# cristicretu/diri

> The best way to work with coding agents. Run fifty in parallel, let them spawn each other, review everything in one place.

## Fast Facts & Telemetry
- **Canonical URL**: https://githubrepo.cloud/repo/cristicretu/diri
- **GitHub Source**: https://github.com/cristicretu/diri
- **Global Rank**: #9,705 on GitHubRepo
- **Stars**: 425
- **Forks**: 33
- **Open Issues**: 41
- **Primary Language**: Rust
- **Software License**: Apache-2.0
- **Last Commit / Push**: Oct 7, 2026
- **Topics**: claude-code, codex, coding-agents, gpui, rust

## Project Overview
diri is an open-source project written in Rust: A native workspace for coding agents. Run in parallel, review in place.

Engineered for speed, consistency, and developer ease, it solves common hurdles in claude-code, codex, coding-agents. 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 Rust 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
cargo install diri
```

### macOS
```bash
cargo install diri # or: brew install diri
```

### Windows
```bash
cargo install diri
```

## System Requirements & Architecture
- **Supported Platforms**: macOS, Linux, Windows
- **Runtime Dependencies**: Rust toolchain (rustc / cargo >= 1.70)
- **Architecture**: x86_64, ARM64 (Apple Silicon & Graviton)

## How It Works
diri coordinates its core functionality through a modular Rust 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 diri.

### 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 Rust, seeking reliable, tested, and actively maintained tooling for production workloads.

## Strengths
- Active community backing with 336 GitHub stars and verified adoption.
- Permissive open-source distribution under the Apache-2.0 license.
- Built in Rust 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 Rust 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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