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---
sidebar_position: 2
title: Configure LLM Provider
---
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
import { PanelLeft } from 'lucide-react';
import { ModelSelectionTip } from '@site/src/components/ModelSelectionTip';
# Supported LLM Providers
goose is compatible with a wide range of LLM providers, allowing you to choose and integrate your preferred model.
:::tip Model Selection
<ModelSelectionTip/>
[Berkeley Function-Calling Leaderboard][function-calling-leaderboard] can be a good guide for selecting models.
:::
## Available Providers
| Provider | Description | Parameters |
|-----------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| [Amazon Bedrock](https://aws.amazon.com/bedrock/) | Offers a variety of foundation models, including Claude, Jurassic-2, and others. **AWS environment variables must be set in advance, not configured through `goose configure`** | Credential auth: `AWS_PROFILE`, or `AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY`, `AWS_REGION`<br /><br />Bearer token auth: `AWS_BEARER_TOKEN_BEDROCK` and `AWS_REGION`, `AWS_DEFAULT_REGION`, or `AWS_PROFILE` |
| [Amazon SageMaker TGI](https://docs.aws.amazon.com/sagemaker/latest/dg/realtime-endpoints.html) | Run Text Generation Inference models through Amazon SageMaker endpoints. **AWS credentials must be configured in advance.** | `SAGEMAKER_ENDPOINT_NAME`, `AWS_REGION` (optional), `AWS_PROFILE` (optional) |
| [Anthropic](https://www.anthropic.com/) | Offers Claude, an advanced AI model for natural language tasks. | `ANTHROPIC_API_KEY`, `ANTHROPIC_HOST` (optional) |
| [Azure OpenAI](https://learn.microsoft.com/en-us/azure/ai-services/openai/) | Access Azure-hosted OpenAI models, including GPT-4 and GPT-3.5. Supports both API key and Azure credential chain authentication. | `AZURE_OPENAI_ENDPOINT`, `AZURE_OPENAI_DEPLOYMENT_NAME`, `AZURE_OPENAI_API_KEY` (optional) |
| [ChatGPT Codex](https://chatgpt.com/codex) | Access GPT-5 Codex models optimized for code generation and understanding. **Requires a ChatGPT Plus/Pro subscription.** | No manual key. Uses browser-based OAuth authentication for both CLI and Desktop. |
| [Databricks](https://www.databricks.com/) | Unified data analytics and AI platform for building and deploying models. | `DATABRICKS_HOST`, `DATABRICKS_TOKEN` |
| [Docker Model Runner](https://docs.docker.com/ai/model-runner/) | Local models running in Docker Desktop or Docker CE with OpenAI-compatible API endpoints. **Because this provider runs locally, you must first [download a model](#local-llms).** | `OPENAI_HOST`, `OPENAI_BASE_PATH` |
| [Gemini](https://ai.google.dev/gemini-api/docs) | Advanced LLMs by Google with multimodal capabilities (text, images). | `GOOGLE_API_KEY` |
| [GCP Vertex AI](https://cloud.google.com/vertex-ai) | Google Cloud's Vertex AI platform, supporting Gemini and Claude models. **Credentials must be [configured in advance](https://cloud.google.com/vertex-ai/docs/authentication).** | `GCP_PROJECT_ID`, `GCP_LOCATION` and optionally `GCP_MAX_RATE_LIMIT_RETRIES` (5), `GCP_MAX_OVERLOADED_RETRIES` (5), `GCP_INITIAL_RETRY_INTERVAL_MS` (5000), `GCP_BACKOFF_MULTIPLIER` (2.0), `GCP_MAX_RETRY_INTERVAL_MS` (320_000). |
| [GitHub Copilot](https://docs.github.com/en/copilot/using-github-copilot/ai-models) | Access to AI models from OpenAI, Anthropic, Google, and other providers through GitHub's Copilot infrastructure. **GitHub account with Copilot access required.** | No manual key. Uses [device flow authentication](#github-copilot-authentication) for both CLI and Desktop. |
| [Groq](https://groq.com/) | High-performance inference hardware and tools for LLMs. | `GROQ_API_KEY` |
| [LiteLLM](https://docs.litellm.ai/docs/) | LiteLLM proxy supporting multiple models with automatic prompt caching and unified API access. | `LITELLM_HOST`, `LITELLM_BASE_PATH` (optional), `LITELLM_API_KEY` (optional), `LITELLM_CUSTOM_HEADERS` (optional), `LITELLM_TIMEOUT` (optional) |
| [Mistral AI](https://mistral.ai/) | Provides access to Mistral models including general-purpose models, specialized coding models (Codestral), and multimodal models (Pixtral). | `MISTRAL_API_KEY` |
| [Ollama](https://ollama.com/) | Local model runner supporting Qwen, Llama, DeepSeek, and other open-source models. **Because this provider runs locally, you must first [download and run a model](#local-llms).** | `OLLAMA_HOST` |
| [OpenAI](https://platform.openai.com/api-keys) | Provides gpt-4o, o1, and other advanced language models. Also supports OpenAI-compatible endpoints (e.g., self-hosted LLaMA, vLLM, KServe). **o1-mini and o1-preview are not supported because goose uses tool calling.** | `OPENAI_API_KEY`, `OPENAI_HOST` (optional), `OPENAI_ORGANIZATION` (optional), `OPENAI_PROJECT` (optional), `OPENAI_CUSTOM_HEADERS` (optional) |
| [OpenRouter](https://openrouter.ai/) | API gateway for unified access to various models with features like rate-limiting management. | `OPENROUTER_API_KEY` |
| [OVHcloud AI](https://www.ovhcloud.com/en/public-cloud/ai-endpoints/) | Provides access to open-source models including Qwen, Llama, Mistral, and DeepSeek through AI Endpoints service. | `OVHCLOUD_API_KEY` |
