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Agent with web_search tool.
Demonstrates the WebSearchTool for searching the internet and reading web pages:
1. Direct tool usage (no LLM) — quick search, deep search, URL read
2. Agent with web search
3. Agent with deep search (fetch_content=True)
Configure your LLM via environment variables:
LLM_API_KEY, LLM_BASE_URL, LLM_MODEL
Run:
python -m examples.web_search_example
"""
import os
from builder import GraphBuilder
from execution import MACPRunner
from tools import WebSearchTool, create_openai_caller, get_registry
# ── Helpers ──────────────────────────────────────────────────────────────────
def _setup_tools() -> None:
get_registry().register(WebSearchTool(max_results=3, max_content_length=2000, fetch_content=False, timeout=15))
def _create_llm():
return create_openai_caller(
base_url=os.getenv("LLM_BASE_URL", "http://localhost:8000/v1"),
api_key=os.getenv("LLM_API_KEY", "your-api-key"),
model=os.getenv("LLM_MODEL", "gpt-4o-mini"),
temperature=0.1,
)
def _header(title: str) -> None:
print(f"\n{'─' * 60}\n {title}\n{'─' * 60}")
# ── Example 1: Direct tool usage ────────────────────────────────────────────
def example_direct_usage():
"""Three WebSearchTool modes without any LLM."""
_header("1 · Direct WebSearchTool Usage (no LLM)")
# Quick search
print("\n a) Quick search (titles + snippets):")
result = WebSearchTool(max_results=3, fetch_content=False).execute(query="Python programming")
print(f" {result.output[:400]}" if result.success else f" Failed: {result.error}")
# Deep search
print("\n b) Deep search (full page content):")
result = WebSearchTool(max_results=2, fetch_content=True, max_content_length=1000).execute(query="Python asyncio")
print(f" {result.output[:400]}" if result.success else f" Failed: {result.error}")
# Direct URL
print("\n c) Read specific URL:")
result = WebSearchTool().execute(url="https://httpbin.org/html")
print(f" {result.output[:300]}" if result.success else f" Failed: {result.error}")
# ── Example 2: Agent with web search ────────────────────────────────────────
def example_agent_search():
"""Agent searches the web and summarises results."""
_header("2 · Agent with Web Search")
builder = GraphBuilder()
builder.add_agent(
agent_id="researcher",
display_name="Web Researcher",
persona="a research assistant",
description="Searches the web for current information.",
tools=["web_search"],
)
builder.add_task(query="Search for 'Python asyncio tutorial' and summarise the key concepts")
builder.connect_task_to_agents(agent_ids=["researcher"])
result = MACPRunner(llm_caller=_create_llm()).run_round(builder.build())
print(f" Result: {result.final_answer}")
# ── Example 3: Agent with deep search ───────────────────────────────────────
def example_agent_deep_search():
"""Agent searches with fetch_content=True to read full pages."""
_header("3 · Agent with Deep Search")
# Re-register with fetch_content enabled
get_registry().register(WebSearchTool(max_results=2, fetch_content=True, max_content_length=2000, timeout=15))
builder = GraphBuilder()
builder.add_agent(
agent_id="deep_researcher",
display_name="Deep Researcher",
persona="a thorough researcher",
description="Searches the web and reads full page content.",
tools=["web_search"],
)
builder.add_task(query="Search for 'FastAPI tutorial' and read the full content")
builder.connect_task_to_agents(agent_ids=["deep_researcher"])
result = MACPRunner(llm_caller=_create_llm()).run_round(builder.build())
print(f" Result: {result.final_answer}")
# ── Main ─────────────────────────────────────────────────────────────────────
def main():
_setup_tools()
example_direct_usage()
example_agent_search()
example_agent_deep_search()
print(f"\n{'=' * 60}")
print("All web search examples completed ✅")
if __name__ == "__main__":
main()
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