TrustGraph vs CrewAI

TrustGraph and CrewAI both enable building multi-agent AI systems, but they differ fundamentally in how agents share knowledge, coordinate tasks, and maintain context.

At a Glance

Core Philosophy

TrustGraph: Graph-Native Agent System

TrustGraph agents operate on shared Knowledge Graphs:

// Agents share a unified Knowledge Graph
const agent = await trustgraph.createAgent({
  type: "research-analyst",
  knowledgeGraph: sharedGraph,
  capabilities: ["query", "reason", "update"],
  permissions: {
    read: ["entities", "relationships"],
    write: ["annotations", "insights"],
  },
});

// Agent automatically has access to all graph knowledge
const result = await agent.execute({
  task: "Analyze market trends for product X",
  reasoning: "multi-hop",
  updateGraph: true, // Findings added to shared graph
});

// Other agents immediately see updates
const synthesizer = await trustgraph.createAgent({
  type: "report-writer",
  knowledgeGraph: sharedGraph, // Same graph!
});

Key Benefits:

• All agents share the same knowledge representation
• No duplicate information or synchronization needed
• Graph structure prevents knowledge inconsistencies
• Transparent provenance for every fact

CrewAI: Role-Based Agent Collaboration

CrewAI organizes agents by roles and responsibilities:

from crewai import Agent, Task, Crew

# Define agents with specific roles
researcher = Agent(
    role="Research Analyst",
    goal="Gather market intelligence",
    backstory="Expert at finding and analyzing market data",
    tools=[search_tool, scraper_tool],
    verbose=True
)

writer = Agent(
    role="Content Writer",
    goal="Create compelling reports",
    backstory="Skilled at synthesizing information",
    tools=[writing_tool],
    verbose=True
)

# Define tasks
research_task = Task(
    description="Research market trends for product X",
    agent=researcher
)

writing_task = Task(
    description="Write report based on research",
    agent=writer,
    context=[research_task]  # Gets output from research
)

# Orchestrate execution
crew = Crew(
    agents=[researcher, writer],
    tasks=[research_task, writing_task],
    process="sequential"  # or "hierarchical"
)

result = crew.kickoff()

Key Characteristics:

• Agents defined by roles and goals
• Knowledge passed through task outputs
• Tools provide agent capabilities
• Flexible process orchestration

Knowledge Management

TrustGraph: Shared Graph Memory

Unified knowledge representation:

// All agents work with the same graph
const multiAgentSystem = await trustgraph.createMultiAgent({
  researcher: {
    role: "research",
    capabilities: ["extract-entities", "find-relationships"],
  },
  analyzer: {
    role: "analyze",
    capabilities: ["reason", "compute-metrics"],
  },
  coordinator: {
    role: "orchestrate",
    capabilities: ["plan", "coordinate"],
  },
  // All share the same Knowledge Graph
  sharedKnowledgeGraph: mainGraph,
});

// Researcher adds findings
await multiAgentSystem.researcher.execute({
  task: "Extract key concepts from documents",
});

// Graph now contains new entities and relationships
// Analyzer immediately has access without data transfer
await multiAgentSystem.analyzer.execute({
  task: "Find patterns in recent findings",
  // Automatically queries the updated graph
});

// Knowledge is structured and queryable
const insights = await mainGraph.query({
  cypher: `
    MATCH (a:Agent)-[:DISCOVERED]->(e:Entity)-[:RELATES_TO]->(c:Concept)
    WHERE a.id = 'researcher'
    RETURN e, c
  `
});

Advantages:

• Zero knowledge duplication
• Instant synchronization
• Structured, queryable knowledge
• Full audit trail of agent contributions

CrewAI: Context Passing

Output-based knowledge sharing:

# Researcher output becomes context for next agent
research_task = Task(
    description="Research competitors",
    agent=researcher,
    expected_output="List of competitors with key features"
)

analysis_task = Task(
    description="Analyze competitive landscape",
    agent=analyzer,
    context=[research_task]  # Receives research output as text
)

