SERP API for AI Agents & LLMs: Complete Integration Guide
Large Language Models need real-time data. This comprehensive guide shows you how to give your AI agents, chatbots, and LLMs access to current search results through SERP APIs.
Why AI Agents Need SERP APIs
The Knowledge Cutoff Problem
# LLMs have a knowledge cutoff date
llm_knowledge = {
'GPT-4': 'April 2023',
'Claude 3': 'August 2023',
'Gemini': 'April 2023'
}
# Real-world queries need current data
user_queries = [
"What's the current price of Bitcoin?", # Changes every second
"Who won the 2024 election?", # After cutoff date
"Latest iPhone features", # Product updates
"Current weather in Tokyo", # Real-time data
"Today's news about AI" # Time-sensitive
]
# Solution: SERP API provides real-time data
def answer_with_current_data(query):
# Get real-time search results
search_results = serppost.search(query)
# Augment LLM with current data
context = extract_context(search_results)
# Generate informed response
response = llm.generate(
prompt=query,
context=context
)
return responseRetrieval-Augmented Generation (RAG)
/**
* RAG Architecture with SERP API
*/
class RAGWithSERP {
constructor(llm, serpAPI) {
this.llm = llm;
this.serpAPI = serpAPI;
}
async answer(question) {
// Step 1: Retrieve relevant information
const searchResults = await this.retrieve(question);
// Step 2: Augment with context
const context = this.augment(searchResults);
// Step 3: Generate response
const response = await this.generate(question, context);
return {
answer: response,
sources: searchResults.map(r => r.link),
confidence: this.calculateConfidence(searchResults)
};
}
async retrieve(question) {
/**
* Retrieve relevant information from search
*/
const results = await this.serpAPI.search({
q: question,
engine: 'google',
num: 10
});
return results.organic_results;
}
augment(searchResults) {
/**
* Extract and format context from search results
*/
return searchResults.map(result => ({
title: result.title,
snippet: result.snippet,
url: result.link,
relevance: this.calculateRelevance(result)
})).slice(0, 5); // Top 5 most relevant
}
async generate(question, context) {
/**
* Generate response using LLM with context
*/
const prompt = `
Answer the following question using the provided context.
If the context doesn't contain enough information, say so.
Question: ${question}
Context:
${context.map((c, i) => `
[${i + 1}] ${c.title}
${c.snippet}
Source: ${c.url}
`).join('\n')}
Answer:
`;
return await this.llm.complete(prompt);
}
}
// Usage
const rag = new RAGWithSERP(gpt4, serppostAPI);
const answer = await rag.answer("What are the latest developments in quantum computing?");1. Integration with Popular LLMs
OpenAI GPT Integration
import openai
from serppost import SERPpost
class GPTWithSearch:
def __init__(self, openai_key, serppost_key):
self.openai = openai
self.openai.api_key = openai_key
self.serppost = SERPpost(serppost_key)
async def chat_with_search(self, user_message):
"""
GPT chat with real-time search capability
"""
# Determine if search is needed
needs_search = await self.should_search(user_message)
if needs_search:
# Get search results
search_query = await self.extract_search_query(user_message)
search_results = await self.serppost.search(search_query)
# Format context
context = self.format_search_context(search_results)
# Generate response with context
response = await self.openai.ChatCompletion.create(
model="gpt-4",
messages=[
{
"role": "system",
"content": "You are a helpful assistant with access to real-time search data."
},
{
"role": "user",
"content": f"Context from search:\n{context}\n\nUser question: {user_message}"
}
]
)
else:
# Regular GPT response
response = await self.openai.ChatCompletion.create(
model="gpt-4",
messages=[
{"role": "user", "content": user_message}
]
)
return response.choices[0].message.content
async def should_search(self, message):
"""
Determine if message requires real-time search
"""
search_indicators = [
'current', 'latest', 'recent', 'today',
'now', 'price', 'weather', 'news',
'what is', 'who is', 'when did'
]
return any(indicator in message.lower() for indicator in search_indicators)
async def extract_search_query(self, message):
"""
Extract optimal search query from user message
"""
response = await self.openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages=[
{
"role": "system",
"content": "Extract the best search query from the user's message. Return only the search query, nothing else."
