LangGraph agents can call tools in loops, which means an unconstrained search tool can burn through credits on a single query. This tutorial builds a LangGraph agent with Scavio search as a tool node that tracks credit usage per conversation and stops searching when it hits a configurable budget limit. Each search costs $0.005.
Prerequisites
- Python 3.8+
- langgraph and langchain installed
- A Scavio API key from scavio.dev
- An LLM API key (OpenAI, Anthropic, or local)
Walkthrough
Step 1: Define the budget-aware search tool
Create a LangGraph-compatible tool that tracks and limits search spend.
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'}))Step 2: Build the LangGraph agent graph
Create the agent graph with search tool node and conditional routing.
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.')Step 3: Run the agent with budget tracking
Execute the agent on a research question and monitor credit usage.
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)Step 4: Add multi-platform search strategy
Configure the agent to search across platforms for richer answers.
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, ${search_state["cost"]:.3f}')
return result['messages'][-1].content
research_with_sources('best search API for AI agents', max_searches=8)Python Example
Python
import os, requests
from langchain_core.tools import tool
SH = {'x-api-key': os.environ['SCAVIO_API_KEY'], 'Content-Type': 'application/json'}
budget = {'n': 0, 'max': 5}
@tool
def web_search(query: str) -> str:
"""Search the web for current information."""
if budget['n'] >= budget['max']:
return 'Budget reached. Use existing results.'
data = requests.post('https://api.scavio.dev/api/v1/search',
headers=SH, json={'query': query, 'country_code': 'us'}).json()
budget['n'] += 1
results = data.get('organic_results', [])[:3]
return '\n'.join(f"{r['title'][:50]}" for r in results)
print(web_search.invoke({'query': 'langgraph tutorial'}))
print(f'Budget: {budget["n"]}/{budget["max"]} (${budget["n"] * 0.005:.3f})')JavaScript Example
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)})`);Expected Output
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