1# Configuration
2
3There are various aspects about the Agent Panel that you can customize.
4All 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"`.
5
6Alternatively, 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".
7
8## LLM Providers
9
10Zed supports multiple large language model providers.
11Here's an overview of the supported providers and tool call support:
12
13| Provider | Tool Use Supported |
14| ----------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
15| [Amazon Bedrock](#amazon-bedrock) | Depends on the model |
16| [Anthropic](#anthropic) | ✅ |
17| [DeepSeek](#deepseek) | ✅ |
18| [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)) |
19| [Google AI](#google-ai) | ✅ |
20| [LM Studio](#lmstudio) | ✅ |
21| [Mistral](#mistral) | ✅ |
22| [Ollama](#ollama) | ✅ |
23| [OpenAI](#openai) | ✅ |
24| [OpenAI API Compatible](#openai-api-compatible) | 🚫 |
25| [OpenRouter](#openrouter) | ✅ |
26| [Vercel](#vercel-v0) | ✅ |
27| [xAI](#xai) | ✅ |
28
29## Use Your Own Keys {#use-your-own-keys}
30
31While 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.
32Below, you can learn how to do that for each provider.
33
34> 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.
35
36### Amazon Bedrock {#amazon-bedrock}
37
38> ✅ Supports tool use with models that support streaming tool use.
39> 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).
40
41To use Amazon Bedrock's models, an AWS authentication is required.
42Ensure your credentials have the following permissions set up:
43
44- `bedrock:InvokeModelWithResponseStream`
45- `bedrock:InvokeModel`
46- `bedrock:ConverseStream`
47
48Your IAM policy should look similar to:
49
50```json
51{
52 "Version": "2012-10-17",
53 "Statement": [
54 {
55 "Effect": "Allow",
56 "Action": [
57 "bedrock:InvokeModel",
58 "bedrock:InvokeModelWithResponseStream",
59 "bedrock:ConverseStream"
60 ],
61 "Resource": "*"
62 }
63 ]
64}
65```
66
67With that done, choose one of the two authentication methods:
68
69#### Authentication via Named Profile (Recommended)
70
711. Ensure you have the AWS CLI installed and configured with a named profile
722. Open your `settings.json` (`zed: open settings`) and include the `bedrock` key under `language_models` with the following settings:
73 ```json
74 {
75 "language_models": {
76 "bedrock": {
77 "authentication_method": "named_profile",
78 "region": "your-aws-region",
79 "profile": "your-profile-name"
80 }
81 }
82 }
83 ```
84
85#### Authentication via Static Credentials
86
87While 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.
88To do this:
89
901. 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).
912. Create security credentials for that User, save them and keep them secure.
923. Open the Agent Configuration with (`agent: open configuration`) and go to the Amazon Bedrock section
934. Copy the credentials from Step 2 into the respective **Access Key ID**, **Secret Access Key**, and **Region** fields.
94
95#### Cross-Region Inference
96
97The 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.
98With Cross-Region inference, you can distribute traffic across multiple AWS Regions, enabling higher throughput.
99
100For 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`.
101Cross-Region inference requests are kept within the AWS Regions that are part of the geography where the data originally resides.
102For example, a request made within the US is kept within the AWS Regions in the US.
103
104Although 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.
105All data will be transmitted encrypted across Amazon's secure network.
106
107We 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).
108
109For 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).
110
111### Anthropic {#anthropic}
112
113> ✅ Supports tool use
114
115You can use Anthropic models by choosing it via the model dropdown in the Agent Panel.
116
1171. Sign up for Anthropic and [create an API key](https://console.anthropic.com/settings/keys)
1182. Make sure that your Anthropic account has credits
1193. Open the settings view (`agent: open configuration`) and go to the Anthropic section
1204. Enter your Anthropic API key
121
122Even 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.
123
124Zed will also use the `ANTHROPIC_API_KEY` environment variable if it's defined.
125
126#### Custom Models {#anthropic-custom-models}
127
128You can add custom models to the Anthropic provider by adding the following to your Zed `settings.json`:
129
130```json
131{
132 "language_models": {
133 "anthropic": {
134 "available_models": [
135 {
136 "name": "claude-3-5-sonnet-20240620",
137 "display_name": "Sonnet 2024-June",
138 "max_tokens": 128000,
139 "max_output_tokens": 2560,
140 "cache_configuration": {
141 "max_cache_anchors": 10,
142 "min_total_token": 10000,
143 "should_speculate": false
144 },
145 "tool_override": "some-model-that-supports-toolcalling"
146 }
147 ]
148 }
149 }
150}
151```
152
153Custom models will be listed in the model dropdown in the Agent Panel.
