Implement complete MistralClient with browser automation and universal SDK

[codegen-sh]

Summary

• Implemented comprehensive SDK architecture with BaseAIClient abstract class for universal AI provider support
• Created fully functional MistralClient with Playwright browser automation for chat.mistral.ai
• Built OpenAI-compatible response format and dynamic client registry system
• Added robust authentication handling for complex web flows (Terms of Service, two-step auth, Memory modals)

Features Implemented

🏗️ Core Architecture

• BaseAIClient: Abstract base class with unified interface for all AI providers
• ClientConfig: Configuration management with provider types, auth methods, and models
• ChatRequest/ChatResponse: OpenAI-compatible data structures
• ClientRegistry: Dynamic provider registration and management system

🤖 MistralClient Implementation

• Browser Automation: Full Playwright integration with headless/visible modes
• Authentication Flow: Handles email/password login with two-step verification
• Modal Management: Automatically dismisses Terms of Service and Memory feature modals
• Session Management: Persistent browser context for efficient chat operations
• Error Handling: Comprehensive error handling with screenshot debugging

🔧 Additional Components

• DeepSeekClient: Placeholder implementation ready for extension
• OpenAIClient/AnthropicClient: Placeholder implementations for API-based providers
• Test Suite: Complete integration tests with credential validation
• Logging: Detailed logging for monitoring and debugging

Test Plan

✅ Authentication Flow

• Terms of Service modal acceptance
• Login button detection and interaction
• Email/password input handling
• Two-step authentication support
• Memory modal dismissal
• Session persistence

✅ SDK Integration

• Import system: from clients import MistralClient
• Client initialization with auto_auth
• OpenAI-compatible response format
• Error handling and logging

✅ Browser Automation

• Playwright browser management
• Element detection with multiple fallback strategies
• Proper cleanup and resource management
• Screenshot debugging capabilities

Usage Example

from clients import MistralClient

# Initialize client with auto-authentication
client = MistralClient(
    email="user@example.com",
    password="password",
    auto_auth=True,
    headless=True
)

# Send message with OpenAI-compatible response
response = client.chat(
    message="What is your model name and what is 2+3?",
    model="web_mistral"
)

print(f"Response: {response.content}")
print(f"Model: {response.model}")
print(f"Provider: {response.provider}")

Architecture Benefits

• Scalable: Easy to add new AI providers through inheritance
• Unified: Single interface for all providers (web scraping + APIs)
• OpenAI Compatible: Drop-in replacement for OpenAI client
• Robust: Comprehensive error handling and debugging tools
• Flexible: Supports various authentication methods and provider types

🤖 Generated with Claude Code

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Summary by cubic

Implements a universal SDK for AI providers and a fully working MistralClient that automates chat.mistral.ai with Playwright. Provides an OpenAI-compatible chat interface with robust auth and session handling.

• New Features
  • BaseAIClient with OpenAI-compatible request/response types and a unified interface.
  • ClientRegistry for dynamic provider registration.
  • MistralClient with Playwright automation, headless/visible modes, and persistent sessions.
  • Auth flow handling: email/password, two-step verification, Terms of Service, and Memory modals.
  • Strong error handling, screenshots for debugging, and detailed logging.
  • Integration tests covering auth and chat flows.
  • Scaffolding for DeepSeek, OpenAI, and Anthropic clients.