> ## Documentation Index > Fetch the complete documentation index at: https://docs.requesty.ai/llms.txt > Use this file to discover all available pages before exploring further. # Langdock > Connect Langdock to Requesty and give your workspace access to any model in the Requesty Model Library [Langdock](https://www.langdock.com) is an enterprise AI workspace for chat, agents, and workflows. It supports OpenAI-compatible providers, so you can point it at Requesty and use any model in the [Model Library](https://app.requesty.ai/model-list) from a single key, with Requesty analytics, approved-model lists, and fallback policies applied to every request. Setup has three parts: add a Requesty key to Langdock, add each model you want to expose as a custom model, and test it in chat. ## 1. Create a Requesty API key 1. Open the [API Keys page](https://app.requesty.ai/api-keys) in the Requesty platform. 2. Click **Create Key**, give it a name such as `Langdock`, and copy the key. Optionally restrict which models this key can use with [Approved Models](/features/approved-models) or an [Access List](/features/access-lists). Langdock only exposes the models you add manually in step 3, but limiting the key is good hygiene. ## 2. Add the key in Langdock 1. In Langdock go to **Settings > Models** and scroll down to **Keys**. 2. Click **Add key**. Langdock Settings > Models with the Add key button highlighted 3. Choose **OpenAI Compatible** as the provider type and fill in the form: | Field | Value | | - | - | | Key name | Any internal name, e.g. `Requesty` | | Base URL | `https://router.requesty.ai/v1` (or `https://router.eu.requesty.ai/v1` for EU-only routing) | | API Key | The Requesty key from step 1 | | Hosting Provider | `Requesty` (shown to your users) | | Custom headers | Leave empty | 4. Click **Save**. The key appears under **Keys** with the status `Not used` until a model references it. Langdock OpenAI Compatible key form filled in with the Requesty base URL ## 3. Add models as custom models Langdock does not fetch the model list from the provider. Every model you want to use across the organisation has to be added individually as a custom model. ### 3a. Open the custom model wizard In **Settings > Models**, select the **Custom** tab and click **Add custom model**. Custom tab in Langdock with the Add custom model button highlighted ### 3b. Set up the model manually The wizard opens on **Choose from configurations**, which lists Langdock's pre-configured providers. Ignore that list and click **set up manually** in the bottom right corner. Langdock model wizard with the set up manually link highlighted ### 3c. Configure the model Fill in the **Configure** step: | Field | Value | | - | - | | Type | **Completion** | | Model Name | Display name shown to users, e.g. `DeepSeek v4.1 Flash` | | Provider Model Name | The model ID exactly as you would call it on Requesty, e.g. `deepseek-v4.1-flash` | | Provider | **Other** | | Region | **Global** (or **EU** if you used the EU router URL) | Leave **Visible to users**, **Available in agents**, and **Advanced capability** enabled unless you want to restrict the model. Click **Continue**. Langdock Configure step with model name and provider model name filled in A bare name like `deepseek-v4.1-flash` is a [Managed Policy](/features/managed-policies): Requesty picks the best provider for that model on every request and handles fallbacks for you. Append `@eu` (e.g. `deepseek-v4.1-flash@eu`) to keep routing on EU-hosted endpoints. To pin a specific provider instead, use the `provider/model-name` ID from the [Model Library](https://app.requesty.ai/model-list), for example `anthropic/claude-sonnet-4-6`. Custom policies from your organisation work too with their `policy/` prefix. ### 3d. Configure the deployment and test On the **Deployment** step: 1. Under **API Key**, select the Requesty key you created in step 2. 2. Set **Model Name / ID** to the same Requesty model ID. 3. Leave **Tokens per minute limit** empty unless you want Langdock to rate-limit this model. 4. Click **Test & continue**. Langdock sends a test request through Requesty and moves to the **Review** step on success. Langdock Deployment step with the Requesty key selected Check the summary on the **Review** step and click **Save model**. Langdock Review step showing the DeepSeek v4.1 Flash model routed through the Requesty key Repeat this section for every model you want to offer. Each one reuses the same Requesty key, so you only manage a single credential. ## 4. Test in chat Open a new chat in Langdock, pick the model from the model selector, and send a message. The response is served through Requesty and shows up in your [Requesty logs](https://app.requesty.ai/logs) and [analytics](https://app.requesty.ai/analytics). Langdock chat using the DeepSeek v4.1 Flash model through Requesty ## Troubleshooting * **Test & continue fails**: confirm the **Model Name / ID** matches an entry in the [Model Library](https://app.requesty.ai/model-list) and that the key is allowed to use it under [Approved Models](/features/approved-models). * **401 Unauthorized**: the API key was pasted incorrectly or has been revoked. Edit the key under **Keys** and paste it again. * **Model missing from the chat selector**: make sure **Visible to users** is enabled on the model and that the deployment toggle is on. ## Resources * [Langdock Help Center](https://docs.langdock.com) * [Requesty Model Library](https://app.requesty.ai/model-list) * [Requesty API Keys](https://app.requesty.ai/api-keys) * [Requesty Platform](https://app.requesty.ai) This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.