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A ready-to-run example is available here!
The LLMProfileStore class provides a centralized mechanism for managing LLM configurations. Define a profile once, reuse it everywhere — across scripts, sessions, and even machines.

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Benefits

  • Persistence: Saves model parameters (API keys, temperature, max tokens, …) to a stable disk format.
  • Reusability: Import a defined profile into any script or session with a single identifier.
  • Portability: Simplifies the synchronization of model configurations across different machines or deployment environments.

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How It Works

1

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Create a Store

The store manages a directory of JSON profile files. By default it uses ~/.openhands/profiles, but you can point it anywhere.
from openhands.sdk import LLMProfileStore

# Default location: ~/.openhands/profiles
store = LLMProfileStore()

# Or bring your own directory
store = LLMProfileStore(base_dir="./my-profiles")
2

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Save a Profile

Got an LLM configured just right? Save it for later.
from pydantic import SecretStr
from openhands.sdk import LLM, LLMProfileStore

fast_llm = LLM(
    usage_id="fast",
    model="anthropic/claude-sonnet-4-5-20250929",
    api_key=SecretStr("sk-..."),
    temperature=0.0,
)

store = LLMProfileStore()
store.save("fast", fast_llm)
API keys are excluded by default for security. Pass include_secrets=True to the save method if you wish to persist them; otherwise, they will be read from the environment at load time.
3

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Load a Profile

Next time you need that LLM, just load it:
# Same model, ready to go.
llm = store.load("fast")
4

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List and Clean Up

See what you’ve got, delete what you don’t need:
print(store.list())   # ['fast.json', 'creative.json']

store.delete("creative")
print(store.list())   # ['fast.json']

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Good to Know

Profile names must be simple filenames (no slashes, no dots at the start).

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Ready-to-run Example

This example is available on GitHub: examples/01_standalone_sdk/37_llm_profile_store.py
examples/01_standalone_sdk/37_llm_profile_store.py
"""Example: Using LLMProfileStore to save and reuse LLM configurations.

LLMProfileStore persists LLM configurations as JSON files, so you can define
a profile once and reload it across sessions without repeating setup code.
"""

import os
import tempfile

from pydantic import SecretStr

from openhands.sdk import LLM, LLMProfileStore


# Use a temporary directory so this example doesn't pollute your home folder.
# In real usage you can omit base_dir to use the default (~/.openhands/profiles).
store = LLMProfileStore(base_dir=tempfile.mkdtemp())


# 1. Create two LLM profiles with different usage

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

fast_llm = LLM(
    usage_id="fast",
    model=model,
    api_key=SecretStr(api_key),
    base_url=base_url,
    temperature=0.0,
)

creative_llm = LLM(
    usage_id="creative",
    model=model,
    api_key=SecretStr(api_key),
    base_url=base_url,
    temperature=0.9,
)

# 2. Save profiles

# Note that secrets are excluded by default for safety.
store.save("fast", fast_llm)
store.save("creative", creative_llm)

# To persist the API key as well, pass `include_secrets=True`:
# store.save("fast", fast_llm, include_secrets=True)

# 3. List available persisted profiles

print(f"Stored profiles: {store.list()}")

# 4. Load a profile

loaded = store.load("fast")
assert isinstance(loaded, LLM)
print(
    "Loaded profile. "
    f"usage:{loaded.usage_id}, "
    f"model: {loaded.model}, "
    f"temperature: {loaded.temperature}."
)

# 5. Delete a profile

store.delete("creative")
print(f"After deletion: {store.list()}")

print("EXAMPLE_COST: 0")
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