Automation Cloud admin guide

Last updated Feb 19, 2026

Configuring LLMs

Note: LLM configurations is available on the following licensing plans:

Unified Pricing: Enterprise Platform, Standard Platform, Basic Platform, App Test Platform Enterprise, App Test Platform Standard.
Flex: Advanced Platform, Flex Standard Platform.

The LLM configurations tab allows you to integrate your existing AI subscriptions while maintaining the governance framework provided by UiPath. You can:

Replace UiPath LLM subscription: Replace UiPath-managed subscriptions with your own, provided they match the same model family and version already supported by the UiPath product. This allows for seamless swapping of UiPath-managed models with your subscribed models.
Add your own LLM: Use any LLM that meets the product's compatibility criteria. To ensure smooth integration, your chosen LLM must pass a series of tests initiated through a probe call before it can be used within the UiPath ecosystem.

Configuring LLMs preserves most of the governance benefits of the AI Trust Layer, including policy enforcement via Automation Ops and detailed audit logs. However, model governance policies are specifically designed for UiPath-managed LLMs. This means that if you disable a particular model through an AI Trust Layer policy, the restriction only applies to the UiPath-managed version of that model. Your own configured models of the same type remain unaffected.

When leveraging the option to use your own LLM or subscription, keep the following points in mind:

Compatibility requirements: Your chosen LLM or subscription must align with the model family and version currently supported by the UiPath product.
Setup: Make sure you properly configure and maintain all required LLMs in the custom setup. If any component is missing, outdated, or incorrectly configured, your custom setup may cease to function. In such cases, the system will automatically revert to a UiPath-managed LLM to ensure continuity of service, unless UiPath LLMs are turned off through an Automation Ops policy.
Cost-saving: If your custom LLM setup is complete, correct, and meets all necessary requirements, you may be eligible for a Reduced Consumption Rate.

Setting up an LLM connection

LLM connections rely on Integration Service to establish the connection to your own models. You can create connections to the following providers:

Azure Open AI
Open AI
Amazon Bedrock
Google Vertex
Open AI V1 Compliant LLM – Use this option to connect to any LLM provider whose API follows the OpenAI V1 standard. For details, refer to the OpenAI V1 Compliant LLM connector documentation.

To set up a new connection, follow these steps:

Create a connection in Integration Service to your provider of choice. For connector-specific authentication details, see the Integration Service user guide.

Note: To prevent unauthorized access, create the Integration Service connection in a private, non-shared folder.
Navigate to Admin > AI Trust Layer > LLM Configurations.
Select the tenant and folder where you want to configure the connection.
Select Add configuration.
Select the Product and Feature.
Choose how you want to configure:
- Replace UiPath LLM subscription – Use your own connection instead of a UiPath-managed one.
- Add your own LLM – Add an additional LLM configuration managed entirely by you.
  Note: When configuring your own LLM, you can optionally restrict which large language models are available for use in your organization. If you want to ensure that only your custom models are used, you can disable UiPath-managed third-party models by applying an AI Trust Layer policy. Check the Models section in the AI Trust Layer policies documentation.
Set up the connection for Replace UiPath LLM subscription:
1. Folder – Select the folder where the configuration will be stored.
2. Replaceable LLM – From the dropdown, select the UiPath LLM you want to replace.
3. Connector – Select your connector (e.g., Microsoft Azure OpenAI).
4. Connection – Choose your Integration Service connection. If none are available, select Add new connection to be redirected to Integration Service.
5. LLM identifier – Enter the identifier for your model.
  - For Azure-hosted models, enter the model identifier.
  - For AWS Bedrock cross-region inference, enter the inference profile ID instead of the model ID.
Set up the connection for Add your own LLM:
1. Folder – Select the folder where the configuration will be stored.
2. Displayed (LLM) name – Provide an alias for your LLM.
3. Connector – Select your connector (e.g., Microsoft Azure OpenAI).
4. Connection – Choose your Integration Service connection.
5. LLM identifier – Enter the identifier for your model.
  - For Azure-hosted models, enter the model identifier.
  - For AWS Bedrock cross-region inference, enter the inference profile ID instead of the model ID.
Select Test configuration to check that the model is reachable and meets the required criteria.
UiPath can confirm reachability, verifying the exact model used is your responsibility.
If the test is successful, select Save to activate the connection.

