Langflow vs Flowise: when each platform fits in your stack
If your shortlist is Langflow and Flowise, the first fact to weigh is maintenance: Flowise's core repository is archived and read-only, while Langflow is actively released, with v1.12.6 published on October 13, 2026 and weekly patch releases through September and October. Flowise's maintainers ended core team presence on August 31, 2026, so no further releases, security fixes or issue triage come from the FlowiseAI core team. Langflow fits teams that want an active upstream and Python-customizable components, particularly where the people extending the platform already write Python and expect to track new releases without forking.
Flowise still fits a specific team: one that already runs it on Node.js, has Node.js 20 or later operating skills, and accepts maintaining the code itself. The source remains under Apache 2.0 for everything outside the enterprise directory and files with explicit copyright notices, so an internal fork is a legitimate path, but it moves patching, dependency upgrades and CVE response onto your staff or your implementation partner. Confirm with your security team whether that ownership model is acceptable before you commit to running an archived codebase in a regulated environment.
The decision turns on three questions. First, who owns maintenance for the next several years. Second, how each platform is configured for production: Langflow ships with automatic login disabled and requires LANGFLOW_SUPERUSER_PASSWORD, uses SQLite by default and recommends PostgreSQL for production, while Flowise also defaults to SQLite and supports MySQL, PostgreSQL and MariaDB, so compare the authentication and database setup you would need. Third, how many existing flows must move, because rebuilding Flowise flows as Langflow flows is real migration work. Inventory your flows and components before you choose.
Which scenario favors Langflow and which favors Flowise
This table matches common team situations to the platform that fits them better, and names the condition behind each choice. Read each row as a starting point, then confirm the condition against your own stack, staffing and security requirements. Flowise is archived, so any choice of Flowise now means taking on its maintenance.
| Scenario | Better fit | Condition that makes it so |
|---|---|---|
| New internal RAG assistant with no existing tooling | Langflow | Your team is comfortable writing Python components and wants a Python-based stack that the core team still releases weekly. |
| Existing Flowise deployment with many flows | Depends on your fork plan | Choose Langflow only if you can rebuild flows and test them; keep Flowise only if you accept a fork and must own every security fix from August 2026 onward. |
| Node.js-only engineering team | Flowise | Your developers work in TypeScript and JavaScript, your ops tooling is Node-based, and you are prepared to maintain an archived codebase or a community fork. |
| Python-heavy data team | Langflow | Your data scientists want to write and test custom components in Python and deploy flows as APIs or MCP servers without a language change. |
| Regulated environment that needs vendor security fixes | Langflow | Your assessor expects a vendor with an active release line; Flowise has no further releases, security fixes or issue triage from its core team, so confirm that gap with your risk owner. |
| Short proof of concept on a laptop | Either | Both install quickly for evaluation; pick the language your team will keep, because the rebuild cost grows once flows and data accumulate. |
| Flows that need a production database | Langflow | Both support PostgreSQL, but Langflow defaults to SQLite and its docs recommend PostgreSQL for production; plan that configuration on either platform. |
| Flows that depend on Flowise enterprise features | Confirm before choosing | Some Flowise code sits under a commercial license, so check which features you use and whether a license covers them before you move or fork. |
How Langflow and Flowise are built and deployed
Both platforms are visual builders for LLM flows that you run in your own infrastructure, but they are built on different runtimes and reach production through different paths. The cards below cover what each stack means for the team that installs, patches and extends it, and what to confirm before you commit.
Language and runtime
Langflow is a Python application, so its components, dependencies and deployment tooling sit in the Python ecosystem that many data and ML teams already run. Flowise is a Node.js server with a React UI, so its operators need Node.js 20 or later and JavaScript tooling skills. Check which runtime your platform team already patches, scans and supports, because that choice drives your day-two workload more than the UI does.
Component model and extension
Langflow components are Python classes that you can customize directly, so a team with Python developers can write, test and version its own nodes. Flowise ships a separate components package of third-party node integrations inside a monorepo. Flowise's own maintainers have stopped work on it, so any node you add or patch becomes your responsibility to maintain in a fork.
