Hunter Bown
6f1158a2d7
DeepSeek TUI - Unofficial terminal UI + CLI for DeepSeek models. Features: - Interactive TUI with multiple modes (Normal, Plan, Agent, YOLO, RLM, Duo) - Comprehensive tool access with approval gating - File operations, shell execution, task management - Sub-agent system for parallel work - MCP integration for external tool servers - Session management and skills system - Cross-platform support (macOS, Linux, Windows) 🤖 Generated with [Claude Code](https://claude.ai/code)
197 lines
9.7 KiB
Markdown
197 lines
9.7 KiB
Markdown
# DeepSeek CLI Architecture
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This document provides an overview of the DeepSeek CLI architecture for developers and contributors.
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## High-Level Overview
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```
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┌─────────────────────────────────────────────────────────────────┐
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│ User Interface │
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│ ┌─────────────────┐ ┌─────────────────┐ ┌────────────────┐ │
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│ │ TUI (ratatui) │ │ One-shot Mode │ │ Config/CLI │ │
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│ └────────┬────────┘ └────────┬────────┘ └────────┬───────┘ │
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└───────────┼─────────────────────┼────────────────────┼──────────┘
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│ │ │
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▼ ▼ ▼
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┌─────────────────────────────────────────────────────────────────┐
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│ Core Engine │
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│ ┌─────────────────────────────────────────────────────────┐ │
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│ │ Agent Loop (core/engine.rs) │ │
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│ │ ┌─────────┐ ┌─────────────┐ ┌──────────────────────┐ │ │
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│ │ │ Session │ │ Turn Mgmt │ │ Tool Orchestration │ │ │
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│ │ └─────────┘ └─────────────┘ └──────────────────────┘ │ │
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│ └─────────────────────────────────────────────────────────┘ │
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└─────────────────────────────────────────────────────────────────┘
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│ │ │
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▼ ▼ ▼
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┌─────────────────────────────────────────────────────────────────┐
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│ Tool & Extension Layer │
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│ ┌──────────┐ ┌──────────┐ ┌─────────┐ ┌────────────────┐ │
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│ │ Tools │ │ Skills │ │ Hooks │ │ MCP Servers │ │
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│ │ (shell, │ │ (plugins)│ │ (pre/ │ │ (external) │ │
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│ │ file) │ │ │ │ post) │ │ │ │
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│ └──────────┘ └──────────┘ └─────────┘ └────────────────┘ │
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└─────────────────────────────────────────────────────────────────┘
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│ │ │
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▼ ▼ ▼
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┌─────────────────────────────────────────────────────────────────┐
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│ LLM Layer │
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│ ┌──────────────────────────────────────────────────────────┐ │
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│ │ LLM Client Abstraction (llm_client.rs) │ │
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│ │ ┌─────────────────┐ ┌─────────────────────────────┐ │ │
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│ │ │ DeepSeek Client │ │ Compatible Client (DeepSeek)│ │ │
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│ │ │ (client.rs) │ │ (client.rs) │ │ │
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│ │ └─────────────────┘ └─────────────────────────────┘ │ │
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│ └──────────────────────────────────────────────────────────┘ │
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└─────────────────────────────────────────────────────────────────┘
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```
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## Module Organization
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### Entry Point
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- **`main.rs`** - CLI argument parsing (clap), configuration loading, entry point routing
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### Core Components
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- **`core/`** - Main engine components
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- `engine.rs` - Agent loop, message processing, tool execution orchestration
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- `session.rs` - Session state management
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- `turn.rs` - Turn-based conversation handling
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- `events.rs` - Event system for UI updates
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- `ops.rs` - Core operations
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### Configuration
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- **`config.rs`** - Configuration loading, profiles, environment variables
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- **`settings.rs`** - Runtime settings management
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### LLM Integration
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- **`client.rs`** - HTTP client for DeepSeek's OpenAI-compatible Responses API (with chat fallback)
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- **`llm_client.rs`** - Abstract LLM client trait with retry logic
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- **`models.rs`** - Data structures for API requests/responses
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#### DeepSeek API Endpoints
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DeepSeek exposes OpenAI-compatible endpoints. The CLI uses:
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- `https://api.deepseek.com/v1/responses` - preferred Responses API
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- `https://api.deepseek.com/v1/chat/completions` - fallback if Responses is unavailable
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The engine uses `handle_deepseek_turn()` to drive the agent loop against the
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Responses API (with automatic fallback if needed).
