Langflow

Visual low-code framework for building and deploying LLM-powered applications and agents.

4.2 (6)

Pregledal Daniel Nikulshyn·Posodobljeno maj 2026

RAG Python Agents Open Source Low-Code LLM API Visual Builder

Pregled

Langflow is an open-source visual development environment for designing applications built on large language models. Through a drag-and-drop interface, users can wire together prompts, models, vector stores, memory, tools, and custom logic to create chatbots, RAG pipelines, and autonomous agents without writing extensive boilerplate code. Each flow can be tested directly in the editor and exported as an API endpoint, making it suitable for both rapid prototyping and production deployment. Langflow supports a wide range of providers and integrations, including major LLMs, embedding models, and databases, and lets developers extend functionality with custom Python components when more control is needed.

Ključne funkcije

Drag-and-drop flow builder
Built-in support for major LLM providers
Integrated RAG and vector database connectors
Agent and tool orchestration
API export for deployment
Custom component creation in Python

Primeri uporabe

Prototype LLM Chatbots Visually

Quickly design and test chatbot flows by dragging prompts, models, and memory components into a visual canvas without writing extensive boilerplate code.

Build RAG Pipelines

Connect vector databases, embedding models, and LLMs to create retrieval-augmented generation workflows that answer questions over custom knowledge bases.

Deploy Flows as Production APIs

Export completed flows as API endpoints, enabling teams to integrate LLM-powered functionality into existing applications and production systems.

Orchestrate Autonomous Agents

Wire together tools, models, and custom Python components to build agents that can reason, call external services, and execute multi-step tasks.

Prednosti in slabosti

Prednosti

Open-source with active community
Intuitive visual interface speeds up prototyping
Broad integrations with LLMs, vector stores, and tools
Flows can be exposed as APIs for production use
Extensible with custom Python components

Slabosti

Complex flows can become difficult to manage visually
Learning curve for users new to LLM concepts
Self-hosting requires some technical setup

Ocene

4.2

Povprečje iz 6 ocen.

Prijavi se za oddajo ocene.

Leila Hassan

Solid for our team

We rolled this out across the team last quarter and open-source with active community. Built-in support for major LLM providers fits neatly into how we already work, and aPI export for deployment removed a step we used to do by hand. Learning curve for users new to LLM concepts, which is the main caveat, but it has held up under daily use.

Pierre Dubois

Does the job

Pretty happy overall. API export for deployment just works and extensible with custom Python components. Learning curve for users new to LLM concepts can be annoying, but no dealbreakers — I'd recommend it to a friend without hesitating.

Ingrid Bauer

Compared a few options

Evaluated this against two competitors. Where it wins: custom component creation in Python and broad integrations with LLMs, vector stores, and tools. On balance the feature set — especially integrated RAG and vector database connectors — justifies the 5 stars for our use case.

Tariq Aziz

Compared a few options

Evaluated this against two competitors. Where it wins: agent and tool orchestration and flows can be exposed as APIs for production use. Where it lags: self-hosting requires some technical setup. On balance the feature set — especially built-in support for major LLM providers — justifies the 4 stars for our use case.

Grace Okafor

Compared a few options

Evaluated this against two competitors. Where it wins: drag-and-drop flow builder and open-source with active community. Where it lags: complex flows can become difficult to manage visually. On balance the feature set — especially agent and tool orchestration — justifies the 4 stars for our use case.

Liam O’Connor

Compared a few options

Evaluated this against two competitors. Where it wins: built-in support for major LLM providers and open-source with active community. Where it lags: complex flows can become difficult to manage visually. On balance the feature set — especially custom component creation in Python — justifies the 4 stars for our use case.