How Sinas compares
Most of these solve a different problem
You probably arrived with one tool in mind: n8n, LangChain, Cursor, a hosted assistant API. Most of them are solving a job Sinas is not, and the other way round. So instead of scoring features, each card below says what that tool is for, where it overlaps with us, and when it is the better choice.
Tool by tool
What each one replaces
The comparisons we are asked about most: what each tool is genuinely for, and where Sinas is the better fit.
n8n and workflow automation
A workflow tool moves data between SaaS products when something happens. Sinas holds the application itself: agents whose tools are granted one by one, state in a store with a schema, and access checked at the moment a tool runs rather than when it is offered. It has its own schedules and webhooks, so plenty of teams end up running one, not both.
Choose Sinas when the thing you are building has users, permissions and data of its own, and not just two SaaS tools that need connecting.
n8n
Zapier
Make
Coding harnesses (pi, Claude Code, Cursor)
A harness is the software around a model that makes it an agent: tool dispatch, memory, a sandbox, guardrails. The ones in fashion are built for one developer on one machine, with code as the subject. Sinas is a harness with an application backend attached: users and roles, state that outlives the session, your own database behind reviewed queries, and an API your product calls. Same idea, aimed at software you ship rather than software you are writing.
Choose Sinas when more than one person uses it, and the agent needs roles, persistent state and an API your product can call.
Pi
Claude Code
Cursor
LangChain and LangGraph
Frameworks you build with, in your codebase, on your infrastructure. Sinas is a running instance where OTP login, scoped API keys, role-based permissions, execution history, schema'd state, file collections and a container sandbox already exist, are administered from a console, and export as a single YAML file you can apply to another instance.
Choose Sinas when you would rather configure the runtime than operate it, and still take every resource with you as YAML.
LangChain
LangGraph
Dify, Flowise and the visual builders
Strong for assembling a chat experience quickly. Sinas targets the case where AI is part of a product you sell: a namespace per tenant on one instance, per-tenant API keys, permissions written per resource and scope, and every agent, function and query reviewable in git.
Choose Sinas when you are shipping to your own customers and need tenant isolation, per-tenant keys and access checked when a tool runs.
Dify
Flowise
OpenAI Assistants and hosted agent APIs
One vendor's models, one vendor's runtime, your data in their account. In Sinas the provider is a resource with an endpoint, chosen per agent, so switching to Mistral or to a local model through Ollama is a config change. There is an OpenAI-compatible endpoint too, so your existing client often does not change at all.
Choose Sinas when the model provider should be a config value, including one on your own hardware, and the data stays in your instance.
OpenAI
Bedrock
Vertex AI
Building it yourself
Every AI feature needs authentication, permissions, execution logging, memory, file handling and a safe place to run code. None of it is the feature, and all of it still has to be maintained afterwards. Sinas is that layer, open source, so you can read it before trusting it and leave with your configuration if you outgrow it.
Choose Sinas when the auth, permissions, execution history, memory and sandbox are not the part you want to own and maintain.
Your codebase
Still not sure?