https://github.com/alexdredmon/arcode
đšī¸ AI development tool
https://github.com/alexdredmon/arcode
ai development llm open-source tool
Last synced: 6 months ago
JSON representation
đšī¸ AI development tool
- Host: GitHub
- URL: https://github.com/alexdredmon/arcode
- Owner: alexdredmon
- License: mit
- Created: 2024-06-01T18:40:57.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2025-01-27T11:46:40.000Z (over 1 year ago)
- Last Synced: 2025-11-28T01:44:34.469Z (7 months ago)
- Topics: ai, development, llm, open-source, tool
- Language: Python
- Homepage: https://www.arcodeai.com
- Size: 7.39 MB
- Stars: 33
- Watchers: 3
- Forks: 5
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Codeowners: .github/CODEOWNERS
Awesome Lists containing this project
README
# arcode
## Description
Arcode is a command-line tool designed to help facilitate holistic software development via LLM. It allows users to generate feature implementations for a given codebase based on user-defined requirements.
## Features
- Provide requirements and followup feedback to auto-build changes
- Single button press confirmation + implementation
- Supplies context from your local files and select remote resources
- Optional limiting of file context based on relevancy to features
- Support for various providers/models via LiteLLM
## Demo
### Provide your requirements:
### Review and approve changes:
## Examples:
Generate a plan and optionally write the changes based on requirements:
```bash
arcode "Implement authentication"
```
Use with input from a file:
```bash
cat feature_requirements.txt | arcode --dir ./my_codebase
```
Provide remote resources
```bash
arcode "Follow the latest docs in the provided resources to add a LangChain SQL query chain to retrieve relevant reporting details from the databse given user input" --resources="https://api.python.langchain.com/en/latest/chains/langchain.chains.sql_database.query.create_sql_query_chain.html#langchain.chains.sql_database.query.create_sql_query_chain"
```
## Usage
Run Arcode with and pass it your requirements:
```bash
arcode "Build feature X"
```
Or if you're running the .py version, run with one of the following:
```bash
./arcode.py "Build feature X"
python arcode.py "Build feature X"
```
## Configuration
You can pass configuration via CLI arguments or by creating an `arcodeconf.yml` file in your `~/.conf/` directory (i.e. global config at `~/.config/arcodeconf.yml`) and/or in the root of your project working directory. An `arcodeconf.yml` file can set arguments and environment variables for a project.
Sample `arcodeconf.yml`:
```yaml
args:
model: anthropic/claude-3-opus-20240229
ignore:
- build
- dist
- secrets
resources:
- https://example.com/resource1
- https://example.com/resource2
env:
ANTHROPIC_API_KEY: 3xampl3
```
## Models
For best results, use one of the following models:
- `anthropic/claude-3-5-sonnet-20240620`
- `bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0`
- `openai/gpt-4o`
- `azure/gpt-4o`
Other popular models include:
- `anthropic/claude-3-opus-20240229`
- `bedrock/anthropic.claude-3-opus-20240229-v1:0`
- `openai/gpt-4`
- `azure/gpt-4`
Popular supported embedding models include:
- `openai/text-embedding-3-small`
## API Keys
Set API keys based on the provider(s) you're using - these values should be present in either your shell environment, an `.env` file in the working directory, or the `env` section of an `arcodeconf.yml` file located in the working directory.
### OpenAI
- `OPENAI_API_KEY`
### Anthropic
- `ANTHROPIC_API_KEY`
### Gemini
- `GEMINI_API_KEY`
### Azure
- `AZURE_API_KEY`
- `AZURE_API_BASE`
- `AZURE_API_VERSION`
### AWS Bedrock
- `AWS_ACCESS_KEY_ID`
- `AWS_SECRET_ACCESS_KEY`
- `AWS_REGION_NAME`
## Arguments:
```bash
usage: arcode [-h] [--dir DIR] [--auto-write AUTO_WRITE] [--focused FOCUSED]
[--model MODEL] [--max-tokens MAX_TOKENS]
[--model-embedding MODEL_EMBEDDING]
[--mode {implement,question}] [--ignore [IGNORE ...]]
[--resources [RESOURCES ...]] [--images [IMAGES ...]] [--debug]
[--models [MODELS]] [--max-estimated-cost MAX_ESTIMATED_COST]
[--max-file-size MAX_FILE_SIZE] [--temperature TEMPERATURE]
[requirements ...]
arcode: AI driven development tool
positional arguments:
requirements Requirements for features to build on the codebase or
question to ask about the codebase.
options:
-h, --help show this help message and exit
--dir DIR The working directory of the codebase, default to
current directory.
--auto-write AUTO_WRITE
Whether or not to immediately write the changeset.
Useful when piping to arcode, e.g. cat feature.txt |
arcode
--focused FOCUSED Enable focused mode to limit file context provided
based on relevancy using embeddings - accepts an
integer containing number of file chunks to limit
context to.
--model MODEL LLM provider/model to use with LiteLLM, default to
openai/gpt-4o.
--max-tokens MAX_TOKENS
Max number of output tokens
--model-embedding MODEL_EMBEDDING
LLM provider/model to use for embeddings with LiteLLM,
default to openai/text-embedding-3-small.
--mode {implement,question}
Mode for the tool: "implement" for feature building
and "question" for asking questions about the
codebase.
--ignore [IGNORE ...]
Additional ignore patterns to use when parsing
.gitignore
--resources [RESOURCES ...]
List of URLs to fetch and include in the prompt
context
--images [IMAGES ...]
List of image file paths to include in the prompt
context
--debug Enable debug mode for additional output
--models [MODELS] List available models. Optionally provide a filter
string.
--max-estimated-cost MAX_ESTIMATED_COST
Maximum estimated cost allowed. Actions with a larger
estimated cost will not be allowed to execute.
(integer or float with up to two decimal places)
--max-file-size MAX_FILE_SIZE
Maximum file size in bytes for files to be included in
the prompt.
--temperature TEMPERATURE
Temperature to use for the LLM
```
## Install
1. Ensure [Homebrew](https://brew.sh/) is installed
2. Ensure `libmagic` is installed (or run `brew install libmagic`)
3. Tap `alexdredmon/arcode`
```bash
brew tap alexdredmon/arcode
```
4. Install arcode
```bash
brew install arcode
```
## Development Setup
1. Clone the repository:
```bash
git clone https://github.com/alexdredmon/arcode
cd arcode
```
2. Install pyenv (if not already installed):
```bash
brew install pyenv
```
3. Install Python 3.9 using pyenv:
```bash
pyenv install 3.9
```
4. Set local Python version:
```bash
pyenv local 3.9
```
5. Create & activate virtual environment:
```bash
python -m venv venv
source venv/bin/activate
```
6. Install the required dependencies:
```bash
pip install -r requirements.txt
```
7. Set your OpenAI API key:
```bash
export OPENAI_API_KEY=
```
or alternatively set in a .env file:
```bash
OPENAI_API_KEY=
```
## Build
1. Build a standalone executable via:
```bash
./scripts/build.sh
```
2. A binary will be created in `./dist/arcode/arcode`
3. Add the resulting binary to your PATH to run it anywhere:
```bash
export PATH="/Users/yourusername/path/to/dist/arcode:$PATH"
```
## Homebrew Installation
To install Arcode via Homebrew:
1. Clone the repository and navigate into the project directory:
```bash
git clone https://github.com/alexdredmon/arcode
cd arcode
```
2. Run the build script to generate the standalone executable and Homebrew formula:
```bash
./scripts/build.sh
```
3. Move the generated formula into your local Homebrew formula directory:
```bash
mkdir -p $(brew --prefix)/Homebrew/Library/Taps/homebrew/homebrew-core/Formula/
cp Formula/arcode.rb $(brew --prefix)/Homebrew/Library/Taps/homebrew/homebrew-core/Formula/
```
4. Install the formula:
```bash
brew install arcode
```
## Upgrade
To upgrade to the latest version, simply:
```bash
brew update
brew upgrade arcode
```
## Tests
1. Run tests via:
```bash
./scripts/run_tests.sh
```
## License
This project is licensed under the MIT License. See the LICENSE file for more details.
## Contributing
Contributions are welcome! Please fork the repository and submit a pull request with your changes.