Which AI agent framework should I use?

Picking an AI agent framework is less about which is "best" and more about which matches the shape of your work. A single agent that needs to remember your client preferences is a different problem from a pipeline that coordinates five tools with handoffs. The frameworks below solve different problems well — and we build on most of them, so this is a comparison, not a pitch for one.

Start with the quiz if you want a fast recommendation, or read the table and the "how to choose" section for the nuance.

The comparison at a glance

This is the short version. The row that answers the question in the title is"Wins when"; the rest of the page fills in the why.

Hermes vs OpenClaw — the one most teams actually face

If you only read one section, read this. These two cover the majority of business-agent builds, and the choice comes down to coordination.

Choose Hermes when one agent can own the whole job. It keeps persistent memory across every run, writes and refines its own skills, and gets sharper each week. If the work is "handle this inbox," "write this recurring report," or "qualify these leads the way we always do," a single learning agent is simpler and cheaper than orchestrating a team.

Choose OpenClaw when the work is a relay race — the agent has to hand off between systems and sub-agents. A lead pipeline that enriches a CRM record, drafts an email, pings Slack, and books a meeting is a coordination problem. OpenClaw's orchestration model handles the handoffs and shared state that a single agent would fumble. We go deeper inHermes vs OpenClaw.

CrewAI and LangGraph — for engineering-led teams

Both are excellent if you have engineers who want to own the build. CrewAImodels work as roles and crews — intuitive for prototyping multi-agent systems in Python, and a common choice when a dev team wants to move fast without designing the orchestration from scratch. We reach for it when the client's own team will maintain the thing and just wants a strong starting point.

LangGraph models the agent as an explicit graph with state, which makes complex, branching, auditable control flow far easier to reason about and debug. If your workflow has loops, human checkpoints mid-flight, or rules about what can happen after what, LangGraph's structure pays for itself. The trade-off is real: more flexibility means more code to write and maintain, and a steeper ramp for non-engineers. For many business workflows the orchestration is real but not that complex, which is why we often land on OpenClaw or a light custom layer instead of standing up LangGraph. SeeCrewAI alternatives andLangGraph agent development for the longer version.

n8n — when the job is connecting your SaaS stack

n8n is not an "agent framework" in the autonomous sense, but it comes up constantly because most business automation is really "move data between these tools." If your workflow is mostly triggers and connectors — new row → summarize → post to Slack — n8n with AI nodes gets you there with almost no code, and your ops person can read and tweak the flow.

The limit is depth. The moment the logic gets conditional, needs memory across runs, or has to make a judgment call, n8n starts fighting you — you end up bolting on code anyway. We point teams at n8n when the task is "wire these apps together," and at a real agent framework when the task is "decide and act." Knowing which one you have saves weeks of forcing the wrong tool.

n8n is also not the only buy-instead-of-build option, and it is aimed at internal plumbing rather than customer conversations. If the work is a support or sales conversation on a channel a vendor already covers, compare theno-code AI agent platforms before you pick any framework at all — a platform you can be live on this week beats a framework you have to staff. Framework choice barely moves the calendar either way:the first working agent lands in days on any of these, and the difference shows up in the hardening weeks that follow.

Custom build — when nothing off the shelf fits

Sometimes the right answer is a bespoke architecture. We built for a healthcare-adjacent client who needed role-based access, full audit logging of every action, and a model-agnostic setup so they were never locked to one provider. No off-the-shelf framework checked all three cleanly, so we built the agent layer directly on their infrastructure.

A custom build is more work up front and needs someone to own it, but it's the only path when the requirements are genuinely unique or regulated. We scope this in thecustom AI agent development engagement — and we're straight about it: if a standard framework would do, we'll say so and save you the build.

How we actually decide for a client

The framework choice sounds technical; in practice it follows from three questions we ask in every blueprint. Take a B2B team we talked to: thirty demo requests a week, data already in HubSpot and Gmail, and a rep who wanted to approve the send. That's one agent, memory helps, low coordination — Hermes, and we said so in the first call.

Contrast a logistics firm whose "lead" was really a handoff chain across four internal systems with approvals at two points. No single agent could own that; it needed OpenClaw's orchestration. Same goal — fewer dead leads — completely different framework. The shape of the work decided it, not a preference. If you're unsure where you land, that's exactly what the free blueprint is for.

How to choose — a decision checklist

• One agent can own it, and it needs memory? → Hermes.
• Multiple tools with handoffs and shared state? → OpenClaw.
• Engineering team wants to own a multi-agent prototype? → CrewAI.
• Complex, branching, auditable control flow? → LangGraph.
• Mostly moving data between SaaS apps, low code? → n8n.
• Unique, regulated, or no framework fits? → Custom.