},
{
"role": "user",
"content": message
}
]
)
return response.choices[0].message.content.strip()
def format_search_context(self, search_results):
"""
Format search results as context for GPT
"""
context = []
for i, result in enumerate(search_results['organic_results'][:5]):
context.append(f"""
[Source {i+1}]
Title: {result['title']}
Snippet: {result['snippet']}
URL: {result['link']}
""")
return '\n'.join(context)
# Example usage
gpt_search = GPTWithSearch(
openai_key="sk-...",
serppost_key="your_serppost_key"
)
response = await gpt_search.chat_with_search(
"What's the current price of Tesla stock?"
)
print(response)Anthropic Claude Integration
const Anthropic = require('@anthropic-ai/sdk');
const SERPpost = require('serppost');
class ClaudeWithSearch {
constructor(anthropicKey, serppostKey) {
this.claude = new Anthropic({ apiKey: anthropicKey });
this.serppost = new SERPpost(serppostKey);
}
async chat(message, conversationHistory = []) {
/**
* Claude chat with search augmentation
*/
// Check if search is needed
const searchNeeded = this.needsSearch(message);
let systemPrompt = "You are Claude, a helpful AI assistant.";
if (searchNeeded) {
// Perform search
const searchQuery = await this.extractSearchQuery(message);
const searchResults = await this.serppost.search({
q: searchQuery,
engine: 'google'
});
// Add search context to system prompt
const searchContext = this.formatSearchResults(searchResults);
systemPrompt += `\n\nYou have access to the following current information from search:\n${searchContext}`;
}
// Generate response
const response = await this.claude.messages.create({
model: 'claude-3-opus-20240229',
max_tokens: 1024,
system: systemPrompt,
messages: [
...conversationHistory,
{ role: 'user', content: message }
]
});
return {
response: response.content[0].text,
searchUsed: searchNeeded,
sources: searchNeeded ? this.extractSources(searchResults) : []
};
}
needsSearch(message) {
/**
* Determine if message requires search
*/
const patterns = [
/what('s| is) (the )?(current|latest|recent)/i,
/today('s)?/i,
/right now/i,
/as of (today|now)/i,
/(price|cost|value) of/i
];
return patterns.some(pattern => pattern.test(message));
}
formatSearchResults(results) {
/**
* Format search results for Claude
*/
return results.organic_results
.slice(0, 5)
.map((result, i) => `
[${i + 1}] ${result.title}
${result.snippet}
Source: ${result.link}
`)
.join('\n');
}
}
// Usage
const claude = new ClaudeWithSearch(
'your_anthropic_key',
'your_serppost_key'
);
const result = await claude.chat(
"What are the latest developments in AI regulation?"
);
console.log(result.response);
console.log('Sources:', result.sources);2. LangChain Integration
LangChain Tool Implementation
from langchain.tools import Tool
from langchain.agents import initialize_agent, AgentType
from langchain.llms import OpenAI
from serppost import SERPpost
class SERPpostTool:
"""
LangChain tool for SERPpost API
"""
def __init__(self, api_key):
self.serppost = SERPpost(api_key)
def search(self, query: str) -> str:
"""
Search using SERPpost and return formatted results
"""
try:
results = self.serppost.search(query)
# Format results for LLM consumption
formatted = []
for i, result in enumerate(results['organic_results'][:5]):
formatted.append(f"""
Result {i+1}:
Title: {result['title']}
Snippet: {result['snippet']}
URL: {result['link']}
""")
return '\n'.join(formatted)
except Exception as e:
return f"Search error: {str(e)}"
def get_tool(self):
"""
Return LangChain Tool object
"""
return Tool(
name="Search",
func=self.search,
description="""
Useful for when you need to answer questions about current events,
prices, weather, news, or any information that requires real-time data.
Input should be a search query string.
"""
)
# Create LangChain agent with search capability
serppost_tool = SERPpostTool(api_key="your_serppost_key")
tools = [
serppost_tool.get_tool(),
# Add other tools as needed
]
llm = OpenAI(temperature=0)
agent = initialize_agent(
tools=tools,
llm=llm,
agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
verbose=True
)
# Use the agent
response = agent.run("What's the current weather in San Francisco?")
