docs: Restructure and improve AI configuration docs (#35133)
Adding a number of settings that weren't documented, restructuring things a bit to separate what is model-related settings from agent panel usage-related settings, adding the recently introduced `disable_ai` key, and more. Release Notes: - Improved docs around configuring and using AI in Zed
This commit is contained in:
Danilo Leal
@@ -1,735 +1,20 @@
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# Configuration
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There are various aspects about the Agent Panel that you can customize.
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All of them can be seen by either visiting [the Configuring Zed page](../configuring-zed.md#agent) or by running the `zed: open default settings` action and searching for `"agent"`.
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When using AI in Zed, you can customize several aspects:
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Alternatively, you can also visit the panel's Settings view by running the `agent: open configuration` action or going to the top-right menu and hitting "Settings".
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1. Which [LLM providers](./llm-providers.md) you can use
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2. [Model parameters and usage](./agent-settings.md#model-settings)
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3. [Interactions with the Agent Panel](./agent-settings.md#agent-panel-settings)
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## LLM Providers
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## Turning AI off entirely
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Zed supports multiple large language model providers.
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Here's an overview of the supported providers and tool call support:
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| Provider | Tool Use Supported |
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| ----------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| [Amazon Bedrock](#amazon-bedrock) | Depends on the model |
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| [Anthropic](#anthropic) | ✅ |
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| [DeepSeek](#deepseek) | ✅ |
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| [GitHub Copilot Chat](#github-copilot-chat) | For some models ([link](https://github.com/zed-industries/zed/blob/9e0330ba7d848755c9734bf456c716bddf0973f3/crates/language_models/src/provider/copilot_chat.rs#L189-L198)) |
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| [Google AI](#google-ai) | ✅ |
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| [LM Studio](#lmstudio) | ✅ |
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| [Mistral](#mistral) | ✅ |
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| [Ollama](#ollama) | ✅ |
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| [OpenAI](#openai) | ✅ |
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| [OpenAI API Compatible](#openai-api-compatible) | 🚫 |
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| [OpenRouter](#openrouter) | ✅ |
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| [Vercel](#vercel-v0) | ✅ |
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| [xAI](#xai) | ✅ |
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## Use Your Own Keys {#use-your-own-keys}
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While Zed offers hosted versions of models through [our various plans](./plans-and-usage.md), we're always happy to support users wanting to supply their own API keys.
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Below, you can learn how to do that for each provider.
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> Using your own API keys is _free_—you do not need to subscribe to a Zed plan to use our AI features with your own keys.
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### Amazon Bedrock {#amazon-bedrock}
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> ✅ Supports tool use with models that support streaming tool use.
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> More details can be found in the [Amazon Bedrock's Tool Use documentation](https://docs.aws.amazon.com/bedrock/latest/userguide/conversation-inference-supported-models-features.html).
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To use Amazon Bedrock's models, an AWS authentication is required.
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Ensure your credentials have the following permissions set up:
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- `bedrock:InvokeModelWithResponseStream`
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- `bedrock:InvokeModel`
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- `bedrock:ConverseStream`
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Your IAM policy should look similar to:
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We want to respect users who want to use Zed without interacting with AI whatsoever.
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To do that, add the following key to your `settings.json`:
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```json
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{
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"Version": "2012-10-17",
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"Statement": [
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{
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"Effect": "Allow",
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"Action": [
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"bedrock:InvokeModel",
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"bedrock:InvokeModelWithResponseStream",
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"bedrock:ConverseStream"
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],
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"Resource": "*"
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}
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]
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"disable_ai": true
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}
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```
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With that done, choose one of the two authentication methods:
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#### Authentication via Named Profile (Recommended)
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1. Ensure you have the AWS CLI installed and configured with a named profile
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2. Open your `settings.json` (`zed: open settings`) and include the `bedrock` key under `language_models` with the following settings:
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```json
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{
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"language_models": {
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"bedrock": {
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"authentication_method": "named_profile",
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"region": "your-aws-region",
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"profile": "your-profile-name"
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}
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}
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}
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```
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#### Authentication via Static Credentials
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While it's possible to configure through the Agent Panel settings UI by entering your AWS access key and secret directly, we recommend using named profiles instead for better security practices.