| [Ramalama](https://ramalama.ai/) | Local model using native [OCI](https://opencontainers.org/) container runtimes, [CNCF](https://www.cncf.io/) tools, and supporting models as OCI artifacts. Ramalama API is a compatible alternative to Ollama and can be used with the goose Ollama provider. Supports Qwen, Llama, DeepSeek, and other open-source models. **Because this provider runs locally, you must first [download and run a model](#local-llms).** | `OLLAMA_HOST` |
| [Snowflake](https://docs.snowflake.com/user-guide/snowflake-cortex/aisql#choosing-a-model) | Access the latest models using Snowflake Cortex services, including Claude models. **Requires a Snowflake account and programmatic access token (PAT)**. | `SNOWFLAKE_HOST`, `SNOWFLAKE_TOKEN` |
| [Tetrate Agent Router Service](https://router.tetrate.ai) | Unified API gateway for AI models including Claude, Gemini, GPT, open-weight models, and others. Supports PKCE authentication flow for secure API key generation. | `TETRATE_API_KEY`, `TETRATE_HOST` (optional) |
| [Venice AI](https://venice.ai/home) | Provides access to open source models like Llama, Mistral, and Qwen while prioritizing user privacy. **Requires an account and an [API key](https://docs.venice.ai/overview/guides/generating-api-key)**. | `VENICE_API_KEY`, `VENICE_HOST` (optional), `VENICE_BASE_PATH` (optional), `VENICE_MODELS_PATH` (optional) |
| [xAI](https://x.ai/) | Access to xAI's Grok models including grok-3, grok-3-mini, and grok-3-fast with 131,072 token context window. | `XAI_API_KEY`, `XAI_HOST` (optional) |
:::tip Prompt Caching for Claude Models
goose automatically enables Anthropic's [prompt caching](https://platform.claude.com/docs/en/build-with-claude/prompt-caching) when using Claude models via Anthropic, Databricks, OpenRouter, and LiteLLM providers. This adds `cache_control` markers to requests, which can reduce costs for longer conversations by caching frequently-used context. See the [provider implementations](https://github.com/block/goose/tree/main/crates/goose/src/providers) for technical details.
:::
### CLI Providers
goose also supports special "pass-through" providers that work with existing CLI tools, allowing you to use your subscriptions instead of paying per token:
| Provider | Description | Requirements |
|-----------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| [Claude Code](https://www.anthropic.com/claude-code) (`claude-code`) | Uses Anthropic's Claude CLI tool with your Claude Code subscription. Provides access to Claude with 200K context limit. | Claude CLI installed and authenticated, active Claude Code subscription |
| [OpenAI Codex](https://developers.openai.com/codex/cli) (`codex`) | Uses OpenAI's Codex CLI tool with your ChatGPT Plus/Pro subscription. Provides access to GPT-5 models with up to 400K context limit. | Codex CLI installed and authenticated, active ChatGPT Plus/Pro subscription |
| [Cursor Agent](https://docs.cursor.com/en/cli/overview) (`cursor-agent`) | Uses Cursor's AI CLI tool with your Cursor subscription. Provides access to GPT-5, Claude 4, and other models through the cursor-agent command-line interface. | cursor-agent CLI installed and authenticated |
| [Gemini CLI](https://ai.google.dev/gemini-api/docs) (`gemini-cli`) | Uses Google's Gemini CLI tool with your Google AI subscription. Provides access to Gemini with 1M context limit. | Gemini CLI installed and authenticated |
:::tip CLI Providers
CLI providers are cost-effective alternatives that use your existing subscriptions. They work differently from API providers as they execute CLI commands and integrate with the tools' native capabilities. See the [CLI Providers guide](/docs/guides/cli-providers) for detailed setup instructions.
:::
## Configure Provider and Model
To configure your chosen provider, see available options, or select a model, visit the `Models` tab in goose Desktop or run `goose configure` in the CLI.
<Tabs groupId="interface">
<TabItem value="ui" label="goose Desktop" default>
**First-time users:**
On the welcome screen the first time you open goose, you have these options:
- **Quick Setup with API Key** - goose will automatically configure your provider based on your API key
- **[ChatGPT Subscription](https://chatgpt.com/codex)** - Sign in with your ChatGPT Plus/Pro credentials to access GPT-5 Codex models
- **[Agent Router by Tetrate](https://tetrate.io/products/tetrate-agent-router-service)** - Access multiple AI models with automatic setup
- **[OpenRouter](https://openrouter.ai/)** - Access 200+ models with one API using pay-per-use pricing
- **Other Providers** - Manually configure additional providers through settings
<Tabs groupId="setup">
<TabItem value="apikey" label="Quick Setup" default>
1. Choose `Quick Setup with API Key`.
2. Enter your API key from your provider (for example, OpenAI, Anthropic, or Google).
3. goose will automatically detect your provider and configure the connection.
4. When setup is complete, you're ready to begin your first session.
</TabItem>
<TabItem value="chatgpt" label="ChatGPT Subscription">
1. Choose `ChatGPT Subscription`.
2. goose will open a browser window for you to sign in with the credentials of your active ChatGPT Plus or Pro subscription.
