#1490 feat(ManagedBrowser): add viewport size configuration for browser launch

Fix/proxy deprecation

.gitignore

@@ -265,7 +265,7 @@ CLAUDE.md
 tests/**/test_site
 tests/**/reports
 tests/**/benchmark_reports
-
+test_scripts/
 docs/**/data
 .codecat/
 

crawl4ai/adaptive_crawler.py

@@ -19,7 +19,7 @@ import re
 from pathlib import Path
 
 from crawl4ai.async_webcrawler import AsyncWebCrawler
-from crawl4ai.async_configs import CrawlerRunConfig, LinkPreviewConfig
+from crawl4ai.async_configs import CrawlerRunConfig, LinkPreviewConfig, LLMConfig
 from crawl4ai.models import Link, CrawlResult
 import numpy as np
 
@@ -178,7 +178,7 @@ class AdaptiveConfig:
     
     # Embedding strategy parameters
     embedding_model: str = "sentence-transformers/all-MiniLM-L6-v2"
-    embedding_llm_config: Optional[Dict] = None  # Separate config for embeddings
+    embedding_llm_config: Optional[Union[LLMConfig, Dict]] = None  # Separate config for embeddings
     n_query_variations: int = 10
     coverage_threshold: float = 0.85
     alpha_shape_alpha: float = 0.5
@@ -250,6 +250,30 @@ class AdaptiveConfig:
         assert 0 <= self.embedding_quality_max_confidence <= 1, "embedding_quality_max_confidence must be between 0 and 1"
         assert self.embedding_quality_scale_factor > 0, "embedding_quality_scale_factor must be positive"
         assert 0 <= self.embedding_min_confidence_threshold <= 1, "embedding_min_confidence_threshold must be between 0 and 1"
+    
+    @property
+    def _embedding_llm_config_dict(self) -> Optional[Dict]:
+        """Convert LLMConfig to dict format for backward compatibility."""
+        if self.embedding_llm_config is None:
+            return None
+        
+        if isinstance(self.embedding_llm_config, dict):
+            # Already a dict - return as-is for backward compatibility
+            return self.embedding_llm_config
+        
+        # Convert LLMConfig object to dict format
+        return {
+            'provider': self.embedding_llm_config.provider,
+            'api_token': self.embedding_llm_config.api_token,
+            'base_url': getattr(self.embedding_llm_config, 'base_url', None),
+            'temperature': getattr(self.embedding_llm_config, 'temperature', None),
+            'max_tokens': getattr(self.embedding_llm_config, 'max_tokens', None),
+            'top_p': getattr(self.embedding_llm_config, 'top_p', None),
+            'frequency_penalty': getattr(self.embedding_llm_config, 'frequency_penalty', None),
+            'presence_penalty': getattr(self.embedding_llm_config, 'presence_penalty', None),
+            'stop': getattr(self.embedding_llm_config, 'stop', None),
+            'n': getattr(self.embedding_llm_config, 'n', None),
+        }
 
 
 class CrawlStrategy(ABC):
@@ -593,7 +617,7 @@ class StatisticalStrategy(CrawlStrategy):
 class EmbeddingStrategy(CrawlStrategy):
     """Embedding-based adaptive crawling using semantic space coverage"""
     
-    def __init__(self, embedding_model: str = None, llm_config: Dict = None):
+    def __init__(self, embedding_model: str = None, llm_config: Union[LLMConfig, Dict] = None):
         self.embedding_model = embedding_model or "sentence-transformers/all-MiniLM-L6-v2"
         self.llm_config = llm_config
         self._embedding_cache = {}
@@ -605,14 +629,24 @@ class EmbeddingStrategy(CrawlStrategy):
         self._kb_embeddings_hash = None  # Track KB changes
         self._validation_embeddings_cache = None  # Cache validation query embeddings
         self._kb_similarity_threshold = 0.95  # Threshold for deduplication
+    
+    def _get_embedding_llm_config_dict(self) -> Dict:
+        """Get embedding LLM config as dict with fallback to default."""
+        if hasattr(self, 'config') and self.config:
+            config_dict = self.config._embedding_llm_config_dict
+            if config_dict:
+                return config_dict
+        
+        # Fallback to default if no config provided
+        return {
+            'provider': 'openai/text-embedding-3-small',
+            'api_token': os.getenv('OPENAI_API_KEY')
+        }
         
     async def _get_embeddings(self, texts: List[str]) -> Any:
         """Get embeddings using configured method"""
         from .utils import get_text_embeddings
-        embedding_llm_config = {
-            'provider': 'openai/text-embedding-3-small',
-            'api_token': os.getenv('OPENAI_API_KEY')
-        }
+        embedding_llm_config = self._get_embedding_llm_config_dict()
         return await get_text_embeddings(
             texts, 
             embedding_llm_config,
@@ -679,8 +713,20 @@ class EmbeddingStrategy(CrawlStrategy):
         Return as a JSON array of strings."""
         
