LLM Providers

To use AI in Zed, you need to have at least one large language model provider set up.

You can do that by either subscribing to one of Zed's plans, or by using API keys you already have for the supported providers.

Use Your Own Keys

If you already have an API key for an existing LLM provider—say Anthropic or OpenAI, for example—you can insert them in Zed and use the Agent Panel for free.

You can add your API key to a given provider either via the Agent Panel's settings UI or directly via the settings.json through the language_models key.

Supported Providers

Here's all the supported LLM providers for which you can use your own API keys:

ProviderTool Use Supported
Amazon BedrockDepends on the model
Anthropic✅
DeepSeek✅
GitHub Copilot ChatFor some models (link)
Google AI✅
LM Studio✅
Mistral✅
Ollama✅
OpenAI✅
OpenAI API Compatible✅
OpenRouter✅
Vercel✅
xAI✅

Amazon Bedrock

✅ Supports tool use with models that support streaming tool use. More details can be found in the Amazon Bedrock's Tool Use documentation.

To use Amazon Bedrock's models, an AWS authentication is required. Ensure your credentials have the following permissions set up:

  • bedrock:InvokeModelWithResponseStream
  • bedrock:InvokeModel
  • bedrock:ConverseStream

Your IAM policy should look similar to:

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "bedrock:InvokeModel",
        "bedrock:InvokeModelWithResponseStream",
        "bedrock:ConverseStream"
      ],
      "Resource": "*"
    }
  ]
}

With that done, choose one of the two authentication methods:

  1. Ensure you have the AWS CLI installed and configured with a named profile
  2. Open your settings.json (zed: open settings) and include the bedrock key under language_models with the following settings: 
    {
  "language_models": {
    "bedrock": {
      "authentication_method": "named_profile",
      "region": "your-aws-region",
      "profile": "your-profile-name"
    }
  }
}

Authentication via Static Credentials

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. To do this:

  1. Create an IAM User that you can assume in the IAM Console.
  2. Create security credentials for that User, save them and keep them secure.
  3. Open the Agent Configuration with (agent: open configuration) and go to the Amazon Bedrock section
  4. Copy the credentials from Step 2 into the respective Access Key ID, Secret Access Key, and Region fields.

Cross-Region Inference

The Zed implementation of Amazon Bedrock uses Cross-Region inference for all the models and region combinations that support it. With Cross-Region inference, you can distribute traffic across multiple AWS Regions, enabling higher throughput.

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. Cross-Region inference requests are kept within the AWS Regions that are part of the geography where the data originally resides. For example, a request made within the US is kept within the AWS Regions in the US.

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. All data will be transmitted encrypted across Amazon's secure network.

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.

For the most up-to-date supported regions and models, refer to the Supported Models and Regions for Cross Region inference.

Anthropic

✅ Supports tool use

You can use Anthropic models by choosing them via the model dropdown in the Agent Panel.

  1. Sign up for Anthropic and create an API key
  2. Make sure that your Anthropic account has credits
  3. Open the settings view (agent: open configuration) and go to the Anthropic section
  4. Enter your Anthropic API key

Even if you pay for Claude Pro, you will still have to pay for additional credits to use it via the API.

Zed will also use the ANTHROPIC_API_KEY environment variable if it's defined.

Custom Models

You can add custom models to the Anthropic provider by adding the following to your Zed settings.json:

{
  "language_models": {
    "anthropic": {
      "available_models": [
        {
          "name": "claude-3-5-sonnet-20240620",
          "display_name": "Sonnet 2024-June",
          "max_tokens": 128000,
          "max_output_tokens": 2560,
          "cache_configuration": {
            "max_cache_anchors": 10,
            "min_total_token": 10000,
            "should_speculate": false
          },
          "tool_override": "some-model-that-supports-toolcalling"
        }
      ]
    }
  }
}

Custom models will be listed in the model dropdown in the Agent Panel.

You can configure a model to use extended thinking (if it supports it) by changing the mode in your model's configuration to thinking, for example:

{
  "name": "claude-sonnet-4-latest",
  "display_name": "claude-sonnet-4-thinking",
  "max_tokens": 200000,
  "mode": {
    "type": "thinking",
    "budget_tokens": 4_096
  }
}

DeepSeek

✅ Supports tool use

  1. Visit the DeepSeek platform and create an API key
  2. Open the settings view (agent: open configuration) and go to the DeepSeek section
  3. Enter your DeepSeek API key

The DeepSeek API key will be saved in your keychain.

Zed will also use the DEEPSEEK_API_KEY environment variable if it's defined.

Custom Models

The Zed agent comes pre-configured to use the latest version for common models (DeepSeek Chat, DeepSeek Reasoner). 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:

{
  "language_models": {
    "deepseek": {
      "api_url": "https://api.deepseek.com",
      "available_models": [
        {
          "name": "deepseek-chat",
          "display_name": "DeepSeek Chat",
          "max_tokens": 64000
        },
        {
          "name": "deepseek-reasoner",
          "display_name": "DeepSeek Reasoner",
          "max_tokens": 64000,
          "max_output_tokens": 4096
        }
      ]
    }
  }
}

Custom models will be listed in the model dropdown in the Agent Panel. You can also modify the api_url to use a custom endpoint if needed.

