obot-platform · calvinmclean · Apr 16, 2026 · Apr 16, 2026 · Apr 17, 2026 · Apr 21, 2026
diff --git a/apiclient/types/model.go b/apiclient/types/model.go
@@ -14,6 +14,7 @@ type ModelManifest struct {
 	Alias         string     `json:"alias,omitempty"`
 	Active        bool       `json:"active"`
 	Usage         ModelUsage `json:"usage"`
+	Dialect       string     `json:"dialect,omitempty"`
 }
 
 type ModelList List[Model]

diff --git a/apiclient/types/modelprovider.go b/apiclient/types/modelprovider.go
@@ -5,6 +5,9 @@ type CommonProviderMetadata struct {
 	IconDark    string `json:"iconDark,omitempty"`
 	Description string `json:"description,omitempty"`
 	Link        string `json:"link,omitempty"`
+	// Dialect specifies the LLM API format used by this provider
+	// (e.g. "AnthropicMessages", "OpenAIChatCompletions", "OpenAIResponses").
+	Dialect string `json:"dialect,omitempty"`
 }
 
 type CommonProviderStatus struct {

diff --git a/docs/docs/configuration/model-providers.md b/docs/docs/configuration/model-providers.md
@@ -17,7 +17,7 @@ Obot supports a variety of model providers, including:
 - Google
 
 **Enterprise**
-- [Azure OpenAI / Microsoft Foundry](#azure-openai-enterprise-only)
+- [Azure OpenAI / Microsoft Foundry](#azure-enterprise-only)
 - Amazon Bedrock
 - Google Vertex (Gemini models)
 
@@ -71,51 +71,70 @@ Setting a default model here does not automatically grant users access to it. Us
 
 ### Instructions for configuring specific providers
 
-#### Azure OpenAI (Enterprise only)
+#### Azure (Enterprise only)
 
-The Azure OpenAI model provider supports legacy Azure OpenAI resources. Microsoft Foundry works with API key authentication as well.
-
-This model provider supports two forms of authentication: API keys and Microsoft Entra.
+Obot supports two Azure providers, each with a different authentication method. These are compatible with both Azure OpenAI deployments and Foundry deployments.
 
 ##### API Key Authentication
 
-In the Azure OpenAI or Microsoft Foundry UI, you can find your API key after you have set up at least one deployment, as well as your endpoint URL.
-Both of these values are required when you configure this model provider in Obot.
+Use the **Azure** provider for API key-based authentication.
 
-Additionally, you must manually specify the names of all of your deployments, as the API key does not provide the ability to list them.
-The format is `name:type`, for example, `gpt-5.2:reasoning-llm`. The supported types are `llm`, `reasoning-llm`, `text-embedding`, and `image-generation`.
-If no type is specified, Obot will assume the type is `llm`.
+In the Azure portal, find your API key and endpoint URL after setting up at least one deployment — both are required.
 
-If the type specified is `llm` or none at all, and the deployment name starts with the name of a known reasoning model (such as `o3` or `gpt-5`), Obot
-will automatically treat it as a reasoning model.
+You must also specify deployment names. The format is a comma-separated list of deployment names, optionally as `model:deployment` pairs (e.g. `gpt-4o,gpt-4o-mini` or `gpt-4o:my-gpt4o,gpt-4o-mini:my-mini`).
 
-##### Microsoft Entra Authentication
+You can also optionally specify the API version (defaults to `2025-01-01-preview`).
 
-:::important
-At this time, Microsoft Entra authentication is only supported for Azure OpenAI deployments and not for the newer Microsoft Foundry deployments.
-:::
+##### Microsoft Entra ID Authentication
+
+Use the **Azure (Entra ID)** provider for service principal authentication via Microsoft Entra ID. Deployments are discovered automatically from the Azure Management API.
+
+###### 1. Create a service principal
+
+```bash
+az ad sp create-for-rbac --name "<sp-name>" \
+  --role "Cognitive Services OpenAI User" \
+  --scopes /subscriptions/<subscription-id>/resourceGroups/<resource-group>/providers/Microsoft.CognitiveServices/accounts/<account-name>
+```
+
+This outputs the `appId` (Client ID), `password` (Client Secret), and `tenant` (Tenant ID) needed below.
+
+###### 2. Find your resource details
+
+```bash
+az cognitiveservices account show \
+  --name <account-name> \
+  --resource-group <resource-group> \
+  --query "{endpoint:properties.endpoint, id:id}"
+```
 
-Instead of using an API key, you can set up a Microsoft Entra app registration as a service principal to use Azure OpenAI.
+###### 3. Configure the provider
 
-Obot requires the Client ID, Client Secret, and Tenant ID of the Entra app, as well as the Endpoint URL, Subscription ID, and Resource Group from Azure OpenAI/Microsoft Foundry.
+Obot requires:
+- **Azure Endpoint** — your Azure OpenAI endpoint URL (`https://<resource_name>.openai.azure.com`)
+- **Client ID** — the Entra app's application (client) ID
+- **Client Secret** — the Entra app's client secret
+- **Tenant ID** — the Entra app's tenant ID
+- **Subscription ID** — the Azure subscription ID containing the Cognitive Services account
+- **Resource Group** — the resource group containing the Cognitive Services account
+- **Account Name** — the Cognitive Services account name
 
-You do NOT need to manually specify your deployment names, as the Entra app's credentials will be sufficient to list them.
+You can also optionally specify the API version (defaults to `2025-01-01-preview`).
 
