These are the essential, official links that every Langflow developer and contributor should bookmark. They are the most authoritative sources for information, downloads, and updates.
- Official Website: https://www.langflow.org/
- Documentation: Comprehensive guide covering installation, quickstarts, tutorials, and release notes for the framework. https://docs.langflow.org/
- Core GitHub Repository: The main source code repository (MIT license) for the Langflow framework. https://github.com/langflow-ai/langflow
- PyPI Package: The official Python package for installing Langflow via
piporuv. https://pypi.org/project/langflow/ - Desktop Application: A downloadable desktop version of Langflow for quick and easy local setup. https://www.langflow.org/desktop
- Official Blog: The main blog featuring news, release announcements, and how-to guides. https://www.langflow.org/blog/
- Security Advisories: A dedicated page listing published security vulnerabilities and advisories for the project. https://github.com/langflow-ai/langflow/security/advisories
Engage with the Langflow community for support, to share your projects, and to stay up-to-date. The community is most active on Discord and GitHub.
- Discord: The official community hub for real-time support, discussions, and showcasing projects. https://discord.gg/EqksyE2EX9
- GitHub Discussions: A forum for general questions, ideas, polls, and showing off projects. https://github.com/langflow-ai/langflow/discussions
- GitHub Issues: The primary place for reporting bugs, tracking tasks, and requesting features. https://github.com/langflow-ai/langflow/issues
- X/Twitter: Official channel for the latest news, updates, and release announcements. https://x.com/langflow_ai
- YouTube: Official channel providing video tutorials, workshops, and feature demonstrations. https://www.youtube.com/@Langflow
Langflow offers multiple installation and deployment paths, from a simple desktop app to scalable Kubernetes deployments.
| Method | Description | Key Details & Command |
|---|---|---|
| Desktop | Standalone, easy-to-upgrade application for getting started quickly. | For macOS (13+) and Windows. Download from https://www.langflow.org/desktop. |
| Python Package (uv/pip) | Install the open-source package for command-line use and environment management. | Requires Python 3.10-3.13 and uv. uv pip install langflow |
| Docker | Run Langflow in an isolated container for consistent, reproducible deployments. | docker run -p 7860:7860 langflowai/langflow:latest |
| Kubernetes (Helm) | Official Helm charts for scalable and robust deployments on a Kubernetes cluster. | helm repo add langflow https://langflow-ai.github.io/langflow-helm-charts |
| Google Cloud Platform | A specific, official guide for deploying Langflow on Google Cloud Platform. | Official step-by-step guide for deploying on GCP infrastructure. https://docs.langflow.org/deployment-gcp |
| Render | A deployment guide for deploying Langflow on the Render cloud platform. | Official guide for one-click deployment and hosting on Render. https://docs.langflow.org/deployment-render |
Whether you're a beginner or an advanced user, these resources provide structured paths to master Langflow.
This curated list is the best starting point for developers new to Langflow.
- Langflow Quickstart: The official guide for beginners to load, run, and serve template flows. https://docs.langflow.org/get-started-quickstart
- LangFlow Full Course For Beginners (Video): A complete video course by Tech With Tim on how to build AI applications. https://www.youtube.com/watch?v=kBG5dQe394s
- Build a RAG Based LLM App in 20 Minutes! (Video): A popular YouTube tutorial on building Retrieval-Augmented Generation apps with Langflow. https://www.youtube.com/watch?v=qaEVUhoKS8M
- Getting Started with Langflow AI (Article): A tutorial focused on effortlessly building an AI chatbot without writing any code. https://sms.com/blog/getting-started-with-langflow-ai
- Building Your First AI Application with LangFlow (Article): A tutorial on creating AI applications with Langflow without writing any code. https://medium.com/@techlatest.net/building-your-first-ai-application-with-langflow-5a3ccb40f05d
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Langflow Learning Journey: A dedicated platform from the Langflow team for mastering AI flow development. https://langflow.academy/
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Langflow: A Guide With Demo Project (Article): A DataCamp tutorial explaining how to install Langflow and build AI agent workflows. https://www.datacamp.com/tutorial/langflow
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A Beginner's Guide to Building Agents in Langflow (Article): Focuses on setting up the canvas, adding memory, and incorporating tools for agents. https://www.datastax.com/blog/guide-to-building-agents-in-langflow
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Workshop: Building Your First AI Agent with Langflow (Video): A hands-on workshop video on the practical steps of building an AI agent. https://www.youtube.com/watch?v=mn1ZnlqnQlg
Explore complex topics like multi-agent systems, function calling, and local LLMs.
