uipath-llm-client 1.9.5

UiPath LLM Client

UiPath LLM Client

A Python client for interacting with UiPath's LLM services. This package provides both a low-level HTTP client and a LangChain integration for accessing LLMs through UiPath's infrastructure.

Architecture Overview

This repository is organized as a monorepo with the following packages:

• uipath_llm_client (root): Core HTTP client with authentication, retry logic, and request handling
• uipath_langchain_client (packages/): LangChain-compatible chat models and embeddings

Supported Backends

The client supports two UiPath backends:

Backend	Description	Default
AgentHub	UiPath's AgentHub infrastructure with automatic CLI-based authentication	Yes
LLMGateway	UiPath's LLM Gateway with S2S authentication	No

Supported Providers

Provider	Chat Models	Embeddings	Vendor Type
OpenAI/Azure	GPT-4o, GPT-4, etc.	text-embedding-3-large/small	openai
Google	Gemini 2.5, Gemini 2.0, etc.	text-embedding-004	vertexai
Anthropic	Claude Sonnet 4.5, etc.	-	awsbedrock, vertexai
AWS Bedrock	Claude, Titan, etc.	Titan Embeddings, etc.	awsbedrock
Fireworks AI	Various open-source models	Various	openai
Azure AI	Various Azure AI models	Various	azure

Installation

Using pip

# Base installation (core client only)
pip install uipath-llm-client

# With optional provider extras for passthrough mode
pip install "uipath-llm-client[openai]"      # OpenAI/Azure OpenAI models
pip install "uipath-llm-client[google]"      # Google Gemini models
pip install "uipath-llm-client[anthropic]"   # Anthropic Claude models
pip install "uipath-llm-client[all]"         # All of the above

For LangChain support, use the separate package: pip install uipath-langchain-client.

Using uv

1. Add the custom index to your pyproject.toml:

[[tool.uv.index]]
name = "uipath"
url = "https://uipath.pkgs.visualstudio.com/_packaging/ml-packages/pypi/simple/"
publish-url = "https://uipath.pkgs.visualstudio.com/_packaging/ml-packages/pypi/upload/"

2. Install the packages:

# Core client
uv add uipath-llm-client

# LangChain integration with all providers
uv add "uipath-langchain-client[all]"

Configuration

Platform Backend (AgentHub / Orchestrator)

The Platform backend uses the UiPath CLI for authentication. Both "agenthub" (default) and "orchestrator" share the same settings — the EndpointManager selects the correct URL paths automatically.

# Authenticate via CLI (populates .uipath/.auth.json)
uv run uipath auth login

# Or set environment variables directly
export UIPATH_URL="https://cloud.uipath.com/org/tenant"
export UIPATH_ORGANIZATION_ID="your-org-id"
export UIPATH_TENANT_ID="your-tenant-id"
export UIPATH_ACCESS_TOKEN="your-access-token"

# Optional: select backend (default: "agenthub")
export UIPATH_LLM_SERVICE="agenthub"   # or "orchestrator"

LLMGateway Backend

To use the LLMGateway backend, set the following environment variables:

# Select the backend
export UIPATH_LLM_SERVICE="llmgateway"

# Required configuration
export LLMGW_URL="https://your-llmgw-url.com"
export LLMGW_SEMANTIC_ORG_ID="your-org-id"
export LLMGW_SEMANTIC_TENANT_ID="your-tenant-id"
export LLMGW_REQUESTING_PRODUCT="your-product-name"
export LLMGW_REQUESTING_FEATURE="your-feature-name"

# Authentication (choose one)
export LLMGW_ACCESS_TOKEN="your-access-token"
# OR for S2S authentication:
export LLMGW_CLIENT_ID="your-client-id"
export LLMGW_CLIENT_SECRET="your-client-secret"

# Optional tracking
export LLMGW_SEMANTIC_USER_ID="your-user-id"

Settings Reference

PlatformSettings

Configuration settings for UiPath Platform client requests. PlatformSettings is a unified settings class that serves both AgentHub and Orchestrator backends — the EndpointManager transparently selects the correct endpoints based on service availability.