Installation paths
Langflow installs as a Python package (uv recommended), as a Docker image or as Langflow Desktop for Windows and macOS. Flowise installs with npm or with Docker Compose. Pick the path that matches how your organization ships software, and confirm that your chosen image and package sources pass your internal supply chain review before rollout.
Production and high availability
Langflow's deployment guidance covers Docker, remote servers behind Caddy or Nginx with HTTPS, and Kubernetes for production-grade, high-availability deployments. Flowise's supported path is self-hosting with npm or Docker Compose, and no high-availability reference architecture was supplied for it. Ask your platform team to define the replica, database and ingress design before you pick a platform for multi-team use.
Default database
Both platforms default to SQLite when no database is configured, which is suitable for evaluation but not for concurrent users. Langflow's docs recommend PostgreSQL for production because of concurrency, data integrity and scalability. Flowise also supports MySQL, PostgreSQL and MariaDB. Plan a managed or self-hosted PostgreSQL instance, with backups, before any shared deployment on either platform.
Hosted offering
Flowise offers a separate Flowise Cloud hosted service alongside self-hosted deployments. Langflow's README and release materials describe the open-source project and its self-hosted installs, and no hosted service was part of the supplied facts for Langflow. If data residency or a managed service matters, confirm the hosting terms, data location and support scope for each option in writing before you compare them on cost.
What the Apache 2.0 and MIT licenses cover in each platform
Langflow is released under the MIT license, according to the license metadata of the langflow-ai/langflow GitHub repository. The MIT license is a permissive grant: you can run, modify, embed and redistribute the code with the copyright notice kept, and the supplied facts do not describe any field-of-use restriction on the repository's license. The supplied facts do not describe Langflow editions or a split between open-source and paid tiers, so do not assume the whole product is available under MIT without checking.
- Repository license: The MIT license applies to the langflow-ai/langflow repository as published on GitHub.
- Editions: The supplied facts do not show a paid or enterprise tier for Langflow. Ask the vendor directly whether any feature you plan to use is offered under separate terms.
- Authentication: Official release packages and Docker images disable automatic login by default and require a superuser password set through
LANGFLOW_SUPERUSER_PASSWORD. This is configuration, not a licensing term, so it does not change what the license grants. - Modification: Because the license is permissive, your team can change components and keep internal forks. Your legal team should still confirm how your own policy treats internal modifications.
- Third-party components: Flows can call external models, databases and tools, each under its own license. The MIT grant for Langflow does not cover those dependencies.
Confirm with your counsel and the vendor which features sit under the MIT grant before you commit, especially any feature you need for identity or audit.
Flowise is not a single-license project. According to its LICENSE.md, the rest of the code is Apache 2.0, while content under packages/server/src/enterprise and files with an explicit copyright notice, such as IdentityManager.ts, are under a commercial license. This means the open-source label applies to most of the repository but not to every file. Because the Flowise repository is now archived and read-only, the Apache 2.0 code remains available, but no future releases will come from the core team.
- Apache 2.0 core: Most of the server, UI and components code is Apache 2.0, so you can use, modify and redistribute those files under its terms.
- Commercial enterprise directory: Anything under packages/server/src/enterprise needs a commercial license, not the Apache 2.0 grant.
- Identity manager file: IdentityManager.ts carries its own copyright notice and falls under the commercial license, so identity-related code may not be covered by the open-source terms.
- Archived status: The source remains on GitHub under Apache 2.0, and the announcement encourages forks for internal updates, but you would own the maintenance of any fork.
- File-by-file review: Because the boundary is set per file and directory, your legal and engineering teams should map each file you deploy to its license before relying on it.
Confirm which identity and access features you need, such as single sign-on, role-based access or audit logs, and whether each sits in the Apache 2.0 code or the commercial part before you choose Flowise.
