Build APIs, AI Agents, and Automations visually.
Deploy anywhere instantly.
Design backends on a visual canvas — REST APIs, AI chatbots, webhook automations, or anything in between. Deploy to Tensorify's cloud in one click, or run on your own laptop, VPS, or server. Your agent, your infrastructure, your rules.
You've tried everything.
Nothing quite works.
Automation tools can't handle AI agents. AI frameworks can't handle webhooks. Tensorify does both — visually, debuggably, and without lock-in.
Zapier hits a ceiling. Scripts rot.
You need conditional branching, real business logic, and the ability to debug when it breaks. Zapier gives you a flowchart. Custom handlers work until they don't — and then you're tailing logs at 2am with no idea what happened.
AI agent frameworks need PhD-level glue code
LangChain, LlamaIndex, CrewAI — they all require stitching together LLMs, tools, memory, and deployment in Python. No visibility into what the agent actually does. When it hallucinates or loops, good luck debugging it.
No one tool does both
Automation platforms don't do AI agents. AI builders don't do webhooks and REST APIs. You end up with Zapier for automation, Langflow for agents, and custom code holding it all together.
Two paths, one canvas
Whether you're automating webhooks or building an AI agent, the workflow is the same: design visually, deploy anywhere, debug with full visibility.
Automation Track
APIs, Webhooks, IntegrationsDesign the logic on a visual canvas
Drag and connect nodes — webhook trigger → verify payload → branch on conditions → call APIs → respond. See the entire flow at once instead of reading scattered handler files.
Deploy to cloud or self-host
Deploy to Tensorify Cloud with one click, or install the CLI runner on your laptop, VPS, or server. One runner manages all your workflows. Zero inbound ports.
Debug node by node
Click a node and run the workflow up to that point. It stops — showing every variable, input, and output. No more guessing what the data looked like three steps in.
AI Agent Track
Chatbots, RAG, AssistantsPick a template or start from scratch
Start with an AI chatbot template or drag an AI Agent node onto a blank canvas. Configure the LLM, system prompt, and tools visually.
Add memory, tools, and RAG
Wire Window Memory for conversations, Qdrant for vector retrieval, or MCP Tool Provider for external integrations. All visual, no framework code.
Chat from the Playground
Open the Playground, send a message, and watch your agent respond with streaming. Deploy as an OpenAI-compatible endpoint when ready.
Or skip the steps. Open the AI Assistant in any canvas and describe what you want — the AI handles design, plugin selection, and wiring for both tracks.
The only visual builder for both automation and AI agents
Automation tools don't do AI. AI builders don't do webhooks. Tensorify does both — with debugging, export, and self-hosting.
| Feature | Tensorify | Zapier | n8n | Flowise / Langflow |
|---|---|---|---|---|
| Visual workflow builder | Canvas + 16 plugins, exports Python | Visual but limited logic | Visual, flexible, Docker required | Visual for AI only, no webhooks/APIs |
| AI agents with tool calling | ReAct loop, multi-provider LLMs, tools, streaming | No native AI agents | Basic LLM nodes | Core feature, visual agent builder |
| OpenAI-compatible endpoint | Any workflow as a chat completions model | Not supported | Not supported | Flowise supports, Langflow partial |
| Conversation memory | Window memory + Qdrant vector (RAG) | Not supported | Limited via code | Built-in memory options |
| Webhook / REST API automation | Full HTTP triggers, branching, transforms | Core feature, limited branching | Core feature, flexible | Not designed for automation |
| Node-by-node debugging | Stop at any node, inspect every variable | Execution logs only | Node execution logs | Limited trace views |
| Interactive Playground | API + Chat modes, SSE, session memory | Not available | Not available | Chat testing available |
| Self-host / run anywhere | Cloud + CLI runner (laptop, VPS, server) | Cloud only | Docker self-host | Docker self-host (some) |
| Python code export | Full portable Python export | No export | JSON export only | No code export |
| MCP tool ecosystem | MCP Tool Provider plugin | Not supported | Not supported | Partial MCP support |
| SSE streaming | Native streaming for agents and APIs | Not supported | Not supported | Supported |
APIs, agents, and automations.
One canvas. Full visibility.
Built for developers building backends — from Stripe webhook handlers to AI chatbots with RAG and tool calling.
AI Agent Builder
Build chatbots, assistants, and reasoning agents with tool calling, memory, and streaming. Deploy as OpenAI-compatible endpoints accessible from any SDK.
Visual Workflow Canvas
Drag nodes, wire edges, build production backends. 16+ plugins for triggers, actions, logic, and AI — from webhook handlers to full RAG systems.
OpenAI-Compatible Deployment
Any workflow becomes a model endpoint. Set the API Trigger to openai-chat and connect from Python, Node.js, or curl using the standard chat completions format.
Interactive Playground
Test APIs and chat with agents directly from the canvas. SSE streaming, session memory, and full tool call visibility — before you deploy anything.
