> ## Documentation Index
> Fetch the complete documentation index at: https://docs.orq.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# LlamaIndex Agents integration
> Connect LlamaIndex Agents to Orq.ai's AI Router for complete observability, built-in reliability, and access to 300+ LLMs across 20+ providers.
Route your LLM calls through the AI Router with a single base URL change. Zero vendor lock-in: always run on the best model at the lowest cost for your use case.
## AI Router
### Overview
LlamaIndex Agents is an agentic framework built on top of LlamaIndex that enables LLMs to reason, use tools, and execute multi-step workflows. By connecting LlamaIndex Agents to Orq.ai's AI Router, you get production-ready agents with enterprise-grade capabilities without changing your existing code.
### Key Benefits
Orq.ai's AI Router enhances your LlamaIndex Agents with:
Track every agent step, tool use, and reasoning trace with detailed analytics
Automatic fallbacks, retries, and load balancing for production resilience
Real-time cost tracking and spend management across all your AI operations
Access 300+ LLMs and 20+ providers through a single, unified integration
### Prerequisites
Before integrating LlamaIndex Agents with Orq.ai, ensure you have:
* An Orq.ai account and [API Key](/docs/administer/api-keys)
* Python 3.8 or higher
* LlamaIndex installed in your project
To setup your API key, see [API keys & Endpoints](/docs/administer/api-keys).
### Installation
Install LlamaIndex and required dependencies:
```bash theme={"theme":{"light":"github-light","dark":"github-dark"}}
pip install llama-index llama-index-llms-openai-like
```
### Configuration
Configure LlamaIndex Agents to use Orq.ai's AI Router with the `OpenAILike` class:
```python Python theme={"theme":{"light":"github-light","dark":"github-dark"}}
from llama_index.llms.openai_like import OpenAILike
import os
# Configure OpenAI-compatible LLM with Orq.ai AI Router
llm = OpenAILike(
model="gpt-4o",
api_key=os.getenv("ORQ_API_KEY"),
api_base="https://api.orq.ai/v3/router",
is_chat_model=True,
)
```
> **api\_base**: `https://api.orq.ai/v3/router`
### Basic Agent Example
Here's a complete example using `ReActAgent` with a simple tool:
```python Python theme={"theme":{"light":"github-light","dark":"github-dark"}}
import asyncio
from llama_index.core.agent.workflow import ReActAgent
from llama_index.core.tools import FunctionTool
from llama_index.llms.openai_like import OpenAILike
import os
# Configure LLM with Orq.ai AI Router
llm = OpenAILike(
model="gpt-4o",
api_key=os.getenv("ORQ_API_KEY"),
api_base="https://api.orq.ai/v3/router",
is_chat_model=True,
)
# Define a tool
def get_weather(location: str) -> str:
"""Get the current weather for a location."""
return f"The weather in {location} is sunny and 72°F"
# Create agent
agent = ReActAgent(
tools=[FunctionTool.from_defaults(get_weather)],
llm=llm,
)
async def main():
response = await agent.run("What's the weather in San Francisco?")
print(response)
asyncio.run(main())
```
### Agent with Multiple Tools
Build agents with multiple tools for complex reasoning:
```python Python theme={"theme":{"light":"github-light","dark":"github-dark"}}
import asyncio
from llama_index.core.agent.workflow import ReActAgent
from llama_index.core.tools import FunctionTool
from llama_index.llms.openai_like import OpenAILike
import os
llm = OpenAILike(
model="gpt-4o",
api_key=os.getenv("ORQ_API_KEY"),
api_base="https://api.orq.ai/v3/router",
is_chat_model=True,
)
def add(a: int, b: int) -> int:
"""Add two integers."""
return a + b
def multiply(a: int, b: int) -> int:
"""Multiply two integers."""
return a * b
def get_company_info(company: str) -> str:
"""Get basic information about a company."""
companies = {
"openai": "OpenAI is an AI research company founded in 2015.",
"anthropic": "Anthropic is an AI safety company founded in 2021.",
}
return companies.get(company.lower(), f"No information found for {company}")
agent = ReActAgent(
tools=[
FunctionTool.from_defaults(add),
FunctionTool.from_defaults(multiply),
FunctionTool.from_defaults(get_company_info),
],
llm=llm,
system_prompt="You are a helpful assistant with access to math and company information tools.",
)
async def main():
response = await agent.run("What is 15 * 4, and tell me about Anthropic?")
print(response)
asyncio.run(main())
```
### Model Selection
With Orq.ai, you can use any supported model from 20+ providers:
```python Python theme={"theme":{"light":"github-light","dark":"github-dark"}}
import asyncio
from llama_index.core.agent.workflow import ReActAgent
from llama_index.core.tools import FunctionTool
from llama_index.llms.openai_like import OpenAILike
import os
def get_time(timezone: str) -> str:
"""Get the current time in a timezone."""
return f"The current time in {timezone} is 14:30"
# Use Claude
claude_llm = OpenAILike(
model="claude-sonnet-4-5-20250929",
api_key=os.getenv("ORQ_API_KEY"),
api_base="https://api.orq.ai/v3/router",
is_chat_model=True,
)
# Use Gemini
gemini_llm = OpenAILike(
model="gemini-2.5-flash",
api_key=os.getenv("ORQ_API_KEY"),
api_base="https://api.orq.ai/v3/router",
is_chat_model=True,
)
agent = ReActAgent(
tools=[FunctionTool.from_defaults(get_time)],
llm=claude_llm,
)
async def main():
response = await agent.run("What time is it in Tokyo?")
print(response)
asyncio.run(main())
```
## Evaluations & Experiments
Once your agents are running, use **Evaluatorq** to score outputs across a dataset and **Experiments** to compare configurations side-by-side.
Run parallel evaluations across your agents and compare results.
Compare agent configurations and view results in the AI Studio.