Langflow vs Nexent

A side-by-side look at pricing, capabilities, pros, cons, and our editorial scores.

Tagline

Langflow

Open-source visual builder for LangChain-style AI agents and RAG pipelines.

Nexent

Open-source, zero-code platform for spinning up production-grade AI agents from a single natural-language prompt.

Pricing

Langflow

Freemium· Open-source free; hosted free tier + paid enterprise via DataStax

Nexent

Free· Free, open-source (MIT); self-hosted infra + model API costs apply

Free trial

Langflow

Yes

Nexent

Yes

API

Langflow

Yes

Nexent

Yes

Platforms

Langflow

webslack

Nexent

api

Open source

Langflow

Yes · MIT

Nexent

Yes

GitHub stars

Langflow

155,378

checked 2026-09-29

Nexent

—

Last GitHub push

Langflow

2026-09-29

Nexent

—

First commit

Langflow

2023-02

Nexent

—

Model used

Langflow

Multi-model

Nexent

Multi-model (OpenAI-compatible: any LLM/Embedding/VLM/STT/TTS)

Best for

Langflow

Pick Langflow if you're a Python developer who wants a visual scratchpad for LangChain-style agents and RAG flows without giving up code-level control.

Nexent

Pick Nexent if you want a self-hosted, MCP-native agent platform you can describe in plain language instead of wiring up in a canvas editor.

Not for

Langflow

Skip it if you're a non-technical user hoping to build production agents by dragging boxes, or if you need a stable, slow-moving platform.

Nexent

Skip it if you want a managed SaaS, a polished low-code UI, or a stable v1 API to build a production product on this quarter.

Editorial score

Langflow

8.1 / 10

Nexent

7.2 / 10

Use cases

Langflow

agent-prototypingrag-pipelinesworkflow-automationllm-orchestrationchatbot-development

Nexent

multi-agent-orchestrationzero-code-agentsknowledge-base-ragenterprise-automationmcp-tool-integration

Pros

Langflow

Open source and self-hostable with a permissive license
Visual graph maps cleanly to LangChain concepts developers already know
Broad LLM and vector-DB coverage out of the box
Every node is editable Python, not a locked black box
Flows deploy as APIs without extra glue code

Nexent

MIT-licensed and fully self-hostable on Docker or Kubernetes
Prompt-to-agent generation skips drag-and-drop canvas entirely
Model-agnostic across LLM, embedding, vision, STT and TTS slots
Built-in multi-tenancy, RBAC, A2A protocol, and agent marketplace
Knowledge base ingests 20+ document formats out of the box

Cons

Langflow

Fast-moving codebase; version upgrades can break existing flows
Visual metaphor still assumes LangChain-level familiarity
Hosted tier is tied to DataStax's Astra ecosystem

Nexent

Self-host only; no managed cloud offering to point at
Young project (v2.0); APIs and abstractions still evolving
Documentation is partly Chinese-first and uneven in English

Website

Langflow

www.langflow.org

Nexent

nexent.tech

Editorial score: rule-based, 0–10, from AI-assisted profile inputs (see /methodology) — not a user rating; “—” means unscored. “Not listed” means we have no record of it, not that it is absent. GitHub figures and prices carry the date they were checked or captured; prices are shown as published, unconverted.

Pick Langflow if

✅ Open source and self-hostable with a permissive license
✅ Visual graph maps cleanly to LangChain concepts developers already know
✅ Broad LLM and vector-DB coverage out of the box
✅ Every node is editable Python, not a locked black box

Pick Nexent if

✅ MIT-licensed and fully self-hostable on Docker or Kubernetes
✅ Prompt-to-agent generation skips drag-and-drop canvas entirely
✅ Model-agnostic across LLM, embedding, vision, STT and TTS slots
✅ Built-in multi-tenancy, RBAC, A2A protocol, and agent marketplace

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