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To do this:
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1. Create an IAM User that you can assume in the [IAM Console](https://us-east-1.console.aws.amazon.com/iam/home?region=us-east-1#/users).
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2. Create security credentials for that User, save them and keep them secure.
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3. Open the Agent Configuration with (`agent: open configuration`) and go to the Amazon Bedrock section
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4. Copy the credentials from Step 2 into the respective **Access Key ID**, **Secret Access Key**, and **Region** fields.
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#### Cross-Region Inference
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The Zed implementation of Amazon Bedrock uses [Cross-Region inference](https://docs.aws.amazon.com/bedrock/latest/userguide/cross-region-inference.html) for all the models and region combinations that support it.
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With Cross-Region inference, you can distribute traffic across multiple AWS Regions, enabling higher throughput.
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For example, if you use `Claude Sonnet 3.7 Thinking` from `us-east-1`, it may be processed across the US regions, namely: `us-east-1`, `us-east-2`, or `us-west-2`.
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Cross-Region inference requests are kept within the AWS Regions that are part of the geography where the data originally resides.
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For example, a request made within the US is kept within the AWS Regions in the US.
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Although the data remains stored only in the source Region, your input prompts and output results might move outside of your source Region during cross-Region inference.
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All data will be transmitted encrypted across Amazon's secure network.
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We will support Cross-Region inference for each of the models on a best-effort basis, please refer to the [Cross-Region Inference method Code](https://github.com/zed-industries/zed/blob/main/crates/bedrock/src/models.rs#L297).
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For the most up-to-date supported regions and models, refer to the [Supported Models and Regions for Cross Region inference](https://docs.aws.amazon.com/bedrock/latest/userguide/inference-profiles-support.html).
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### Anthropic {#anthropic}
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> ✅ Supports tool use
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You can use Anthropic models by choosing it via the model dropdown in the Agent Panel.
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1. Sign up for Anthropic and [create an API key](https://console.anthropic.com/settings/keys)
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2. Make sure that your Anthropic account has credits
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3. Open the settings view (`agent: open configuration`) and go to the Anthropic section
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4. Enter your Anthropic API key
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Even if you pay for Claude Pro, you will still have to [pay for additional credits](https://console.anthropic.com/settings/plans) to use it via the API.
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Zed will also use the `ANTHROPIC_API_KEY` environment variable if it's defined.
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#### Custom Models {#anthropic-custom-models}
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You can add custom models to the Anthropic provider by adding the following to your Zed `settings.json`:
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```json
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{
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"language_models": {
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"anthropic": {
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"available_models": [
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{
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"name": "claude-3-5-sonnet-20240620",
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"display_name": "Sonnet 2024-June",
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"max_tokens": 128000,
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"max_output_tokens": 2560,
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"cache_configuration": {
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"max_cache_anchors": 10,
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"min_total_token": 10000,
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"should_speculate": false
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},
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"tool_override": "some-model-that-supports-toolcalling"
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}
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]
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}
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}
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}
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```
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Custom models will be listed in the model dropdown in the Agent Panel.
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You can configure a model to use [extended thinking](https://docs.anthropic.com/en/docs/about-claude/models/extended-thinking-models) (if it supports it) by changing the mode in your model's configuration to `thinking`, for example:
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```json
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{
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"name": "claude-sonnet-4-latest",
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"display_name": "claude-sonnet-4-thinking",
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"max_tokens": 200000,
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"mode": {
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"type": "thinking",
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"budget_tokens": 4_096
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}
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}
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```
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### DeepSeek {#deepseek}
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> ✅ Supports tool use
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1. Visit the DeepSeek platform and [create an API key](https://platform.deepseek.com/api_keys)
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2. Open the settings view (`agent: open configuration`) and go to the DeepSeek section
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3. Enter your DeepSeek API key
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The DeepSeek API key will be saved in your keychain.
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Zed will also use the `DEEPSEEK_API_KEY` environment variable if it's defined.
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#### Custom Models {#deepseek-custom-models}
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The Zed agent comes pre-configured to use the latest version for common models (DeepSeek Chat, DeepSeek Reasoner).
