Webhooks, cron schedules, and app events trigger chains of nodes that fetch, transform, and route data: n8n is a workflow automation platform built around a visual, node-based editor. It ships with 400+ built-in integrations covering databases like Postgres, SaaS tools like Slack and HubSpot, and every major AI provider. When a pre-built node does not exist, the HTTP Request node calls any REST API, and the Code node runs JavaScript or Python inline, so you are never blocked by a missing connector. Workflows execute as directed graphs with branching, loops, error handling, and sub-workflows, and every run is logged for inspection and replay during debugging. It also includes LangChain-based nodes for building AI agents with tool calling and memory. Self-hosting on RepoCloud gives you unlimited workflow executions with no per-task pricing, and all data stays on your instance. Runs on Node.js with SQLite by default; add Postgres and Redis queue mode when you need to scale workers horizontally.

With over 46,000 GitHub stars and one of the largest communities in data engineering, Apache Airflow is the workflow orchestration platform that lets teams define, schedule, and monitor complex data pipelines as Python code through directed acyclic graphs. Airflow 3.x introduced a modernized architecture with a task execution API, the Language Task SDK for writing task implementations in Java and Go alongside Python, asset-based partitioning with FanOutMapper and FixedKeyMapper for data-driven scheduling, a first-class state store for tasks and assets, pluggable retry policies, and a redesigned React-based web UI built on FastAPI. The provider ecosystem ships 80+ packages covering AWS, Google Cloud, Azure, Snowflake, Databricks, Apache Spark, Apache Kafka, PostgreSQL, MySQL, MongoDB, Slack, HTTP, SSH, Docker, Kubernetes, and dozens more, enabling a single deployment to orchestrate jobs across multi-cloud and on-premises infrastructure. The scheduler supports cron expressions, timetable plugins, data-aware scheduling triggered by asset events, and dynamic task generation through Python loops and conditionals. Built-in operators include BashOperator, PythonOperator, DockerOperator, KubernetesPodOperator, and sensor operators that poll external systems. The web UI provides DAG visualization with Gantt charts, grid views, and graph views, task instance logs, SLA monitoring, connection and variable management, and role-based access control. Deployment options include standalone mode, Docker Compose with CeleryExecutor or KubernetesExecutor, Helm charts for Kubernetes, and managed cloud services. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache License 2.0 licensed.

Agents that keep working after you close the tab: a13n gives a team one shared place to define them, run them, and watch what they did. In the Console, an administrator adds models from OpenAI, Anthropic, Gemini, Bedrock, Azure, OpenRouter, DeepSeek, Ollama, and others, then creates agents with instructions, skills, memories, connections, browsers, and sandbox environments. A Try agent button opens a conversation, and the Debug view lays out every run step by step, with model calls, tool calls, token counts, cache rates, and durations. Sessions and schedules keep a history of conversations and recurring jobs. The Usage page charts spend, runs, requests, and tokens by agent, model, or member across a time range. Findings analyzes traces, flags answer quality and tool failures, and lets reviewers confirm or dismiss each one with a suggested improvement. Each agent has a Connect to app screen with its ID, service URL, and API key, so applications can call it over an HTTP API or SDK. Workers lease runs from PostgreSQL and save checkpoints to object storage, so interrupted work resumes. Customer support teams, internal platform groups, and product builders use it to ship agent features without writing their own orchestration layer. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache-2.0 licensed.

A control room for AI agents, where every run is recorded, capped, and ready to resume after a crash. Mission Control opens first with agent and run counts, tokens, success rate, and cost per day. A Templates page applies ready-made agents for code review, KYB due diligence, lead scoring, market monitoring, and release notes in a single screen. Every run gets a Report tab with the finished answer and a Timeline tab with each message, tool call, and charge. Tools are set to allow, ask, or deny. Spend caps pause a run instead of failing it. An agent can stop and ask a person a question. The Playground starts a run from a form and shows the same request as curl, so each agent doubles as an API. Triggers start capped runs on a schedule, from a signed webhook, or on a GitHub pull request. Learning mode keeps notes, skills, and workflows from each job so repeat work needs fewer tokens. Automated code review, customer research, and competitor digests are typical recurring jobs to hand it. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache-2.0 licensed.

