TrustGraph vs LlamaIndex

TrustGraph and LlamaIndex both help developers build context-aware AI applications, but they differ fundamentally in how they structure and retrieve information.

At a Glance

Core Philosophy

TrustGraph: Graph-Native Platform

TrustGraph treats relationships as first-class citizens:

// Build interconnected Knowledge Graph
await trustgraph.buildGraph({
  sources: ["documents/"],
  relationships: ["semantic", "hierarchical", "temporal"],
  reasoning: "graph-based",
});

// Query with relationship awareness
const result = await trustgraph.queryGraph({
  query: "How are X and Y connected?",
  maxDepth: 3,  // Follow relationship chains
  includeRelated: true,
});

// Rich structured context
{
  entities: [...],
  relationships: [
    { source: "X", type: "influences", target: "Y", weight: 0.8 }
  ],
  paths: [["X", "influences", "Y", "requires", "Z"]]
}

LlamaIndex: Index-First Framework

LlamaIndex organizes data through hierarchical indexes:

from llama_index import VectorStoreIndex, Document

# Build index from documents
documents = [Document(text="..."), ...]
index = VectorStoreIndex.from_documents(documents)

# Query the index
query_engine = index.as_query_engine()
response = query_engine.query("How are X and Y connected?")

# Returns synthesized text answer
# Relationships must be in text, not structured

Data Structuring

TrustGraph: Knowledge Graph Construction

Automatic entity and relationship extraction:

// TrustGraph automatically extracts:
// - Entities: People, organizations, concepts, events
// - Relationships: Semantic links, hierarchies, temporal sequences
// - Attributes: Properties of entities
// - Provenance: Source document tracking

const graph = await trustgraph.ingest({
  sources: ["company_docs/"],
  graphConfig: {
    extractEntities: true,
    linkingStrategy: "semantic",
    ontology: "custom-schema.ttl",
  }
});

// Resulting graph structure
{
  nodes: [
    { id: "person_1", type: "Person", name: "John Smith", role: "CEO" },
    { id: "company_1", type: "Organization", name: "Acme Corp" }
  ],
  edges: [
    { from: "person_1", to: "company_1", type: "employed_by", since: "2020" }
  ]
}

LlamaIndex: Hierarchical Indexing

Document-centric indexing:

# LlamaIndex creates indexes, not graphs
from llama_index import TreeIndex, VectorStoreIndex

# Options:
# - VectorStoreIndex: Flat embedding-based retrieval
# - TreeIndex: Hierarchical summarization
# - KeywordTableIndex: Keyword-based lookup
# - KnowledgeGraphIndex: Limited graph support (metadata-based)

index = VectorStoreIndex.from_documents(docs)

# Data remains text-based
# Relationships embedded in text, not structured

Note: LlamaIndex has KnowledgeGraphIndex but it's primarily metadata-based, not a true graph database with traversal capabilities.

Retrieval Strategies

TrustGraph: Graph Traversal + Vector Search

Hybrid approach:

1. Vector search: Find semantically relevant entities
2. Graph traversal: Explore relationships and connections
3. Path reasoning: Identify multi-hop relationship chains
4. Context assembly: Build comprehensive subgraph

const context = await trustgraph.retrieve({
  query: "Impact of AI on healthcare",
  strategy: "graph-rag",
  vectorTopK: 10,      // Initial vector search
  graphDepth: 2,       // Expand relationships
  includeAttributes: true,
});

// Returns structured subgraph with:
// - Relevant entities (AI, healthcare, specific applications)
// - Relationships (impacts, enables, requires)
// - Attributes (dates, metrics, sources)
// - Reasoning paths

LlamaIndex: Index-Based Retrieval

Index-specific strategies:

# Vector similarity retrieval
vector_index = VectorStoreIndex.from_documents(docs)
vector_response = vector_index.query("query")

# Hierarchical tree retrieval
tree_index = TreeIndex.from_documents(docs)
tree_response = tree_index.query("query")

# Keyword-based retrieval
keyword_index = KeywordTableIndex.from_documents(docs)
keyword_response = keyword_index.query("query")

