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LLM CLI Tool Brainstorm

Core Concept

A CLI tool that provides Claude Code-like capabilities but with the flexibility to switch between different LLM models (Claude, GPT, Gemini). This tool will be publishable as a package for others to easily install and use.

Architecture

Core Components

  1. CLI Interface - Command parsing, user interaction
  2. Model Manager - Handles model selection and API integration
  3. Context Manager - Maintains conversation history
  4. Tool Framework - Integrates system tools for file operations, web access, etc.
  5. Output Formatter - Renders responses appropriately for terminal

Model Integration

  • Abstract adapter pattern for different LLM APIs
  • Standardized prompt formatting across models
  • Configuration system for API keys and preferences
  • Capability detection for model-specific features

User Experience

Model Selection

# Set default model
$ llm config set-default-model gemini-2.5-pro

# Use specific model for current session
$ llm --model gpt-o1

Configuration

$ llm config add-api-key claude CLAUDE_API_KEY
$ llm config add-api-key openai OPENAI_API_KEY
$ llm config add-api-key google GOOGLE_API_KEY

Session Management

  • Persistent conversations with model switching
  • Ability to export/import sessions
  • Session isolation for different projects
  • Context management with /compact command:
    • Warns when approaching token limits (e.g., Gemini 2.5 Pro's 1M context)
    • Generates conversation summary on demand
    • Carries summary forward to new context window
    • Allows for theoretically infinite conversation length

Technical Considerations

API Differences

  • Handle token limits per model
  • Account for different capabilities (code generation, tools, etc.)
  • Normalize response formats for consistent UX
  • Model-specific Tool Implementation - Hardcode specific tool sets for OpenAI and Gemini initially

Authentication

  • Secure storage of API keys
  • Support for organization accounts and custom endpoints

Performance

  • Streaming responses for all models
  • Local caching of contexts to minimize token usage
  • Efficient file handling for large codebases

Implementation Plan

  1. Start with basic CLI framework (Click, Typer, or similar)
  2. Implement Gemini 2.5 Pro integration first:
    • Basic conversation capability
    • Token tracking and context management
    • /compact command implementation
  3. Add basic file operations and tool integration
  4. Expand to OpenAI models
  5. Enhance with advanced tools (search, web access)
  6. Package for distribution (PyPI/npm)
  7. Create documentation and examples
  8. Refine UX based on user feedback

Distribution Strategy

Package Publishing

  • Publish to package managers (PyPI for Python, npm for Node.js)
  • Create easy installation process (pip install llm-cli or npm install -g llm-cli)
  • Version management and release cycle

First-Run Experience

  • Interactive configuration on first run
  • API key setup wizard
  • Default model selection
  • Quick tutorial/example workflow

Open Questions

  • How to handle tool capabilities that differ between models?
  • What's the best local storage approach for conversation history?
  • How to implement efficient codebase indexing for context?
  • Should we support plugins/extensions for custom tools?