You choose between them via the UIPATH_LLM_SERVICE environment variable (or the backend parameter in get_default_client_settings()):

Value	Description
"agenthub" (default)	Routes requests through AgentHub endpoints
"orchestrator"	Routes requests through Orchestrator endpoints

Both values create a PlatformSettings instance — the difference is in how EndpointManager resolves the URL paths.

from uipath.llm_client.settings import get_default_client_settings, PlatformSettings

# Option 1: Factory (reads UIPATH_LLM_SERVICE, defaults to "agenthub")
settings = get_default_client_settings()

# Option 2: Explicit backend
settings = get_default_client_settings(backend="agenthub")
settings = get_default_client_settings(backend="orchestrator")

# Option 3: Direct instantiation
settings = PlatformSettings()

AgentHub

AgentHub is the default backend. It routes LLM requests through UiPath's AgentHub service, which provides model discovery, routing, and tracing capabilities.

# Select the backend (default, can be omitted)
export UIPATH_LLM_SERVICE="agenthub"

# Core settings (populated automatically by `uipath auth login`)
export UIPATH_URL="https://cloud.uipath.com/org/tenant"
export UIPATH_ORGANIZATION_ID="your-org-id"
export UIPATH_TENANT_ID="your-tenant-id"
export UIPATH_ACCESS_TOKEN="your-access-token"

# Optional: AgentHub configuration for discovery (default: "agentsruntime")
export UIPATH_AGENTHUB_CONFIG="agentsruntime"

# Optional: tracing
export UIPATH_PROCESS_KEY="your-process-key"
export UIPATH_JOB_KEY="your-job-key"

Orchestrator

Orchestrator uses the same PlatformSettings and authentication as AgentHub, but routes requests through Orchestrator endpoints instead.

# Select the backend
export UIPATH_LLM_SERVICE="orchestrator"

# Core settings (same as AgentHub)
export UIPATH_URL="https://cloud.uipath.com/org/tenant"
export UIPATH_ORGANIZATION_ID="your-org-id"
export UIPATH_TENANT_ID="your-tenant-id"
export UIPATH_ACCESS_TOKEN="your-access-token"

# Optional: tracing
export UIPATH_PROCESS_KEY="your-process-key"
export UIPATH_JOB_KEY="your-job-key"

PlatformSettings Attributes

Attribute	Environment Variable	Type	Default	Description
access_token	UIPATH_ACCESS_TOKEN	SecretStr | None	None	Access token for authentication (populated by uipath auth login)
base_url	UIPATH_URL	str | None	None	Base URL of the UiPath Platform API
tenant_id	UIPATH_TENANT_ID	str | None	None	Tenant ID for request routing
organization_id	UIPATH_ORGANIZATION_ID	str | None	None	Organization ID for request routing
agenthub_config	UIPATH_AGENTHUB_CONFIG	str | None	"agentsruntime"	AgentHub configuration for discovery
process_key	UIPATH_PROCESS_KEY	str | None	None	Process key for tracing
job_key	UIPATH_JOB_KEY	str | None	None	Job key for tracing

Authentication behavior:

• All four core fields (access_token, base_url, tenant_id, organization_id) are required
• Run uipath auth login to populate them automatically via the UiPath CLI
• The access token is validated against the local .uipath/.auth.json file
• Token refresh is handled automatically using the refresh token from the auth file

LLMGatewaySettings

Configuration settings for LLM Gateway client requests. These settings control routing, authentication, and tracking for requests to LLM Gateway.

from uipath.llm_client.settings import LLMGatewaySettings

settings = LLMGatewaySettings(
    base_url="https://your-llmgw-url.com",
    org_id="your-org-id",
    tenant_id="your-tenant-id",
    requesting_product="your-product",
    requesting_feature="your-feature",
    client_id="your-client-id",           # For S2S auth
    client_secret="your-client-secret",   # For S2S auth
)