print(response)Advanced LangChain RAG Chain
from langchain.chains import RetrievalQA
from langchain.vectorstores import FAISS
from langchain.embeddings import OpenAIEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.docstore.document import Document
class SERPRAGChain:
"""
Advanced RAG chain using SERP results
"""
def __init__(self, openai_key, serppost_key):
self.serppost = SERPpost(serppost_key)
self.embeddings = OpenAIEmbeddings(openai_api_key=openai_key)
self.llm = OpenAI(temperature=0, openai_api_key=openai_key)
async def query(self, question):
"""
Query with RAG using SERP results
"""
# Step 1: Get search results
search_results = await self.serppost.search(question)
# Step 2: Scrape full content from top results
documents = await self.scrape_results(search_results)
# Step 3: Split into chunks
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=200
)
splits = text_splitter.split_documents(documents)
# Step 4: Create vector store
vectorstore = FAISS.from_documents(splits, self.embeddings)
# Step 5: Create retrieval chain
qa_chain = RetrievalQA.from_chain_type(
llm=self.llm,
chain_type="stuff",
retriever=vectorstore.as_retriever(
search_kwargs={"k": 5}
),
return_source_documents=True
)
# Step 6: Get answer
result = qa_chain({"query": question})
return {
'answer': result['result'],
'sources': [doc.metadata['source'] for doc in result['source_documents']]
}
async def scrape_results(self, search_results):
"""
Scrape full content from search results
"""
documents = []
for result in search_results['organic_results'][:5]:
try:
# Use SERPpost web scraping API
content = await self.serppost.scrape(result['link'])
doc = Document(
page_content=content['text'],
metadata={
'source': result['link'],
'title': result['title']
}
)
documents.append(doc)
except Exception as e:
print(f"Failed to scrape {result['link']}: {e}")
return documents
# Usage
rag = SERPRAGChain(
openai_key="sk-...",
serppost_key="your_serppost_key"
)
result = await rag.query("Explain the latest breakthroughs in quantum computing")
print(result['answer'])
print("Sources:", result['sources'])3. AutoGPT & Autonomous Agents
AutoGPT Plugin
from typing import Any, Dict, List
import json
class SERPpostPlugin:
"""
AutoGPT plugin for SERPpost
"""
def __init__(self):
self.name = "serppost_search"
self.description = "Search the web for current information"
self.serppost = None
def can_handle_post_prompt(self) -> bool:
return True
def can_handle_on_planning(self) -> bool:
return True
def can_handle_post_planning(self, response: str) -> bool:
return True
def can_handle_pre_instruction(self) -> bool:
return True
def can_handle_on_instruction(self, messages: List[Dict[str, Any]]) -> bool:
return True
def can_handle_post_instruction(self, response: str) -> bool:
return True
def can_handle_pre_command(
self, command_name: str, arguments: Dict[str, Any]
) -> bool:
return command_name == "serppost_search"
def can_handle_post_command(
self, command_name: str, response: str
) -> bool:
return command_name == "serppost_search"
def post_prompt(self, prompt: str) -> str:
"""
Add search capability to prompt
"""
return prompt + """
You have access to a search command:
- serppost_search: Search the web for current information
Arguments: {"query": "search query"}
"""
def on_planning(
self, prompt: str, messages: List[Dict[str, Any]]
) -> List[Dict[str, Any]]:
"""
Add search planning capability
"""
return messages
def pre_command(
self, command_name: str, arguments: Dict[str, Any]
) -> Dict[str, Any]:
"""
Execute search command
"""
if command_name == "serppost_search":
query = arguments.get("query", "")
if not self.serppost:
from serppost import SERPpost
import os
self.serppost = SERPpost(os.getenv("SERPPOST_API_KEY"))
results = self.serppost.search(query)
# Format results
formatted = {
"results": [
{
"title": r["title"],
"snippet": r["snippet"],
"url": r["link"]
}
for r in results["organic_results"][:5]
]
}
arguments["search_results"] = json.dumps(formatted)
return argumentsCustom AI Agent with Tools
class AIAgentWithTools {
constructor(llm, tools) {
this.llm = llm;
this.tools = tools;
this.conversationHistory = [];
}
async run(task) {
/**
* Run autonomous agent with tool access
*/
let iterations = 0;
const maxIterations = 10;
while (iterations < maxIterations) {
// Get next action from LLM
const action = await this.getNextAction(task);
if (action.type === 'final_answer') {
return action.content;
}
// Execute tool
const toolResult = await this.executeTool(
action.tool,
action.arguments
);
// Add to history
this.conversationHistory.push({
action: action,
result: toolResult
});
iterations++;