Managing existing LLM connections

You can perform the following actions on your existing connections:

Check status – Verify the status of your Integration Service connection. This action ensures that the connection is active and functioning correctly.
Edit – Modify any parameters of your existing connection.
Disable – Temporarily suspend the connection. When disabled, the connection remains visible in your list but doesn't route any calls. You can re-enable the connection when needed.
Delete – Permanently remove the connection from your system. This action disables the connection and removes it from your list.

Configuring LLMs for your product

Each product supports specific large language models (LLMs) and versions. Use the table below to identify the supported models and versions for your product.

You can connect your own LLM using one of the following providers: Amazon Web Services, Google Vertex, Microsoft Azure OpenAI, or OpenAI V1 Compliant. Follow the steps outlined in the previous section to create a connection.

Product	Feature	LLM	Version
Agents¹	Design, Evaluate & Deploy	Anthropic	anthropic.claude-3.5-sonnet-20240620-v1:0 anthropic.claude-3.5-sonnet-20241022-v2:0 anthropic.claude-3.7-sonnet-20250219-v1:0 anthropic.claude-3-haiku-20240307-v1:0
Agents¹	Design, Evaluate & Deploy	OpenAI	gpt-4o-2024-05-13 gpt-4o-2024-08-06 gpt-4o-2024-11-20 gpt-4o-mini-2025-04-14 gpt-4o-mini-2024-07-18
Autopilot for everyone	Chat	Anthropic	anthropic.claude-3.5-sonnet-20240620-v1:0 anthropic.claude-3.7-sonnet-20250219-v1:0
Autopilot for everyone	Chat	OpenAI	gpt-4o-mini-2024-07-18
GenAI Activities	Build, Test & Deploy	Anthropic	anthropic.claude-3-5-sonnet-20240620-v1:0 anthropic.claude-3-5-sonnet-20241022-v2:0 anthropic.claude-3-7-sonnet-20250219-v1:0 anthropic.claude-sonnet-4-20250514-v1:0 anthropic.claude-sonnet-4-5-20250929-v1:0 anthropic.claude-haiku-4-5-20251001-v1:0
		Google	gemini-2.0-flash-001 gemini-2.5-pro gemini-2.5-flash
		OpenAI	gpt-5-2025-08-07 gpt-5-mini-2025-08-07 gpt-5-nano-2025-08-07 gpt-5.1-2025-11-13 gpt-4o-2024-11-20 gpt-4o-mini-2024-07-18
Healing Agent	Workflow Recovery	Google	gemini-2.5-flash
Healing Agent	Workflow Recovery	OpenAI	gpt-4o-2024-08-06
UI Automation	ScreenPlay	Anthropic	anthropic.claude-sonnet-4-5-20250929-v1:0
		Google	gemini-2.5-flash
		OpenAI	gpt-4.1-mini-2025-04-14 gpt-4.1-2025-04-14 gpt-5-2025-08-07 gpt-5-mini-2025-08-07 computer-use-preview-2025-03-11
Test Manager	Autopilot (generate and manage tests)	Anthropic	anthropic.claude-3.7-sonnet-20250219-v1:0 (to be replaced with anthropic.claude-4.5-sonnet in March 2026)
		Google	gemini-2.5-pro gemini-2.5-flash
		OpenAI	gpt-4o-2024-11-20

¹ When configuring your model deployment for agents, ensure that your LLM supports the following capabilities:

Tool (function) calling – Your model must be able to call tools or functions during execution.
Disabling parallel tool calls – If supported by your LLM provider, the model should offer the option to disable parallel tool calls.

Note: When using custom models, the system cannot determine the model’s true token capacity. Agents default to a 4096 token limit, even if the underlying model supports a higher value. This behavior is intentional, as UiPath cannot infer token limits for customer-defined deployments.

On this page

Setting up an LLM connection
Managing existing LLM connections
Configuring LLMs for your product

Was this page helpful?

PREVIOUSManaging Autopilot for Everyone

NEXTGoverning contextual data for GenAI features

Support and Services

Get The Help You Need

UiPath Academy

Learning RPA - Automation Courses

UiPath Forum

UiPath Community Forum

Trust and Security

Cookies Policy