Attribute	Environment Variable	Type	Required	Description
base_url	LLMGW_URL	str	Yes	Base URL of the LLM Gateway
org_id	LLMGW_SEMANTIC_ORG_ID	str	Yes	Organization ID for request routing
tenant_id	LLMGW_SEMANTIC_TENANT_ID	str	Yes	Tenant ID for request routing
requesting_product	LLMGW_REQUESTING_PRODUCT	str	Yes	Product name making the request (for tracking)
requesting_feature	LLMGW_REQUESTING_FEATURE	str	Yes	Feature name making the request (for tracking)
access_token	LLMGW_ACCESS_TOKEN	SecretStr | None	Conditional	Access token for authentication
client_id	LLMGW_CLIENT_ID	SecretStr | None	Conditional	Client ID for S2S authentication
client_secret	LLMGW_CLIENT_SECRET	SecretStr | None	Conditional	Client secret for S2S authentication
user_id	LLMGW_SEMANTIC_USER_ID	str | None	No	User ID for tracking and billing
action_id	LLMGW_ACTION_ID	str | None	No	Action ID for tracking
operation_code	LLMGW_OPERATION_CODE	str | None	No	Operation code to identify BYO models
additional_headers	LLMGW_ADDITIONAL_HEADERS	Mapping[str, str]	No	Additional custom headers to include in requests

Authentication behavior:

• Either access_token OR both client_id and client_secret must be provided
• S2S authentication uses client_id/client_secret to obtain tokens automatically

Usage Examples

Quick Start with Direct Client Classes

The simplest way to get started - settings are automatically loaded from environment variables:

from uipath_langchain_client.clients.openai.chat_models import UiPathAzureChatOpenAI

# No settings needed - uses defaults from environment (AgentHub backend)
chat = UiPathAzureChatOpenAI(model="gpt-4o-2024-11-20")
response = chat.invoke("What is the capital of France?")
print(response.content)

Using Different Providers

from uipath_langchain_client.clients.openai.chat_models import UiPathAzureChatOpenAI
from uipath_langchain_client.clients.google.chat_models import UiPathChatGoogleGenerativeAI
from uipath_langchain_client.clients.anthropic.chat_models import UiPathChatAnthropic
from uipath_langchain_client.clients.openai.embeddings import UiPathAzureOpenAIEmbeddings

# OpenAI/Azure models
openai_chat = UiPathAzureChatOpenAI(model="gpt-4o-2024-11-20")
response = openai_chat.invoke("Hello!")
print(response.content)

# Google Gemini models
gemini_chat = UiPathChatGoogleGenerativeAI(model="gemini-2.5-flash")
response = gemini_chat.invoke("Hello!")
print(response.content)

# Anthropic Claude models (via AWS Bedrock)
claude_chat = UiPathChatAnthropic(model="anthropic.claude-sonnet-4-5-20250929-v1:0", vendor_type="awsbedrock")
response = claude_chat.invoke("Hello!")
print(response.content)

# Embeddings
embeddings = UiPathAzureOpenAIEmbeddings(model="text-embedding-3-large")
vectors = embeddings.embed_documents(["Hello world", "How are you?"])
print(f"Generated {len(vectors)} embeddings of dimension {len(vectors[0])}")

Using Factory Functions (Auto-Detect Vendor)

Factory functions automatically detect the model vendor but require settings to be passed:

from uipath_langchain_client import get_chat_model, get_embedding_model
from uipath.llm_client.settings import get_default_client_settings

settings = get_default_client_settings()

# Create a chat model - vendor is auto-detected from model name
chat_model = get_chat_model(model_name="gpt-4o-2024-11-20", client_settings=settings)
response = chat_model.invoke("What is the capital of France?")
print(response.content)

# Create an embeddings model
embeddings_model = get_embedding_model(model_name="text-embedding-3-large", client_settings=settings)
vectors = embeddings_model.embed_documents(["Hello world", "How are you?"])