}
return "Max iterations reached";
}
async getNextAction(task) {
/**
* Determine next action using LLM
*/
const prompt = this.buildPrompt(task);
const response = await this.llm.complete(prompt);
return this.parseAction(response);
}
buildPrompt(task) {
/**
* Build prompt with available tools and history
*/
const toolDescriptions = this.tools.map(tool => `
- ${tool.name}: ${tool.description}
Arguments: ${JSON.stringify(tool.arguments)}
`).join('\n');
const history = this.conversationHistory.map(h => `
Action: ${h.action.tool}(${JSON.stringify(h.action.arguments)})
Result: ${h.result}
`).join('\n');
return `
You are an AI agent with access to the following tools:
${toolDescriptions}
Task: ${task}
Previous actions:
${history}
What should you do next? Respond in this format:
Thought: [your reasoning]
Action: [tool_name]
Arguments: [JSON arguments]
Or if you have the final answer:
Thought: [your reasoning]
Final Answer: [the answer]
`;
}
async executeTool(toolName, arguments) {
/**
* Execute specified tool
*/
const tool = this.tools.find(t => t.name === toolName);
if (!tool) {
return `Error: Tool ${toolName} not found`;
}
try {
return await tool.execute(arguments);
} catch (error) {
return `Error executing ${toolName}: ${error.message}`;
}
}
}
// Define search tool
const searchTool = {
name: 'search',
description: 'Search the web for current information',
arguments: { query: 'string' },
execute: async (args) => {
const results = await serppostAPI.search(args.query);
return JSON.stringify(results.organic_results.slice(0, 5));
}
};
// Create and run agent
const agent = new AIAgentWithTools(gpt4, [searchTool]);
const result = await agent.run(
"Find the current price of Bitcoin and explain why it changed"
);4. Function Calling & Tool Use
OpenAI Function Calling
import openai
from serppost import SERPpost
class GPTFunctionCalling:
def __init__(self, openai_key, serppost_key):
self.openai = openai
self.openai.api_key = openai_key
self.serppost = SERPpost(serppost_key)
def get_functions(self):
"""
Define available functions for GPT
"""
return [
{
"name": "search_web",
"description": "Search the web for current information",
"parameters": {
"type": "object",
"properties": {
"query": {
"type": "string",
"description": "The search query"
},
"engine": {
"type": "string",
"enum": ["google", "bing"],
"description": "Search engine to use"
},
"num_results": {
"type": "integer",
"description": "Number of results to return",
"default": 5
}
},
"required": ["query"]
}
},
{
"name": "scrape_webpage",
"description": "Scrape content from a specific webpage",
"parameters": {
"type": "object",
"properties": {
"url": {
"type": "string",
"description": "The URL to scrape"
}
},
"required": ["url"]
}
}
]
async def chat(self, user_message):
"""
Chat with function calling capability
"""
messages = [
{"role": "system", "content": "You are a helpful assistant with access to web search."},
{"role": "user", "content": user_message}
]
# Initial GPT call
response = await self.openai.ChatCompletion.create(
model="gpt-4",
messages=messages,
functions=self.get_functions(),
function_call="auto"
)
message = response.choices[0].message
# Check if GPT wants to call a function
if message.get("function_call"):
function_name = message["function_call"]["name"]
function_args = json.loads(message["function_call"]["arguments"])
# Execute function
if function_name == "search_web":
function_response = await self.search_web(**function_args)
elif function_name == "scrape_webpage":
function_response = await self.scrape_webpage(**function_args)
# Add function response to messages
messages.append(message)
messages.append({
"role": "function",
"name": function_name,
"content": json.dumps(function_response)
})
# Get final response from GPT
final_response = await self.openai.ChatCompletion.create(
model="gpt-4",
messages=messages
)
return final_response.choices[0].message.content
return message.content
async def search_web(self, query, engine="google", num_results=5):
"""
Execute web search
"""
results = await self.serppost.search({
"q": query,
"engine": engine
})
return {
"results": [
{
"title": r["title"],
"snippet": r["snippet"],
"url": r["link"]
}
for r in results["organic_results"][:num_results]
]
}
async def scrape_webpage(self, url):
"""
Scrape webpage content
"""
content = await self.serppost.scrape(url)
return {
"url": url,
"title": content.get("title", ""),
"text": content.get("text", "")[:5000] # Limit to 5000 chars
}
# Usage
gpt = GPTFunctionCalling(
openai_key="sk-...",
serppost_key="your_serppost_key"
)
response = await gpt.chat("What are the top 3 AI news stories today?")
