Complete Guide to AI Agent Tools and APIs

AI agents are the next evolution of software — systems that don't just process data but autonomously plan, reason, and take action. The tools and APIs that power these agents have exploded in number and capability over the past year, making it harder than ever to pick the right stack.

This guide covers the AI agent landscape: the LLM APIs that provide reasoning, the framework SDKs that orchestrate behavior, and the tool APIs that give agents real-world capabilities like search, scraping, and code execution.

Key Takeaways

• LLM APIs (OpenAI, Anthropic, Google) provide the reasoning foundation — pick based on cost, speed, and capability needs
• Agent frameworks (LangChain, CrewAI, AutoGen) add orchestration, memory, and tool-calling patterns
• Tool APIs (SearchHive, browser automation, code execution) give agents the ability to interact with the real world
• SearchHive's unified API (search + scrape + research) replaces 3–4 separate tool APIs in most agent stacks
• The best agent stacks are minimal — too many tools creates decision paralysis and increases latency

Section 1: LLM APIs — The Reasoning Engine

Every AI agent starts with a language model that can reason about tasks, generate plans, and interpret tool outputs. The three major providers dominate:

OpenAI GPT-4o / o3

OpenAI's models remain the most widely used for agent workloads. GPT-4o offers the best balance of capability and cost for general-purpose agents. The o-series (o1, o3) adds chain-of-thought reasoning for complex multi-step tasks.

Pricing: GPT-4o — $2.50/1M input tokens, $10/1M output tokens. o3 — $10/1M input, $40/1M output.

Best for: General-purpose agents, production workloads where reliability matters most.

from openai import OpenAI

client = OpenAI(api_key="your_key")

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "system", "content": "You are a research assistant that uses web search to answer questions."},
        {"role": "user", "content": "What are the current interest rates for 30-year fixed mortgages?"}
    ],
    tools=[{
        "type": "function",
        "function": {
            "name": "web_search",
            "description": "Search the web for current information",
            "parameters": {
                "type": "object",
                "properties": {
                    "query": {"type": "string", "description": "Search query"}
                },
                "required": ["query"]
            }
        }
    }]
)

# Check if the model wants to call a tool
if response.choices[0].message.tool_calls:
    tool_call = response.choices[0].message.tool_calls[0]
    print(f"Agent wants to search: {tool_call.function.arguments}")

Anthropic Claude

Claude excels at long-context tasks, nuanced reasoning, and following complex instructions. Claude 3.5 Sonnet offers strong agent capabilities at a competitive price point.

Pricing: Claude 3.5 Sonnet — $3/1M input, $15/1M output. Claude 3 Opus — $15/1M input, $75/1M output.

Best for: Agents that need careful reasoning, long document analysis, or nuanced instruction following.

Google Gemini

Gemini 2.0 offers strong multilingual capabilities and the largest context window (2M tokens). Its tool-calling implementation is clean and well-documented.

Pricing: Gemini 2.0 Flash — $0.10/1M input, $0.40/1M output. Gemini 2.5 Pro — $1.25/1M input, $10/1M output.

Best for: Cost-sensitive agents, multilingual applications, and agents processing very large documents.

Section 2: Agent Frameworks — Orchestration Layer

Agent frameworks handle the complexity of tool calling, memory management, multi-step reasoning, and error recovery.

LangChain / LangGraph

The most widely adopted agent framework. LangGraph adds stateful graph-based orchestration on top of LangChain's tool-calling primitives.

Best for: Production agents that need complex state management, human-in-the-loop patterns, and robust error handling.

CrewAI

CrewAI takes a team-based approach — you define multiple agent "roles" that collaborate on tasks, each with their own LLM, tools, and instructions.

Best for: Multi-agent systems where different specialists (researcher, writer, coder) collaborate.

Microsoft AutoGen

AutoGen enables conversational multi-agent systems where agents can chat with each other to solve problems. Built by Microsoft Research.

Best for: Research and prototyping multi-agent collaboration patterns.

Minimalist Approach: Direct Tool Calling

For many use cases, you don't need a framework at all. Direct tool calling with the OpenAI or Anthropic SDK handles 80% of agent patterns:

import requests
from openai import OpenAI

client = OpenAI()

def web_search(query):
    """Search the web using SearchHive SwiftSearch"""
    resp = requests.get(
        "https://api.searchhive.dev/v1/swiftsearch",
        headers={"Authorization": "Bearer YOUR_KEY"},
        params={"query": query, "limit": 5}
    )
    results = resp.json()["data"]["results"]
    return "\n".join(f"- {r['title']}: {r['snippet']}" for r in results)

def scrape_page(url):
    """Extract content from a web page using SearchHive ScrapeForge"""
    resp = requests.post(
        "https://api.searchhive.dev/v1/scrapeforge",
        headers={"Authorization": "Bearer YOUR_KEY"},
        json={"url": url, "render_js": True}
    )
    return resp.json()["data"]["content"][:3000]

