The visual canvas for durable AI workflows. Drag, connect, deploy. Durable execution handles the retries, the long-running jobs, and the cost tracking you'd otherwise build yourself.
They work in the demo. They work in the screenshot. They work until an API times out, or the model returns an empty string, or the job runs for more than 30 seconds, or someone tries to actually use the thing at scale. Then they stop working.
Making an AI agent that holds up in production is a second, much harder job — vector databases, retry logic, async queues, cost tracking, observability. The people who understand what AI can do for their business shouldn't have to build all of that themselves.
You describe the workflow. Knitch runs it. Every step is checkpointed, every failure retries automatically, every token is tracked. The agents you build on the canvas run for hours and cost what you expect.
Pick any one and someone sells it. Stitched together, they're a workflow you can actually ship.
A working agent takes under ten minutes. Webhook input, AI node with agent mode, output. No infrastructure to configure. The hard parts are already built.
Knowledge bases, vector search, hybrid retrieval, all pre-wired. Drop in documents or connect a source and the agent can search and reason over your content. No pgvector. No embedding pipeline.
Not an OpenAI wrapper. Assign Claude, GPT, Haiku, Gemini, or an open model per node. The right model for each step, with the cost and latency you picked for a reason.
A Dynamic Orchestrator that takes a goal and spawns sub-agents to handle it. Parallel fan-out, synthesis, evaluator loops. Not on the roadmap — shipping today.
Every run produces a full trace. Cost by node, tokens in and out, latency per step, tool calls made, embeddings retrieved. Point at the specific LLM call that's expensive and decide what to do about it.
Every weave on Knitch fits into one of three shapes. Start with the simplest that still solves the problem.
One AI node. It reasons, picks tools, loops until done. Best for focused tasks: triage, classification, extraction, generation.
Multiple AI nodes in sequence or branching. Each handles a phase and passes structured output to the next. Best for pipelines with distinct stages.
One Orchestrator node takes a goal, spawns sub-agents to handle it, and synthesizes the result. Best for complex tasks where sub-tasks can't be predicted up-front.
An email arrives. An agent reads it, classifies the issue, routes it to the right team, creates the ticket, drafts a response. Autonomously. With full cost tracking. Using a different model for each step.
Built on Knitch in eight nodes and about seven minutes of wiring. The full weave is below.
Open it on the canvas and it already knows your whole workflow — every node, your knowledge bases, your stored data, and what each node's last run cost. Ask why a step is slow, which model a node should use, or where a number came from. It searches with the same retrieval your agents use, points to the exact node it means, and tells you what to change and why. It reads and explains, it doesn't touch your workflow.
sonnet-4.6. haiku-4.5 is a better fit, near-identical accuracy on classification at a fraction of the cost, roughly $0.0003 versus $0.0036 per call. On a high-volume node like this, that difference is most of its spend.
Open Classify's panel and switch its model to haiku-4.5, and keep sonnet on @Draft Reply, where wording quality matters.
It answers using the exact models, retrieval, and tools your nodes run on — so what it tells you matches what the workflow actually does.
Every node it names is a live chip. Click @Draft Reply and the canvas centers on that node.
Tokens, cost, and what each step did — pulled from your real executions, not guessed. Ask where a number came from and it shows you.
Zapier can't do agents. Trigger.dev wants you to write TypeScript. Knitch is for the person in between.
| Zapier · Make | Trigger.dev · Inngest | Knitch | |
|---|---|---|---|
| Who builds it | Anyone | Developers only | AI-aware, not a developer |
| AI capability | Basic AI steps | Full control, code required | Full agentic capability, no code |
| RAG / knowledge | Not available | Build it yourself | Pre-built, day one |
| Multi-agent | Not available | Build it yourself | Built in, visual |
| Model choice | Limited | Full control | Multi-provider, per node |
| Cost observability | Minimal | Depends | Full trace per run |
| Time to first agent | N/A | Days to weeks | Under ten minutes |
Every run produces a complete trace: cost breakdown by node, tokens per agent and sub-agent, latency per step, tool calls made, embeddings retrieved. No black box. When the bill shows up, you'll know why.
You focus on what the agents should do. Knitch handles the rest.
Every node checkpointed. Failures retry automatically with exponential backoff. Long-running jobs survive deploys and crashes — a weave that starts on Monday can finish on Thursday.
Classify & Route. Extract & Transform. Agentic Loop. Knowledge-Augmented. Parallel Orchestration. Persistent Chat Agent. Start from a pattern or build from scratch.
Four trigger types. No cron daemon to run. No server to SSH into. Point a URL at Knitch and an agent picks up from there.
Use your Anthropic, OpenAI, Google, xAI, DeepSeek, or Mistral account. Knitch passes through. You pay what the model costs — nothing stacked on top per token.
Usage-based, tracked by the same telemetry that powers cost observability. The bill matches the trace.
Build, test, and ship your first agent. Generous limits on runs, tokens, and knowledge base size. Everything you need for a real proof of concept.
Start free →For teams running agents in production. Usage-based pricing that tracks cost observability — you pay for what ran, and the trace tells you why.
Choose Pro →SSO and SAML, audit logs, RBAC, bring-your-own-key, dedicated support, and a custom SLA. Talk to us.
Talk to us →See the full tier breakdown on the pricing page.
Ten minutes to a working agent. No credit card. No infrastructure.