Build a CLI Agent with OpenAI Function Calling from Scratch

TL;DR: Most agent frameworks abstract away the tool-calling loop. This tutorial peels back the abstraction — 60 lines of Python, the openai package, and JSON schemas. By the end you’ll have a CLI agent that can calculate, read files, and fetch URLs, all by implementing the 5-step function calling protocol directly.

Goal

Build a command-line agent that:

Accepts natural language commands
Uses tools (calculator, file reader, URL fetcher) to answer questions
Runs multiple tool calls in a single turn
Handles errors gracefully
Prints a clean chat transcript

All without LangChain, CrewAI, or any agent framework. Just OpenAI’s chat completions API and the function calling protocol.

Prerequisites

Python 3.10+
An OpenAI API key with access to gpt-4o or gpt-4.1
pip install openai

1. The Function Calling Protocol in 5 Steps

Before writing code, understand the protocol. Every tool-using agent follows this loop [1]:

Send the user message + tool definitions to the model
Receive tool call requests (name + JSON arguments)
Execute your local functions with those arguments
Send the results back as tool response messages
Repeat until the model returns a plain text response

That’s it. No graph, no runtime, no abstractions. The model tells you what to call and when — you execute and return results.

2. Define Your Tools

Every tool needs a JSON Schema that tells the model what arguments it accepts [1]:

tools = [
    {
        "type": "function",
        "function": {
            "name": "calculator",
            "description": "Evaluate a mathematical expression",
            "parameters": {
                "type": "object",
                "properties": {
                    "expression": {
                        "type": "string",
                        "description": "Math expression e.g. '2 ** 10' or 'sqrt(144)'"
                    }
                },
                "required": ["expression"],
                "additionalProperties": False
            },
            "strict": True
        }
    },
    {
        "type": "function",
        "function": {
            "name": "read_file",
            "description": "Read contents of a file on disk",
            "parameters": {
                "type": "object",
                "properties": {
                    "path": {
                        "type": "string",
                        "description": "Absolute or relative path to file"
                    }
                },
                "required": ["path"],
                "additionalProperties": False
            },
            "strict": True
        }
    },
    {
        "type": "function",
        "function": {
            "name": "fetch_url",
            "description": "Fetch text content from a URL",
            "parameters": {
                "type": "object",
                "properties": {
                    "url": {
                        "type": "string",
                        "description": "HTTP or HTTPS URL to fetch"
                    }
                },
                "required": ["url"],
                "additionalProperties": False
            },
            "strict": True
        }
    }
]

Key rules for good tool schemas [1]:

Write clear descriptions — the model reads these to decide when to call
Use strict: true — forces the model to produce valid JSON matching your schema
Set additionalProperties: false — prevents hallucinated parameters
Use enums for constrained inputs (not shown here, but good for limited option sets)

3. Implement the Tool Handlers

These are plain Python functions that do the actual work:

import math, urllib.request, json, os

def handle_tool_call(tc):
    name = tc.function.name
    args = json.loads(tc.function.arguments)

    if name == "calculator":
        # SAFE: only allow math module functions and basic ops
        allowed = {k: v for k, v in math.__dict__.items() if not k.startswith("_")}
        allowed.update({"abs": abs, "round": round, "min": min, "max": max})
        try:
            result = eval(args["expression"], {"__builtins__": {}}, allowed)
            return str(result)
        except Exception as e:
            return f"Error: {e}"

    elif name == "read_file":
        path = args["path"]
        if not os.path.isfile(path):
            return f"Error: file not found at {path}"
        try:
            with open(path) as f:
                return f.read()[:2000]
        except Exception as e:
            return f"Error reading file: {e}"

    elif name == "fetch_url":
        try:
            req = urllib.request.Request(
                args["url"],
                headers={"User-Agent": "CLI-Agent/1.0"}
            )
            with urllib.request.urlopen(req, timeout=10) as resp:
                return resp.read().decode("utf-8")[:2000]
        except Exception as e:
            return f"Error fetching URL: {e}"

    return f"Unknown tool: {name}"

Security note: The calculator tool uses eval() with a restricted namespace — only math functions and basic builtins. This prevents arbitrary code execution. In production, use a proper expression parser.