- Building Multi-Agents Systems with Langflow: A guide on creating complex, collaborative multi-agent systems using various architectural patterns. https://valentinaalto.medium.com/building-multi-agents-systems-with-langflow-bbff3dd25591
- Building Deep Research Agents with Langflow: Learn to build multi-agent systems for deep research using frameworks like JigsawStack. https://www.langflow.org/blog/web-search-in-your-ai-agents-a-langflow-tutorial
- MCP Server Documentation: Official documentation for setting up and using the Model Context Protocol (MCP) client. https://docs.langflow.org/mcp-client
- How to Host Your AI Agents and MCP Servers on Langflow: A guide on deploying Langflow projects, including agents and MCP servers for interoperability. https://www.langflow.org/blog/how-to-host-langflow
- Introducing Custom Components: A blog post on creating and configuring custom components to extend Langflow's functionality. https://www.langflow.org/blog/introducing-custom-components
- Web Search in Your AI Agents: A Langflow Tutorial: Official tutorial on integrating real-time web search capabilities into your AI agents. https://www.langflow.org/blog/web-search-in-your-ai-agents-a-langflow-tutorial
- Unlocking Local AI: Using Ollama with Agents: A guide on setting up and using local models with Ollama as agents. https://www.langflow.org/blog/local-ai-using-ollama-with-agents
- GitHub Issue: Organizational Management: A discussion on adding organizational management and multi-tenancy features to Langflow. langflow-ai/langflow#1716
Guides and articles focused on building and optimizing Retrieval-Augmented Generation (RAG) pipelines.
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Build a Multi-Query RAG pipeline in Langflow: A step-by-step guide on implementing a multi-query RAG workflow to enhance retrieval. https://medium.com/profull-stack/build-a-multi-query-rag-pipeline-in-langflow-9f77953c754a
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Introducing Astra DB Hybrid Search: Highlights using Langflow to experiment with hybrid search for improved relevance. https://www.datastax.com/blog/introducing-astra-db-hybrid-search
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Launch Week Day 5 - Graph RAG: Introduces the Graph RAG component, designed to improve accuracy using data relationships. https://www.langflow.org/blog/launch-week-day-5-graph-rag
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Beyond Basic RAG: Retrieval Weighting: Discusses strategies to prioritize more relevant information from different retrieved sources. https://www.langflow.org/blog/beyond-basic-rag-retrieval-weighting
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Building RAG Systems with Langflow: a step-by-step Guide: Details components for data loading, chunking, embedding, and retrieval flows. https://medium.com/@alexrodriguesj/building-rag-systems-with-langflow-a-step-by-step-guide-e35ee537b9cc
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Langfuse Integration Guide: Official guidance on integrating Langfuse for deep tracing, monitoring, and evaluation. https://docs.langflow.org/integrations-langfuse
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LangSmith Integration Guide: Instructions for setting up LangSmith to monitor, observe, and evaluate LLM applications. https://docs.langflow.org/integrations-langsmith
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Case Study: Building an AI Resume Assistant App with RAG: A practical RAG application for parsing resumes and comparing them against job descriptions. https://www.langflow.org/blog/building-resumai-langflow-astra-db-openai
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Case Study: Aparavi Financial Services Chatbot: A case study on building a secure financial services RAG chatbot with Langflow. https://medium.com/@mansi.more943/how-aparavi-and-langflow-created-a-financial-services-chatbot-without-data-risks-adf3390cc9f3
Langflow connects to a wide array of LLMs, vector databases, and data sources.