         # Use the LLM for query generation
-        provider = self.llm_config.get('provider', 'openai/gpt-4o-mini') if self.llm_config else 'openai/gpt-4o-mini'
-        api_token = self.llm_config.get('api_token') if self.llm_config else None
+        # Convert LLMConfig to dict if needed
+        llm_config_dict = None
+        if self.llm_config:
+            if isinstance(self.llm_config, dict):
+                llm_config_dict = self.llm_config
+            else:
+                # Convert LLMConfig object to dict
+                llm_config_dict = {
+                    'provider': self.llm_config.provider,
+                    'api_token': self.llm_config.api_token
+                }
+        
+        provider = llm_config_dict.get('provider', 'openai/gpt-4o-mini') if llm_config_dict else 'openai/gpt-4o-mini'
+        api_token = llm_config_dict.get('api_token') if llm_config_dict else None
         
         # response = perform_completion_with_backoff(
         #     provider=provider,
@@ -843,10 +889,7 @@ class EmbeddingStrategy(CrawlStrategy):
         
         # Batch embed only uncached links
         if texts_to_embed:
-            embedding_llm_config = {
-                'provider': 'openai/text-embedding-3-small',
-                'api_token': os.getenv('OPENAI_API_KEY')
-            }
+            embedding_llm_config = self._get_embedding_llm_config_dict()
             new_embeddings = await get_text_embeddings(texts_to_embed, embedding_llm_config, self.embedding_model)
 
             # Cache the new embeddings
@@ -1184,10 +1227,7 @@ class EmbeddingStrategy(CrawlStrategy):
             return
             
         # Get embeddings for new texts
-        embedding_llm_config = {
-            'provider': 'openai/text-embedding-3-small',
-            'api_token': os.getenv('OPENAI_API_KEY')
-        }        
+        embedding_llm_config = self._get_embedding_llm_config_dict()      
         new_embeddings = await get_text_embeddings(new_texts, embedding_llm_config, self.embedding_model)
 
         # Deduplicate embeddings before adding to KB
@@ -1256,10 +1296,12 @@ class AdaptiveCrawler:
         if strategy_name == "statistical":
             return StatisticalStrategy()
         elif strategy_name == "embedding":
-            return EmbeddingStrategy(
+            strategy = EmbeddingStrategy(
                 embedding_model=self.config.embedding_model,
                 llm_config=self.config.embedding_llm_config
             )
+            strategy.config = self.config  # Pass config to strategy
+            return strategy
         else:
             raise ValueError(f"Unknown strategy: {strategy_name}")
     

crawl4ai/async_configs.py

@@ -1,5 +1,6 @@
 import os
 from typing import Union
+import warnings
 from .config import (
     DEFAULT_PROVIDER,
     DEFAULT_PROVIDER_API_KEY,
@@ -257,24 +258,39 @@ class ProxyConfig:
     
     @staticmethod
     def from_string(proxy_str: str) -> "ProxyConfig":
-        """Create a ProxyConfig from a string in the format 'ip:port:username:password'."""
-        parts = proxy_str.split(":")
-        if len(parts) == 4:  # ip:port:username:password
+        """Create a ProxyConfig from a string.
+
+        Supported formats:
+        - 'http://username:password@ip:port'
+        - 'http://ip:port'
+        - 'socks5://ip:port'
+        - 'ip:port:username:password'
+        - 'ip:port'
+        """
+        s = (proxy_str or "").strip()
+        # URL with credentials
+        if "@" in s and "://" in s:
+            auth_part, server_part = s.split("@", 1)
+            protocol, credentials = auth_part.split("://", 1)
+            if ":" in credentials:
+                username, password = credentials.split(":", 1)
+                return ProxyConfig(
+                    server=f"{protocol}://{server_part}",
+                    username=username,
+                    password=password,
+                )
+        # URL without credentials (keep scheme)
+        if "://" in s and "@" not in s:
+            return ProxyConfig(server=s)
+        # Colon separated forms
+        parts = s.split(":")
+        if len(parts) == 4:
             ip, port, username, password = parts
-            return ProxyConfig(
-                server=f"http://{ip}:{port}",
-                username=username,
-                password=password,
-                ip=ip
-            )
-        elif len(parts) == 2:  # ip:port only
+            return ProxyConfig(server=f"http://{ip}:{port}", username=username, password=password)
+        if len(parts) == 2:
             ip, port = parts
-            return ProxyConfig(
-                server=f"http://{ip}:{port}",
-                ip=ip
-            )
-        else:
-            raise ValueError(f"Invalid proxy string format: {proxy_str}")
+            return ProxyConfig(server=f"http://{ip}:{port}")
+        raise ValueError(f"Invalid proxy string format: {proxy_str}")
     