Retool's job, self-hosted: ToolJet is an open-source low-code platform for building internal tools, dashboards, and admin panels. Apps are assembled in a drag-and-drop visual builder with 60+ responsive components, including tables, charts, forms, and lists, and connected to 80+ data sources: PostgreSQL, MySQL, MongoDB, REST and GraphQL APIs, cloud storage, and common SaaS tools. When visual configuration is not enough, you can run JavaScript or Python inline for queries and transformations. A built-in no-code database (ToolJet Database) covers apps that need their own tables without provisioning an external database, Workflows add node-based automation for background jobs with dedicated worker containers and a Redis-backed queue, and multi-page apps with multiplayer editing, inline comments, and mentions support team development. Security is designed for internal data: credentials are AES-256-GCM encrypted, data flows proxy-only through your server so database contents never reach a third-party cloud, and granular per-app access control plus SSO gate each tool. Where Retool-style platforms bill per builder and sometimes per end user, the self-hosted Community Edition serves unlimited builders and users at hosting cost, and full source availability means the platform itself can be forked, audited, and extended. The stack is Node.js and React on PostgreSQL, deployed via Docker.

Toonflow, an open-source AI studio for short dramas, animated comics, and short videos, lets you plan, generate, and direct every shot as nodes on a canvas that runs on your own server. Text nodes hold the story, characters, and shot descriptions. Image generation nodes create character sheets with close-up, front, side, and back views. Video generation nodes take numbered reference images. Aspect ratio, resolution, and duration options depend on the selected model. Connectors link the nodes, so a character sheet made once is reusable in every shot. The 3D director's studio blocks a scene with camera framing, lighting, and keyframes. Export the scene as a video node. An assistant panel reads the project and reports which assets and videos have been generated. It offers next steps: generate a shot, revise a prompt, or start a new canvas. Finished images and clips preview on the canvas and download for further production work. Models come from third-party APIs, local ComfyUI workflows, or local language models. Plugins for nodes, skills, tools, and agents install from the marketplace. MCP links external tools and services. Projects, settings, and plugins live in one data directory. The interface offers 21 languages. There is no built-in login, so a firewall or an authenticating reverse proxy decides who can reach it. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

Dify turns the notoriously complex process of building production-grade AI applications into a visual drag-and-drop experience that teams can actually ship and maintain. With over 87,000 GitHub stars and backing from prominent investors, the platform has become the go-to open-source LLMOps solution for organizations that refuse to be locked into proprietary AI stacks. The visual workflow canvas lets developers wire together LLM calls, conditional logic, iteration loops, tool invocations, and human-in-the-loop checkpoints without writing boilerplate integration code. Its RAG pipeline engine handles the full document lifecycle from ingestion of PDFs, Word documents, and HTML through configurable chunking strategies, embedding with models from OpenAI or open-source alternatives, vector storage in Weaviate, Qdrant, Pinecone, or pgvector, and hybrid semantic-plus-keyword retrieval with citation tracking. Dify integrates with hundreds of model providers including OpenAI GPT-4o, Anthropic Claude, Google Gemini, Mistral, Llama, and any OpenAI-compatible endpoint like Ollama for fully local inference. The agent framework supports both ReAct and function-calling strategies with 50-plus built-in tools spanning Google Search, DALL-E, Stable Diffusion, WolframAlpha, and custom API definitions. Published apps can be deployed as hosted web interfaces, embedded chat widgets, REST API endpoints, or MCP-compatible tools. Enterprise features include role-based access control, SSO integration, and audit logging. A built-in marketplace enables teams to share and reuse model providers, tools, and workflow templates across projects. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache 2.0 licensed with an open-source community edition.

