEY="your_anthropic_api_key_here"
AGENTOPS_API_KEY="your_agentops_api_key_here"
```
Then load the environment variables in your Python code:
```python theme={null}
from dotenv import load_dotenv
import os
# Load environment variables from .env file
load_dotenv()
# Set up environment variables with fallback values
os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY")
os.environ["ANTHROPIC_API_KEY"] = os.getenv("ANTHROPIC_API_KEY")
os.environ["AGENTOPS_API_KEY"] = os.getenv("AGENTOPS_API_KEY")
```
## Usage
The simplest way to integrate AgentOps with LiteLLM is to set up the success\_callback.
```python theme={null}
import litellm
from litellm import completion
# Configure LiteLLM to use AgentOps
litellm.success_callback = ["agentops"]
# Make completion requests with LiteLLM
response = completion(
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": "Hello, how are you?"}]
)
print(response.choices[0].message.content)
```
## Examples
```python Streaming theme={null}
import litellm
from litellm import completion
# Configure LiteLLM to use AgentOps
litellm.success_callback = ["agentops"]
# Make a streaming completion request
response = completion(
model="gpt-4",
messages=[{"role": "user", "content": "Write a short poem about AI."}],
stream=True
)
# Process the streaming response
for chunk in response:
if chunk.choices[0].delta.content:
print(chunk.choices[0].delta.content, end="", flush=True)
print() # Add a newline at the end
```
```python Multi-Provider theme={null}
import litellm
from litellm import completion
# Configure LiteLLM to use AgentOps
litellm.success_callback = ["agentops"]
# OpenAI request
openai_response = completion(
model="gpt-4",
messages=[{"role": "user", "content": "What are the advantages of GPT-4?"}]
)
print("OpenAI Response:", openai_response.choices[0].message.content)
# Anthropic request using the same interface
anthropic_response = completion(
model="anthropic/claude-3-opus-20240229",
messages=[{"role": "user", "content": "What are the advantages of Claude?"}]
)
print("Anthropic Response:", anthropic_response.choices[0].message.content)
# All requests across different providers are automatically tracked by AgentOps
```
## More Examples
For more information on integrating AgentOps with LiteLLM, refer to the [LiteLLM documentation on AgentOps integration](https://docs.litellm.ai/docs/observability/agentops_integration).