# Returns text chunks, not structured relationships

Hallucination Prevention

TrustGraph: Graph-Grounded Constraints

Structural validation:

// Graph structure prevents hallucinations
const response = await trustgraph.generate({
  prompt: "Explain X's relationship to Y",
  context: graphContext,
  groundingMode: "strict",  // Only reference graph entities
});

// Automatic validation
const validation = validateAgainstGraph(response, graphContext);
// Flags any entities or relationships not in graph

Benefits:

• LLM cannot invent entities not in graph
• Relationships must exist in graph structure
• Citations link to specific graph nodes
• Transparent provenance

LlamaIndex: Chunk-Based Validation

Source citation and node references:

response = query_engine.query("query", response_mode="tree_summarize")

# Check source nodes
source_nodes = response.source_nodes
for node in source_nodes:
    print(node.node.text)  # Original text chunk
    print(node.score)       # Relevance score

# Manual validation required
# LLM might still hallucinate beyond chunks

Agent Architecture

TrustGraph: Graph-Based Multi-Agent

Agents operate on Knowledge Graphs:

const agent = await trustgraph.createAgent({
  type: "graph-query",
  capabilities: ["query", "reason", "update"],
  knowledgeGraph: mainGraph,
});

// Agent understands graph structure
const result = await agent.execute({
  task: "Find all companies influenced by trend X",
  reasoning: "multi-hop",
  maxSteps: 5,
});

// Multi-agent collaboration
const agents = await trustgraph.createMultiAgent({
  researcher: { role: "query", graph: mainGraph },
  analyzer: { role: "reason", graph: mainGraph },
  coordinator: { role: "orchestrate" }
});

LlamaIndex: Tool-Based Agents

Agents use indexes as tools:

from llama_index.agent import OpenAIAgent
from llama_index.tools import QueryEngineTool

# Convert indexes to tools
vector_tool = QueryEngineTool.from_defaults(
    query_engine=vector_index.as_query_engine(),
    name="vector_search",
    description="Search documents by similarity"
)

tree_tool = QueryEngineTool.from_defaults(
    query_engine=tree_index.as_query_engine(),
    name="summarize",
    description="Hierarchical summarization"
)

# Agent selects tools
agent = OpenAIAgent.from_tools([vector_tool, tree_tool])
response = agent.chat("Analyze X")

Deployment & Infrastructure

TrustGraph: Complete Platform

Fully containerized system:

# docker-compose.yml includes:
services:
  knowledge-graph:
    image: trustgraph/graph-store
  vector-store:
    image: trustgraph/vector-db
  agent-orchestrator:
    image: trustgraph/agents
  api-gateway:
    image: trustgraph/api
  monitoring:
    image: trustgraph/observability

Out-of-box features:

• Multi-tenancy
• Authentication & authorization
• Observability dashboards
• Cost monitoring
• Audit logging

LlamaIndex: Framework + Your Infrastructure

You provide infrastructure:

# You deploy and manage:
# - Application server
# - Vector database (Pinecone, Weaviate, etc.)
# - LLM API keys
# - Monitoring (optional)
# - Security (your responsibility)

Performance & Scaling

TrustGraph

• Graph databases: Neo4j, Cassandra, Memgraph, FalkorDB
• Distributed scaling: Cassandra for massive graphs
• Caching: Intelligent subgraph caching
• Optimization: Graph query optimization built-in

LlamaIndex

• Vector stores: Pinecone, Weaviate, Chroma, many others
• Scaling: Depends on chosen vector store
• Caching: Application-level (manual implementation)
• Optimization: Index-specific tuning

Advanced Features

TrustGraph

✅ Knowledge Cores: Modular, swappable knowledge bases ✅ 3D Graph Visualization: Interactive graph exploration ✅ Ontology Support: Schema-driven graph construction ✅ Temporal Relationships: Time-aware connections ✅ MCP Integration: Native Model Context Protocol support ✅ Multi-modal: Text, structured data, future multimedia support

