Architecture and Primary Job
LangGraph is a low-level orchestration runtime rather than a catalog of prebuilt agents. LangChain's current documentation places durable execution, streaming, human-in-the-loop, and persistence at the center of the product, and it explicitly says LangChain components are optional. Teams model state and control flow through graph or functional APIs, which makes branching, loops, pauses, and long-running work visible in code. That foundation is strongest when the workflow itself must behave predictably under failure and review.
LlamaIndex Workflows uses an event-driven, step-based model. A step receives a typed event, performs work such as retrieval, an LLM call, shared-state update, or human request, and returns another event that activates the next compatible step. Branches can be ordinary conditionals, loops return events to earlier steps, and concurrent work can dispatch lists or use Context APIs. This is a natural fit for data applications because retrieval, reranking, citation, synthesis, and agent routing can share one Python workflow vocabulary.
State, Persistence, and Recovery
LangGraph separates thread-scoped graph state from cross-thread application data. Checkpointers persist state snapshots for conversation continuity, human review, time travel, and fault tolerance; stores retain application-defined facts or preferences beyond one thread. Production deployments must replace in-memory checkpointing with a persistent option such as PostgreSQL or SQLite and set retention policies because checkpoints can grow over long conversations. The contract is explicit enough to design recovery before the first production incident.
LlamaIndex Workflows supports shared per-run state through `ctx.store`, while Resource objects hold clients, indexes, models, and configuration that should not live in serialized state. Current docs also link a durable-workflow path that checkpoints workflow context and resumes a run after restart. That closes an important historical gap, but teams still choose the durability integration and define which resources can be recreated. For data-heavy pipelines, this separation keeps serialized workflow state smaller while retrieval infrastructure remains an injected dependency.
Human Review and Execution Control
LangGraph interrupts pause execution at a chosen point, persist the exact graph state, and wait until a caller resumes with `Command`. A `thread_id` acts as the persistent cursor; reusing it loads the same checkpoint, while a new value starts a new thread. Official guidance requires a durable checkpointer in production and warns that a resumed node restarts from its beginning, so side effects before an interrupt must be idempotent. Those details make approval flows implementable rather than merely illustrative.
LlamaIndex can ask for human input inside a step and documents interactive, stateful human-in-the-loop workflow examples. Its typed event graph is validated before execution: start and stop events must exist, produced events need consumers, consumed events need producers, and accidental dead ends are reported. This validation catches structural mistakes early. LangGraph still wins high-risk approval orchestration because checkpoint, resume, replay, and interrupt semantics are more central and extensively specified, while LlamaIndex's advantage is keeping review close to data-processing steps.
RAG, Documents, and Multi-Agent Work
LlamaIndex has the clearer data-workflow advantage. Its official examples cover RAG with reranking, citation query engines, corrective RAG, query planning, and parallel execution; the broader framework supplies loaders, indexes, retrievers, query engines, and vector-store integrations. AgentWorkflow adds a built-in multi-agent handoff pattern that manages agents, state, tools, and streamed events with little setup. A team building document ingestion and grounded synthesis can therefore stay within one component system from source data to agent response.
LangGraph is deliberately more framework-agnostic. A node can wrap a retriever, LlamaIndex component, ordinary function, tool call, or another graph, while the runtime focuses on state transitions and operational behavior. This creates a viable hybrid: use LlamaIndex for document parsing, indexing, retrieval, and citation, then place the multi-step agent around those calls in LangGraph when durable approvals or complex recovery matter. The tradeoff is integration ownership; state schemas and trace boundaries must be designed across two ecosystems.
Migration, Integration, and Maintenance
LlamaIndex's older QueryPipeline abstraction is in feature-freeze and deprecation, with official guidance directing orchestration users to Workflows. New code should not present QueryPipeline as the current strategic surface, and existing pipelines need an event-and-step migration plan rather than a cosmetic rename. Workflows can be installed through `llama-index-core` or as the standalone `llama-index-workflows` package, which helps teams adopt the orchestration layer without every managed cloud feature.
LangGraph's current product layering is also explicit: LangChain provides higher-level agent abstractions and integrations, LangGraph supplies the orchestration runtime, and LangSmith is a separate platform for tracing, evaluation, prompts, and deployment. A project can use LangGraph without LangChain, but choosing LangSmith or a custom telemetry/deployment stack is a separate operational decision. This separation reduces hidden coupling, although teams must budget for schema evolution, checkpoint migrations, and tests around branching or replay behavior.
Verdict and Selection Matrix
LangGraph wins when the primary requirement is a production agent runtime: state must survive interruptions, sensitive actions need approval, failures must resume from known checkpoints, and operators need inspectable branches and replay. Choose it for long-running research, service automation, multi-stage operations, or agents whose workflow topology is part of the product contract. The cost is additional architecture and persistence work, but those are deliberate controls rather than application glue discovered after launch.
Choose LlamaIndex when retrieval and document transformation are the center of gravity and orchestration exists mainly to move data through ingestion, reranking, citation, synthesis, and agent handoffs. Its event-driven Workflows and AgentWorkflow can cover substantial agent logic, so this is not a claim that LlamaIndex lacks orchestration. LangGraph remains the overall winner because it makes durable execution and human control the core abstraction; the strongest carve-out, and often the strongest hybrid, is LlamaIndex data primitives inside a LangGraph-managed runtime.