A collection of Python SDKs that enable developers to build and deploy AI agents to the UiPath Cloud Platform using different agent frameworks. These packages provide programmatic interaction with UiPath Cloud Platform services and human-in-the-loop (HITL) semantics through Action Center integration.
All packages are extensions to the UiPath Python SDK and implement the UiPath Runtime Protocol.
Build agents using the LlamaIndex framework with support for RAG, multiple LLM providers, and state persistence.
- Installation:
pip install uipath-llamaindex - Documentation: packages/uipath-llamaindex/
- Samples: packages/uipath-llamaindex/samples/
Check out these sample projects to see the SDK in action.
- Python 3.11 or higher
- UiPath Automation Cloud account
Choose the agent framework that best fits your needs and follow the installation instructions for that package:
- LlamaIndex: For RAG applications and stateful workflows → See LlamaIndex documentation
- OpenAI Agents: For multi-agent systems → See OpenAI Agents documentation
- Quick Start Guide
- LlamaIndex Package Documentation
- OpenAI Agents Package Documentation
- Sample Projects
This repository is organized as a UV workspace with multiple packages:
uipath-llamaindex-python/
├── packages/
│ ├── uipath-llamaindex/ # LlamaIndex runtime
│ └── uipath-openai-agents/ # OpenAI Agents runtime
├── docs/ # Shared documentation
└── pyproject.toml # Workspace configuration
This repository uses UV for workspace management:
# Install UV
curl -LsSf https://astral.sh/uv/install.sh | sh
# Clone the repository
git clone https://github.com/UiPath/uipath-llamaindex-python.git
cd uipath-llamaindex-python
# Install all dependencies
uv sync --all-extras
# Run tests for all packages
uv run pytest
# Build a specific package
uv build --package uipath-llamaindex
uv build --package uipath-openai-agentsCheck out uipath-dev - an interactive terminal application for building, testing, and debugging UiPath Python runtimes, agents, and automation scripts.
Please read our contribution guidelines before submitting a pull request.
A huge thank-you to the open-source community and the maintainers of the libraries that make this project possible:
- LlamaIndex for providing a powerful framework for building stateful LLM applications
- OpenAI for the Agents framework and APIs
- OpenInference for observability and instrumentation support
- Pydantic for reliable, typed configuration and validation
See LICENSE for details.