DSPy

DSPy connects to OpenAI-compatible APIs through LiteLLM. Prefix the Radium model with openai/, then provide Radium’s API key and base URL.

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 DSPy

mkdir radium-dspy && cd radium-dspy
python3 -m venv .venv
source .venv/bin/activate
python -m pip install dspy

mkdir radium-dspy; cd radium-dspy
py -m venv .venv
.venv\Scripts\Activate.ps1
python -m pip install dspy

2. Set your Radium credentials

export RADIUM_API_KEY="your-radium-api-key"
export RADIUM_MODEL="hal-1.0"

$env:RADIUM_API_KEY = "your-radium-api-key"
$env:RADIUM_MODEL = "hal-1.0"

3. Create main.py

import os
import dspy

api_key = os.getenv("RADIUM_API_KEY")
if not api_key:
    raise SystemExit("Set RADIUM_API_KEY before running this program.")

model = os.getenv("RADIUM_MODEL", "hal-1.0")
lm = dspy.LM(
    f"openai/{model}",
    api_key=api_key,
    api_base="https://api.radium.cloud/v1",
    max_tokens=512,
    temperature=0,
    timeout=90,
)
dspy.configure(lm=lm)

responses = lm(
    messages=[
        {"role": "system", "content": "Follow the user's instruction exactly."},
        {"role": "user", "content": "Reply with exactly: Radium connected."},
    ]
)
raw = responses[0] if responses else None
content = raw.get("text") if isinstance(raw, dict) else raw
if not content or not str(content).strip():
    raise RuntimeError("Radium returned no visible text.")
print(f"RADIUM_RESPONSE: {str(content).strip()}")

DSPy may return a string or a dictionary containing text and reasoning_content. The code handles both response shapes.

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 DSPy program

Configure the global language model, then keep using your existing signatures and modules:

lm = dspy.LM(
    "openai/hal-1.0",
    api_key=os.environ["RADIUM_API_KEY"],
    api_base="https://api.radium.cloud/v1",
    max_tokens=512,
)
dspy.configure(lm=lm)

DSPy optimizers can make many model calls. Test latency and usage on a small dataset before starting a large optimization.

Tool-calling note

Known issue with hal-1.0: DSPy ReAct executed tools and returned grounded answers with all three models. With hal-1.0, DSPy logged an output-truncation warning during the agent trace at both 512 and 1024 output tokens, though the tool still ran once and the final answer was correct. Test longer ReAct workflows before relying on them in production.

Troubleshooting

Problem	Fix
Authentication errors	Verify the key in the active terminal
Provider errors	Include `openai/` before the model ID passed to `dspy.LM`
Empty output	Retain the response-shape normalization shown above
Large optimizer jobs	Reduce the dataset first to confirm expected request volume

Validation

These instructions were verified with Python 3.12.7 and dspy==3.3.0 on August 20, 2026. You do not need that exact package version.

Text generation passed with hal-1.0, clarke-1.0, and tycho-1.0; ReAct tool calling passed with the caveat above.

Getting started

Models

Switching

Core concepts

Tools

Guides

Enterprise

DSPy

DSPy

Before you start

1. Create a project and install DSPy

2. Set your Radium credentials

3. Create main.py

4. Run it

Choose a model

Migrate an existing DSPy program

Tool-calling note

Troubleshooting

Validation

Next steps

API quickstart

Tool calling

​DSPy

​Before you start

​1. Create a project and install DSPy

​2. Set your Radium credentials

​3. Create main.py

​4. Run it

​Choose a model

​Migrate an existing DSPy program

​Tool-calling note

​Troubleshooting

​Validation

​Next steps

API quickstart

Tool calling

DSPy

Before you start

1. Create a project and install DSPy

2. Set your Radium credentials

3. Create main.py

4. Run it

Choose a model

Migrate an existing DSPy program

Tool-calling note

Troubleshooting

Validation

Next steps