FastGPT lets you build production AI agents and knowledge base chatbots through a visual drag-and-drop workflow editor, connecting any LLM provider to your documents with retrieval-augmented generation that cites sources and reduces hallucination. The workflow canvas chains LLM calls, conditional branching, HTTP requests, code sandbox execution, and plugin nodes into complex conversation flows and agent skill pipelines without writing backend code. The knowledge base engine ingests documents in ten formats (TXT, Markdown, HTML, PDF, DOCX, PPTX, CSV, XLSX, URL scraping, and CSV batch import) then applies automatic chunking, hybrid vector retrieval with semantic reranking, and QA-pair splitting to deliver accurate, citation-backed answers. FastGPT connects to virtually any LLM provider through its AI Proxy aggregation layer: OpenAI GPT-4o, Anthropic Claude, Google Gemini, DeepSeek, Qwen, ERNIE Bot, and models hosted via Ollama all work through a unified OpenAI-compatible API. Bidirectional MCP support enables agents to call external tools and expose their own capabilities to other systems. Completed applications can be shared via login-free links, embedded as iframe widgets, or integrated with WeCom, Lark, DingTalk, and WeChat Official Accounts through the published REST API. Application operation logs, conversation annotation, and per-model usage analytics provide full lifecycle governance for compliance-sensitive deployments. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. FastGPT Open Source License (Apache 2.0 based) licensed.

Recurring chores should get cheaper every time you run them, and Umpteenth is a self-hosted agent service built on that idea: describe a job once in plain language, and each run teaches it to need less help. A job starts on a schedule, a webhook call, the API, or the Run now button, and each run gets a fresh disposable container destroyed afterwards. The first run is an Explore run where the agent works out the steps; afterwards a reflection step reads the transcript and writes short learnings plus reusable toolkit scripts into a numbered playbook version. Later runs are Assisted, starting from those learnings, and after three successful assisted runs that take the same steps, the job graduates: reflection writes a main script, and scripted runs execute it alone, spending no model tokens unless a check asks for a judgment call. If a scripted check fails, the agent takes over in the same sandbox, finishes the job, and repairs the script. The job page charts cost per run dropping toward zero. Connect HTTP MCP servers such as GitHub or Slack; model keys and MCP credentials stay on the host and never enter the sandbox. Each job sets its own schedule, model, reach, and per run spending limit. Deploy with Docker Compose, or on Kubernetes with the Helm chart, where every sandbox is a pod. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. AGPL-3.0 licensed.

Describe the change in chat, and LoopForge opens a Docker sandbox, checks out your repositories, hands the work to specialist agent roles, and opens the pull request. That is the conversational path: a Main Bot coordinates planning, implementation, review, and testing roles, each with its own prompt, executor, model, rules, skills, and tools, and you can queue or steer messages mid-reply, then pause, resume, or take over a run at any point. When a way of working proves itself, you freeze it as an SOP: a visual canvas of agent steps, deterministic script steps, human gates, and sub-workflows that every requirement then follows in the same order with the same checks. Four built-in SOPs cover backend delivery, frontend delivery, product proposals, and bug fixes, and each one can be copied, edited, and versioned per project. A Needs review inbox collects every gate waiting on you, with per-node status, tool traces, logs, and artifact snapshots as the evidence behind each approval. Recurring AI tasks run on schedules with the same workspace and workflow access, and insights reports track engineering metrics and token costs per run and per conversation. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

Preloop is a self-hostable control plane that governs every AI agent tool call and model call, and can run ticket-triggered agent work that returns as reviewable pull requests. An MCP firewall can allow, deny, or hold any tool call for approval, with require-justification rules written as YAML and CEL policy. Human approvals arrive on mobile, watch, Slack, Mattermost, email, webhook, or the command line, and agents can ask a person a question before continuing. The model gateway speaks OpenAI, Anthropic, and Gemini formats, applies budgets and allowed-model lists, and attributes token spend by model, agent, session, API key, flow, and user. Flows start agents from GitHub, GitLab, Bitbucket Cloud, Jira, webhooks, or CI jobs, in hosted containers or on private runners that connect over an outbound WebSocket. Twenty-one presets review pull requests, implement issues, triage tickets, scan for vulnerabilities, and collect evidence packs. Operators can steer a running agent from the console, phone, or watch with notes, then review the resulting pull request. One session timeline records tool calls, model calls, policy decisions, approvals, spend, outcomes, and synchronized recordings. Fleet Map shows people, devices, agents, models, and observed work in one view. An account kill switch blocks gateway and tool traffic and stops managed runs, with audited recovery. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache-2.0 licensed.

Give an AI agent a price list and it drafts the quote; hand it an invoice and it checks the numbers against purchase and receiving records before anyone pays. Atom is a self-hosted AI agent platform for business automation that wraps every agent action in governance: permissions, ordered policies authored in a canvas editor, human approval checkpoints, and durable audit records. Work starts from your files and instructions, with demonstrated workflows that map unfamiliar spreadsheet columns, calculate against source records, and produce cited Word or PowerPoint documents you can inspect line by line. Corrections become reusable teaching: saved field mappings, commercial terms, and contextual lessons carry into new work, and each job keeps the accepted snapshot it started with. Questions, approvals, and waits survive restarts, completed operations are retained, and uncertain effects must be reconciled before anything retries, so long-running business processes do not silently double-act. Results keep provenance back to the source records and operations that produced them, and unresolved requirements block completion instead of being invented away. You bring the model: configure a hosted provider or a local OpenAI-compatible endpoint, route by capability and cost, and run everything in a single-tenant workspace with user, role, and workspace boundaries. A starter sample runs with no model or cloud key at all. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. AGPL-3.0 licensed.

SuperPlane is an open-source engineering automation platform that turns routine backlog tickets into verified, review-ready pull requests while orchestrating complex multi-system workflows across your developer tools. Engineers use the visual infinite canvas to assemble event-driven pipelines with trigger nodes, conditional routing, loops, and human-in-the-loop approval gates. You can delegate pull request code reviews, automated QA testing, documentation generation, and release management to coding agents operating within strictly defined policy boundaries. Custom operational consoles let teams build live monitoring dashboards displaying key delivery metrics, active incident states, and execution histories from connected tools. Integrations connect natively with GitHub, GitLab, Datadog, PagerDuty, Slack, AWS, and leading AI models through webhook events and bi-directional actions. Every automation run maintains durable state persistence across process restarts, ensuring failed steps resume with full input payloads and debugging context intact. Developers can define reusable work orders, configure canvas memory for long-term state retention, and execute fleet commands over SSH or containerized runners. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache 2.0 licensed.

Cotal puts your AI agents in one shared room: Claude Code, Codex, and OpenCode discover each other, message channels, hand off work, and ask you to approve the result. Post to a channel and every subscriber hears it; message one peer and the durable inbox holds it until they are free; address a role like reviewer and one available reviewer claims the job. Presence shows who is actually there, and attention modes decide which channel traffic may interrupt an agent mid-task. A manager starts and stops agent processes in tmux or cmux, here or on remote hosts you authorize, without sitting between their messages. For work that waits and branches, Cotal Lang programs launch agents, pause at human checkpoints with deadlines, and resume from their recorded steps after an interruption. Services join the same space by publishing command contracts that authorized agents can discover and invoke; the bundled Linear adapter shows the pattern. Supervise from a terminal console with the live roster and activity feed, or from the read-only browser dashboard and its graph of agents and channels. Spaces stay separate, every message carries a checked identity, and per-agent grants decide who may delegate, spawn teammates, or call a service. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache-2.0 licensed.

