# dataiku/kiji-proxy

> Privacy proxy for your OpenAI requests

## Fast Facts & Telemetry
- **Canonical URL**: https://githubrepo.cloud/repo/dataiku/kiji-proxy
- **GitHub Source**: https://github.com/dataiku/kiji-proxy
- **Global Rank**: #9,601 on GitHubRepo
- **Stars**: 438
- **Forks**: 51
- **Open Issues**: 42
- **Primary Language**: Go
- **Software License**: Apache-2.0
- **Last Commit / Push**: Oct 16, 2026
- **Topics**: ai-gateway, data-anonymization, data-masking, llm-security, pii-detection, privacy, privacy-protection, privacy-proxy

## Project Overview
kiji-proxy is an open-source project written in Go: Privacy proxy for your OpenAI requests.

Engineered for speed, consistency, and developer ease, it solves common hurdles in ai-gateway, data-anonymization, data-masking. It provides clear interfaces, comprehensive configuration options, and seamless integration with existing tools across the modern development stack.

## Key Features
- Optimized execution pipeline written in Go for predictable speed.
- Zero-friction configuration with comprehensive sensible defaults out of the box.
- Cross-platform runtime support across Linux, macOS, and Windows environments.
- Strong typing and modular architecture designed for easy extension and maintainability.
- Standardized CLI and API interfaces for smooth integration into CI/CD workflows.
- Active community maintenance with regular dependency updates and security patches.

## Installation & Quick Start
### Linux
```bash
go install github.com/dataiku/kiji-proxy@latest
```

### macOS
```bash
go install github.com/dataiku/kiji-proxy@latest
```

### Windows
```bash
go install github.com/dataiku/kiji-proxy@latest
```

## System Requirements & Architecture
- **Supported Platforms**: macOS, Linux, Windows
- **Runtime Dependencies**: Go >= 1.21 runtime environment
- **Architecture**: x86_64, ARM64 (Apple Silicon & Graviton)

## How It Works
kiji-proxy coordinates its core functionality through a modular Go pipeline. It parses configuration parameters, validates inputs, and resolves dependencies asynchronously. By minimizing runtime overhead and keeping allocations localized, it delivers predictable performance in both local development environments and automated production workloads.

## Production Use Cases
### Autonomous AI Agents
Orchestrate intelligent workflows and tool-calling routines with kiji-proxy.

### Model Inference & Prompting
Integrate fast, local or cloud-hosted generative AI models directly into production code.

### Context Memory & RAG
Augment language models with dynamic vector retrieval and structured project memory.

### Developer Productivity
Automate repetitive engineering tasks, code generation, and test creation using AI agents.

## Target Audience
Developers and engineering teams building with Go, seeking reliable, tested, and actively maintained tooling for production workloads.

## Strengths
- Active community backing with 438 GitHub stars and verified adoption.
- Permissive open-source distribution under the Apache-2.0 license.
- Built in Go for high execution speed and developer familiarity.
- Cross-platform compatibility across modern Linux, macOS, and Windows environments.
- Clean modular design allowing flexible configuration and pipeline integration.

## Considerations
- Requires familiarity with Go and modern CLI workflows.
- Ecosystem extensions may require manual configuration depending on environment constraints.
- Active development roadmap means breaking API changes may occur across major versions.

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