> ## Documentation Index
> Fetch the complete documentation index at: https://allhandsai-fix-security-analyzer-examples.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Interactive Terminal

> Enable agents to interact with terminal applications like ipython, python REPL, and other interactive CLI tools.

The `BashTool` provides agents with the ability to interact with terminal applications that require back-and-forth communication, such as Python's interactive mode, ipython, database CLIs, and other REPL environments. This enables agents to execute commands within these interactive sessions, receive output, and send follow-up commands based on the results.

<Note>
  This example is available on GitHub: [examples/01\_standalone\_sdk/06\_interactive\_terminal\_w\_reasoning.py](https://github.com/OpenHands/software-agent-sdk/blob/main/examples/01_standalone_sdk/06_interactive_terminal_w_reasoning.py)
</Note>

```python icon="python" expandable examples/01_standalone_sdk/06_interactive_terminal_w_reasoning.py theme={null}
import os

from pydantic import SecretStr

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


logger = get_logger(__name__)

# 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", "openhands/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
cwd = os.getcwd()
tools = [
    Tool(
        name=TerminalTool.name,
        params={"no_change_timeout_seconds": 3},
    )
]

# 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(
    "Enter python interactive mode by directly running `python3`, then tell me "
    "the current time, and exit python interactive mode."
)
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]}")
```

```bash Running the Example theme={null}
export LLM_API_KEY="your-api-key"
cd agent-sdk
uv run python examples/01_standalone_sdk/06_interactive_terminal_w_reasoning.py
```

## How It Works

```python highlight={6} theme={null}
cwd = os.getcwd()
register_tool("BashTool", BashTool)
tools = [
    Tool(
        name="BashTool",
        params={"no_change_timeout_seconds": 3},
    )
]
```

The `BashTool` is configured with a `no_change_timeout_seconds` parameter that determines how long to wait for terminal updates before sending the output back to the agent.

In the example above, the agent should:

1. Enters Python's interactive mode by running `python3`
2. Executes Python code to get the current time
3. Exits the Python interpreter

The `BashTool` maintains the session state throughout these interactions, allowing the agent to send multiple commands within the same terminal session. Review the [BashTool](https://github.com/OpenHands/software-agent-sdk/blob/main/openhands-tools/openhands/tools/execute_bash/definition.py) and [terminal source code](https://github.com/OpenHands/software-agent-sdk/blob/main/openhands-tools/openhands/tools/execute_bash/terminal/terminal_session.py) to better understand how the interactive session is configured and managed.

## Next Steps

* **[Custom Tools](/sdk/guides/custom-tools)** - Create your own tools for specific use cases