Как создать своего первого AI-агента в 2026: полный гайд для новичков

while response.stop_reason == "tool_use":   # модель хочет инструмент
    result = run_tool(block.name, block.input)   # ты его выполняешь
    messages.append(tool_result(result))         # возвращаешь результат, цикл повторяется

pip install anthropic
export ANTHROPIC_API_KEY=sk-ant-...      # ключ берётся на console.anthropic.com

from anthropic import Anthropic
 
client = Anthropic()   # читает ANTHROPIC_API_KEY из окружения
 
response = client.messages.create(
    model="claude-haiku-4-5",   # дёшево и быстро; позже поменяешь на sonnet/opus
    max_tokens=1024,
    messages=[{"role": "user", "content": "Одним предложением: что такое AI-агент?"}],
)
 
print(response.content[0].text)

TOOLS = [
    {
        "name": "get_current_time",
        "description": "Return the current date and time. Call when the user asks the time or date.",
        "input_schema": {"type": "object", "properties": {}},
    },
]
 
response = client.messages.create(
    model="claude-haiku-4-5",
    max_tokens=1024,
    tools=TOOLS,
    messages=[{"role": "user", "content": "Сколько сейчас времени?"}],
)
 
print(response.stop_reason)   # -> "tool_use"

from datetime import datetime
 
def get_current_time() -> str:
    return datetime.now().strftime("%Y-%m-%d %H:%M:%S")

# agent.py — крошечный AI-агент на чистом Python. Без фреймворков.
# Setup:  pip install anthropic  &&  export ANTHROPIC_API_KEY=sk-ant-...
import ast
import operator
from datetime import datetime
 
from anthropic import Anthropic
 
client = Anthropic()
MODEL = "claude-haiku-4-5"   # дёшево; поменяй на "claude-sonnet-4-6" или "claude-opus-4-8"
 
# --- 1. Инструменты: обычные функции Python ---------------------------------
 
def get_current_time() -> str:
    """The model has no clock — this gives it one."""
    return datetime.now().strftime("%Y-%m-%d %H:%M:%S")
 
_OPS = {
    ast.Add: operator.add, ast.Sub: operator.sub,
    ast.Mult: operator.mul, ast.Div: operator.truediv,
    ast.Pow: operator.pow, ast.USub: operator.neg,
}
 
def _eval(node):
    # A tiny, safe arithmetic evaluator — never eval() untrusted input.
    if isinstance(node, ast.Constant):
        return node.value
    if isinstance(node, ast.BinOp):
        return _OPS[type(node.op)](_eval(node.left), _eval(node.right))
    if isinstance(node, ast.UnaryOp):
        return _OPS[type(node.op)](_eval(node.operand))
    raise ValueError("unsupported expression")
 
def calculate(expression: str) -> str:
    """Deterministic math — don't trust an LLM to multiply big numbers."""
    return str(_eval(ast.parse(expression, mode="eval").body))
 
# --- 2. Описываем инструменты для модели (JSON Schema) ---------------------
 
TOOLS = [
    {
        "name": "get_current_time",
        "description": "Return the current date and time. Call when the user asks the time or date.",
        "input_schema": {"type": "object", "properties": {}},
    },
    {
        "name": "calculate",
        "description": "Evaluate a basic arithmetic expression like '23 * 47 + 10'. Call for any math.",
        "input_schema": {
            "type": "object",
            "properties": {
                "expression": {"type": "string", "description": "e.g. '2 ** 10 / 4'"},
            },
            "required": ["expression"],
        },
    },
]
 
def run_tool(name: str, tool_input: dict) -> str:
    if name == "get_current_time":
        return get_current_time()
    if name == "calculate":
        return calculate(tool_input["expression"])
    return f"Unknown tool: {name}"
 
# --- 3. Цикл агента --------------------------------------------------------
 
def agent_turn(messages: list) -> str:
    """Run one user turn to completion, executing tools until the model is done."""
    for _ in range(10):   # жёсткий лимит, чтобы модель не зациклилась навсегда
        response = client.messages.create(
            model=MODEL,
            max_tokens=1024,
            tools=TOOLS,
            messages=messages,
        )
        # Keep the assistant turn (incl. any tool_use blocks) in memory.
        messages.append({"role": "assistant", "content": response.content})
 
        if response.stop_reason != "tool_use":
            return "".join(b.text for b in response.content if b.type == "text")
 
        # The model asked for tools. Run them, feed the results back, loop.
        results = []
        for block in response.content:
            if block.type == "tool_use":
                output = run_tool(block.name, block.input)
                results.append({
                    "type": "tool_result",
                    "tool_use_id": block.id,   # must match the tool_use block
                    "content": output,
                })
        messages.append({"role": "user", "content": results})
    return "Stopped: too many tool calls."
 
# --- 4. Чат-цикл — разговор и ЕСТЬ память -----------------------------------
 
if __name__ == "__main__":
    messages = []   # весь разговор живёт здесь
    print("Agent ready. Ctrl-C to quit.\n")
    while True:
        messages.append({"role": "user", "content": input("you> ")})
        print(f"agent> {agent_turn(messages)}\n")

import json, pathlib
 
STORE = pathlib.Path("memory.json")
messages = json.loads(STORE.read_text()) if STORE.exists() else []
# ... после чат-цикла или при выходе:
STORE.write_text(json.dumps(messages, default=lambda o: o.model_dump()))

with client.messages.stream(
    model=MODEL,
    max_tokens=1024,
    messages=[{"role": "user", "content": "Объясни AI-агентов пятилетнему ребёнку."}],
) as stream:
    for text in stream.text_stream:
        print(text, end="", flush=True)
    print()