> For the complete documentation index and Markdown links, see [llms.txt](/docs/llms.txt).
# Use a Gateway
Use a gateway when you route model requests through a platform such as OpenRouter, Vercel AI Gateway, Amazon Bedrock, or another OpenAI-compatible service.
| Gateway | Zed AI features | External Agents | Terminal Threads | Notes |
| ------------------------- | --------------- | --------------- | ---------------- | -------------------------------------------- |
| OpenRouter | Yes | Separate config | Separate config | Uses OpenRouter API access |
| Vercel AI Gateway | Yes | Separate config | Separate config | Uses Vercel AI Gateway API access |
| Amazon Bedrock | Yes | Separate config | Separate config | Uses AWS credentials or Bedrock bearer token |
| OpenAI-compatible gateway | Yes | Separate config | Separate config | Configure base URL, model, and key |
## OpenRouter {#openrouter}
Use OpenRouter when you want to route Zed AI features through OpenRouter.
1. Visit [OpenRouter](https://openrouter.ai) and create an account.
2. Generate an API key from your [OpenRouter keys page](https://openrouter.ai/keys).
3. Open **Settings → AI → LLM Providers** with agent: open settings and find the OpenRouter row.
4. Enter your OpenRouter API key.
Zed also reads `OPENROUTER_API_KEY` from the local Zed process environment.
When using OpenRouter as your assistant provider, explicitly select a model in your settings:
```json
{
"agent": {
"default_model": {
"provider": "openrouter",
"model": "openrouter/auto"
}
}
}
```
The `openrouter/auto` model routes requests to an available model selected by OpenRouter. You can also specify any model available through OpenRouter's API.
### OpenRouter Custom Models {#openrouter-custom-models}
You can add custom models to the OpenRouter provider in settings:
```json
{
"language_models": {
"open_router": {
"api_url": "https://openrouter.ai/api/v1",
"available_models": [
{
"name": "google/gemini-2.0-flash-thinking-exp",
"display_name": "Gemini 2.0 Flash (Thinking)",
"max_tokens": 200000,
"max_output_tokens": 8192,
"supports_tools": true,
"supports_images": true,
"mode": {
"type": "thinking",
"budget_tokens": 8000
}
}
]
}
}
}
```
Custom model entries support fields such as `name`, `display_name`, `max_tokens`, `max_output_tokens`, `max_completion_tokens`, `supports_tools`, `supports_images`, and `mode`.
### OpenRouter Provider Routing {#openrouter-provider-routing}
You can control how OpenRouter routes a custom model request among upstream providers with the `provider` object on each model entry.
Supported fields include `order`, `allow_fallbacks`, `require_parameters`, `data_collection`, `only`, `ignore`, `quantizations`, and `sort`.
```json
{
"language_models": {
"open_router": {
"available_models": [
{
"name": "openrouter/auto",
"display_name": "Auto Router",
"max_tokens": 2000000,
"supports_tools": true,
"provider": {
"order": ["anthropic", "openai"],
"allow_fallbacks": true,
"require_parameters": true,
"data_collection": "allow"
}
}
]
}
}
}
```
## Vercel AI Gateway {#vercel-ai-gateway}
Use Vercel AI Gateway when you want to route Zed AI features through Vercel.
1. Create an API key from your Vercel AI Gateway keys page.
2. Open **Settings → AI → LLM Providers** with agent: open settings and find the Vercel AI Gateway row.
3. Enter your Vercel AI Gateway API key.
Zed also reads `VERCEL_AI_GATEWAY_API_KEY` from the local Zed process environment.
You can set a custom endpoint for Vercel AI Gateway in settings:
```json
{
"language_models": {
"vercel_ai_gateway": {
"api_url": "https://ai-gateway.vercel.sh/v1"
}
}
}
```
## Amazon Bedrock {#amazon-bedrock}
Use Amazon Bedrock when you want model access through AWS.
Bedrock supports models that support streaming tool use. See [Amazon Bedrock's Tool Use documentation](https://docs.aws.amazon.com/bedrock/latest/userguide/conversation-inference-supported-models-features.html).
