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Given a claim or news statement, a team of specialized AI agents searches the internet for evidence, analyzes its credibility, and produces a structured verdict report.\n\n## How It Works\n\nThe crew runs three agents in a sequential pipeline:\n\n| Agent | Role | Responsibility |\n|---|---|---|\n| `jurnalist` | Senior Investigative Journalist | Searches the internet for credible sources, studies, and official statements related to the claim |\n| `fact_checker` | Truth Judge & Logic Analyst | Evaluates the gathered evidence and delivers a verdict (True / False / Partially False) with a confidence score (1–100) |\n| `redactor` | Editor-in-Chief | Compiles the verdict into a clean, professionally formatted Markdown report |\n\n### Tasks\n\n1. **`research_task`** — The journalist searches the web for evidence (pro and con) and produces a research report with source links.\n2. **`fact_check_task`** — The fact-checker analyzes the research report and outputs a verdict with a confidence score and main argument.\n3. **`reporting_task`** — The editor assembles the final report saved to `raport_final.md`.\n\n### Tools\n\n- **Internet Search** (`Cautare pe Internet`) — A DuckDuckGo-powered search tool used by the journalist agent to retrieve live web results.\n\n### LLM\n\nThe crew uses **Groq's `llama-3.3-70b-versatile`** model via [LiteLLM](https://docs.litellm.ai/) routing.\n\n## Project Structure\n\n```\nagentic-ai-fake-news-crew/\n├── knowledge/\n│   └── user_preference.txt        # Optional user context injected into the crew\n├── src/fake_news_crew/\n│   ├── config/\n│   │   ├── agents.yaml            # Agent definitions (role, goal, backstory)\n│   │   └── tasks.yaml             # Task definitions (description, expected_output, agent)\n│   ├── tools/\n│   │   └── custom_tool.py         # Scaffold for additional custom tools\n│   ├── crew.py                    # Crew orchestration (agents, tasks, LLM, tools)\n│   └── main.py                    # Entry point — defines the input claim and runs the crew\n├── raport_final.md                # Generated output report (created after each run)\n├── pyproject.toml\n└── .env                           # API keys\n```\n\n## Installation\n\nEnsure you have Python >=3.10, <3.14 installed. This project uses [uv](https://docs.astral.sh/uv/) for dependency management.\n\n```bash\n# Install uv if you haven't already\npip install uv\n\n# Install project dependencies\ncrewai install\n```\n\n## Configuration\n\nCreate a `.env` file in the project root and add your Groq API key:\n\n```env\nGROQ_API_KEY=your_groq_api_key_here\n```\n\nTo change the claim being fact-checked, edit the `subiect` input in `src/fake_news_crew/main.py`:\n\n```python\ninput_date = {\n    'subiect': 'Your claim or news statement here.'\n}\n```\n\nYou can also customize agents and tasks:\n\n- `src/fake_news_crew/config/agents.yaml` — agent roles, goals, and backstories\n- `src/fake_news_crew/config/tasks.yaml` — task descriptions and expected outputs\n- `src/fake_news_crew/crew.py` — LLM settings, tool assignments, crew process\n\n## Running the Project\n\n```bash\ncrewai run\n```\n\nThe crew will research the claim, analyze the evidence, and write the verdict to `raport_final.md` in the project root.\n\n### Example Output\n\nThe generated report (`raport_final.md`) includes:\n\n1. 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