     @staticmethod
     def from_dict(proxy_dict: Dict) -> "ProxyConfig":
@@ -438,6 +454,7 @@ class BrowserConfig:
         host: str = "localhost",
         enable_stealth: bool = False,
     ):
+        
         self.browser_type = browser_type
         self.headless = headless 
         self.browser_mode = browser_mode
@@ -450,13 +467,22 @@ class BrowserConfig:
         if self.browser_type in ["firefox", "webkit"]:
             self.channel = ""
             self.chrome_channel = ""
+        if proxy:
+            warnings.warn("The 'proxy' parameter is deprecated and will be removed in a future release. Use 'proxy_config' instead.", UserWarning)
         self.proxy = proxy
         self.proxy_config = proxy_config
         if isinstance(self.proxy_config, dict):
             self.proxy_config = ProxyConfig.from_dict(self.proxy_config)
         if isinstance(self.proxy_config, str):
             self.proxy_config = ProxyConfig.from_string(self.proxy_config)
-
+        
+        if self.proxy and self.proxy_config:
+            warnings.warn("Both 'proxy' and 'proxy_config' are provided. 'proxy_config' will take precedence.", UserWarning)
+            self.proxy = None
+        elif self.proxy:
+            # Convert proxy string to ProxyConfig if proxy_config is not provided
+            self.proxy_config = ProxyConfig.from_string(self.proxy)
+            self.proxy = None
 
         self.viewport_width = viewport_width
         self.viewport_height = viewport_height

crawl4ai/browser_manager.py

@@ -15,6 +15,7 @@ from .js_snippet import load_js_script
 from .config import DOWNLOAD_PAGE_TIMEOUT
 from .async_configs import BrowserConfig, CrawlerRunConfig
 from .utils import get_chromium_path
+import warnings
 
 
 BROWSER_DISABLE_OPTIONS = [
@@ -368,6 +369,9 @@ class ManagedBrowser:
             ]
             if self.headless:
                 flags.append("--headless=new")
+            # Add viewport flag if specified in config
+            if self.browser_config.viewport_height and self.browser_config.viewport_width:
+                flags.append(f"--window-size={self.browser_config.viewport_width},{self.browser_config.viewport_height}")
             # merge common launch flags
             flags.extend(self.build_browser_flags(self.browser_config))
         elif self.browser_type == "firefox":
@@ -741,17 +745,18 @@ class BrowserManager:
             )
             os.makedirs(browser_args["downloads_path"], exist_ok=True)
 
-        if self.config.proxy or self.config.proxy_config:
+        if self.config.proxy:
+            warnings.warn(
+                "BrowserConfig.proxy is deprecated and ignored. Use proxy_config instead.",
+                DeprecationWarning,
+            )
+        if self.config.proxy_config:
             from playwright.async_api import ProxySettings
 
-            proxy_settings = (
-                ProxySettings(server=self.config.proxy)
-                if self.config.proxy
-                else ProxySettings(
-                    server=self.config.proxy_config.server,
-                    username=self.config.proxy_config.username,
-                    password=self.config.proxy_config.password,
-                )
+            proxy_settings = ProxySettings(
+                server=self.config.proxy_config.server,
+                username=self.config.proxy_config.username,
+                password=self.config.proxy_config.password,
             )
             browser_args["proxy"] = proxy_settings
 

crawl4ai/deep_crawling/bff_strategy.py

@@ -122,11 +122,6 @@ class BestFirstCrawlingStrategy(DeepCrawlStrategy):
                 
             valid_links.append(base_url)
             
-        # If we have more valid links than capacity, limit them
-        if len(valid_links) > remaining_capacity:
-            valid_links = valid_links[:remaining_capacity]
-            self.logger.info(f"Limiting to {remaining_capacity} URLs due to max_pages limit")
-            
         # Record the new depths and add to next_links
         for url in valid_links:
             depths[url] = new_depth
@@ -146,7 +141,8 @@ class BestFirstCrawlingStrategy(DeepCrawlStrategy):
         """
         queue: asyncio.PriorityQueue = asyncio.PriorityQueue()
         # Push the initial URL with score 0 and depth 0.
-        await queue.put((0, 0, start_url, None))
+        initial_score = self.url_scorer.score(start_url) if self.url_scorer else 0
+        await queue.put((-initial_score, 0, start_url, None))
         visited: Set[str] = set()
         depths: Dict[str, int] = {start_url: 0}
 
@@ -193,7 +189,7 @@ class BestFirstCrawlingStrategy(DeepCrawlStrategy):
                 result.metadata = result.metadata or {}
                 result.metadata["depth"] = depth
                 result.metadata["parent_url"] = parent_url
-                result.metadata["score"] = score
+                result.metadata["score"] = -score
                 
                 # Count only successful crawls toward max_pages limit
                 if result.success:
@@ -214,7 +210,7 @@ class BestFirstCrawlingStrategy(DeepCrawlStrategy):
                     for new_url, new_parent in new_links:
                         new_depth = depths.get(new_url, depth + 1)
                         new_score = self.url_scorer.score(new_url) if self.url_scorer else 0
-                        await queue.put((new_score, new_depth, new_url, new_parent))
+                        await queue.put((-new_score, new_depth, new_url, new_parent))
 
         # End of crawl.
 

deploy/docker/auth.py

@@ -28,25 +28,43 @@ def create_access_token(data: dict, expires_delta: Optional[timedelta] = None) -
     signing_key = get_jwk_from_secret(SECRET_KEY)
     return instance.encode(to_encode, signing_key, alg='HS256')
 