Capability comparison for LLM flow builders
This table compares Langflow and Flowise on license, language, release status, databases, authentication, observability, deployment and flow deployment. Read the rows marked to confirm as open questions for a test environment, since the supplied facts do not settle them. The main takeaway is that Flowise's license is still Apache 2.0 but its core team has stopped releasing, which shifts the decision toward who will maintain the code.
| Capability | Langflow | Flowise |
|---|---|---|
| License | MIT for the langflow-ai/langflow repository | Apache 2.0 for most code; enterprise directory and files with explicit copyright notices are commercial |
| Implementation language | Python, with Python-customizable components | Node.js server, React UI and a components package |
| Release status | Weekly patch releases through September and October 2026; v1.12.6 published October 13, 2026 | Code freeze July 29, 2026; repository archived read-only August 13, 2026; last release flowise@3.1.4 on July 29, 2026 |
| Default database | SQLite | SQLite when no database variables are set |
| Production databases | PostgreSQL recommended in the docs for concurrency, integrity and scale | SQLite, MySQL, PostgreSQL and MariaDB |
| Authentication default | Automatic login disabled; superuser password set through LANGFLOW_SUPERUSER_PASSWORD in official packages and images | Confirm the default user model and any SSO or enterprise identity options in your own test environment |
| Observability integrations | Listed in the README: LangSmith, Langfuse and others | Confirm supported tracing integrations with the maintainers or in a test deployment |
| Deployment paths | Python package, Docker image or Langflow Desktop for Windows and macOS; Caddy or Nginx with HTTPS; Kubernetes for high availability | npm on Node.js 20 or later or Docker Compose; hosted Flowise Cloud also offered alongside self-hosting |
| Flows as API, JSON or MCP | Flows deploy as an API, as JSON export and as an MCP server | Confirm export formats and MCP support against the current codebase before migrating |
Why the Flowise archive changes the maintenance math
The Flowise archive moves the maintenance question from a vendor roadmap to your own change control. The maintainers announced a code freeze on July 29, 2026, archived the GitHub repository to read-only on August 13, 2026, and ended core team presence on August 31, 2026. The last release, flowise@3.1.4, shipped on July 29, 2026. From here, no further releases, security fixes or issue triage come from the FlowiseAI core team. A Flowise deployment that stays as it is keeps running, but every dependency inside it (Node.js packages, the React UI, third-party node integrations in the components package) will age without a maintainer. Any vulnerability disclosed after the freeze will not get an upstream patch from the core team.
The maintainers named two paths. The source remains on GitHub under Apache 2.0, and the announcement encourages teams to fork the repository for internal updates or to follow community-led forks. Those are different commitments. An internal fork means your team owns every patch, rebuild and test cycle, and the Apache 2.0 license allows that. A community-led fork means you depend on another group's priorities, release cadence and security response, and the community-led projects we are aware of may differ in scope. Confirm the status, license and release history of any fork you consider before you build on it. Flowise's *LICENSE.md* also carves out commercial-licensed code under packages/server/src/enterprise and files such as IdentityManager.ts, so a fork inherits those boundaries too.
Ask two questions before you choose. First, who would patch a dependency vulnerability in your Flowise host within your normal response window, and is that person named in a runbook? Second, who would own the fork if you adopt one, including its branches, its security advisories and the decision to stop merging upstream? If the answer to either is unclear, the archive has already changed your maintenance math, whether or not you have migrated. The Langflow releases page shows the release cadence an actively maintained alternative keeps, and the Langflow migration section of this page covers what rebuilding your flows involves.
How to rebuild Flowise flows in Langflow
This sequence moves a Flowise deployment to Langflow in a controlled order, from inventory through a pilot rebuild, database move and parallel run, so you finish with a tested Langflow environment and a documented cutover decision.
