LangGraph vs Cassandra for real-time apps: Which Should You Use?

LangGraph and Cassandra solve different problems, and confusing them will waste time.

LangGraph is an orchestration framework for building stateful LLM agents with nodes, edges, checkpoints, and tool execution. Cassandra is a distributed wide-column database built for high-write, low-latency data storage at massive scale. For real-time apps, use Cassandra for the data plane and LangGraph only if the app’s “real-time” behavior depends on agent reasoning or workflow orchestration.

Quick Comparison

When LangGraph Wins

Use LangGraph when the “real-time app” is really a live decision system with branching logic.

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  You need an agent that reacts to incoming events and chooses tools dynamically.

  • •Example: a claims triage assistant that reads a policy update event, calls a retrieval tool, then routes to fraud checks or straight-through processing.
  • •StateGraph is the right abstraction here because you want explicit nodes and transitions, not a pile of ad hoc async functions.
• •

  You need durable multi-step workflows with recovery.

  • •LangGraph’s checkpointing lets you persist state between steps using checkpointers like MemorySaver or database-backed persistence.
  • •That matters when an LLM call fails mid-flow and you want to resume from the last valid node instead of restarting everything.
• •

  You need human approval in the loop.

  • •Real-time banking workflows often require “pause here until analyst approves.”
  • •LangGraph handles this style of flow well because the graph can stop at a node boundary and continue later with updated state.
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  You are building an assistant where context management is the core problem.

  • •MessagesState, reducers like add_messages, and explicit state updates make it easier to control what the model sees next.
  • •This is useful for support bots or internal ops copilots where conversation history drives every next action.

LangGraph wins when correctness comes from orchestration. It is not your primary datastore; it is your control plane.

When Cassandra Wins

Use Cassandra when the app needs to serve lots of users with predictable latency under heavy write load.

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  You need to store fast-changing application state at scale.

  • •Examples: user presence, session metadata, device telemetry, transaction events.
  • •Cassandra handles this well because writes are append-friendly and reads stay fast when your partition key matches your access pattern.
• •

  You need horizontal scale without turning every query into a bottleneck.

  • •With proper modeling around partition keys and clustering columns, Cassandra gives you low-latency reads across many nodes.
  • •That is what you want for dashboards refreshing every second or event feeds serving thousands of concurrent users.
• •

  You need time-ordered access patterns.

  • •Tables modeled by tenant + bucket + timestamp work well for real-time event streams.
  • •Use CQL features like TTLs to expire transient records automatically.
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  You care about operational durability more than workflow logic.

  • •Replication factor settings and consistency levels like ONE, QUORUM, or LOCAL_QUORUM let you tune availability versus consistency.
  • •That makes Cassandra a strong fit for payment activity logs, alert streams, and audit trails where losing write throughput is worse than having eventual consistency.

Cassandra wins when the hard part is storing and serving data reliably under pressure. It does not reason about business steps; it stores them.

For real-time apps Specifically

If your app needs instant state reads/writes at scale, pick Cassandra. If your app needs dynamic decision-making across multiple steps before producing an outcome, add LangGraph on top of your system. The clean architecture is usually both: Cassandra as the durable backend store and LangGraph as the orchestration layer for agentic flows.

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.