-def verify_token(credentials: HTTPAuthorizationCredentials = Depends(security)) -> Dict:
+def verify_token(credentials: HTTPAuthorizationCredentials) -> Dict:
     """Verify the JWT token from the Authorization header."""
-
-    if credentials is None:
-        return None
+    
+    if not credentials or not credentials.credentials:
+        raise HTTPException(
+            status_code=401, 
+            detail="No token provided",
+            headers={"WWW-Authenticate": "Bearer"}
+        )
+    
     token = credentials.credentials
     verifying_key = get_jwk_from_secret(SECRET_KEY)
     try:
         payload = instance.decode(token, verifying_key, do_time_check=True, algorithms='HS256')
         return payload
-    except Exception:
-        raise HTTPException(status_code=401, detail="Invalid or expired token")
+    except Exception as e:
+        raise HTTPException(
+            status_code=401, 
+            detail=f"Invalid or expired token: {str(e)}",
+            headers={"WWW-Authenticate": "Bearer"}
+        )
 
 
 def get_token_dependency(config: Dict):
     """Return the token dependency if JWT is enabled, else a function that returns None."""
-
+    
     if config.get("security", {}).get("jwt_enabled", False):
-        return verify_token
+        def jwt_required(credentials: HTTPAuthorizationCredentials = Depends(security)) -> Dict:
+            """Enforce JWT authentication when enabled."""
+            if credentials is None:
+                raise HTTPException(
+                    status_code=401, 
+                    detail="Authentication required. Please provide a valid Bearer token.",
+                    headers={"WWW-Authenticate": "Bearer"}
+                )
+            return verify_token(credentials)
+        return jwt_required
     else:
         return lambda: None
 

deploy/docker/c4ai-code-context.md

@@ -7520,17 +7520,18 @@ class BrowserManager:
             )
             os.makedirs(browser_args["downloads_path"], exist_ok=True)
 
-        if self.config.proxy or self.config.proxy_config:
+        if self.config.proxy:
+            warnings.warn(
+                "BrowserConfig.proxy is deprecated and ignored. Use proxy_config instead.",
+                DeprecationWarning,
+            )
+        if self.config.proxy_config:
             from playwright.async_api import ProxySettings
 
-            proxy_settings = (
-                ProxySettings(server=self.config.proxy)
-                if self.config.proxy
-                else ProxySettings(
-                    server=self.config.proxy_config.server,
-                    username=self.config.proxy_config.username,
-                    password=self.config.proxy_config.password,
-                )
+            proxy_settings = ProxySettings(
+                server=self.config.proxy_config.server,
+                username=self.config.proxy_config.username,
+                password=self.config.proxy_config.password,
             )
             browser_args["proxy"] = proxy_settings
 

deploy/docker/config.yml

@@ -38,8 +38,8 @@ rate_limiting:
 
 # Security Configuration
 security:
-  enabled: false 
-  jwt_enabled: false 
+  enabled: false
+  jwt_enabled: false
   https_redirect: false
   trusted_hosts: ["*"]
   headers:

docs/examples/adaptive_crawling/llm_config_example.py

@@ -0,0 +1,154 @@
+import asyncio
+import os
+from crawl4ai import AsyncWebCrawler, AdaptiveCrawler, AdaptiveConfig, LLMConfig
+
+
+async def test_configuration(name: str, config: AdaptiveConfig, url: str, query: str):
+    """Test a specific configuration"""
+    print(f"\n{'='*60}")
+    print(f"Configuration: {name}")
+    print(f"{'='*60}")
+    
+    async with AsyncWebCrawler(verbose=False) as crawler:
+        adaptive = AdaptiveCrawler(crawler, config)
+        result = await adaptive.digest(start_url=url, query=query)
+        
+        print("\n" + "="*50)
+        print("CRAWL STATISTICS")
+        print("="*50)
+        adaptive.print_stats(detailed=False)
+        
+        # Get the most relevant content found
+        print("\n" + "="*50)
+        print("MOST RELEVANT PAGES")
+        print("="*50)
+        
+        relevant_pages = adaptive.get_relevant_content(top_k=5)
+        for i, page in enumerate(relevant_pages, 1):
+            print(f"\n{i}. {page['url']}")
+            print(f"   Relevance Score: {page['score']:.2%}")
+            
+            # Show a snippet of the content
+            content = page['content'] or ""
+            if content:
+                snippet = content[:200].replace('\n', ' ')
+                if len(content) > 200:
+                    snippet += "..."
+                print(f"   Preview: {snippet}")
+        
+        print(f"\n{'='*50}")
+        print(f"Pages crawled: {len(result.crawled_urls)}")
+        print(f"Final confidence: {adaptive.confidence:.1%}")
+        print(f"Stopped reason: {result.metrics.get('stopped_reason', 'max_pages')}")
+        
+        if result.metrics.get('is_irrelevant', False):
+            print("⚠️  Query detected as irrelevant!")
+        
+        return result
+
+
+async def llm_embedding():
+    """Demonstrate various embedding configurations"""
+    
+    print("EMBEDDING STRATEGY CONFIGURATION EXAMPLES")
+    print("=" * 60)
+    
+    # Base URL and query for testing
+    test_url = "https://docs.python.org/3/library/asyncio.html"
+    
+    openai_llm_config = LLMConfig(
+        provider='openai/text-embedding-3-small',
+        api_token=os.getenv('OPENAI_API_KEY'),
+        temperature=0.7,
+        max_tokens=2000
+    )
+    config_openai = AdaptiveConfig(
+        strategy="embedding",
+        max_pages=10,
+        
+        # Use OpenAI embeddings
+        embedding_llm_config=openai_llm_config,
+        # embedding_llm_config={
+        #     'provider': 'openai/text-embedding-3-small',
+        #     'api_token': os.getenv('OPENAI_API_KEY')
+        # },
+        
+        # OpenAI embeddings are high quality, can be stricter
+        embedding_k_exp=4.0,
+        n_query_variations=12
+    )
+    
+    await test_configuration(
+        "OpenAI Embeddings",
+        config_openai,
+        test_url,
+        # "event-driven architecture patterns"
+        "async await context managers coroutines"
+    )
+    return
+    
+    
+
+async def basic_adaptive_crawling():
+    """Basic adaptive crawling example"""
+    
+    # Initialize the crawler
+    async with AsyncWebCrawler(verbose=True) as crawler:
+        # Create an adaptive crawler with default settings (statistical strategy)
+        adaptive = AdaptiveCrawler(crawler)
+        
+        # Note: You can also use embedding strategy for semantic understanding:
+        # from crawl4ai import AdaptiveConfig
+        # config = AdaptiveConfig(strategy="embedding")
+        # adaptive = AdaptiveCrawler(crawler, config)
+        
+        # Start adaptive crawling
+        print("Starting adaptive crawl for Python async programming information...")
+        result = await adaptive.digest(
+            start_url="https://docs.python.org/3/library/asyncio.html",
+            query="async await context managers coroutines"
+        )
+        
+        # Display crawl statistics
+        print("\n" + "="*50)
+        print("CRAWL STATISTICS")
+        print("="*50)
+        adaptive.print_stats(detailed=False)
+        
+        # Get the most relevant content found
+        print("\n" + "="*50)
+        print("MOST RELEVANT PAGES")
+        print("="*50)
+        
+        relevant_pages = adaptive.get_relevant_content(top_k=5)
+        for i, page in enumerate(relevant_pages, 1):
+            print(f"\n{i}. {page['url']}")
+            print(f"   Relevance Score: {page['score']:.2%}")
+            
+            # Show a snippet of the content
+            content = page['content'] or ""
+            if content:
+                snippet = content[:200].replace('\n', ' ')
+                if len(content) > 200:
+                    snippet += "..."
+                print(f"   Preview: {snippet}")
+        
+        # Show final confidence
+        print(f"\n{'='*50}")
+        print(f"Final Confidence: {adaptive.confidence:.2%}")
+        print(f"Total Pages Crawled: {len(result.crawled_urls)}")
+        print(f"Knowledge Base Size: {len(adaptive.state.knowledge_base)} documents")
+        
+        
+        if adaptive.confidence >= 0.8:
+            print("✓ High confidence - can answer detailed questions about async Python")
+        elif adaptive.confidence >= 0.6:
+            print("~ Moderate confidence - can answer basic questions") 
+        else:
+            print("✗ Low confidence - need more information")
+
+
+
+if __name__ == "__main__":
+    asyncio.run(llm_embedding())
+    # asyncio.run(basic_adaptive_crawling())

docs/md_v2/advanced/proxy-security.md

@@ -7,13 +7,13 @@ Simple proxy configuration with `BrowserConfig`:
 ```python
 from crawl4ai.async_configs import BrowserConfig
 
-# Using proxy URL
-browser_config = BrowserConfig(proxy="http://proxy.example.com:8080")
+# Using HTTP proxy
+browser_config = BrowserConfig(proxy_config={"server": "http://proxy.example.com:8080"})
 async with AsyncWebCrawler(config=browser_config) as crawler:
     result = await crawler.arun(url="https://example.com")
 
 # Using SOCKS proxy
-browser_config = BrowserConfig(proxy="socks5://proxy.example.com:1080")
+browser_config = BrowserConfig(proxy_config={"server": "socks5://proxy.example.com:1080"})
 async with AsyncWebCrawler(config=browser_config) as crawler:
     result = await crawler.arun(url="https://example.com")
 ```
@@ -25,7 +25,11 @@ Use an authenticated proxy with `BrowserConfig`:
 ```python
 from crawl4ai.async_configs import BrowserConfig
 
-browser_config = BrowserConfig(proxy="http://[username]:[password]@[host]:[port]")
+browser_config = BrowserConfig(proxy_config={
+    "server": "http://[host]:[port]",
+    "username": "[username]",
+    "password": "[password]",
+})
 async with AsyncWebCrawler(config=browser_config) as crawler:
     result = await crawler.arun(url="https://example.com")
 ```