3. Authorize goose to access your ChatGPT subscription.
4. When you return to goose Desktop, you're ready to begin your first session.
</TabItem>
<TabItem value="tetrate" label="Agent Router">
We recommend new users start with Agent Router by Tetrate. Tetrate provides access to multiple AI models with built-in rate limiting and automatic failover.
:::info Free Credits Offer
You'll receive $10 in free credits the first time you automatically authenticate with Tetrate through goose. This offer is available to both new and existing Tetrate users.
:::
1. Choose `Agent Router by Tetrate`.
2. goose will open a browser window for you to authenticate with Tetrate, or create a new account if you don't have one already.
3. When you return to goose Desktop, you're ready to begin your first session.
</TabItem>
<TabItem value="openrouter" label="OpenRouter">
1. Choose `Automatic setup with OpenRouter`.
2. goose will open a browser window for you to authenticate with OpenRouter, or create a new account if you don't have one already.
3. When you return to the goose Desktop, you're ready to begin your first session.
</TabItem>
<TabItem value="others" label="Other Providers">
1. If you have a specific provider you want to use with goose, and an API key from that provider, choose `Other Providers`.
2. Find the provider of your choice and click its `Configure` button. If you don't see your provider in the list, click `Add Custom Provider` at the bottom of the window to [configure a custom provider](#configure-custom-provider).
3. Depending on your provider, you'll need to input your API Key, API Host, or other optional [parameters](#available-providers). Click the `Submit` button to authenticate and begin your first session.
:::info Ollama Model Detection
For Ollama users, all locally installed models display automatically in the model selection dropdown.
:::
</TabItem>
</Tabs>
**To update your LLM provider and API key:**
1. Click the <PanelLeft className="inline" size={16} /> button in the top-left to open the sidebar
2. Click the `Settings` button on the sidebar
3. Click the `Models` tab
4. Click `Configure providers`
5. Click your provider in the list
6. Add your API key and other required configurations, then click `Submit`
**To change your current model:**
1. Click the <PanelLeft className="inline" size={16} /> button in the top-left to open the sidebar
2. Click the `Settings` button on the sidebar
3. Click the `Models` tab
4. Click `Switch models`
5. Choose from your configured providers in the dropdown, or select `Use other provider` to configure a new one
6. Select a model from the available options, or choose `Use custom model` to enter a specific model name
7. Click `Select model` to confirm your choice
:::tip Shortcut
For faster access, click your current model name at the bottom of the app and choose `Change Model`.
:::
**To start over with provider and model configuration:**
1. Click the <PanelLeft className="inline" size={16} /> button in the top-left to open the sidebar
2. Click the `Settings` button on the sidebar
3. Click the `Models` tab
4. Click `Reset Provider and Model` to clear your current settings and return to the welcome screen
</TabItem>
<TabItem value="cli" label="goose CLI">
1. In your terminal, run the following command:
```sh
goose configure
```
2. Select `Configure Providers` from the menu and press `Enter`.
```
┌ goose-configure
│
◆ What would you like to configure?
// highlight-start
│ ● Configure Providers (Change provider or update credentials)
// highlight-end
│ ○ Custom Providers
│ ○ Add Extension
│ ○ Toggle Extensions
│ ○ Remove Extension
│ ○ goose Settings
└
```
3. Choose a model provider and press `Enter`. Use the arrow keys (↑/↓) to move through the options.
```
┌ goose-configure
│
◇ What would you like to configure?
│ Configure Providers
│
◆ Which model provider should we use?
│ ○ Amazon Bedrock
│ ○ Amazon SageMaker TGI
// highlight-start
│ ● Anthropic (Claude and other models from Anthropic)
// highlight-end
│ ○ Azure OpenAI
│ ○ Claude Code CLI
│ ○ ...
└
```
4. Enter your API key (and any other configuration details) when prompted.
```
┌ goose-configure
│
◇ What would you like to configure?
│ Configure Providers
│
◇ Which model provider should we use?
│ Anthropic
│
◆ Provider Anthropic requires ANTHROPIC_API_KEY, please enter a value
// highlight-start
│ ▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪
// highlight-end
└
```
If you're just changing models, skip any prompts to update the provider configuration.
5. Enter your desired `ANTHROPIC_HOST` or press `Enter` to use the default.
```
◆ Provider Anthropic requires ANTHROPIC_HOST, please enter a value
// highlight-start
│ https://api.anthropic.com (default)
// highlight-end
```
6. Choose the model you want to use. Depending on the provider, you can:
- Select the model from a list
- Search for the model by name
- Enter the model name directly
```
│
◇ Model fetch complete
│
◇ Select a model:
// highlight-start
│ claude-sonnet-4-5 (default)
// highlight-end
│
◒ Checking your configuration...
└ Configuration saved successfully
```
This change takes effect the next time you start a session.
:::note
`goose configure` doesn't support entering custom model names. To use a model not in the provider's list, use goose Desktop or edit the `GOOSE_MODEL` variable in your [`config.yaml`](/docs/guides/config-files) directly.
:::
:::tip
Set the model for an individual session using the [`run` command](/docs/guides/goose-cli-commands#run-options):
```bash
goose run --model claude-sonnet-4-0 -t "initial prompt"
```
:::
</TabItem>
</Tabs>
### Using Custom OpenAI Endpoints
The built-in OpenAI provider can connect to OpenAI's official API (`api.openai.com`) or any OpenAI-compatible endpoint, such as:
- Self-hosted LLMs (e.g., LLaMA, Mistral) using vLLM or KServe
- Private OpenAI-compatible API servers
- Enterprise deployments requiring data governance and security compliance
- OpenAI API proxies or gateways
:::tip Custom Provider Option
Need to connect to multiple OpenAI-compatible endpoints? [Configure custom providers](#configure-custom-provider) instead for easier switching and better organization, as well as custom naming and shareable configurations.
