When you actually need multi-agent
Before comparing frameworks, the honest question: do you actually need multi-agent orchestration? The answer is often no. Many workflows that look like they need "multiple agents" are better modeled as a single well-scoped agent that calls tools, with orchestration handled by deterministic application code around it.
Multi-agent makes sense when the sub-tasks genuinely require separate context, separate policies, or separate capabilities — not just when the overall workflow is long or branching. A customer support agent that triages, resolves tier-1 issues, and hands off to humans doesn't need to be three agents; it's one agent with multiple tools and an escalation policy.
The tell-tale sign that multi-agent is worth the complexity: you would write distinctly different policy constitutions for different steps of the workflow. If your policy is uniform, your agent should be too.
CrewAI
CrewAI models multi-agent systems as a "crew" of role-defined agents that pass tasks between each other. Each agent has a role, goal, backstory, and tool set. The framework handles message routing, task assignment, and result aggregation.
Where it works well: workflows that decompose naturally into role-based responsibilities (researcher → analyst → writer patterns, or triage → specialist → reviewer patterns). The abstraction is intuitive, the setup is fast, and the code is readable.
Where it struggles: complex branching logic, heavy state across many steps, and production observability. CrewAI's default abstractions are high-level, which is great for prototypes and less great when you need to debug why a specific tool call in step 4 produced an unexpected result. Instrumentation is getting better but is not yet at parity with graph-based approaches.
Our take: good for focused, role-based workflows and great for prototyping. For production deployments with meaningful branching or stateful workflows, we typically reach for LangGraph or build orchestration in application code around a single agent.
LangGraph
LangGraph models multi-agent systems as state machines: a directed graph of nodes, each of which is either an agent or a deterministic step, connected by edges that represent transitions. The state of the workflow is a typed object that nodes read from and write to.
Where it works well: complex workflows with conditional branching, loops, parallel steps, human-in-the-loop interruption, and explicit state. The explicitness is the point — you can see, serialize, and checkpoint the state at every node. Debugging is concrete: what was in state when this node ran?
Where it struggles: fast prototyping. The graph construction is more verbose than CrewAI's crew definition, and for simple workflows the abstraction adds friction without paying back. Team ramp-up on graph-based programming models can take a week or two.
Our take: the right choice for production multi-agent workflows that involve real state, branching, and operational demands. Most of our complex enterprise deployments that genuinely need multi-agent land on LangGraph.
Agno
Agno (formerly Phidata) focuses on agent composability and memory. It provides built-in primitives for agents with memory, knowledge bases, and tool use, plus patterns for composing workflows out of them. The philosophy is "agents with batteries included" rather than a framework-heavy DSL.
Where it works well: teams building AI assistants with persistent memory, knowledge integration, and multi-turn workflows. The abstractions for memory, tools, and RAG are cleanly integrated, and the development experience is fast.
Where it struggles: very complex orchestration with deep branching. Agno is strong on the agent side and lighter on the orchestration side — if your workflow needs LangGraph-style explicit state machines, you'll feel Agno's simpler model as a constraint.
Our take: a great choice for teams building assistant-style agents where memory and knowledge integration are first-class concerns. Less ideal when orchestration complexity is the dominant concern.
The pattern that often wins: one agent + deterministic orchestration
The pattern that wins more often than it should in enterprise deployments is the boring one: a single well-designed agent with a clean tool surface, orchestrated by deterministic application code that handles branching, retries, and state.
Why: orchestration done in application code is trivially debuggable, observable, and testable with standard engineering tools. Agent reasoning is reserved for the steps that actually need reasoning. The contract between application code and agent is explicit.
This doesn't scale to every workflow — genuinely agentic systems where the path through the work is itself reasoned about by the model need framework-level support. But a lot of what gets branded as "multi-agent" is really "agent plus workflow," and the simpler architecture is the better one.
How we choose in GrowTK engagements
In discovery, we map the workflow steps, ask which ones need reasoning, and identify where state flows. If the branching is simple and the state is lightweight, we go single-agent + deterministic orchestration. If state is rich and branching is complex, we go LangGraph. If it's role-based and relatively linear, CrewAI. If assistant-style with heavy memory requirements, Agno.
Across all four choices, the non-negotiable shared properties are the same: every agent operates under an explicit constitution, every tool call is logged, every escalation path is explicit, and production deployments have structured observability from day one. The framework choice is less important than the governance layer that sits around it.