Use Search API with the Anthropic SDK

pip install anthropic perplexityai
export ANTHROPIC_API_KEY="your_anthropic_key"
export PERPLEXITY_API_KEY="your_perplexity_key"

WEB_SEARCH_TOOL_DESCRIPTION = """\
Searches the web for current and factual information to answer user queries, returning relevant results with titles, URLs, and content snippets, similar to Google or Bing. Intended for questions about up-to-date or externally verified information beyond your knowledge cutoff. The tool works best with an array of short, keyword-focused queries. Complex queries that require multi-step reasoning are not supported. Time-sensitive queries are supported if the date is included in the query.

Best practices for using this tool:
- Limit the number of queries in each request to a maximum of three to maintain efficiency.
- For multi-entity questions, break them into separate, single-entity queries:
  - Preferred:
    [
      "Brand A protein powder review",
      "Brand B protein powder review"
    ]
  - Not recommended:
    [
      "Brand A vs Brand B protein powder review"
    ]

- For simple queries, keep each query straightforward and focused:
  - Preferred: ["inflation rate Canada"]
  - Not recommended: ["What is the inflation rate in Canada?"]

Each query should be short to ensure optimal tool performance. Make sure all provided examples and generated queries follow this guideline."""

QUERIES_PARAM_DESCRIPTION = (
    "An array of keyword-based search queries. Each query should be short, "
    "as longer queries may reduce performance. Do not provide more than three "
    "queries to maintain efficiency."
)

WEB_SEARCH_TOOL = {
    "name": "web_search",
    "description": WEB_SEARCH_TOOL_DESCRIPTION,
    "input_schema": {
        "type": "object",
        "properties": {
            "queries": {
                "type": "array",
                "description": QUERIES_PARAM_DESCRIPTION,
                "items": {"type": "string"},
                "minItems": 1,
                "maxItems": 3,
            },
        },
        "required": ["queries"],
    },
}

from perplexity import Perplexity

perplexity = Perplexity()

def run_web_search(queries: list[str]) -> str:
    """Call Perplexity Search and format the results for the model."""
    response = perplexity.search.create(query=queries, max_results=5)

    lines = []
    for result in response.results:
        snippet = (result.snippet or "").strip().replace("\n", " ")
        if len(snippet) > 400:
            snippet = snippet[:400] + "…"
        lines.append(f"- {result.title}\n  {result.url}\n  {snippet}")
    return "\n\n".join(lines) if lines else "No results."

import json
from anthropic import Anthropic

claude = Anthropic()

def chat_with_search(user_prompt: str, model: str = "claude-sonnet-4-6") -> str:
    messages = [{"role": "user", "content": user_prompt}]

    while True:
        response = claude.messages.create(
            model=model,
            max_tokens=1024,
            tools=[WEB_SEARCH_TOOL],
            messages=messages,
        )

        if response.stop_reason != "tool_use":
            # Final answer — pull text blocks out of the assistant message.
            return "".join(
                block.text for block in response.content if block.type == "text"
            )

        # Append the assistant's tool-call turn to history.
        messages.append({"role": "assistant", "content": response.content})

        # Run every tool call in this turn and pair results by tool_use_id.
        tool_results = []
        for block in response.content:
            if block.type != "tool_use":
                continue
            if block.name == "web_search":
                output = run_web_search(block.input["queries"])
            else:
                output = json.dumps({"error": f"unknown tool: {block.name}"})
            tool_results.append({
                "type": "tool_result",
                "tool_use_id": block.id,
                "content": output,
            })

        messages.append({"role": "user", "content": tool_results})


if __name__ == "__main__":
    answer = chat_with_search(
        "What were the major AI infrastructure announcements this week?"
    )
    print(answer)

with claude.messages.stream(
    model="claude-sonnet-4-6",
    max_tokens=1024,
    tools=[WEB_SEARCH_TOOL],
    messages=messages,
) as stream:
    for text in stream.text_stream:
        print(text, end="", flush=True)
    final = stream.get_final_message()

# Inspect final.stop_reason and final.content for tool_use blocks, then
# resume the loop the same way as in the non-streaming version.