AI agent platform comparison: n8n, LangGraph, Lindy

Short answer: n8n, LangGraph, and Lindy are three different categories of tool, not three candidates for the same slot. "AI agent platform" is a label doing a lot of work right now. It covers visual workflow builders, developer frameworks, and hosted assistants, and those are not the same product category. Comparing them on a single scorecard produces a meaningless answer. The useful question is what kind of work you are trying to get done, and who will own the thing after launch. This piece looks at n8n, LangGraph, and Lindy as three different tools, with a comparison table, a pilot checklist, and no absolute winner.

n8n: AI workflows on a visual canvas

n8n documents AI workflows that connect model providers, tools, and memory, with support for multiple models. You build the workflow on a visual canvas, which makes the shape of the automation visible to anyone who looks at it.

The trade-off is ownership. A visual workflow still needs someone to watch it: credentials expire, model providers change, and steps fail in ways that need a human to notice. Before adopting n8n, decide who that person is and budget their time. The visual approach lowers the skill floor for building. It does not remove the operational burden of running.

LangGraph: an orchestration runtime for stateful agents

LangGraph describes itself as a low-level orchestration framework and runtime for long-running, stateful agents. Its documented features include durable execution, streaming, and human-in-the-loop patterns, and it lets you combine deterministic steps with LLM steps in one flow.

The phrase to notice is "low-level." This is a toolkit for developers building an application, not a turnkey no-code service. It fits when the agent has to survive interruptions, wait on people, and keep state over time, and when you have engineers who want control over how that happens. If nobody on the team writes code, this is the wrong starting point.

Lindy: a hosted assistant built around where you already work

Lindy is a hosted team AI assistant, described by its vendor as centered on Slack and connections to the tools a team already uses. The appeal is obvious: it arrives where conversations already happen instead of asking the team to adopt a new surface.

One honest caveat: everything in this section is the vendor's description of its own product. We have not run independent tests, and claims on a product site should be treated as claims. If you evaluate Lindy, verify the specific behaviors you care about in a trial, with your own tools and your own data.

How to choose

No winner, by design. As an editorial rule: if the work is a set of defined steps with AI inside them, n8n's visual approach is the natural category. If the work is a long-running agent with state, retries, and human checkpoints, LangGraph is the category for a team with developers. If the work is giving the team an assistant inside Slack, Lindy is the category to evaluate, carefully. You can combine two tools for different jobs when the pilot shows a clear boundary.

A pilot checklist before you commit

1. Name the workflow and its trigger. "When this happens, do that." If you cannot finish the sentence, the pilot is not ready.
2. Decide the failure path first. What happens when the model is wrong? A hypothetical: an assistant drafts supplier emails and a person approves each one before it sends.
3. Map the data. Decide what the workflow may touch, what leaves your systems, and who reviews that choice.
4. Pick the category, not the brand: visual workflow, developer runtime, or hosted assistant.
5. Build one end-to-end path with a hypothetical example: an inbound email, a drafted reply, a human approval step.
6. Run the pilot alongside the manual version for enough representative runs to measure failures and repair time.
7. Ask who maintains this in month six, and what they will need to do it.
8. Expand by one step at a time, and only after the previous step has run clean for a while.

FAQ

Is one of these the best AI agent platform? No. They are different categories with different owners and different failure modes. The best pick is the one that matches your workflow and the team that will run it.

Can a non-technical team use these tools? n8n's visual canvas lowers the barrier to building, but someone still has to maintain the workflows. LangGraph is low-level by its own description and assumes developers. Lindy is a hosted assistant you configure, which asks the least of the team.

Do we need to choose one and commit? No. You can pair a hosted assistant with a workflow tool when their responsibilities are distinct and the pilot supports it. Start with one pilot, prove it, then extend.

Are vendor claims about capabilities enough to decide? Treat them as a starting list, not evidence. A pilot on your own tasks is more informative than relying on feature pages alone. If you want a hand scoping that pilot, see our AI automation services or get in touch.

Sources: n8n docs, Advanced AI; LangChain docs, LangGraph overview; Lindy.