154
155You 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:
156
157```json
158{
159 "name": "claude-sonnet-4-latest",
160 "display_name": "claude-sonnet-4-thinking",
161 "max_tokens": 200000,
162 "mode": {
163 "type": "thinking",
164 "budget_tokens": 4_096
165 }
166}
167```
168
169### DeepSeek {#deepseek}
170
171> ✅ Supports tool use
172
1731. Visit the DeepSeek platform and [create an API key](https://platform.deepseek.com/api_keys)
1742. Open the settings view (`agent: open configuration`) and go to the DeepSeek section
1753. Enter your DeepSeek API key
176
177The DeepSeek API key will be saved in your keychain.
178
179Zed will also use the `DEEPSEEK_API_KEY` environment variable if it's defined.
180
181#### Custom Models {#deepseek-custom-models}
182
183The Zed agent comes pre-configured to use the latest version for common models (DeepSeek Chat, DeepSeek Reasoner).
184If you wish to use alternate models or customize the API endpoint, you can do so by adding the following to your Zed `settings.json`:
185
186```json
187{
188 "language_models": {
189 "deepseek": {
190 "api_url": "https://api.deepseek.com",
191 "available_models": [
192 {
193 "name": "deepseek-chat",
194 "display_name": "DeepSeek Chat",
195 "max_tokens": 64000
196 },
197 {
198 "name": "deepseek-reasoner",
199 "display_name": "DeepSeek Reasoner",
200 "max_tokens": 64000,
201 "max_output_tokens": 4096
202 }
203 ]
204 }
205 }
206}
207```
208
209Custom models will be listed in the model dropdown in the Agent Panel.
210You can also modify the `api_url` to use a custom endpoint if needed.
211
212### GitHub Copilot Chat {#github-copilot-chat}
213
214> ✅ Supports tool use in some cases.
215> 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.
216
217You can use GitHub Copilot Chat with the Zed agent by choosing it via the model dropdown in the Agent Panel.
218
2191. Open the settings view (`agent: open configuration`) and go to the GitHub Copilot Chat section
2202. Click on `Sign in to use GitHub Copilot`, follow the steps shown in the modal.
221
222Alternatively, you can provide an OAuth token via the `GH_COPILOT_TOKEN` environment variable.
223
224> **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).
225
226### Google AI {#google-ai}
227
228> ✅ Supports tool use
229
230You can use Gemini models with the Zed agent by choosing it via the model dropdown in the Agent Panel.
231
2321. Go to the Google AI Studio site and [create an API key](https://aistudio.google.com/app/apikey).
2332. Open the settings view (`agent: open configuration`) and go to the Google AI section
2343. Enter your Google AI API key and press enter.
235
236The Google AI API key will be saved in your keychain.
237
238Zed will also use the `GOOGLE_AI_API_KEY` environment variable if it's defined.
239
240#### Custom Models {#google-ai-custom-models}
241
242By 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.
243
244Here is an example of a custom Google AI model you could add to your Zed `settings.json`:
245
246```json
247{
248 "language_models": {
249 "google": {
250 "available_models": [
251 {
252 "name": "gemini-2.5-flash-preview-05-20",
253 "display_name": "Gemini 2.5 Flash (Thinking)",
254 "max_tokens": 1000000,
255 "mode": {
256 "type": "thinking",
257 "budget_tokens": 24000
258 }
259 }
260 ]
261 }
262 }
263}
264```
265
266Custom models will be listed in the model dropdown in the Agent Panel.
267
268### LM Studio {#lmstudio}
269
270> ✅ Supports tool use
271
2721. Download and install [the latest version of LM Studio](https://lmstudio.ai/download)
2732. 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:
274
275 ```sh
276 lms get qwen2.5-coder-7b
277 ```
278
2793. Make sure the LM Studio API server is running by executing:
280
281 ```sh
282 lms server start
283 ```
284
285Tip: 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.
286
287### Mistral {#mistral}
288
289> ✅ Supports tool use
290
2911. Visit the Mistral platform and [create an API key](https://console.mistral.ai/api-keys/)
2922. Open the configuration view (`agent: open configuration`) and navigate to the Mistral section
2933. Enter your Mistral API key
294
295The Mistral API key will be saved in your keychain.
