what is an agent swarm?

an agent swarm is many simple agents whose local interactions produce useful collective behaviour with no central controller, borrowed from swarm intelligence research on ants and flocking. in current llm practice the word is used loosely for any group of agents that hand work between each other, which is not really a swarm.

what are the top 10 agentic frameworks?

the ten that matter are langgraph, crewai, autogen, the openai agents sdk, the claude agent sdk, llamaindex workflows, smolagents, pydanticai, semantic kernel, and mastra. they split into graph-based control, role-based teams, provider sdks, and typed application frameworks. all are free.

what is a multi-agent system?

a multi-agent system is several autonomous agents sharing an environment, each with its own goals and information, coordinating or competing toward an outcome no single one controls. the term comes from 1980s distributed ai research. language models made it commercially interesting, but the coordination problems are the old ones.

what is the best multi-agent system?

it depends on whether you want to build one or run one. among frameworks, langgraph leads on explicit control and crewai on speed to a working team. among platforms, the differentiator is not the model but memory, permission scoping, isolation, and whether there is a gate before irreversible actions.

aldena learnupdated2026-08-04

what is the difference between crewai and agent swarm?

crewai is a python framework that models agents as a crew: named roles, assigned tasks, and a sequential or hierarchical process. agent swarm is a pattern, and also openai's now-superseded library, where lightweight agents hand control to each other with no coordinator. crewai is structured, swarm is emergent.

the two designs side by side

	crewai	swarm pattern
structure	roles, tasks, and an explicit process	agents that hand off to each other
coordination	sequential or hierarchical, declared upfront	decided at runtime by whoever holds control
mental model	a team with a project plan	a relay where control is passed
predictable	yes, mostly	less so, by design
debugging	inspect the failing task	replay the handoff chain
best for	repeatable processes with known stages	routing problems where the next specialist depends on the input

crewai in one paragraph

You define agents with a role, a goal, and a backstory, define tasks with expected outputs, and assemble them into a crew with a process. Sequential runs tasks in order. Hierarchical adds a manager agent that delegates and reviews. The structure is declared before anything runs, which makes it easy to reason about and easy to explain.

The trade-off is rigidity. When a task needs a stage nobody declared, crewai does not improvise well, and you go back and edit the crew.

swarm in one paragraph

Openai's Swarm was an experimental library with two primitives: agents with instructions and tools, and handoffs where one agent transfers the conversation to another. No orchestrator, no shared plan, no task list. Control moves with the conversation. It has since been superseded by the Agents SDK, which keeps handoffs and adds guardrails and tracing.

The trade-off is legibility. Handoff chains are easy to write and hard to reason about once they branch, and a loop between two agents passing control back and forth is a bug you find on the invoice.

which one to reach for

Crewai when the process has stages you can name: research, then draft, then review, then publish. The declared structure is the value.

Swarm-style handoffs when the right specialist depends on the input and you cannot enumerate the routes. Customer triage is the canonical fit.

Neither if you have not yet run a single agent on twenty real tasks and written down where it failed. The most common expensive mistake in this category is reaching for multi-agent before proving one agent is insufficient.

the thing both leave to you

Permissions, spend caps, durable memory, audit, and a gate before irreversible actions. Neither framework provides them. That work is identical in every team that builds it, it takes a quarter rather than a weekend, and it is the actual reason homegrown agent systems stall.

how this works in aldena

Aldena is the crewai shape with the leftover list already built. Roles are prebuilt, eleven of them, and coordination is an org chart you arrange rather than a process you declare in code, so a manager delegates down its own line and a reviewer checks the result.

The parts the frameworks skip are the product: isolated rooms with their own server, durable memory at 50 private and 100 shared entries, tool permissions per agent, and an approval gate on by default before anything irreversible.

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