-After you have created your Entra app registration, you need to go to your Azure OpenAI resource in the Azure portal and add a new role assignment for the app registration, as a service principal.
-It needs to have the `Cognitive Services OpenAI User` role.
+The service principal requires at minimum the `Cognitive Services OpenAI User` or `Cognitive Services User` role on the account to read deployments. Deployments are discovered automatically — each deployment's base model name becomes the model ID exposed to Obot.
 
-See the [Microsoft docs](https://learn.microsoft.com/en-us/azure/ai-foundry/openai/how-to/role-based-access-control?view=foundry-classic#add-role-assignment-to-an-azure-openai-resource) for more details.
+See the [Microsoft docs](https://learn.microsoft.com/en-us/azure/foundry/foundry-models/how-to/configure-entra-id) for more details.
 
 #### Ollama
 
 [Ollama](https://ollama.ai/) allows you to run LLMs locally. Two configuration steps are required to use it with Obot:
 
 1. **Expose Ollama to the network** - By default, Ollama only binds to `127.0.0.1:11434`. Since Obot runs in a container, `localhost` addresses resolve to Obot's container, not your host. Set `OLLAMA_HOST=0.0.0.0` before starting Ollama, then use your host's IP address in the endpoint URL.
 
-2. **Use the OpenAI-compatible endpoint** - The endpoint must include the `/v1/` path:
+2. **Set the Ollama host**
    ```
-   http://<your-host-ip>:11434/v1/
+   http://<your-host-ip>:11434
    ```
-   Using `http://<host>:11434/` without `/v1/` will result in validation errors.
+   - If you are running Obot in Docker, you should use `http://host.docker.internal:11434`. For linux users, run Obot in Docker with this additional flag `--add-host=host.docker.internal:host-gateway` or use an alternative method of allowing the container to access the host network
 
 See [Ollama's FAQ](https://docs.ollama.com/faq) for platform-specific instructions on setting `OLLAMA_HOST`.
diff --git a/docs/docs/configuration/server-configuration.md b/docs/docs/configuration/server-configuration.md
@@ -44,8 +44,8 @@ The Obot server is configured via environment variables. The following configura
 | `OBOT_SERVER_AUDIT_LOGS_COMPRESS_FILE` | Controls whether or not to compress audit log files | `true` |
 | `OBOT_SERVER_AUDIT_LOGS_USE_PATH_STYLE` | Whether to use path style for S3 | - |
 | `OBOT_SERVER_MCPBASE_IMAGE` | Deploy MCP servers in the kubernetes cluster or using docker with this base image. | `ghcr.io/obot-platform/mcp-images/phat:v0.20.2` |
-| `OBOT_SERVER_MCPREMOTE_SHIM_BASE_IMAGE` | Deploy MCP remote shim servers in the cluster using this base image. | `ghcr.io/nanobot-ai/nanobot:v0.0.68` |
-| `OBOT_SERVER_NANOBOT_AGENT_IMAGE` | Deploy the Nanobot agent in the cluster using this image. | `ghcr.io/nanobot-ai/nanobot-agent:v0.0.68` |
+| `OBOT_SERVER_MCPREMOTE_SHIM_BASE_IMAGE` | Deploy MCP remote shim servers in the cluster using this base image. | `ghcr.io/nanobot-ai/nanobot:v0.0.69` |
+| `OBOT_SERVER_NANOBOT_AGENT_IMAGE` | Deploy the Nanobot agent in the cluster using this image. | `ghcr.io/nanobot-ai/nanobot-agent:v0.0.69` |
 | `OBOT_SERVER_MCPHTTPWEBHOOK_BASE_IMAGE` | Deploy MCP HTTP webhook servers in the cluster using this base image. | `ghcr.io/obot-platform/mcp-images/http-webhook-mcp-converter:v0.20.2` |
 | `OBOT_SERVER_MCPRUNTIME_BACKEND` | The runtime backend to use for running MCP servers: docker, kubernetes, or local. | `kubernetes` in the helm chart, `docker` otherwise |
 | `OBOT_SERVER_MCPCLUSTER_DOMAIN` | The cluster domain to use for MCP services. Only matters if `OBOT_SERVER_MCPBASE_IMAGE` is set. | `cluster.local` |