The API supports:
- System LLM integrations: OpenAI, Azure OpenAI, Anthropic, Gemini, Amazon Bedrock, and Kore.ai XO GPT.
- Custom LLM integrations.
- Token usage limits and usage notifications.
- Partial updates for existing integrations.
- Deletion of configured integrations.
Path Parameters
Body Parameters
ForPUT, use the following parameters to configure or update an LLM Integration.
For
DELETE, use the following parameters to delete an LLM Integration configuration.
Sample Request for PUT
- In
dynamicConfig,name, anddescriptionshouldn’t be passed for system models. - To edit the name of an existing multi-instance integration (OpenAI, Azure OpenAI, Bedrock, Custom LLMs), pass the previous name as
previousName, and the latest name asintegrationName.
Sample Request for DELETE
- Only
integrationIdis required forkorexo,gemini, andanthropicintegrations.
Enabling Token Usage
- Use the following sample token usage payload to enable token usage.
- If you don’t want to enable token usage, use the following payload.
Request Body Examples
OpenAI- Use
openaias theintegrationId. - In OpenAI,
dynamicModelConfigis only for custom models. - To enable custom models in OpenAI, the following fields are mandatory for first-time creation:
type,name,modelId,desc. - To edit the existing integration name, you need to pass
previousName.
- Use
azureas theintegrationId. - To enable system and custom models in Azure OpenAI, use
dynamicModelConfig. dynamicModelConfigis mandatory when enabling Azure OpenAI for the first time.- You can save the config only when at least one of the models is enabled (
toggle: true). - In the payload, the
tenantis a subdomain. - To enable custom models in Azure OpenAI, the following fields are mandatory for first-time creation:
type,name,modelId,desc,toggle,deploymentId. - To enable system models in Azure OpenAI, the following fields are mandatory for first-time creation:
type,modelId,toggle,deploymentId. - The following are the supported system model IDs for Azure OpenAI integration:
- GPT-3.5 Turbo
- GPT-4
- GPT-4-32K
- GPT-4 Turbo
- GPT-4o
- GPT-4o-mini
- GPT-5.4
- GPT-5-mini
- GPT-5.1
- GPT-5.2
- GPT-5.4 Mini
- GPT-5.4 Nano
- Use
anthropicas theintegrationId. - No need to pass
integrationNamefor Anthropic integration. - To enable system and custom models in Anthropic, use
dynamicModelConfig. dynamicModelConfigis mandatory when enabling Anthropic for the first time.- To enable custom models in Anthropic, the following fields are mandatory for first-time creation:
type,name,modelId,desc,toggle. - To enable system models in Anthropic, the following fields are mandatory for first-time creation:
type,modelId,toggle. - The following are the supported system model IDs for Anthropic integration:
- Claude Haiku 4.5
- Claude Opus 4.6
- Claude Sonnet 4.5
- Claude Sonnet 4.6
- Use
geminias theintegrationId. - No need to pass
integrationNamefor Gemini integration. - To enable system and custom models in Gemini, use
dynamicModelConfig. dynamicModelConfigis mandatory when enabling Gemini for the first time.- To enable custom models in Gemini, the following fields are mandatory for first-time creation:
type,name,modelId,desc,toggle. - To enable system models in Gemini, the following fields are mandatory for first-time creation:
type,modelId,toggle. - The following are the supported system model IDs for Gemini integration:
- Gemini 2.5 Flash
- Gemini 2.5 Flash-Lite
- Gemini 2.5 Pro
- Gemini 3 Flash Preview
- Gemini 3.1 Pro Preview
- Use
amazon_bedrockas theintegrationId. - For Amazon Bedrock, provide the model configuration in the request payload to validate the model.
- Use
isTestCallto validate the Bedrock model configuration.
- Use
custom_llmas theintegrationId. - For Custom LLMs, provide the model configuration in the request payload to validate the model.
- Use
korexoas theintegrationId. - Use the following flags to enable the respective models:
textrephrase– Text Rephrasingconversationsummary– Summarizing Conversationaa_conversationsummary– Summarizing AgentAI ConversationvectorGeneration– EmbeddingsdialogGPT– DialogGPTanswerGeneration– Answer Generation