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Find the right AI tool in 2 minutes. Independent reviews and honest comparisons of 890+ AI tools.

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AI app platform🟡Low Code
F

Flowise

Flowise supports visual flow builder, agent orchestration, api deployment for prototyping assistants and low-code agent workflows.

Starting atFree
Visit Flowise →
💡

In Plain English

Flowise supports visual flow builder, agent orchestration, api deployment for prototyping assistants and low-code agent workflows.

OverviewFeaturesPricingGetting StartedUse CasesIntegrationsLimitationsFAQSecurityAlternatives

Overview

Flowise is a ai app platform product intended for prototyping assistants and low-code agent workflows. Its core product areas include visual flow builder, agent orchestration, api deployment. For builders and business teams, the practical value is reducing the amount of manual work needed to move from an idea or recurring process to a usable result. A sensible evaluation should start with one bounded workflow, representative data, and a clear success measure such as turnaround time, output quality, adoption, or reduced handoffs. Teams should also test permissions, export options, collaboration controls, and how easily a human can review or correct the system's work before relying on it in a production process. Flowise is relevant to Model Context Protocol adoption. It acts as an MCP client, which means flowise can use mcp-connected tools in visual agent flows. This can make it easier to connect agent experiences to governed tools or context without building a separate proprietary connector for every client. Pricing could not be reliably extracted from the vendor pricing page during this run, so no price figures are asserted here. Before purchase, verify current plan limits, usage charges, included seats, API access, data retention, support, and enterprise security terms directly with the vendor. This profile is therefore useful as a structured discovery record, but commercial details require manual confirmation. The strongest fit is a team with a concrete ai app platform workflow and an owner who can validate outputs. It is less suitable when requirements are undefined, review responsibility is unclear, or regulated data would be introduced before security and contractual checks are complete.

🦞

Using with OpenClaw

▼

Integrate Flowise with OpenClaw through available APIs or create custom skills for specific workflows and automation tasks.

Use Case Example:

Extend OpenClaw's capabilities by connecting to Flowise for specialized functionality and data processing.

Learn about OpenClaw →
🎨

Vibe Coding Friendly?

▼
Difficulty:beginner
No-Code Friendly ✨

Standard web service with documented APIs suitable for vibe coding approaches.

Learn about Vibe Coding →

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Editorial Review

Strong bridge between low-code builders and developer-owned LangChain-style workflows. Main caution: The /pricing route redirected to sign-in during research, so pricing should be manually verified before publishing.

Key Features

Agentflow for Multi-Agent Orchestration+

Build multi-agent systems with workflow orchestration distributed across multiple coordinated agents. Each agent can have its own tools, memory, and instructions, with handoffs between agents for complex task decomposition.

Chatflow with RAG and Tool Calling+

Build single-agent systems and chatbots with support for tool calling and knowledge retrieval (RAG) from various data sources. Supports document formats including TXT, PDF, RTF, DOC, HTML, CSV, MD, and SQL.

Human-in-the-Loop (HITL) Workflows+

Allow humans to review tasks performed by agents within the feedback loop before final execution. This is critical for regulated industries and high-stakes decisions where AI outputs need human validation.

Observability with Execution Traces+

Full execution traces support Prometheus, OpenTelemetry, and other observability tools out of the box. Track every node execution, LLM call, tool invocation, and token usage across your workflows.

Developer-Friendly API, SDK, and Embed+

Extend and integrate to your applications using REST APIs, TypeScript and Python SDKs, and an embeddable chat widget. Deploy any chatflow as a /api/v1/prediction/:id endpoint with a single click.

Pricing Plans

Freemium / Open Source

View Details →
See Full Pricing →Free vs Paid →Is it worth it? →

Ready to get started with Flowise?

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Getting Started with Flowise

  1. 1Install Flowise locally via npm (npm install -g flowise) or use Docker deployment with official flowise/flowise image
  2. 2Launch Flowise server and navigate to http://localhost:3000 to access the visual builder interface
  3. 3Configure your first LLM provider (OpenAI, Anthropic, or local Ollama) by adding API credentials in the settings
  4. 4Create your first chatflow by dragging a Chat Model node and connecting it to a Simple Chain for basic functionality
  5. 5Test your chatflow using the built-in chat interface, then deploy as API endpoint with one-click deployment
Ready to start? Try Flowise →

Best Use Cases

🎯

Prototyping assistants

⚡

Low-code agent workflows

Integration Ecosystem

33 integrations

Flowise works with these platforms and services:

🧠 LLM Providers
OpenAIAnthropicGoogleCohereMistralOllama
📊 Vector Databases
PineconeWeaviateQdrantChromaMilvuspgvector
☁️ Cloud Platforms
AWSGCPAzureRailway
💬 Communication
SlackDiscordEmailTwilio
📇 CRM
HubSpot
🗄️ Databases
PostgreSQLMySQLMongoDBSupabase
📈 Monitoring
LangSmithLangfuse
💾 Storage
S3
⚡ Code Execution
Docker
🔗 Other
GitHubNotionZapierMake
View full Integration Matrix →

Limitations & What It Can't Do

We believe in transparent reviews. Here's what Flowise doesn't handle well:

  • ⚠Cannot export chatflows as standalone code — applications must run within the Flowise runtime environment
  • ⚠Custom components require TypeScript development knowledge and understanding of Flowise's specific node architecture
  • ⚠No built-in evaluation or testing framework — quality assessment requires external tooling like LangSmith or manual testing
  • ⚠Scaling beyond a single instance requires manual load balancing configuration and shared PostgreSQL/storage setup
  • ⚠Visual canvas becomes cluttered with workflows containing many conditional branches or 50+ nodes, reducing maintainability

Pros & Cons

✓ Pros

  • ✓Apache 2.0 self-hosting offers strong control and flexibility
  • ✓Visual editing speeds up prototypes and stakeholder demos
  • ✓Many connectors cover common model and retrieval stacks
  • ✓Each flow can become an API rather than staying a diagram

✗ Cons

  • ✗Complex canvases can become hard to maintain
  • ✗Self-hosters own authentication, upgrades, monitoring, and scaling
  • ✗Breaking changes require version pinning and testing
  • ✗Cloud execution limits can affect chatty or high-volume agents

Frequently Asked Questions

Do I need to know LangChain to use Flowise?+

It helps significantly. Flowise visualizes LangChain/LlamaIndex components — understanding what a retriever, chain, or agent does makes the visual builder much more effective. You can start with simple chatflows using pre-built templates, but deeper customization benefits from framework knowledge.

How does Flowise compare to Langflow?+

Both are visual LangChain builders, but they target different ecosystems. Flowise is Node.js-based, while Langflow is Python-based — important for deployment preferences and team skill sets.

Can I export Flowise chatflows as code?+

Flowise doesn't directly export chatflows as standalone Python/TypeScript code. Chatflows are stored as JSON configurations that Flowise interprets at runtime via its Node.js engine. If you need standalone code, use the chatflow design as a reference to implement equivalent logic directly with LangChain.

What's the best way to deploy Flowise in production?+

Docker deployment on a cloud VM or container platform (AWS ECS, Google Cloud Run, Kubernetes) is the most common production approach. Use PostgreSQL for persistent storage of chatflow configurations and conversation history.

Is Flowise free to use, and what does the enterprise version offer?+

Yes, Flowise is fully open-source and free to self-host via npm or Docker — install it with a single command (npm install -g flowise) and run npx flowise start. The enterprise tier adds managed hosting, SSO, advanced security, and dedicated support.

🔒 Security & Compliance

—
SOC2
Unknown
—
GDPR
Unknown
—
HIPAA
Unknown
—
SSO
Unknown
✅
Self-Hosted
Yes
✅
On-Prem
Yes
✅
RBAC
Yes
—
Audit Log
Unknown
✅
API Key Auth
Yes
✅
Open Source
Yes
—
Encryption at Rest
Unknown
✅
Encryption in Transit
Yes
Data Retention: configurable
Data Residency: SELF-HOSTED DEPLOYMENTS ALLOW USER-CONTROLLED DATA RESIDENCY

Recent Updates

View all updates →
🔄

Multi-Agent Workflows

v2.1.0

Visual builder support for multi-agent conversations and handoffs.

Feb 14, 2026Source
🦞

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What's New in 2026

Flowise has expanded its Agentflow capabilities for multi-agent orchestration, added Human-in-the-Loop (HITL) workflows for regulated industries, and improved observability with Prometheus and OpenTelemetry support. The platform continues to grow its community marketplace and component library.

Alternatives to Flowise

CrewAI

AI Agents

Open-source Python framework for orchestrating role-playing, autonomous AI agents that collaborate as a 'crew' to complete complex tasks.

Microsoft AutoGen

Multi-Agent Builders

Microsoft's open-source framework for building multi-agent AI systems with asynchronous, event-driven architecture.

LangGraph

AI agent framework

LangGraph is LangChain's open-source framework for building stateful, durable, multi-agent workflows in Python and JavaScript with graph-based control flow.

Microsoft Semantic Kernel

AI Agent Builders

SDK for integrating cutting-edge LLM technology into applications, with support for building AI agents and connecting model capabilities into existing app workflows.

View All Alternatives & Detailed Comparison →

User Reviews

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Quick Info

Category

AI app platform

Website

flowiseai.com
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