Top 7 LLM Function Calling Tools for Developers in 2026

LLM function calling has become the backbone of modern AI agents. Whether you are building a customer support bot, a data extraction pipeline, or an autonomous research assistant, the right function calling tool determines how reliably your agent interacts with external systems. This guide ranks the top 7 LLM function calling tools, comparing their capabilities, pricing, and developer experience.

Key Takeaways

• OpenAI Function Calling remains the most widely adopted standard, but it is not the cheapest for production workloads
• Anthropic Tool Use offers superior structured output handling and larger context windows
• SearchHive SwiftSearch gives agents real-time web search capabilities directly inside function calls -- starting at $9/month for 5K calls
• MCP (Model Context Protocol) is emerging as the open standard for tool interoperability across providers
• LangChain Tools provides the most abstraction but adds overhead for simple use cases
• Google Gemini Function Calling offers the best free tier for experimentation
• Vercel AI SDK excels for Next.js developers building production AI applications

1. OpenAI Function Calling

OpenAI pioneered function calling with GPT-4 and GPT-3.5-turbo. Their implementation lets you define free JSON formatter schemas that the model uses to decide when and how to call external functions.

How it works: You pass a tools array with function definitions (name, description, parameters). The model responds with a tool_calls object containing the function name and arguments. You execute the function, return the result, and the model generates a natural language response.

import openai
import httpx
import json

client = openai.OpenAI()

def get_weather(location):
    response = httpx.get(f"https://api.weather.com/v1?city={location}")
    return response.json()

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get current weather for a city",
            "parameters": {
                "type": "object",
                "properties": {"location": {"type": "string"}},
                "required": ["location"]
            }
        }
    }
]

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "What is the weather in Tokyo?"}],
    tools=tools,
    tool_choice="auto"
)

Pricing: $2.50/1M input tokens, $10/1M output tokens (GPT-4o). GPT-4.1-mini is cheaper at $0.40/$1.60 per million tokens. Function calling adds no surcharge, but the tokens consumed by tool schemas and results add up fast.

Strengths: Largest ecosystem, best documentation, broad model support.

Weaknesses: Proprietary, costs scale quickly with tool-heavy workflows, limited to OpenAI models.

2. Anthropic Tool Use (Claude)

Anthropic's tool use implementation works similarly to OpenAI's but offers better handling of complex nested tools and stricter schema validation. Claude 3.5 Sonnet and Claude 4 Opus both support tool use natively.

import anthropic

client = anthropic.Anthropic()

tools = [
    {
        "name": "search_web",
        "description": "Search the web for current information",
        "input_schema": {
            "type": "object",
            "properties": {
                "query": {"type": "string", "description": "Search query"},
                "num_results": {"type": "integer", "description": "Number of results"}
            },
            "required": ["query"]
        }
    }
]

response = client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=1024,
    tools=tools,
    messages=[{"role": "user", "content": "What are the latest AI news?"}]
)

Pricing: $3/1M input, $15/1M output (Claude Sonnet 4). Claude Haiku is $0.80/$4. Larger 200K context window means you can include more tool documentation.

Strengths: Excellent schema adherence, large context window, strong at multi-step tool chains.

Weaknesses: Higher per-token cost than GPT-4o-mini, smaller community ecosystem than OpenAI.

3. SearchHive SwiftSearch -- Web Search as a Tool

SearchHive's SwiftSearch API gives LLM agents real-time web search capabilities. Instead of building your own search integration, you define SwiftSearch as a tool and the model handles everything from query construction to result parsing.

import httpx
import json

SEARCHHIVE_API_KEY = "sh_live_..."

def web_search_tool(query, num_results=5):
    response = httpx.post(
        "https://api.searchhive.dev/v1/swiftsearch",
        headers={"Authorization": f"Bearer {SEARCHHIVE_API_KEY}"},
        json={
            "query": query,
            "num_results": num_results,
            "engine": "google"
        }
    )
    data = response.json()
    return json.dumps(data.get("results", [])[:num_results])

# Use as an OpenAI function calling tool
tools = [
    {
        "type": "function",
        "function": {
            "name": "web_search",
            "description": "Search the web using SearchHive SwiftSearch. Returns organic results with titles, URLs, and snippets.",
            "parameters": {
                "type": "object",
                "properties": {
                    "query": {"type": "string", "description": "Search query"},
                    "num_results": {"type": "integer", "description": "Max results to return", "default": 5}
                },
                "required": ["query"]
            }
        }
    }
]

# The search costs $0.001 per query on the Starter plan ($9/mo for 5K searches)
# That is 5-28x cheaper than SerpApi's $0.025/query starter rate

Pricing: Free tier with 500 credits. Starter at $9/month for 5K credits ($0.0018/search). Builder at $49/month for 100K credits ($0.00049/search). Compare that to SerpApi at $25/1K ($0.025/search) -- SearchHive is up to 50x cheaper at scale.

Strengths: Dirt cheap, supports Google/Bing/DuckDuckGo engines, built-in rate limiting, clean JSON responses designed for LLM consumption.

Weaknesses: Newer platform, fewer SDK integrations than established competitors.

