https://github.com/inference-gateway/coder
An open-source, AI-powered coding agent - your smart coding companion for generating reliable solutions, automating bug fixes, and optimizing workflows to help you build and iterate faster.
https://github.com/inference-gateway/coder
automation bot bots ci coder coding development github gitlab gitops
Last synced: 3 months ago
JSON representation
An open-source, AI-powered coding agent - your smart coding companion for generating reliable solutions, automating bug fixes, and optimizing workflows to help you build and iterate faster.
- Host: GitHub
- URL: https://github.com/inference-gateway/coder
- Owner: inference-gateway
- License: mit
- Created: 2025-01-06T23:38:13.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2025-03-20T22:28:11.000Z (4 months ago)
- Last Synced: 2025-03-20T22:33:48.730Z (4 months ago)
- Topics: automation, bot, bots, ci, coder, coding, development, github, gitlab, gitops
- Language: Rust
- Homepage:
- Size: 536 KB
- Stars: 1
- Watchers: 1
- Forks: 1
- Open Issues: 8
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
Awesome Lists containing this project
README
🦀 Coder 🦀
The tool you want to hide from your boss.
Table of content:
- [What is Coder?](#what-is-coder)
- [Why Coder?](#why-coder)
- [Key Features](#key-features)
- [Installation](#installation)
- [Usage](#usage)
- [Using Docker](#using-docker)
- [Configuration](#configuration)
- [Development](#development)
- [Prerequisites](#prerequisites)
- [Getting Started](#getting-started)
- [Contributing](#contributing)
- [License](#license)
### What is Coder?
Coder is an AI-powered assistant that automatically reviews issues and creates pull requests/merge requests with suggested fixes for GitHub and GitLab repositories. It is built with Rust for performance and reliability, and it supports the latest reasoning LLMs (DeepSeek-r1) for code analysis and improvement.
### Why Coder?
Unlike many tools that are either open-source UIs bloated and loaded with dependencies or costly, proprietary solutions, Coder is simple, free, and runs directly from your terminal - giving you true freedom. You can also automate it in remote environments like Kubernetes to parallelize tasks, control resources, and scale on demand. With a lightweight footprint and zero extra dependencies, Coder seamlessly integrates on Kubernetes, Docker, or locally, adapts to your project structure, and supports configurable AI models.
Coder understands project structure rather than just a specific code snippet or a file. It can analyze the project and suggest fixes based on the context of the project. It can also fetch the latest data when activated, thanks to RAG-enabled reasoning. It supports the latest reasoning LLMs (DeepSeek-r1) for code analysis and improvement, and it can be configured to use only the AI models with tool-use support.
### Key Features
- 📜 Open-source and free to use
- 🚀 Automatic issue analysis and code review
- 🤖 AI-generated code fixes and improvements
- 🔗 Support for both GitHub PRs and GitLab MRs
- ⚡ Built with Rust for performance and reliability
- 🎛️ Configurable AI model selection (only the ones with tool-use support)
- 🤖 Works with the latest and greatest reasoning LLMs (DeepSeek-r1)
- 🔄 RAG-enabled reasoning: fetches the latest data when activated 🚀
- 🗂️ Project structure aware analysis
- 📦 Easy to install and use
- 💻 Run directly from your terminal – enjoy true freedom from vendor lock-in! 🔓
- 🌐 Cross-platform support
- 🚀 Runs on ☸️ Kubernetes, 🐳 Docker, and 💻 local environments
- 🤖 Automate remote tasks with Kubernetes: parallelize, scale, and control resources on demand
- 📦 Lightweight footprint - binary weight ~ 11MB container weight ~ 37MB
- 🚀 Easy to deploy
### Installation
Just run:
```bash
curl -sSL https://raw.githubusercontent.com/inference-gateway/coder/refs/heads/main/install.sh | CODER_VERSION=latest sh -
```
### Usage
1. Initialize the project:
```bash
coder init
```
This will generate configuration files in the .coder directory. Review them and configure the assistant.
2. Index the project:
```bash
coder index
```
This will index the project files and prepare all the information necessary for the assistant so it's easily digestible.
3. Send the assistant to fix an issue:
```bash
coder fix --issue=#1
```
### Using Docker
You can also use Docker to run the assistant:
```bash
cp .env.coder.example .env
docker run -v $(pwd):/app -w /app --env-file .env ghcr.io/inference-gateway/coder:latest init
docker run -v $(pwd):/app -w /app --env-file .env ghcr.io/inference-gateway/coder:latest index
docker run -v $(pwd):/app -w /app --env-file .env ghcr.io/inference-gateway/coder:latest fix --issue=#1
```
### Configuration
Via Environment Variables:
```
CODER_INFERENCE_GATEWAY_URL=http://localhost:8080
CODER_SCM_NAME=github # The SCM you are using (e.g. github, gitlab)
CODER_SCM_TOKEN= # The token you use to authenticate with the SCM
CODER_SCM_USERNAME= # The username you use to authenticate with the SCM
CODER_SCM_REPOSITORY= # The repository name
CODER_AGENT_MODEL= # The name of the agent
CODER_AGENT_PROVIDER= # The provider of the agent
CODER_AGENT_MAX_TOKENS= # The maximum tokens the agent can use per request
CODER_LANGUAGE_NAME= # The main language of the project
CODER_LANGUAGE_ANALYSE= # The command you use for static analysis
CODER_LANGUAGE_LINTER= # The command you use for linting
CODER_LANGUAGE_TEST_COMMAND= # The command you use for testing
CODER_LANGUAGE_DOCS_URL= # The URL for the documentations so the agent can reference them
```
Or using the file `.coder/config.yaml` - you can customize the configuration by editing this file.
Running:
```bash
coder init
```
Will result in the following available configurations:
```yaml
---
# AI Coder Configuration
language:
name: rust # The language of the project
analyse: cargo clippy # The command you use for static analysis
linter: cargo fmt # The command you use for linting
test_command: cargo test # The command you use for testing
docs_url: https://docs.rs # The URL for the documentations so the agent can reference them
scm:
name: github # The SCM you are using (e.g. github, gitlab)
owner: owner # The owner of the repository
repository: repo # The repository name
# Extra validation for issue templates if you want reliable Pull Requests generation - it's better to have a good issue template
issue_template: |-
## Description
## Steps to Reproduce
## Expected Behavior
## Actual Behavior
## Environment
agent:
model: deepseek-r1-distill-llama-70b # The name of the LLM
provider: groq # The provider of the LLM
max_tokens: 4000 # The maximum tokens the agent can use per request
api:
endpoint: http://localhost:8080 # The endpoint of the deployed Inference-Gateway API
```
### Development
#### Prerequisites
- Docker
- Devcontainers
#### Getting Started
For development tasks, you can use the following commands:
1. To start the backend gateway:
```bash
task gateway
```
2. To build the project:
```bash
task build
```
3. To execute the agent coder:
```bash
task run
```
4. To execute a specific command of the coder:
```bash
task run --
```
5. To run the tests:
```bash
task test
```
### Contributing
1. Fork the repository
2. Create a new branch
3. Make your changes
4. Commit your changes
5. Push your changes
6. Create a new pull request
### License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.