
Code-Atlas
A C++ implementation of Open Interpreter, based on llama.cpp. / Open Interpreter 的 C++ 实现,基于 llama.cpp
Stars: 62

Code Atlas is a lightweight interpreter developed in C++ that supports the execution of multi-language code snippets and partial Markdown rendering. It consumes significantly lower resources compared to similar tools, making it suitable for resource-limited devices. It leverages llama.cpp for local large-model inference and supports cloud-based large-model APIs. The tool provides features for code execution, Markdown rendering, local AI inference, and resource efficiency.
README:
This project is a lightweight interpreter developed in C++, inspired by Open Interpreter. It supports the execution of multi-language code snippets and partial Markdown rendering. Compared to Open Interpreter, this project has significantly lower resource consumption, making it more suitable for resource-limited devices. Additionally, it leverages llama.cpp
for local large-model inference, providing an efficient and secure execution environment. It also supports cloud-based large-model APIs, configurable via config.json.
-
Code Execution:
- Supports scripting languages like Python, Batch, PowerShell, and Shell/Bash.
- Executable code blocks are highlighted in purple, while others are shown in yellow.
-
Markdown Rendering:
- Supports the following Markdown elements:
- Headings (levels 1–4)
- Bold and Italic
- Code blocks
- Output is color-coded for enhanced readability.
- Supports the following Markdown elements:
-
Local AI Inference:
- Uses
llama.cpp
for efficient local large-language model inference. - Compatible with
gguf
models, such as the Qwen2.5-Interpreter, fine-tuned for this task. - Configuration: Set
API
toFalse
in config.json.
- Uses
-
Cloud-Based Large Models:
- Supports HTTPS API-based cloud models like OpenAI.
- Configuration: Set
API
toTrue
in config.json and provide the base URL and API key.
-
Resource Efficiency:
- Significantly reduces memory and computational resource requirements compared to Open Interpreter.
- Download the project source code and compile it using a compatible C++ compiler, or download the precompiled executable (Releases).
g++ code-atlas.cpp -o code-atlas.exe -std=c++17 -lcurl -lws2_32
-
Download or compile llama.cpp, ensuring the following files are available:
llama.cli.exe
- Necessary
.dll
files - A compatible
gguf
model (e.g., Qwen2.5-Interpreter) or an accessible API.
-
Place the following files in the same directory:
- The project's executable
llama.cli.exe
- Required
.dll
files - Your chosen
gguf
model config.json
-
Run the program. Note that administrator permissions might be required on some systems.
- [ ] Support more platforms (e.g., Linux, macOS).
- [ ] Develop a graphical user interface (GUI) for enhanced usability.
- [ ] Allow users to choose the programming language for code execution.
- [ ] Expand supported programming languages beyond Python, Batch, PowerShell, and Shell/Bash.
- [ ] Add plugin support to extend functionality.
- [x] Support additional AI model APIs, such as OpenAI.
- [ ] Enhance sandboxing mechanisms for improved execution security.
- [ ] Extend Markdown rendering to support more elements.
- [ ] Support voice mode for hands-free operation.
This project is inspired by Open Interpreter, which we admire for its innovative approach to integrating natural language and programming. By optimizing performance, this project is implemented in C++ with specific enhancements for Windows systems while retaining the intuitive and powerful core design.
Additionally, the Qwen2.5-Interpreter model, fine-tuned from Qwen2.5-0.5B, showcases the project's potential for task optimization and performance enhancement.
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