crew.py

"""
Crew definition for the agents service.

LLM is provided via LiteLLM against Hugging Face. Set the model id in the environment — see
AGENTS_LLM_MODEL in .env.example (LiteLLM form: huggingface/<org>/<model>).

Pick a model that your Hugging Face account can run via Inference Providers (check the model's Hub page
for Inference / provider badges), or set AGENTS_LLM_BASE_URL to your own OpenAI-compatible endpoint.

Qwen/WebWorld-32B is not served on the public Inference Providers router; use it only with
AGENTS_LLM_BASE_URL (e.g. your own HF Inference Endpoint or vLLM).

If a plain huggingface/<org>/<model> call fails, LiteLLM supports:
    huggingface/<provider>/<org>/<model>
only when that provider is listed on the model card for this model (do not guess the provider).

Required environment:
    HF_TOKEN (or HUGGINGFACE_HUB_TOKEN)
    AGENTS_LLM_MODEL

Optional:
    AGENTS_LLM_TEMPERATURE   float, default 0.5
    AGENTS_LLM_BASE_URL      OpenAI-compatible base URL (e.g. HF Inference Endpoint)
"""
from __future__ import annotations

import os
from typing import List

from crewai import Agent, Crew, LLM, Process, Task
from crewai.agents.agent_builder.base_agent import BaseAgent
from crewai.project import CrewBase, agent, crew, task


def _resolve_hf_token() -> str:
    token = (os.getenv("HF_TOKEN") or os.getenv("HUGGINGFACE_HUB_TOKEN") or "").strip()
    if (token.startswith('"') and token.endswith('"')) or (token.startswith("'") and token.endswith("'")):
        token = token[1:-1].strip()
    if not token:
        raise RuntimeError(
            "Missing Hugging Face token. Set HF_TOKEN (or HUGGINGFACE_HUB_TOKEN) in the "
            "agents service environment."
        )
    return token


def _strip_optional_env_quotes(value: str) -> str:
    v = value.strip()
    if (v.startswith('"') and v.endswith('"')) or (v.startswith("'") and v.endswith("'")):
        v = v[1:-1].strip()
    return v


def _resolve_llm_model() -> str:
    raw = (os.getenv("AGENTS_LLM_MODEL") or "").strip()
    raw = _strip_optional_env_quotes(raw)
    if not raw:
        raise RuntimeError(
            "Missing AGENTS_LLM_MODEL. Set it in the environment (see .env.example), e.g. "
            "huggingface/Qwen/Qwen2.5-7B-Instruct"
        )
    return raw


def _build_llm() -> LLM:
    model = _resolve_llm_model()
    temperature = float(os.getenv("AGENTS_LLM_TEMPERATURE", "0.5"))

    hf_token = _resolve_hf_token()
    os.environ["HF_TOKEN"] = hf_token
    os.environ["HUGGINGFACE_HUB_TOKEN"] = hf_token

    base_url = (os.getenv("AGENTS_LLM_BASE_URL") or "").strip().rstrip("/")
    base_url = _strip_optional_env_quotes(base_url) if base_url else ""

    if "webworld" in model.lower() and not base_url:
        raise RuntimeError(
            "AGENTS_LLM_MODEL is set to a WebWorld model, which Hugging Face Inference Providers "
            "does not host. Set AGENTS_LLM_MODEL to a routed instruct model, or run WebWorld on your "
            "own endpoint and set AGENTS_LLM_BASE_URL to that OpenAI-compatible base URL."
        )

    if base_url:
        return LLM(
            model=model,
            base_url=base_url,
            api_key=hf_token,
            temperature=temperature,
        )

    return LLM(model=model, api_key=hf_token, temperature=temperature)


@CrewBase
class ContentCrew:
    """LinkedIn post writing crew — runs inside the agents service."""

    agents: List[BaseAgent]
    tasks: List[Task]

    @agent
    def writer_agent(self) -> Agent:
        return Agent(
            config=self.agents_config["writer_agent"],
            llm=_build_llm(),
            max_tokens=420,
            verbose=False,
        )

    @agent
    def editor_agent(self) -> Agent:
        return Agent(
            config=self.agents_config["editor_agent"],
            llm=_build_llm(),
            max_tokens=380,
            verbose=False,
        )

    @task
    def write_post_task(self) -> Task:
        return Task(config=self.tasks_config["write_post_task"])

    @task
    def edit_post_task(self) -> Task:
        return Task(config=self.tasks_config["edit_post_task"])

    @crew
    def crew(self) -> Crew:
        return Crew(
            agents=self.agents,
            tasks=self.tasks,
            process=Process.sequential,
            verbose=False,
        )