Plataformas de desarrollo visual para diseñar, probar y desplegar flujos de trabajo y pipelines de agentes de inteligencia artificial automatizados.
Langflow is a low-code platform for designing and deploying multi-step AI agent pipelines and large language model sequences. It provides a visual environment to map logic and data flow between components, serving as an orchestrator for managing conversations and data retrieval across multiple autonomous agents. The platform distinguishes itself through a drag-and-drop interface that allows for the construction of complex AI pipelines without extensive boilerplate code. It enables the conversion of these internal workflows into standardized tools for external connectivity via the Model Contex
Langflow is a low-code platform with a visual drag-and-drop interface purpose-built for designing and deploying multi-step AI agent pipelines and LLM sequences, covering the core requirements of visual editing, multi-model LLM integration, and deployment via API endpoints — making it a direct and comprehensive fit for building agentic workflows with minimal hand-coding.
Langflow is a visual interface for building and orchestrating workflows, allowing users to construct complex systems through a drag-and-drop canvas. It provides tools for managing autonomous agents, configuring memory settings, and integrating custom code-based components. Users can organize their work into projects, track component versions, and group multiple elements into reusable units. The platform includes an interactive playground for testing workflows, monitoring tool calls, and debugging chat sessions with unique identifiers. Once built, workflows can be executed via RESTful or OpenA
Langflow is a visual drag-and-drop workflow builder for AI agents and LLM pipelines, with built-in support for multi-model integration, tool calling, prompt chaining, and production API deployment — exactly matching the low-code visual AI workflow builder this search is after.
This project is a React-based framework for constructing interactive, node-based visual interfaces. It provides a platform for building canvases where users define, connect, and organize logical processes, data pipelines, or complex workflows through a graphical interface. By utilizing a modular component architecture, it enables the development of low-code environments, visual programming tools, and interactive diagramming applications. The framework distinguishes itself through a declarative approach where state changes automatically synchronize with the visual representation of nodes and e
xyflow/xyflow is a React-based framework for building node-based visual interfaces, which is the right kind of tool for creating a visual AI workflow builder, but it is a lower-level UI component library rather than a ready-to-use AI agent pipeline designer with built-in LLM integration, conditional branching, or deployment features.
cc-wf-studio is a suite of tools for visually designing, refining, and exporting AI agent workflows. It provides a visual automation orchestrator and an LLM agent workflow designer that allow users to create multi-agent sequences and tool integrations using a drag-and-drop canvas. The project features a converter that transforms these visual agent designs into markdown-formatted commands and skills for use with artificial intelligence coding assistants. It also includes an AI-driven workflow editor that enables the modification of agent logic through natural language conversations. The platf
This repository is a visual drag-and-drop AI agent workflow designer with multi-agent orchestration, tool integration, human-in-the-loop support, and a natural-language editing interface, matching all key requirements for a low-code agentic pipeline builder.
This project is a containerized local AI infrastructure stack designed to deploy large language models and vector databases on private hardware. It functions as an orchestration platform that combines AI runners, knowledge graphs, and a visual workflow builder for creating agentic chatflows and automating tasks via tool integration. The platform distinguishes itself through a low-code approach to agent orchestration, utilizing a visual interface to design complex sequences and connect agents to external tools and search engines. It includes a dedicated local observability stack to track promp
This repository packages a full local AI infrastructure platform with a visual workflow builder for designing agentic pipelines, supporting LLM integration and tool calling through a low-code interface — making it a comprehensive fit for your search.
Botpress is a conversational AI builder and LLM agent platform used to design chatbot workflows and orchestrate agents powered by large language models. It provides a framework for managing the entire lifecycle of these agents, from initial creation through to deployment across various production environments. The platform includes a custom integration SDK for developing and publishing third-party connectors that extend agent capabilities. These tools allow for the creation of custom plugins that connect AI agents to external APIs and third-party services. The system supports both visual des
Botpress is a conversational AI platform with a visual workflow editor for designing and deploying LLM-powered agent pipelines, directly matching the search for a low-code agentic workflow builder with multi-model support, conditional logic, tool calling, and production deployment.
Flowise is a low-code platform designed for building and deploying complex language model workflows through a visual, node-based interface. It functions as an orchestrator for autonomous multi-agent systems, allowing users to construct conversational pipelines by connecting language models, memory stores, and external tools on a drag-and-drop canvas. The platform distinguishes itself through its support for sophisticated agentic patterns, including supervisor-worker delegation and iterative reasoning strategies. Users can design directed acyclic graphs to manage conditional branching, state p
Flowise is a low-code visual platform for designing and deploying multi-step AI agent workflows using a drag-and-drop node editor, with support for LLM integration, conditional branching, tool calling, and production APIs — exactly matching the search for a visual agentic pipeline builder.