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If you wish to use alternate models or customize the API endpoint, you can do so by adding the following to your Zed `settings.json`:
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```json
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{
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"language_models": {
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"deepseek": {
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"api_url": "https://api.deepseek.com",
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"available_models": [
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{
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"name": "deepseek-chat",
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"display_name": "DeepSeek Chat",
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"max_tokens": 64000
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},
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{
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"name": "deepseek-reasoner",
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"display_name": "DeepSeek Reasoner",
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"max_tokens": 64000,
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"max_output_tokens": 4096
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}
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]
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}
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}
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}
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```
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Custom models will be listed in the model dropdown in the Agent Panel.
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You can also modify the `api_url` to use a custom endpoint if needed.
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### GitHub Copilot Chat {#github-copilot-chat}
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> ✅ Supports tool use in some cases.
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> Visit [the Copilot Chat code](https://github.com/zed-industries/zed/blob/9e0330ba7d848755c9734bf456c716bddf0973f3/crates/language_models/src/provider/copilot_chat.rs#L189-L198) for the supported subset.
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You can use GitHub Copilot Chat with the Zed agent by choosing it via the model dropdown in the Agent Panel.
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1. Open the settings view (`agent: open configuration`) and go to the GitHub Copilot Chat section
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2. Click on `Sign in to use GitHub Copilot`, follow the steps shown in the modal.
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Alternatively, you can provide an OAuth token via the `GH_COPILOT_TOKEN` environment variable.
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> **Note**: If you don't see specific models in the dropdown, you may need to enable them in your [GitHub Copilot settings](https://github.com/settings/copilot/features).
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To use Copilot Enterprise with Zed (for both agent and inline completions), you must configure your enterprise endpoint as described in [Configuring GitHub Copilot Enterprise](./edit-prediction.md#github-copilot-enterprise).
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### Google AI {#google-ai}
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> ✅ Supports tool use
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You can use Gemini models with the Zed agent by choosing it via the model dropdown in the Agent Panel.
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1. Go to the Google AI Studio site and [create an API key](https://aistudio.google.com/app/apikey).
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2. Open the settings view (`agent: open configuration`) and go to the Google AI section
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3. Enter your Google AI API key and press enter.
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The Google AI API key will be saved in your keychain.
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Zed will also use the `GEMINI_API_KEY` environment variable if it's defined. See [Using Gemini API keys](Using Gemini API keys) in the Gemini docs for more.
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#### Custom Models {#google-ai-custom-models}
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By default, Zed will use `stable` versions of models, but you can use specific versions of models, including [experimental models](https://ai.google.dev/gemini-api/docs/models/experimental-models). You can configure a model to use [thinking mode](https://ai.google.dev/gemini-api/docs/thinking) (if it supports it) by adding a `mode` configuration to your model. This is useful for controlling reasoning token usage and response speed. If not specified, Gemini will automatically choose the thinking budget.
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Here is an example of a custom Google AI model you could add to your Zed `settings.json`:
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```json
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{
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"language_models": {
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"google": {
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"available_models": [
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{
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"name": "gemini-2.5-flash-preview-05-20",
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"display_name": "Gemini 2.5 Flash (Thinking)",
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"max_tokens": 1000000,
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"mode": {
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"type": "thinking",
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"budget_tokens": 24000
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}
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}
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]
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}
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}
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}
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```
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Custom models will be listed in the model dropdown in the Agent Panel.
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### LM Studio {#lmstudio}
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> ✅ Supports tool use
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1. Download and install [the latest version of LM Studio](https://lmstudio.ai/download)
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2. In the app press `cmd/ctrl-shift-m` and download at least one model (e.g., qwen2.5-coder-7b). Alternatively, you can get models via the LM Studio CLI:
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```sh
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lms get qwen2.5-coder-7b
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```
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3. Make sure the LM Studio API server is running by executing:
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```sh
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lms server start
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```
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Tip: Set [LM Studio as a login item](https://lmstudio.ai/docs/advanced/headless#run-the-llm-service-on-machine-login) to automate running the LM Studio server.
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### Mistral {#mistral}
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> ✅ Supports tool use
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1. Visit the Mistral platform and [create an API key](https://console.mistral.ai/api-keys/)
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2. Open the configuration view (`agent: open configuration`) and navigate to the Mistral section
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3. Enter your Mistral API key
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The Mistral API key will be saved in your keychain.
