# Configuration

This page covers SDK-wide defaults that you usually set once during application startup, such as the default OpenAI key or client, the default OpenAI API shape, tracing export defaults, and logging behavior.

These defaults still apply to sandbox-based workflows, but sandbox workspaces, sandbox clients, and session reuse are configured separately.

If you need to configure a specific agent or run instead, start with:

-   [Agents](agents.md) for instructions, tools, output types, handoffs, and guardrails on a plain `Agent`.
-   [Running agents](running_agents.md) for `RunConfig`, sessions, and conversation-state options.
-   [Sandbox agents](sandbox/guide.md) for `SandboxRunConfig`, manifests, capabilities, and sandbox-client-specific workspace setup.
-   [Models](models/index.md) for model selection and provider configuration.
-   [Tracing](tracing.md) for per-run tracing metadata and custom trace processors.

## API keys and clients

By default, the SDK uses the `OPENAI_API_KEY` environment variable for LLM requests and tracing. The key is resolved when the SDK first creates an OpenAI client (lazy initialization), so set the environment variable before your first model call. If you are unable to set that environment variable before your app starts, you can use the [set_default_openai_key()][agents.set_default_openai_key] function to set the key.

```python
from agents import set_default_openai_key

set_default_openai_key("sk-...")
```

Alternatively, you can also configure an OpenAI client to be used. By default, the SDK creates an `AsyncOpenAI` instance, using the API key from the environment variable or the default key set above. You can change this by using the [set_default_openai_client()][agents.set_default_openai_client] function.

```python
from openai import AsyncOpenAI
from agents import set_default_openai_client

custom_client = AsyncOpenAI(base_url="...", api_key="...")
set_default_openai_client(custom_client)
```

If you prefer environment-based endpoint configuration, the default OpenAI provider also reads `OPENAI_BASE_URL`. When you enable Responses websocket transport, it also reads `OPENAI_WEBSOCKET_BASE_URL` for the websocket `/responses` endpoint.

```bash
export OPENAI_BASE_URL="https://your-openai-compatible-endpoint.example/v1"
export OPENAI_WEBSOCKET_BASE_URL="wss://your-openai-compatible-endpoint.example/v1"
```

Finally, you can also customize the OpenAI API that is used. By default, we use the OpenAI Responses API. You can override this to use the Chat Completions API by using the [set_default_openai_api()][agents.set_default_openai_api] function.

```python
from agents import set_default_openai_api

set_default_openai_api("chat_completions")
```

## OpenAI provider defaults

OpenAI-backed providers also read SDK-wide defaults when they resolve model names. Use [`set_default_openai_responses_transport()`][agents.set_default_openai_responses_transport] to make OpenAI Responses models use websocket transport by default:

```python
from agents import set_default_openai_responses_transport

set_default_openai_responses_transport("websocket")
```

This affects OpenAI Responses models resolved by the default OpenAI provider. For provider-level setup, connection reuse, keepalive options, and custom websocket endpoints, see [Responses WebSocket transport](models/index.md#responses-websocket-transport).

If your OpenAI setup expects provider-level agent registration metadata, configure a default harness ID once at startup:

```python
from agents import set_default_openai_harness

set_default_openai_harness("your-harness-id")
```

You can also pass the full registration object:

```python
from agents import OpenAIAgentRegistrationConfig, set_default_openai_agent_registration

set_default_openai_agent_registration(
    OpenAIAgentRegistrationConfig(harness_id="your-harness-id")
)
```

If no SDK default is set, OpenAI-backed providers fall back to the `OPENAI_AGENT_HARNESS_ID` environment variable. When a harness ID is configured, the SDK adds it to trace metadata as `agent_harness_id` unless that key is already present in `RunConfig.trace_metadata`.

## Tracing

Tracing is enabled by default. By default it uses the same OpenAI API key as your model requests from the section above (that is, the environment variable or the default key you set). You can specifically set the API key used for tracing by using the [`set_tracing_export_api_key`][agents.set_tracing_export_api_key] function.

```python
from agents import set_tracing_export_api_key

set_tracing_export_api_key("sk-...")
```

If your model traffic uses one key or client but tracing should use a different OpenAI key, pass `use_for_tracing=False` when setting the default key or client, then configure tracing separately. The same pattern works with [`set_default_openai_key()`][agents.set_default_openai_key] if you are not using a custom client.

```python
from openai import AsyncOpenAI
from agents import (
    set_default_openai_client,
    set_tracing_export_api_key,
)

custom_client = AsyncOpenAI(base_url="https://your-openai-compatible-endpoint.example/v1", api_key="provider-key")
set_default_openai_client(custom_client, use_for_tracing=False)

set_tracing_export_api_key("sk-tracing")
```

If you need to attribute traces to a specific organization or project when using the default exporter, set these environment variables before your app starts:

```bash
export OPENAI_ORG_ID="org_..."
export OPENAI_PROJECT_ID="proj_..."
```

You can also set a tracing API key per run without changing the global exporter.

```python
from agents import Runner, RunConfig

await Runner.run(
    agent,
    input="Hello",
    run_config=RunConfig(tracing={"api_key": "sk-tracing-123"}),
)
```

You can also disable tracing entirely by using the [`set_tracing_disabled()`][agents.set_tracing_disabled] function.

```python
from agents import set_tracing_disabled

set_tracing_disabled(True)
```

If you want to keep tracing enabled but exclude potentially sensitive inputs/outputs from trace payloads, set [`RunConfig.trace_include_sensitive_data`][agents.run.RunConfig.trace_include_sensitive_data] to `False`:

```python
from agents import Runner, RunConfig

await Runner.run(
    agent,
    input="Hello",
    run_config=RunConfig(trace_include_sensitive_data=False),
)
```

You can also change the default without code by setting this environment variable before your app starts:

```bash
export OPENAI_AGENTS_TRACE_INCLUDE_SENSITIVE_DATA=0
```

For full tracing controls, see the [tracing guide](tracing.md).

## Debug logging

The SDK defines two Python loggers (`openai.agents` and `openai.agents.tracing`) and does not attach handlers by default. Logs follow your application's Python logging configuration.

To enable verbose logging, use the [`enable_verbose_stdout_logging()`][agents.enable_verbose_stdout_logging] function.

```python
from agents import enable_verbose_stdout_logging

enable_verbose_stdout_logging()
```

Alternatively, you can customize the logs by adding handlers, filters, formatters, etc. You can read more in the [Python logging guide](https://docs.python.org/3/howto/logging.html).

```python
import logging

logger = logging.getLogger("openai.agents") # or openai.agents.tracing for the Tracing logger

# To make all logs show up
logger.setLevel(logging.DEBUG)
# To make info and above show up
logger.setLevel(logging.INFO)
# To make warning and above show up
logger.setLevel(logging.WARNING)
# etc

# You can customize this as needed, but this will output to `stderr` by default
logger.addHandler(logging.StreamHandler())
```

### Sensitive data in logs

Certain logs may contain sensitive data (for example, user data).

By default, the SDK does **not** log LLM inputs/outputs or tool inputs/outputs. These protections are controlled by:

```bash
OPENAI_AGENTS_DONT_LOG_MODEL_DATA=1
OPENAI_AGENTS_DONT_LOG_TOOL_DATA=1
```

If you need to include this data temporarily for debugging, set either variable to `0` (or `false`) before your app starts:

```bash
export OPENAI_AGENTS_DONT_LOG_MODEL_DATA=0
export OPENAI_AGENTS_DONT_LOG_TOOL_DATA=0
```