Use LlamaIndex with Radium

LlamaIndex provides 
OpenAILike for third-party OpenAI-compatible APIs. Use it to connect a LlamaIndex application to Radium.
Before you start
You need Python 3.10 or newer, a Radium API key, and a terminal or PowerShell window.
1. Create a project and install LlamaIndex
macOS or Linux:
mkdir radium-llamaindex
cd radium-llamaindex
python3 -m venv .venv
source .venv/bin/activate
python -m pip install llama-index-llms-openai-like
Windows PowerShell:
mkdir radium-llamaindex
cd radium-llamaindex
py -m venv .venv
.venv\Scripts\Activate.ps1
python -m pip install llama-index-llms-openai-like
2. Set your Radium credentials
macOS or Linux:
export RADIUM_API_KEY="your-radium-api-key"
export RADIUM_MODEL="hal-1.0"
Windows PowerShell:
$env:RADIUM_API_KEY = "your-radium-api-key"
$env:RADIUM_MODEL = "hal-1.0"
3. Create 
main.py
import os
from llama_index.core.llms import ChatMessage
from llama_index.llms.openai_like import OpenAILike
api_key = os.getenv("RADIUM_API_KEY")
if not api_key:
    raise SystemExit("Set RADIUM_API_KEY before running this program.")
llm = OpenAILike(
    model=os.getenv("RADIUM_MODEL", "hal-1.0"),
    api_key=api_key,
    api_base="https://api.radium.cloud/v1",
    is_chat_model=True,
    max_tokens=512,
    temperature=0,
    timeout=90,
)
response = llm.chat(
    [
        ChatMessage(role="system", content="Follow the user's instruction exactly."),
        ChatMessage(role="user", content="Reply with exactly: Radium connected."),
    ]
)
content = response.message.content
if not content or not content.strip():
    raise RuntimeError("Radium returned no visible text.")
print(f"RADIUM_RESPONSE: {content.strip()}")
LlamaIndex calls the endpoint setting 
api_base, not 
base_url.
4. Run it
python main.py
Expected output:
RADIUM_RESPONSE: Radium connected.
Choose a model
export RADIUM_MODEL="clarke-1.0"  # or hal-1.0 or tycho-1.0
python main.py
Migrate an existing LlamaIndex app
Replace its current LLM object:
llm = OpenAILike(
    model="hal-1.0",
    api_key=os.environ["RADIUM_API_KEY"],
    api_base="https://api.radium.cloud/v1",
    is_chat_model=True,
    max_tokens=512,
)
Pass 
llm to the existing index, query engine, workflow, or agent. A RAG application still needs a separate embedding model; do not use these chat model IDs as embedding model IDs.
Troubleshooting
Authentication errors: check 
RADIUM_API_KEY in the active terminal.
404: keep 
/v1 in 
api_base and use an exact Radium model ID.
Empty output: use at least 512 output tokens.
Import errors: activate 
.venv and reinstall the pinned package.
Validation
Version note: these instructions were verified with Python 3.12.7 and 
llama-index-llms-openai-like==0.7.2 on August 20, 2026. You do not need that exact package version. Text generation and 
FunctionAgent tool calling passed with 
hal-1.0, 
clarke-1.0, and 
tycho-1.0.
Reference: LlamaIndex OpenAILike integration.
Next
API quickstart, for calling Radium directly
Tool calling, for the 
tool_use and 
tool_result contract