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Zed will also use the `MISTRAL_API_KEY` environment variable if it's defined.
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#### Custom Models {#mistral-custom-models}
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The Zed agent comes pre-configured with several Mistral models (codestral-latest, mistral-large-latest, mistral-medium-latest, mistral-small-latest, open-mistral-nemo, and open-codestral-mamba).
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All the default models support tool use.
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If you wish to use alternate models or customize their parameters, you can do so by adding the following to your Zed `settings.json`:
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```json
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{
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"language_models": {
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"mistral": {
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"api_url": "https://api.mistral.ai/v1",
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"available_models": [
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{
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"name": "mistral-tiny-latest",
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"display_name": "Mistral Tiny",
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"max_tokens": 32000,
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"max_output_tokens": 4096,
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"max_completion_tokens": 1024,
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"supports_tools": true,
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"supports_images": false
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}
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]
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}
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}
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}
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```
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Custom models will be listed in the model dropdown in the Agent Panel.
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### Ollama {#ollama}
|
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> ✅ Supports tool use
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||||
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Download and install Ollama from [ollama.com/download](https://ollama.com/download) (Linux or macOS) and ensure it's running with `ollama --version`.
|
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1. Download one of the [available models](https://ollama.com/models), for example, for `mistral`:
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```sh
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ollama pull mistral
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```
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2. Make sure that the Ollama server is running. You can start it either via running Ollama.app (macOS) or launching:
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```sh
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ollama serve
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```
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3. In the Agent Panel, select one of the Ollama models using the model dropdown.
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#### Ollama Context Length {#ollama-context}
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Zed has pre-configured maximum context lengths (`max_tokens`) to match the capabilities of common models.
|
||||
Zed API requests to Ollama include this as the `num_ctx` parameter, but the default values do not exceed `16384` so users with ~16GB of RAM are able to use most models out of the box.
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||||
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||||
See [get_max_tokens in ollama.rs](https://github.com/zed-industries/zed/blob/main/crates/ollama/src/ollama.rs) for a complete set of defaults.
|
||||
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||||
> **Note**: Token counts displayed in the Agent Panel are only estimates and will differ from the model's native tokenizer.
|
||||
|
||||
Depending on your hardware or use-case you may wish to limit or increase the context length for a specific model via settings.json:
|
||||
|
||||
```json
|
||||
{
|
||||
"language_models": {
|
||||
"ollama": {
|
||||
"api_url": "http://localhost:11434",
|
||||
"available_models": [
|
||||
{
|
||||
"name": "qwen2.5-coder",
|
||||
"display_name": "qwen 2.5 coder 32K",
|
||||
"max_tokens": 32768,
|
||||
"supports_tools": true,
|
||||
"supports_thinking": true,
|
||||
"supports_images": true
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
If you specify a context length that is too large for your hardware, Ollama will log an error.
|
||||
You can watch these logs by running: `tail -f ~/.ollama/logs/ollama.log` (macOS) or `journalctl -u ollama -f` (Linux).
|
||||
Depending on the memory available on your machine, you may need to adjust the context length to a smaller value.
|
||||
|
||||
You may also optionally specify a value for `keep_alive` for each available model.
|
||||
This can be an integer (seconds) or alternatively a string duration like "5m", "10m", "1h", "1d", etc.
|
||||
For example, `"keep_alive": "120s"` will allow the remote server to unload the model (freeing up GPU VRAM) after 120 seconds.
|
||||
|
||||
The `supports_tools` option controls whether the model will use additional tools.
|
||||
If the model is tagged with `tools` in the Ollama catalog, this option should be supplied, and the built-in profiles `Ask` and `Write` can be used.
|
||||
If the model is not tagged with `tools` in the Ollama catalog, this option can still be supplied with the value `true`; however, be aware that only the `Minimal` built-in profile will work.
|
||||
|
||||
The `supports_thinking` option controls whether the model will perform an explicit "thinking" (reasoning) pass before producing its final answer.
|
||||
If the model is tagged with `thinking` in the Ollama catalog, set this option and you can use it in Zed.
|
||||
|
||||
The `supports_images` option enables the model's vision capabilities, allowing it to process images included in the conversation context.
