Deep Research
Learn to build autonomous AI research agents that perform multi-hop web research, entity extraction, and knowledge synthesis.
Choose Your Framework
Build the same deep research agent with your preferred orchestration framework:
Best for: TypeScript developers, modern web apps
⏱️ Time: 30-40 minutes 🎯 Level: Advanced
What you'll build:
Entity-driven research using knowledge graphs
Pre-ingestion filtering with native reranking
Autonomous convergence detection
Multi-source synthesis with citations
Best for: Python developers, high-performance systems
⏱️ Time: 30-40 minutes 🎯 Level: Advanced
Why Agno:
5000x faster than LangGraph
50x less memory usage
Simpler code (just Python functions!)
Same algorithm, cleaner implementation
What All Tutorials Cover
Every tutorial teaches the same 5-phase research algorithm, implemented in different frameworks:
Phase 1: Seed Acquisition
Start from a URL or search query to establish initial knowledge base
Phase 2: Entity-Driven Discovery
Extract entities from your knowledge graph (Person, Organization, Category)
Phase 3: Intelligent Expansion
Search web for each entity, filter before ingesting (key innovation!)
Phase 4: Convergence Detection
Automatically detect when research has converged (novelty scoring)
Phase 5: Multi-Source Synthesis
Generate comprehensive reports from 100+ sources using summary-based RAG
Key Innovations
1. Pre-Ingestion Filtering
Analyze 50 sources, ingest only top 8
Uses Graphlit's native reranker
Significantly faster with better quality
2. Diminishing Returns Detection
Agent knows when to stop researching
Based on novelty scoring (% new sources in top 10)
No manual intervention needed
3. Summary-Based RAG
Scales beyond traditional RAG (10-20 docs → 100+)
Operates on optimized summaries
Fast and accurate
What Graphlit Provides
All frameworks use the same Graphlit SDK:
✅ Automatic entity extraction (during ingestion) ✅ Knowledge graph (Schema.org/JSON-LD) ✅ Native reranker (enables pre-filtering) ✅ Exa search (built-in, no API key needed) ✅ Summary-based RAG (scalable synthesis) ✅ Multi-source citations
Time saved: 12-14 weeks of infrastructure development
Coming Soon
More framework tutorials:
LangGraph (Python) - Graph-based state machines
Vercel Workflow (TypeScript) - Deterministic, durable orchestration
Choose Your Tutorial
TypeScript developer? → Start with Mastra
Python developer? → Start with Agno
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