The best-loved open-source visual agent builder was archived in August 2026 — what a self-hosted Flowise deployment can do now, and what moving to an owned, maintained platform involves
On ibl.ai you own all the code and the data, run it model-agnostic across any LLM, and pay with no per-seat pricing — so you can deploy anywhere, from your own cloud to a fully air-gapped network.
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Flowise and ibl.ai both leave you holding the code. Flowise is Apache-2.0 and self-hosted; with ibl.ai you own all the code and the data, deployed inside your perimeter. Ownership was never the gap between them — maintenance now is.
Workday acquired Flowise in August 2025. On July 29, 2026 the team stopped feature development and pull-request review; the GitHub repository was archived on August 13, and core-team support ended August 31, per the project's own notice.
The code remains Apache-2.0, and the team encourages forks. A running Flowise instance keeps working — but security fixes, new model integrations and dependency upgrades are now your team's job, or a community fork's.
ibl.ai is maintained, model-agnostic, has no per-seat pricing, and deploys anywhere: your cloud, a VPC, on-premise or air-gapped. Flowise's visual builder remains the friendlier canvas. ibl.ai is family-owned and operated from New York, NY.
by ibl.ai
Owned agentic AI platformby FlowiseAI (archived; Apache 2.0)
Visual LLM and agent builder| Criteria | ibl.ai | Flowise |
|---|---|---|
| Source code you receive | Platform source under license; the OS frontend and infra-cli are MIT on GitHub. | Apache-2.0 and fully self-hostable; the archived repository stays readable and forkable. |
| Upstream maintenance | Actively developed, with releases published on the ibl.ai updates feed. | Archived August 13, 2026; npm packages and Docker images marked deprecated; core support ended August 31. |
| Pricing shape | Commercial license, usage-based with no per-seat pricing. | Free to run; the cost is the engineers who now maintain the fork. |
| Model choice | Provider cards include Amazon, Anthropic, Google, Microsoft, NVIDIA, OpenAI and xAI; switch per agent. | Broad at archive time; new providers and model APIs now need someone to write the nodes. |
| Criteria | ibl.ai | Flowise |
|---|---|---|
| Visual builder | Workflow canvas with Agent, File search, Guardrails, MCP, If/else, While, User approval and state nodes. | Flowise's signature strength: a mature drag-and-drop canvas with a very large node library. |
| Time to a first working flow | Agents configure in minutes; integrating systems of record takes longer. | Minutes from docker run to a chatbot — still true for an existing install. |
| Memory & RAG | Global and agent memories; datasets from files, URLs, crawls, Drive, OneDrive and GitHub on a retrain schedule. | Memory, loader and vector-store nodes you compose per flow. |
| Criteria | ibl.ai | Flowise |
|---|---|---|
| Guardrails & PII | Moderation and safety prompts, PII redact/mask/block across 13 entity types. | Moderation nodes exist; organization-wide policy is yours to assemble and now to maintain. |
| Evals | Benchmarks up to 10,000 rows, LLM-as-Judge and human review, CSV export. | Evaluations were a Cloud and Enterprise plan feature, not part of the self-hosted open-source edition. |
| Users, RBAC & audit | SSO, RBAC, SCIM, spend caps and per-agent audit logs. | Whatever identity and audit features your edition shipped stop evolving; extending them is now yours. |
A maintained platform keeps absorbing dependency CVEs, model API changes and new providers as part of the license.
An archived Flowise keeps running exactly as deployed. The risk accrues slowly: unpatched dependencies and model APIs that change while the nodes do not.
Nothing breaks on day one. Plan for the month a dependency CVE or a model deprecation lands, and decide now who owns that fix.
Migrating trades the visual canvas you know for a maintained platform with governance, memory, evals and engineers accountable to your timeline.
Forking keeps every flow untouched and costs nothing upfront, but your team becomes the upstream for security, upgrades and new models.
Fork if Flowise is a small internal tool and you have a maintainer. Migrate if users depend on it in production.
ibl.ai's Workflow canvas covers branching, loops, approvals, guardrails and MCP, built for governed business processes rather than node-by-node LLM plumbing.
Flowise's canvas is more granular and has a far larger node library, and many teams find it the fastest way to sketch an LLM pipeline.
Flowise wins the canvas. ibl.ai wins what surrounds it: identity, memory, data, evals and a maintained roadmap.
A fork of an Apache-2.0 codebase you already run is cheap to keep, provided someone owns patching it.
Users depending on an unmaintained runtime is a growing operational risk; a maintained platform with RBAC and audit removes it.
Compliance reviewers ask who patches the software. After August 2026, for Flowise the answer is your own team.
Keep sketching in Flowise if you like the canvas, and run the agents your organization relies on on a maintained platform.
Timeline: Three to eight weeks, depending on the number of flows and custom nodes
Timeline: Days, then ongoing maintenance
ibl.ai is the agentic AI platform where you own all the code and the data. You self-host the entire stack inside your own perimeter, run it model-agnostic across any LLM and switch anytime, and pay by usage with no per-seat pricing — so you can deploy anywhere: your cloud, on-premise, GovCloud, or fully air-gapped.
ibl.ai keeps what Flowise users valued — self-hosting and code you control — and adds what an archived project can no longer provide: a maintained roadmap, governance, memory, evaluation and accountable support. Agentic OS is the platform, with a Workflow canvas for branching agents; Agentic Vibe gives coding agents 157 skills to build on it. You own all the code and the data, and forward-deployed engineers can rebuild your highest-traffic flows with you.
Full source code under a perpetual license, running on your infrastructure. Not API access to someone else's platform — the stack itself is yours.
Run any LLM — Claude, GPT, Gemini, Llama, Command, or your own fine-tune — and switch providers without rewriting the platform.
Usage-based billing against a budget cap you set. Cost tracks what your organization actually uses, not how many people you employ.
Your cloud, your VPC, on-premise, GovCloud, or a fully air-gapped network with no outbound connectivity.
1.6M+ users across 400+ organizations run the platform this way, including NVIDIA, MIT, and Syracuse University.
ibl.ai is family-owned and operated from New York, NY — a U.S.-headquartered, domestically-owned long-term partner, not a vendor that sells licenses and moves on.
Every statement this page makes about Flowise was checked against the primary source below as of 2026-10-20. If something has changed, tell us and we will correct it.
See how ibl.ai deploys AI agents you own and control—on your infrastructure, integrated with your systems.