Using the Normalized API (Provider-Agnostic)

The normalized API provides a consistent interface across all LLM providers:

from uipath_langchain_client import get_chat_model
from uipath.llm_client.settings import get_default_client_settings

settings = get_default_client_settings()

# Use normalized API for provider-agnostic calls
chat_model = get_chat_model(
    model_name="gpt-4o-2024-11-20",
    client_settings=settings,
    client_type="normalized",
)

# Works the same way regardless of the underlying provider
response = chat_model.invoke("Explain quantum computing in simple terms.")
print(response.content)

Streaming Responses

All chat models support streaming for real-time output:

from uipath_langchain_client.clients.openai.chat_models import UiPathAzureChatOpenAI

chat_model = UiPathAzureChatOpenAI(model="gpt-4o-2024-11-20")

for chunk in chat_model.stream("Write a short poem about coding."):
    print(chunk.content, end="", flush=True)
print()

Async Operations

For async/await support:

import asyncio
from uipath_langchain_client.clients.openai.chat_models import UiPathAzureChatOpenAI

async def main():
    chat_model = UiPathAzureChatOpenAI(model="gpt-4o-2024-11-20")
    
    # Async invoke
    response = await chat_model.ainvoke("What is 2 + 2?")
    print(response.content)
    
    # Async streaming
    async for chunk in chat_model.astream("Tell me a joke."):
        print(chunk.content, end="", flush=True)
    print()

asyncio.run(main())

Tool/Function Calling

Use tools with LangChain's standard interface:

from uipath_langchain_client.clients.openai.chat_models import UiPathAzureChatOpenAI
from langchain_core.tools import tool

@tool
def get_weather(city: str) -> str:
    """Get the current weather for a city."""
    return f"The weather in {city} is sunny and 72°F."

@tool
def calculate(expression: str) -> str:
    """Evaluate a mathematical expression."""
    return str(eval(expression))

chat_model = UiPathAzureChatOpenAI(model="gpt-4o-2024-11-20")

# Bind tools to the model
model_with_tools = chat_model.bind_tools([get_weather, calculate])

# The model can now use tools
response = model_with_tools.invoke("What's the weather in Paris?")
print(response.tool_calls)

Using with LangChain Agents

Integrate with LangChain's agent framework:

from uipath_langchain_client.clients.openai.chat_models import UiPathAzureChatOpenAI
from langchain_core.tools import tool
from langgraph.prebuilt import create_react_agent

@tool
def search(query: str) -> str:
    """Search for information."""
    return f"Search results for: {query}"

chat_model = UiPathAzureChatOpenAI(model="gpt-4o-2024-11-20")
agent = create_react_agent(chat_model, [search])

# Run the agent
result = agent.invoke({"messages": [("user", "Search for Python tutorials")]})
print(result["messages"][-1].content)

Native SDK Wrappers (Without LangChain)

The core uipath_llm_client package provides thin wrappers around native vendor SDKs. These are drop-in replacements that route requests through UiPath's infrastructure while preserving the original SDK's interface:

from uipath.llm_client.clients.openai import UiPathOpenAI, UiPathAzureOpenAI

# Drop-in replacement for openai.OpenAI — routes through UiPath
client = UiPathOpenAI(model_name="gpt-4o-2024-11-20")
response = client.chat.completions.create(
    model="gpt-4o-2024-11-20",
    messages=[{"role": "user", "content": "Hello!"}],
)
print(response.choices[0].message.content)

# Azure OpenAI variant
azure_client = UiPathAzureOpenAI(model_name="gpt-4o-2024-11-20")

from uipath.llm_client.clients.anthropic import UiPathAnthropic

# Drop-in replacement for anthropic.Anthropic
client = UiPathAnthropic(model_name="anthropic.claude-sonnet-4-5-20250929-v1:0")
response = client.messages.create(
    model="anthropic.claude-sonnet-4-5-20250929-v1:0",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Hello!"}],
)
print(response.content[0].text)

from uipath.llm_client.clients.google import UiPathGoogle

# Drop-in replacement for google.genai.Client
client = UiPathGoogle(model_name="gemini-2.5-flash")
response = client.models.generate_content(
    model="gemini-2.5-flash",
    contents="Hello!",
)
print(response.text)

