A Python package for interacting with the DeepSeek AI chat API. This package provides a clean interface to interact with DeepSeek's chat model, with support for streaming responses, thinking process visibility, and web search capabilities.

• and reverse wasm like it was required here
• whop.com/reverser-academy (beta)

⚠️ Service Notice: DeepSeek API is currently experiencing high load. Work is in progress to integrate additional API providers. Please expect intermittent errors.

📝 Note: If you encounter any errors, please ensure you are using the latest version of this library. The DeepSeek API may change frequently, and updates are released to maintain compatibility.

• 🔄 Streaming Responses: Real-time interaction with token-by-token output
• 🤔 Thinking Process: Optional visibility into the model's reasoning steps
• 🔍 Web Search: Optional integration for up-to-date information
• 💬 Session Management: Persistent chat sessions with conversation history
• ⚡ Efficient PoW: WebAssembly-based proof of work implementation
• 🛡️ Error Handling: Comprehensive error handling with specific exceptions
• ⏱️ No Timeouts: Designed for long-running conversations without timeouts
• 🧵 Thread Support: Parent message tracking for threaded conversations

1. Clone the repository:

git clone https://github.com/yourusername/deepseek4free.git cd deepseek4free

2. Install dependencies:

pip install -r requirements.txt

To use this package, you need a DeepSeek auth token. Here's how to obtain it:

If you know how to use chrome devtools, simply run this snipped in the console:

JSON.parse(localStorage.getItem("userToken")).value

1. Visit chat.deepseek.com
2. Log in to your account
3. Open browser developer tools (F12 or right-click > Inspect)
4. Go to Application tab (if not visible, click >> to see more tabs)
5. In the left sidebar, expand "Local Storage"
6. Click on "https://chat.deepseek.com"
7. Find the key named userToken
8. Copy "value" - this is your authentication token

Alternatively, you can get the token from network requests:

1. Visit chat.deepseek.com
2. Log in to your account
3. Open browser developer tools (F12)
4. Go to Network tab
5. Make any request in the chat
6. Find the request headers
7. Copy the authorization token (without 'Bearer ' prefix)

If you encounter Cloudflare challenges ("Just a moment..." page), you'll need to get a cf_clearance cookie. Run this command:

python -m dsk.bypass

This will:

1. Open an undetected browser
2. Visit DeepSeek and solve the Cloudflare challenge
3. Capture and save the cf_clearance cookie
4. The cookie will be automatically used in future requests

You only need to run this when:

• You get Cloudflare challenges in your requests
• Your existing cf_clearance cookie expires
• You see the error "Please wait a few minutes before trying again"

The captured cookie will be stored in dsk/cookies.json and automatically used by the API.

from dsk.api import DeepSeekAPI # Initialize with your auth token api = DeepSeekAPI("YOUR_AUTH_TOKEN") # Create a new chat session chat_id = api.create_chat_session() # Simple chat completion prompt = "What is Python?" for chunk in api.chat_completion(chat_id, prompt): if chunk['type'] == 'text': print(chunk['content'], end='', flush=True)

The thinking process shows the model's reasoning steps:

# With thinking process enabled for chunk in api.chat_completion( chat_id, "Explain quantum computing", thinking_enabled=True ): if chunk['type'] == 'thinking': print(f"🤔 Thinking: {chunk['content']}") elif chunk['type'] == 'text': print(chunk['content'], end='', flush=True)

Enable web search for up-to-date information:

# With web search enabled for chunk in api.chat_completion( chat_id, "What are the latest developments in AI?", thinking_enabled=True, search_enabled=True ): if chunk['type'] == 'thinking': print(f"🔍 Searching: {chunk['content']}") elif chunk['type'] == 'text': print(chunk['content'], end='', flush=True)

Create threaded conversations by tracking parent messages:

# Start a conversation chat_id = api.create_chat_session() # Send initial message parent_id = None for chunk in api.chat_completion(chat_id, "Tell me about neural networks"): if chunk['type'] == 'text': print(chunk['content'], end='', flush=True) elif 'message_id' in chunk: parent_id = chunk['message_id'] # Send follow-up question in the thread for chunk in api.chat_completion( chat_id, "How do they compare to other ML models?", parent_message_id=parent_id ): if chunk['type'] == 'text': print(chunk['content'], end='', flush=True)

The package provides specific exceptions for different error scenarios:

from dsk.api import ( DeepSeekAPI, AuthenticationError, RateLimitError, NetworkError, CloudflareError, APIError ) try: api = DeepSeekAPI("YOUR_AUTH_TOKEN") chat_id = api.create_chat_session() for chunk in api.chat_completion(chat_id, "Your prompt here"): if chunk['type'] == 'text': print(chunk['content'], end='', flush=True) except AuthenticationError: print("Authentication failed. Please check your token.") except RateLimitError: print("Rate limit exceeded. Please wait before making more requests.") except CloudflareError as e: print(f"Cloudflare protection encountered: {str(e)}") except NetworkError: print("Network error occurred. Check your internet connection.") except APIError as e: print(f"API error occurred: {str(e)}")