Not an agent. Not memory. Not search.

It depends which layer you mean. LangChain and CrewAI orchestrate the steps inside an agent. Zapier, n8n and Make orchestrate fixed trigger-to-action recipes between apps. Daiton sits underneath both and runs whole agentic loops — lead follow-up, the morning brief, a QA gate on every deliverable — where the agents reason over one shared, sourced brain and adapt when the inputs change, and anything irreversible waits for your approval. For a product team already running Claude Code, Cursor and Copilot, the missing piece is usually not another orchestrator; it is the system of record all of them read from.

Integration platforms — MuleSoft, Zapier, n8n, Make — move data between systems and leave you to decide what is true. Daiton discovers your stack from your email receipts (CRM, finance, code, docs, ads, automations) and unifies it into one validated, cited system of record, where every fact carries its source, who asserted it, its confidence and its freshness. Correct a fact once and every agent gets the correction, forever. If what you need is a pipe, use an iPaaS. If what you need is one answer every agent can trust, that is the layer Daiton adds.

Linear, Notion and ClickUp are where remote dev teams track the work, and Daiton does not replace them — it connects to them. What it adds is the layer a tracker does not have: a live command center showing every agent and session in flight with status, tokens and full traces, and loops that turn the work you repeat — the morning brief, the weekly KPI report, a QA gate on every deliverable — into something your agents run on a schedule. The ticket still lives in your tracker; the context every agent needs lives in Daiton.

Claude Code, Cursor, GitHub Copilot, Windsurf, Cline and Aider are the coding agents teams actually run today, and Daiton competes with none of them. The problem is that they do not know what the others did: every session starts from zero, so you re-explain the company, the client and the codebase daily. Daiton connects to all of them over MCP and gives them one shared, sourced brain — so memory is cross-agent instead of trapped in one tool, and the same document is not re-read, and re-billed, fifteen times a week.

A hub is only as good as what it knows. Zapier, n8n and Make run fixed trigger-to-action recipes; Daiton runs the whole agentic loop, with agents that reason over the shared brain and adapt when the inputs change. It also finds your toolchain for you: Daiton reads your email receipts and connects the tools you already pay for — HubSpot, Salesforce, Close, Stripe, QuickBooks, GitHub, Linear, Notion, Slack, Zapier itself — then puts every agent working across them on one live board.

Compare them on the four things that actually differ. One: does a fact carry its source, its asserter and a confidence score, or is it an extracted blob? Two: is the memory shared across every agent, or scoped per app and per run? Three: can you see every agent in flight — status, tokens, full traces — or only after-the-fact logs? Four: does anything irreversible wait for your go? The table above scores memory APIs, agent frameworks and enterprise search against Daiton on exactly those axes.

That category is narrower than it sounds. Per-app memory layers (Mem0, Zep, Letta, Cognee, Supermemory) and built-in AI memory (ChatGPT memory, Claude memory) give one app, or one model, a better memory. A shared brain means every agent and every teammate reads and writes the same validated facts, portable across models. In Daiton that brain is bitemporal — it tracks how a fact changed over time and flags contradictions rather than waiting to be asked — and answering from it costs roughly 190 tokens and about 4ms, against 12,000–134,000 tokens to re-read the raw sources every time. That gap is where the 60–700× dedupe figure comes from.