All native SDK wrappers are available in sync and async variants:

Class	SDK	Description
UiPathOpenAI / UiPathAsyncOpenAI	openai.OpenAI	OpenAI models (BYO)
UiPathAzureOpenAI / UiPathAsyncAzureOpenAI	openai.AzureOpenAI	Azure OpenAI models
UiPathAnthropic / UiPathAsyncAnthropic	anthropic.Anthropic	Anthropic models
UiPathAnthropicBedrock / UiPathAsyncAnthropicBedrock	anthropic.AnthropicBedrock	Anthropic via AWS Bedrock
UiPathAnthropicVertex / UiPathAsyncAnthropicVertex	anthropic.AnthropicVertex	Anthropic via Vertex AI
UiPathAnthropicFoundry / UiPathAsyncAnthropicFoundry	anthropic.AnthropicFoundry	Anthropic via Azure Foundry
UiPathGoogle	google.genai.Client	Google Gemini models

Low-Level HTTP Client

For completely custom HTTP requests, use the low-level HTTPX client directly:

from uipath.llm_client import UiPathHttpxClient
from uipath.llm_client.settings import UiPathAPIConfig, get_default_client_settings

settings = get_default_client_settings()

# Create a low-level HTTP client with UiPath auth and routing
client = UiPathHttpxClient(
    base_url=settings.build_base_url(model_name="gpt-4o-2024-11-20"),
    auth=settings.build_auth_pipeline(),
    headers=settings.build_auth_headers(model_name="gpt-4o-2024-11-20"),
    model_name="gpt-4o-2024-11-20",
    api_config=UiPathAPIConfig(
        api_type="completions",
        client_type="passthrough",
        vendor_type="openai",
        api_flavor="chat-completions",
    ),
    max_retries=2,
)

# Make a raw HTTP request
response = client.post(
    "/chat/completions",
    json={
        "model": "gpt-4o-2024-11-20",
        "messages": [{"role": "user", "content": "Hello!"}],
        "max_tokens": 100,
    },
)
response.raise_for_status()
print(response.json())

Custom Configuration

Pass custom settings when you need more control:

from uipath_langchain_client.clients.openai.chat_models import UiPathAzureChatOpenAI
from uipath.llm_client.settings import PlatformSettings
from uipath.llm_client.utils.retry import RetryConfig

# Custom settings for Platform (AgentHub/Orchestrator)
settings = PlatformSettings()

# With retry configuration
retry_config: RetryConfig = {
    "initial_delay": 2.0,
    "max_delay": 60.0,
    "exp_base": 2.0,
    "jitter": 1.0,
}

chat_model = UiPathAzureChatOpenAI(
    model="gpt-4o-2024-11-20",
    client_settings=settings,
    max_retries=3,
    retry_config=retry_config,
)

Switching Between Backends

from uipath_langchain_client.clients.openai.chat_models import UiPathAzureChatOpenAI
from uipath.llm_client.settings import get_default_client_settings

# Explicitly specify the backend
agenthub_settings = get_default_client_settings(backend="agenthub")
llmgw_settings = get_default_client_settings(backend="llmgateway")

chat = UiPathAzureChatOpenAI(model="gpt-4o-2024-11-20", client_settings=llmgw_settings)

# Or use environment variable (no code changes needed)
# export UIPATH_LLM_SERVICE="llmgateway"

Using LLMGatewaySettings Directly

You can instantiate LLMGatewaySettings directly for full control over configuration:

With Direct Client Classes:

from uipath_langchain_client.clients.openai.chat_models import UiPathAzureChatOpenAI
from uipath_langchain_client.clients.google.chat_models import UiPathChatGoogleGenerativeAI
from uipath_langchain_client.clients.openai.embeddings import UiPathAzureOpenAIEmbeddings
from uipath.llm_client.settings import LLMGatewaySettings