296
297Zed will also use the `MISTRAL_API_KEY` environment variable if it's defined.
298
299#### Custom Models {#mistral-custom-models}
300
301The 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).
302All the default models support tool use.
303If you wish to use alternate models or customize their parameters, you can do so by adding the following to your Zed `settings.json`:
304
305```json
306{
307 "language_models": {
308 "mistral": {
309 "api_url": "https://api.mistral.ai/v1",
310 "available_models": [
311 {
312 "name": "mistral-tiny-latest",
313 "display_name": "Mistral Tiny",
314 "max_tokens": 32000,
315 "max_output_tokens": 4096,
316 "max_completion_tokens": 1024,
317 "supports_tools": true,
318 "supports_images": false
319 }
320 ]
321 }
322 }
323}
324```
325
326Custom models will be listed in the model dropdown in the Agent Panel.
327
328### Ollama {#ollama}
329
330> ✅ Supports tool use
331
332Download and install Ollama from [ollama.com/download](https://ollama.com/download) (Linux or macOS) and ensure it's running with `ollama --version`.
333
3341. Download one of the [available models](https://ollama.com/models), for example, for `mistral`:
335
336 ```sh
337 ollama pull mistral
338 ```
339
3402. Make sure that the Ollama server is running. You can start it either via running Ollama.app (macOS) or launching:
341
342 ```sh
343 ollama serve
344 ```
345
3463. In the Agent Panel, select one of the Ollama models using the model dropdown.
347
348#### Ollama Context Length {#ollama-context}
349
350Zed has pre-configured maximum context lengths (`max_tokens`) to match the capabilities of common models.
351Zed 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.
352
353See [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.
354
355> **Note**: Token counts displayed in the Agent Panel are only estimates and will differ from the model's native tokenizer.
356
357Depending on your hardware or use-case you may wish to limit or increase the context length for a specific model via settings.json:
358
359```json
360{
361 "language_models": {
362 "ollama": {
363 "api_url": "http://localhost:11434",
364 "available_models": [
365 {
366 "name": "qwen2.5-coder",
367 "display_name": "qwen 2.5 coder 32K",
368 "max_tokens": 32768,
369 "supports_tools": true,
370 "supports_thinking": true,
371 "supports_images": true
372 }
373 ]
374 }
375 }
376}
377```
378
379If you specify a context length that is too large for your hardware, Ollama will log an error.
380You can watch these logs by running: `tail -f ~/.ollama/logs/ollama.log` (macOS) or `journalctl -u ollama -f` (Linux).
381Depending on the memory available on your machine, you may need to adjust the context length to a smaller value.
382
383You may also optionally specify a value for `keep_alive` for each available model.
384This can be an integer (seconds) or alternatively a string duration like "5m", "10m", "1h", "1d", etc.
385For example, `"keep_alive": "120s"` will allow the remote server to unload the model (freeing up GPU VRAM) after 120 seconds.
386
387The `supports_tools` option controls whether the model will use additional tools.
388If 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.
389If 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.
390
391The `supports_thinking` option controls whether the model will perform an explicit "thinking" (reasoning) pass before producing its final answer.
392If the model is tagged with `thinking` in the Ollama catalog, set this option and you can use it in Zed.
393
394The `supports_images` option enables the model's vision capabilities, allowing it to process images included in the conversation context.
395If the model is tagged with `vision` in the Ollama catalog, set this option and you can use it in Zed.
396
397### OpenAI {#openai}
398
399> ✅ Supports tool use
400
4011. Visit the OpenAI platform and [create an API key](https://platform.openai.com/account/api-keys)
4022. Make sure that your OpenAI account has credits
4033. Open the settings view (`agent: open configuration`) and go to the OpenAI section
4044. Enter your OpenAI API key
405
406The OpenAI API key will be saved in your keychain.
407
408Zed will also use the `OPENAI_API_KEY` environment variable if it's defined.
409
410#### Custom Models {#openai-custom-models}
411
412The 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).
