Langflow Pre-Release v1.0.0rc0

Feature complete, stability, and control… check! ✅. Ready for Takeoff 🚀

We are almost there! Langflow v1.0.0rc0 is our final pre-release. Today we’re releasing a feature-complete update with all the new components, features, and flows we’ve been working towards, with much improved stability. It is a big step towards our vision of creating the most powerful visual framework for building multi-agent and RAG applications out there — open-source, Python-powered, and fully agnostic. The official 1.0 release is very close!

The Vision

Langflow 1.0 isn’t just a showroom of pre-built AI components; it’s the manufacturing plant! This release delivers the fundamental building blocks required to create, customize, and share your own reusable AI components. It brings control over advanced AI pipelines with a variety of models, data sources, and APIs.

Let’s dive into what’s new and why it matters! 🤿

Same Component, Multiple Outputs

Components can now have more than a single output, allowing for unique flexibility in creating complex flows. The game-changer is output routing — it allows for so many new capabilities it’s almost silly to describe!

1. Branch to one or more subsequent components;
2. Apply logic operations like if/else and exploit decision-making;
3. Create classification models that choose between paths;
4. Enable the development of agent architectures from scratch;
5. Build an orchestrator that routes between agents.

Components can now have multiple outputs

Flow + Composition

Some components run in real-time, while others take their time… Langflow brings the best of both flow and composition, allowing components to work with both immediate execution and modular building. This means that easy flows are really easy and the complex ones are still possible!

• Flow: Data is processed by one component and then passed to the next component in line for immediate execution.
• Composition: Allows components to not only forward data but also share states for modular building.

For example, a flow can sequentially process text, and after a few steps, trigger an agent. It can access functions that wait to be called or to respond. This blend of flow and composition brings an unprecedented level of flexibility and control to data workflows in LLM-based apps and agents that use multiple models and APIs working together to achieve tasks.

Components can forward data, state, and access functions

Output Preview

Each component now includes an output visualizer that opens a pop-up screen, allowing you to easily inspect and monitor transmissions between components. It provides instant feedback on your workflows, letting you see results as they are processed. 🔍

Preview the output of a component to inspect results at each step

Inputs and Outputs Handling

Inputs and outputs finally make more sense to us, and hopefully to you too.

We’re proposing Langflow-native types to keep things consistent, but not limited to use any Python type. For instance, a Chat Input component sends out what we call a Message object (text + metadata like date, time, and sender), but maybe you want to introduce external types from your favorite Python package? Go wild. Each native type will have their own visualization modes and will evolve according to new integrations added.

And there’s so much more — an entirely redesigned code structure for custom components, multi-modal capabilities and even editable memory sessions! We’re working 24/7 to finalize everything and we’re beyond excited to have you on this journey with us. Keep sending your feedback on GitHub and Discord, and thanks for being a part of this! 🌟

Sincerely,

The Langflow Team 🚀