goose supports various environment variables that allow you to customize its behavior. This guide provides a comprehensive list of available environment variables grouped by their functionality.
| `GOOSE_PROVIDER` | Specifies the LLM provider to use | [See available providers](/docs/getting-started/providers#available-providers) | None (must be [configured](/docs/getting-started/providers#configure-provider)) |
| `GOOSE_MODEL` | Specifies which model to use from the provider | Model name (e.g., "gpt-4", "claude-sonnet-4-20250514") | None (must be configured) |
| `GOOSE_TEMPERATURE` | Sets the [temperature](https://medium.com/@kelseyywang/a-comprehensive-guide-to-llm-temperature-%EF%B8%8F-363a40bbc91f) for model responses | Float between 0.0 and 1.0 | Model-specific default |
These variables are needed when using custom endpoints, enterprise deployments, or specific provider implementations.
| Variable | Purpose | Values | Default |
|----------|---------|---------|---------|
| `GOOSE_PROVIDER__TYPE` | The specific type/implementation of the provider | [See available providers](/docs/getting-started/providers#available-providers) | Derived from GOOSE_PROVIDER |
| `GOOSE_PROVIDER__HOST` | Custom API endpoint for the provider | URL (e.g., "https://api.openai.com") | Provider-specific default |
| `GOOSE_PROVIDER__API_KEY` | Authentication key for the provider | API key string | None |
These variables configure a [lead/worker model pattern](/docs/tutorials/lead-worker) where a powerful lead model handles initial planning and complex reasoning, then switches to a faster/cheaper worker model for execution. The switch happens automatically based on your settings.
The experimental [AutoPilot](/docs/guides/multi-model/autopilot) feature provides intelligent, context-aware model switching. Configure models for different roles using the `x-advanced-models` setting.
| `GOOSE_LEAD_PROVIDER` | Provider for the lead model | [See available providers](/docs/getting-started/providers#available-providers) | Falls back to `GOOSE_PROVIDER` |
| `GOOSE_LEAD_TURNS` | Number of initial turns using the lead model before switching to the worker model | Integer | 3 |
| `GOOSE_LEAD_FAILURE_THRESHOLD` | Consecutive failures before fallback to the lead model | Integer | 2 |
| `GOOSE_LEAD_FALLBACK_TURNS` | Number of turns to use the lead model in fallback mode | Integer | 2 |
A _turn_ is one complete prompt-response interaction. Here's how it works with the default settings:
The lead model and worker model names are displayed at the start of the goose CLI session. If you don't export a `GOOSE_MODEL` for your session, the worker model defaults to the `GOOSE_MODEL` in your [configuration file](/docs/guides/config-files).
| `GOOSE_PLANNER_PROVIDER` | Specifies which provider to use for planning mode | [See available providers](/docs/getting-started/providers#available-providers) | Falls back to GOOSE_PROVIDER |
| `GOOSE_PLANNER_MODEL` | Specifies which model to use for planning mode | Model name (e.g., "gpt-4", "claude-sonnet-4-20250514")| Falls back to GOOSE_MODEL |
| `GOOSE_MAX_TURNS` | [Maximum number of turns](/docs/guides/sessions/smart-context-management#maximum-turns) allowed without user input | Integer (e.g., 10, 50, 100) | 1000 |
| `GOOSE_SUBAGENT_MAX_TURNS` | Sets the maximum turns allowed for a [subagent](/docs/guides/subagents) to complete before timeout | Integer (e.g., 25) | 25 |
| `GOOSE_AUTO_COMPACT_THRESHOLD` | Set the percentage threshold at which goose [automatically summarizes your session](/docs/guides/sessions/smart-context-management#automatic-compaction). | Float between 0.0 and 1.0 (disabled at 0.0) | 0.8 |
These variables allow you to override the default context window size (token limit) for your models. This is particularly useful when using [LiteLLM proxies](https://docs.litellm.ai/docs/providers/litellm_proxy) or custom models that don't match goose's predefined model patterns.
| `GOOSE_CONTEXT_LIMIT` | Override context limit for the main model | Integer (number of tokens) | Model-specific default or 128,000 |
| `GOOSE_LEAD_CONTEXT_LIMIT` | Override context limit for the lead model in [lead/worker mode](/docs/tutorials/lead-worker) | Integer (number of tokens) | Falls back to `GOOSE_CONTEXT_LIMIT` or model default |
| `GOOSE_WORKER_CONTEXT_LIMIT` | Override context limit for the worker model in lead/worker mode | Integer (number of tokens) | Falls back to `GOOSE_CONTEXT_LIMIT` or model default |
| `GOOSE_PLANNER_CONTEXT_LIMIT` | Override context limit for the [planner model](/docs/guides/multi-model/creating-plans) | Integer (number of tokens) | Falls back to `GOOSE_CONTEXT_LIMIT` or model default |
| `GOOSE_TOOLSHIM_OLLAMA_MODEL` | Specifies the model for [tool call interpretation](/docs/experimental/ollama) | Model name (e.g. llama3.2, qwen2.5) | System default |
| `GOOSE_CLI_TOOL_PARAMS_TRUNCATION_MAX_LENGTH` | Maximum length for tool parameter values before truncation in CLI output (not in debug mode) | Integer | 40 |
These variables configure [AI-powered code editing](/docs/guides/enhanced-code-editing) for the Developer extension's `str_replace` tool. All three variables must be set and non-empty for the feature to activate.
| Variable | Purpose | Values | Default |
|----------|---------|---------|---------|
| `GOOSE_EDITOR_API_KEY` | API key for the code editing model | API key string | None |
| `GOOSE_EDITOR_HOST` | API endpoint for the code editing model | URL (e.g., "https://api.openai.com/v1") | None |
These variables control security related features.
| Variable | Purpose | Values | Default |
|----------|---------|---------|---------|
| `GOOSE_ALLOWLIST` | Controls which extensions can be loaded | URL for [allowed extensions](/docs/guides/allowlist) list | Unset |
| `GOOSE_DISABLE_KEYRING` | Disables the system keyring for secret storage | Set to any value (e.g., "1", "true", "yes") to disable. The actual value doesn't matter, only whether the variable is set. | Unset (keyring enabled) |
:::tip
When the keyring is disabled, secrets are stored here:
Beyond Goose's built-in [logging system](/docs/guides/logs), you can export telemetry to external observability platforms for advanced monitoring, performance analysis, and production insights.
### OpenTelemetry Protocol (OTLP)
Configure goose to export traces and metrics to any OTLP-compatible observability platform.
OTLP is the standard protocol for sending telemetry collected by [OpenTelemetry](https://opentelemetry.io/docs/). When configured, goose exports telemetry asynchronously and flushes on exit.
These variables enable experimental features that are in active development. These may change or be removed in future releases. Use with caution in production environments.
| `ALPHA_FEATURES` | Enables experimental alpha features—check the feature docs to see if this flag is required | "true", "1" (case insensitive) to enable | false |
Sometimes you want goose to use different commands or have different shell behavior than your normal terminal usage. For example, you might want goose to use a different tool, or prevent goose from running long-running development servers that could hang the AI agent. This is most useful when using goose CLI, where shell commands are executed directly in your terminal environment.