AI app for it and development · no coding needed
Visual multi-agent workflow delivery workspace
Reduce tool sprawl and keep workflow data, versions and traces inside the buyer's own environment.
Made for: Engineering teams and automation builders running AI workflows and multi-agent systems
What it does for you
The problem
Teams rent several separate tools to build, test, deploy and monitor AI workflows, and their data and version history sit outside their control.
What it gives you
A deployed, monitored multi-agent workflow owned by the buyer
What you give it
Workflow definitionsmodel connectionscode modulesoperational settings
Build your own version of Langflow Desktop, Langflow and more
One app with what these 5 AI tools do, yours to keep and change: Langflow Desktop, Langflow, Lamatic 3.0, ROMA, Clevrr Computer.
Everything these tools do, in one app
- Visual workflow builder Lets users create AI workflows by dragging and dropping components instead of writing code.Found in Langflow Desktop, Langflow, Lamatic 3.0
- Multi-agent system support Enables building and managing systems where multiple AI agents work together.Found in Langflow, Lamatic 3.0, ROMA
- Multiple model integrations Connects to various language models and data sources, including local and external APIs.Found in Langflow Desktop, Langflow, ROMA
- Python customization Provides full access to Python code for advanced modifications and control.Found in Langflow
- Local execution Runs on the user's desktop for data privacy and offline access.Found in Langflow Desktop
- Real-time testing and debugging Allows testing and debugging workflows within the environment as they run.Found in Langflow Desktop
- Workflow export and sharing Enables exporting and sharing workflows for collaboration or deployment.Found in Langflow Desktop
- Cloud deployment Offers deployment to the cloud, including serverless edge options.Found in Langflow, Lamatic 3.0
- Self-hosting Allows users to host the platform on their own infrastructure.Found in Langflow
- Role-based access control Manages user permissions and access within the platform.Found in Lamatic 3.0
- Integrated versioning Tracks changes and versions of workflows or agents.Found in Lamatic 3.0
- Data ETL and vector DB support Includes built-in data extraction, transformation, loading, and vector database support.Found in Lamatic 3.0
- Session memory management Manages memory across sessions for agents.Found in Lamatic 3.0
- Agent optimization toolkit Provides fallbacks, retries, A/B testing, and parallel model execution for reliable runs.Found in Lamatic 3.0
- GitHub-native version control Integrates with GitHub for version control and environment support.Found in Lamatic 3.0
- Open-source SDK and GraphQL API Offers an SDK and a federated GraphQL API for integration.Found in Lamatic 3.0
- Real-time traces and logging Provides real-time traces, experiment logging, and a Prompt IDE.Found in Lamatic 3.0
- AgentKit templates Includes 1-click templates for quick agent launch.Found in Lamatic 3.0
- Recursive hierarchical structure Breaks down complex problems using a recursive, hierarchical approach.Found in ROMA
- Pre-built agent examples Provides ready-made examples like task solvers, research agents, and finance agents.Found in ROMA
- Automated data analysis Performs data analysis automatically with customizable parameters.Found in Clevrr Computer
- Third-party app integration Connects with popular third-party applications for seamless workflows.Found in Clevrr Computer
- Natural language command processing Interprets user commands using natural language processing.Found in Clevrr Computer
- Real-time performance monitoring Monitors performance and provides feedback in real time.Found in Clevrr Computer
- Cloud-based access Enables access from multiple devices via the cloud.Found in Clevrr Computer
How it works, step by step
- Build AI workflows by dragging and dropping components
- Manage multiple agents working together in one system
- Connect local and external language models and data sources
- Edit underlying Python code for advanced control
- Run workflows locally on the desktop for privacy and offline use
- Test and debug workflows in real time as they run
- Export and share workflows for collaboration or deployment
- Deploy workflows to cloud and serverless edge targets
- Self-host the platform on the buyer's own infrastructure
- Apply role-based access control to users and projects
- Track workflow and agent versions with integrated versioning
- Run data ETL and connect vector databases
- Manage session memory across agent conversations
- Apply fallbacks, retries, A/B tests and parallel model execution
- Sync versions and environments through GitHub
- Expose an open-source SDK and federated GraphQL API
- Record real-time traces, experiment logs and prompt edits
- Launch from one-click AgentKit templates
- Break complex problems into recursive hierarchical steps
- Start from pre-built task, research and finance agent examples
- Run automated data analysis with customizable parameters
- Connect third-party applications into workflows
- Interpret natural language commands for workflow actions
- Monitor performance and return feedback in real time
- Access the workspace from multiple devices via the cloud
Build it yourself with your AI system
Build this app yourself, no coding needed
Start with a quick version you can try in a few minutes. Like it? Then build the full app by copying and pasting our step-by-step instructions: everything is prepared for you.