:::
#### Configuration Parameters
| Parameter | Required | Description |
|-----------|----------|-------------|
| `OPENAI_API_KEY` | Yes | Authentication key for the API |
| `OPENAI_HOST` | No | Custom endpoint URL (defaults to api.openai.com) |
| `OPENAI_ORGANIZATION` | No | Organization ID for usage tracking and governance |
| `OPENAI_PROJECT` | No | Project identifier for resource management |
| `OPENAI_CUSTOM_HEADERS` | No | Additional headers to include in the request. Can be set via environment variable, configuration file, or CLI, in the format `HEADER_A=VALUE_A,HEADER_B=VALUE_B`. |
#### Example Configurations
<Tabs groupId="deployment">
<TabItem value="vllm" label="vLLM Self-Hosted" default>
If you're running LLaMA or other models using vLLM with OpenAI compatibility:
```sh
OPENAI_HOST=https://your-vllm-endpoint.internal
OPENAI_API_KEY=your-internal-api-key
```
</TabItem>
<TabItem value="kserve" label="KServe Deployment">
For models deployed on Kubernetes using KServe:
```sh
OPENAI_HOST=https://kserve-gateway.your-cluster
OPENAI_API_KEY=your-kserve-api-key
OPENAI_ORGANIZATION=your-org-id
OPENAI_PROJECT=ml-serving
```
</TabItem>
<TabItem value="enterprise" label="Enterprise OpenAI">
For enterprise OpenAI deployments with governance:
```sh
OPENAI_API_KEY=your-api-key
OPENAI_ORGANIZATION=org-id123
OPENAI_PROJECT=compliance-approved
```
</TabItem>
<TabItem value="custom-headers" label="Custom Headers">
For OpenAI-compatible endpoints that require custom headers:
```sh
OPENAI_API_KEY=your-api-key
OPENAI_ORGANIZATION=org-id123
OPENAI_PROJECT=compliance-approved
OPENAI_CUSTOM_HEADERS="X-Header-A=abc,X-Header-B=def"
```
</TabItem>
</Tabs>
#### Setup Instructions
<Tabs groupId="interface">
<TabItem value="ui" label="goose Desktop" default>
1. Click the <PanelLeft className="inline" size={16} /> button in the top-left to open the sidebar
2. Click the `Settings` button on the sidebar
3. Click the `Models` tab
4. Click `Configure providers`
5. Click `OpenAI` in the provider list
6. Fill in your configuration details:
- API Key (required)
- Host URL (for custom endpoints)
- Organization ID (for usage tracking)
- Project (for resource management)
7. Click `Submit`
</TabItem>
<TabItem value="cli" label="goose CLI">
1. Run `goose configure`
2. Select `Configure Providers`
3. Choose `OpenAI` as the provider
4. Enter your configuration when prompted:
- API key
- Host URL (if using custom endpoint)
- Organization ID (if using organization tracking)
- Project identifier (if using project management)
</TabItem>
</Tabs>
:::tip Enterprise Deployment
For enterprise deployments, you can pre-configure these values using environment variables or configuration files to ensure consistent governance across your organization.
:::
## Configure Custom Provider
Create custom providers to connect to services that aren't [already supported](#available-providers) or customize how you connect to them. Custom providers appear in goose's provider list and can be selected like any other provider.
**Benefits:**
- **Multiple endpoints**: Switch between different services (e.g., vLLM, corporate proxy, OpenAI)
- **Pre-configured models**: Store a list of preferred models
- **Shareable configuration**: JSON files can be shared across teams or checked into repos
- **Custom naming**: Show "Corporate API" instead of "OpenAI" in the UI
- **Separate credentials**: Assign each provider its own API key
Custom providers must use OpenAI, Anthropic, or Ollama compatible API formats. They can include custom headers for additional authentication, API keys, tokens, or tenant identifiers. Each custom provider maps to a JSON configuration file.
**To add a custom provider:**
<Tabs groupId="interface">
<TabItem value="ui" label="goose Desktop" default>
1. Click the <PanelLeft className="inline" size={16} /> button in the top-left to open the sidebar
2. Click the `Settings` button on the sidebar
3. Click the `Models` tab
4. Click `Configure providers`
5. Click `Add Custom Provider` at the bottom of the window
6. Fill in the provider details:
- **Provider Type**:
- `OpenAI Compatible` (most common)
- `Anthropic Compatible`
- `Ollama Compatible`
- **Display Name**: A friendly name for the provider
- **API URL**: The base URL of the API endpoint
- **Authentication**:
- **API Key**: The API key, which is accessed using a custom environment variable and stored in the keychain (or `secrets.yaml` if the keyring is disabled)
- For providers that don't require authorization (e.g., local models like Ollama, vLLM, LM Studio, or internal APIs), uncheck the **"This provider requires an API key"** checkbox
- **Available Models**: Comma-separated list of available model names
- **Streaming Support**: Whether the API supports streaming responses (click to toggle)
7. Click `Create Provider`
:::info Custom Headers
Currently, custom headers can't be defined in goose Desktop. As a workaround, edit the provider configuration file after creation.
