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README.md
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README.md
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# G3 General Purpose AI Agent
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# G3
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A code-first AI agent that helps you complete tasks by writing and executing code or scripts.
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An experiment in a code-first AI agent that helps you complete tasks by writing and executing code.
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## Philosophy
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G3 doesn't just give you advice - **it writes and runs code to solve your problems**. Whether you need to:
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- Process data files
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- Automate workflows
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- Scrape websites
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- Manipulate files
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- Analyze logs
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- Generate reports
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- Set up environments
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G3 will write the appropriate scripts (Python, Bash, JavaScript, etc.) and can execute them for you.
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## Features
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- **Code-First Approach**: Always tries to solve problems with executable code
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- **Multi-Language Support**: Generates Python, Bash, JavaScript, Rust, and more
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- **Modular Architecture**: Clean separation between CLI, core engine, and LLM providers
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- **Multiple LLM Providers**: Support for OpenAI, Anthropic, and embedded open-weights models
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- **Local Model Support**: Run completely offline with embedded GGUF models via llama.cpp
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- **Interactive Mode**: Chat with the AI and watch it solve problems in real-time
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- **Task Automation**: Create reusable automation scripts
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## Installation
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```bash
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cargo install --path .
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```
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## Configuration
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Create a configuration file at `~/.config/g3/config.toml`:
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### Cloud Providers
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```toml
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[providers]
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default_provider = "anthropic"
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[providers.anthropic]
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api_key = "your-anthropic-api-key"
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model = "claude-3-5-sonnet-20241022"
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max_tokens = 4096
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temperature = 0.1
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[providers.openai]
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api_key = "your-openai-api-key"
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model = "gpt-4"
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max_tokens = 2048
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temperature = 0.1
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```
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### Local Embedded Models
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For completely offline operation with open-weights models:
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```toml
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[providers]
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default_provider = "embedded"
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[providers.embedded]
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# Path to your GGUF model file
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model_path = "~/.cache/g3/models/codellama-7b-instruct.Q4_K_M.gguf"
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model_type = "codellama"
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context_length = 4096
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max_tokens = 2048
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temperature = 0.1
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# Number of layers to offload to GPU (0 for CPU only)
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gpu_layers = 32
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# Number of CPU threads to use
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threads = 8
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```
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**Getting Models**: Download GGUF models from [Hugging Face](https://huggingface.co/models?library=gguf) (search for "GGUF"). Popular options:
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- [CodeLlama 7B Instruct](https://huggingface.co/TheBloke/CodeLlama-7B-Instruct-GGUF)
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- [Llama 2 7B Chat](https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGUF)
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- [Mistral 7B Instruct](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GGUF)
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### Agent Settings
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```toml
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[agent]
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max_context_length = 8192
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enable_streaming = true
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timeout_seconds = 60
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```
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## Usage
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### Interactive Mode (Default)
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Simply run G3 to start interactive mode:
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```bash
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g3
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```
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Example interactions:
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```
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g3> Process this CSV file and show me the top 10 customers by revenue
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# G3 writes Python pandas script to analyze the CSV
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g3> Set up a backup script for my home directory
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# G3 creates a bash script with rsync/tar commands
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g3> Download all images from this webpage
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# G3 writes a Python script with requests/BeautifulSoup
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g3> Convert these JSON files to a SQLite database
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# G3 creates a Python script to parse JSON and insert into SQLite
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```
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### Direct Commands
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You can also use G3 with direct commands:
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```bash
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# Solve any task with code
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g3 task "merge these PDF files into one"
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g3 task "find all TODO comments in my codebase"
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# Create automation scripts
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g3 automate "daily backup of my projects folder"
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g3 automate "resize all images in a folder to 800px width"
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# Data processing
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g3 data "analyze this log file for error patterns"
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g3 data "convert CSV to JSON with validation"
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# Legacy code commands (still supported)
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g3 analyze src/main.rs
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g3 generate "fibonacci function" --output fib.py
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g3 review src/lib.rs
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```
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## Architecture
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G3 follows the Unix philosophy with modular, composable components:
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- **g3-cli**: Command-line interface
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- **g3-core**: Core agent logic and orchestration
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- **g3-providers**: LLM provider abstractions
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- **g3-config**: Configuration management
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See [DESIGN.md](DESIGN.md) for detailed architecture documentation.
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## Development
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```bash
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# Build all crates
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cargo build
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# Run tests
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cargo test
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# Run with debug logging
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RUST_LOG=debug cargo run -- analyze src/
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```
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## License
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MIT
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