Langflow vs Vellum
Compare coding AI Tools
Low code builder for agentic and RAG apps with visual nodes deployments and MCP servers that is open source and easy to self host for teams.
Vellum is an AI agent building platform that combines a prompt playground, evaluation tools, and hosted agent apps so teams can iterate on LLM workflows with debugging and knowledge base support, starting with a free tier and upgrading for more credits.
Feature Tags Comparison
Key Features
- Visual node editor that builds agent and RAG graphs without boilerplate
- Open source core that you can self host with your own keys
- Live testing panel for prompts retrievers and tool calls
- Exports and imports as JSON for version control and reviews
- Deploy as an API or shareable UI for quick stakeholder testing
- Supports major LLMs vector stores and tool libraries
- Free and Pro plans: Pricing starts at $0 with 50 credits and Pro at $25 with 200 builder credits so solo builders can scale testing
- Prompt playground: Compare models side by side and iterate prompts systematically instead of relying on subjective testing
- Evaluations framework: Run repeatable quality tests at scale to detect regressions and track improvements across prompt versions
- Hosted agent apps: Share working agents with teammates through hosted apps for demos
- reviews
- and stakeholder feedback cycles
Use Cases
- Prototype an LLM answer engine with retrieval and feedback
- Design an agent that calls APIs and checks constraints
- Let analysts tweak prompts without touching backend code
- Share an internal UI for reviews and red team sessions
- Export flows to git for code review and change tracking
- Spin up demos for sales or research without devops work
- Agent prototyping: Build an agent by chatting with AI then refine logic with low code steps and controlled prompt versions
- Prompt iteration: Compare LLM outputs side by side and select prompts that improve accuracy and reduce unwanted variation
- Regression testing: Run evaluations on a saved dataset before release to catch quality drops after model or prompt changes
- RAG apps: Attach a knowledge base and test retrieval behavior with representative questions and strict document scope rules
- Stakeholder demos: Publish hosted agent apps so product and compliance reviewers can test behavior without local setup steps
- Model selection: Evaluate providers and self hosted options with the same tasks to choose the best cost and latency mix for production
Perfect For
LLM engineers data teams product managers educators and startups who want a visual builder that still exports real artifacts
product managers, ML engineers, software engineers, data scientists, AI platform teams, prompt engineers, QA and reliability teams, startups building LLM features, teams shipping agent workflows
Capabilities
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