Flowise vs LangGraph

Detailed side-by-side comparison to help you choose the right tool

Flowise

🟡Low Code

AI App Builder

Flowise is an open-source visual builder for LLM apps, RAG pipelines, and multi-agent workflows that you can self-host for free or run on Flowise Cloud.

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Starting Price

Free

LangGraph

🔴Developer

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.

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Starting Price

Free

Feature Comparison

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FeatureFlowiseLangGraph
CategoryAI App BuilderAI agent framework
Pricing Plans22 tiers8 tiers
Starting PriceFreeFree
Key Features
  • Visual node-based builder for AI agents and chatflows
  • Agentflow multi-agent orchestration
  • Chat assistants with RAG and tool calling
  • Graph-based workflow orchestration
  • Deterministic state machine execution
  • Human-in-the-loop workflows

💡 Our Take

Choose Flowise if your team values visual development speed and includes non-engineers who can configure workflows without writing code. Choose LangGraph if you're a senior LangChain developer who needs fine-grained control over agent state machines and cyclic graphs.

Flowise - Pros & Cons

Pros

  • Truly open source; self-host gives you full control of data and prompts
  • Visual canvas dramatically shortens the prototype-to-demo loop
  • Huge integration surface inherited from LangChain and LlamaIndex
  • MCP client support means new tool ecosystems plug in without code
  • Active community: 30k+ GitHub stars, frequent releases, Discord support

Cons

  • Visual graphs get unwieldy at scale; complex flows can become hard to maintain
  • Some breaking changes between versions; pin and test before upgrading
  • Observability and evals are basic compared to dedicated platforms
  • Production deployment (auth, rate limiting, monitoring) is on you for self-host
  • Cloud pricing is competitive but execution limits can bite for chatty agents

LangGraph - Pros & Cons

Pros

  • Open-source library is MIT-licensed and runs anywhere without platform lock-in
  • Native checkpointing makes durable, resumable, human-in-the-loop agents straightforward
  • First-class multi-agent patterns: supervisor, hierarchical, sequential, parallel branches
  • Tight integration with LangSmith for production observability, evaluations, and replays
  • Active maintenance from the LangChain team with frequent releases and strong community

Cons

  • More verbose than LangChain for simple agents — explicit state schemas and edge functions add overhead
  • LangSmith trace pricing ($2.50/1k base traces) is a real cost at production scale
  • LCU + deployment-minute billing makes pricing harder to predict than seat-only competitors
  • Steeper learning curve than role-based frameworks like CrewAI for newcomers
  • Best documented in Python; JavaScript SDK exists but lags in features

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🔒 Security & Compliance Comparison

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Security FeatureFlowiseLangGraph
SOC2✅ Yes
GDPR✅ Yes
HIPAA
SSO✅ Yes
Self-Hosted✅ Yes🔀 Hybrid
On-Prem✅ Yes✅ Yes
RBAC✅ Yes✅ Yes
Audit Log✅ Yes
Open Source✅ Yes✅ Yes
API Key Auth✅ Yes✅ Yes
Encryption at Rest✅ Yes
Encryption in Transit✅ Yes✅ Yes
Data Residencyself-hosted deployments allow user-controlled data residency
Data Retentionconfigurableconfigurable
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