docs/md_v2/api/parameters.md

@@ -23,7 +23,7 @@ browser_cfg = BrowserConfig(
 | **`headless`**        | `bool` (default: `True`)               | Headless means no visible UI. `False` is handy for debugging.                                                                         |
 | **`viewport_width`**  | `int` (default: `1080`)                | Initial page width (in px). Useful for testing responsive layouts.                                                                    |
 | **`viewport_height`** | `int` (default: `600`)                 | Initial page height (in px).                                                                                                          |
-| **`proxy`**           | `str` (default: `None`)                | Single-proxy URL if you want all traffic to go through it, e.g. `"http://user:pass@proxy:8080"`.                                      |
+| **`proxy`**           | `str` (deprecated)                      | Deprecated. Use `proxy_config` instead. If set, it will be auto-converted internally. |
 | **`proxy_config`**    | `dict` (default: `None`)               | For advanced or multi-proxy needs, specify details like `{"server": "...", "username": "...", ...}`.                                  |
 | **`use_persistent_context`** | `bool` (default: `False`)       | If `True`, uses a **persistent** browser context (keep cookies, sessions across runs). Also sets `use_managed_browser=True`.          |
 | **`user_data_dir`**   | `str or None` (default: `None`)        | Directory to store user data (profiles, cookies). Must be set if you want permanent sessions.                                         |

docs/md_v2/core/adaptive-crawling.md

@@ -108,7 +108,19 @@ config = AdaptiveConfig(
     embedding_min_confidence_threshold=0.1  # Stop if completely irrelevant
 )
 
-# With custom embedding provider (e.g., OpenAI)
+# With custom LLM provider for query expansion (recommended)
+from crawl4ai import LLMConfig
+
+config = AdaptiveConfig(
+    strategy="embedding",
+    embedding_llm_config=LLMConfig(
+        provider='openai/text-embedding-3-small',
+        api_token='your-api-key',
+        temperature=0.7
+    )
+)
+
+# Alternative: Dictionary format (backward compatible)
 config = AdaptiveConfig(
     strategy="embedding",
     embedding_llm_config={

tests/adaptive/test_llm_embedding.py

@@ -0,0 +1,154 @@
+import asyncio
+import os
+from crawl4ai import AsyncWebCrawler, AdaptiveCrawler, AdaptiveConfig, LLMConfig
+
+
+async def test_configuration(name: str, config: AdaptiveConfig, url: str, query: str):
+    """Test a specific configuration"""
+    print(f"\n{'='*60}")
+    print(f"Configuration: {name}")
+    print(f"{'='*60}")
+    
+    async with AsyncWebCrawler(verbose=False) as crawler:
+        adaptive = AdaptiveCrawler(crawler, config)
+        result = await adaptive.digest(start_url=url, query=query)
+        
+        print("\n" + "="*50)
+        print("CRAWL STATISTICS")
+        print("="*50)
+        adaptive.print_stats(detailed=False)
+        
+        # Get the most relevant content found
+        print("\n" + "="*50)
+        print("MOST RELEVANT PAGES")
+        print("="*50)
+        
+        relevant_pages = adaptive.get_relevant_content(top_k=5)
+        for i, page in enumerate(relevant_pages, 1):
+            print(f"\n{i}. {page['url']}")
+            print(f"   Relevance Score: {page['score']:.2%}")
+            
+            # Show a snippet of the content
+            content = page['content'] or ""
+            if content:
+                snippet = content[:200].replace('\n', ' ')
+                if len(content) > 200:
+                    snippet += "..."
+                print(f"   Preview: {snippet}")
+        
+        print(f"\n{'='*50}")
+        print(f"Pages crawled: {len(result.crawled_urls)}")
+        print(f"Final confidence: {adaptive.confidence:.1%}")