:::
</TabItem>
<TabItem value="cli" label="goose CLI">
1. In your terminal, run the following command:
```sh
goose configure
```
2. Select `Custom Providers`. Use the arrow keys (↑/↓) to move through the options.
```sh
┌ goose-configure
│
◆ What would you like to configure?
│ ○ Configure Providers
// highlight-start
│ ● Custom Providers (Add custom provider with compatible API)
// highlight-end
│ ○ Add Extension
│ ○ Toggle Extensions
│ ○ Remove Extension
│ ○ goose Settings
└
```
3. Select `Add A Custom Provider`
```sh
┌ goose-configure
│
◇ What would you like to configure?
│ Custom Providers
│
◆ What would you like to do?
// highlight-start
│ ● Add A Custom Provider (Add a new OpenAI/Anthropic/Ollama compatible Provider)
// highlight-end
│ ○ Remove Custom Provider
└
```
4. Follow the prompts to enter the provider details:
- **API Type**:
- `OpenAI Compatible` (most common)
- `Anthropic Compatible`
- `Ollama Compatible`
- **Name**: A friendly name for the provider
- **API URL**: The base URL of the API endpoint
- **Authentication Required**: Answer "Yes" if your provider needs an API key, or "No" if authentication is not required
- If Yes: You'll be prompted to enter your **API Key** (stored securely in the keychain or `secrets.yaml`)
- If No: The API key prompt is skipped
- **Available Models**: Comma-separated list of available model names
- **Streaming Support**: Whether the API supports streaming responses
- **Custom Headers**: Any additional header names and values
:::info Custom Headers
Currently, custom headers can only be defined for OpenAI compatible providers in the CLI. For Anthropic or Ollama compatible providers, edit the provider configuration file after creation.
:::
</TabItem>
<TabItem value="config" label="Config File">
First create a JSON file in the `custom_providers` directory:
- macOS/Linux: `~/.config/goose/custom_providers/`
- Windows: `%APPDATA%\Block\goose\config\custom_providers\`
Example `custom_corp_api.json` configuration file:
```json
{
"name": "custom_corp_api",
"engine": "openai",
"display_name": "Corporate API",
"description": "Custom Corporate API provider",
"api_key_env": "CUSTOM_CORP_API_API_KEY",
"base_url": "https://api.company.com/v1/chat/completions",
"models": [
{
"name": "gpt-4o",
"context_limit": 128000
},
{
"name": "gpt-3.5-turbo",
"context_limit": 16385
}
],
"headers": {
"x-origin-client-id": "YOUR_CLIENT_ID",
"x-origin-secret": "YOUR_SECRET_VALUE"
},
"supports_streaming": true,
"requires_auth": true
}
```
Then use the `api_key_env` to set the key for your session. For example:
```bash
export CUSTOM_CORP_API_API_KEY="your-api-key"
goose session start --provider custom_corp_api
```
:::tip Keychain Key Storage
If you want to store the API key in the `goose` keychain, update the provider in goose Desktop and enter the key. This provides secure, persistent storage and allows goose to connect natively to the provider.
:::
</TabItem>
</Tabs>
**To update a custom provider:**
<Tabs groupId="interface">
<TabItem value="ui" label="goose Desktop" default>
1. Click the <PanelLeft className="inline" size={16} /> button in the top-left to open the sidebar
2. Click the `Settings` button on the sidebar
3. Click the `Models` tab
4. Click `Configure providers`
5. Click on your custom provider in the list
6. Update the fields you want to change
7. Click `Update Provider`
</TabItem>
<TabItem value="cli" label="goose CLI">
1. In your terminal, run the following command:
```sh
goose configure
```
2. Select `Configure Providers` from the menu and press `Enter`.
```sh
┌ goose-configure
│
◆ What would you like to configure?
// highlight-start
│ ● Configure Providers (Change provider or update credentials)
// highlight-end
│ ○ Custom Providers
│ ○ Add Extension
│ ○ Toggle Extensions
│ ○ Remove Extension
│ ○ goose Settings
└
```
3. Select the custom provider you want to update and press `Enter`. Use the arrow keys (↑/↓) to move through the options.
```sh
┌ goose-configure
│
◇ What would you like to configure?
│ Configure Providers
│
◆ Which model provider should we use?
│ ○ Amazon Bedrock
│ ○ Amazon SageMaker TGI
│ ○ Anthropic
│ ○ Azure OpenAI
│ ○ Claude Code CLI
// highlight-start
│ ● Corporate API (Custom Corporate API provider)
// highlight-end
│ ○ Cursor Agent
│ ○ ...
└
```
4. Follow the prompts to update the fields.
</TabItem>
<TabItem value="config" label="Config File">
Open the custom provider configuration file in the `custom_providers` directory:
- macOS/Linux: `~/.config/goose/custom_providers/`
- Windows: `%APPDATA%\Block\goose\config\custom_providers\`
Update the fields you want to change and save your changes.
</TabItem>
</Tabs>
Your changes are available in your next goose session.
**To remove a custom provider:**
<Tabs groupId="interface">
<TabItem value="ui" label="goose Desktop" default>
Currently you cannot remove custom providers using goose Desktop.
</TabItem>
<TabItem value="cli" label="goose CLI">
1. In your terminal, run the following command:
```sh
goose configure
```
2. Select `Custom Providers`. Use the arrow keys (↑/↓) to move through the options.
```sh
┌ goose-configure
│
◆ What would you like to configure?