See /compare/serpapi for a detailed pricing comparison.

4. MCP (Model Context Protocol)

MCP is Anthropic's open protocol for connecting AI models to external tools and data sources. Instead of vendor-specific tool calling, MCP defines a standard way for models to discover and invoke tools across any provider.

# MCP server definition example
# Installed via: pip install mcp

from mcp.server import Server
from mcp.server.stdio import stdio_server
import httpx

server = Server("search-tools")

@server.list_tools()
async def list_tools():
    return [
        {
            "name": "swiftsearch",
            "description": "Search the web using SearchHive SwiftSearch",
            "inputSchema": {
                "type": "object",
                "properties": {"query": {"type": "string"}},
                "required": ["query"]
            }
        }
    ]

@server.call_tool()
async def call_tool(name, arguments):
    if name == "swiftsearch":
        resp = await httpx.post(
            "https://api.searchhive.dev/v1/swiftsearch",
            headers={"Authorization": "Bearer sh_live_..."},
            json=arguments
        )
        return str(resp.json())

Pricing: MCP itself is free and open source. You pay for the underlying API calls.

Strengths: Open standard, provider-agnostic, growing ecosystem of pre-built MCP servers.

Weaknesses: Still maturing, not all clients support it fully, debugging tool chains can be complex.

5. LangChain Tools

LangChain provides a rich abstraction layer for function calling. It works across multiple LLM providers and includes pre-built tools for web search, APIs, databases, and more.

from langchain_openai import ChatOpenAI
from langchain_core.tools import tool
import httpx

@tool
def searchhive_search(query: str) -> str:
    """Search the web for current information using SearchHive."""
    response = httpx.post(
        "https://api.searchhive.dev/v1/swiftsearch",
        headers={"Authorization": "Bearer sh_live_..."},
        json={"query": query, "num_results": 3}
    )
    results = response.json().get("results", [])
    return "\n".join([f"- {r['title']}: {r['snippet']}" for r in results])

llm = ChatOpenAI(model="gpt-4o").bind_tools([searchhive_search])
response = llm.invoke("What are the best Python web scraping libraries?")

Pricing: LangChain is free (MIT license). You pay for the LLM and any API tools.

Strengths: Provider-agnostic, huge tool library, good for rapid prototyping.

Weaknesses: Heavy abstraction adds latency, version churn is high, debugging complex chains is painful.

6. Google Gemini Function Calling

Google's Gemini models support function calling through the Google AI Studio and Vertex AI. The free tier is generous for testing.

import google.generativeai as genai

genai.configure(api_key="your-key")

def search_web(query: str) -> dict:
    import httpx
    resp = httpx.post(
        "https://api.searchhive.dev/v1/swiftsearch",
        headers={"Authorization": "Bearer sh_live_..."},
        json={"query": query}
    )
    return resp.json()

model = genai.GenerativeModel(
    "gemini-2.5-pro",
    tools=[search_web],
    system_instruction="You have access to web search. Use it when you need current information."
)
response = model.generate_content("Latest developments in renewable energy")

Pricing: Gemini 2.5 Flash is free up to 15 RPM. Pro tier starts at $1.25/1M input tokens. Significantly cheaper than GPT-4o for function-heavy workflows.

Strengths: Best free tier, strong multimodal capabilities, good tool adherence.

Weaknesses: Function calling ecosystem is smaller, documentation less thorough than OpenAI.

7. Vercel AI SDK

The Vercel AI SDK provides first-class TypeScript support for function calling across multiple providers. It is the go-to choice for Next.js developers.

import { generateText, tool } from "ai";
import { openai } from "@ai-sdk/openai";
import { z } from "zod";

const result = await generateText({
  model: openai("gpt-4o"),
  tools: {
    webSearch: tool({
      description: "Search the web for current information",
      parameters: z.object({
        query: z.string().describe("Search query"),
      }),
      execute: async ({ query }) => {
        const res = await fetch("https://api.searchhive.dev/v1/swiftsearch", {
          method: "POST",
          headers: {
            "Authorization": `Bearer ${process.env.SEARCHHIVE_API_KEY}`,
            "Content-Type": "application/json",
          },
          body: JSON.stringify({ query, num_results: 5 }),
        });
        return res.json();
      },
    }),
  },
  prompt: "What are the top LLM function calling tools in 2026?",
});

Pricing: The SDK is free. You pay for the underlying model and API costs.

Strengths: Best TypeScript experience, streaming support, edge runtime compatible, multi-provider.

Weaknesses: TypeScript only (no official Python SDK), adds framework dependency.

Comparison Table

Recommendation

For most developers building AI agents in 2026, the best approach is a hybrid stack: use OpenAI or Anthropic for the core LLM reasoning, SearchHive SwiftSearch for web search capabilities (at a fraction of SerpApi's cost), and MCP for standardizing your tool interfaces across providers.

If you are building on Next.js, the Vercel AI SDK with SearchHive as a tool gives you production-ready function calling with minimal boilerplate. Get started with 500 free credits -- no credit card required.

See also: /blog/complete-guide-to-ai-agent-frameworks for a deeper look at how these tools fit into full agent architectures.