Langdock comes with prebuilt configurations for the most popular AI models. You can pick from this catalog and we fill in all the provider settings automatically. All you need to do is provide your model credentials, and we handle the rest.Prebuilt configurations are available for the most popular completion, image generation, and embedding models.
Navigate to Workspace Settings -> Models and click Add custom model.Select a completion model from the catalog. When you choose a pre-configured model, Langdock keeps all settings up to date. Pricing, context windows, and capabilities always reflect the latest from your provider.If your model isn’t in the catalog, you can Set up your model manually.
If you selected a pre-configured model, Langdock has already filled in the technical settings. You only need to decide how to make it available in your workspace. If you chose manual setup, you’ll configure everything yourself, from provider details to pricing and capabilities.Open the section below that matches your setup.
Pre-configured model
Langdock handles all the technical settings for you. The only decisions left are how you want to roll the model out to your workspace.
Set the model to invisible while you test it. Make it visible once you’re satisfied with how it responds.
Decide whether the model should be available when building assistants.
Mark it as a premium model if you don’t want users setting it as their personal default.
Use maintenance mode to pause usage without removing the model.
If you chose to set up the model manually, you now need to configure the technical parameters yourself. This includes general model info, connection settings, pricing, and capability flags that Langdock would otherwise manage for you.
Set up your model manually
Setting up a model manually gives you full control over every configuration parameter. Keep in mind that you are responsible for keeping these settings accurate. Langdock won’t automatically update things like context window size or pricing when a provider makes changes.General
Provider: the organization that built the model (e.g. OpenAI, Anthropic).
Provider model name: the model identifier used by your provider.
Region: where the model is hosted. Shown to users as EU, US, or Global.
Description (EN / DE): optional, max 150 characters each.
Configure
Context window size: the model’s context window in tokens.
Max output tokens: maximum tokens the model can generate per response.
Pricing
Token prices are used for usage tracking and cost reporting.
Input token price (USD / 1M tokens)
Output token price (USD / 1M tokens)
Cache read token price (USD / 1M tokens)
Cache write token price (USD / 1M tokens)
Capabilities
Image analysis: enable if the model supports image analysis.
Canvas: enable to allow the model to create documents using the canvas tool.
Supports tools: enable if the model supports tool calling.
Supports temperature: if disabled, the temperature parameter will not be sent to the model.
Always show reasoning: for models that natively produce reasoning output (e.g. o3, o4 Mini).
Use Responses API: use OpenAI’s Responses API instead of Chat Completions.
A deployment connects your model to a specific provider endpoint using a model key. Select a key you have already configured in Model Keys, or add a new one.
Key: select an existing model key or add a new one.
Model identifier: the deployment name or model ID as defined in your provider’s configuration.
Tokens per minute (TPM): optional. Limit how many tokens per minute can be processed through this deployment.
Once you have tested the model successfully, review the final settings before saving and making it available to your workspace.You have now added your own completion model to Langdock 🎉
Navigate to Workspace Settings -> Models and click Add custom model.Select an image generation model from the catalog. When you choose a pre-configured model, Langdock keeps all settings up to date. Pricing, context windows, and capabilities always reflect the latest from your provider.If your model isn’t in the catalog, you can Set up your model manually.
If you selected a pre-configured model, Langdock has already filled in the technical settings. You only need to decide how to make it available in your workspace. If you chose manual setup, you’ll configure everything yourself, from provider details to pricing and capabilities.Open the section below that matches your setup.
Pre-configured model
Langdock handles all the technical settings for you. You only need to set the region and decide how the model is available in your workspace.
Region: select where the model is hosted (EU, US, or Global). This is shown to users so they know where their data is processed.
Visible to users: when enabled, this model will be available for users in the workspace. Keep it off while you test it.
Max images per user: optional. Set a limit on how many images a user can generate within a time window (e.g. 50 per 3 hours).
If you chose to set up the model manually, you now need to configure the technical parameters yourself. This includes general model info, description, usage limits, and capability flags that Langdock would otherwise manage for you.
Set up your model manually
Setting up a model manually gives you full control over every configuration parameter. Keep in mind that you are responsible for keeping these settings accurate. Langdock won’t automatically update them when a provider makes changes.General
Model name: the display name shown to users.
Provider model name: the model identifier used by your provider.
Provider: the organization that built the model (e.g. OpenAI, Google).
Region: where the model is hosted. Shown to users as EU, US, or Global.
Visibility
Visible to users: when enabled, this model will be available for users in the workspace.
Description
Description (EN): optional, max 150 characters.
Description (DE): optional, max 150 characters.
Configure
Max images per user: optional. Set a limit on how many images a user can generate within a time window (e.g. 50 per 3 hours).
Advanced
Image generation: all image models can generate images by default.
Image editing: enable if the model supports editing existing images.
A deployment connects your model to a specific provider endpoint using a model key. Select a key you have already configured in Model Keys, or add a new one.
Key: select an existing model key or add a new one.
Model identifier: the deployment name or model ID as defined in your provider’s configuration.
Tokens per minute (TPM): optional. Limit how many tokens per minute can be processed through this deployment.
Once you have tested the model successfully, review the final settings before saving and making it available to your workspace.You have now added your own image generation model to Langdock 🎉
Navigate to Workspace Settings -> Models and click Add custom model.Select an embedding model from the catalog. When you choose a pre-configured model, Langdock keeps all settings up to date. Pricing, context windows, and capabilities always reflect the latest from your provider.If your model isn’t in the catalog, you can Set up your model manually.
If you selected a pre-configured model, Langdock has already filled in the technical settings. You only need to decide how to make it available in your workspace. If you chose manual setup, you’ll configure everything yourself, from provider details to pricing and capabilities.Open the section below that matches your setup.
Pre-configured model
Langdock handles all the technical settings for you. The only decision left is whether to make the model available to users.
Visible to users: when enabled, this model will be available for users in the workspace. Keep it off while you test it.
If you chose to set up the model manually, you now need to configure the technical parameters yourself. This includes general model info, connection settings, and capability flags that Langdock would otherwise manage for you.
Set up your model manually
Setting up a model manually gives you full control over every configuration parameter. Keep in mind that you are responsible for keeping these settings accurate. Langdock won’t automatically update them when a provider makes changes.Visibility
Visible to users: when enabled, this model will be available for users in the workspace.
Configure
Vector Similarity Threshold: minimum similarity score (0.1–1) required for embedding matches. Higher values return more relevant but fewer results. Defaults to 0.5.
A deployment connects your model to a specific provider endpoint using a model key. Select a key you have already configured in Model Keys, or add a new one.
Key: select an existing model key or add a new one.
Model identifier: the deployment name or model ID as defined in your provider’s configuration.
Once you have tested the model successfully, review the final settings before saving and making it available to your workspace.You have now added your own embedding model to Langdock 🎉
