TrustGraph vs Cognee

TrustGraph and Cognee both build on Knowledge Graphs for AI applications, but they differ in their focus: production-ready enterprise platform versus developer-friendly cognitive framework.

At a Glance

Core Philosophy

TrustGraph: Production-Ready Platform

TrustGraph provides a complete, deployable system:

# Single command deployment
docker compose -f trustgraph_system.yaml up -d

# Full stack includes:
# - Knowledge Graph store (choice of Neo4j, Cassandra, etc.)
# - Vector database (Qdrant by default)
# - Agent orchestration engine
# - API gateway with authentication
# - Monitoring and observability (Prometheus + Grafana)
# - Cost tracking
# - Audit logging

// Everything integrated and ready
const trustgraph = new TrustGraph({
  endpoint: "https://your-deployment.com",
  apiKey: process.env.TRUSTGRAPH_API_KEY,
});

// Start using immediately
await trustgraph.ingest({
  sources: ["documents/"],
  graphConfig: "production-optimized",
});

const result = await trustgraph.query({
  query: "Find relationships between X and Y",
  reasoning: "multi-hop",
});

Key characteristics:

• Infrastructure included
• Production-ready security
• Built-in monitoring
• Enterprise features
• Minimal setup required

Cognee: Cognitive Development Framework

Cognee provides building blocks for cognitive systems:

import cognee

# Configure components
cognee.config.set_llm_provider("openai")
cognee.config.set_vector_db_provider("weaviate")
cognee.config.set_graph_db_provider("neo4j")

# You build the system
await cognee.add("document.txt")
await cognee.cognify()  # Process and build graph

# Query the knowledge
results = await cognee.search("QUERY", "Find relationships")

Key characteristics:

• Flexible framework
• Configure your stack
• Build custom cognitive systems
• Developer-focused
• More setup required

Architecture & Deployment

TrustGraph: Integrated Platform

Out-of-box components:

# docker-compose.yml
services:
  # Graph storage
  knowledge-graph:
    image: trustgraph/neo4j
    volumes:
      - graph-data:/data
    environment:
      - NEO4J_AUTH=neo4j/trustgraph

  # Vector storage
  vector-store:
    image: trustgraph/qdrant
    volumes:
      - vector-data:/qdrant/storage

  # Agent orchestration
  agent-orchestrator:
    image: trustgraph/agents
    depends_on:
      - knowledge-graph
      - vector-store

  # API Gateway
  api-gateway:
    image: trustgraph/api
    ports:
      - "8080:8080"
    environment:
      - AUTH_ENABLED=true
      - RBAC_ENABLED=true

  # Observability
  monitoring:
    image: trustgraph/observability
    ports:
      - "3000:3000"  # Grafana
      - "9090:9090"  # Prometheus

  # Message queue
  pulsar:
    image: trustgraph/pulsar

Built-in features:

• Multi-tenancy
• Authentication & authorization
• API rate limiting
• Cost tracking per tenant
• Audit logging
• Health monitoring
• Backup & restore

Cognee: Framework Integration

You assemble the stack:

# Install Cognee
pip install cognee

# Configure your infrastructure
import cognee
from cognee.infrastructure import Neo4jConfig, WeaviateConfig

# Set up graph database
cognee.config.graph_db = Neo4jConfig(
    uri="bolt://your-neo4j:7687",
    username="neo4j",
    password="your-password"
)

# Set up vector database
cognee.config.vector_db = WeaviateConfig(
    url="http://your-weaviate:8080",
    api_key="your-key"
)

# Your application code
async def build_knowledge():
    await cognee.add([doc1, doc2, doc3])
    await cognee.cognify()

# You handle:
# - Deployment (Docker, K8s, etc.)
# - Monitoring (your choice)
# - Security (your implementation)
# - Scaling (your strategy)
# - Multi-tenancy (if needed)

Knowledge Processing

TrustGraph: Automated Pipeline

Integrated ingestion and processing:

