Skip to content

CLIver Overview

CLIver is an AI-powered command-line interface tool that enhances your terminal experience with intelligent capabilities. It integrates with MCP (Model Context Protocol) servers and various LLM providers to provide an interactive CLI experience.

Design Goals

CLIver is built as a dual-layer system:

  • API Layer (TaskExecutor): The core engine — embeddable in any Python application. It handles LLM inference, tool calling, Re-Act loops, permissions, and workflow execution with no dependency on CLI concerns (no terminal, no stdin, no prompt_toolkit). This is the layer you use when integrating CLIver as a library.
  • CLI Layer (Cliver class + Click commands): A thin interactive shell on top of TaskExecutor for terminal users. Provides Rich-formatted output, prompt_toolkit input, and slash commands.

Any feature built at the API layer (permissions, workflows, skills, memory) works identically whether invoked from the CLI or from your own Python code.

Key Features

Core Capabilities

  • Multi-LLM Support: Connect to various language models served by various providers (DeepSeek, OpenAI, Qwen3-coder on OpenAI compatible servers, vLLM, and more in the future)
  • MCP Integration: Seamlessly integrate with Model Context Protocol servers for enhanced functionality
  • Builtin Tools: 17 tools (12 core, 5 contextual) for file I/O, shell, web, planning, memory, identity, and skills
  • Tool Permissions: Resource-aware permission system controlling which tools can execute and what resources they can access
  • Skills: LLM-driven skill activation from SKILL.md files for specialized tasks
  • Memory & Identity: Agent memory and identity profiles with multi-agent isolation
  • Session Management: Conversation history with LLM-based compression and session persistence
  • Configurable Workflows: Define and execute complex workflows using YAML configuration files
  • Task Scheduling: Schedule workflows with cron expressions
  • Token Usage: Track and view token usage statistics per model and session
  • Extensible Architecture: Easy to extend with custom commands and backends

Usage Modes

CLIver operates in two primary modes:

  1. Interactive and Batch CLI Mode: Direct command-line interaction for immediate responses and operations
  2. Library Mode: Python library integration for embedding LLM capabilities in your applications

Architecture

CLIver follows a modular architecture that allows for easy extension:

graph TD
    A[CLIver Class] --> B[ConfigManager]
    A --> C[TaskExecutor]
    A --> D[CLI Interface]

    B --> E[LLM Model Config]
    B --> F[MCP Server Config]

    C --> G[LLM Inference Engines]
    C --> H[MCP & Builtin Tools Integration]
    C --> I[Workflow Engine]
    C --> N[Permission Manager]
    C --> O[Agent Profile]

    G --> J[Ollama Provider]
    G --> K[OpenAI Provider]
    G --> L[vLLM Provider]

    H --> M[MCPServersCaller]

    O --> P[Memory]
    O --> Q[Identity]
    O --> R[Sessions]
    O --> S[Tasks]

    style A fill:#e1f5fe
    style B fill:#f3e5f5
    style C fill:#f3e5f5
    style D fill:#f3e5f5
    style G fill:#e8f5e8
    style H fill:#e8f5e8
    style I fill:#e8f5e8
    style O fill:#fff3e0

Getting Started

To start using CLIver, visit our Installation Guide to set up the tool on your system, followed by the Configuration Guide to connect to your preferred LLM provider.