print(response)Claude Tool Use
const Anthropic = require('@anthropic-ai/sdk');
class ClaudeToolUse {
constructor(anthropicKey, serppostKey) {
this.claude = new Anthropic({ apiKey: anthropicKey });
this.serppost = new SERPpost(serppostKey);
}
getTools() {
/**
* Define tools for Claude
*/
return [
{
name: 'search',
description: 'Search the web for current information. Use this when you need up-to-date information.',
input_schema: {
type: 'object',
properties: {
query: {
type: 'string',
description: 'The search query'
},
engine: {
type: 'string',
enum: ['google', 'bing'],
description: 'Search engine to use',
default: 'google'
}
},
required: ['query']
}
},
{
name: 'scrape',
description: 'Extract content from a specific webpage',
input_schema: {
type: 'object',
properties: {
url: {
type: 'string',
description: 'The URL to scrape'
}
},
required: ['url']
}
}
];
}
async chat(message) {
/**
* Chat with tool use capability
*/
const messages = [{ role: 'user', content: message }];
let response = await this.claude.messages.create({
model: 'claude-3-opus-20240229',
max_tokens: 4096,
tools: this.getTools(),
messages: messages
});
// Process tool use
while (response.stop_reason === 'tool_use') {
const toolUse = response.content.find(
block => block.type === 'tool_use'
);
// Execute tool
const toolResult = await this.executeTool(
toolUse.name,
toolUse.input
);
// Add tool result to messages
messages.push({
role: 'assistant',
content: response.content
});
messages.push({
role: 'user',
content: [
{
type: 'tool_result',
tool_use_id: toolUse.id,
content: JSON.stringify(toolResult)
}
]
});
// Get next response
response = await this.claude.messages.create({
model: 'claude-3-opus-20240229',
max_tokens: 4096,
tools: this.getTools(),
messages: messages
});
}
return response.content[0].text;
}
async executeTool(toolName, input) {
/**
* Execute specified tool
*/
if (toolName === 'search') {
const results = await this.serppost.search({
q: input.query,
engine: input.engine || 'google'
});
return {
results: results.organic_results.slice(0, 5).map(r => ({
title: r.title,
snippet: r.snippet,
url: r.link
}))
};
}
if (toolName === 'scrape') {
const content = await this.serppost.scrape(input.url);
return {
url: input.url,
title: content.title,
text: content.text.substring(0, 5000)
};
}
throw new Error(`Unknown tool: ${toolName}`);
}
}
// Usage
const claude = new ClaudeToolUse(
'your_anthropic_key',
'your_serppost_key'
);
const response = await claude.chat(
"Find information about the latest SpaceX launch and summarize it"
);
console.log(response);5. Chatbot Integration
Custom Chatbot with Memory
from typing import List, Dict
import json
class ChatbotWithSearch:
"""
Chatbot with search capability and conversation memory
"""
def __init__(self, llm, serppost_key):
self.llm = llm
self.serppost = SERPpost(serppost_key)
self.conversation_history = []
self.search_cache = {}
async def chat(self, user_message: str) -> Dict:
"""
Process user message and return response
"""
# Add to history
self.conversation_history.append({
"role": "user",
"content": user_message
})
# Determine if search is needed
search_needed = await self.analyze_intent(user_message)
search_results = None
if search_needed:
# Extract search query
search_query = await self.extract_search_query(user_message)
# Check cache
if search_query in self.search_cache:
search_results = self.search_cache[search_query]
else:
# Perform search
search_results = await self.serppost.search(search_query)
self.search_cache[search_query] = search_results
# Generate response
response = await self.generate_response(
user_message,
search_results
)
# Add to history
self.conversation_history.append({
"role": "assistant",
"content": response,
"search_used": search_needed,
"sources": self.extract_sources(search_results) if search_results else []
})
return {
"response": response,
"search_used": search_needed,
"sources": self.extract_sources(search_results) if search_results else [],
"conversation_id": self.get_conversation_id()
}
async def analyze_intent(self, message: str) -> bool:
"""
Analyze if message requires search
"""
prompt = f"""
Analyze if this message requires real-time web search:
"{message}"
Consider:
- Does it ask about current events, prices, or time-sensitive information?
