OpenJarvisvsLangflow

Full side-by-side comparison — features, pricing, platforms, and which one wins in 2026.

OpenJarvis

AI Agent Frameworks

Local-first personal AI agents that run with Ollama

Langflow

Automation Platforms

Visual framework for building multi-agent AI apps

FeatureOpenJarvisLangflow
CategoryAI Agent FrameworksAutomation Platforms
PricingFree (open-source)Free (open-source) + Cloud
GitHub Stars
5k
More stars
35k
PlatformsmacOS, Linux, Windows, WSL2, DockerLinux, macOS, Windows, Docker, Web
Key Features
  • Local-first personal AI agents
  • Built-in Ollama support
  • Morning briefing preset
  • Deep research across web and local documents
  • Code assistant preset
  • Local engines: Ollama, vLLM, SGLang, llama.cpp
  • Optional cloud engines
  • Energy, cost and latency-aware routing
  • Visual builder
  • Drag-and-drop
  • Component library
  • API export
  • Multi-model
Pros
  • + Strong fit for Ollama-based local agent workflows
  • + Apache-2.0 open-source project
  • + Ships ready-to-run presets instead of only framework primitives
  • + Supports both local engines and optional cloud escalation
  • + Built around privacy, cost, latency and energy as first-class constraints
  • + Intuitive visual builder
  • + LangChain ecosystem integration
  • + Easy prototyping
  • + Export as Python code
  • + Growing component library
Cons
  • Young v1.0 project with fast-moving docs and releases
  • Local-first does not mean cloud-free unless configured that way
  • Personal-agent presets may need access to sensitive local files, email or calendar data
  • Efficiency claims are project-reported and should be tested on your own workloads
  • Tied to LangChain ecosystem
  • Limited for very custom logic
  • Can be slow for complex flows
  • Still maturing
Tags
open-sourcelocal-firstpersonal-aiagentsollamalocal-airesearchpython
no-codevisuallangchainopen-source

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