|
||||
If the model is tagged with `vision` in the Ollama catalog, set this option and you can use it in Zed.
|
||||
|
||||
### OpenAI {#openai}
|
||||
|
||||
> ✅ Supports tool use
|
||||
|
||||
1. Visit the OpenAI platform and [create an API key](https://platform.openai.com/account/api-keys)
|
||||
2. Make sure that your OpenAI account has credits
|
||||
3. Open the settings view (`agent: open configuration`) and go to the OpenAI section
|
||||
4. Enter your OpenAI API key
|
||||
|
||||
The OpenAI API key will be saved in your keychain.
|
||||
|
||||
Zed will also use the `OPENAI_API_KEY` environment variable if it's defined.
|
||||
|
||||
#### Custom Models {#openai-custom-models}
|
||||
|
||||
The Zed agent comes pre-configured to use the latest version for common models (GPT-3.5 Turbo, GPT-4, GPT-4 Turbo, GPT-4o, GPT-4o mini).
|
||||
To use alternate models, perhaps a preview release or a dated model release, or if you wish to control the request parameters, you can do so by adding the following to your Zed `settings.json`:
|
||||
|
||||
```json
|
||||
{
|
||||
"language_models": {
|
||||
"openai": {
|
||||
"available_models": [
|
||||
{
|
||||
"name": "gpt-4o-2024-08-06",
|
||||
"display_name": "GPT 4o Summer 2024",
|
||||
"max_tokens": 128000
|
||||
},
|
||||
{
|
||||
"name": "o1-mini",
|
||||
"display_name": "o1-mini",
|
||||
"max_tokens": 128000,
|
||||
"max_completion_tokens": 20000
|
||||
}
|
||||
],
|
||||
"version": "1"
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
You must provide the model's context window in the `max_tokens` parameter; this can be found in the [OpenAI model documentation](https://platform.openai.com/docs/models).
|
||||
|
||||
OpenAI `o1` models should set `max_completion_tokens` as well to avoid incurring high reasoning token costs.
|
||||
Custom models will be listed in the model dropdown in the Agent Panel.
|
||||
|
||||
### OpenAI API Compatible {#openai-api-compatible}
|
||||
|
||||
Zed supports using [OpenAI compatible APIs](https://platform.openai.com/docs/api-reference/chat) by specifying a custom `api_url` and `available_models` for the OpenAI provider. This is useful for connecting to other hosted services (like Together AI, Anyscale, etc.) or local models.
|
||||
|
||||
To configure a compatible API, you can add a custom API URL for OpenAI either via the UI (currently available only in Preview) or by editing your `settings.json`.
|
||||
|
||||
For example, to connect to [Together AI](https://www.together.ai/) via the UI:
|
||||
|
||||
1. Get an API key from your [Together AI account](https://api.together.ai/settings/api-keys).
|
||||
2. Go to the Agent Panel's settings view, click on the "Add Provider" button, and then on the "OpenAI" menu item
|
||||
3. Add the requested fields, such as `api_url`, `api_key`, available models, and others
|
||||
|
||||
Alternatively, you can also add it via the `settings.json`:
|
||||
|
||||
```json
|
||||
{
|
||||
"language_models": {
|
||||
"openai": {
|
||||
"api_url": "https://api.together.xyz/v1",
|
||||
"api_key": "YOUR_TOGETHER_AI_API_KEY",
|
||||
"available_models": [
|
||||
{
|
||||
"name": "mistralai/Mixtral-8x7B-Instruct-v0.1",
|
||||
"display_name": "Together Mixtral 8x7B",
|
||||
"max_tokens": 32768,
|
||||
"supports_tools": true
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### OpenRouter {#openrouter}
|
||||
|
||||
> ✅ Supports tool use
|
||||
|
||||
OpenRouter provides access to multiple AI models through a single API. It supports tool use for compatible models.
|
||||
|
||||
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 the settings view (`agent: open configuration`) and go to the OpenRouter section
|
||||
4. Enter your OpenRouter API key
|
||||
|
||||
The OpenRouter API key will be saved in your keychain.
|
||||
|
||||
Zed will also use the `OPENROUTER_API_KEY` environment variable if it's defined.