Promptflow is a development framework and orchestrator for building applications powered by large language models. It functions as a suite of tools for designing, orchestrating, and deploying AI workflows by linking prompts, custom Python code, and language models into executable sequences. The project is distinguished by a visual AI workflow designer that allows for the creation of directed acyclic graphs of logic nodes. It provides a dedicated prompt engineering environment for versioning and comparing templates, alongside stateful execution tracing to record function calls and variable val
Promptflow is a development framework with a visual DAG-based workflow designer for orchestrating multi-step AI pipelines using prompts, code, and LLMs — it directly matches the requirement for a low-code visual AI workflow builder with support for prompt chaining, deployment, and execution tracing.
pyspur is a visual workflow builder for AI agents, with support for multiple LLMs, human-in-the-loop, and graph-based pipeline design, making it a solid match for low-code prototyping and production of multi-step agentic workflows.
n8n is a workflow automation platform that combines a visual interface with code-based extensibility to design, orchestrate, and manage automated processes. It provides a comprehensive suite of tools for data transformation, filtering, and storage, allowing users to build complex logic through conditional branching, looping, and sub-workflow execution. The platform supports both pre-built integration nodes and custom code execution in JavaScript or Python, enabling connectivity with a wide range of external services and APIs. The platform includes a suite of generative AI capabilities, such a
n8n is a low-code workflow automation platform with a visual drag-and-drop editor, LLM integration, conditional branching, tool calling, and deployment options, making it a direct fit for visually designing and orchestrating multi-step AI agent workflows with minimal coding.
Dify is an open-source platform for building, orchestrating, and deploying generative AI applications and autonomous agents. It provides a visual development environment that allows users to design complex, multi-step logic chains and conversational flows, which can then be published as APIs, web interfaces, or embedded widgets. The platform acts as a centralized infrastructure layer, managing model connections, prompt templates, and knowledge retrieval to support scalable AI-powered services. What distinguishes the platform is its focus on stateful application design and workflow orchestrati
Dify is a low-code visual platform for building and orchestrating multi-step AI agent workflows with a drag-and-drop editor, LLM integration, conditional logic, tool calling, and deployment to production APIs, directly matching the search for a visual AI workflow builder.
Aigcpanel is a visual workflow automation tool and model lifecycle manager designed for generative AI media pipelines. It provides a unified interface to install, launch, and configure both local and remote AI model endpoints, acting as an orchestration platform for large language models and AI tools. The system features a drag-and-drop node editor for chaining AI models and scripts into automated processing pipelines. It distinguishes itself with a breakpoint-aware execution model that allows users to pause and resume long media tasks from specific points in the workflow. Additionally, it in
Aigcpanel is a visual drag-and-drop node editor for chaining generative AI models into automated pipelines, making it a clear low-code AI workflow builder, though its focus on media tasks and lack of explicit conditional branching or tool calling keep it from being a flagship example.
PraisonAI is an autonomous AI agent platform that coordinates multiple LLM-powered agents for research, planning, and execution of complex workflows. It functions as a multi-agent orchestration framework, a workflow builder, and a Model Context Protocol server, while also providing retrieval-augmented generation through vector knowledge bases. Agents can interact via CLI, web, or standardized protocols with sandboxed code execution. The platform distinguishes itself with a rich set of agent communication protocols, including A2A, REST, WebSocket, voice and telephony integration, and MCP, allo
PraisonAI is a multi-agent orchestration framework for coordinating LLM-powered agents in complex workflows, which fits the category of an AI workflow builder, though its primary interface is code and CLI rather than a visual drag-and-drop editor, and it lacks explicit mention of a visual workflow designer, conditional branching, or human-in-the-loop approval features.
Mastra is an orchestration framework designed for building, deploying, and managing autonomous AI agents and multi-agent systems. It provides a comprehensive suite of primitives for creating resilient AI applications, including durable workflow orchestration, event-driven agent loops, and semantic memory management. By integrating these core components, the platform enables developers to build complex, multi-step processes that can reason about goals and execute tasks without manual intervention. The framework distinguishes itself through its focus on observability and secure, isolated execut
Mastra is a TypeScript orchestration framework for building and managing autonomous AI agents with durable workflow orchestration and event-driven loops, which fits the category of an agentic pipeline designer, but its code-first approach and lack of a visual drag-and-drop editor mean it is a narrower, developer-oriented tool rather than a low-code visual builder.
CrewAI is a multi-agent orchestration framework designed for building autonomous systems that execute complex, multi-step workflows. It provides a development platform where specialized agents are defined with specific roles, goals, and tool sets to perform tasks collaboratively. By leveraging a declarative workflow engine, the system manages task dependencies, state transitions, and execution logic, allowing for the creation of structured, stateful sequences of operations. The framework distinguishes itself through its hierarchical management capabilities, which utilize manager agents to coo
CrewAI is a multi-agent orchestration framework for building autonomous AI workflows, but it is code-first (Python) rather than a visual drag-and-drop builder, so it fits the category of an agentic pipeline designer but lacks the low-code visual editor this search prioritizes.
ComfyUI is a modular generative AI workflow orchestrator and node-based GUI for designing and executing complex diffusion model pipelines. It functions as both a visual interface for building generative logic graphs and a programmable backend API that exposes diffusion model operations for external integration. The system distinguishes itself through a graph-based execution model that supports differential workflow execution, re-running only modified nodes to reduce computation. It features dynamic model offloading to manage memory between system RAM and GPU VRAM and utilizes metadata-embedde
ComfyUI is a node-based visual workflow builder for generative AI pipelines, which fits the low-code visual designer intent, though it is primarily focused on diffusion models rather than general LLM agent orchestration with tool calling and human-in-the-loop approval.