AutoGen vs Cassandra for AI agents: Which Should You Use?

AutoGen and Cassandra solve different problems. AutoGen is an agent orchestration framework for building multi-agent LLM workflows; Cassandra is a distributed database built for high-write, low-latency, always-on data storage. If you are building AI agents, pick AutoGen for the agent logic and Cassandra only when you need durable state at scale.

Quick Comparison

When AutoGen Wins

Use AutoGen when the core problem is agent behavior, not data storage.

• •

  You need multi-agent collaboration

  • •Example: one agent drafts a claims summary, another checks policy language, a third verifies compliance.
  • •AutoGen gives you GroupChat and GroupChatManager so agents can pass work around without you hand-rolling the conversation loop.
• •

  You need tool execution inside a controlled workflow

  • •Example: an underwriting assistant that calls pricing APIs, runs Python calculations, then asks a human for approval.
  • •AssistantAgent plus UserProxyAgent is the right shape here because it cleanly separates reasoning from execution.
• •

  You want to build iterative task loops

  • •Example: an agent writes code with code_execution_config, runs it in a sandbox, inspects output, then retries.
  • •That feedback loop is where AutoGen earns its keep.
• •

  You are prototyping agentic product behavior quickly

  • •Example: internal support triage bots or document analysis assistants where the team needs to test prompts, tools, and handoffs fast.
  • •AutoGen gets you to working behavior faster than designing your own orchestration layer.

When Cassandra Wins

Use Cassandra when the hard problem is storing massive amounts of state reliably across nodes.

• •

  You need durable memory for lots of agents

  • •Example: millions of chat events or interaction logs that must survive restarts and be queryable by tenant and time.
  • •Cassandra handles append-heavy workloads better than stuffing everything into a relational database.
• •

  You need predictable low-latency writes at scale

  • •Example: every agent action emits an event record for audit or replay.
  • •Cassandra’s write path is built for this kind of workload.
• •

  You are operating in a multi-region environment

  • •Example: a global insurance platform where agents run close to users in different regions.
  • •Cassandra’s replication model fits distributed deployments better than most single-primary databases.
• •

  Your access pattern is known in advance

  • •Example: fetch all interactions for tenant_id + session_id, or all events for a policy number over the last 30 days.
  • •Cassandra works well when you model tables around queries instead of trying to ad hoc search everything later.

For AI agents Specifically

My recommendation is simple: use AutoGen as the orchestration layer and Cassandra as the persistence layer only if your agent system needs serious scale or auditability. Do not try to choose between them as substitutes; they sit at different layers of the stack.

If you are building one or ten production agents, start with AutoGen and store state in Postgres unless you already know you need Cassandra’s distribution model. Bring in Cassandra when event volume explodes or when compliance requires high-throughput immutable history across regions.

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.