Langflow Review: Visual Agent Builder (146k Stars)
TL;DR: Langflow is one of the strongest visual tools for building AI agents and workflows, and its star count puts it near the top of its category. It shines for prototyping and learning. Production deployments need more thought before you commit.
If you have ever tried to wire up an AI agent by hand, you know the drill: a Python file that starts clean and ends up as a wall of glue code you stop wanting to touch. Langflow takes a different route. You drag boxes onto a canvas, connect them with lines, and watch your data move from one step to the next. It looks less like programming and more like sketching a flowchart that happens to run.
That approach has earned it a serious following. The GitHub repository sits near 150k stars as of mid-2026, which makes it one of the most-watched projects of its kind. (The 146k figure in our title was accurate around April; the repo has kept climbing since.) For Australian teams weighing whether to build agents in code or on a canvas, that popularity is a useful signal: a big community means more components, faster fixes, and plenty of people who have hit the same wall you are about to.
The catch is the one that follows every low-code tool around. Building something fast and getting it to survive real traffic are two different problems. We spent time inside Langflow to see where that line falls, and where the visual model starts to fight you instead of help you.
One thing worth flagging up front: Langflow is owned by DataStax, which IBM acquired in a deal announced in early 2025. That ownership does not change the open-source license, but it is context the product pages tend to leave out.
What Is Langflow?
Langflow is a visual builder for AI agents and workflows. It is its own open-source Python framework with its own component system, not a front-end bolted onto LangChain (an older framing that has stuck around longer than it should, LangChain is now just one optional bundle among many). Here is what you get:
- Drag-and-drop interface for building agents
- Pre-built components covering a large library of integrations (LangChain is one of several bundles, not the whole story)
- Custom components, write Python when you need logic the built-ins do not cover
- API export, deploy a flow as a REST endpoint (or an MCP server)
- Real-time testing, debug on the canvas as you build
Price: Free and MIT-licensed. Managed hosting is available through partners. Note: the DataStax-hosted Langflow cloud service shut down on 9 April 2026, so if you read older guides promising "DataStax Cloud hosting," that option is gone, third-party pricing breakdowns point to options like IBM watsonx or Render instead.
Building an Agent
We built a research agent in about 20 minutes:
- Dragged "Chat Input" → "OpenAI" → "Web Search" → "Output"
- Configured the search tool (SerpAPI key)
- Added a "Memory" component for conversation history
- Exported it as an API
- Tested with curl, worked first try
The visual layout made it obvious where data flowed. When something looked off, you could see it on the canvas instead of squinting at a Python traceback. That is the real pitch: you spend less time guessing what your code is doing.
Component Library
Langflow ships a deep component library across these categories. The counts below are our own tally from testing rather than figures pulled from official docs, so treat them as a guide:
| Category | Count | Examples |
|---|---|---|
| LLMs | 15 | OpenAI, Anthropic, Ollama, Cohere |
| Tools | 40 | Search, Calculator, Wikipedia, APIs |
| Memory | 8 | Buffer, Vector, Redis, Postgres |
| Vector Stores | 12 | Pinecone, Chroma, Weaviate, FAISS |
| Loaders | 30 | PDF, CSV, URL, GitHub, Notion |
| Output | 10 | Chat, Text, JSON, File |
Performance
The numbers below come from our own self-hosted testing. We could not find independent benchmarks to confirm them, so read them as ballpark figures from one setup rather than published results:
| Metric | Value |
|---|---|
| Flow execution time | 200-500ms for simple flows |
| Complex multi-agent flows | 2-5 seconds |
| Memory usage | 150-300 MB base |
| Concurrent requests | 50-100 (self-hosted) |
Pros and Cons
| Pros | Cons |
|---|---|
| Very fast prototyping | Complex flows can turn into spaghetti |
| Huge component library | Performance overhead compared to code |
| Good way to learn agent building | Debugging complex flows gets hard |
| Active development | Self-hosting means you maintain it |
| Free and open source | Some components lag behind |
Verdict
Score: 8.4/10 (our subjective rating, other 2026 reviews land lower, some around 7.2/10, so weigh it against your own needs)
Langflow is the fastest way we have found to prototype an AI agent. The canvas is easy to read and the component library covers most of what you will reach for. For production, export to code and run it properly. For learning and quick experiments, it is hard to beat.
One caveat on currency: we tested v1.3 (an early-2025 release). As of June 2026 Langflow has moved well past that, with v1.10.0 shipping on 9 June 2026 (the 1.9 release notes cover much of what changed in between). Expect newer versions to have more components and rougher edges sanded down, so check the current release before you judge it on our notes.
*Published June 16, 2026 | Langflow v1.3 tested (self-hosted)*