|
||||
|
||||
#### Custom Models {#openrouter-custom-models}
|
||||
|
||||
You can add custom models to the OpenRouter provider by adding the following to your Zed `settings.json`:
|
||||
|
||||
```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
|
||||
}
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
The available configuration options for each model are:
|
||||
|
||||
- `name` (required): The model identifier used by OpenRouter
|
||||
- `display_name` (optional): A human-readable name shown in the UI
|
||||
- `max_tokens` (required): The model's context window size
|
||||
- `max_output_tokens` (optional): Maximum tokens the model can generate
|
||||
- `max_completion_tokens` (optional): Maximum completion tokens
|
||||
- `supports_tools` (optional): Whether the model supports tool/function calling
|
||||
- `supports_images` (optional): Whether the model supports image inputs
|
||||
- `mode` (optional): Special mode configuration for thinking models
|
||||
|
||||
You can find available models and their specifications on the [OpenRouter models page](https://openrouter.ai/models).
|
||||
|
||||
Custom models will be listed in the model dropdown in the Agent Panel.
|
||||
|
||||
### Vercel v0 {#vercel-v0}
|
||||
|
||||
> ✅ Supports tool use
|
||||
|
||||
[Vercel v0](https://vercel.com/docs/v0/api) is an expert model for generating full-stack apps, with framework-aware completions optimized for modern stacks like Next.js and Vercel.
|
||||
It supports text and image inputs and provides fast streaming responses.
|
||||
|
||||
The v0 models are [OpenAI-compatible models](/#openai-api-compatible), but Vercel is listed as first-class provider in the panel's settings view.
|
||||
|
||||
To start using it with Zed, ensure you have first created a [v0 API key](https://v0.dev/chat/settings/keys).
|
||||
Once you have it, paste it directly into the Vercel provider section in the panel's settings view.
|
||||
|
||||
You should then find it as `v0-1.5-md` in the model dropdown in the Agent Panel.
|
||||
|
||||
### xAI {#xai}
|
||||
|
||||
> ✅ Supports tool use
|
||||
|
||||
Zed has first-class support for [xAI](https://x.ai/) models. You can use your own API key to access Grok models.
|
||||
|
||||
1. [Create an API key in the xAI Console](https://console.x.ai/team/default/api-keys)
|
||||
2. Open the settings view (`agent: open configuration`) and go to the **xAI** section
|
||||
3. Enter your xAI API key
|
||||
|
||||
The xAI API key will be saved in your keychain. Zed will also use the `XAI_API_KEY` environment variable if it's defined.
|
||||
|
||||
> **Note:** While the xAI API is OpenAI-compatible, Zed has first-class support for it as a dedicated provider. For the best experience, we recommend using the dedicated `x_ai` provider configuration instead of the [OpenAI API Compatible](#openai-api-compatible) method.
|
||||
|
||||
#### Custom Models {#xai-custom-models}
|
||||
|
||||
The Zed agent comes pre-configured with common Grok models. If you wish to use alternate models or customize their parameters, you can do so by adding the following to your Zed `settings.json`:
|
||||
|
||||
```json
|
||||
{
|
||||
"language_models": {
|
||||
"x_ai": {
|
||||
"api_url": "https://api.x.ai/v1",
|
||||
"available_models": [
|
||||
{
|
||||
"name": "grok-1.5",
|
||||
"display_name": "Grok 1.5",
|
||||
"max_tokens": 131072,
|
||||
"max_output_tokens": 8192
|
||||
},
|
||||
{
|
||||
"name": "grok-1.5v",
|
||||
"display_name": "Grok 1.5V (Vision)",
|
||||
"max_tokens": 131072,
|
||||
"max_output_tokens": 8192,
|
||||
"supports_images": true
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## Advanced Configuration {#advanced-configuration}
|
||||
|
||||
### Custom Provider Endpoints {#custom-provider-endpoint}
|
||||
|
||||
You can use a custom API endpoint for different providers, as long as it's compatible with the provider's API structure.
|
||||
To do so, add the following to your `settings.json`:
|
||||
|
||||
```json
|
||||
{
|
||||
"language_models": {
|
||||
"some-provider": {
|
||||
"api_url": "http://localhost:11434"
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
Where `some-provider` can be any of the following values: `anthropic`, `google`, `ollama`, `openai`.
