Lesson 03 — Raw Deep Agent

"""Raw DeepAgent — no custom tools, no domain knowledge."""

from __future__ import annotations

import json
import os
from datetime import datetime, timezone
from pathlib import Path
from typing import Any

from deepagents import create_deep_agent
from dotenv import load_dotenv
from langchain_core.language_models import BaseChatModel
from langchain_openai import ChatOpenAI

from src.schemas import Receipt

load_dotenv()

INSTRUCTIONS = """You are a business analyst assistant for ACME Colombia S.A.S.
You do NOT yet have any tools to query business data. If you cannot answer with
evidence, say so explicitly and do not fabricate numbers.

When you cannot answer, respond with a JSON block:
{"status": "no_capability", "reason": "<why>", "needed_skill": "<what would help>"}

## Scope

You are a domain agent (business analyst). You do NOT run shell commands, git
operations, or filesystem writes outside your own virtual FS. If the user asks
for any of those, respond with no_capability — that is the coding agent's job
(Claude Code, Pi, etc.), not yours. Ver `AGENTS.md` for the coding-agent policy.
"""


def build_llm() -> BaseChatModel:
    api_key = os.environ["OPENROUTER_API_KEY"]
    model = os.environ.get("OPENROUTER_MODEL", "anthropic/claude-haiku-4.5")
    return ChatOpenAI(
        base_url="https://openrouter.ai/api/v1",
        api_key=api_key,  # type: ignore[arg-type]
        model=model,
        temperature=0.0,
    )


def build_agent(llm: BaseChatModel | None = None) -> Any:
    return create_deep_agent(
        tools=[],
        instructions=INSTRUCTIONS,
        model=llm or build_llm(),
    )


def write_receipt(event: str, payload: dict[str, Any]) -> Path:
    receipts_dir = Path(os.environ.get("RECEIPTS_DIR", ".receipts"))
    receipts_dir.mkdir(exist_ok=True)
    path = receipts_dir / "session.jsonl"
    receipt = Receipt(
        ts=datetime.now(tz=timezone.utc),
        actor="agent",
        event=event,
        payload=payload,
    )
    with path.open("a", encoding="utf-8") as f:
        f.write(receipt.model_dump_json() + "\n")
    return path

"""CLI entrypoint — one question at a time."""

from __future__ import annotations

import json

import typer
from rich import print as rprint

from src.agent import build_agent, write_receipt

app = typer.Typer(add_completion=False, no_args_is_help=True)


@app.command()
def ask(question: str) -> None:
    """Ask the raw DeepAgent a business question."""
    agent = build_agent()
    result = agent.invoke({"messages": [{"role": "user", "content": question}]})
    reply = result["messages"][-1].content
    rprint("[bold cyan]Agent:[/bold cyan]", reply)

    if "no_capability" in reply:
        try:
            parsed = json.loads(reply[reply.index("{") : reply.rindex("}") + 1])
        except (ValueError, json.JSONDecodeError):
            parsed = {"raw_reply": reply}
        path = write_receipt("no_capability", {"question": question, **parsed})
        rprint(f"[dim]receipt → {path}[/dim]")


if __name__ == "__main__":
    app()