# Minimal agent loop
messages = [{"role": "user", "content": "Research the top 3 Python web frameworks in 2025"}]
tools = [
    {"type": "function", "function": {"name": "web_search", "description": "Search the web", "parameters": {"type": "object", "properties": {"query": {"type": "string"}}, "required": ["query"]}}},
    {"type": "function", "function": {"name": "scrape_page", "description": "Extract page content", "parameters": {"type": "object", "properties": {"url": {"type": "string"}}, "required": ["url"]}}}
]

for _ in range(10):  # max iterations
    response = client.chat.completions.create(
        model="gpt-4o", messages=messages, tools=tools
    )
    msg = response.choices[0].message
    messages.append(msg)

    if not msg.tool_calls:
        print(msg.content)
        break

    for tc in msg.tool_calls:
        if tc.function.name == "web_search":
            result = web_search(eval(tc.function.arguments)["query"])
        elif tc.function.name == "scrape_page":
            result = scrape_page(eval(tc.function.arguments)["url"])
        messages.append({"role": "tool", "tool_call_id": tc.id, "content": result})

Section 3: Tool APIs — Real-World Capabilities

An agent is only as useful as the tools it can call. Here are the essential tool categories:

Web Search

Agents need to search the web for current information. Options:

• SearchHive SwiftSearch — $9/mo starting, universal search with structured results
• Tavily — $0.008/credit, purpose-built for AI agent search
• Exa — $7/1K requests, AI-native search with neural ranking
• SerpAPI — $25/1K, Google SERP data
• Brave Search API — $5/1K, privacy-focused search

import requests

# SearchHive — returns structured results with titles, URLs, snippets
resp = requests.get(
    "https://api.searchhive.dev/v1/swiftsearch",
    headers={"Authorization": "Bearer YOUR_KEY"},
    params={"query": "latest AI agent frameworks comparison", "limit": 5}
)
for r in resp.json()["data"]["results"]:
    print(f"[{r['title']}] {r['url']}")

Web Scraping

For extracting data from specific pages:

• SearchHive ScrapeForge — Unified scraping with JS rendering, proxy rotation, and structured extraction
• Firecrawl — $16/3K pages, markdown conversion with crawling
• Jina AI Reader — Free for basic use, single-page extraction
• ScrapingBee — $49/250K requests, proxy rotation included

Code Execution

Agents that write code need to run it:

• E2B — Sandboxed code execution environments for AI agents
• Modal — Serverless Python execution with GPU support
• Jupyter kernels — For interactive, stateful code execution

File and Data Access

• Google Drive API — Document access and search
• Notion API — Knowledge base integration
• Postgres/Supabase — Structured data queries via SQL tools

Section 4: Best Practices for Building AI Agents

1. Start Minimal, Add Complexity as Needed

Don't reach for a framework on day one. Start with direct tool calling and a simple loop. Add LangGraph or CrewAI only when you hit limitations like complex state management or multi-agent coordination.

2. Design Tools with Clear Inputs and Outputs

Every tool function should have:

• A clear, specific description (the LLM uses this to decide when to call it)
• Well-typed parameters with descriptions
• Consistent output format (ideally a string the LLM can reason about)
• Error handling that returns helpful messages (not stack traces)

3. Implement Guardrails

• Maximum iterations — Always cap the agent loop to prevent infinite tool-calling
• Cost tracking — Monitor token usage and tool API costs per agent run
• Human approval — For high-stakes actions (sending emails, making purchases), require human confirmation
• Output validation — Parse and validate agent outputs before using them

4. Use SearchHive to Replace Multiple Tools

Most agent stacks need web search, page scraping, and deep research. Instead of integrating 3–4 separate APIs:

import requests

headers = {"Authorization": "Bearer YOUR_SEARCHHIVE_KEY"}

# One API for search, scraping, and research
# SwiftSearch for finding information
search = requests.get("https://api.searchhive.dev/v1/swiftsearch",
    headers=headers, params={"query": " competitor pricing", "limit": 5})

# ScrapeForge for extracting specific data
scrape = requests.post("https://api.searchhive.dev/v1/scrapeforge",
    headers=headers, json={"url": "https://example.com", "extract": {"price": ".price"}})

# DeepDive for multi-step research
research = requests.post("https://api.searchhive.dev/v1/deepdive",
    headers=headers, json={"query": " market analysis for X industry", "depth": "deep"})

One API key, one authentication pattern, one billing dashboard. This simplifies agent development significantly.

Conclusion

The AI agent tool landscape is vast but the winning approach is simple: start with a strong LLM (GPT-4o or Claude), add a minimal tool-calling loop, and expand with frameworks only when you need them. For tool APIs, consolidate where possible — SearchHive's unified search + scrape + research API replaces three separate integrations and gives your agent broad web capabilities from a single provider.

Get started building AI agents with SearchHive's free tier — 500 free credits covering SwiftSearch, ScrapeForge, and DeepDive. No credit card required. Check the API documentation for integration guides and examples.

For more on building with SearchHive, see SearchHive vs SerpAPI for AI agents and the SearchHive developer tutorials.