# Chat Mode Implementation Summary

## Overview

Added a simple, interactive chat mode to AgentOS similar to Gemini, Claude Code, and Codex CLI. Users can now have real-time conversations with various LLM providers through the command line.

## What's New

### 1. **New Chat Command**

```bash
agentos chat [OPTIONS]
```

**Options:**

- `--provider/-p` - Choose LLM provider (github, gemini, cohere, openai, claude, ollama)
- `--model/-m` - Specify a model (uses provider default if not specified)
- `--temperature` - Adjust response creativity (0.0-1.0)
- `--system-prompt/-s` - Provide custom instructions for the AI

### 2. **Files Created**

#### Core Implementation

- **[agentos/cli/cli_cmd_chat.py](../agentos/cli/cli_cmd_chat.py)** (175 lines)
  - Main chat mode implementation
  - Interactive loop with command handling
  - Rich terminal UI with markdown rendering
  - Supports all 6 LLM providers

#### Documentation

- **[MD/CHAT_MODE.md](./CHAT_MODE.md)**
  - Complete user guide
  - Usage examples for each provider
  - Configuration instructions
  - Troubleshooting tips

#### Examples

- **[examples/chat-demo.py](../examples/chat-demo.py)**
  - Demo script showing all usage patterns
  - Configuration guide
  - Feature overview

### 3. **Files Modified**

#### CLI Integration

- **[agentos/cli/cli_parser.py](../agentos/cli/cli_parser.py)**

  - Added chat command parser
  - Updated examples section

- **[agentos/cli/cli_commands.py](../agentos/cli/cli_commands.py)**

  - Exported `cmd_chat` function

- **[agentos/agentos.py](../agentos.py)**
  - Imported and registered chat command
  - Added command dispatcher mapping

## Usage Examples

### Start with default (OpenAI):

```bash
agentos chat
```

### Use different providers:

```bash
agentos chat --provider claude
agentos chat --provider gemini --temperature 0.3
agentos chat --provider ollama --model llama2
agentos chat --provider github
```

### With custom settings:

```bash
agentos chat --provider openai --model gpt-4 --temperature 0.5
agentos chat --system-prompt "You are a Python expert"
```

## Supported LLM Providers

| Provider   | Model                        | Requires API Key | Notes                     |
| ---------- | ---------------------------- | ---------------- | ------------------------- |
| **OpenAI** | gpt-4o-mini                  | ✓ OPENAI_API_KEY | Default, fast & capable   |
| **Claude** | claude-3-5-haiku             | ✓ CLAUDE_API_KEY | Great for complex tasks   |
| **Gemini** | models/gemini-2.0-flash-lite | ✓ GEMINI_API_KEY | Google's latest           |
| **GitHub** | openai/gpt-4o-mini           | ✓ GIT_HUB_TOKEN  | Free tier available       |
| **Cohere** | command-xlarge-nightly       | ✓ COHERE_API_KEY | Specialized capabilities  |
| **Ollama** | phi3                         | ✗ None           | Local only, fully offline |

## In-Chat Commands

Within an active chat session:

- **exit** / **quit** - End the session
- **clear** - Clear chat history
- **status** - Show message count
- **help** - Show available commands

## Features

✨ **Rich Terminal UI**

- Color-coded messages
- Markdown rendering for responses
- Beautiful panels and formatting
- Syntax highlighting support

📝 **Conversation Management**

- Maintains chat history across messages
- Clear history when needed
- Track conversation status

🔄 **Multi-Provider**

- Seamless provider switching
- Auto-selects provider defaults
- Compatible with all AgentOS LLM integrations

⚙️ **Customization**

- Adjustable temperature parameter
- Custom system prompts
- Model selection per provider
- Verbose logging support

🔒 **Offline Capable**

- Ollama support for local models
- No internet required with local setup
- Privacy-friendly option

## How It Works

1. User runs `agentos chat [options]`
2. Parser validates provider and loads arguments
3. Chat session initializes with welcome banner
4. User enters messages in interactive loop
5. Each message is sent to the chosen LLM provider
6. Response is displayed with rich formatting
7. Conversation context is maintained
8. Commands are processed (exit, clear, help, etc.)
9. Session continues until user exits

## Implementation Highlights

### Multi-Provider Support

- Uses existing `agentos/llm/answerer.py` for provider abstraction
- Leverages 6 pre-integrated LLM providers
- Fallback to provider defaults

### Rich UI

- Gracefully handles missing Rich library
- Falls back to plain text if Rich unavailable
- Beautiful error handling and status messages

### Chat History Management

- Leverages existing `agentos/core/utils.py` chat_history
- Maintains context across turns
- Simple clear mechanism

### Error Handling

- Validates provider selection
- Handles API errors gracefully
- Supports keyboard interrupt (Ctrl+C)
- Clear error messages

## Testing

The implementation has been verified:

- ✓ Chat command appears in main help
- ✓ Chat subcommand help works correctly
- ✓ All provider options are listed
- ✓ Command parser integration successful
- ✓ Import chain validation passed

## Next Steps (Optional Enhancements)

Potential future additions:

1. Save/load conversation transcripts
2. Code syntax highlighting for code blocks
3. Export conversations as markdown/PDF
4. Multi-turn prompt templates
5. Chat mode presets for common scenarios
6. Integration with agent pipelines
7. Web UI for chat mode
8. Voice/audio input support

## Configuration

### Environment Setup

Create or update `.env` file:

```env
OPENAI_API_KEY=sk-...
CLAUDE_API_KEY=sk-ant-...
GEMINI_API_KEY=...
GIT_HUB_TOKEN=github_pat_...
COHERE_API_KEY=...
```

### Ollama Setup (Optional)

```bash
# Install Ollama from https://ollama.ai
ollama serve              # Start Ollama in terminal 1
ollama pull phi3          # Download a model in terminal 2
agentos chat --provider ollama
```

## Related Documentation

- Main README: [README.md](../README.md)
- Quick Reference: [QUICK_REFERENCE.md](./QUICK_REFERENCE.md)
- Production Guide: [PRODUCTION_READY.md](./PRODUCTION_READY.md)
- Chat Mode Guide: [CHAT_MODE.md](./CHAT_MODE.md)

## Summary

The chat mode transforms AgentOS into a versatile conversational AI interface. It provides:

- Simple CLI access to multiple LLM providers
- Rich, user-friendly terminal experience
- Full context preservation across turns
- Flexibility with customizable parameters
- Support for both cloud and local models

Perfect for:

- Quick AI consultations
- Learning and exploration
- Code assistance
- Brainstorming
- General-purpose conversations