GitHub Copilot Chat

✅ Supports tool use in some cases. Visit the Copilot Chat code for the supported subset.

You can use GitHub Copilot Chat with the Zed agent by choosing it via the model dropdown in the Agent Panel.

  1. Open the settings view (agent: open configuration) and go to the GitHub Copilot Chat section
  2. Click on Sign in to use GitHub Copilot, follow the steps shown in the modal.

Alternatively, you can provide an OAuth token via the GH_COPILOT_TOKEN environment variable.

Note: If you don't see specific models in the dropdown, you may need to enable them in your GitHub Copilot settings.

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.

Google AI

✅ Supports tool use

You can use Gemini models with the Zed agent by choosing it via the model dropdown in the Agent Panel.

  1. Go to the Google AI Studio site and create an API key.
  2. Open the settings view (agent: open configuration) and go to the Google AI section
  3. Enter your Google AI API key and press enter.

The Google AI API key will be saved in your keychain.

Zed will also use the GEMINI_API_KEY environment variable if it's defined. See Using Gemini API keys in the Gemini docs for more.

Custom Models

By default, Zed will use stable versions of models, but you can use specific versions of models, including experimental models. You can configure a model to use thinking mode (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.

Here is an example of a custom Google AI model you could add to your Zed settings.json:

{
  "language_models": {
    "google": {
      "available_models": [
        {
          "name": "gemini-2.5-flash-preview-05-20",
          "display_name": "Gemini 2.5 Flash (Thinking)",
          "max_tokens": 1000000,
          "mode": {
            "type": "thinking",
            "budget_tokens": 24000
          }
        }
      ]
    }
  }
}

Custom models will be listed in the model dropdown in the Agent Panel.

LM Studio

✅ Supports tool use

  1. Download and install the latest version of LM Studio

  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:

    lms get qwen2.5-coder-7b

  3. Make sure the LM Studio API server is running by executing:

    lms server start

Tip: Set LM Studio as a login item to automate running the LM Studio server.

Mistral

✅ Supports tool use

  1. Visit the Mistral platform and create an API key
  2. Open the configuration view (agent: open configuration) and navigate to the Mistral section
  3. Enter your Mistral API key

The Mistral API key will be saved in your keychain.

Zed will also use the MISTRAL_API_KEY environment variable if it's defined.

Custom Models

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). All the default models support tool use. If you wish to use alternate models or customize their parameters, you can do so by adding the following to your Zed settings.json:

{
  "language_models": {
    "mistral": {
      "api_url": "https://api.mistral.ai/v1",
      "available_models": [
        {
          "name": "mistral-tiny-latest",
          "display_name": "Mistral Tiny",
          "max_tokens": 32000,
          "max_output_tokens": 4096,
          "max_completion_tokens": 1024,
          "supports_tools": true,
          "supports_images": false
        }
      ]
    }
  }
}

Custom models will be listed in the model dropdown in the Agent Panel.

Ollama

✅ Supports tool use

Download and install Ollama from ollama.com/download (Linux or macOS) and ensure it's running with ollama --version.

  1. Download one of the available models, for example, for mistral:

    ollama pull mistral

  2. Make sure that the Ollama server is running. You can start it either via running Ollama.app (macOS) or launching:

    ollama serve

  3. In the Agent Panel, select one of the Ollama models using the model dropdown.

Ollama Context Length

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.

See get_max_tokens in ollama.rs for a complete set of defaults.

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:

{
  "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

✅ Supports tool use

  1. Visit the OpenAI platform and create an API key
  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

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:

{
  "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.

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

Zed 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.

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 via the UI:

  1. Get an API key from your Together AI account.
  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:

{
  "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

✅ Supports tool use

OpenRouter provides access to multiple AI models through a single API. It supports tool use for compatible models.

  1. Visit OpenRouter and create an account
  2. Generate an API key from your OpenRouter keys page
  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

You can add custom models to the OpenRouter provider by adding the following to your Zed 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
          }
        }
      ]
    }
  }
}

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.

Custom models will be listed in the model dropdown in the Agent Panel.

Vercel v0

✅ Supports tool use

Vercel v0 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, 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. 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

✅ Supports tool use

Zed has first-class support for xAI models. You can use your own API key to access Grok models.

  1. Create an API key in the xAI Console
  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 method.

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:

{
  "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
        }
      ]
    }
  }
}

Custom Provider Endpoints

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:

{
  "language_models": {
    "some-provider": {
      "api_url": "http://localhost:11434"
    }
  }
}

Currently, some-provider can be any of the following values: anthropic, google, ollama, openai.

This is the same infrastructure that powers models that are, for example, OpenAI-compatible.