Llm Console
What is Llm Console
LLM-Console is a cross-platform, vendor-agnostic command-line interface designed for interacting with various Language Learning Models (LLMs). It supports multiple LLMs, including those from OpenAI, Anthropic, Google, and local PyTorch inference.
Use cases
Use cases for LLM-Console include querying real-time data (like current time in different regions), generating structured data formats (like TOML), and facilitating interactive conversations with language models.
How to use
To use LLM-Console, install it via pip with the command ‘pip install llm-console’. After installation, run the interactive wizard by typing ‘llm’ to configure your connection to a language model. You can then interact with the model by typing commands like ‘llm “Wazzup, LLM”’.
Key features
Key features of LLM-Console include flexible configuration through a .env file, extremely fast and parallel LLM usage, and model-agnostic capabilities that allow it to work with various LLM providers.
Where to use
LLM-Console can be used in various fields such as software development, data analysis, and any area requiring natural language processing or interaction with language models.
Clients Supporting MCP
The following are the main client software that supports the Model Context Protocol. Click the link to visit the official website for more information.
Overview
What is Llm Console
LLM-Console is a cross-platform, vendor-agnostic command-line interface designed for interacting with various Language Learning Models (LLMs). It supports multiple LLMs, including those from OpenAI, Anthropic, Google, and local PyTorch inference.
Use cases
Use cases for LLM-Console include querying real-time data (like current time in different regions), generating structured data formats (like TOML), and facilitating interactive conversations with language models.
How to use
To use LLM-Console, install it via pip with the command ‘pip install llm-console’. After installation, run the interactive wizard by typing ‘llm’ to configure your connection to a language model. You can then interact with the model by typing commands like ‘llm “Wazzup, LLM”’.
Key features
Key features of LLM-Console include flexible configuration through a .env file, extremely fast and parallel LLM usage, and model-agnostic capabilities that allow it to work with various LLM providers.
Where to use
LLM-Console can be used in various fields such as software development, data analysis, and any area requiring natural language processing or interaction with language models.
Clients Supporting MCP
The following are the main client software that supports the Model Context Protocol. Click the link to visit the official website for more information.
Content
LLM-Console
LLM-Console is a cross-platform vendor-agnostic command-line interface for LLMs.
Development Status: bookmark it and go away, it is still in early development.
✨ Features
- @todo
- Flexible configuration via
.envfile - Extremely fast, parallel LLM usage
- Model-agnostic (OpenAI, Anthropic, Google, local PyTorch inference, etc.)
🚀 Quickstart
# Install LLM Console via pip
pip install llm-console
# Run the interactive wizard to configure the connection to your language model.
llm
# Talk to your Language Model
llm "Wazzup, LLM"
Usage Examples
llm --mcp https://time.mcp.inevitable.fyi/mcp what is current time in Ukraine? answer in H:i:s, no additional text
> 16:31:12
>llm --mcp https://time.mcp.inevitable.fyi/mcp H:i time across a Europe, in valid toml, no text before of after toml [EuropeTime] London = "2024-06-10T13:38:23+01:00" Paris = "2024-06-10T14:38:23+02:00" Berlin = "2024-06-10T14:38:23+02:00" Madrid = "2024-06-10T14:38:23+02:00" Rome = "2024-06-10T14:38:23+02:00" Athens = "2024-06-10T15:38:23+03:00" Istanbul = "2024-06-10T16:38:23+03:00"
🤝 Contributing
We ❤️ contributions! See CONTRIBUTING.md.
📝 License
Licensed under the MIT License.
© 2022—2025 Vitalii Stepanenko
Dev Tools Supporting MCP
The following are the main code editors that support the Model Context Protocol. Click the link to visit the official website for more information.