# Optional shared memory
from crewai.memory import ShortTermMemory, LongTermMemory

crew = Crew(
    agents=[researcher, analyzer],
    tasks=[research_task, analysis_task],
    memory=True,  # Enable memory
    short_term_memory=ShortTermMemory(),
    long_term_memory=LongTermMemory()
)

Characteristics:

• Knowledge passed as text outputs
• Optional memory systems (short-term, long-term)
• Agents retrieve relevant context from memory
• Less structured than graph representation

Agent Coordination

TrustGraph: Graph-Aware Orchestration

Agents coordinate through graph operations:

// Define agent network with graph-based coordination
const agentNetwork = await trustgraph.createAgentNetwork({
  topology: "hierarchical",
  agents: {
    supervisor: {
      type: "coordinator",
      capabilities: ["plan", "delegate", "synthesize"],
      subordinates: ["researcher1", "researcher2", "analyst"],
    },
    researcher1: {
      type: "web-researcher",
      capabilities: ["search", "extract", "validate"],
    },
    researcher2: {
      type: "document-researcher",
      capabilities: ["read-docs", "extract", "summarize"],
    },
    analyst: {
      type: "data-analyst",
      capabilities: ["analyze", "compute", "visualize"],
    },
  },
  coordinationGraph: coordinationGraph,  // Tracks dependencies
});

// Supervisor plans based on graph structure
const plan = await agentNetwork.supervisor.plan({
  objective: "Complete market analysis",
  constraints: ["use latest data", "cite sources"],
});

// Execution follows graph dependencies
// Results automatically merge into Knowledge Graph
const result = await agentNetwork.execute(plan);

CrewAI: Process-Based Orchestration

Sequential or hierarchical execution:

from crewai import Crew, Process

# Sequential process - one after another
sequential_crew = Crew(
    agents=[researcher, analyst, writer],
    tasks=[research_task, analysis_task, writing_task],
    process=Process.sequential
)

# Hierarchical process - manager delegates to workers
hierarchical_crew = Crew(
    agents=[manager, researcher, analyst, writer],
    tasks=[research_task, analysis_task, writing_task],
    process=Process.hierarchical,
    manager_llm="gpt-4"  # Manager coordinates
)

# Custom process control
from crewai import Task

conditional_task = Task(
    description="Additional research if needed",
    agent=researcher,
    condition=lambda context: needs_more_research(context)
)

Reasoning Capabilities

TrustGraph: Graph-Based Reasoning

Multi-hop relationship reasoning:

// Agent can traverse relationships for complex reasoning
const reasoning = await agent.reason({
  query: "How does technology X impact industry Y?",
  strategy: "graph-traversal",
  maxDepth: 4,  // Follow relationship chains
  includeInference: true,
});

// Returns:
{
  directImpacts: [
    { entity: "Technology X", relation: "disrupts", target: "Process A" }
  ],
  indirectImpacts: [
    {
      path: ["Technology X", "enables", "Tool B", "improves", "Efficiency C", "benefits", "Industry Y"],
      confidence: 0.87
    }
  ],
  supportingEvidence: [
    { fact: "...", source: "doc_123", timestamp: "2025-01-10" }
  ]
}

CrewAI: Tool-Based Reasoning

Chain-of-thought with tools:

# Agent uses tools to reason
analyst = Agent(
    role="Strategic Analyst",
    goal="Provide strategic insights",
    tools=[
        calculator_tool,
        search_tool,
        analysis_tool
    ],
    verbose=True,
    allow_delegation=True  # Can delegate to other agents
)

# Chain of thought emerges from tool use
task = Task(
    description="""
    Analyze impact of technology X on industry Y:
    1. Research current state of industry Y
    2. Identify how technology X is being used
    3. Calculate potential efficiency gains
    4. Synthesize strategic implications
    """,
    agent=analyst
)

