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Arsitekturnya dibuat modular agar agent, task, atau tool baru bisa ditambahkan tanpa mengubah jalur eksekusi inti.\n\n## Fitur\n\n- Local LLM via Ollama dengan model yang didukung: `llama3.2`, `llama3.1:8b`, `gemma2:9b`\n- 4 agent utama:\n  - Researcher Agent\n  - Writer Agent\n  - Critic Agent\n  - Manager Agent\n- Workflow siap pakai:\n  - `article`\n  - `social`\n  - `business`\n  - `competitor`\n  - `assistant`\n- CLI berbasis Typer\n- Streamlit UI\n- Custom tools:\n  - DuckDuckGo web search\n  - Wikipedia search\n  - file read/write\n  - Python interpreter\n- Logging, error handling, output persistence, dan `.env` configuration\n\n## Struktur Project\n\n```text\nai-agent/\n├── main.py\n├── crew.py\n├── streamlit_app.py\n├── agents/\n│   ├── __init__.py\n│   └── factory.py\n├── tasks/\n│   ├── __init__.py\n│   └── factory.py\n├── tools/\n│   ├── __init__.py\n│   ├── file_tools.py\n│   ├── python_tool.py\n│   ├── web_search.py\n│   └── wikipedia_tool.py\n├── config/\n│   ├── __init__.py\n│   ├── llm.py\n│   └── settings.py\n├── utils/\n│   ├── __init__.py\n│   ├── files.py\n│   ├── health.py\n│   └── logging.py\n├── scripts/\n│   └── setup_ollama.sh\n├── outputs/\n├── .env.example\n├── requirements.txt\n└── README.md\n```\n\n## Instalasi\n\n1. Gunakan Python `3.12` bila tersedia. Di mesin ini ada `python3.12`, `python3.13`, dan `python3.14`; rekomendasi default untuk project ini adalah `3.12` agar dependensi AI cenderung lebih stabil.\n\n2. Buat virtual environment dan aktifkan:\n\n```bash\npython3.12 -m venv .venv\nsource .venv/bin/activate\n```\n\nJika ingin setup otomatis:\n\n```bash\nchmod +x scripts/bootstrap_env.sh\n./scripts/bootstrap_env.sh\nsource .venv/bin/activate\n```\n\n3. Install dependensi:\n\n```bash\npip install -r requirements.txt\n```\n\n4. Siapkan `.env`:\n\n```bash\ncp .env.example .env\n```\n\n5. Install dan jalankan Ollama, lalu pull model:\n\n```bash\nchmod +x scripts/setup_ollama.sh\n./scripts/setup_ollama.sh llama3.2\n```\n\n6. Cek status Ollama:\n\n```bash\npython main.py health-check\n```\n\n## Menjalankan via CLI\n\nLihat workflow yang tersedia:\n\n```bash\npython main.py list-workflows\n```\n\nJalankan workflow artikel:\n\n```bash\npython main.py run \"Masa depan AI agents untuk UMKM\" --workflow article\n```\n\nJalankan workflow kompetitor:\n\n```bash\npython main.py run \"Notion AI alternatives\" --workflow competitor --context \"Fokus pada SaaS untuk tim kecil\"\n```\n\nSimpan output ke file tertentu:\n\n```bash\npython main.py run \"AI untuk personal productivity\" --workflow assistant --output outputs/custom-report.md\n```\n\n## Menjalankan Streamlit UI\n\n```bash\npython main.py ui --port 8501\n```\n\nAtau langsung:\n\n```bash\nstreamlit run streamlit_app.py\n```\n\n## Cara Menambah Agent Baru\n\n1. Buka `agents/factory.py`.\n2. Tambahkan definisi `Agent(...)` baru sesuai role, goal, backstory, dan tools.\n3. Masukkan agent itu ke `AgentBundle` bila memang perlu dipakai di workflow umum.\n4. Bila agent baru hanya dipakai pada workflow khusus, buat bundle atau builder terpisah agar scope tetap jelas.\n\n## Cara Menambah Task atau Workflow Baru\n\n1. Tambahkan deskripsi workflow ke `WORKFLOW_DESCRIPTIONS` di `tasks/factory.py`.\n2. Buat builder baru, misalnya `_build_customer_support_tasks`.\n3. Daftarkan builder itu ke mapping `builders`.\n4. 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