Configuring the Assistant

Providers

The following providers are supported:

To configure different providers, run assistant: show configuration in the command palette, or click on the hamburger menu at the top-right of the assistant panel and select "Configure".

1

This provider does not support the /workflow command.

To further customize providers, you can use settings.json to do that as follows:

Zed AI

A hosted service providing convenient and performant support for AI-enabled coding in Zed, powered by Anthropic's Claude 3.5 Sonnet and accessible just by signing in.

Anthropic

You can use Claude 3.5 Sonnet via Zed AI for free. To use other Anthropic models you will need to configure it by providing your own API key.

  1. Sign up for Anthropic and create an API key
  2. Make sure that your Anthropic account has credits
  3. Open the configuration view (assistant: show configuration) and navigate 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.

Anthropic 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 assistant panel.

GitHub Copilot Chat

You can use GitHub Copilot chat with the Zed assistant by choosing it via the model dropdown in the assistant panel.

Google AI

You can use Gemini 1.5 Pro/Flash with the Zed assistant by choosing it via the model dropdown in the assistant panel.

  1. Go the Google AI Studio site and create an API key.
  2. Open the configuration view (assistant: show configuration) and navigate 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 GOOGLE_AI_API_KEY environment variable if it's defined.

Google AI custom models

By default Zed will use stable versions of models, but you can use specific versions of models, including experimental models with the Google AI provider by adding the following to your Zed settings.json:

{
  "language_models": {
    "google": {
      "available_models": [
        {
          "name": "gemini-1.5-flash-latest",
          "display_name": "Gemini 1.5 Flash (Latest)",
          "max_tokens": 1000000
        }
      ]
    }
  }
}

Custom models will be listed in the model dropdown in the assistant panel.

Ollama

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 assistant 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 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: Tokens counts displayed in the assistant panel are only estimates and will differ from the models 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
        }
      ]
    }
  }
}

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 alternately 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 120seconds.

OpenAI

  1. Visit the OpenAI platform and create an API key
  2. Make sure that your OpenAI account has credits
  3. Open the configuration view (assistant: show configuration) and navigate 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.

OpenAI Custom Models

The Zed Assistant 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). If you wish to use alternate models, perhaps a preview release or a dated model release or 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": [
        {
          "provider": "openai",
          "name": "gpt-4o-2024-08-06",
          "max_tokens": 128000
        },
        {
          "name": "o1-mini",
          "display_name": "o1-mini",
          "max_tokens": 128000,
          "max_completion_tokens": 20000
        }
      ]
    }
  }
}

You must provide the model's Context Window in the max_tokens parameter, this can be found OpenAI Model Docs. 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 assistant panel.

Advanced configuration

Example Configuration

{
  "assistant": {
    "enabled": true,
    "show_hints": true,
    "button": true,
    "dock": "right"
    "default_width": 480,
    "default_model": {
      "provider": "zed.dev",
      "model": "claude-3-5-sonnet"
    },
    "version": "2",
  }
}
keytypedefaultdescription
enabledbooleantrueSetting this to false will completely disable the assistant
show_hintsbooleantrueWhether to to show hints in the editor explaining how to use assistant
buttonbooleantrueShow the assistant icon in the status bar
dockstring"right"The default dock position for the assistant panel. Can be ["left", "right", "bottom"]
default_heightstringnullThe pixel height of the assistant panel when docked to the bottom
default_widthstringnullThe pixel width of the assistant panel when docked to the left or right

Custom endpoints

You can use a custom API endpoint for different providers, as long as it's compatible with the providers API structure.

To do so, add the following to your Zed settings.json:

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

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

Configuring the default model

The default model can be set via the model dropdown in the assistant panel's top-right corner. Selecting a model saves it as the default. You can also manually edit the default_model object in your settings:

{
  "assistant": {
    "version": "2",
    "default_model": {
      "provider": "zed.dev",
      "model": "claude-3-5-sonnet"
    }
  }
}

Configuring alternative models for inline 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. For example, the following configuration will generate two outputs for every assist. One with Claude 3.5 Sonnet, and one with GPT-4o.

{
  "assistant": {
    "default_model": {
      "provider": "zed.dev",
      "model": "claude-3-5-sonnet"
    },
    "inline_alternatives": [
      {
        "provider": "zed.dev",
        "model": "gpt-4o"
      }
    ],
    "version": "2"
  }
}