// Comprehensive ingestion pipeline
await trustgraph.ingest({
  sources: [
    "s3://bucket/documents/",
    "postgres://db/tables",
    "api://external-service",
  ],
  graphConfig: {
    extractEntities: true,
    linkingStrategy: "semantic",
    ontology: "schema.ttl",
    deduplication: "automatic",
    validation: "strict",
  },
  processing: {
    chunking: "adaptive",
    embeddings: "openai-3-large",
    parallel: true,
    batchSize: 1000,
  },
  monitoring: {
    trackProgress: true,
    errorHandling: "retry-with-backoff",
    notifications: ["email@company.com"],
  },
});

// Real-time monitoring
const status = await trustgraph.getIngestionStatus();
console.log(`Processed: ${status.documentsProcessed}/${status.totalDocuments}`);
console.log(`Entities extracted: ${status.entitiesCreated}`);
console.log(`Relationships: ${status.relationshipsCreated}`);

Cognee: Programmable Processing

Build custom pipelines:

import cognee
from cognee.tasks import Document, Task

# Define custom processing
async def custom_pipeline():
    # Add data
    documents = await load_documents()
    await cognee.add(documents)

    # Custom processing tasks
    await cognee.cognify(
        tasks=[
            Task.extract_entities,
            Task.extract_relationships,
            Task.build_graph,
            CustomTask.domain_specific_processing,
        ]
    )

    # Your validation logic
    validate_knowledge_quality()

# More control, more code
await custom_pipeline()

Query & Retrieval

TrustGraph: Hybrid Query System

Graph + vector unified queries:

// Declarative query with multiple strategies
const results = await trustgraph.query({
  query: "Impact of AI on healthcare",
  strategy: "graph-rag",

  // Vector search parameters
  vectorTopK: 10,
  vectorThreshold: 0.7,

  // Graph traversal parameters
  graphDepth: 3,
  relationshipTypes: ["influences", "enables", "requires"],

  // Reasoning parameters
  reasoning: "multi-hop",
  includeInferences: true,

  // Output configuration
  format: "structured",
  includeProvenance: true,
});

// Rich structured response
{
  entities: [...],
  relationships: [...],
  inferences: [...],
  sources: [...],
  confidence: 0.89
}

Cognee: Search Interface

Query the cognitive system:

# Search with different modes
results = await cognee.search(
    "QUERY",
    "Impact of AI on healthcare"
)

# Or use graph queries directly
results = await cognee.graph.query("""
    MATCH (ai:Technology {name: 'AI'})-[r]->(domain:Domain {name: 'Healthcare'})
    RETURN ai, r, domain
""")

# Returns results based on configuration
# More manual assembly of context

Agent Support

TrustGraph: Graph-Based Agents

Native agent orchestration:

// Create agents with graph awareness
const agentSystem = await trustgraph.createMultiAgent({
  researcher: {
    role: "research",
    capabilities: ["query-graph", "extract-entities", "validate"],
    knowledgeGraph: mainGraph,
  },
  analyzer: {
    role: "analyze",
    capabilities: ["reason", "infer", "compute-metrics"],
    knowledgeGraph: mainGraph,
  },
  coordinator: {
    role: "orchestrate",
    capabilities: ["plan", "coordinate", "synthesize"],
  },
});

// Execute coordinated task
const result = await agentSystem.execute({
  objective: "Comprehensive market analysis",
  coordinationStrategy: "hierarchical",
  monitoring: true,
});

// Built-in monitoring
const metrics = await agentSystem.getMetrics();
console.log(`Tasks completed: ${metrics.tasksCompleted}`);
console.log(`Graph queries: ${metrics.graphQueries}`);
console.log(`Cost: $${metrics.totalCost}`);

Cognee: Cognitive Agents

Framework for building agents:

# Build agents using Cognee's cognitive framework
from cognee.agents import CognitiveAgent

class ResearchAgent(CognitiveAgent):
    async def process(self, task):
        # Query knowledge
        knowledge = await cognee.search("QUERY", task.query)

