Large language models are smart. Very smart. But on their own, they just generate text. What if you want them to do something? Like book a meeting. Call an API. Update a database. Or send an email?
That is where function calling comes in.
OpenAI made function calling popular. It lets you define structured functions. The model decides when to use them. And it returns clean JSON you can execute in your app.
But OpenAI is not alone.
Many tools now help you execute structured tasks with LLMs. Some are simple. Some are powerful. Some are built for developers. Others are low-code.
Let’s explore five great alternatives.
TLDR: Function calling lets large language models trigger structured actions like API calls and database updates. OpenAI made it famous, but other tools now offer similar or even better capabilities. Platforms like Anthropic, Google Vertex AI, LangChain, LlamaIndex, and Microsoft Semantic Kernel provide powerful ways to connect LLMs to real-world tools. The right choice depends on your stack, workflow, and how much control you want.
What Is AI Function Calling?
Before we dive in, let’s keep it simple.
Function calling allows an LLM to:
- Understand user intent
- Choose a predefined function
- Return structured arguments
- Trigger real-world actions
Instead of getting messy text like:
“You should call the weather API with New York as the city.”
You get clean structured JSON like:
{
"function": "get_weather",
"arguments": {
"city": "New York"
}
}
That is powerful.
It turns AI from a chatbot into a system operator.
1. Anthropic Tool Use (Claude)
Anthropic’s Claude models support tool use. It works very much like OpenAI function calling.
You define tools with:
- Name
- Description
- Input schema
Claude then decides when to call the tool. It produces structured JSON output.
Why people like it:
- Strong reasoning abilities
- Very large context windows
- Clean tool execution format
Claude is especially good at:
- Multi-step workflows
- Document-heavy pipelines
- Complex reasoning before taking action
If your app needs long memory and thoughtful decisions before triggering tools, this is a strong option.
It feels calm and deliberate. Like a careful assistant.
2. Google Vertex AI Function Calling (Gemini)
Google’s Gemini models inside Vertex AI also support function calling.
Google calls this “function declarations.”
The concept is similar:
- You define the function schema
- Gemini selects the right function
- It returns structured arguments
What makes it shine?
- Deep integration with Google Cloud
- Strong multimodal capabilities
- Enterprise-ready infrastructure
If your company already uses:
- BigQuery
- Cloud Run
- Firebase
This may feel natural.
Gemini is also good at mixing text, images, and structured tasks. That makes it powerful for apps that process different data types.
3. LangChain Agents and Tools
LangChain is not an LLM. It is a framework.
It makes orchestration easier.
Think of it as glue between:
- LLMs
- APIs
- Databases
- External tools
LangChain agents can:
- Decide which tool to call
- Chain multiple steps
- Reason between actions
It works with OpenAI. Anthropic. Google. And others.
Why developers love it:
- Flexible architecture
- Strong community
- Works across model providers
You are not locked into one LLM vendor.
That is a big advantage.
But it requires more engineering effort. It is not plug-and-play. It rewards those who like control.
If you are building serious AI workflows, LangChain often becomes part of the stack.
4. LlamaIndex Tool Integration
LlamaIndex started as a retrieval framework.
Now it is much more.
It supports agents and structured tool use.
LlamaIndex focuses heavily on:
- Data retrieval
- Indexing
- Knowledge augmentation
With tool integration, your LLM can:
- Query internal documents
- Call APIs
- Trigger workflows
- Combine retrieval with action
This is powerful for:
- Customer support systems
- Internal company knowledge bots
- Research assistants
It feels like giving your AI both memory and hands.
5. Microsoft Semantic Kernel
Semantic Kernel is Microsoft’s AI orchestration SDK.
It is built with structured AI actions in mind.
It uses a concept called “skills.”
Skills are functions the model can call.
These can be:
- Native code functions
- API endpoints
- Prompt-based functions
It works well with:
- Azure OpenAI
- .NET environments
- Enterprise systems
What makes it special?
- Strong enterprise integration
- Planning capabilities
- Structured execution flows
If you live in the Microsoft ecosystem, this feels like home.
It is structured. Predictable. Enterprise-ready.
Comparison Chart
| Tool | Best For | Vendor Lock-In | Ease of Use | Enterprise Ready |
|---|---|---|---|---|
| Anthropic Tool Use | Reasoning-heavy workflows | Medium | Easy | Yes |
| Google Vertex AI | Cloud-native apps | Medium | Medium | Yes |
| LangChain | Custom AI agents | Low | Medium to Hard | Yes |
| LlamaIndex | Data-focused assistants | Low | Medium | Yes |
| Semantic Kernel | Microsoft ecosystem apps | Medium | Medium | Yes |
How to Choose the Right One
Start with questions.
1. What is your stack?
- Google Cloud → Vertex AI may be ideal.
- Azure → Semantic Kernel fits well.
- Multi-model setup → LangChain works nicely.
2. How complex are your workflows?
- Simple API calls → Native function calling is enough.
- Multi-step reasoning → Claude or LangChain may shine.
3. Do you need heavy document retrieval?
- Yes → LlamaIndex is strong.
- No → Simpler tool calling may be enough.
4. Do you want flexibility?
Frameworks like LangChain and LlamaIndex reduce vendor lock-in.
Using built-in function calling ties you more closely to one provider.
Why Function Calling Changes Everything
Without function calling, LLMs are talkers.
With function calling, they are doers.
That changes apps completely.
Examples:
- Booking flights automatically
- Updating CRM records
- Triggering Slack messages
- Generating reports and emailing them
This is where AI stops being a chatbot.
It becomes infrastructure.
Smart infrastructure.
A Simple Mental Model
Think of it like this:
- The user talks.
- The LLM thinks.
- A function executes.
- The world changes.
That loop is the future of AI applications.
And the tools above help you build it.
Final Thoughts
OpenAI popularized function calling. And it is still excellent.
But today, you have options.
Anthropic brings careful reasoning.
Google brings cloud power.
LangChain brings flexibility.
LlamaIndex brings smart data handling.
Semantic Kernel brings enterprise structure.
Each tool turns language models into action engines.
The right choice depends on your ecosystem. Your team. Your ambition.
But one thing is clear.
The future of AI is not just conversation.
It is execution.
And function calling is how we get there.