- Does it reference "today", "latest", "current", etc.?
- Can it be answered with general knowledge?
Respond with only "YES" or "NO".
"""
response = await self.llm.complete(prompt)
return "YES" in response.upper()
async def extract_search_query(self, message: str) -> str:
"""
Extract optimal search query
"""
prompt = f"""
Extract the best search query from this message:
"{message}"
Return only the search query, nothing else.
"""
response = await self.llm.complete(prompt)
return response.strip()
async def generate_response(
self,
user_message: str,
search_results: Dict = None
) -> str:
"""
Generate chatbot response
"""
# Build context from conversation history
context = self.build_context()
# Add search results if available
if search_results:
search_context = self.format_search_results(search_results)
context += f"\n\nCurrent information from search:\n{search_context}"
# Generate response
prompt = f"""
{context}
User: {user_message}
Assistant: {response}
"""
response = await self.llm.complete(prompt)
return response.strip()
def build_context(self) -> str:
"""Build context from conversation history"""
recent_history = self.conversation_history[-10:] # Last 10 messages
return "\n".join([
f"{msg['role'].capitalize()}: {msg['content']}"
for msg in recent_history
])
def format_search_results(self, results: Dict) -> str:
"""Format search results for context"""
formatted = []
for i, result in enumerate(results['organic_results'][:3]):
formatted.append(f"[{i+1}] {result['title']}: {result['snippet']}")
return "\n".join(formatted)
def extract_sources(self, results: Dict) -> List[str]:
"""Extract source URLs"""
if not results:
return []
return [r['link'] for r in results['organic_results'][:3]]6. Best Practices for AI Integration
Caching Strategy
class SmartSearchCache {
constructor(ttl = 3600) {
this.cache = new Map();
this.ttl = ttl * 1000; // Convert to ms
}
async get(query, searchFunc) {
const key = this.normalizeQuery(query);
const cached = this.cache.get(key);
if (cached && Date.now() - cached.timestamp < this.ttl) {
return { ...cached.data, fromCache: true };
}
// Fetch fresh data
const data = await searchFunc(query);
this.cache.set(key, {
data,
timestamp: Date.now()
});
return { ...data, fromCache: false };
}
normalizeQuery(query) {
return query.toLowerCase().trim().replace(/\s+/g, ' ');
}
}Cost Optimization
class CostOptimizedSearch:
"""Optimize search costs for AI applications"""
def __init__(self, serppost_key, budget_per_day=100):
self.serppost = SERPpost(serppost_key)
self.budget_per_day = budget_per_day
self.daily_spend = 0
self.request_count = 0
async def search(self, query):
"""Search with cost tracking"""
cost_per_request = 0.004
if self.daily_spend + cost_per_request > self.budget_per_day:
raise Exception("Daily budget exceeded")
results = await self.serppost.search(query)
self.daily_spend += cost_per_request
self.request_count += 1
return results7. Production Deployment
Monitoring & Logging
import logging
from datetime import datetime
class AISearchMonitor:
def __init__(self):
self.logger = logging.getLogger(__name__)
self.metrics = {
'total_searches': 0,
'cache_hits': 0,
'errors': 0,
'avg_latency': 0
}
async def monitored_search(self, query, search_func):
start_time = datetime.now()
try:
result = await search_func(query)
self.metrics['total_searches'] += 1
latency = (datetime.now() - start_time).total_seconds()
self.update_latency(latency)
self.logger.info(f"Search successful: {query} ({latency}s)")
return result
except Exception as e:
self.metrics['errors'] += 1
self.logger.error(f"Search failed: {query} - {str(e)}")
raiseConclusion
Key Takeaways
- RAG is Essential: Combine LLMs with real-time search for accurate, current responses
- Function Calling: Use native function calling for seamless tool integration
- Caching Saves Money: Implement smart caching to reduce API costs by 70%
- Monitor Everything: Track usage, costs, and performance in production
Quick Start
- Get API key - Free 1,000 requests
- Read docs - Integration guides
- Try playground - Test before coding
- View blog - Learn from tutorials and guides
Related Resources
About the Author: Dr. Kevin Zhang is the AI Research Lead at SERPpost with a PhD in Machine Learning from Stanford. He specializes in RAG systems, LLM optimization, and has published 15+ papers on information retrieval and natural language processing.
Ready to power your AI with real-time search? Start building with SERPpost today.