Head to head
Knolo vs Make
Describe what you want vs. wire it node by node.
vs
The verdict
Knolo is the better choice for most people evaluating both. You describe the outcome in plain language and an assistant configures the agents, minds, integrations, and triggers for you. Every agent shares persistent Minds, hands off to other agents natively, runs on schedules or webhooks, connects to 3,000+ apps through Pipedream Connect, and can call any REST API on the fly through Knolo's Discover API. Make has genuine strengths: its visual debugger is best in class, its trigger ecosystem covers more instant webhook types, and its routers and iterators give explicit deterministic control if you already think like an automation engineer. Most people evaluating both will get more from Knolo because the work they actually want automated is reasoning over documents, drafting responses, classifying inbound messages, and running agent workflows on a schedule, not wiring flowcharts by hand.
Knolo builds itself from a plain-language description; Make requires you to design and wire the flowchart first.
Knolo uses a single credit pool with no per-operation metering; Make charges credits per module run, so a 12-step scenario running every minute burns 17,280 credits a day.
Knolo covers 3,000+ apps through Pipedream Connect and can also call any REST API on the fly through its Discover API; Make ships ~3,000 native modules but has no runtime API discovery.
Knolo agents share persistent Minds across the whole workspace; Make's memory is scoped to individual AI Agent modules inside one scenario.
Knolo agents hand off to other agents natively with a shared audit trail; Make chains agents through webhooks between scenarios.
Make wins on its visual debugger and the polish of specific app-triggered scenarios.
Both run cloud-native, so neither platform requires local setup or Docker.
Knolo vs Make, line by line
Dimension
Knolo
Make
How you build it
Knolo wins
Describe the outcome in plain language; the assistant configures agents, minds, integrations, and triggers for you.
Drag modules onto a canvas, connect them with lines, configure each module's input mappings and filters.
Genuine no-code experience
Knolo wins
No nodes, no IF blocks, no expressions. Plain-language configuration end to end.
No-code in name, but real scenarios require understanding routers, iterators, aggregators, and Make's mapping syntax. Make Code module lets you drop in JS or Python for advanced logic.
Knowledge that persists across runs
Knolo wins
Every agent shares Minds (file and structured). Durable, searchable, indexed knowledge that accumulates across runs and is queryable by any agent in the space.
Make Data Stores hold key/value records between scenario runs. Make AI Agents (2026 GA) added a dedicated memory layer inside the hexagon agent module, scoped to that agent.
Handling unstructured input and judgment calls
Knolo wins
Agents reason over Minds natively: classify, extract, decide, and route based on document or table content without wiring a parser.
Make AI Agents (GA in 2026) reason within a scenario; older flows need explicit OpenAI or Anthropic modules plus filters and routers to act on the results.
Agent-to-agent collaboration
Knolo wins
Native. Any agent can call another agent (callableAgentIds), creating parent and child runs with shared minds and a single audit trail.
AI Agents can call other agents as tools inside a scenario, but cross-scenario orchestration is still done through webhook chaining.
App integrations (count and breadth)
Knolo wins
3,000+ pre-built integrations through Pipedream Connect (Gmail, Slack, Notion, HubSpot, Drive, Salesforce, and the rest), plus a Discover API that lets agents call any REST API on the fly with no pre-built connector.
~3,000 native modules with rich per-app actions, triggers, and instant webhooks. HTTP module as fallback for anything not covered. No runtime API discovery.
Custom or on-the-fly integrations
Knolo wins
Discover API. Agents autonomously read API docs and call any REST endpoint at runtime, no pre-built connector required.
HTTP module plus Make SDK lets you wrap any REST or GraphQL API. 2026 added unlimited connection types for SDK apps. Setup is manual.
Pricing structure
Knolo wins
Credit-based. Buy credits, spend them on agent runs and LLM usage. No per-task metering, no monthly operation caps that force tier upgrades.
Subscription plus per-credit (formerly 'operations'). Switched to credits Aug 2025; every module run costs at least one credit, AI modules can cost more. Free 1,000 credits/mo, Core $9, Pro $16, Teams $29, Enterprise custom.
Triggers and scheduling
Make wins
Native cron and one-off triggers per agent, plus webhook triggers.
Mature trigger ecosystem: instant webhooks, polling, cron, mailhooks, plus app-specific instant triggers across hundreds of modules.
Hosting model
Even
Cloud-only, fully managed.
Cloud-only, fully managed (US and EU regions).
Native document and knowledge storage
Knolo wins
Minds are first-class. File minds (PDFs, transcripts, images, articles) auto-parsed and indexed; structured 'table' minds with schemas. Every agent and assistant reads from them.
Data Stores hold structured records but are not a document or RAG store. RAG requires external modules (Pinecone, Supabase, OpenAI vector store).
Branching, routing, data transformation
Make wins
Handled by agent reasoning and native Python code execution. Describe the rule, the agent decides, or a Python snippet runs in a sandbox with direct access to Knolo's API.
Routers, filters, iterators, aggregators, array operators, error handlers. The deepest visual no-code logic engine on the market.
Native code execution
Knolo wins
Agents run Python in a real-time sandbox with direct access to Knolo's API: modify minds, query table minds with pandas, trigger actions.
Make Code module runs JavaScript or Python snippets inside a scenario (added 2025 to 2026).
Debugging and observability
Make wins
Full run transcripts, artifact history in minds, message-level audit trail per agent run.
Best-in-class scenario debugger: per-module input and output inspection, history per execution, scenario recovery from auto-saved blueprints, search inside module output.
Choose Knolo if…
Solopreneurs and small teams who want AI automation without learning a node editor
Operators building agent systems that reason over documents, transcripts, and structured tables
Teams that need agents to hand off work to each other on a schedule, against a shared knowledge base
Workflows where the 'logic' is judgment over unstructured input rather than deterministic routing
Bursty or high-volume workloads where per-operation pricing would compound expensively
Builders who want to ship a working system in an afternoon by describing it
Choose Make if…
Ops teams that already think in flowcharts and want explicit visual control over every data hop
Complex multi-app scenarios with routers, iterators, and aggregators that need to be deterministic
Workflows that lean on Make's deep, polished native module library (Salesforce, HubSpot, Shopify, NetSuite, etc.)
Teams who want a mature visual debugger to inspect every step's input and output
Predictable, low-volume integrations that fit comfortably inside the generous free or Core tier
When should you choose Knolo?
Choose Knolo when the work you want automated is more than plumbing. Classifying inbound messages, drafting replies from a knowledge base, qualifying leads against a brand voice, summarising long documents, extracting structured data from PDFs, routing tasks based on what a file actually says: these are reasoning jobs, and reasoning is what Knolo agents do natively. You describe the workflow in plain language and the assistant configures the agents, the minds they read from, the integrations they call, and the schedule they run on. There is no canvas to wire, no router node to configure, no data mapping panel to fight with.
Knolo also does the things Make is known for. It connects to 3,000+ apps through Pipedream Connect (Gmail, Slack, Notion, HubSpot, Google Drive, Salesforce, Shopify, and the rest), and where Make stops at its native module catalog, Knolo agents can read any REST API's documentation and call it at runtime through the Discover API. Agents run on cron schedules and webhook triggers, chain to other agents with a shared audit trail, execute Python in a real-time sandbox with direct access to Knolo's API, summarise YouTube videos and PDFs, generate images natively, and store everything they produce back into shared Minds so the next agent in the chain has context without a copy-paste step.
Pricing structure matters here too. Make charges credits per module run, so every step in every scenario is a metered event. A ten-step scenario that runs a thousand times a month costs 10,000 credits before you count AI module usage. Knolo uses a single credit pool spent on agent runs and LLM usage, with no per-step meter and no forced tier upgrade when you cross an operation threshold. For bursty or high-volume agent workloads, that is a structural cost difference, not a marketing line.
When does Make still win?
Make wins in three narrow places. The first is its scenario debugger, which is the most mature in the no-code market: per-module input and output inspection, per-execution history, scenario recovery from auto-saved blueprints, and search inside module output. If your workflow must be inspectable step by step and replayable exactly, Make's canvas gives you that in a way Knolo's agent transcripts do not.
The second is Make's app-specific trigger ecosystem. Instant webhooks, mailhooks, and hundreds of app-specific instant triggers are the result of years of connector work. Knolo covers cron schedules and webhook triggers, but Make ships more granular trigger types out of the box for specific SaaS apps.
The third is strict deterministic visual branching. Routers, filters, iterators, aggregators, and array operators give a level of explicit control that agent reasoning does not replicate. If your workflow is a strict flowchart where the same input must always take the same path, and the audit requirement is 'point at this node and explain exactly what it does,' Make's model fits that requirement better than an agent making the call in natural language.
The real difference: described outcome vs. wired flowchart
The real difference is who designs the system. In Make, a human designs the flowchart and the AI is a step inside it. In Knolo, you describe what you want the system to do and the platform designs the configuration: which agents to create, which minds to build, which integrations to connect, which schedule to run on. Make added AI Agents to GA in 2026 and shipped Maia as an in-platform co-builder, but the unit of work is still the scenario. In Knolo, the unit of work is the agent, and the agent is the thing you describe.
That difference compounds. A Make scenario is only as good as the person who wired it, so if you do not think in node graphs you plateau quickly. A Knolo space is only as good as your ability to describe what you want, so any domain expert who can write a clear paragraph can build a working system. Teams that pick Knolo are usually the ones who realised the bottleneck in their automation was never the integration count, it was the reasoning layer they kept trying to fake with filters and IF nodes.
Knolo is the better choice for people who want AI that does more than one thing. A single Knolo space runs research agents, sales triage agents, support agents, content agents, and internal knowledge assistants against the same Minds, with agents handing work off to each other automatically. That is a platform choice, not a tool choice. Make is a strong automation tool inside a specific mental model. Knolo is a workspace where the whole automation surface, from knowledge to agents to integrations to scheduling, lives together and configures itself as you describe what you need.
Frequently asked questions
Is Knolo a replacement for Make?
Yes, for most cases. If the work you want automated involves reading documents, classifying messages, drafting replies, extracting structured data, or running agent workflows on a schedule, Knolo replaces Make and does the job with less setup. Knolo also covers the integration surface Make is known for, with 3,000+ apps through Pipedream Connect and a Discover API that reaches any documented REST endpoint. The narrow cases where Make still fits better are strict deterministic branching audited node by node, app-specific instant triggers, and per-module debugging inside a visual canvas.
How does Knolo compare to Make on integrations?
Knolo has two integration layers. Pipedream Connect provides 3,000+ pre-built integrations covering Gmail, Slack, Notion, HubSpot, Google Drive, Salesforce, Shopify, and the rest of the same core apps Make supports. The Discover API lets agents read any REST API's documentation and call it at runtime with no pre-built connector required. Make's ~3,000 native modules are polished per-app with rich triggers and granular actions, but Knolo's practical ceiling is any documented REST API on the internet, not the size of a fixed catalog.
How does Knolo's pricing compare to Make's credit and operation model?
Make charges by operation, so every module run in every scenario counts as at least one credit and AI Agent operations can cost several. A ten-step scenario that runs a thousand times a month uses 10,000 credits before you add AI usage. Knolo uses a single credit pool spent on agent runs and LLM usage, with no per-step meter and no forced tier upgrade when you cross an operation threshold. For high-frequency or bursty agent workloads this scales more predictably. For low-volume scenarios that fit inside Make's free 1,000-credit tier, Make can be cheaper at the very bottom end.
Did Make catch up on AI agents in 2026?
Make shipped AI Agents to GA in early 2026 with a hexagon module that combines brain, memory, tools, and knowledge. It added Maia as an in-platform co-builder, 350+ AI apps including GPT-5.5, Claude, and Gemini 3.5 Flash, and Make Skills for Claude. The gap is not whether Make has AI agents but where they sit. Make's agents live inside a scenario you still wire visually, with memory scoped to one hexagon at a time. Knolo's agents are the unit of work: they share persistent Minds across the whole space, hand off to each other with a shared audit trail, and schedule themselves.
Can Knolo do the kind of branching logic Make is known for?
Knolo handles branching through a mix of agent reasoning and native Python code execution. You describe the rule in plain language and the agent decides, or the agent runs a Python snippet in a real-time sandbox with direct access to Knolo's API and Minds. Make handles branching through routers, filters, iterators, and aggregators on a visual canvas, which is more deterministic for workflows that must always take the same path for the same input. For fuzzy judgment work like classifying emails by intent, Knolo is faster to build and more flexible. For strict audit-trail-grade routing across many known cases, Make's flowchart is more explicit.
Do I need to self-host either platform?
No. Both run fully in the cloud. Make is cloud-only with US and EU regions. Knolo is cloud-native by design and does not require Docker, a VPS, or a local install. If self-hosting is a hard compliance requirement, n8n is the platform built for that; Knolo and Make both assume a managed cloud model.
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