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For the complete documentation index, see llms.txt. This page is also available as Markdown.

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:

Mastra (TypeScript)

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

Agno (Python)

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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