SHAI

shai is a coding agent, your pair programming buddy that lives in the terminal. Written in rust with love <3

Features

• Interactive coding agent - Chat with shai in your terminal to write code, fix bugs, and get answers
• Headless mode - Pipe prompts directly into shai for scripting and automation
• HTTP server - Run shai as a service with OpenAI-compatible APIs and SSE streaming
• Shell assistant - Automatically suggests fixes when commands fail in your terminal
• Project context - Load project-specific information via SHAI.md files
• MCP Support - Configure specialized agents with MCP and OAuth support
• Multiple LLM providers - Works with OVHCloud, OpenAI, and other compatible endpoints

Installation

Latest stable release

Install the latest release with the following command:

curl -fsSL https://raw.githubusercontent.com/ovh/shai/main/install.sh | sh

Nightly version

Install the last unstable version with the following command:

curl -fsSL https://raw.githubusercontent.com/ovh/shai/main/install.sh | SHAI_RELEASE=unstable sh

The shai binary will be installed in $HOME/.local/bin

Quick Start

By default shai uses OVHcloud as an anonymous user meaning you will be rate limited! If you want to sign in with your account or select another provider, run:

shai auth

Once you have a provider set up, you can run shai:

shai

Usage

Interactive Mode

Simply run shai to start the interactive coding agent. You can chat with shai and it will help you write code, fix bugs, and answer questions.

Headless Mode

Shai can also run in headless mode without user interface. In that case simply pipe a prompt into shai, it will stream event in the stderr:

echo "make me a hello world in main.py" | shai

You can also instruct shai to return the entire conversation as a trace once it is done:

echo "make me a hello world in main.py" | shai 2>/dev/null --trace

This is handy because you can chain shai calls:

echo "make me a hello world in main.py" | shai --trace | shai "now run it!"

HTTP Server Mode

You can run shai as an HTTP service with SSE streaming support. This mode provides multiple API endpoints:

shai serve --port 3000

Available API endpoints:

• POST /v1/chat/completions - OpenAI Chat Completions API (ephemeral mode)
• POST /v1/responses - OpenAI Responses API (stateful/stateless)
• GET /v1/responses/{id} - Get response by ID
• POST /v1/responses/{id}/cancel - Cancel a response
• POST /v1/multimodal - Simple multimodal API (streaming)
• POST /v1/multimodal/{session_id} - Simple multimodal API (with session)

Options:

• --port <PORT> - Port to bind to (default: 3000)
• --ephemeral - Use ephemeral mode (spawn new agent per request)
• [AGENT] - Agent name to use for persistent session

Shell Assistant

shai can also act as a shell assistant in case a command failed and will propose you a fix. This works by injecting command hook while monitoring your terminal output. Your last terminal output along with the last command and error code will be sent for analysis to the llm provider.

To start hooking your shell with shai simply type:

shai on

For instance:

To stop shai from monitoring your shell you can type:

shai off

Configuration

Project Context File

You can create a SHAI.md file at the root of your project containing any information you want Shai to know about the project (architecture, build steps, important directories, etc.). Shai will automatically load this file as additional context.

Custom Agents (with MCP)

Instead of a single global configuration, you can create custom agent in a separate configuration.

.ovh.config contains an example of a custom configuration with an remote MCP server configured.

Place this file in ~/.config/shai/agents/ovh.config, you can then list the agents available with:

curl https://raw.githubusercontent.com/ovh/shai/refs/heads/main/.ovh.config -o ~/.config/shai/agents/ovh.config
shai agent list

You can run shai with this specific agent with the agent subcommand:

shai agent ovh

OVHCloud Endpoints

OVHCloud provides compatible LLM endpoints for using shai with tools. Start by creating a Public Cloud project in your OVHCloud account, then head to AI Endpoints and retreive your API key. After setting it in shai, you can:

• choose one of the models with function calling feature (e.g., gpt-oss-120b, gpt-oss-20b, Mistral-​Small-​3.2-​24B-​Instruct-​2506) for best performance ;
• choose any other model forcing structured output (/set so option).

Development

Build The Project

Simply build the project with cargo

git clone git@github.com:ovh/shai.git
cd shai
cargo build --release