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To connect an AI client to an OpenAI-compatible endpoint, enter the endpoint’s base URL, the API key it expects, and the model ID it serves. The setup location and supported features differ by client. The 12 entries below are configuration paths, not 12 distinct products: Zed appears twice because it supports both a settings screen and a settings-file example.
Before you configure a client
Get the endpoint’s documented base URL, authentication requirements, and exact model ID from the service or local server you plan to use. Follow its URL instructions exactly: some endpoints expect a base URL ending in /v1, while others use a different route structure. Do not add /v1 twice.
Check which API protocol the endpoint accepts. “OpenAI-compatible” describes an API surface; it does not guarantee that every OpenAI feature or protocol is implemented. A client may offer Chat Completions, Responses, or both, and the endpoint must support the protocol the client sends.
- Base URL: the endpoint’s documented API address, with the correct route prefix.
- API key: a valid key for hosted services, or whatever the local server’s documentation specifies. Some local servers allow a blank key or a placeholder.
- Model ID: the exact identifier the endpoint expects. It may differ from a familiar commercial model name.
12 AI client configuration paths
These examples cover the configuration forms identified in the available product and integration guidance. Except where a specific UI path or setting is stated, labels and workflows may change between versions; confirm the current form in the client’s own documentation before applying it.
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| Client or configuration path | Where to configure it | What to enter or check |
|---|---|---|
| Open WebUI | Settings → Admin → Connections → Manage OpenAI API Connections → Add Connection | Enter the URL and API key. If model discovery is unavailable, manually allowlist the model ID. |
| Zed provider settings | Agent Settings → LLM Providers → Add Provider | Set a provider name, API URL, model ID, and context window. Review the model’s capability settings as well. |
| LibreChat | Configure a custom endpoint in librechat.yaml |
The integration example uses baseURL and apiKey. Check the current LibreChat schema and endpoint structure. |
| AnythingLLM | Select “Generic OpenAI” in the provider setup | Set base_url and api_key; confirm the current field labels in your version. |
| Dify | Select an OpenAI-API-compatible model provider | Set base_url and api_key in the model-provider workflow. |
| Continue | The cited example uses ~/.continue/config.json |
Its example fields are provider set to openai, apiBase, apiKey, and model. Check your installed version’s configuration format. |
| Cline | Choose API Provider = “OpenAI Compatible” | Enter the endpoint URL and key; check the current UI for the model field and any model-specific requirements. |
| Aider | Use command-line options | The documented example pattern is aider --openai-api-base <base-url> --openai-api-key <api-key> --model openai/<model-id>. Substitute the actual values and avoid exposing a secret in shared shell history or logs. |
| Cursor | Settings → Models → Override OpenAI Base URL | Set the base URL and API key. Verify your Cursor plan and model restrictions in current product documentation. |
| n8n | Configure credentials and a custom Base URL in the OpenAI node | The exact fields can depend on the node and n8n version. |
| OpenAI Python SDK-based application | Initialize the SDK client in the application | Supply base_url, api_key, and the endpoint’s model ID. Follow the current SDK documentation for initialization syntax. |
| Zed settings-file configuration | Use the language_models.openai_compatible settings structure |
Set an endpoint URL such as https://example.com/v1 only when that matches your provider’s instructions, and specify an available model name and accurate context limit. |
The examples do not establish a canonical list of exactly 12 separate AI client products. They are 12 configuration paths, including two ways to configure Zed and one SDK integration pattern.
Configure and test the connection
- Confirm provider support. Find the client’s OpenAI-compatible or custom-provider option. If it only supports a fixed provider or endpoint, a custom URL may not be possible.
- Enter the documented base URL. Use the endpoint’s exact route structure. Do not guess or duplicate a suffix such as
/v1. - Set authentication. Enter the required API key. For a local server, follow its instructions rather than assuming that a hosted-service key is needed.
- Set the model ID. Select it from the client’s list if available; otherwise, use its manual model field or allowlist where supported.
- Send a plain chat request. Confirm a basic response before testing tools, streaming, image input, embeddings, or other capabilities.
- Test each needed feature separately. A successful chat does not prove that tool calls, streaming, images, or retrieval features work.
What the endpoint needs to support
For Open WebUI, POST /v1/chat/completions is the required route for core chat. Its documentation recommends GET /v1/models for model discovery; when that route is unavailable, the documented workaround is to add model IDs to the allowlist manually. Embeddings are needed for retrieval-augmented generation (RAG), while audio and image routes enable additional features.
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These route details are specific to Open WebUI’s guidance, not a universal contract for every client. Check both the client and endpoint documentation for the protocol and routes they support.
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- Model list is empty: the endpoint may not expose a compatible
/modelsroute, or the client may not discover it. Enter the model ID manually if the client permits it. - Chat works but tools fail: the endpoint may not implement tool calling in the format the client expects. Streaming tool-call formats can differ even between APIs described as OpenAI-compatible.
- Images or embeddings fail: these require routes and model capabilities beyond basic chat. Verify support on both sides.
- Protocol mismatch: the client may send Chat Completions while the endpoint expects the Responses API, or vice versa. Confirm the expected protocol before changing unrelated settings.
- Context errors: check the model’s actual context limit and any context-window value configured in the client.
Zed’s documented defaults for OpenAI-compatible models mark tools and chat completions as supported, but images and parallel tool calls as unsupported. Its settings allow capabilities to be adjusted, including disabling chat completions for a model that only works with the Responses API. These defaults illustrate why a connection alone is not proof of feature parity.
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Local servers, Docker, and network access
When a client runs in a container or directly in a browser, the address that reaches the model server may differ from the address that works on the host. Test reachability from the client’s runtime environment, not only from a browser on the host machine. Browser-direct connections may also require the endpoint to permit cross-origin requests (CORS).
Open WebUI in Docker connecting to a host server
If Open WebUI runs in Docker and the model server runs on the host, its documentation says to use host.docker.internal instead of localhost in the connection URL. A container’s localhost refers to that container, not automatically to the host.
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LM Studio with Open WebUI
For LM Studio’s local server, the documented Open WebUI URL is http://localhost:1234/v1. Start LM Studio’s Local Server first. The documentation says the API key may be blank or set to lm-studio.
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Troubleshoot in this order
- Compare the base URL and route suffix with the endpoint’s official instructions.
- Check network access from the client process or container; for browser-direct requests, check CORS as well.
- Determine whether the endpoint exposes a model-list route. If discovery fails, enter or allowlist the exact model ID manually where possible.
- Test a basic chat request before trying tools or other advanced features.
- Confirm that the client’s API protocol matches the endpoint’s supported protocol.
- Compare support for streaming, tool calls, images, embeddings, and context limits on both sides.
- Keep API keys out of public examples, screenshots, and shared configuration files.
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