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A ready-to-run example is available here!
Use the LLM registry to manage multiple LLM providers and dynamically switch between models.

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Using the Registry

You can add LLMs to the registry using the .add method and retrieve them later using the .get() method. 
main_llm = LLM(
    usage_id="agent",
    model=model,
    base_url=base_url,
    api_key=SecretStr(api_key),
)

# define the registry and add an LLM
llm_registry = LLMRegistry()
llm_registry.add(main_llm)
...
# retrieve the LLM by its usage ID
llm = llm_registry.get("agent")

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Ready-to-run Example

This example is available on GitHub: examples/01_standalone_sdk/05_use_llm_registry.py
examples/01_standalone_sdk/05_use_llm_registry.py
import os

from pydantic import SecretStr

from openhands.sdk import (
    LLM,
    Agent,
    Conversation,
    Event,
    LLMConvertibleEvent,
    LLMRegistry,
    Message,
    TextContent,
    get_logger,
)
from openhands.sdk.tool import Tool
from openhands.tools.terminal import TerminalTool


logger = get_logger(__name__)

# Configure LLM using LLMRegistry
api_key = os.getenv("LLM_API_KEY")
assert api_key is not None, "LLM_API_KEY environment variable is not set."
model = os.getenv("LLM_MODEL", "anthropic/claude-sonnet-4-5-20250929")
base_url = os.getenv("LLM_BASE_URL")

# Create LLM instance
main_llm = LLM(
    usage_id="agent",
    model=model,
    base_url=base_url,
    api_key=SecretStr(api_key),
)

# Create LLM registry and add the LLM
llm_registry = LLMRegistry()
llm_registry.add(main_llm)

# Get LLM from registry
llm = llm_registry.get("agent")

# Tools
cwd = os.getcwd()
tools = [Tool(name=TerminalTool.name)]

# Agent
agent = Agent(llm=llm, tools=tools)

llm_messages = []  # collect raw LLM messages


def conversation_callback(event: Event):
    if isinstance(event, LLMConvertibleEvent):
        llm_messages.append(event.to_llm_message())


conversation = Conversation(
    agent=agent, callbacks=[conversation_callback], workspace=cwd
)

conversation.send_message("Please echo 'Hello!'")
conversation.run()

print("=" * 100)
print("Conversation finished. Got the following LLM messages:")
for i, message in enumerate(llm_messages):
    print(f"Message {i}: {str(message)[:200]}")

print("=" * 100)
print(f"LLM Registry usage IDs: {llm_registry.list_usage_ids()}")

# Demonstrate getting the same LLM instance from registry
same_llm = llm_registry.get("agent")
print(f"Same LLM instance: {llm is same_llm}")

# Demonstrate requesting a completion directly from an LLM
resp = llm.completion(
    messages=[
        Message(role="user", content=[TextContent(text="Say hello in one word.")])
    ]
)
# Access the response content via OpenHands LLMResponse
msg = resp.message
texts = [c.text for c in msg.content if isinstance(c, TextContent)]
print(f"Direct completion response: {texts[0] if texts else str(msg)}")

# Report cost
cost = llm.metrics.accumulated_cost
print(f"EXAMPLE_COST: {cost}")
You can run the example code as-is.
The model name should follow the LiteLLM convention: provider/model_name (e.g., anthropic/claude-sonnet-4-5-20250929, openai/gpt-4o). The LLM_API_KEY should be the API key for your chosen provider.
ChatGPT Plus/Pro subscribers: You can use LLM.subscription_login() to authenticate with your ChatGPT account and access Codex models without consuming API credits. See the LLM Subscriptions guide for details.

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Next Steps