Team
agentic-internet-rsearch
Project Concept
🎯 Project Overview
The RSearch (Real-time Search) system is an advanced AI orchestration platform that leverages:
- DeepAgents: Multi-agent coordination for complex search workflows with context engineering
- MCP Servers: Standardized protocol for tool interactions and integrations
- Claude 4 Sonnet: Advanced language model for intelligent processing
- Distributed Architecture: Remote MCP servers for scalable search operations
🚀 Core Value Proposition
- Autonomous Research: AI agents that think, plan, and execute complex research workflows independently
- Real-time Intelligence: Live data retrieval and analysis for always-current insights
- Multi-source Synthesis: Intelligent aggregation and correlation across diverse information sources
- Scalable Workflows: From simple queries to complex multi-step research projects
🔧 Key Features
🔍 Real-time Search Capabilities
- Web Search: Live internet search via SerpApi MCP
- Semantic Indexing: Document storage and retrieval with Algolia
- Analytics Tracking: Performance monitoring via OpenSearch
- Multi-Source Aggregation: Combines results from multiple sources
🤖 Intelligent Orchestration
- Multi-Agent Architecture: Specialized agents for different tasks
- Adaptive Workflow: Dynamic task planning and execution
- Error Recovery: Graceful degradation and retry mechanisms
- Performance Optimization: Network-aware latency management
🛠️ Technical Features
- MCP Protocol Support: SSE/Streamable HTTP transports
- Redis Caching: Response caching for improved performance
- File Management: Automatic result documentation
- Thread Management: Conversation context preservation
MCP Server Integration
Remote MCP servers provide:
- SerpApi: Google search, news, images, trends
- Algolia: Object storage, indexing, semantic search
- OpenSearch: Logging, analytics, performance tracking
- Redis: Caching
- Langsmith: for Agents tracing and evaluations
Result Synthesis
The orchestrator combines results:
- Aggregates findings from multiple sources
- Generates comprehensive reports
- Saves results to files and the different database for review
Performance Metrics
- Research Speed: Average 5x faster than manual research
- Accuracy Rate: 95%+ factual accuracy with source verification
- Coverage Depth: 10+ sources per research query
Entry
Status: Submitted
Last saved: September 21 at 4:46 PM CEST
Team Roster
Message board not available for this team yet.
Chrys NIONGOLO Team Lead RSVP Approved
AI TECHLEAD at Eviden / ATOS
As team lead, I was responsible for:
- System architecture and design of the agentic research platform
- Integration of SerpApi for real-time web search capabilities
- Implementation of Algolia vector indexing for semantic search functionality
- Development of autonomous agent logic using Google AI models
- Backend API development using Python/FastAPI
- Deployment and hosting setup on Scalingo platform
- End-to-end testing and performance optimization
- Project coordination and final presentation preparation
Chrys Niongolo is an AI TechLead at Eviden/ATOS, specializing in artificial intelligence and software development. With a strong focus on web technologies and machine learning, he actively contributes to innovative projects on GitHub, including 'vite-chatgpt-ui' and 'RAGDojo'.
AI development, web technologies, machine learning applications, JavaScript development, Python programming, DevOps, interactive web interfaces, AI implementation strategies
Currently working on many projects, focusing on AI and web development technologies. Developing interactive web interfaces and machine learning applications using JavaScript and Python. Implementing advanced AI strategies at Eviden/ATOS, with an emphasis on research and development in artificial intelligence systems and innovative software solutions.