DeerFlow Research Engine
A sophisticated LangGraph-based research pipeline that orchestrates multiple AI agents to conduct deep, multi-source research with automatic synthesis and citation.
How It Works
Query Analysis
Lead agent breaks down the research query into sub-questions and distributes tasks.
Parallel Research
Multiple sub-agents search web sources simultaneously using Tavily API.
Data Synthesis
Results are collated, deduplicated, and synthesized into coherent findings.
Report Generation
Final report with citations, sources, and structured output delivered.
Key Features
Parallel Sub-Agent Execution
Runs up to 8 research agents simultaneously, reducing research time from hours to minutes.
Tavily Web Search Integration
High-quality web search with automatic source verification and credibility scoring.
Intelligent Context Management
Smart token management prevents context overflow while maintaining research depth.
Automated Citation Tracking
Every claim is linked to its source with full citation formatting for reports.
Multi-Model Routing
Uses Kimi K2.5 for fast research, Claude for synthesis, optimized for cost and quality.
Persistent Thread Memory
Research threads maintain context across sessions for ongoing investigations.
Technical Achievements
4
Silent Bugs Fixed
Including recursion limit, model naming, and context overflow issues.
8x
Speed Improvement
Parallel execution vs sequential research workflows.
100%
Uptime
Fully operational with Docker containerization.
Technology Stack
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