413To 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`:
414
415```json
416{
417 "language_models": {
418 "openai": {
419 "available_models": [
420 {
421 "name": "gpt-4o-2024-08-06",
422 "display_name": "GPT 4o Summer 2024",
423 "max_tokens": 128000
424 },
425 {
426 "name": "o1-mini",
427 "display_name": "o1-mini",
428 "max_tokens": 128000,
429 "max_completion_tokens": 20000
430 }
431 ],
432 "version": "1"
433 }
434 }
435}
436```
437
438You 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).
439
440OpenAI `o1` models should set `max_completion_tokens` as well to avoid incurring high reasoning token costs.
441Custom models will be listed in the model dropdown in the Agent Panel.
442
443### OpenAI API Compatible {#openai-api-compatible}
444
445Zed supports using OpenAI compatible APIs by specifying a custom `endpoint` and `available_models` for the OpenAI provider.
446
447Zed supports using OpenAI compatible APIs 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.
448
449To configure a compatible API, you can add a custom API URL for OpenAI either via the UI or by editing your `settings.json`. For example, to connect to [Together AI](https://www.together.ai/):
450
4511. Get an API key from your [Together AI account](https://api.together.ai/settings/api-keys).
4522. Add the following to your `settings.json`:
453
454```json
455{
456 "language_models": {
457 "openai": {
458 "api_url": "https://api.together.xyz/v1",
459 "api_key": "YOUR_TOGETHER_AI_API_KEY",
460 "available_models": [
461 {
462 "name": "mistralai/Mixtral-8x7B-Instruct-v0.1",
463 "display_name": "Together Mixtral 8x7B",
464 "max_tokens": 32768,
465 "supports_tools": true
466 }
467 ]
468 }
469 }
470}
471```
472
473### OpenRouter {#openrouter}
474
475> ✅ Supports tool use
476
477OpenRouter provides access to multiple AI models through a single API. It supports tool use for compatible models.
478
4791. Visit [OpenRouter](https://openrouter.ai) and create an account
4802. Generate an API key from your [OpenRouter keys page](https://openrouter.ai/keys)
4813. Open the settings view (`agent: open configuration`) and go to the OpenRouter section
4824. Enter your OpenRouter API key
483
484The OpenRouter API key will be saved in your keychain.
485
486Zed will also use the `OPENROUTER_API_KEY` environment variable if it's defined.
487
488#### Custom Models {#openrouter-custom-models}
489
490You can add custom models to the OpenRouter provider by adding the following to your Zed `settings.json`:
491
492```json
493{
494 "language_models": {
495 "open_router": {
496 "api_url": "https://openrouter.ai/api/v1",
497 "available_models": [
498 {
499 "name": "google/gemini-2.0-flash-thinking-exp",
500 "display_name": "Gemini 2.0 Flash (Thinking)",
501 "max_tokens": 200000,
502 "max_output_tokens": 8192,
503 "supports_tools": true,
504 "supports_images": true,
505 "mode": {
506 "type": "thinking",
507 "budget_tokens": 8000
508 }
509 }
510 ]
511 }
512 }
513}
514```
515
516The available configuration options for each model are:
517
518- `name` (required): The model identifier used by OpenRouter
519- `display_name` (optional): A human-readable name shown in the UI
520- `max_tokens` (required): The model's context window size
521- `max_output_tokens` (optional): Maximum tokens the model can generate
522- `max_completion_tokens` (optional): Maximum completion tokens
523- `supports_tools` (optional): Whether the model supports tool/function calling
524- `supports_images` (optional): Whether the model supports image inputs
525- `mode` (optional): Special mode configuration for thinking models
526
527You can find available models and their specifications on the [OpenRouter models page](https://openrouter.ai/models).
528
529Custom models will be listed in the model dropdown in the Agent Panel.
530
531### Vercel v0 {#vercel-v0}
532
533> ✅ Supports tool use
534
535[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.
536It supports text and image inputs and provides fast streaming responses.
537
538The v0 models are [OpenAI-compatible models](/#openai-api-compatible), but Vercel is listed as first-class provider in the panel's settings view.
539
540To start using it with Zed, ensure you have first created a [v0 API key](https://v0.dev/chat/settings/keys).
541Once you have it, paste it directly into the Vercel provider section in the panel's settings view.
542
543You should then find it as `v0-1.5-md` in the model dropdown in the Agent Panel.
544
545### xAI {#xai}
546
547> ✅ Supports tool use
548
549Zed has first-class support for [xAI](https://x.ai/) models. You can use your own API key to access Grok models.
