A newer version of the Streamlit SDK is available:
1.54.0
title: Multi Agent Research Assistant With Tavily
emoji: 🐨
colorFrom: gray
colorTo: blue
sdk: streamlit
sdk_version: 1.52.0
app_file: app.py
pinned: false
license: mit
short_description: An autonomous Agentic AI system with Tavily web search
🤖 Multi-Agent Research Assistant
🌟 Overview
This project implements a production-ready Agentic AI system featuring four specialized agents that collaborate to conduct research, analyze information, and generate high-quality reports. The system autonomously selects the appropriate tools (web search, calculator, knowledge base) based on query context, demonstrating true agentic behavior.
✨ Features
- 🧠 Agentic AI Architecture: Autonomous decision-making with dynamic tool selection
- 🤝 Multi-Agent Collaboration: Four specialized agents working in concert
- 🔄 Iterative Refinement: Self-improving through critic feedback loops
- 🔍 Intelligent Search: AI-optimized web search with Tavily + internal knowledge base
- 🎨 Professional UI: Clean, modern Streamlit interface with real-time visualization
- 📊 Source Attribution: Full transparency with citations and confidence scores
🚀 Live Demo
Try it here: [https://huggingface.co/spaces/GhufranAI/Multi_Agent_Research_Assistant_with_Tavily]
🛠️ Tech Stack
- LangGraph - Agentic workflow orchestration
- Tavily - AI-optimized search API
- Llama 3.1 8B - Language model
- Streamlit - Web interface
- Pydantic - Data validation
📊 Architecture
- Agent Responsibilities
🔍 Researcher Agent
Role: Information gathering & tool orchestration
Tools: Web search (Tavily), Calculator, Knowledge base
Decision Making: Analyzes query to select optimal tool
- "latest news" → Web search
- "calculate 25*4" → Calculator
- "explain AI" → Knowledge base
Output: Raw information with source attribution
📊 Analyst Agent
- Role: Extract insights from research findings
- Capabilities: Pattern recognition, theme identification
- Output: Key points and implications
✍️ Writer Agent
- Role: Synthesize research into professional report
- Format: Executive summary + findings + implications + sources
- Output: Structured, citation-rich report
🎯 Critic Agent
- Role: Quality assurance & improvement trigger
- Evaluation: Scores report on completeness, clarity, sourcing
- Decision: Approve (≥8/10) or request revision
- Output: Quality score + feedback
🎯 Use Cases
- Research current events
- Answer complex questions
- Perform calculations
- Generate comprehensive reports
📸 Screenshots
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💻 Local Setup
pip install -r requirements.txt
streamlit run app.py
🔑 API Keys
- Hugging Face: https://huggingface.co/settings/tokens (FREE)
- Tavily: https://tavily.com/ (1,000 searches/month free)
📝 License
MIT
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference