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Snapshot 0 · 2026-11-04T12:47:24Z · Python · Apache-2.0
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GitHub description at capture

Perform data science on data that remains in someone else's server
README at capture

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# PySyft v2

> **`syft` 0.10+ is the successor of `syft-client`.** `syft` is the sync engine (`import syft as sy`); datasets and jobs live in `syft-rds` (`from syft_rds import login_do, login_ds`). If you depend on the legacy PySyft ≤0.9 API, pin `syft<0.10`.

[![Unit Tests](https://github.com/OpenMined/pysyft/actions/workflows/unit-tests.yml/badge.svg?branch=dev)](https://github.com/OpenMined/PySyft/actions/workflows/unit-tests.yml?query=branch%3Adev)
[![Integration Tests](https://github.com/OpenMined/pysyft/actions/workflows/integration-tests.yml/badge.svg?branch=dev)](https://github.com/OpenMined/pysyft/actions/workflows/integration-tests.yml?query=branch%3Adev)
[![PyPI](https://img.shields.io/pypi/v/syft)](https://pypi.org/project/syft/)
[![Python 3.10+](https://img.shields.io/python/required-version-toml?tomlFilePath=https%3A%2F%2Fraw.githubusercontent.com%2FOpenMined%2Fpysyft%2Fdev%2Fpyproject.toml)](https://github.com/OpenMined/pysyft)
[![License: Apache 2.0](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://github.com/OpenMined/pysyft/blob/main/pyproject.toml)

PySyft allows data scientists run computations which are reviewed and run by data owners on private data. Users securely communicate through cloud storage their organizations already use (Google Drive, Microsoft 365, etc.) or serverless p2p chnannels like webRTC. No new infrastructure required and all communication between be peers can be signed and encrypted.

## Docs

- [Workflow](https://github.com/OpenMined/PySyft/blob/dev/docs/workflow.md) — End-to-end privacy-preserving data analysis workflow
- [API Reference](https://github.com/OpenMined/PySyft/blob/dev/docs/API.md) — All public client methods and properties
- [Authentication & Setup](https://github.com/OpenMined/PySyft/blob/dev/docs/auth.md) — Google Cloud OAuth setup for local/Jupyter usage
- [Background Services](https://github.com/OpenMined/PySyft/blob/dev/packages/syft-bg/README.md) — Email notifications, running api calls, and TUI dashboard
- [Connections](https://github.com/OpenMined/PySyft/blob/dev/docs/connections.md) — How the Google Drive transport layer works
- [Permissions](https://github.com/OpenMined/PySyft/blob/dev/packages/syft-permissions/docs/permission-user-docs.md) - Permissions for syft
- [Enclaves](https://github.com/OpenMined/PySyft/tree/dev/packages/syft-enclave) - Enclaves with syft

## Tutorials

- [Double blind LLM evals in enclaves](https://github.com/OpenMined/PySyft/blob/dev/notebooks/enclave/gemma/colab) — a researcher evaluates a private model on a private benchmark inside an enclave, with neither side revealing its assets
- [LLM user logs analysis](https://github.com/OpenMined/PySyft/blob/dev/notebooks/ai_audit/external/DS_Tutorial_v2.ipynb) — submit jobs against private LLM user logs and get back only the approved results ([data owner notebook](https://github.com/OpenMined/PySyft/blob/dev/notebooks/ai_audit/internal/DO_Tutorial_V3.ipynb))

## Features

- **Privacy-preserving** — Private data never leaves the data owner's machine; only approved results are shared
- **Transport-agnostic** — Works over Google Drive today, extensible to any file-based transport
- **Offline-first** — Full functionality even when peers are offline; changes sync when connectivity resumes
- **Peer-to-peer with explicit auth** — Data owners must approve each collaborator before any data flows
- **Isolated job execution** — Jobs run in sandboxed Python virtual environments with controlled access to private data
- **Dataset sharing with mock/private separation** — Data scientists explore mock data, then submit jobs that run on the real thing

## Quick Start

**Before you start:** this quick start assumes Google Colab, where login is a browser pop-up and works out of the box. On a local machine or in Jupyter you first need a Google Cloud OAuth token: follow [docs/auth.md](https://github.com/OpenMined/PySyft/blob/dev/docs/auth.md) (about 15 minutes the first time), then pass it as `token_path` to `login_do` / `login_ds`. Each role needs its own Google account.