Sign in to see how to build it yourself
Build a quick version to try, or get the full app pack for Visual multi-agent workflow delivery workspace with the step-by-step building instructions. You don't need any technical skills: you copy, paste and answer a few questions. Both are included in the membership.
4 Have it built for you days to a few weeks
Rather not do it yourself, or want it fully tailored to your data, your way of working and your brand? Nexibeo builds Visual multi-agent workflow delivery workspace with you.
What's in the app pack
Included in the Complete AI Training membership.
- The building instructions your AI follows, step by step
- The questions your AI will ask you about your business before it starts
- A clickable demo you can open in your browser, to see how it should work
- A detailed blueprint of the screens, the information it keeps and the checks it runs
Become a member to get the app packAlready a member? Sign in
The files, for the technically curious
- START-HERE.mdHow to build it with your own AI (read first)3 KB
- README.mdOverview and links5 KB
- questions.mdQuestions to answer before you build2 KB
- prompt-cloudflare.mdThe full build prompt, hosted on Cloudflare26 KB
- prompt-vps.mdThe same build on your own server (Docker)26 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria13 KB
- demo/index.htmlThe working demo on sample data202 KB
Questions
Do I need to know how to code?
No. You copy and paste the prompts on this page into ChatGPT or Claude, and the AI does the building. When it asks you something, you answer in your own words.
What does it cost?
The quick version, the app pack and the step-by-step instructions are for members: you pay the membership price, not a price per app (see the plans). Building the full app uses your own ChatGPT or Claude subscription. Putting it online is often cheap or no cost at the start, and your AI tells you before anything costs money.
How long does it take?
The quick version: about two minutes. The real app: an afternoon for a first version you can use, longer if you want every feature.
Can I change it to fit my business?
Yes. Tell your AI what to change in plain words, like “add a column for the price” or “use our logo and colours”. Or have Nexibeo build and customise it for you.
More detailsHow the AI works, safeguards and what to build first
Reduce tool sprawl and keep workflow data, versions and traces inside the buyer's own environment. For engineering teams and automation builders running AI workflows and multi-agent systems, convert visual workflow definitions, model connections, code modules and operational settings into a deployed, monitored multi-agent workflow owned by the buyer. The benefit is a testable hypothesis, measured through deployed workflows per builder hour and failed runs after release; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect workflow definitions, model connections, code modules and operational settings, then follow this sequence: 1. Build AI workflows by dragging and dropping components. 2. Manage multiple agents working together in one system. 3. Connect local and external language models and data sources. Resolve uncertain cases with qualified reviewers, approve a deployed, monitored multi-agent workflow owned by the buyer, and measure deployed workflows per builder hour and failed runs after release against a documented baseline.
How the AI works
Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the three stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One fixed deployment target and approved model list; final architecture and production checks remain engineering. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Preserve source attribution, code accuracy and usage permissions. Engineering owners approve substantive changes and deployment scope. One fixed deployment target and approved model list; final architecture and production checks remain engineering. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.
What to build first
Pilot scope: One fixed deployment target and approved model list; final architecture and production checks remain engineering. Implement one approved input format, a bounded representative case set and the first two task modules: build AI workflows by dragging and dropping components; manage multiple agents working together in one system. Support the third module with operator review: connect local and external language models and data sources. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.
What it can connect to
Buyer-owned repositories, model providers and permitted data sources. Cloud and edge deployment targets, GitHub, vector databases and third-party applications. Start with file exchange and validate destination specifications before promising direct deployment. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.
The screens in detail
Primary screens: Workflow canvas, Run and trace inspector, Deployment and access console. Use a thumbnail gallery for projects, a large central drag-and-drop canvas, and a right-hand panel for components, model settings and code. Let users compare workflow versions side by side. Display draft, tested, deployed and failed states. Provide a shared run link with traces anchored to the relevant step. Make the task-specific outcome a deployed, monitored multi-agent workflow owned by the buyer visible beside its evidence, review state and value baseline.
