AI Agent Builder Tools Compared: n8n, Flowise, Langflow

The right AI agent builder depends on who builds and where the agent runs: pick n8n if the agent lives inside business automations, Flowise or Langflow for visual LLM apps, and a code SDK such as the OpenAI Agents SDK, Claude Agent SDK or LangGraph if developers want full control. Hosted platforms sit on top of these when you would rather not run the agent loop yourself. The differences that matter most are memory, tools, hosting and licence terms, and those are what this page compares.

Every fact below comes from the vendor's own docs, pricing page or licence file, linked inline and checked October 2026. We have not scored these tools on our own benchmarks; the matrix records what each vendor documents, not how fast or accurate it is.

Models are the other half of the cost. Our LLM API pricing comparison covers what OpenAI, Anthropic, Gemini and open models charge per token, which matters more than the builder's own price once an agent is busy.

AI agent builder comparison matrix

The matrix below sticks to four questions: how the agent remembers, how it calls tools, where it runs, and what the licence lets you do. Each cell is taken from the docs linked in the sections that follow.

One pattern is clear from the docs: every option now supports the Model Context Protocol, so tool access is less of a differentiator than it was. The real splits are licence terms, whether memory is a setting or something you engineer, and who operates the runtime.

What is the best AI agent builder for your project?

"Best" depends on two axes: whether the people building are comfortable in code, and whether the agent is mainly about connecting business apps or mainly about LLM reasoning. Place your project in the grid, then read the matching section.

If you are still unsure, the order that usually costs least to change is: prototype visually, learn what memory and tools the agent truly needs, then decide whether to keep it in the visual tool or rebuild it in code.

Which no code AI agent builder should you use?

The three visual builders all let you wire a model, memory and tools on a canvas. They differ in what surrounds the agent.

n8n

n8n's AI Agent node takes a chat model and one or more tools, and the agent decides which tools to call; the docs say you must connect at least one tool sub-node. For memory, n8n's memory guide lists Simple Memory for the current session plus nodes for Redis Chat Memory, Postgres Chat Memory, Motorhead, Xata and Zep, and a Chat Memory Manager for advanced cases. Its MCP Client Tool lets an agent call tools on an external MCP server. The strength is everything around the agent: the same workflow can trigger on a form, call the agent and write results to a CRM.

Licensing is the part to read twice. n8n uses its Sustainable Use License, a fair-code licence that allows use for your own internal business purposes or for non-commercial or personal use, and n8n notes it is not an OSI open source licence. Plan prices are on n8n's pricing page and broken down in our n8n pricing comparison; for a self-hosted starting point with local models, see the n8n AI starter kit.

Flowise

Flowise's Agentflow V2 Agent node lets you choose a model, the tools it may use (each with an optional human-input flag), document stores or vector stores for knowledge, and memory with settings for memory type, window size and token limit. Its tools and MCP tutorial covers prebuilt tools, Custom Tools and Custom MCP for any MCP server. You can self-host with npm or Docker, per the getting started guide, or use Flowise Cloud. The licence file puts most of the code under Apache 2.0, with the enterprise directory under a commercial licence.

Langflow

Langflow's Agent component has built-in chat memory that is on by default and uses Langflow storage, per its Message History docs; the Message History component adds external stores such as Redis or Mem0 when you need them. The MCP Tools component exposes an MCP server's functions as agent tools, and Langflow can also serve your flows as an MCP server. You can run Langflow Desktop, the Python package or Docker, and the repository is MIT licensed. For a side-by-side of the two LLM-first builders against n8n, see Flowise vs n8n.

Code SDKs: OpenAI, Claude and LangGraph

Code SDKs trade the canvas for version control, tests and full control of the loop. They are free to use as libraries; you pay for the model calls and wherever you run them.

OpenAI Agents SDK

The OpenAI Agents SDK ships for Python and TypeScript under the MIT licence. Its sessions keep conversation history across runs, with SQLite and Redis implementations among the options. The tools guide lists function tools, hosted OpenAI tools such as web search, file search and code interpreter, agents as tools, and MCP; the TypeScript MCP guide names hosted, Streamable HTTP and stdio servers. Hosted tools run on OpenAI's side, so they suit OpenAI models specifically.

Claude Agent SDK

Anthropic's Agent SDK overview describes it as the same tools, agent loop and context management that power Claude Code, for Python and TypeScript. It includes built-in tools to read, write and edit files, run commands and search the web, plus hooks, subagents, MCP, permissions and sessions you can resume or fork. The licence section says use is governed by Anthropic's Commercial Terms of Service, and Anthropic asks third-party products to use API key authentication rather than claude.ai login. If you already use Claude Code, our guide to Claude Code access options explains the plans and API route.

LangGraph

LangGraph is MIT licensed and builds agents as graphs of steps. Its persistence docs split memory cleanly: checkpointers for short-term, thread-scoped memory, and stores for long-term memory across threads. LangChain now ships MCP support in its own namespace, replacing the separate adapters package. For hosting you can run it yourself or use LangSmith Deployment; LangChain's docs say self-hosted deployments of that platform need an Enterprise plan.

Hosted platforms: when someone else runs the agent

A hosted platform runs the loop, the sandbox or the builder for you. That removes server work but ties you to the vendor's roadmap, as the Agent Builder change below shows.

Sources: Claude Managed Agents overview, LangSmith self-hosted overview and OpenAI ChatKit guide, all checked October 2026. OpenAI's docs say existing Agent Builder users can continue during a transition window and point new work to ChatKit with your own server-side agent.

If you want to turn an agent into something customers pay for, our AI SaaS program covers packaging, pricing and shipping AI tools as products. For patterns that hold up across all of these builders, see our guide to agentic AI workflows.

Mistakes to avoid when choosing an AI agent builder

• Choosing on tool count alone. Every option here documents MCP support, so most services you need can be reached from any of them. Compare memory, hosting and licence instead.
• Skipping the licence. MIT, Apache 2.0, a fair-code licence and commercial terms allow very different things once you charge customers. Read the licence file in the repository, not a summary of it.
• Leaving memory on defaults. Session memory that lives in a local database or in process memory may vanish on restart or stay on one server. Decide where conversation data is stored before real users arrive, and check what your privacy policy says about it.
• Giving the agent every tool. More tools mean more ways to call the wrong one. Start with the few tools the task needs, and use the approval or human-input options the builder offers for anything that writes or sends.
• Building on a hosted product without an exit. OpenAI's Agent Builder deprecation shows hosted builders can change. Prefer options that let you export the agent as code or a portable file.
• Ignoring model cost. The builder is often free or cheap; the model calls are not. Estimate tokens per conversation before you pick a model tier.

Appendix: first agent in each, from the docs

These are condensed from each vendor's quickstart, checked October 2026. They show the shortest documented path, not a tested build; follow the linked page for current versions and requirements.

Sources: Flowise getting started, Langflow installation, OpenAI Agents SDK quickstart, Claude Agent SDK quickstart and LangGraph persistence.

AI agent builders: FAQ