TrustGraph vs. Agent Orchestration Frameworks

LangChain, LangGraph, LlamaIndex, and CrewAI are powerful frameworks for orchestrating LLM interactions and building multi-agent workflows. They excel at the application layer—managing prompts, chains, tool calls, and agent coordination.​

But they stop where your real challenges begin.

These frameworks assume you already have production-grade infrastructure: data pipelines streaming real-time context, knowledge graphs capturing domain relationships, vector databases indexed and queryable, and deployment infrastructure that scales. TrustGraph provides all of that, plus agent orchestration, in one unified platform.

The Infrastructure Gap

Where They Excel, Where TrustGraph Takes Over

LangChain & LangGraph: Prompt Orchestration Powerhouse
LangChain and LangGraph are exceptional at building stateful, multi-step workflows with branching logic and tool integration. LangGraph's graph-based state machines provide traceable, debuggable flows perfect for customer support agents with escalation paths or research pipelines.​

The Gap: LangChain assumes your data infrastructure already exists. You must separately build and maintain streaming data pipelines, construct knowledge graphs, deploy vector databases, and orchestrate all these components. LangChain connects your agent to tools—but you build the tools.​

TrustGraph Solution: We provide the complete infrastructure underneath LangChain-style orchestration. Data streams in continuously, knowledge graphs self-construct from your domain data, and vector retrieval is integrated. You can still use LangChain patterns on top—but with TrustGraph, the entire context stack is included.​

LlamaIndex: RAG-First, Infrastructure-Second
LlamaIndex excels at retrieval-augmented generation (RAG)—connecting LLMs to your documents through vector search and semantic retrieval. Its data connectors, indexing strategies, and query interfaces simplify building RAG pipelines.​

The Gap: LlamaIndex focuses on the query stage of RAG. You still need to build the ingestion layer (how real-time data flows in), the transformation layer (cleaning, chunking, embedding), and the deployment layer (containerization, scaling, observability). For production RAG at scale, you're orchestrating separate tools: vector DBs, embedding models, chunk stores, and retrieval caches.​

TrustGraph Solution: TrustGraph treats RAG as one layer in a complete post-training stack. We handle real-time data ingestion, automated knowledge graph construction (which enhances retrieval beyond pure vector similarity), multi-index vector search, and LLM integration—all in one deployment. LlamaIndex is a query engine; TrustGraph is the entire data-to-agent platform.​

CrewAI: Multi-Agent Collaboration
CrewAI shines in role-based multi-agent systems where specialized agents (researcher, analyst, writer) collaborate on complex tasks. Its hierarchical workflows and task delegation mirror human teams, reducing hallucinations through specialization.​

The Gap: CrewAI orchestrates agent interactions, not data infrastructure. Each agent needs context—where does it come from? You must build separate pipelines for data ingestion, knowledge representation, and retrieval. When agents need real-time market data, customer transaction graphs, or regulatory documents, you wire those systems manually.​

TrustGraph Solution: TrustGraph provides the unified data plane that multi-agent systems need. All agents—whether orchestrated by CrewAI, LangGraph, or custom code—can query the same knowledge graphs, retrieve from the same vector indices, and access the same streaming data sources. We're the infrastructure layer that makes multi-agent collaboration data-aware and production-ready.​

Use TrustGraph When...

• You need production infrastructure, not just a library. LangChain, LlamaIndex, and CrewAI are excellent application frameworks, but they're not platforms. TrustGraph deploys as a complete stack.​

• Post-training workflows matter. You're not just doing inference—you're continuously ingesting data, updating knowledge graphs, fine-tuning retrieval, and optimizing agent context. TrustGraph is built for the post-training era.​

• Data sovereignty is non-negotiable. Run TrustGraph locally, on-premises, or in your VPC. Your data never leaves your infrastructure. Agent orchestration frameworks are libraries—they don't control where your data goes.​

• You're building agents for enterprises. Compliance, observability, and auditability aren't optional. TrustGraph provides built-in monitoring, lineage tracking, and governance that frameworks alone cannot deliver.​

Use LangChain/LlamaIndex/CrewAI When...

• You already have infrastructure. If you've built robust data pipelines, deployed vector databases, and have MLOps tooling, these frameworks integrate beautifully.​

• You're prototyping rapidly. For experimentation and proof-of-concept work, lightweight libraries are perfect. TrustGraph is for when you're ready to move to production scale.​

• You want maximum flexibility. These frameworks are unopinionated about infrastructure. TrustGraph makes architectural decisions to provide an integrated experience.​

The Bottom Line
LangChain, LangGraph, LlamaIndex, and CrewAI are not competitors to TrustGraph—they're complementary.

Use them for agent orchestration logic. Use TrustGraph for everything those agents need to be intelligent: real-time data streams, knowledge graphs, vector retrieval, post-training pipelines, and production deployment infrastructure.

TrustGraph is the agentic context stack. The frameworks are the orchestration layer. Together, they build production AI.​

TrustGraph on GitHub: https://github.com/trustgraph-ai/trustgraph