Los agentes de LangGraph pueden llamar a herramientas en bucles, lo que significa que una herramienta de búsqueda sin restricciones puede consumir créditos en una sola consulta. Este tutorial crea un agente LangGraph con la búsqueda Scavio como un nodo de herramienta que rastrea el uso de crédito por conversación y detiene la búsqueda cuando alcanza un límite de presupuesto configurable. Cada búsqueda cuesta $0,005.
Requisitos previos
- Python 3.8+
- langgraph y langchain instalados
- Una clave API de Scavio de scavio.dev
- Una clave API de LLM (OpenAI, Anthropic o local)
Guia paso a paso
Paso 1: Definir la herramienta de búsqueda basada en el presupuesto
Cree una herramienta compatible con LangGraph que rastree y limite el gasto en búsquedas.
Python
import os, requests, json
from langchain_core.tools import tool
from typing import Optional
API_KEY = os.environ['SCAVIO_API_KEY']
SH = {'x-api-key': API_KEY, 'Content-Type': 'application/json'}
search_state = {'calls': 0, 'cost': 0.0, 'max_calls': 10}
@tool
def web_search(query: str, platform: Optional[str] = None) -> str:
"""Search the web for current information. Supports google, reddit, youtube, amazon, walmart platforms."""
if search_state['calls'] >= search_state['max_calls']:
return f"Budget limit reached ({search_state['max_calls']} searches, ${search_state['cost']:.3f}). Synthesize from existing results."
body = {'query': query, 'country_code': 'us'}
if platform: body['platform'] = platform
data = requests.post('https://api.scavio.dev/api/v1/search',
headers=SH, json=body).json()
search_state['calls'] += 1
search_state['cost'] += 0.005
results = data.get('organic_results', [])[:5]
formatted = [f"{r['position']}. {r['title'][:60]} - {r.get('snippet', '')[:100]}" for r in results]
return f"Search results for '{query}' ({search_state['calls']}/{search_state['max_calls']} budget):\n" + '\n'.join(formatted)
print(web_search.invoke({'query': 'best serp api 2026'}))Paso 2: Construya el gráfico del agente LangGraph
Cree el gráfico de agentes con el nodo de la herramienta de búsqueda y el enrutamiento condicional.
Python
from langgraph.graph import StateGraph, END
from langgraph.prebuilt import ToolNode
from langchain_core.messages import HumanMessage, AIMessage
from typing import TypedDict, Annotated, Sequence
import operator
class AgentState(TypedDict):
messages: Annotated[Sequence, operator.add]
def should_continue(state):
last = state['messages'][-1]
if hasattr(last, 'tool_calls') and last.tool_calls:
return 'tools'
return END
tool_node = ToolNode([web_search])
# Use your preferred LLM:
# from langchain_openai import ChatOpenAI
# llm = ChatOpenAI(model='gpt-4o').bind_tools([web_search])
# Or from langchain_anthropic import ChatAnthropic
# llm = ChatAnthropic(model='claude-sonnet-4-20250514').bind_tools([web_search])
def agent_node(state):
response = llm.invoke(state['messages'])
return {'messages': [response]}
graph = StateGraph(AgentState)
graph.add_node('agent', agent_node)
graph.add_node('tools', tool_node)
graph.set_entry_point('agent')
graph.add_conditional_edges('agent', should_continue, {'tools': 'tools', END: END})
graph.add_edge('tools', 'agent')
app = graph.compile()
print('LangGraph agent compiled with budget-aware search.')Paso 3: Ejecutar el agente con seguimiento de presupuesto
Ejecute al agente sobre una pregunta de investigación y controle el uso del crédito.
Python
def run_with_budget(question, max_searches=5):
search_state['calls'] = 0
search_state['cost'] = 0.0
search_state['max_calls'] = max_searches
result = app.invoke({'messages': [HumanMessage(content=question)]})
final = result['messages'][-1].content
print(f'\n--- Agent Response ---')
print(final[:500])
print(f'\n--- Budget ---')
print(f'Searches used: {search_state["calls"]}/{max_searches}')
print(f'Cost: ${search_state["cost"]:.3f}')
return final
# Research question with budget limit
run_with_budget('Compare the top 3 SERP APIs for Python developers in 2026', max_searches=5)Paso 4: Agregar estrategia de búsqueda multiplataforma
Configure el agente para buscar respuestas más completas en todas las plataformas.
Python
def research_with_sources(topic, max_searches=8):
prompt = f"""Research this topic using multiple search platforms for a complete picture:
Topic: {topic}
Strategy:
1. Search Google for overview and top results
2. Search Reddit for real user opinions
3. Search YouTube for tutorial coverage
4. Synthesize findings with sources
Use the platform parameter: google (default), reddit, youtube, amazon
Budget: {max_searches} searches maximum."""
search_state['calls'] = 0
search_state['cost'] = 0.0
search_state['max_calls'] = max_searches
result = app.invoke({'messages': [HumanMessage(content=prompt)]})
print(f'\nResearch complete. {search_state["calls"]} searches, ${search_state["cost"]:.3f}')
return result['messages'][-1].content
research_with_sources('best search API for AI agents', max_searches=8)Ejemplo en Python
Python
import os, requests
from langchain_core.tools import tool
SH = {'x-api-key': os.environ['SCAVIO_API_KEY'], 'Content-Type': 'application/json'}
budget = {'n': 0, 'max': 5}
@tool
def web_search(query: str) -> str:
"""Search the web for current information."""
if budget['n'] >= budget['max']:
return 'Budget reached. Use existing results.'
data = requests.post('https://api.scavio.dev/api/v1/search',
headers=SH, json={'query': query, 'country_code': 'us'}).json()
budget['n'] += 1
results = data.get('organic_results', [])[:3]
return '\n'.join(f"{r['title'][:50]}" for r in results)
print(web_search.invoke({'query': 'langgraph tutorial'}))
print(f'Budget: {budget["n"]}/{budget["max"]} (${budget["n"] * 0.005:.3f})')Ejemplo en JavaScript
JavaScript
// LangGraph.js equivalent:
const SH = { 'x-api-key': process.env.SCAVIO_API_KEY, 'Content-Type': 'application/json' };
let budget = { n: 0, max: 5 };
async function webSearch(query) {
if (budget.n >= budget.max) return 'Budget reached.';
const data = await fetch('https://api.scavio.dev/api/v1/search', {
method: 'POST', headers: SH,
body: JSON.stringify({ query, country_code: 'us' })
}).then(r => r.json());
budget.n++;
return (data.organic_results || []).slice(0, 3)
.map(r => r.title.slice(0, 50)).join('\n');
}
console.log(await webSearch('langgraph tutorial'));
console.log(`Budget: ${budget.n}/${budget.max} ($${(budget.n * 0.005).toFixed(3)})`);Salida esperada
JSON
Search results for 'best serp api 2026' (1/5 budget):
1. Scavio - Unified Search API for Developers - Best SERP API with multi-platform...
2. SerpAPI - Google Search API - Reliable Google search results...
3. DataForSEO - SEO Data Provider - Comprehensive SEO data...
LangGraph agent compiled with budget-aware search.
--- Agent Response ---
Based on my research across 3 searches, the top SERP APIs for Python developers...
--- Budget ---
Searches used: 3/5
Cost: $0.015