Secret Registry

Provide environment variables and secrets to agent workspace securely.

A ready-to-run example is available here!

The Secret Registry provides a secure way to handle sensitive data in your agent’s workspace. It automatically detects secret references in bash commands, injects them as environment variables when needed, and masks secret values in command outputs to prevent accidental exposure.

Injecting Secrets

Use the update_secrets() method to add secrets to your conversation.

Secrets can be provided as static strings or as callable functions that dynamically retrieve values, enabling integration with external secret stores and credential management systems:

from openhands.sdk.conversation.secret_source import SecretSource

# Static secret
conversation.update_secrets({"SECRET_TOKEN": "my-secret-token-value"})

# Dynamic secret using SecretSource
class MySecretSource(SecretSource):
    def get_value(self) -> str:
        return "callable-based-secret"

conversation.update_secrets({"SECRET_FUNCTION_TOKEN": MySecretSource()})

Ready-to-run Example

This example is available on GitHub: examples/01_standalone_sdk/12_custom_secrets.py

examples/01_standalone_sdk/12_custom_secrets.py

import os

from pydantic import SecretStr

from openhands.sdk import (
    LLM,
    Agent,
    Conversation,
)
from openhands.sdk.secret import SecretSource
from openhands.sdk.tool import Tool
from openhands.tools.file_editor import FileEditorTool
from openhands.tools.terminal import TerminalTool


# Configure LLM
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")
llm = LLM(
    usage_id="agent",
    model=model,
    base_url=base_url,
    api_key=SecretStr(api_key),
)

# Tools
tools = [
    Tool(name=TerminalTool.name),
    Tool(name=FileEditorTool.name),
]

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


class MySecretSource(SecretSource):
    def get_value(self) -> str:
        return "callable-based-secret"


conversation.update_secrets(
    {"SECRET_TOKEN": "my-secret-token-value", "SECRET_FUNCTION_TOKEN": MySecretSource()}
)

conversation.send_message("just echo $SECRET_TOKEN")

conversation.run()

conversation.send_message("just echo $SECRET_FUNCTION_TOKEN")

conversation.run()

# 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.

Next Steps

• MCP Integration - Connect to MCP
• Security Analyzer - Add security validation