│ ○ Configure Providers
// highlight-start
│ ● Custom Providers (Add custom provider with compatible API)
// highlight-end
│ ○ Add Extension
│ ○ Toggle Extensions
│ ○ Remove Extension
│ ○ goose Settings
└
```
3. Select `Remove Custom Provider`.
```sh
┌ goose-configure
│
◇ What would you like to configure?
│ Custom Providers
│
◆ What would you like to do?
│ ○ Add A Custom Provider
// highlight-start
│ ● Remove Custom Provider (Remove an existing custom provider)
// highlight-end
└
```
4. Select the custom provider you want to remove.
The provider configuration file is removed from the `custom_providers` directory and the key is removed from the keychain.
</TabItem>
<TabItem value="config" label="Config File">
:::tip
If the provider's API key is stored in the keychain, use goose CLI to remove the custom provider. This also removes the stored API key.
:::
Delete the custom provider configuration file in the `custom_providers` directory:
- macOS/Linux: `~/.config/goose/custom_providers/`
- Windows: `%APPDATA%\Block\goose\config\custom_providers\`
</TabItem>
</Tabs>
## Using goose for Free
goose is a free and open source AI agent that you can start using right away, but not all supported [LLM Providers][providers] provide a free tier.
Below, we outline a couple of free options and how to get started with them.
:::warning Limitations
These free options are a great way to get started with goose and explore its capabilities. However, you may need to upgrade your LLM for better performance.
:::
### Groq
Groq provides free access to open source models with high-speed inference. To use Groq with goose, you need an API key from [Groq Console](https://console.groq.com/keys).
Groq offers several open source models that support tool calling:
- **moonshotai/kimi-k2-instruct** - Mixture-of-Experts model with 1 trillion parameters, optimized for agentic intelligence and tool use
- **qwen/qwen3-32b** - 32.8 billion parameter model with advanced reasoning and multilingual capabilities
- **gemma2-9b-it** - Google's Gemma 2 model with instruction tuning
- **llama-3.3-70b-versatile** - Meta's Llama 3.3 model for versatile applications
To set up Groq with goose, follow these steps:
<Tabs groupId="interface">
<TabItem value="ui" label="goose Desktop" default>
**To update your LLM provider and API key:**
1. Click the <PanelLeft className="inline" size={16} /> button in the top-left to open the sidebar.
2. Click the `Settings` button on the sidebar.
3. Click the `Models` tab.
4. Click `Configure Providers`
5. Choose `Groq` as provider from the list.
6. Click `Configure`, enter your API key, and click `Submit`.
</TabItem>
<TabItem value="cli" label="goose CLI">
1. Run:
```sh
goose configure
```
2. Select `Configure Providers` from the menu.
3. Follow the prompts to choose `Groq` as the provider.
4. Enter your API key when prompted.
5. Enter the Groq model of your choice (e.g., `moonshotai/kimi-k2-instruct`).
</TabItem>
</Tabs>
### Google Gemini
Google Gemini provides a free tier. To start using the Gemini API with goose, you need an API Key from [Google AI studio](https://aistudio.google.com/app/apikey).
To set up Google Gemini with goose, follow these steps:
<Tabs groupId="interface">
<TabItem value="ui" label="goose Desktop" default>
**To update your LLM provider and API key:**
1. Click the <PanelLeft className="inline" size={16} /> button in the top-left to open the sidebar.
2. Click the `Settings` button on the sidebar.
3. Click the `Models` tab.
4. Click `Configure Providers`
5. Choose `Google Gemini` as provider from the list.
6. Click `Configure`, enter your API key, and click `Submit`.
</TabItem>
<TabItem value="cli" label="goose CLI">
1. Run:
```sh
goose configure
```
2. Select `Configure Providers` from the menu.
3. Follow the prompts to choose `Google Gemini` as the provider.
4. Enter your API key when prompted.
5. Enter the Gemini model of your choice.
```
┌ goose-configure
│
◇ What would you like to configure?
│ Configure Providers
│
◇ Which model provider should we use?
│ Google Gemini
│
◇ Provider Google Gemini requires GOOGLE_API_KEY, please enter a value
│▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪
│
◇ Enter a model from that provider:
│ gemini-2.0-flash-exp
│
◇ Hello! You're all set and ready to go, feel free to ask me anything!
│
└ Configuration saved successfully
```
</TabItem>
</Tabs>
### Local LLMs
goose is a local AI agent, and by using a local LLM, you keep your data private, maintain full control over your environment, and can work entirely offline without relying on cloud access. However, please note that local LLMs require a bit more set up before you can use one of them with goose.
:::warning Limited Support for models without tool calling
goose extensively uses tool calling, so models without it can only do chat completion. If using models without tool calling, all goose [extensions must be disabled](/docs/getting-started/using-extensions#enablingdisabling-extensions).
:::
Here are some local providers we support:
<Tabs groupId="local-llms">
<TabItem value="ollama" label="Ollama" default>
<Tabs groupId="ollama-models">
<TabItem value="ramalala" label="Ramalala">
1. [Download Ramalama](https://github.com/containers/ramalama?tab=readme-ov-file#install).
2. In a terminal, run any Ollama [model supporting tool-calling](https://ollama.com/search?c=tools) or [GGUF format HuggingFace Model](https://huggingface.co/search/full-text?q=%22tools+support%22+%2B+%22gguf%22&type=model):
The `--runtime-args="--jinja"` flag is required for Ramalama to work with the goose Ollama provider.