- OpenAI: Official bundle for OpenAI models, supporting text generation, embeddings, and agentic functions. Requires an OpenAI API key. Supports models like GPT-4. https://docs.langflow.org/bundles-openai
- Azure OpenAI: Seamless integration for using Azure-hosted OpenAI models in RAG and agent applications. Requires Azure endpoint URL, deployment name, and an API key. https://www.youtube.com/watch?v=gAYZP-LUbwc
- Anthropic: Dedicated bundle for using Anthropic's Claude models, including Claude 3.5 Haiku. Requires an Anthropic API key. Also supports MCP. https://docs.langflow.org/bundles-anthropic
- AWS Bedrock: Official bundle for accessing various foundation models hosted on Amazon Bedrock. Requires specifying AWS region and using standard AWS authentication. https://docs.langflow.org/bundles-amazon
- Google Vertex AI: Integration for using Google's models like text-bison for generation and embeddings. Auth via service account JSON or GOOGLE_APPLICATION_CREDENTIALS env var. https://docs.langflow.org/bundles-google
- MistralAI: Dedicated bundle for accessing MistralAI models like open-mixtral-8x7b. Requires a MistralAI API key. The base URL is configurable. https://docs.langflow.org/bundles-mistralai
- Groq: Build blazing-fast LLM applications using Groq's high-speed LPU Inference Engine. Integration is built-in and requires a Groq API key. https://docs.langflow.org/bundles-groq
- Ollama (Local): Run local LLMs like Llama 3 and other open-source models directly with Langflow. No API key needed. Set the local host URL of the Ollama instance. https://docs.langflow.org/bundles-ollama
- NVIDIA: Official bundle for NVIDIA NIMs, G-Assist, and leveraging local RTX GPU acceleration. Requires an NVIDIA API key. Includes components for generation, embeddings, and reranking. https://docs.langflow.org/bundles-nvidia
- Meta (Llama): Use Meta's Llama models within Langflow by running them locally via Ollama. Integration is handled through the Ollama component by specifying the local Llama model. https://docs.langflow.org/bundles-meta
- DataStax Astra DB: Native integration for vector and hybrid search, central to the DataStax Langflow ecosystem. https://docs.langflow.org/bundles-datastax
- Pinecone: Official bundle for connecting to Pinecone to index, store, and retrieve vector embeddings. https://docs.langflow.org/bundles-pinecone
- Qdrant: Component for using Qdrant as a vector store for similarity search in RAG pipelines. https://docs.langflow.org/bundles-qdrant
- Weaviate: Connect to a Weaviate instance for scalable vector search and data storage. https://docs.langflow.org/bundles-weaviate
- Milvus: Integration for using Milvus, a popular open-source vector database, in your flows. https://docs.langflow.org/bundles-milvus
- Redis: Use Redis as a vector store for low-latency retrieval and caching within Langflow. https://docs.langflow.org/bundles-redis
- MongoDB: Connect to MongoDB Atlas to leverage its integrated vector search capabilities. https://docs.langflow.org/bundles-mongodb
- Chroma DB: Use the open-source Chroma DB for in-memory or persistent vector storage and retrieval. https://docs.langflow.org/bundles-chroma
- Vectara: Integration with Vectara's end-to-end platform for building powerful RAG applications. https://docs.langflow.org/bundles-vectara
- Notion: Connect to Notion to use pages and databases as a data source for agents. https://docs.langflow.org/bundles-notion
- Wikipedia: A tool for agents to query and retrieve information directly from Wikipedia articles. https://docs.langflow.org/bundles-wikipedia
- Bing Search: Provide agents with real-time web search capabilities using the Bing Search API. https://docs.langflow.org/bundles-bing
Integrate these tools to trace, monitor, and evaluate your Langflow applications.