        # Process with LLM
        result = await self.llm.process(knowledge)

        # Update knowledge if needed
        if task.update_knowledge:
            await cognee.add(result)
            await cognee.cognify()

        return result

# Orchestration is your responsibility
researcher = ResearchAgent()
result = await researcher.process(task)

Production Features

TrustGraph: Enterprise-Ready

Built-in production features:

✅ Multi-tenancy: Isolated knowledge graphs per tenant ✅ Authentication: OAuth, SAML, API keys ✅ Authorization: Role-based access control (RBAC) ✅ Monitoring: Real-time dashboards ✅ Cost tracking: Per-tenant LLM and compute costs ✅ Audit logging: Complete activity logs ✅ Health checks: Service health monitoring ✅ Rate limiting: API rate limits per tenant ✅ Backup/Restore: Automated backups ✅ High availability: Multi-node deployment ✅ Documentation: Complete API docs and guides

// Production configuration
const trustgraph = new TrustGraph({
  deployment: "production",
  tenantId: "company-xyz",
  auth: {
    method: "oauth2",
    provider: "okta",
  },
  monitoring: {
    enabled: true,
    metrics: ["latency", "throughput", "costs"],
    alerts: ["email", "slack"],
  },
  rateLimit: {
    requestsPerMinute: 1000,
    burstLimit: 100,
  },
});

Cognee: DIY Production

You implement production features:

# You add production features as needed
import cognee
from your_auth import authenticate
from your_monitoring import track_metrics
from your_tenancy import get_tenant_config

async def production_query(user, query):
    # Implement authentication
    if not authenticate(user):
        raise Unauthorized()

    # Implement tenancy
    config = get_tenant_config(user.tenant_id)
    cognee.config.update(config)

    # Track metrics
    with track_metrics(user.tenant_id):
        results = await cognee.search("QUERY", query)

    return results

# More flexibility, more work

Use Case Recommendations

Choose TrustGraph For:

1. Enterprise Deployments

   • Need production-ready infrastructure
   • Multi-tenant SaaS applications
   • Compliance and audit requirements
   • Enterprise security needs

2. Rapid Deployment

   • Want to focus on application logic
   • Don't want to build infrastructure
   • Need monitoring and observability
   • Prefer integrated solutions

3. Mission-Critical Applications

   • Healthcare, financial, legal systems
   • Need high availability
   • Require robust security
   • Must track costs and usage

4. Teams Without DevOps

   • Limited infrastructure expertise
   • Small teams focused on product
   • Want managed components
   • Need support options

Choose Cognee For:

1. Research & Development

   • Building cognitive architectures
   • Experimenting with approaches
   • Academic research
   • Novel cognitive systems

2. Custom Requirements

   • Need specific graph/vector DB
   • Custom processing pipelines
   • Unique architecture needs
   • Full control over stack

3. Developer-Focused Projects

   • Python-native development
   • Framework over platform
   • Willing to build infrastructure
   • Want maximum flexibility

4. Open-Source Contribution

   • Want to contribute to framework
   • Build on cognitive architecture
   • Extend framework capabilities
   • Community-driven development

Technical Capabilities

TrustGraph

Knowledge Graph:

• Multiple graph databases: Neo4j, Cassandra, Memgraph, FalkorDB
• Distributed scaling with Cassandra
• Graph query optimization
• Temporal graph support

Vector Search:

• Qdrant (default), Pinecone, Milvus
• Hybrid search (graph + vector)
• Configurable embedding models
• Optimized retrieval

Agents:

• Multi-agent orchestration
• Graph-aware agents
• Built-in coordination
• Cost tracking per agent

Integrations:

• 40+ LLM providers
• MCP native support
• Apache Pulsar messaging
• S3, databases, APIs

Cognee

Knowledge Processing:

• Flexible pipeline configuration
• Custom task definitions
• Extensible architecture
• Python-native

Graph & Vector:

• Neo4j, Weaviate, Qdrant
• Configurable providers
• Graph + vector hybrid
• Custom backends possible

Cognitive Framework:

• Modular cognitive architecture
• Custom cognitive tasks
• Extensible agent framework
• Research-oriented

Integrations:

• OpenAI, Anthropic, local models
• Various vector stores
• Graph database options
• Python ecosystem

Performance & Scaling

TrustGraph

• Optimization: Pre-optimized for production
• Caching: Multi-level caching built-in
• Scaling: Horizontal scaling configured
• Monitoring: Performance metrics included
• Tuning: Production tuning applied

Cognee

• Optimization: You optimize as needed
• Caching: Implement your caching
• Scaling: Design your scaling strategy
• Monitoring: Add your monitoring
• Tuning: Tune your deployment

Developer Experience

TrustGraph

// Simple, production-ready API
import { TrustGraph } from "@trustgraph/sdk";

const tg = new TrustGraph({ apiKey: process.env.API_KEY });

// Everything just works
await tg.ingest({ sources: ["docs/"] });
const results = await tg.query({ query: "..." });

// Monitoring built-in
const health = await tg.health();
const metrics = await tg.metrics();

Cognee

# Flexible, configurable framework
import cognee

# More configuration needed
cognee.config.set_llm_provider("openai")
cognee.config.set_vector_db_provider("weaviate")
cognee.config.set_graph_db_provider("neo4j")

# Build your system
await cognee.add(documents)
await cognee.cognify()
results = await cognee.search("QUERY", query)

# Add monitoring yourself

Community & Support

TrustGraph

• Enterprise support available
• Professional documentation
• Production deployment guides
• Architecture consultation
• SLA options

Cognee

• Open-source community
• GitHub discussions
• Community contributions
• Research collaborations
• Documentation in progress

Pricing

TrustGraph

• Open source: Free
• Self-hosted: Infrastructure costs
• Enterprise support: Optional paid support
• Cloud: Coming soon

Cognee

• Open source: Free
• Infrastructure: Your costs (DBs, compute, LLMs)
• Support: Community-driven

Migration & Integration

From Cognee to TrustGraph

Why migrate:

• Need production infrastructure
• Want built-in monitoring and security
• Require enterprise features
• Reduce operational overhead

Migration approach:

# Export from Cognee
graph_data = await cognee.graph.export()
vector_data = await cognee.vector_db.export()

# Import to TrustGraph
trustgraph.import_graph(graph_data)
trustgraph.import_vectors(vector_data)

# Update application code
# TrustGraph SDK provides similar concepts

Using Cognee as Development Environment

Complementary use:

# Develop and test with Cognee
import cognee

# Build prototype
await cognee.add(sample_documents)
await cognee.cognify()
results = await cognee.search("QUERY", "test query")

# Export knowledge for TrustGraph production deployment
knowledge = await cognee.export_knowledge()

# Deploy to TrustGraph for production
trustgraph.import_knowledge(knowledge)

Conclusion

TrustGraph and Cognee serve different needs in the Knowledge Graph AI space:

Choose TrustGraph when you need:

• Production-ready infrastructure
• Enterprise features out-of-box
• Minimal operational overhead
• Integrated monitoring and security
• Fast time-to-production
• Support and SLAs

Choose Cognee when you need:

• Flexible cognitive framework
• Maximum customization
• Research and experimentation
• Python-native development
• DIY infrastructure approach
• Open-source contributions

For production deployments and enterprise applications, TrustGraph's complete platform significantly reduces time-to-market and operational burden. For research projects and custom cognitive architectures, Cognee's flexible framework provides excellent building blocks.

Additional Resources

Cognee:

• Official Website
• Documentation

TrustGraph:

• TrustGraph Website
• TrustGraph Documentation
• TrustGraph GitHub

Next Steps

• TrustGraph Documentation
• Deploy TrustGraph
• TrustGraph vs LangChain
• Understanding Knowledge Graphs