How do I set up Tenere with my local Ollama instance?

Install Ollama and run a model server, then configure the URL and model name in Tenere's config.toml file under the [ollama] section, as per the README instructions. Ensure the server is accessible at the specified URL.

Tenere vs chatblade: which is better for terminal LLM chat?

Tenere offers a full TUI with Vim keybindings and multiple backends, ideal for interactive sessions. Chatblade is more focused on command-line queries and scripting, so choose based on whether you prefer a GUI-like interface or pure CLI efficiency.

Can Tenere work completely offline?

Yes, if you use backends like llama.cpp or Ollama with locally hosted models, Tenere can operate offline without an internet connection. The ChatGPT backend requires online access for API calls.

How to customize key bindings in Tenere?

Modify the [key_bindings] section in the config.toml file, but note that most custom bindings require using Ctrl key combinations to avoid conflicts with Vim modes, as mentioned in the README's note.

Does Tenere support streaming responses from LLMs?

Yes, the README mentions a key binding 'ctrl + t' to stop the stream response, indicating streaming is supported. However, configuration details for adjusting stream behavior are not extensively documented.

Is Tenere suitable for team collaboration on chat logs?

No, Tenere is designed for individual use with local chat history saved to files. It lacks features for sharing, synchronizing, or real-time co-editing of chats, making it poor for collaborative workflows.

Open-Awesome

tenere

GPL-3.0Rustv0.11.3

A terminal user interface (TUI) for interacting with multiple large language model backends, featuring Vim keybindings and chat history.

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675 stars33 forks0 contributors

What is tenere?

Tenere is a terminal user interface (TUI) for interacting with large language models. It provides a keyboard-focused chat interface that supports multiple backends including ChatGPT, llama.cpp, and Ollama, allowing developers to query LLMs directly from their terminal.

Target Audience

Developers and command-line enthusiasts who prefer working in the terminal and want efficient, keyboard-driven access to various LLM backends without switching between different tools or web interfaces.

Value Proposition

Tenere offers a unified, efficient terminal interface for multiple LLM providers with Vim keybindings for power users, local configuration support, and the ability to work entirely offline with self-hosted models.

Overview

🤖 TUI for LLMs

Use Cases

Best For

Querying LLMs from the terminal without leaving your development workflow
Using self-hosted LLM models like those served via llama.cpp or Ollama
Keyboard-focused developers who prefer Vim-style navigation
Working with LLMs on remote servers or headless environments
Maintaining chat history and saving conversations locally
Mobile development on Android via nix-on-droid

Not Ideal For

Projects requiring graphical user interfaces or rich media display
Teams needing real-time collaborative chat features or multi-user access
Users who prefer mouse-driven interfaces and are unfamiliar with Vim keybindings
Applications that require programmatic API access or automation rather than interactive chat

Pros & Cons

Pros

Flexible Backend Integration

Supports ChatGPT, llama.cpp, and Ollama from a single interface, allowing easy switching between cloud-based and self-hosted LLMs as highlighted in the README.

Vim-Style Keybindings

Implements comprehensive Vim navigation and editing modes for efficient text manipulation, catering to power users who prefer keyboard-driven workflows.

Cross-Platform Availability

Runs on Linux, macOS, Windows, and Android via nix-on-droid, making it versatile for different environments, including mobile development.

Local Data Management

Automatically saves and loads chat history to files, supporting offline use and easy reference of past conversations, as detailed in the features list.

Cons

Manual Configuration Overhead

Requires setting up backend servers and API keys through TOML files or environment variables, which can be complex and time-consuming for new users.

Limited Accessibility

Heavily reliant on Vim keybindings with restricted customization (e.g., Ctrl key requirements), making it less user-friendly for those not proficient in Vim.

Basic Feature Set

Lacks advanced features like plugin support, multi-model conversations, or extensive UI customization compared to more mature LLM tools.

Frequently Asked Questions

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