LangGraph vs Cassandra for RAG: Which Should You Use?

LangGraph and Cassandra solve different problems. LangGraph is an orchestration framework for building stateful LLM workflows with nodes, edges, checkpoints, and tool calls; Cassandra is a distributed database built to store and retrieve data at scale. For RAG, use LangGraph to control the retrieval-and-generation workflow, and use Cassandra only if you need the database layer behind your retrieval system.

Quick Comparison

When LangGraph Wins

Use LangGraph when your RAG pipeline is not just “retrieve then answer.” If you need query rewriting, multi-hop retrieval, fallback retrieval sources, or conditional routing based on confidence scores, StateGraph is the right abstraction.

LangGraph also wins when the workflow needs control flow that a plain chain cannot express cleanly.

• •

  Multi-step retrieval

  • •Example: rewrite the user question with one node, retrieve from a vector store in another node, then run a reranker before generation.
  • •This is exactly what graphs are good at: explicit state passed between nodes.
• •

  Human approval or escalation

  • •Example: in insurance claims support, let the graph pause after retrieval and route to an analyst if policy text conflicts with claim details.
  • •LangGraph supports checkpointing and resuming stateful execution through its persistence patterns.
• •

  Branching based on evidence

  • •Example: if retrieved documents score below a threshold, branch to web search or an internal knowledge base.
  • •That kind of conditional logic is much cleaner with graph edges than with imperative glue code.
• •

  Tool-heavy assistants

  • •Example: retrieve policy docs, call a calculator tool for premium estimates, then generate an answer with citations.
  • •LangGraph handles tool invocation as part of the workflow instead of burying it in ad hoc application code.

A practical pattern looks like this:

from langgraph.graph import StateGraph

graph = StateGraph(MyState)
graph.add_node("rewrite", rewrite_query)
graph.add_node("retrieve", retrieve_docs)
graph.add_node("generate", generate_answer)

graph.set_entry_point("rewrite")
graph.add_edge("rewrite", "retrieve")
graph.add_edge("retrieve", "generate")

app = graph.compile()
result = app.invoke({"question": "What does my policy cover?"})

That structure matters when your RAG system needs auditability and deterministic execution paths.

When Cassandra Wins

Use Cassandra when your main problem is storage and retrieval at massive scale. If you have millions of documents or events and need predictable writes across multiple nodes with high availability, Cassandra is the better infrastructure choice.

Cassandra wins when RAG is part of a larger data platform rather than an LLM-first application.

• •

  High-volume ingestion

  • •Example: continuously storing claim notes, call transcripts, policy updates, or event logs.
  • •Cassandra handles write-heavy workloads far better than most application databases.
• •

  Operational durability

  • •Example: you want replicas across regions and can tolerate eventual consistency where appropriate.
  • •Cassandra’s replication model is designed for this kind of resilience.
• •

  Query-pattern-driven access

  • •Example: fetch all chunks for a given customer ID or all documents for a case ID quickly.
  • •With proper partition keys and clustering columns, Cassandra gives stable low-latency reads.
• •

  Existing Cassandra estate

  • •Example: your company already runs Cassandra for customer records or telemetry.
  • •Adding RAG metadata there is simpler than introducing another datastore just for embeddings/chunks.

A typical storage schema might look like this:

CREATE TABLE rag_chunks (
    tenant_id text,
    doc_id text,
    chunk_id timeuuid,
    content text,
    embedding blob,
    source text,
    created_at timestamp,
    PRIMARY KEY ((tenant_id), doc_id, chunk_id)
) WITH CLUSTERING ORDER BY (doc_id ASC, chunk_id ASC);

That works well if your retrieval layer already knows how to query by tenant or document family. It does not replace orchestration logic; it only stores data efficiently.

For RAG Specifically

My recommendation is simple: use LangGraph for the RAG workflow and Cassandra only as one possible backing store. If you are choosing one tool to build the application logic around retrieval-augmented generation, pick LangGraph every time.

Cassandra is storage infrastructure. LangGraph is the control plane for the actual RAG experience: rewrite → retrieve → rerank → generate → escalate if needed.

Keep learning

• •The complete AI Agents Roadmap — my full 8-step breakdown
• •Free: The AI Agent Starter Kit — PDF checklist + starter code
• •Work with me — I build AI for banks and insurance companies

By Cyprian Aarons, AI Consultant at Topiax.