# Create LLMGatewaySettings with explicit configuration
settings = LLMGatewaySettings(
    base_url="https://your-llmgw-url.com",
    org_id="your-org-id",
    tenant_id="your-tenant-id",
    requesting_product="my-product",
    requesting_feature="my-feature",
    client_id="your-client-id",
    client_secret="your-client-secret",
    user_id="optional-user-id",  # Optional: for tracking
)

# Use with OpenAI/Azure chat model
openai_chat = UiPathAzureChatOpenAI(
    model="gpt-4o-2024-11-20",
    settings=settings,
)
response = openai_chat.invoke("Hello!")
print(response.content)

# Use with Google Gemini
gemini_chat = UiPathChatGoogleGenerativeAI(
    model="gemini-2.5-flash",
    settings=settings,
)
response = gemini_chat.invoke("Hello!")
print(response.content)

# Use with embeddings
embeddings = UiPathAzureOpenAIEmbeddings(
    model="text-embedding-3-large",
    settings=settings,
)
vectors = embeddings.embed_documents(["Hello world"])

With Factory Methods:

from uipath_langchain_client import get_chat_model, get_embedding_model
from uipath.llm_client.settings import LLMGatewaySettings

# Create LLMGatewaySettings
settings = LLMGatewaySettings(
    base_url="https://your-llmgw-url.com",
    org_id="your-org-id",
    tenant_id="your-tenant-id",
    requesting_product="my-product",
    requesting_feature="my-feature",
    client_id="your-client-id",
    client_secret="your-client-secret",
)

# Factory auto-detects vendor from model name
chat_model = get_chat_model(
    model_name="gpt-4o-2024-11-20",
    client_settings=settings,
)
response = chat_model.invoke("What is the capital of France?")
print(response.content)

# Use normalized API for provider-agnostic interface
normalized_chat = get_chat_model(
    model_name="gemini-2.5-flash",
    client_settings=settings,
    client_type="normalized",
)
response = normalized_chat.invoke("Explain quantum computing.")
print(response.content)

# Embeddings with factory
embeddings = get_embedding_model(
    model_name="text-embedding-3-large",
    client_settings=settings,
)
vectors = embeddings.embed_documents(["Hello", "World"])

Bring Your Own (BYO) Model Connections

If you have enrolled your own model deployment into UiPath's LLMGateway, you can use it by providing your BYO connection ID. This allows you to route requests through LLMGateway to your custom-enrolled models.

from uipath_langchain_client.clients.openai.chat_models import UiPathAzureChatOpenAI

# Use your BYO connection ID from LLMGateway enrollment
chat = UiPathAzureChatOpenAI(
    model="your-custom-model-name",
    byo_connection_id="your-byo-connection-id",  # UUID from LLMGateway enrollment
)

response = chat.invoke("Hello from my custom model!")
print(response.content)

This works with any client class:

from uipath_langchain_client.clients.google.chat_models import UiPathChatGoogleGenerativeAI
from uipath_langchain_client.clients.openai.embeddings import UiPathAzureOpenAIEmbeddings

# BYO chat model
byo_chat = UiPathChatGoogleGenerativeAI(
    model="my-custom-gemini",
    byo_connection_id="f1d29b49-0c7b-4c01-8bc4-fc1b7d918a87",
)

# BYO embeddings model
byo_embeddings = UiPathAzureOpenAIEmbeddings(
    model="my-custom-embeddings",
    byo_connection_id="a2e38c51-1d8a-5e02-9cd5-ge2c8e029b98",
)

Error Handling

The client provides a hierarchy of typed exceptions for handling API errors. All exceptions extend UiPathAPIError (which extends httpx.HTTPStatusError):

from uipath.llm_client import (
    UiPathAPIError,
    UiPathAuthenticationError,
    UiPathRateLimitError,
    UiPathNotFoundError,
)
from uipath_langchain_client.clients.openai.chat_models import UiPathAzureChatOpenAI

chat = UiPathAzureChatOpenAI(model="gpt-4o-2024-11-20")

try:
    response = chat.invoke("Hello!")
except UiPathRateLimitError as e:
    print(f"Rate limited. Retry after: {e.retry_after} seconds")
except UiPathAuthenticationError:
    print("Authentication failed — check your credentials")
except UiPathAPIError as e:
    print(f"API error {e.status_code}: {e.message}")

