Fighting misinformation through AI-powered multi-source analysis
In an era of information overload, distinguishing fact from fiction has become increasingly difficult:
- Media Bias: Every outlet has its own perspective, leading to selective reporting
- Echo Chambers: Algorithms feed us content that confirms our existing beliefs
- Clickbait Culture: Sensational headlines prioritize engagement over accuracy
- No Single Source of Truth: Different outlets report the same event with conflicting details
- Manual Fact-Checking is Slow: Traditional fact-checking can't keep pace with the 24/7 news cycle
Result: Readers are left confused, misinformed, or trapped in filter bubbles.
FactNews is an advanced analytical platform that cross-verifies news information across multiple global media outlets. By employing a council of Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG), we identify consensus, highlight contested facts, and provide transparent, evidence-based summaries of current events.
| Pillar | Description |
|---|---|
| Multi-Source Verification | No single source is treated as definitive. We aggregate from 20+ international outlets. |
| Consensus Identification | Mathematical and semantic analysis calculates agreement between reports. |
| Transparent Sourcing | Every claim links to its original source for independent verification. |
Automated ingestion from 20+ major news outlets via RSS feeds:
- International: BBC, Reuters, Al Jazeera, DW, France24
- US: CNN, Fox News, New York Times, Washington Post, Wall Street Journal
- Tech: TechCrunch, Wired, The Verge
- And more...
Multiple LLMs deliberate in parallel, then a judge synthesizes their conclusions:
- Supports OpenAI, Anthropic, Google, DeepSeek, Grok, Cerebras, and more
- Reduces individual model bias through consensus
- Judge LLM produces balanced, well-reasoned synthesis
- Optimized chunk-level semantic search
- Multi-tiered embedding cache (Redis + local NPZ + API fallback)
- Fast numpy vector operations without heavy vector databases
- Identifies where sources agree vs disagree
- Highlights potential media bias
- Shows contested facts with supporting evidence from each source
- Daily AI-generated newspaper edition
- Curated stories with consensus analysis
- Topic clustering for organized reading
- Compare responses from multiple AI models side-by-side
- See how different models interpret the same news
- Understand model-specific biases
┌─────────────────────────────────────────────────────────────────────────────┐
│ FACTNEWS PIPELINE │
└─────────────────────────────────────────────────────────────────────────────┘
┌──────────────┐ ┌──────────────┐ ┌──────────────┐
│ RSS Feeds │────▶│ Scraper │────▶│ Chunker │
│ (20+ srcs) │ │ (Articles) │ │ (Segments) │
└──────────────┘ └──────────────┘ └──────────────┘
│
▼
┌──────────────┐ ┌──────────────┐ ┌──────────────┐
│ Response │◀────│ Model │◀────│ Embedding │
│ Cache │ │ Council │ │ Cache │
└──────────────┘ └──────────────┘ └──────────────┘
│ │ ▲
│ ▼ │
│ ┌──────────────┐ │
│ │ Judge │ │
│ │ LLM │ │
│ └──────────────┘ │
│ │ │
▼ ▼ │
┌──────────────┐ ┌──────────────┐ ┌──────────────┐
│ Frontend │◀────│ Consensus │◀────│ RAG Query │
│ (Next.js) │ │ Report │ │ Engine │
└──────────────┘ └──────────────┘ └──────────────┘
- Ingest: RSS feeds pull articles from 20+ sources continuously
- Process: Articles are chunked, embedded, and stored with metadata
- Query: User asks a question about current events
- Retrieve: RAG engine finds relevant chunks across all sources
- Deliberate: Multiple LLMs analyze the evidence in parallel
- Judge: A judge LLM synthesizes findings into a consensus report
- Present: User sees consensus, divergences, and original sources
backend/
├── app.py # FastAPI routes & endpoints
├── rag_optimized.py # ChunkRAG with multi-tier cache
├── embedding_cache.py # Redis embedding store
├── response_cache.py # L1/L2 response caching
├── cache.py # Redis connection utility
├── lru_cache.py # In-memory LRU cache
├── chunker.py # News chunking logic
├── rss_ingester.py # RSS feed ingestion
├── scraper.py # Web scraping utilities
├── sources_catalog.py # Source catalog by country
├── pulse.py # AI Pulse arena logic
├── ai_newspaper.py # Newspaper edition generator
├── clustering.py # Article clustering
│
└── inference/ # Multi-provider LLM layer
├── __init__.py # get_provider, ModelCouncil
├── factory.py # Provider factory
├── config.py # Provider configurations
├── base.py # InferenceProvider base
├── council.py # ModelCouncil: N + 1 judge
└── providers/ # Provider integrations
├── openai_provider.py
├── anthropic.py
├── deepseek.py
├── google.py
├── grok.py
├── cerebras.py
├── zai.py
├── openrouter.py
└── crusoe.py
frontend/
├── app/ # Next.js App Router
│ ├── arena/ # AI model comparison
│ ├── bookmarks/ # Saved reports
│ ├── feed/ # News feed
│ ├── history/ # Search history
│ ├── search/ # Main search interface
│ ├── sources/ # News sources browser
│ └── trending/ # Trending topics
├── components/ # React components
├── hooks/ # Custom React hooks
├── lib/ # Utilities, API client
└── stores/ # Zustand state management
| Layer | Technologies |
|---|---|
| Frontend | Next.js 16, React 19, TypeScript, Tailwind CSS 4, Zustand |
| Backend | FastAPI, Python 3.11+, Uvicorn |
| AI/ML | OpenAI, Anthropic, Google, DeepSeek, Grok, Cerebras |
| Caching | Redis, File-based NPZ cache, LRU in-memory |
| Processing | NumPy, scikit-learn, newspaper3k, trafilatura |
| Data | RSS feeds, Supabase (pgvector optional) |
| Method | Endpoint | Description |
|---|---|---|
GET |
/ |
Status + active providers |
GET |
/stats |
Article and embedding counts |
GET |
/health |
Health check (Redis, RAG status) |
POST |
/ask |
Generate consensus fact-check |
POST |
/ask/search |
Fast chunk search without AI |
POST |
/ask/stream |
SSE streaming generation |
GET |
/articles |
List articles with pagination |
GET |
/sources/catalog |
All sources by country |
GET/PUT |
/sources/selected |
Manage source selection |
POST |
/refresh-news |
Re-ingest RSS feeds |
POST |
/api/ai-pulse |
Multi-agent arena debate |
GET |
/api/newspaper |
AI-generated newspaper |
- Python 3.11+
- Node.js 18+
- Redis (optional, for enhanced caching)
- API keys for LLM providers
# Navigate to backend
cd backend
# Create virtual environment
python3 -m venv env
source env/bin/activate # Linux/Mac
# env\Scripts\activate # Windows
# Install dependencies
pip install -r requirements.txt
# Create .env file
cat > .env << EOF
OPENAI_API_KEY=your_key_here
CRUSOE_API_KEY=your_key_here
REDIS_URL=redis://localhost:6379
COUNCIL_PROVIDERS=openai,deepseek,anthropic
EOF
# Start server
uvicorn app:app --reload --port 8000# Navigate to frontend
cd frontend
# Install dependencies
npm install
# Create .env.local
echo "NEXT_PUBLIC_API_URL=http://localhost:8000" > .env.local
# Start development server
npm run devVisit http://localhost:3000 to see the app.
- Multi-tiered embedding cache: Redis → local NPZ → OpenAI API
- Fast vector operations: NumPy-based similarity search without heavy Vector DB
- Cross-process safety: FileLock for concurrent NPZ writes
from inference import ModelCouncil
council = ModelCouncil(
providers=["openai", "deepseek", "grok"],
judge="anthropic"
)
result = council.deliberate("Analyze this news story")
print(result["judgment"]["synthesis"])- Works without Redis (falls back to local cache)
- Council deliberation continues if individual providers fail
- Automatic retry with rate-limit handling
- Cut through the noise: Get balanced summaries from multiple sources
- See the full picture: Understand where sources agree and disagree
- Verify independently: Every claim links to original sources
- Study media bias: Analyze how different outlets frame stories
- Track narrative evolution: See how coverage changes over time
- Access structured data: Programmatic API for analysis
- Combat misinformation: Evidence-based consensus over viral claims
- Promote transparency: Open methodology, verifiable sources
- Encourage critical thinking: Show multiple perspectives, let users decide
| Phase | Status | Description |
|---|---|---|
| Phase 1 | Completed | Core backend architecture & basic frontend |
| Phase 2 | Completed | Single-model LLM integration & embeddings |
| Phase 3 | Completed | RSS ingestion & article scraping |
| Phase 4 | In Progress | Vector database migration (Supabase pgvector) |
| Phase 5 | In Progress | Multi-LLM consensus council |
| Phase 6 | Planned | Advanced UI for consensus/divergence visualization |
| Phase 7 | Planned | Browser extension for in-page fact-checking |
| Phase 8 | Planned | Mobile app with push notifications |
We welcome contributions! Please see our contributing guidelines for details on:
- Code of conduct
- Pull request process
- Coding standards
- Testing requirements
This project is licensed under the MIT License - see the LICENSE file for details.
- News outlets providing RSS feeds for public access
- OpenAI, Anthropic, Google, DeepSeek, and other LLM providers
- Open source community for the amazing tools that power this project
Built with purpose during HackEurpoe 2026
"In a world of noise, find the consensus."