- LangSmith: A platform by LangChain for debugging, testing, evaluating, and monitoring LLM applications. https://docs.langflow.org/integrations-langsmith
- Langfuse: Open-source observability and analytics for LLM apps, supporting both cloud and self-hosted instances. https://docs.langflow.org/integrations-langfuse
- LangWatch: An all-in-one LLMOps platform providing monitoring, analytics, evaluations, and alerting for flows. https://docs.langflow.org/integrations-langwatch
- Arize Phoenix: Open-source LLM evaluation and observability platform for tracing and measurement. https://docs.langflow.org/integrations-arize
- Opik: An open-source platform from Comet for evaluating, testing, and monitoring LLM applications. https://docs.langflow.org/integrations-opik
Langflow is under active development, and security is critical. Use these resources to stay informed about vulnerabilities and best practices.
- CVE-2025-3248: Critical RCE vulnerability affecting versions before 1.3.0. Upgrade immediately. https://github.com/langflow-ai/langflow/security/advisories/GHSA-rvqx-wpfh-mfx7
- CVE-2025-57760: High-severity privilege escalation affecting container versions before 1.5.1. Upgrade required. https://github.com/langflow-ai/langflow/security/advisories/GHSA-4gv9-mp8m-592r
- Security Policy: Official policy detailing security practices and how to report vulnerabilities. https://github.com/langflow-ai/langflow/blob/main/SECURITY.md
- Advisories Index: A complete list of published security advisories for the Langflow project. https://github.com/langflow-ai/langflow/security/advisories
- Hardening Guide: Recommendations include disabling auto-login, using a secret key, and an external database. https://docs.langflow.org/deployment-prod-best-practices
Resources for deploying Langflow in production and ensuring it can handle enterprise-level loads.
- Kubernetes Scaling: Official Helm charts for deploying scalable and highly available Langflow instances on Kubernetes. https://github.com/langflow-ai/langflow-helm-charts
- GPU Acceleration (NVIDIA RTX): Guide on enabling local AI agents on RTX PCs for high-performance inference. https://blogs.nvidia.com/blog/rtx-ai-garage-langflow-agents-remix/
- Concurrency & Load Balancing: General guidance on handling concurrent connections and scaling for production loads. https://docs.langflow.org/deployment-prod-best-practices
- Cloud Deployment (Render): Official guide for deploying and scaling Langflow on the Render cloud platform. https://docs.langflow.org/deployment-render
Understand how Langflow fits into the broader ecosystem of LLM application development tools.
- Langflow vs Flowise vs n8n vs Make (Reddit Discussion): A community discussion on Reddit comparing Langflow with other popular workflow automation tools. https://www.reddit.com/r/langflow/comments/1ij66dl/langflow_vs_flowise_vs_n8n_vs_make/
See how Langflow is being used in the real world and keep up with major project milestones.
- DataStax Acquires Langflow (April 4, 2024): DataStax acquired Langflow to enhance GenAI development with a visual, one-click deployment framework. https://www.datastax.com/press-release/datastax-acquires-langflow-to-make-building-genai-apps-100x-easier-faster-more-fun
- Langflow Enables Local AI Agents on RTX PCs (July 31, 2025): NVIDIA's blog highlights how Langflow can be used to create local AI agents on RTX PCs. https://blogs.nvidia.com/blog/rtx-ai-garage-langflow-agents-remix/
- Case Study: ResumAI - AI Resume Assistant: An application using Langflow, Astra DB, and OpenAI to parse and compare resumes. https://www.langflow.org/blog/building-resumai-langflow-astra-db-openai
- Case Study: Aparavi Financial Services Chatbot: Aparavi built a secure financial chatbot with Langflow, reducing data prep time by 90%. https://medium.com/@mansi.more943/how-aparavi-and-langflow-created-a-financial-services-chatbot-without-data-risks-adf3390cc9f3
- Showcase: AI-Powered Email Assistant: A guide and showcase on building an email assistant using Langflow and SendGrid. https://www.langflow.org/blog/from-no-reply-to-please-reply-how-to-build-an-ai-powered-email-assistant-with-langflow-and-sendgrid
- Langflow Reaches 100,000 GitHub Stars (August 14, 2025): Langflow celebrated reaching the 100k GitHub stars milestone, reflecting massive community adoption. https://github.com/langflow-ai/langflow