LlamaIndex

✅ Multiple Index Types: Vector, tree, keyword, graph ✅ Custom Retrievers: Build your own retrieval strategies ✅ Response Synthesis: Various synthesis modes ✅ Streaming Support: Stream responses token-by-token ✅ Callback System: Hook into retrieval pipeline ✅ Multi-modal: Text, images, audio support

Use Case Recommendations

Choose TrustGraph For:

1. Complex Relationship Queries

   • "How is concept A related to concept B?"
   • Multi-hop reasoning requirements
   • Need to understand connection paths

2. High-Accuracy Requirements

   • Medical, legal, financial applications
   • Hallucinations unacceptable
   • Must cite sources with precision

3. Enterprise Knowledge Management

   • Interconnected corporate knowledge
   • Need relationship tracking
   • Compliance and audit requirements

4. Agent Systems with Shared Knowledge

   • Multiple agents querying same graph
   • Agents updating shared knowledge base
   • Complex multi-agent coordination

Choose LlamaIndex For:

1. Document-Centric Applications

   • Primarily querying text documents
   • Document summarization needs
   • Hierarchical information organization

2. Flexible Indexing Strategies

   • Want to experiment with different index types
   • Need custom retrieval strategies
   • Prefer Python ecosystem

3. Rapid Development

   • Quick prototyping
   • Familiar with Python data science stack
   • Don't need graph relationships

4. Existing Vector DB Investment

   • Already using Pinecone, Weaviate, etc.
   • Want lightweight framework
   • Have custom infrastructure

Integration & Ecosystem

TrustGraph

• MCP Native: Built-in Model Context Protocol support
• LLM Agnostic: OpenAI, Anthropic, Google, AWS Bedrock, 40+ providers
• Graph Stores: Neo4j, Cassandra, Memgraph, FalkorDB
• Vector Stores: Qdrant (default), Pinecone, Milvus
• Messaging: Apache Pulsar for event streaming

LlamaIndex

• LlamaHub: Large collection of data connectors
• LLM Support: OpenAI, Anthropic, HuggingFace, local models
• Vector Stores: 20+ integrations
• Frameworks: Integrates with LangChain
• Tools: LlamaIndex TS (TypeScript version)

Migration Path

From LlamaIndex to TrustGraph

Why migrate:

• Need relationship-aware reasoning
• Want graph-grounded accuracy
• Require production platform infrastructure
• Need multi-hop query capabilities

Migration steps:

1. Extract entities from indexed documents
2. Build Knowledge Graph from entities
3. Update queries to use graph traversal
4. Deploy TrustGraph platform

Using Both Together

Complementary use:

# Use LlamaIndex for document ingestion and initial indexing
from llama_index import VectorStoreIndex
index = VectorStoreIndex.from_documents(documents)

# Extract entities and relationships
entities = extract_entities_from_index(index)

# Build TrustGraph Knowledge Graph
trustgraph.ingest_entities(entities)

# Query TrustGraph for relationship-aware retrieval
graph_context = trustgraph_client.query_graph(query)

# Use LlamaIndex for synthesis
response = index.query(context=graph_context, query=query)

Pricing

TrustGraph

• Open source: Free
• Self-hosted: Infrastructure costs
• Enterprise support: Optional

LlamaIndex

• Open source: Free
• LlamaIndex Cloud: Managed service (pricing varies)
• Infrastructure: Your vector DB + LLM API costs

Conclusion

TrustGraph excels when relationships and structure matter:

• Graph-based reasoning
• Multi-hop queries
• Hallucination prevention
• Enterprise deployment

LlamaIndex shines for document-centric applications:

• Flexible indexing strategies
• Document summarization
• Python data science integration
• Rapid prototyping

For applications requiring relationship understanding and grounded reasoning, TrustGraph's Knowledge Graph approach provides significant advantages. For simpler document Q&A with flexible indexing options, LlamaIndex offers an excellent Python-native framework.

Additional Resources

LlamaIndex:

• Official Website
• Documentation

TrustGraph:

• TrustGraph Website
• TrustGraph Documentation
• TrustGraph GitHub

Next Steps

• TrustGraph Documentation
• Build Your First Knowledge Graph
• TrustGraph vs LangChain
• Understanding Knowledge Graphs