With nearly 16,000 GitHub stars, 5.7 million monthly PyPI downloads, and 400+ contributors, Dagster is the most widely adopted asset-centric data orchestration platform — replacing task-oriented schedulers like Apache Airflow with a declarative model where every pipeline is defined as Python functions producing data assets such as tables, datasets, machine learning models, and reports. The built-in asset graph provides automatic lineage tracking across your entire data platform, showing exactly how data flows from ingestion through transformation to downstream consumption in a single unified view. Declarative Automation goes beyond cron scheduling with event-driven conditions that intelligently trigger materializations based on upstream freshness, data quality signals, and dependency state. The integrated data catalog auto-generates documentation from asset metadata, ensuring it never drifts out of sync with production. Native first-class integrations connect dbt, Snowflake, BigQuery, Databricks, Fivetran, Airbyte, Spark, Great Expectations, Tableau, Power BI, AWS, GCP, and Azure without custom glue code. The web UI visualizes asset graphs, run history, schedules, sensors, and partitioned materializations with built-in alerting via Slack and PagerDuty. Dagster Pipes enables executing arbitrary code in external environments including Spark clusters, Kubernetes Jobs, and cloud functions. Deploy via Docker Compose on a single VM with separate containers for the webserver, daemon, and code locations, or use official Helm charts for production Kubernetes with K8sRunLauncher scaling each run as an independent Job. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache-2.0 licensed.

Suparagent turns a single server into a workspace of named AI coworkers that each have a job, a memory, and a folder of their own. Chat with an agent in the browser, pick an engine per conversation, and watch replies stream in as the sidebar keeps every thread searchable. Approval modes decide how much freedom an agent gets: ask first, accept edits, or plan only, so nothing touches a file or runs a command without your say. Routines send a prompt to an agent on chosen weekdays, through a webhook, or on demand, and every run lands in its own conversation. A kanban board with month, week, and upcoming views tracks tasks by owner and priority, and a canvas builds multi-step workflows from schedule triggers, model steps, agent steps, conditions, and approvals. Agents save notes you can edit, delegate to specialists in shared rooms, and reuse saved skills and templates. A usage panel totals turns and tokens by conversation, engine, and day, with a CSV export. Invite teammates with a one-time link, pair a phone, and keep agents working when your laptop is closed. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache-2.0 licensed.

Drag an AI agent onto a canvas beside a Gmail trigger, a condition, and a Google Sheets step, and ByteChef runs the whole thing as one durable workflow that resumes where it stopped. Runs that fail or wait for a human pick up from the step they reached. The visual editor maps values between steps with data pills, and its 250+ connectors double as agent tools and MCP tools. An agent combines a model from twelve LLM providers, including OpenAI, Anthropic, Mistral, and Ollama, with chat memory, knowledge-base retrieval, and tools. It streams structured answers. Flow controls cover conditions, branches, loops, parallel and fork-join paths, subflows, error handling, and approval steps that pause for a person. Workflows start from webhooks, polling, schedules, forms, or app events, and code steps run JavaScript, Python, or Ruby. Workflows can be exposed through an MCP server for Claude Desktop, Cursor, or Windsurf, and ByteChef consumes external MCP servers too. Data Tables add typed relational tables with a spreadsheet-like grid and CSV import and export. The Agent Playbook opens a chat with the agent as you build it. Every run is listed with per-step input, output, and errors. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache 2.0 + EE licensed.

Connect one AI assistant to every chat app your customers already use, then manage it from a single admin panel. Create a bot for Slack, Discord, Telegram, LINE, Lark, DingTalk, WeCom, Matrix, KOOK, or QQ, or embed the Page Bot widget on a website with one script tag. Event routing sends each message to the processor you choose, and a pipeline controls triggers, safety rules, the model and prompt, and how replies are sent. A built-in Debug Chat streams answers as you edit, in private or group sessions. Knowledge bases split uploaded documents into chunks and search them by vector, full-text, or hybrid matching, with query rewriting and reranking options. Add providers like OpenAI, Anthropic, DeepSeek, Gemini, and Ollama, then give each pipeline its own models. The dashboard counts executions, success rate, token usage, and active sessions, and tabs list model calls, user feedback, and error logs. Sessions show the full conversation for each user, with a button to reset context. Install plugins and MCP servers from the extension marketplace to add tools. Use it for a support bot, a community assistant, an internal helpdesk, or a multi-channel front desk. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache-2.0 licensed.