Exception Reference

Exception	HTTP Status	Description
UiPathAPIError	Any	Base exception for all UiPath API errors
UiPathBadRequestError	400	Invalid request parameters
UiPathAuthenticationError	401	Invalid or expired credentials
UiPathPermissionDeniedError	403	Insufficient permissions
UiPathNotFoundError	404	Model or resource not found
UiPathConflictError	409	Request conflicts with current state
UiPathRequestTooLargeError	413	Request payload too large
UiPathUnprocessableEntityError	422	Request is well-formed but semantically invalid
UiPathRateLimitError	429	Rate limit exceeded (has retry_after property)
UiPathInternalServerError	500	Server-side error
UiPathServiceUnavailableError	503	Service temporarily unavailable
UiPathGatewayTimeoutError	504	Gateway timeout
UiPathTooManyRequestsError	529	Anthropic overload (too many requests)

UiPathAPIConfig Reference

The UiPathAPIConfig class controls how requests are routed through UiPath's infrastructure:

from uipath.llm_client.settings import UiPathAPIConfig

config = UiPathAPIConfig(
    api_type="completions",
    client_type="passthrough",
    vendor_type="openai",
    api_flavor="chat-completions",
    api_version="2025-03-01-preview",
)

Field	Type	Default	Description
api_type	"completions" | "embeddings" | None	None	Type of API call
client_type	"passthrough" | "normalized" | None	None	"passthrough" uses vendor-native APIs; "normalized" uses UiPath's unified API
vendor_type	str | None	None	LLM vendor identifier: "openai", "vertexai", "awsbedrock", "anthropic", "azure"
api_flavor	str | None	None	Vendor-specific API flavor (e.g., "chat-completions", "responses", "generate-content", "converse", "invoke", "anthropic-claude")
api_version	str | None	None	Vendor-specific API version (e.g., "2025-03-01-preview", "v1beta1")
freeze_base_url	bool	False	Prevents httpx from modifying the base URL (required for some vendor SDKs)

Advanced Configuration

SSL Configuration

The client supports custom SSL/TLS configuration through environment variables:

Environment Variable	Description
UIPATH_DISABLE_SSL_VERIFY	Set to "1", "true", "yes", or "on" to disable SSL verification (not recommended for production)
SSL_CERT_FILE	Path to a custom SSL certificate file
REQUESTS_CA_BUNDLE	Path to a custom CA bundle file
SSL_CERT_DIR	Path to a directory containing SSL certificate files

By default, the client uses truststore (if available) or falls back to certifi for SSL certificate verification.

Logging

Enable request/response logging by passing a logger instance:

import logging
from uipath_langchain_client.clients.openai.chat_models import UiPathAzureChatOpenAI

logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger("uipath_llm")

chat = UiPathAzureChatOpenAI(
    model="gpt-4o-2024-11-20",
    logger=logger,  # Enables request/response logging with timing
)
response = chat.invoke("Hello!")

The logger will record:

• Request start time and URL
• Response duration (in milliseconds)
• Error responses with status codes and body content

Default Headers

All requests automatically include the following default headers:

Header	Value	Description
X-UiPath-LLMGateway-TimeoutSeconds	295	Server-side timeout for LLM Gateway
X-UiPath-LLMGateway-AllowFull4xxResponse	true	Returns full error response bodies for 4xx errors

Authentication Auto-Refresh

Both AgentHub and LLMGateway authentication pipelines automatically handle token expiry:

• When a request receives a 401 Unauthorized response, the auth pipeline refreshes the token and retries the request
• Token refresh is handled transparently — no user intervention required
• Auth instances use the singleton pattern to reuse tokens across multiple client instances

Development

# Clone and install with dev dependencies
git clone https://github.com/UiPath/uipath-llm-client.git
cd uipath-llm-client
uv sync

# Run tests
uv run pytest

# Format and lint
uv run ruff format .
uv run ruff check .
uv run pyright

Testing

Tests use VCR.py to record and replay HTTP interactions. Cassettes (recorded responses) are stored in tests/cassettes.db (SQLite) via pytest-recording.

