Llama 4 for agents: tool calling and function support

Llama 4 agents tool calling works best when you treat the model’s function output as a strict contract rather than free-form text. This guide lays out an ordered path to wire Llama 4 into an agent loop, from tool schema to execution and parsing, with code you can adapt today.

1. Define tools in Llama 4’s expected schema

The foundation of llama 4 agents tool calling is a JSON-Schema description passed in the tools array. Llama 4 retains the native format introduced in Llama 3.1: each tool is an object with type: "function" and a function block containing name, description, and parameters.

{
  "type": "function",
  "function": {
    "name": "get_weather",
    "description": "Fetch current weather for a location",
    "parameters": {
      "type": "object",
      "properties": {
        "location": {"type": "string", "description": "City, e.g. 'Berlin'"},
        "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}
      },
      "required": ["location"]
    }
  }
}

Keep descriptions terse. Llama 4 uses them to decide when to call, and verbose text inflates prompt size without improving accuracy.

2. Send the initial request

Point an OpenAI-compatible client at your inference endpoint. The snippet below uses a gateway, but the request shape is identical for self-hosted vLLM or TGI serving Llama 4.

from openai import OpenAI

client = OpenAI(base_url="https://api.n4n.ai/v1", api_key="YOUR_KEY")

tools = [get_weather_schema]  # from step 1

resp = client.chat.completions.create(
    model="llama-4-70b",
    messages=[{"role": "user", "content": "What's the weather in Berlin?"}],
    tools=tools,
    tool_choice="auto",
    temperature=0.2,
)

Set temperature low (0.1–0.3). Higher values make Llama 4 more likely to hallucinate parameter names or skip required fields.

Pitfall: some Llama 4 derivatives ignore tool_choice="auto" and always emit a call when tools is present. If you need the model to answer freely, either omit tools or force tool_choice="none" if supported.

3. Extract and validate tool calls

Inspect resp.choices[0].message.tool_calls. Each entry has id, function.name, and function.arguments (a JSON string).

import json
from pydantic import BaseModel, ValidationError

class WeatherArgs(BaseModel):
    location: str
    unit: str = "celsius"

msg = resp.choices[0].message
if msg.tool_calls:
    for call in msg.tool_calls:
        try:
            raw = json.loads(call.function.arguments)
            args = WeatherArgs(**raw)
        except (json.JSONDecodeError, ValidationError) as e:
            print(f"Bad call {call.id}: {e}")
            continue
        print(call.function.name, args.location)

Llama 4 sometimes emits arguments with trailing commas or single quotes. In development, swap json.loads for a tolerant parser (json5) to measure how often the raw output is malformed. In production, reject and retry with a corrective prompt.

Tradeoff: strict pydantic validation adds a few milliseconds but prevents invalid tool executions that could crash downstream services.

4. Execute tools and feed results back

You must echo the assistant message (with tool_calls) into the next request, then append a tool message per call. Llama 4 matches results by tool_call_id.

messages = [
    {"role": "user", "content": "What's the weather in Berlin?"},
    msg,
]

for call in msg.tool_calls:
    args = WeatherArgs(**json.loads(call.function.arguments))
    result = {"temp": 22, "unit": args.unit}  # fake exec
    messages.append({
        "role": "tool",
        "tool_call_id": call.id,
        "content": json.dumps(result),
    })

followup = client.chat.completions.create(
    model="llama-4-70b",
    messages=messages,
    tools=tools,
)

If tool_call_id mismatches, Llama 4 silently drops the result and may invent a value. Log IDs to catch this.

5. Parallel calls and concurrency

Llama 4 can emit multiple tool_calls in one turn. Execute them concurrently, but respect dependencies.

import asyncio

async def run_all(calls):
    async def exec_one(c):
        a = WeatherArgs(**json.loads(c.function.arguments))
        return await execute_tool_async(c.function.name, a)
    return await asyncio.gather(*[exec_one(c) for c in calls])

Caveats:

• The model does not signal which calls depend on others. If call B needs output of call A, serialize manually.
• Some servings cap parallel calls at 4. Test your endpoint’s limit.
• One failed tool in asyncio.gather raises; use return_exceptions=True and handle per-call errors.

6. Common pitfalls and tradeoffs

Weak required-field enforcement

Llama 4 respects required loosely. It may omit a required param and expect you to infer. Always validate server-side; never trust the schema alone.

Context bloat from many tools

With 20+ tools, the schema can eat 1–2k tokens per request. Use dynamic filtering: send only tools relevant to the current step. This cuts latency and reduces wrong-call rates.

Provider variance

Different inference servers parse Llama 4’s output differently. An OpenAI-compatible endpoint that addresses 240+ models can forward your schema uniformly and apply automatic fallback if a Llama 4 provider is rate-limited, but you still must handle heterogeneous response quirks in your parser.

Cache control

Stable tool schemas rarely change. If your gateway honors provider cache-control hints, mark them with cache_control: {"type": "ephemeral"} to avoid re-billing schema tokens each turn.

Hallucinated tool names

Llama 4 occasionally emits a function.name not in your tools list. Post-filter: if name not in registry, treat as unknown and prompt correction.

7. Production checklist

An ordered path for shipping llama 4 agents tool calling:

1. Define tools as JSON Schema; keep names and descriptions short.
2. Call with tools and tool_choice="auto"; use low temperature.
3. Parse tool_calls with tolerant JSON, validate with pydantic.
4. Echo assistant tool_calls and return role: tool messages with matching IDs.
5. Limit parallel calls; serialize dependent ones.
6. Filter tools per step to control context growth.
7. Retry on malformed arguments; fall back to ReAct prompting if schema is ignored.
8. Monitor call success rate, token usage, and fallback frequency.

Following this path gets you a robust Llama 4 agent loop without surprises in production.