|
||||
|
||||
### Default Model {#default-model}
|
||||
|
||||
Zed's hosted LLM service sets `claude-sonnet-4` as the default model.
|
||||
However, you can change it either via the model dropdown in the Agent Panel's bottom-right corner or by manually editing the `default_model` object in your settings:
|
||||
|
||||
```json
|
||||
{
|
||||
"agent": {
|
||||
"version": "2",
|
||||
"default_model": {
|
||||
"provider": "zed.dev",
|
||||
"model": "gpt-4o"
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### Feature-specific Models {#feature-specific-models}
|
||||
|
||||
If a feature-specific model is not set, it will fall back to using the default model, which is the one you set on the Agent Panel.
|
||||
|
||||
You can configure the following feature-specific models:
|
||||
|
||||
- Thread summary model: Used for generating thread summaries
|
||||
- Inline assistant model: Used for the inline assistant feature
|
||||
- Commit message model: Used for generating Git commit messages
|
||||
|
||||
Example configuration:
|
||||
|
||||
```json
|
||||
{
|
||||
"agent": {
|
||||
"version": "2",
|
||||
"default_model": {
|
||||
"provider": "zed.dev",
|
||||
"model": "claude-sonnet-4"
|
||||
},
|
||||
"inline_assistant_model": {
|
||||
"provider": "anthropic",
|
||||
"model": "claude-3-5-sonnet"
|
||||
},
|
||||
"commit_message_model": {
|
||||
"provider": "openai",
|
||||
"model": "gpt-4o-mini"
|
||||
},
|
||||
"thread_summary_model": {
|
||||
"provider": "google",
|
||||
"model": "gemini-2.0-flash"
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### Alternative Models for Inline Assists {#alternative-assists}
|
||||
|
||||
You can configure additional models that will be used to perform inline assists in parallel.
|
||||
When you do this, the inline assist UI will surface controls to cycle between the alternatives generated by each model.
|
||||
|
||||
The models you specify here are always used in _addition_ to your [default model](#default-model).
|
||||
For example, the following configuration will generate two outputs for every assist.
|
||||
One with Claude 3.7 Sonnet, and one with GPT-4o.
|
||||
|
||||
```json
|
||||
{
|
||||
"agent": {
|
||||
"default_model": {
|
||||
"provider": "zed.dev",
|
||||
"model": "claude-sonnet-4"
|
||||
},
|
||||
"inline_alternatives": [
|
||||
{
|
||||
"provider": "zed.dev",
|
||||
"model": "gpt-4o"
|
||||
}
|
||||
],
|
||||
"version": "2"
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### Default View
|
||||
|
||||
Use the `default_view` setting to set change the default view of the Agent Panel.
|
||||
You can choose between `thread` (the default) and `text_thread`:
|
||||
|
||||
```json
|
||||
{
|
||||
"agent": {
|
||||
"default_view": "text_thread"
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### Edit Card
|
||||
|
||||
Use the `expand_edit_card` setting to control whether edit cards show the full diff in the Agent Panel.
|
||||
It is set to `true` by default, but if set to false, the card's height is capped to a certain number of lines, requiring a click to be expanded.
|
||||
|
||||
```json
|
||||
{
|
||||
"agent": {
|
||||
"expand_edit_card": "false"
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
This setting is currently only available in Preview.
|
||||
It should be up in Stable by the next release.
|
||||
|
||||
### Terminal Card
|
||||
|
||||
Use the `expand_terminal_card` setting to control whether terminal cards show the command output in the Agent Panel.
|
||||
It is set to `true` by default, but if set to false, the card will be fully collapsed even while the command is running, requiring a click to be expanded.
|
||||
|
||||
```json
|
||||
{
|
||||
"agent": {
|
||||
"expand_terminal_card": "false"
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
This setting is currently only available in Preview.
|
||||
It should be up in Stable by the next release.
|
||||
Read [the following blog post](https://zed.dev/blog/disable-ai-features) to learn more about our motivation to promote this, as much as we also encourage users to explore AI-assisted programming.
|
||||
|
||||
Reference in New Issue
Block a user