We assume two parties here, a Data Owner (DO) and a Data Scientist (DS), the DS wants to do an analysis on private data of the DO. For brevity we use code blocks, but in practice these would be distributed: each party executes their code on their own machine.

```bash
# Data scientist
uv pip install "syft>=0.10.0" "syft-rds>=0.6.0"
# Data owner (adds the background services)
uv pip install "syft>=0.10.0" "syft-rds>=0.6.0" "syft-bg>=0.3.12"
```

```python
import syft as sy                          # sync engine + helpers (resolve_dataset_file_path, bug_report, ...)
from syft_rds import login_do, login_ds    # datasets + jobs (the Remote Data Science client)
```

```python
# Colab: login is a browser pop-up, nothing to set up
# Local / Jupyter: create a token first, see docs/auth.md
do = login_do(email="do@org.com")  # local: add token_path="token_do.json"
ds = login_ds(email="ds@org.com")  # second account; local: token_path="token_ds.json"

# Peer request & approve
ds.add_peer("do@org.com")
do.approve_peer_request("ds@org.com")

# Create & sync dataset
do.create_dataset(
    name="census",
    mock_path="mock.txt", # create this and add some text
    private_path="private.txt", # create this and add some text
    users=["ds@org.com"],
)
do.sync(); ds.sync()
datasets = ds.datasets.get_all()
```

Write an `analysis.py` that reads the dataset and produces a result in our case this is just the length of the data. Inside a job, `resolve_dataset_file_path` automatically resolves to the **private** data:

```python
# analysis.py
import json
import syft as sy

data_path = sy.resolve_dataset_file_path("census")
with open(data_path, "r") as f:
    data = f.read()

with open("outputs/result.json", "w") as f:
    json.dump({"length": len(data)}, f)
```

Submit the job and retrieve results:

```python
# Submit job
ds.submit_python_job(
    user="do@org.com",
    code_path="analysis.py",
    job_name="analysis",
)
ds.sync(); do.sync()

# Data owner Approves & runs job
do.jobs["ds@org.com"]["analysis"].approve()
do.process_approved_jobs(share_outputs_with_submitter=True)
do.sync(); ds.sync()
result = open(ds.jobs["do@org.com"]["analysis"].output_paths[0]).read()
```

## Packages

| Package                                                                                      | Description                                                       |
| -------------------------------------------------------------------------------------------- | ----------------------------------------------------------------- |
| [`syft-rds`](https://github.com/OpenMined/PySyft/tree/dev/packages/syft-rds)                 | Remote Data Science client: `login_do`/`login_ds`, datasets, jobs |
| [`syft-datasets`](https://github.com/OpenMined/PySyft/tree/dev/packages/syft-datasets)       | Dataset management and sharing                                    |
| [`syft-job`](https://github.com/OpenMined/PySyft/tree/dev/packages/syft-job)                 | Job submission and execution                                      |
| [`syft-permissions`](https://github.com/OpenMined/PySyft/tree/dev/packages/syft-permissions) | Permission system for Syft datasites                              |
| [`syft-perms`](https://github.com/OpenMined/PySyft/tree/dev/packages/syft-perms)             | User-facing permission API for Syft datasites                     |
| [`syft-bg`](https://github.com/OpenMined/PySyft/tree/dev/packages/syft-bg)                   | Background services TUI dashboard for SyftBox                     |
| [`syft-notebook-ui`](https://github.com/OpenMined/PySyft/tree/dev/packages/syft-notebook-ui) | Jupyter notebook display utilities                                |

## Development

```bash
# Install in development mode
uv pip install -e .

# Run tests
just test-unit          # Unit tests (fast, mocked)
just test-integration   # Integration tests (slow, real API)
```

---

Built by [OpenMined](https://openmined.org) — building open-source technology for privacy-preserving data science and AI.

## Support

For questions about PySyft, reach out via `#support` on <a href="https://slack.openmined.org/">Slack</a>.

# Community

Supported by the OpenMined Foundation, the OpenMined Community is an online network of over 17,000 technologists, researchers, and industry professionals keen to _unlock 1000x more data in every scientific field and industry_.

<a href="https://join.slack.com/t/openmined/shared_invite/zt-2hxwk07i9-HO7u5C7XOgou4Z62VU78zA"><img width=150px src="https://img.shields.io/badge/Join_us-%20slack-purple?logo=slack" /></a>

# Contributors

OpenMined and Syft appreciates all contributors, if you would like to fix a bug or suggest a new feature, please reach out via <a href="https://github.com/OpenMined/PySyft/issues">Github</a> or <a href="https://join.slack.com/t/openmined/shared_invite/zt-2hxwk07i9-HO7u5C7XOgou4Z62VU78zA/">Slack</a>!

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# About OpenMined

OpenMined is a non-profit foundation creating technology infrastructure that helps researchers get answers from data without needing a copy or direct access. Our community of technologists is building Syft.

<a href="https://donate.stripe.com/fZe03H0aLdAO59e9AA
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# License

[Apache License 2.0](https://github.com/OpenMined/PySyft/tree/dev/LICENSE)<br />
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