Hallucination Prevention

TrustGraph: Graph Grounding

Facts must exist in graph:

// Agent responses validated against graph
const response = await agent.generate({
  prompt: "Explain relationship between X and Y",
  groundingMode: "strict",  // Only reference graph entities
  validateOutput: true,
});

// Automatic validation
if (!response.validated) {
  console.error("Hallucinated entities:", response.hallucinations);
  // System can reject or flag response
}

// All claims traceable to graph nodes
response.claims.forEach(claim => {
  console.log("Claim:", claim.statement);
  console.log("Source:", claim.graphNode);
  console.log("Provenance:", claim.sourceDocuments);
});

CrewAI: Output Validation

Manual validation approaches:

# Add validation through custom tools
validation_tool = Tool(
    name="Fact Checker",
    func=validate_against_sources,
    description="Verify facts against source documents"
)

reviewer = Agent(
    role="Quality Reviewer",
    goal="Ensure accuracy of outputs",
    tools=[validation_tool],
    backstory="Expert at fact-checking and validation"
)

review_task = Task(
    description="Review and validate the analysis",
    agent=reviewer,
    context=[analysis_task]
)

# Or use guardrails in prompts
analyst = Agent(
    role="Analyst",
    goal="Provide accurate analysis",
    backstory="""Expert analyst who always:
    - Cites sources for claims
    - Distinguishes facts from opinions
    - Says 'I don't know' when uncertain
    """
)

Deployment & Infrastructure

TrustGraph: Complete Platform

Containerized multi-agent platform:

# docker-compose.yml
services:
  knowledge-graph:
    image: trustgraph/graph-store

  agent-orchestrator:
    image: trustgraph/agents
    environment:
      - GRAPH_URL=knowledge-graph:7687
      - VECTOR_URL=vector-store:6333

  vector-store:
    image: trustgraph/vector-db

  monitoring:
    image: trustgraph/observability

Built-in features:

• Agent lifecycle management
• Resource allocation
• Cost tracking per agent
• Performance monitoring
• Audit logging

CrewAI: Python Application

You deploy and manage:

# Your application code
from crewai import Crew
import os

# You handle:
# - Environment configuration
# - LLM API keys
# - Deployment infrastructure
# - Monitoring (if any)
# - Scaling

os.environ["OPENAI_API_KEY"] = "your-key"

crew = Crew(
    agents=[...],
    tasks=[...],
    verbose=True
)

# Deploy as Python application
# (Flask, FastAPI, container, etc.)

Use Case Recommendations

Choose TrustGraph For:

1. Knowledge-Intensive Systems

   • Agents need to build and maintain complex knowledge bases
   • Multiple agents must share consistent understanding
   • Need to query and reason over accumulated knowledge

2. High-Accuracy Requirements

   • Medical, legal, financial domains
   • Must prevent hallucinations
   • Need complete audit trails

3. Long-Running Agent Systems

   • Agents operate continuously
   • Knowledge persists across sessions
   • Learning and adaptation required

4. Complex Relationship Understanding

   • Multi-hop reasoning needed
   • Relationship types matter
   • Context includes entity connections

Choose CrewAI For:

1. Well-Defined Workflows

   • Clear role assignments
   • Linear or hierarchical processes
   • Task dependencies are simple

2. Rapid Prototyping

   • Quick agent experimentation
   • Flexible role definitions
   • Python-native development

3. Tool-Heavy Applications

   • Agents primarily use external tools
   • Less emphasis on shared knowledge
   • Output-focused collaboration

4. Simple Knowledge Requirements

   • Context passing via text is sufficient
   • Don't need complex relationship tracking
   • Memory requirements are basic

Integration & Ecosystem

TrustGraph

• MCP Native: Built-in Model Context Protocol support
• Graph Stores: Neo4j, Cassandra, Memgraph, FalkorDB
• LLMs: OpenAI, Anthropic, Google, AWS Bedrock, 40+ providers
• Messaging: Apache Pulsar for agent communication
• Observability: Built-in monitoring dashboards