Your AWS credentials need these permissions:
- `bedrock:InvokeModelWithResponseStream`
- `bedrock:InvokeModel`
Bedrock supports Zed-prefixed AWS environment variables so Zed does not override or consume your normal AWS credentials:
- `ZED_ACCESS_KEY_ID`
- `ZED_SECRET_ACCESS_KEY`
- `ZED_SESSION_TOKEN`
- `ZED_AWS_PROFILE`
- `ZED_AWS_REGION`
- `ZED_AWS_ENDPOINT`
- `ZED_BEDROCK_BEARER_TOKEN`
### Bedrock Authentication {#bedrock-authentication}
You can authenticate with a named profile, static credentials, or a Bedrock API key.
For a named profile, configure Bedrock in settings:
```json
{
"language_models": {
"bedrock": {
"authentication_method": "named_profile",
"region": "your-aws-region",
"profile": "your-profile-name"
}
}
}
```
For static credentials, open Agent Settings with agent: open settings, go to the Amazon Bedrock section, and enter the access key ID, secret access key, and region.
For a Bedrock API key, choose API key authentication:
```json
{
"language_models": {
"bedrock": {
"authentication_method": "api_key",
"region": "your-aws-region"
}
}
}
```
The API key itself is stored in the system keychain, not in `settings.json`.
### Bedrock Cross-Region Inference {#bedrock-cross-region-inference}
Zed uses [Cross-Region inference](https://docs.aws.amazon.com/bedrock/latest/userguide/cross-region-inference.html) for Bedrock on a best-effort basis.
By default, Zed uses regional inference profiles. To opt into global profiles, add `allow_global`:
```json
{
"language_models": {
"bedrock": {
"authentication_method": "named_profile",
"region": "your-aws-region",
"profile": "your-profile-name",
"allow_global": true
}
}
}
```
Only some models support global inference profiles. See the AWS Bedrock supported models documentation for the current list.
### Bedrock Guardrails {#bedrock-guardrails}
Some AWS environments require a guardrail on every Bedrock API call. Add `guardrail_identifier` to apply a guardrail to all Bedrock requests:
```json
{
"language_models": {
"bedrock": {
"guardrail_identifier": "arn:aws:bedrock:us-east-1:123456789012:guardrail/abc123",
"guardrail_version": "DRAFT"
}
}
}
```
### Bedrock Mantle Models {#bedrock-mantle-models}
Some models, such as the GPT-5.6 family (Sol, Terra, and Luna), GPT-5.5, GPT-5.4, and Grok 4.3, aren't available through Bedrock's Converse API and are only reachable through `bedrock-mantle`, AWS's OpenAI-compatible inference endpoint. Zed routes these models through `bedrock-mantle` automatically; they appear alongside the rest of the Bedrock models in the model picker once you're authenticated, with no extra configuration required.
Mantle models require IAM permissions for the `bedrock-mantle` endpoint (for example via the `AmazonBedrockMantleInferenceAccess` managed policy) in addition to whatever permissions your existing Bedrock credentials already have, and `bedrock-mantle` is only available in [some AWS Regions](https://docs.aws.amazon.com/bedrock/latest/userguide/bedrock-mantle.html#regions). Zed surfaces an error naming the current Region and the supported ones if you try to use a Mantle model outside of them.
#### Custom Bedrock Mantle Models {#bedrock-mantle-custom-models}
You can add custom models served through `bedrock-mantle` with `mantle_available_models`:
```json
{
"language_models": {
"bedrock": {
"mantle_available_models": [
{
"name": "openai.gpt-oss-120b",
"display_name": "GPT-OSS 120B",
"max_tokens": 128000,
"protocol": "chat_completions",
"supports_tools": true,
"supports_images": false,
"supports_thinking": true
}
]
}
}
}
```
`protocol` selects which OpenAI-compatible API the model is called through, and must be either `chat_completions` or `responses`. Set `supports_thinking` to `true` for custom Mantle models that accept OpenAI reasoning effort parameters; Zed will then expose `low`, `medium`, `high`, and `xhigh` in the thinking effort picker, while disabling thinking sends `none`.
## OpenAI-Compatible Gateways {#openai-compatible}
If your gateway exposes an OpenAI-compatible API, configure it with [Use API Access](./use-api-access.md#openai-compatible).