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-and-model)) |
| `GOOSE_MODEL` | Specifies which model to use from the provider | Model name (e.g., "gpt-4", "claude-sonnet-4-20250514") | None (must be [configured](/docs/getting-started/providers#configure-provider-and-model)) |
| `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 |
| `GOOSE_MAX_TOKENS` | Sets the maximum number of tokens for each model response (truncates longer responses) | Positive integer (e.g., 4096, 8192) | 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 |
Define custom model configurations with provider-specific parameters and context limits. This is useful for enabling provider beta features (like extended context windows) or configuring models with specific settings.
| Variable | Purpose | Values | Default |
|----------|---------|---------|---------|
| `GOOSE_PREDEFINED_MODELS` | Define custom model configurations | JSON array of model objects | None |
**Model Configuration Fields:**
| Field | Required | Type | Description |
|-------|----------|------|-------------|
| `id` | No | number | Optional numeric identifier |
| `name` | Yes | string | Model name used to reference this configuration |
| `alias` | No | string | Display name for the model |
| `subtext` | No | string | Additional descriptive text |
| `context_limit` | No | number | Override the default context window size in tokens |
| `request_params` | No | object | Provider-specific parameters included in API requests |
:::info
The `id`, `alias`, and `subtext` fields are currently not used.
:::
When a custom model's `context_limit` is specified, it takes precedence over pattern-matching but can still be overridden by explicit environment variables like [`GOOSE_CONTEXT_LIMIT`](#model-context-limit-overrides).
**Examples**
```bash
# Enable Anthropic's 1M context window with beta header
exportGOOSE_PREDEFINED_MODELS='[
{
"id": 1,
"name": "claude-sonnet-4-1m",
"provider": "anthropic",
"alias": "Claude Sonnet 4 (1M context)",
"subtext": "Anthropic",
"context_limit": 1000000,
"request_params": {
"anthropic_beta": ["context-1m-2025-08-07"]
}
}
]'
# Define multiple custom models
exportGOOSE_PREDEFINED_MODELS='[
{
"id": 1,
"name": "gpt-4-custom",
"provider": "openai",
"alias": "GPT-4 (200k)",
"context_limit": 200000
},
{
"id": 2,
"name": "internal-model",
"provider": "databricks",
"alias": "Internal Model (500k)",
"context_limit": 500000
}
]'
```
Custom context limits and request parameters are applied when the model is used. Custom context limits are displayed in goose CLI's [token usage indicator](/docs/guides/sessions/smart-context-management#token-usage).
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.
| `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_CLI_NEWLINE_KEY` | Customize the keyboard shortcut for [inserting newlines in CLI input](/docs/guides/goose-cli-commands#keyboard-shortcuts) | Single character (e.g., "n", "m") | "j" (Ctrl+J) |
| `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/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 |
| `GOOSE_DEBUG` | Enables debug mode to show full tool parameters without truncation. Can also be toggled during a session using the `/r` [slash command](/docs/guides/goose-cli-commands#slash-commands) | "1", "true" (case insensitive) to enable | false |
| `GOOSE_SEARCH_PATHS` | Additional directories to search for executables when running extensions | JSON array of paths (e.g., `["/usr/local/bin", "~/custom/bin"]`) | System PATH only | No |
These paths are prepended to the system PATH when extensions execute commands, ensuring your custom tools are found without modifying your global PATH.
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 |
| `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) |
| `HTTPS_PROXY` | Proxy URL for HTTPS connections (takes precedence over `HTTP_PROXY` when both are set) | URL (e.g., `http://proxy.company.com:8080`) | None |
| `NO_PROXY` | Hosts to bypass the proxy | Comma-separated list (e.g., `localhost,127.0.0.1,.internal.com`) | None |
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.
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 |
These variables are primarily used for development, testing, and debugging goose itself.
| Variable | Purpose | Values | Default |
|----------|---------|---------|---------|
| `GOOSE_PATH_ROOT` | Override the root directory for all goose data, config, and state files | Absolute path to directory | Platform-specific defaults |
When set, goose creates `config/`, `data/`, and `state/` subdirectories under the specified path. Useful for isolating test environments, running multiple configurations, or CI/CD pipelines.
**Examples**
```bash
# Temporary test environment
exportGOOSE_PATH_ROOT="/tmp/goose-test"
# Isolated environment for a single command
GOOSE_PATH_ROOT="/tmp/goose-isolated" goose run --recipe my-recipe.yaml
# CI/CD usage
GOOSE_PATH_ROOT="$(mktemp -d)" goose run --recipe integration-test.yaml
# Use with developer tools
GOOSE_PATH_ROOT="/tmp/goose-test" ./scripts/goose-db-helper.sh status
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, prevent goose from running `git commit`, or block 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.
When deploying goose in enterprise environments, administrators might need to control behavior and infrastructure, or enforce consistent settings across teams. The following environment variables are commonly used:
**Network and Infrastructure** - Control how goose connects to external services and internal infrastructure:
- [Network Configuration](#network-configuration) - Proxy configuration and network settings
- [Advanced Provider Configuration](#advanced-provider-configuration) - Point to internal LLM endpoints (e.g., Databricks, custom deployments)
- [Model Context Limit Overrides](#model-context-limit-overrides) - Configure context limits for LiteLLM proxies and custom models
**Security and Privacy** - Control security and privacy features:
- [Security and Privacy](#security-and-privacy) - Manage security and privacy settings such as extension loading, secrets storage, and usage data collection