Function Calling

Enable LLMs to use external tools and return structured data.

When to Use

• LLM needs to call APIs or databases
• Extracting structured data from text
• Building AI agents with tool use
• Reliable JSON output from LLMs

Basic Tool Definition (2026 Best Practice)

# OpenAI format with strict mode (2026 recommended)
tools = [{
    "type": "function",
    "function": {
        "name": "search_documents",
        "description": "Search the document database for relevant content",
        "strict": True,  # ← 2026: Enables structured output validation
        "parameters": {
            "type": "object",
            "properties": {
                "query": {
                    "type": "string",
                    "description": "The search query"
                },
                "limit": {
                    "type": "integer",
                    "description": "Max results to return"
                }
            },
            "required": ["query", "limit"],  # All props required when strict
            "additionalProperties": False     # ← 2026: Required for strict mode
        }
    }
}]

# Note: With strict=True:
# - All properties must be listed in "required"
# - additionalProperties must be False
# - No "default" values (provide via code instead)

Tool Execution Loop

async def run_with_tools(messages: list, tools: list) -> str:
    """Execute tool calls until LLM returns final answer."""
    while True:
        response = await llm.chat(messages=messages, tools=tools)

        # Check if LLM wants to call tools
        if not response.tool_calls:
            return response.content

        # Execute each tool call
        for tool_call in response.tool_calls:
            result = await execute_tool(
                tool_call.function.name,
                json.loads(tool_call.function.arguments)
            )

            # Add tool result to conversation
            messages.append({
                "role": "tool",
                "tool_call_id": tool_call.id,
                "content": json.dumps(result)
            })

        # Continue loop (LLM will process tool results)

async def execute_tool(name: str, args: dict) -> any:
    """Route to appropriate tool implementation."""
    tools = {
        "search_documents": search_documents,
        "get_weather": get_weather,
        "calculate": calculate,
    }
    return await tools[name](**args)

Structured Output (Guaranteed JSON)

from pydantic import BaseModel

class Analysis(BaseModel):
    sentiment: str
    confidence: float
    key_points: list[str]

# OpenAI structured output
response = await client.beta.chat.completions.parse(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Analyze this text..."}],
    response_format=Analysis
)

analysis = response.choices[0].message.parsed  # Typed Analysis object

LangChain Tool Binding

from langchain_core.tools import tool
from pydantic import BaseModel, Field

@tool
def search_documents(query: str, limit: int = 5) -> list[dict]:
    """Search the document database.

    Args:
        query: Search query string
        limit: Maximum results to return
    """
    return db.search(query, limit=limit)

# Bind to model
llm_with_tools = llm.bind_tools([search_documents])

# Or with structured output
class SearchResult(BaseModel):
    query: str = Field(description="The search query used")
    results: list[str] = Field(description="Matching documents")

structured_llm = llm.with_structured_output(SearchResult)

Parallel Tool Calls

# OpenAI supports parallel tool calls
response = await llm.chat(
    messages=messages,
    tools=tools,
    parallel_tool_calls=True  # Default in GPT-4o
)

# Handle multiple calls in parallel
if response.tool_calls:
    results = await asyncio.gather(*[
        execute_tool(tc.function.name, json.loads(tc.function.arguments))
        for tc in response.tool_calls
    ])

⚠️ 2026 Compatibility Note:

# Structured outputs with strict=True may not work with parallel_tool_calls
# If using strict mode schemas, disable parallel calls:
response = await llm.chat(
    messages=messages,
    tools=tools_with_strict_true,
    parallel_tool_calls=False  # Required for strict mode reliability
)

Key Decisions

Common Mistakes

• Vague tool descriptions (LLM won't know when to use)
• No input validation (LLM sends bad params)
• Missing error handling (crashes on tool failure)
• Too many tools (LLM gets confused)

Related Skills

• agent-loops - Multi-step tool use with reasoning
• llm-streaming - Streaming with tool calls
• structured-output - Complex output schemas