+        print(f"Stopped reason: {result.metrics.get('stopped_reason', 'max_pages')}")
+        
+        if result.metrics.get('is_irrelevant', False):
+            print("⚠️  Query detected as irrelevant!")
+        
+        return result
+
+
+async def llm_embedding():
+    """Demonstrate various embedding configurations"""
+    
+    print("EMBEDDING STRATEGY CONFIGURATION EXAMPLES")
+    print("=" * 60)
+    
+    # Base URL and query for testing
+    test_url = "https://docs.python.org/3/library/asyncio.html"
+    
+    openai_llm_config = LLMConfig(
+        provider='openai/text-embedding-3-small',
+        api_token=os.getenv('OPENAI_API_KEY'),
+        temperature=0.7,
+        max_tokens=2000
+    )
+    config_openai = AdaptiveConfig(
+        strategy="embedding",
+        max_pages=10,
+        
+        # Use OpenAI embeddings
+        embedding_llm_config=openai_llm_config,
+        # embedding_llm_config={
+        #     'provider': 'openai/text-embedding-3-small',
+        #     'api_token': os.getenv('OPENAI_API_KEY')
+        # },
+        
+        # OpenAI embeddings are high quality, can be stricter
+        embedding_k_exp=4.0,
+        n_query_variations=12
+    )
+    
+    await test_configuration(
+        "OpenAI Embeddings",
+        config_openai,
+        test_url,
+        # "event-driven architecture patterns"
+        "async await context managers coroutines"
+    )
+    return
+    
+    
+
+async def basic_adaptive_crawling():
+    """Basic adaptive crawling example"""
+    
+    # Initialize the crawler
+    async with AsyncWebCrawler(verbose=True) as crawler:
+        # Create an adaptive crawler with default settings (statistical strategy)
+        adaptive = AdaptiveCrawler(crawler)
+        
+        # Note: You can also use embedding strategy for semantic understanding:
+        # from crawl4ai import AdaptiveConfig
+        # config = AdaptiveConfig(strategy="embedding")
+        # adaptive = AdaptiveCrawler(crawler, config)
+        
+        # Start adaptive crawling
+        print("Starting adaptive crawl for Python async programming information...")
+        result = await adaptive.digest(
+            start_url="https://docs.python.org/3/library/asyncio.html",
+            query="async await context managers coroutines"
+        )
+        
+        # Display crawl statistics
+        print("\n" + "="*50)
+        print("CRAWL STATISTICS")
+        print("="*50)
+        adaptive.print_stats(detailed=False)
+        
+        # Get the most relevant content found
+        print("\n" + "="*50)
+        print("MOST RELEVANT PAGES")
+        print("="*50)
+        
+        relevant_pages = adaptive.get_relevant_content(top_k=5)
+        for i, page in enumerate(relevant_pages, 1):
+            print(f"\n{i}. {page['url']}")
+            print(f"   Relevance Score: {page['score']:.2%}")
+            
+            # Show a snippet of the content
+            content = page['content'] or ""
+            if content:
+                snippet = content[:200].replace('\n', ' ')
+                if len(content) > 200:
+                    snippet += "..."
+                print(f"   Preview: {snippet}")
+        
+        # Show final confidence
+        print(f"\n{'='*50}")
+        print(f"Final Confidence: {adaptive.confidence:.2%}")
+        print(f"Total Pages Crawled: {len(result.crawled_urls)}")
+        print(f"Knowledge Base Size: {len(adaptive.state.knowledge_base)} documents")
+        
+        
+        if adaptive.confidence >= 0.8:
+            print("✓ High confidence - can answer detailed questions about async Python")
+        elif adaptive.confidence >= 0.6:
+            print("~ Moderate confidence - can answer basic questions") 
+        else:
+            print("✗ Low confidence - need more information")
+
+
+
+if __name__ == "__main__":
+    asyncio.run(llm_embedding())
+    # asyncio.run(basic_adaptive_crawling())

