If you can't trace it,
you can't trust it.
TraceGraph is a GraphRAG citation explorer that grounds every AI answer in a verifiable knowledge graph. See the entities, follow the relationships, trace the truth.
Built with
Vector search finds text that looks like the answer.
Standard RAG retrieves document chunks based on semantic similarity. It works for simple lookups but fails when questions require connecting information across multiple documents, understanding entity relationships, or providing auditable citation trails.
Graph traversal finds information that is related to the answer.
GraphRAG builds a knowledge graph from your documents — entities, relationships, and community structures. Every answer traces back through the graph to its source, providing the citation chain that regulated industries demand.
Everything you need to trace AI reasoning.
From entity extraction to citation trails, TraceGraph provides the complete toolkit for explainable AI.
Full Citation Trail
Every AI answer traces back through entity chains to source documents. Click any citation to see the exact graph path that led to each claim.
"GraphRAG reduces hallucinations by grounding responses..."
4 Search Modes
Hybrid, Local, Global, or Naive — choose how to traverse the knowledge graph for each query.
Side-by-Side
Compare naive vector search against graph-enhanced retrieval on the same question.
6 Entity Types
Concepts, technologies, organizations, regulations, persons, and documents — each color-coded in the graph.
Works Offline
Ships with sample graph data. No API keys needed to explore the UI.
Resizable Panels
Drag to resize the graph, AI response, and citation trail panels. Read long answers without feeling cramped.
From documents to traceable answers in three steps.
Ingest Documents
Upload PDFs, text files, or any document corpus. TraceGraph chunks and embeds them for retrieval.
Extract Knowledge Graph
LLMs automatically extract entities and relationships, building a structured knowledge graph with community hierarchies.
Query with Citations
Ask questions in natural language. Get answers grounded in the knowledge graph with full citation trails.
How does GraphRAG reduce hallucinations?
GraphRAG grounds responses in verified entity relationships...
Query the knowledge graph in three lines.
import httpx
response = httpx.post("https:">//tracegraph-ls2t.onrender.com/query", json={
"query": "How does GraphRAG reduce hallucinations?",
"mode": "hybrid"
})
result = response.json()
print(result["answer"]) "text-[#6a737d]"># Grounded AI response
print(result["citations"][0]["entity_chain"]) "text-[#6a737d]"># ['GraphRAG', 'Knowledge Graph', 'Structured Grounding']
print(f"Sources: {len(result['citations'])}") "text-[#6a737d]"># Sources: 10Designed for clarity, built for scale.
Built for domains where accuracy is non-negotiable.
Healthcare Decision Support
Knowledge graphs over clinical guidelines, drug interactions, and patient data. UMLS and SNOMED CT entity support.
Pharmacovigilance
VAERS adverse event analysis with cross-document reasoning. Brighton Collaboration case definition support.
EU AI Act Compliance
Article 13 transparency and Article 14 human oversight requirements met through built-in citation traceability.
Clinical Trial Analysis
Multi-hop reasoning across trial data, drug efficacy studies, and safety profiles. ClinicalTrials.gov integration.