Anthropic

pip install anthropic tavily-python

import os
# Set your API keys
os.environ["OPENAI_API_KEY"] = "your-openai-api-key"
os.environ["TAVILY_API_KEY"] = "your-tavily-api-key"

import json
from anthropic import Anthropic
from tavily import TavilyClient

# Initialize clients
client = Anthropic(api_key=os.environ["ANTHROPIC_API_KEY"])
tavily_client = TavilyClient(api_key=os.environ["TAVILY_API_KEY"])
MODEL_NAME = "claude-sonnet-4-20250514"

SYSTEM_PROMPT = (
    "You are a research assistant. Use the tavily_search tool when needed. "
    "After tools run and tool results are provided back to you, produce a concise, well-structured summary "
    "with a short bullet list of key points and a 'Sources' section listing the URLs. "
)

tools = [
    {
        "name": "tavily_search",
        "description": "Search the web using Tavily. Return relevant links & summaries.",
        "input_schema": {
            "type": "object",
            "properties": {
                "query": {"type": "string", "description": "Search query string."},
                "max_results": {"type": "integer", "default": 5},
                "search_depth": {"type": "string", "enum": ["basic", "advanced"]},
            },
            "required": ["query"]
        }
    }
]

def tavily_search(**kwargs):
    return tavily_client.search(**kwargs)

def process_tool_call(name, args):
    if name == "tavily_search":
        return tavily_search(**args)
    raise ValueError(f"Unknown tool: {name}")

def chat_with_claude(user_message: str):
    print(f"\n{'='*50}\nUser Message: {user_message}\n{'='*50}")

    # ---- Call 1: allow tools so Claude can ask for searches ----
    initial_response = client.messages.create(
        model=MODEL_NAME,
        max_tokens=4096,
        system=SYSTEM_PROMPT,
        messages=[{"role": "user", "content": [{"type": "text", "text": user_message}]}],
        tools=tools,
    )

    print("\nInitial Response stop_reason:", initial_response.stop_reason)
    print("Initial content:", initial_response.content)

    # If Claude already answered in text, return it
    if initial_response.stop_reason != "tool_use":
        final_text = next((b.text for b in initial_response.content if getattr(b, "type", None) == "text"), None)
        print("\nFinal Response:", final_text)
        return final_text

    # ---- Execute ALL tool_use blocks from Call 1 ----
    tool_result_blocks = []
    for block in initial_response.content:
        if getattr(block, "type", None) == "tool_use":
            result = process_tool_call(block.name, block.input)
            tool_result_blocks.append({
                "type": "tool_result",
                "tool_use_id": block.id,
                "content": [{"type": "text", "text": json.dumps(result)}],
            })

    # ---- Call 2: NO tools; ask for the final summary from tool results ----
    final_response = client.messages.create(
        model=MODEL_NAME,
        max_tokens=4096,
        system=SYSTEM_PROMPT,
        messages=[
            {"role": "user", "content": [{"type": "text", "text": user_message}]},
            {"role": "assistant", "content": initial_response.content},    # Claude's tool requests
            {"role": "user", "content": tool_result_blocks},    # Your tool results
            {"role": "user", "content": [{"type": "text", "text":
                "Please synthesize the final answer now based on the tool results above. "
                "Include 3–7 bullets and a 'Sources' section with URLs."}]},
        ],
    )

    final_text = next((b.text for b in final_response.content if getattr(b, "type", None) == "text"), None)
    print("\nFinal Response:", final_text)
    return final_text

# Example usage
chat_with_claude("What is trending now in the agents space in 2025?")