Example:
```sh
ramalama serve --runtime-args="--jinja" ollama://qwen2.5
```
3. In a separate terminal window, configure with goose:
```sh
goose configure
```
4. Choose to `Configure Providers`
```
┌ goose-configure
│
◆ What would you like to configure?
│ ● Configure Providers (Change provider or update credentials)
│ ○ Toggle Extensions
│ ○ Add Extension
└
```
5. Choose `Ollama` as the model provider since Ramalama is API compatible and can use the goose Ollama provider
```
┌ goose-configure
│
◇ What would you like to configure?
│ Configure Providers
│
◆ Which model provider should we use?
│ ○ Anthropic
│ ○ Databricks
│ ○ Google Gemini
│ ○ Groq
│ ● Ollama (Local open source models)
│ ○ OpenAI
│ ○ OpenRouter
└
```
6. Enter the host where your model is running
:::info Endpoint
For the Ollama provider, if you don't provide a host, we set it to `localhost:11434`. When constructing the URL, we preprend `http://` if the scheme is not `http` or `https`. Since Ramalama's default port to serve on is 8080, we set `OLLAMA_HOST=http://0.0.0.0:8080`
:::
```
┌ goose-configure
│
◇ What would you like to configure?
│ Configure Providers
│
◇ Which model provider should we use?
│ Ollama
│
◆ Provider Ollama requires OLLAMA_HOST, please enter a value
│ http://0.0.0.0:8080
└
```
7. Enter the model you have running
```
┌ goose-configure
│
◇ What would you like to configure?
│ Configure Providers
│
◇ Which model provider should we use?
│ Ollama
│
◇ Provider Ollama requires OLLAMA_HOST, please enter a value
│ http://0.0.0.0:8080
│
◇ Enter a model from that provider:
│ qwen2.5
│
◇ Welcome! You're all set to explore and utilize my capabilities. Let's get started on solving your problems together!
│
└ Configuration saved successfully
```
:::tip Context Length
If you notice that goose is having trouble using extensions or is ignoring [.goosehints](/docs/guides/context-engineering/using-goosehints), it is likely that the model's default context length of 2048 tokens is too low. Use `ramalama serve` to set the `--ctx-size, -c` option to a [higher value](https://github.com/containers/ramalama/blob/main/docs/ramalama-serve.1.md#--ctx-size--c).
:::
</TabItem>
<TabItem value="deepseek" label="DeepSeek-R1">
The native `DeepSeek-r1` model doesn't support tool calling, however, we have a [custom model](https://ollama.com/michaelneale/deepseek-r1-goose) you can use with goose.
:::warning
Note that this is a 70B model size and requires a powerful device to run smoothly.
:::
1. [Download Ollama](https://ollama.com/download).
2. In a terminal window, run the following command to install the custom DeepSeek-r1 model:
```sh
ollama run michaelneale/deepseek-r1-goose
```
3. In a separate terminal window, configure with goose:
```sh
goose configure
```
4. Choose to `Configure Providers`
```
┌ goose-configure
│
◆ What would you like to configure?
│ ● Configure Providers (Change provider or update credentials)
│ ○ Toggle Extensions
│ ○ Add Extension
└
```
5. Choose `Ollama` as the model provider
```
┌ goose-configure
│
◇ What would you like to configure?
│ Configure Providers
│
◆ Which model provider should we use?
│ ○ Anthropic
│ ○ Databricks
│ ○ Google Gemini
│ ○ Groq
│ ● Ollama (Local open source models)
│ ○ OpenAI
│ ○ OpenRouter
└
```
6. Enter the host where your model is running
```
┌ goose-configure
│
◇ What would you like to configure?
│ Configure Providers
│
◇ Which model provider should we use?
│ Ollama
│
◆ Provider Ollama requires OLLAMA_HOST, please enter a value
│ http://localhost:11434
└
```
7. Enter the installed model from above
```
┌ goose-configure
│
◇ What would you like to configure?
│ Configure Providers
│
◇ Which model provider should we use?
│ Ollama
│
◇ Provider Ollama requires OLLAMA_HOST, please enter a value
│ http://localhost:11434
│
◇ Enter a model from that provider:
│ michaelneale/deepseek-r1-goose
│
◇ Welcome! You're all set to explore and utilize my capabilities. Let's get started on solving your problems together!
│
└ Configuration saved successfully
```
</TabItem>
<TabItem value="others" label="Other Models" default>
1. [Download Ollama](https://ollama.com/download).
2. In a terminal, run any [model supporting tool-calling](https://ollama.com/search?c=tools)
Example:
```sh
ollama run qwen2.5
```
3. In a separate terminal window, configure with goose:
```sh
goose configure
```
4. Choose to `Configure Providers`
```
┌ goose-configure
│
◆ What would you like to configure?
│ ● Configure Providers (Change provider or update credentials)
│ ○ Toggle Extensions
│ ○ Add Extension
└
```
5. Choose `Ollama` as the model provider
```
┌ goose-configure
│
◇ What would you like to configure?
│ Configure Providers
│
◆ Which model provider should we use?
│ ○ Anthropic
│ ○ Databricks
│ ○ Google Gemini
│ ○ Groq
│ ● Ollama (Local open source models)
│ ○ OpenAI
│ ○ OpenRouter
└
```
6. Enter the host where your model is running
:::info Endpoint
For Ollama, if you don't provide a host, we set it to `localhost:11434`.
When constructing the URL, we prepend `http://` if the scheme is not `http` or `https`.