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### Tool System
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- **`tools/`** - Built-in tool implementations
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- `mod.rs` - Tool registry and common types
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- `shell.rs` - Shell command execution
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- `file.rs` - File read/write operations
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- `todo.rs` - Todo list management
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- `plan.rs` - Planning tools
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- `subagent.rs` - Sub-agent spawning
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- `spec.rs` - Tool specifications
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### Extension Systems
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- **`mcp.rs`** - Model Context Protocol client for external tool servers
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- **`skills.rs`** - Plugin/skill loading and execution
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- **`hooks.rs`** - Pre/post execution hooks with conditions
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### User Interface
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- **`tui/`** - Terminal UI components (ratatui-based)
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- `app.rs` - Application state and message handling
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- `ui.rs` - Rendering logic
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- `approval.rs` - Tool approval dialog
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- `clipboard.rs` - Clipboard handling
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- `streaming.rs` - Streaming text collector
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- **`ui.rs`** - Legacy/simple UI utilities
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### Security
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- **`sandbox/`** - macOS sandboxing support
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- `mod.rs` - Sandbox type definitions
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- `policy.rs` - Sandbox policy configuration
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- `seatbelt.rs` - macOS Seatbelt profile generation
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### Utilities
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- **`utils.rs`** - Common utilities
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- **`logging.rs`** - Logging infrastructure
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- **`compaction.rs`** - Context compaction for long conversations
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- **`rlm.rs`** - Reflection/reasoning utilities
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- **`pricing.rs`** - Cost estimation
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- **`prompts.rs`** - System prompt templates
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- **`project_doc.rs`** - Project documentation handling
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- **`session.rs`** - Session serialization
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## Data Flow
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### Interactive Session
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1. User input received in TUI
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2. Input processed by `core/engine.rs`
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3. Message sent to LLM via `llm_client.rs`
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4. Response streamed back, parsed in `client.rs`
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5. Tool calls extracted and executed via `tools/`
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6. Hooks triggered before/after tool execution
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7. Results aggregated and sent back to LLM
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8. Final response rendered in TUI
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### Tool Execution
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1. LLM requests tool via `tool_use` content block
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2. Tool registry looks up handler
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3. Pre-execution hooks run
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4. Approval requested if needed (non-yolo mode)
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5. Tool executed (possibly sandboxed on macOS)
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6. Post-execution hooks run
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7. Result returned to agent loop
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## Extension Points
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### Adding a New Tool
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1. Create handler in `tools/`
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2. Register in `tools/registry.rs`
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3. Add tool specification (name, description, input schema)
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### Adding an MCP Server
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1. Configure in `~/.deepseek/mcp.json`
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2. Server auto-discovered at startup
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3. Tools exposed to LLM automatically
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### Creating a Skill
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1. Create skill directory with `SKILL.md`
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2. Define skill prompt and optional scripts
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3. Place in `~/.deepseek/skills/`
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### Adding Hooks
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Configure in `~/.deepseek/config.toml`:
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```toml
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[[hooks]]
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event = "tool_call_before"
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command = "echo 'Running tool: $TOOL_NAME'"
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```
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## Key Design Decisions
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1. **Streaming-first**: All LLM responses stream for responsiveness
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2. **Tool safety**: Non-yolo mode requires approval for destructive operations
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3. **Extensibility**: MCP, skills, and hooks allow customization without code changes
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4. **Cross-platform**: Core works on Linux/macOS/Windows, sandboxing macOS-only
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5. **Minimal dependencies**: Careful dependency selection for build speed
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## Configuration Files
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- `~/.deepseek/config.toml` - Main configuration
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- `~/.deepseek/mcp.json` - MCP server configuration
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- `~/.deepseek/skills/` - User skills directory
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- `~/.deepseek/sessions/` - Session history
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