Selected-Node Debugging
Run your workflow up to any node and stop there. Inspect every variable, every input, and every output directly in the browser. No terminal. No log hunting.
Run Anywhere
Managed cloud or self-hosted CLI runner on your laptop, VPS, or server. Export to pure Python you fully own. One runner manages all your workflows.
Build with AI and build AI
Use the AI Assistant to build workflows from English. Or use the AI Agent plugin to build chatbots, assistants, and RAG systems that your users interact with.
Build with AI
Every canvas has a built-in Claude-powered assistant. Describe what you want and it builds or modifies your workflow.
Reads your canvas
The AI inspects your live workflow — every node, connection, and setting — before suggesting changes.
Generates and patches
Ask for a new workflow and it builds the full graph. Ask to add a step and it patches just that part.
Knows every plugin
Full access to the plugin catalog — picks the right plugin and configures it correctly.
Build AI
The AI Agent plugin turns your workflow into an intelligent agent with tools, memory, and OpenAI-compatible deployment.
AI Agent with ReAct loop
Multi-provider LLMs (OpenAI, Anthropic, DeepSeek), tool calling, structured output, and streaming.
Memory and RAG
Window memory for conversations, Qdrant vector store for RAG. MCP servers for external tool integrations.
OpenAI-compatible endpoint
Set protocol to openai-chat and your agent becomes a model accessible from any OpenAI SDK.
Pricing
Simple, transparent pricing
Start free for local development. Scale to your team when you're ready.
Starter
For local development
- Unlimited workflows
- 2 members (shared workspace)
- Self-hosted runner
- Visual workflow builder
- Node-by-node debugging
- 500 managed calls/month
- 1 hour managed execution
- 50 AI credits/month
- 3-day execution history
Pro
For individual developers
- Everything in Starter
- 2 members (shared workspace)
- Unlimited webhook URLs
- 50,000 managed calls/month
- 167 hours managed execution
- 1,000 AI credits/month
- 50 concurrent executions
- 30-day execution history
- Priority support
Team
For growing teams
- Everything in Pro
- Unlimited members
- 200,000 managed calls/user
- 40 hours execution/user
- 5,000 AI credits/user/month
- 200 concurrent executions
- Shared quota pooling
- Role-based permissions
- 90-day execution history
- Python Code Export (Zero Lock-in)
2 users = $50/mo (collaboration features included)
Enterprise
For large organizations
- Everything in Team
- 2,000,000 managed calls/month
- 500 hours managed execution
- 50,000 AI credits/month
- 1,000 concurrent executions
- SSO & SAML
- Self-hosted deployment
- SLA guarantee
- HIPAA/SOC2 compliance
- Python Code Export (Zero Lock-in)
- 365-day retention
All tiers include self-hosted runner execution for total privacy. Cloud deployment is optional.
Read the docs to learn more.
Built for builders
who've hit the wall.
Whether you're automating webhooks, building AI agents, or maintaining inherited handlers — Tensorify gives you visibility, control, and infrastructure ownership.
The Builder
You've rebuilt the same webhook boilerplate too many times already.
The AI Builder
Building AI agents shouldn't require PhD-level framework knowledge.
The Integrator
A webhook failure can stay invisible until customers feel it.
The Maintainer
You inherited webhook code that works until it suddenly doesn't.
Debug Loop
The selected-node test loop is the real differentiator
Move faster by eliminating the "push and pray" cycle. Start a runner on your infrastructure, stop the workflow exactly where you want, and inspect exactly what happened.
Start your runner on your machine or VPS
After `tensorify init`, run `tensorify runner start` so your self-hosted runner listens for test and deploy commands from the design canvas.
Feed it real, mock, or captured data
Use a mock, empty, or real captured Stripe webhook payload depending on what you are trying to reproduce.
Stop the workflow exactly where you want
Run the workflow up to any specific node. It stops there, letting you inspect the state before it moves downstream.
See every variable. No guessing.
Use the UI to inspect exactly what the data looks like at that specific moment, without blind logging or production deploys.
Social Proof
Loved by developers and integrators
Developers are building production workflows with Tensorify.
"Tensorify saved me from rewriting webhook handlers for the fourth time. I built the entire Stripe checkout flow in 20 minutes and actually debugged it when it failed."
Sarah Chen
Full-Stack Developer, E-commerce Startup
"We hit Zapier's logic ceiling hard. Tensorify gave us visual branching, and we run everything on our own infrastructure with a self-hosted runner."
Marcus Rodriguez
CTO, B2B SaaS Platform
"The node-by-node debugging is a game changer. I can see exactly what my data looks like at every step. No more tailing logs at 2am wondering what went wrong."
Emily Zhang
Backend Engineer, FinTech Company
Common Questions
Everything you need to know about the platform.
Stop debugging webhook failures
at 2am.
Design the workflow visually. Run it on your own infrastructure. Stop at any node when something looks wrong. Ship integrations that do not break at 2am.