If you're running Ollama on a different server, you'll have to set `OLLAMA_HOST=http://{host}:{port}`.
:::
```
┌ goose-configure
│
◇ What would you like to configure?
│ Configure Providers
│
◇ Which model provider should we use?
│ Ollama
│
◆ Provider Ollama requires OLLAMA_HOST, please enter a value
│ http://localhost:11434
└
```
7. Enter the model you have running
```
┌ goose-configure
│
◇ What would you like to configure?
│ Configure Providers
│
◇ Which model provider should we use?
│ Ollama
│
◇ Provider Ollama requires OLLAMA_HOST, please enter a value
│ http://localhost:11434
│
◇ Enter a model from that provider:
│ qwen2.5
│
◇ Welcome! You're all set to explore and utilize my capabilities. Let's get started on solving your problems together!
│
└ Configuration saved successfully
```
:::tip Context Length
If you notice that goose is having trouble using extensions or is ignoring [.goosehints](/docs/guides/context-engineering/using-goosehints), it is likely that the model's default context length of 4096 tokens is too low. Set the `OLLAMA_CONTEXT_LENGTH` environment variable to a [higher value](https://github.com/ollama/ollama/blob/main/docs/faq.mdx#how-can-i-specify-the-context-window-size).
:::
</TabItem>
</Tabs>
</TabItem>
<TabItem value="docker" label="Docker Model Runner" default>
1. [Get Docker](https://docs.docker.com/get-started/get-docker/)
2. [Enable Docker Model Runner](https://docs.docker.com/ai/model-runner/#enable-dmr-in-docker-desktop)
3. [Pull a model](https://docs.docker.com/ai/model-runner/#pull-a-model), for example, from Docker Hub [AI namespace](https://hub.docker.com/u/ai), [Unsloth](https://hub.docker.com/u/unsloth), or [from HuggingFace](https://www.docker.com/blog/docker-model-runner-on-hugging-face/)
Example:
```sh
docker model pull hf.co/unsloth/gemma-3n-e4b-it-gguf:q6_k
```
4. Configure goose to use Docker Model Runner, using the OpenAI API compatible endpoint:
```sh
goose configure
```
5. Choose to `Configure Providers`
```
┌ goose-configure
│
◆ What would you like to configure?
│ ● Configure Providers (Change provider or update credentials)
│ ○ Toggle Extensions
│ ○ Add Extension
└
```
6. Choose `OpenAI` as the model provider:
```
┌ goose-configure
│
◇ What would you like to configure?
│ Configure Providers
│
◆ Which model provider should we use?
│ ○ Anthropic
│ ○ Amazon Bedrock
│ ○ Claude Code
│ ● OpenAI (GPT-4 and other OpenAI models, including OpenAI compatible ones)
│ ○ OpenRouter
```
7. Configure Docker Model Runner endpoint as the `OPENAI_HOST`:
```
┌ goose-configure
│
◇ What would you like to configure?
│ Configure Providers
│
◇ Which model provider should we use?
│ OpenAI
│
◆ Provider OpenAI requires OPENAI_HOST, please enter a value
│ https://api.openai.com (default)
└
```
The default value for the host-side port Docker Model Runner is 12434, so the `OPENAI_HOST` value could be:
`http://localhost:12434`.
8. Configure the base path:
```
◆ Provider OpenAI requires OPENAI_BASE_PATH, please enter a value
│ v1/chat/completions (default)
└
```
Docker model runner uses `/engines/llama.cpp/v1/chat/completions` for the base path.
9. Finally configure the model available in Docker Model Runner to be used by goose: `hf.co/unsloth/gemma-3n-e4b-it-gguf:q6_k`
```
│
◇ Enter a model from that provider:
│ gpt-4o
│
◒ Checking your configuration...
└ Configuration saved successfully
```
</TabItem>
</Tabs>
## GitHub Copilot Authentication
GitHub Copilot uses a device flow for authentication, so no API keys are required:
1. Run [`goose configure`](#configure-provider-and-model) and select **GitHub Copilot**
2. An eight-character code will be automatically copied to your clipboard
3. A browser will open to GitHub's device activation page
4. Paste the code to authorize the application
5. When you return to goose, GitHub Copilot will be available as a provider in both CLI and Desktop.
## Azure OpenAI Credential Chain
goose supports two authentication methods for Azure OpenAI:
1. **API Key Authentication** - Uses the `AZURE_OPENAI_API_KEY` for direct authentication
2. **Azure Credential Chain** - Uses Azure CLI credentials automatically without requiring an API key
To use the Azure Credential Chain:
- Ensure you're logged in with `az login`
- Have appropriate Azure role assignments for the Azure OpenAI service
- Configure with `goose configure` and select Azure OpenAI, leaving the API key field empty
This method simplifies authentication and enhances security for enterprise environments.
## Multi-Model Configuration
Beyond single-model setups, goose supports [multi-model configurations](/docs/guides/multi-model/) that can use different models and providers for specialized tasks:
- **Lead/Worker Model** - Automatic switching between a lead model for initial turns and a worker model for execution tasks
- **Planning Mode** - Manual planning phase using a dedicated model to create detailed project breakdowns before execution
---
If you have any questions or need help with a specific provider, feel free to reach out to us on [Discord](https://discord.gg/goose-oss) or on the [goose repo](https://github.com/block/goose).
[providers]: /docs/getting-started/providers
[function-calling-leaderboard]: https://gorilla.cs.berkeley.edu/leaderboard.html
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