AutoGen vs Cassandra for multi-agent systems: Which Should You Use?

AutoGen is a framework for orchestrating LLM-driven agents in Python. Cassandra is a different class of tool: a distributed database, not an agent framework, so it only enters this comparison if your multi-agent system needs durable state, event history, and high-write persistence behind the agents.

For multi-agent systems, use AutoGen for orchestration and Cassandra for storage. If you have to pick one for agent behavior, AutoGen is the actual answer.

Quick Comparison

When AutoGen Wins

AutoGen wins when the problem is agent orchestration, not data persistence.

• •

  You need multiple LLM agents to collaborate

  • •Example: one agent gathers facts from internal docs, another drafts a customer response, a third checks policy compliance.
  • •AutoGen’s GroupChat and GroupChatManager are built for this exact flow.
• •

  You want tool-using agents with controlled handoffs

  • •AssistantAgent plus function/tool execution lets you wire agents to APIs like CRM lookup, claims systems, or pricing engines.
  • •This is the right pattern when each agent has a role and a bounded set of actions.
• •

  You need human-in-the-loop workflows

  • •UserProxyAgent is useful when an approval step matters before an action is executed.
  • •In banking or insurance, that matters for payout approval, KYC review, or exception handling.
• •

  You are prototyping the reasoning layer

  • •AutoGen gets you from idea to working multi-agent loop fast.
  • •If you’re validating whether “planner + executor + reviewer” works for your use case, AutoGen is the correct starting point.

Example shape:

from autogen import AssistantAgent, UserProxyAgent

planner = AssistantAgent(name="planner", llm_config={"model": "gpt-4o"})
executor = AssistantAgent(name="executor", llm_config={"model": "gpt-4o"})
human = UserProxyAgent(name="human", human_input_mode="ALWAYS")

That’s already closer to a real multi-agent control plane than anything Cassandra gives you.

When Cassandra Wins

Cassandra wins when the problem is state durability at scale, not agent logic.

• •

  You need massive write throughput

  • •Multi-agent systems generate logs fast: prompts, responses, tool calls, traces, retries.
  • •Cassandra handles append-heavy workloads better than most relational databases once volume climbs.
• •

  You need distributed availability

  • •If your agents run across regions or services and you can’t afford a single database bottleneck, Cassandra fits.
  • •Its replication model is designed for always-on systems.
• •

  You need long-lived conversation or event history

  • •Store every turn of every agent interaction with TTLs or partitioned keys.
  • •That’s useful for auditability in regulated environments where traceability matters more than ad hoc querying.
• •

  You want predictable horizontal scaling

  • •Add nodes when load grows.
  • •For teams running many concurrent agent sessions with high ingest rates, that operational model is cleaner than forcing a relational DB to behave like an event store.

Example schema idea:

CREATE TABLE agent_events (
    tenant_id text,
    session_id text,
    ts timeuuid,
    agent_name text,
    event_type text,
    payload text,
    PRIMARY KEY ((tenant_id, session_id), ts)
) WITH CLUSTERING ORDER BY (ts DESC);

That gives you durable session history without pretending Cassandra is an orchestration engine.

For multi-agent systems Specifically

Use AutoGen to coordinate agents and Cassandra to persist their state. AutoGen gives you AssistantAgent, UserProxyAgent, GroupChat, and GroupChatManager, which are the primitives you actually need for planning and delegation.

Cassandra does not compete here; it complements the stack by storing transcripts, tool outputs, checkpoints, and audit logs at scale. If your goal is building a real multi-agent system in banking or insurance, start with AutoGen and add Cassandra when persistence becomes a production requirement.

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.