llm-chat-term 0.1.3

Chat with LLMs from the terminal

Terminal LLM Chatbot

A python terminal-based chatbot application that integrates with language models using LangChain. Features include multiline text input and streaming responses.

Features

• Terminal-based user interface with rich text formatting
• Support for multiline text input
• Streaming responses from the language model
• Markdown rendering for assistant responses
• You can edit the conversation at any point to add/remove context
• Configurable via YAML

Installation

You can install llm_chat_term by running:

uv tool install llm_chat_term

or if you don't have uv installed:

pip install llm_chat_term

Set up your API key(s)

On first run, a config.yaml with default options is created in your Config dir (e.g. ~/.config/llm_chat_term) Edit it to add your API keys and customize other options. Then re-run the application.

llm:
   openai_api_key: "your_openai_api_key_here""
   anthropic_key: "your_openai_api_key_here"

Usage

Run the chatbot from the command line:

llm_chat_term

Controls

• Type your message and press Alt(Esc)+Enter to send
• Use Enter to add a new line in your message
• Type /exit, exit, or press Ctrl+D to exit the application
• Type /help to view help for available commands
• Press Ctrl+C to interrupt and exit

Configuration

The following environment variables can be set in the config.yaml file:

llm:
  provider: anthropic
  openai_api_key: ""
  anthropic_key: ""
  model: claude-3-7-sonnet-20250219
  temperature: 0.7
  system_prompt:
    You are a helpful assistant responding to a user's questions in a
    terminal environment. The user is an experienced software engineer, your answers
    should be concise and not repetitive. Skip conclusions and summarizations of your
    answers.If the user asks for a change in code, don't return the whole code, just
    the changed segment(s).
ui:
  prompt_symbol: ">>> "
  user: user
  assistant: assistant
colors:
  user: cyan
  assistant: grey39
  system: yellow

Extending

To add new features:

1. Add new LLM providers by extending the LLMClient class
2. Customize the UI by modifying the ChatUI class
3. Add configuration options in config.py

License

MIT