import anthropic

client = anthropic.Anthropic()

response = client.messages.create(
    model="claude-3-7-sonnet-20250219",
    max_tokens=16000,
    thinking={
        "type": "enabled",
        "budget_tokens": 10000  # Allow up to 10K thinking tokens
    },
    messages=[{
        "role": "user",
        "content": "Analyze the time complexity of this algorithm and suggest optimizations..."
    }]
)

# Access thinking blocks
for block in response.content:
    if block.type == "thinking":
        print("Model's reasoning process:")
        print(block.thinking)
    elif block.type == "text":
        print("Final answer:")
        print(block.text)

def review_pull_request(diff: str, context: str) -> dict:
    """
    Use Claude 3.7 Sonnet to review a pull request
    """
    client = anthropic.Anthropic()
    
    response = client.messages.create(
        model="claude-3-7-sonnet-20250219",
        max_tokens=4096,
        system="""You are a senior software engineer performing a code review.
        Analyze the provided diff for:
        1. Security vulnerabilities
        2. Performance issues  
        3. Code maintainability
        4. Test coverage gaps
        5. API design issues
        
        Return structured feedback in JSON format.""",
        messages=[{
            "role": "user",
            "content": f"Review this PR:\n\nContext: {context}\n\nDiff:\n{diff}"
        }]
    )
    
    return parse_review_response(response.content[0].text)

import ast
import anthropic

def generate_docs(source_file: str) -> str:
    """Generate comprehensive documentation for a Python module"""
    
    with open(source_file, 'r') as f:
        code = f.read()
    
    # Parse to extract structure
    tree = ast.parse(code)
    functions = [node.name for node in ast.walk(tree) 
                 if isinstance(node, ast.FunctionDef)]
    classes = [node.name for node in ast.walk(tree) 
               if isinstance(node, ast.ClassDef)]
    
    client = anthropic.Anthropic()
    
    response = client.messages.create(
        model="claude-3-7-sonnet-20250219",
        max_tokens=8192,
        messages=[{
            "role": "user",
            "content": f"""Generate comprehensive Markdown documentation for this Python module.
            
Functions: {functions}
Classes: {classes}

Source code:
```python
{code}

from anthropic import Anthropic

class AgentOrchestrator:
    def __init__(self):
        self.client = Anthropic()
        self.agents = {
            "researcher": ResearchAgent(),
            "coder": CodingAgent(),
            "reviewer": ReviewAgent()
        }
    
    def orchestrate_task(self, task: str) -> str:
        """Use Claude 3.7 Sonnet to coordinate multiple specialized agents"""
        
        # Plan the task
        plan = self.create_plan(task)
        
        results = {}
        for step in plan["steps"]:
            agent_name = step["agent"]
            agent_task = step["task"]
            
            # Execute with appropriate agent
            agent = self.agents[agent_name]
            results[step["id"]] = agent.execute(
                task=agent_task,
                context=results
            )
        
        # Synthesize final result
        return self.synthesize_results(task, results)
    
    def create_plan(self, task: str) -> dict:
        response = self.client.messages.create(
            model="claude-3-7-sonnet-20250219",
            max_tokens=2048,
            messages=[{
                "role": "user",
                "content": f"Create a step-by-step plan for: {task}. Return JSON."
            }]
        )
        return json.loads(response.content[0].text)

response = client.messages.create(
    model="claude-3-7-sonnet-20250219",
    max_tokens=4096,
    system=[
        {
            "type": "text",
            "text": "You are a coding assistant. Here is our large codebase context...",
            "cache_control": {"type": "ephemeral"}  # Cache this!
        }
    ],
    messages=[{
        "role": "user",
        "content": "Add error handling to the authentication module"
    }]
)

# Subsequent calls with same system prompt use cached tokens
# 90% cost reduction on cached portion

# Submit batch of requests at 50% discount
batch = client.beta.messages.batches.create(
    requests=[
        {
            "custom_id": f"task-{i}",
            "params": {
                "model": "claude-3-7-sonnet-20250219",
                "max_tokens": 2048,
                "messages": [{"role": "user", "content": task}]
            }
        }
        for i, task in enumerate(tasks)
    ]
)

import time
import anthropic
from anthropic import RateLimitError, APIError

def call_claude_with_retry(
    client: anthropic.Anthropic,
    max_retries: int = 3,
    **kwargs
) -> anthropic.Message:
    
    for attempt in range(max_retries):
        try:
            return client.messages.create(**kwargs)
        except RateLimitError:
            if attempt == max_retries - 1:
                raise
            wait_time = (2 ** attempt) + (random.random() * 0.5)
            time.sleep(wait_time)
        except APIError as e:
            if e.status_code >= 500:  # Server errors, retry
                if attempt == max_retries - 1:
                    raise
                time.sleep(2 ** attempt)
            else:
                raise  # Client errors, don't retry

from pydantic import BaseModel
from anthropic import Anthropic

class CodeReview(BaseModel):
    severity: str  # "critical", "major", "minor", "info"
    issues: list[dict]
    suggestions: list[str]
    overall_score: int  # 1-10

def get_structured_review(code: str) -> CodeReview:
    client = Anthropic()
    
    response = client.messages.create(
        model="claude-3-7-sonnet-20250219",
        max_tokens=2048,
        messages=[{
            "role": "user",
            "content": f"Review this code and return JSON matching CodeReview schema:\n{code}"
        }]
    )
    
    return CodeReview.model_validate_json(response.content[0].text)

class ConversationManager:
    def __init__(self, max_tokens: int = 100000):
        self.client = Anthropic()
        self.messages = []
        self.max_tokens = max_tokens
        self.token_count = 0
    
    def add_message(self, role: str, content: str):
        tokens = self.estimate_tokens(content)
        
        # Prune old messages if approaching limit
        while self.token_count + tokens > self.max_tokens * 0.8:
            removed = self.messages.pop(0)
            self.token_count -= self.estimate_tokens(removed["content"])
        
        self.messages.append({"role": role, "content": content})
        self.token_count += tokens
    
    def chat(self, user_message: str) -> str:
        self.add_message("user", user_message)
        
        response = self.client.messages.create(
            model="claude-3-7-sonnet-20250219",
            max_tokens=4096,
            messages=self.messages
        )
        
        assistant_message = response.content[0].text
        self.add_message("assistant", assistant_message)
        return assistant_message