Important: Tests must pass locally before submitting a PR. The CI pipeline does not make any real API requests—it only runs tests using the pre-recorded cassettes.

Prerequisites:

• Install Git LFS: brew install git-lfs (macOS) or apt install git-lfs (Ubuntu)
• Initialize Git LFS: git lfs install
• Pull cassettes: git lfs pull

Running tests locally:

# Run all tests using cassettes (no API credentials required)
uv run pytest

# Run specific test files
uv run pytest tests/langchain/
uv run pytest tests/core/

Updating cassettes:

When adding new tests or modifying existing ones that require new API interactions:

1. Set up your environment with valid credentials (see Configuration)
2. Run the tests—VCR will record new interactions automatically
3. Commit the updated cassettes along with your code changes

Note: The CI pipeline validates that all tests pass using the committed cassettes. If your tests require new API calls, you must record and commit the corresponding cassettes for the pipeline to pass.

Project Structure

uipath-llm-client/
├── src/uipath/llm_client/              # Core package
│   ├── httpx_client.py                 # UiPathHttpxClient / UiPathHttpxAsyncClient
│   ├── clients/                        # Native SDK wrappers
│   │   ├── openai/                     # UiPathOpenAI, UiPathAzureOpenAI, etc.
│   │   ├── anthropic/                  # UiPathAnthropic, UiPathAnthropicBedrock, etc.
│   │   ├── google/                     # UiPathGoogle
│   │   ├── normalized/                 # UiPathNormalizedClient (provider-agnostic)
│   │   └── litellm/                    # UiPathLiteLLM (via LiteLLM)
│   ├── settings/                       # Backend-specific settings & auth
│   │   ├── base.py                     # UiPathBaseSettings, UiPathAPIConfig
│   │   ├── platform/                   # PlatformSettings, PlatformAuth
│   │   └── llmgateway/                 # LLMGatewaySettings, LLMGatewayS2SAuth
│   └── utils/                          # Exceptions, retry, logging, SSL
│       ├── exceptions.py               # UiPathAPIError hierarchy (12 classes)
│       ├── retry.py                    # RetryConfig, RetryableHTTPTransport
│       ├── logging.py                  # LoggingConfig
│       └── ssl_config.py              # SSL/TLS configuration
├── packages/
│   ├── uipath_langchain_client/        # LangChain integration
│   │   └── src/uipath_langchain_client/
│   │       ├── base_client.py          # UiPathBaseLLMClient mixin
│   │       ├── factory.py              # get_chat_model(), get_embedding_model()
│   │       └── clients/
│   │           ├── normalized/         # UiPathChat, UiPathEmbeddings
│   │           ├── openai/             # UiPathAzureChatOpenAI, UiPathChatOpenAI, etc.
│   │           ├── google/             # UiPathChatGoogleGenerativeAI, etc.
│   │           ├── anthropic/          # UiPathChatAnthropic
│   │           ├── vertexai/           # UiPathChatAnthropicVertex
│   │           ├── bedrock/            # UiPathChatBedrock, UiPathChatBedrockConverse
│   │           ├── fireworks/          # UiPathChatFireworks, UiPathFireworksEmbeddings
│   │           ├── litellm/           # UiPathChatLiteLLM, UiPathLiteLLMEmbeddings
│   │           └── azure/              # UiPathAzureAIChatCompletionsModel
└── tests/                              # Test suite with VCR cassettes (SQLite)

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contact

For any questions or issues, please contact the maintainers at UiPath GitHub Repository.