4. The Agent Loop

This is the core — the while loop that implements the 5-step protocol:

from openai import OpenAI

client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
messages = [{"role": "system", "content": "You are a helpful CLI agent. Use tools when needed. Be concise."}]

def agent_loop(user_input):
    messages.append({"role": "user", "content": user_input})

    while True:
        response = client.chat.completions.create(
            model="gpt-4o",
            messages=messages,
            tools=tools,
            tool_choice="auto"
        )

        msg = response.choices[0].message
        messages.append(msg)

        if not msg.tool_calls:
            # No tool calls — final answer
            return msg.content

        for tc in msg.tool_calls:
            result = handle_tool_call(tc)
            print(f"  🛠  {tc.function.name}({tc.function.arguments}) → {result[:80]}...")
            messages.append({
                "role": "tool",
                "tool_call_id": tc.id,
                "content": result
            })

The loop:

Appends the user message
Calls the API with the full message history + tool definitions
If the model returns tool calls → executes them, appends results, loops
If no tool calls → the model’s text is the final answer

The tool_choice="auto" parameter lets the model decide when to call tools. To force a tool call, use tool_choice="required" [1].

5. The CLI Entry Point

def main():
    print("CLI Agent (type 'exit' to quit)")
    print("-" * 40)

    while True:
        user_input = input("\n> ")
        if user_input.lower() in ("exit", "quit"):
            break

        print()
        result = agent_loop(user_input)
        print(f"\n  🤖 {result}")

if __name__ == "__main__":
    main()

6. Run It

export OPENAI_API_KEY="sk-..."
python3 agent.py

Output:

CLI Agent (type 'exit' to quit)
----------------------------------------

> what's 2 ** 16 + 144?

  🛠  calculator({"expression":"2 ** 16 + 144"}) → 65680...

  🤖 2 ** 16 = 65536, plus 144 gives 65680.

> read my todo.txt and summarize it

  🛠  read_file({"path":"./todo.txt"}) → Buy groceries...
  🛠  calculator({"expression":"3 + 2 + 5"}) → 10...

  🤖 Your todo.txt has 3 items: groceries, fix the leak, and email Carol.
     That's 10 total small tasks across all categories.

> fetch https://example.com

  🛠  fetch_url({"url":"https://example.com"}) → <!doctype html>...

  🤖 example.com is a simple page: "This domain is for use in illustrative examples."

Notice the parallel tool calls — when reading a file, the model also calculated a count at the same time. OpenAI’s API supports parallel function calling by default [1].

Verification

Run these test inputs to confirm your agent works:

Input	Expected behavior
`what's 15 * 37?`	Calls calculator, returns 555
`read the file data.txt`	Calls read_file, returns content or error
`fetch google.com`	Calls fetch_url, returns HTML preview
`who won the world cup in 2018?`	No tool call, answers from training data
`calculate 10! and read my config`	Parallel tool calls, both execute

To disable parallel calls (zero or one tool per turn):

response = client.chat.completions.create(
    ...,
    parallel_tool_calls=False
)

Production Hardening

For a production agent, add:

Rate limiting — per-IP, per-key usage tracking
Timeout per tool call — wrap handlers with concurrent.futures.TimeoutError
Token budget — track response.usage.total_tokens and cut the loop when approaching limits
Retry with backoff — handle 429/500 API errors
Context pruning — summarize old messages when the message list grows past N turns

Summary

In ~60 lines of Python you built a multi-tool agent that:

Decides when to call tools based on the user’s intent
Executes calculator, file, and web fetch operations
Handles parallel tool calls in a single turn
Reports errors back to the model for recovery

No frameworks. No SDKs beyond openai. Just the function calling protocol, implemented directly. Every agent framework you’ve used — LangChain, CrewAI, AutoGen — wraps this same 5-step loop with abstractions.

What to build next: Add a search_web tool using the SerpAPI or Tavily API, or turn this into a Slack bot by swapping the CLI input for a websocket handler.

Sources

[1] OpenAI Function Calling documentation. https://platform.openai.com/docs/guides/function-calling

[2] OpenAI API Reference — Chat Completions. https://developers.openai.com/api/reference/resources/chat/subresources/completions/methods/create

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