CrewAI

• LLMs: OpenAI, Anthropic, local models via LiteLLM
• Tools: LangChain tools compatible
• Frameworks: Can integrate with LangChain, LlamaIndex
• Deployment: Python-native (FastAPI, Flask, etc.)
• Community: Active Discord, many tutorials

Advanced Features

TrustGraph

✅ Knowledge Graph Agents: Agents that understand graph structure ✅ Provenance Tracking: Every fact traces to source ✅ Multi-hop Reasoning: Follow relationship chains ✅ Graph Visualization: 3D visualization of agent knowledge ✅ Temporal Graphs: Time-aware relationships ✅ Distributed Agents: Scale across multiple nodes

CrewAI

✅ Role-Based Design: Intuitive agent definitions ✅ Multiple Processes: Sequential, hierarchical, custom ✅ Memory Systems: Short-term, long-term, entity memory ✅ Tool Integration: Easy tool addition ✅ Delegation: Agents can delegate to others ✅ Callbacks: Hook into agent execution

Migration Considerations

From CrewAI to TrustGraph

Why migrate:

• Need structured knowledge sharing
• Want graph-based reasoning
• Require hallucination prevention
• Need production platform infrastructure

Migration approach:

# CrewAI agent outputs can become TrustGraph entities
crew_result = crew.kickoff()

# Extract entities and relationships from outputs
entities = extract_entities(crew_result)
relationships = extract_relationships(crew_result)

# Build Knowledge Graph
trustgraph.ingest_entities(entities)
trustgraph.ingest_relationships(relationships)

# Recreate agents as graph-aware
trustgraph_agents = trustgraph.createMultiAgent({
    researcher: { role: "research", graph: mainGraph },
    analyst: { role: "analyze", graph: mainGraph }
})

Using Both Together

Complementary approach:

# Use CrewAI for task orchestration
crew_researcher = Agent(
    role="Researcher",
    tools=[search_tool]
)

research_task = Task(
    description="Gather data",
    agent=crew_researcher
)

# Feed results into TrustGraph
crew_result = crew.kickoff()

# Build Knowledge Graph from results
trustgraph_client.ingest_data(crew_result)

# Use TrustGraph agents for graph-based reasoning
graph_result = trustgraph_client.agents.analyze({
    task: "Find hidden patterns",
    reasoning: "graph-traversal"
})

Performance Characteristics

TrustGraph

• Strength: Complex reasoning over relationships
• Optimization: Graph query optimization
• Scaling: Distributed graph databases
• Overhead: Graph storage and maintenance

CrewAI

• Strength: Quick task execution
• Optimization: LLM call efficiency
• Scaling: Horizontal scaling of Python workers
• Overhead: Minimal, framework is lightweight

Pricing

TrustGraph

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

CrewAI

• Open source: Free
• Costs: LLM API calls (pay per use)
• CrewAI+: Optional paid tier with additional features

Conclusion

TrustGraph and CrewAI excel in different scenarios:

Choose TrustGraph when you need:

• Shared, structured knowledge across agents
• Graph-based reasoning and relationships
• Hallucination prevention through grounding
• Production-ready platform with observability
• Long-term knowledge persistence

Choose CrewAI when you need:

• Role-based agent collaboration
• Rapid prototyping and experimentation
• Simple task orchestration
• Python-native development
• Lightweight framework

For knowledge-intensive, accuracy-critical systems, TrustGraph's Knowledge Graph foundation provides significant advantages. For well-defined workflows with role-based coordination, CrewAI offers an excellent, intuitive framework.

Additional Resources

CrewAI:

• Official Website
• Documentation

TrustGraph:

• TrustGraph Website
• TrustGraph Documentation
• TrustGraph GitHub

Next Steps

• TrustGraph Documentation
• Build Multi-Agent Systems
• TrustGraph vs LangChain
• Understanding Knowledge Graphs