550
5511. [Create an API key in the xAI Console](https://console.x.ai/team/default/api-keys)
5522. Open the settings view (`agent: open configuration`) and go to the **xAI** section
5533. Enter your xAI API key
554
555The xAI API key will be saved in your keychain. Zed will also use the `XAI_API_KEY` environment variable if it's defined.
556
557> **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.
558
559#### Custom Models {#xai-custom-models}
560
561The 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`:
562
563```json
564{
565 "language_models": {
566 "x_ai": {
567 "api_url": "https://api.x.ai/v1",
568 "available_models": [
569 {
570 "name": "grok-1.5",
571 "display_name": "Grok 1.5",
572 "max_tokens": 131072,
573 "max_output_tokens": 8192
574 },
575 {
576 "name": "grok-1.5v",
577 "display_name": "Grok 1.5V (Vision)",
578 "max_tokens": 131072,
579 "max_output_tokens": 8192,
580 "supports_images": true
581 }
582 ]
583 }
584 }
585}
586```
587
588## Advanced Configuration {#advanced-configuration}
589
590### Custom Provider Endpoints {#custom-provider-endpoint}
591
592You can use a custom API endpoint for different providers, as long as it's compatible with the provider's API structure.
593To do so, add the following to your `settings.json`:
594
595```json
596{
597 "language_models": {
598 "some-provider": {
599 "api_url": "http://localhost:11434"
600 }
601 }
602}
603```
604
605Where `some-provider` can be any of the following values: `anthropic`, `google`, `ollama`, `openai`.
606
607### Default Model {#default-model}
608
609Zed's hosted LLM service sets `claude-sonnet-4` as the default model.
610However, 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:
611
612```json
613{
614 "agent": {
615 "version": "2",
616 "default_model": {
617 "provider": "zed.dev",
618 "model": "gpt-4o"
619 }
620 }
621}
622```
623
624### Feature-specific Models {#feature-specific-models}
625
626If 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.
627
628You can configure the following feature-specific models:
629
630- Thread summary model: Used for generating thread summaries
631- Inline assistant model: Used for the inline assistant feature
632- Commit message model: Used for generating Git commit messages
633
634Example configuration:
635
636```json
637{
638 "agent": {
639 "version": "2",
640 "default_model": {
641 "provider": "zed.dev",
642 "model": "claude-sonnet-4"
643 },
644 "inline_assistant_model": {
645 "provider": "anthropic",
646 "model": "claude-3-5-sonnet"
647 },
648 "commit_message_model": {
649 "provider": "openai",
650 "model": "gpt-4o-mini"
651 },
652 "thread_summary_model": {
653 "provider": "google",
654 "model": "gemini-2.0-flash"
655 }
656 }
657}
658```
659
660### Alternative Models for Inline Assists {#alternative-assists}
661
662You can configure additional models that will be used to perform inline assists in parallel.
663When you do this, the inline assist UI will surface controls to cycle between the alternatives generated by each model.
664
665The models you specify here are always used in _addition_ to your [default model](#default-model).
666For example, the following configuration will generate two outputs for every assist.
667One with Claude 3.7 Sonnet, and one with GPT-4o.
668
669```json
670{
671 "agent": {
672 "default_model": {
673 "provider": "zed.dev",
674 "model": "claude-sonnet-4"
675 },
676 "inline_alternatives": [
677 {
678 "provider": "zed.dev",
679 "model": "gpt-4o"
680 }
681 ],
682 "version": "2"
683 }
684}
685```
686
687### Default View
688
689Use the `default_view` setting to set change the default view of the Agent Panel.
690You can choose between `thread` (the default) and `text_thread`:
691
692```json
693{
694 "agent": {
695 "default_view": "text_thread"
696 }
697}
698```
699
700### Edit Card
701
702Use the `expand_edit_card` setting to control whether edit cards show the full diff in the Agent Panel.
703It 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.
704
705```json
706{
707 "agent": {
708 "expand_edit_card": "false"
709 }
710}
711```
712
713This setting is currently only available in Preview.
714It should be up in Stable by the next release.
715
716### Terminal Card
717
718Use the `expand_terminal_card` setting to control whether terminal cards show the command output in the Agent Panel.
719It 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.
720
721```json
722{
723 "agent": {
724 "expand_terminal_card": "false"
725 }
726}
727```
728
729This setting is currently only available in Preview.
730It should be up in Stable by the next release.