tests/async/test_0.4.2_browser_manager.py

@@ -112,7 +112,7 @@ async def test_proxy_settings():
         headless=True,
         verbose=False,
         user_agent="Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36",
-        proxy="http://127.0.0.1:8080",  # Assuming local proxy server for test
+        proxy_config={"server": "http://127.0.0.1:8080"},  # Assuming local proxy server for test
         use_managed_browser=False,
         use_persistent_context=False,
     ) as crawler:

tests/general/test_bff_scoring.py

@@ -0,0 +1,117 @@
+#!/usr/bin/env python3
+"""
+Simple test to verify BestFirstCrawlingStrategy fixes.
+This test crawls a real website and shows that:
+1. Higher-scoring pages are crawled first (priority queue fix)
+2. Links are scored before truncation (link discovery fix)
+"""
+
+import asyncio
+from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig
+from crawl4ai.deep_crawling import BestFirstCrawlingStrategy
+from crawl4ai.deep_crawling.scorers import KeywordRelevanceScorer
+
+async def test_best_first_strategy():
+    """Test BestFirstCrawlingStrategy with keyword scoring"""
+    
+    print("=" * 70)
+    print("Testing BestFirstCrawlingStrategy with Real URL")
+    print("=" * 70)
+    print("\nThis test will:")
+    print("1. Crawl Python.org documentation")
+    print("2. Score pages based on keywords: 'tutorial', 'guide', 'reference'")
+    print("3. Show that higher-scoring pages are crawled first")
+    print("-" * 70)
+    
+    # Create a keyword scorer that prioritizes tutorial/guide pages
+    scorer = KeywordRelevanceScorer(
+        keywords=["tutorial", "guide", "reference", "documentation"],
+        weight=1.0,
+        case_sensitive=False
+    )
+    
+    # Create the strategy with scoring
+    strategy = BestFirstCrawlingStrategy(
+        max_depth=2,          # Crawl 2 levels deep
+        max_pages=10,         # Limit to 10 pages total
+        url_scorer=scorer,    # Use keyword scoring
+        include_external=False  # Only internal links
+    )
+    
+    # Configure browser and crawler
+    browser_config = BrowserConfig(
+        headless=True,    # Run in background
+        verbose=False     # Reduce output noise
+    )
+    
+    crawler_config = CrawlerRunConfig(
+        deep_crawl_strategy=strategy,
+        verbose=False
+    )
+    
+    print("\nStarting crawl of https://docs.python.org/3/")
+    print("Looking for pages with keywords: tutorial, guide, reference, documentation")
+    print("-" * 70)
+    
+    crawled_urls = []
+    
+    async with AsyncWebCrawler(config=browser_config) as crawler:
+        # Crawl and collect results
+        results = await crawler.arun(
+            url="https://docs.python.org/3/",
+            config=crawler_config
+        )
+        
+        # Process results
+        if isinstance(results, list):
+            for result in results:
+                score = result.metadata.get('score', 0) if result.metadata else 0
+                depth = result.metadata.get('depth', 0) if result.metadata else 0
+                crawled_urls.append({
+                    'url': result.url,
+                    'score': score,
+                    'depth': depth,
+                    'success': result.success
+                })
+    
+    print("\n" + "=" * 70)
+    print("CRAWL RESULTS (in order of crawling)")
+    print("=" * 70)
+    
+    for i, item in enumerate(crawled_urls, 1):
+        status = "✓" if item['success'] else "✗"
+        # Highlight high-scoring pages
+        if item['score'] > 0.5:
+            print(f"{i:2}. [{status}] Score: {item['score']:.2f} | Depth: {item['depth']} | {item['url']}")
+            print(f"     ^ HIGH SCORE - Contains keywords!")
+        else:
+            print(f"{i:2}. [{status}] Score: {item['score']:.2f} | Depth: {item['depth']} | {item['url']}")
+    
+    print("\n" + "=" * 70)
+    print("ANALYSIS")
+    print("=" * 70)
+    
+    # Check if higher scores appear early in the crawl
+    scores = [item['score'] for item in crawled_urls[1:]]  # Skip initial URL
+    high_score_indices = [i for i, s in enumerate(scores) if s > 0.3]
+    
+    if high_score_indices and high_score_indices[0] < len(scores) / 2:
+        print("✅ SUCCESS: Higher-scoring pages (with keywords) were crawled early!")
+        print("   This confirms the priority queue fix is working.")
+    else:
+        print("⚠️  Check the crawl order above - higher scores should appear early")
+    
+    # Show score distribution
+    print(f"\nScore Statistics:")
+    print(f"  - Total pages crawled: {len(crawled_urls)}")
+    print(f"  - Average score: {sum(item['score'] for item in crawled_urls) / len(crawled_urls):.2f}")
+    print(f"  - Max score: {max(item['score'] for item in crawled_urls):.2f}")
+    print(f"  - Pages with keywords: {sum(1 for item in crawled_urls if item['score'] > 0.3)}")
+    
+    print("\n" + "=" * 70)
+    print("TEST COMPLETE")
+    print("=" * 70)
+
+if __name__ == "__main__":
+    print("\n🔍 BestFirstCrawlingStrategy Simple Test\n")
+    asyncio.run(test_best_first_strategy())

tests/memory/test_docker_config_gen.py

@@ -24,7 +24,7 @@ CASES = [
     # --- BrowserConfig variants ---
     "BrowserConfig()",
     "BrowserConfig(headless=False, extra_args=['--disable-gpu'])",
-    "BrowserConfig(browser_mode='builtin', proxy='http://1.2.3.4:8080')",
+    "BrowserConfig(browser_mode='builtin', proxy_config={'server': 'http://1.2.3.4:8080'})",
 ]
 
 for code in CASES:

tests/proxy/test_proxy_deprecation.py

@@ -0,0 +1,42 @@
+import warnings
+
+import pytest
+
+from crawl4ai.async_configs import BrowserConfig, ProxyConfig
+
+
+def test_browser_config_proxy_string_emits_deprecation_and_autoconverts():
+    warnings.simplefilter("always", DeprecationWarning)
+
+    proxy_str = "23.95.150.145:6114:username:password"
+    with warnings.catch_warnings(record=True) as caught:
+        cfg = BrowserConfig(proxy=proxy_str, headless=True)
+
+    dep_warnings = [w for w in caught if issubclass(w.category, DeprecationWarning)]
+    assert dep_warnings, "Expected DeprecationWarning when using BrowserConfig(proxy=...)"
+
+    assert cfg.proxy is None, "cfg.proxy should be None after auto-conversion"
+    assert isinstance(cfg.proxy_config, ProxyConfig), "cfg.proxy_config should be ProxyConfig instance"
+    assert cfg.proxy_config.username == "username"
+    assert cfg.proxy_config.password == "password"
+    assert cfg.proxy_config.server.startswith("http://")
+    assert cfg.proxy_config.server.endswith(":6114")
+
+
+def test_browser_config_with_proxy_config_emits_no_deprecation():
+    warnings.simplefilter("always", DeprecationWarning)
+
+    with warnings.catch_warnings(record=True) as caught:
+        cfg = BrowserConfig(
+            headless=True,
+            proxy_config={
+                "server": "http://127.0.0.1:8080",
+                "username": "u",
+                "password": "p",
+            },
+        )
+
+    dep_warnings = [w for w in caught if issubclass(w.category, DeprecationWarning)]
+    assert not dep_warnings, "Did not expect DeprecationWarning when using proxy_config"
+    assert cfg.proxy is None
+    assert isinstance(cfg.proxy_config, ProxyConfig)