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Zed is a high-performance editor with built-in AI assistant support. It can connect to any OpenAI-compatible API, making Bifrost a natural fit for universal model access across providers, plus governance features like virtual keys and built-in observability. Zed editor integration

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Setup

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1. Configure Bifrost Provider

Add Bifrost to Zed’s language_models.openai_compatible configuration. This is typically in your Zed settings (JSON) or workspace config. 
"language_models": {
  "openai_compatible": {
    "Bifrost": {
      "api_url": "http://localhost:8080/openai",
      "available_models": [
        {
          "name": "anthropic/claude-sonnet-4.5",
          "max_tokens": 200000,
          "max_output_tokens": 4096,
          "capabilities": {
            "tools": true,
            "images": true,
            "parallel_tool_calls": true,
            "prompt_cache_key": false
          }
        },
        {
          "name": "openai/gpt-4o",
          "max_tokens": 128000,
          "max_output_tokens": 4096,
          "capabilities": {
            "tools": true,
            "images": true,
            "parallel_tool_calls": true,
            "prompt_cache_key": false
          }
        },
        {
          "name": "openai/gpt-5",
          "max_tokens": 256000,
          "max_output_tokens": 4096,
          "capabilities": {
            "tools": true,
            "images": true,
            "parallel_tool_calls": true,
            "prompt_cache_key": false
          }
        }
      ]
    }
  }
}
Replace http://localhost:8080/openai with your Bifrost gateway URL + /openai.

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2. Model Capabilities

FieldDescription
toolsEnable tool/function calling
imagesEnable image input (vision)
parallel_tool_callsSupport multiple tool calls in one response
prompt_cache_keyEnable prompt caching (set false if not supported)
Use Bifrost model IDs in provider/model format (e.g. openai/gpt-5, anthropic/claude-sonnet-4.5). Ensure these models are configured in Bifrost.

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3. Reload Workspace

After changing the configuration, reload the workspace so Zed recognizes and reloads the provider list.

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Virtual Keys

When Bifrost has virtual key authentication enabled, add an api_key field to the Bifrost provider config (check Zed’s documentation for the exact field name — it may vary by version): 
"Bifrost": {
  "api_url": "http://localhost:8080/openai",
  "api_key": "bf-your-virtual-key-here",
  "available_models": [...]
}
This lets you enforce usage limits, budgets, and access control per user or team. For team deployments, create a separate virtual key for each team or environment — each key can have its own rate limits, budgets, and provider access rules configured in the Bifrost dashboard.

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Model Selection

Zed lets you assign models to different AI features. Use Bifrost model IDs in provider/model format to access any configured provider:
  • Use powerful models like openai/gpt-5 or anthropic/claude-sonnet-4-5-20250929 for complex code generation and refactoring
  • Use fast models like groq/llama-3.3-70b-versatile for quick completions and inline suggestions

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Using Multiple Providers

Bifrost routes requests to the correct provider based on the model name. Use the provider/model-name format to access any configured provider through the single OpenAI-compatible endpoint: 
anthropic/claude-sonnet-4-5-20250929
openai/gpt-5
gemini/gemini-2.5-pro
mistral/mistral-large-latest

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Supported Providers

Bifrost supports the following providers with the provider/model-name format: openai, azure, gemini, vertex, bedrock, mistral, groq, cerebras, cohere, perplexity, xai, ollama, openrouter, huggingface, nebius, parasail, replicate, vllm, sgl
Non-native models must support tool use for Zed’s AI features (code actions, refactoring) to work properly. Models without tool use support will only work for basic chat and completions.
Zed connects to Bifrost via a single OpenAI-compatible endpoint. Bifrost handles routing to the correct provider based on the model name — no per-provider configuration needed.

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Observability

All Zed requests through Bifrost are logged. Monitor them at http://localhost:8080/logs — filter by provider, model, or search through conversation content to track usage.

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Next Steps