Inventory every chatflow and custom component
Your team exports or lists every Flowise chatflow and agentflow, every node type in use, every custom component and every credential and external connection each flow depends on. Record which flows are live, which are used by other systems through the Flowise API, and which are stale. Compare the list with the components in the Flowise repository, since the components package holds the third-party node integrations you may rely on. Done means you have a written inventory that a reviewer can check against the running Flowise instance, and no flow is left unlisted. No effort figure is supplied for this work, so size it from your own inventory.
Map each Flowise node to a Langflow component
For each node in the inventory, identify the Langflow component that covers the same job, such as a chat model, retriever, vector store, memory or tool. Where no equivalent exists, note whether a Python custom component is needed, since Langflow components are Python-customizable. Mark any node whose behavior you cannot match and decide whether to redesign that step. Done means every node has a mapped target or a logged gap with an owner, and the gap list is reviewed before any build starts.
Rebuild one pilot flow in Langflow
Pick a representative, low-risk flow with a retriever or tool call and rebuild it in Langflow using the mapping. Run it in the interactive playground and compare answers, source citations and tool behavior against the Flowise output using the same test inputs. Your team owns this work, or your implementation partner does under a written scope. Done means the pilot produces outputs your domain owner accepts, and the differences are documented as either fixed or accepted.
Move the database from SQLite to PostgreSQL
Langflow uses SQLite by default, and its docs say PostgreSQL is better suited for production because of concurrent users, data integrity and scalability. Configure a PostgreSQL database before you load production flows so that Langflow state is not written to a single SQLite file. Confirm the connection settings in the custom database documentation for your Langflow version, and test a restart to confirm flows persist. Done means a restart retains the pilot flow and the database backup and restore procedure has been tested.
Set the superuser password through the environment variable
Official Langflow packages and Docker images disable automatic login by default and require a superuser password set through LANGFLOW_SUPERUSER_PASSWORD. Set this variable from your secrets store rather than in a shared file, then confirm that login works and that unauthenticated access to the UI and API is refused. Record who holds administrative access and how it is rotated. Done means a login test passes with the configured credential and an unauthenticated request is rejected.
Test the API and MCP endpoints
Flows that other systems call through Flowise need equivalent Langflow endpoints. Deploy the rebuilt flow as an API endpoint, and if you use it as a tool, expose it as an MCP server. Call each endpoint with your existing client code and with authentication enabled, then check response shape, latency and error handling against the Flowise version. Update client integrations where request formats differ. Done means each integrated client gets correct responses from Langflow in your test environment.
Run both platforms in parallel before cutover
Keep Flowise running for the live flows while Langflow serves the same traffic in shadow or a limited user group, and compare outputs and errors over a period you choose from your own risk tolerance. Note that Flowise is archived and read-only, so no security fixes arrive from the core team; plan the retirement date and any fork or patch responsibility in writing. Done means the parallel comparison is signed off by the flow owners and the cutover date and rollback path are recorded.
Cut over and retire the Flowise instance
Switch production traffic to Langflow, keep a read-only copy of the Flowise database and configuration for audit, and remove any credentials that only Flowise used. Confirm monitoring covers the Langflow service and that the runbook names the owner of upgrades. Done means production traffic runs on Langflow, the archived Flowise copy is stored with its access controlled, and the runbook is approved by the operations owner.
What drives the running cost on each platform
Running either platform in your own infrastructure costs more than the software line item. The cards below separate the recurring work (hosting, upgrades, patching, support) from the one-time work (migration and rebuilding flows), and show who carries each cost in a self-hosted model.
Hosting and database footprint
Your team pays the cloud or data center bill for every container, database and proxy. Flowise defaults to SQLite when no database variables are set, and its docs list MySQL, PostgreSQL and MariaDB as alternatives. Langflow also starts on SQLite, and its docs say PostgreSQL is better suited for production because of concurrent users, data integrity and scalability. Ask your team which database each platform will run on, who backs it up, and what the recovery target is. Moving a production SQLite file later is a one-time cost that grows with data volume.
Reverse proxy and HTTPS setup
Both platforms need a TLS-terminating proxy before users can reach them safely. Langflow's deployment docs cover remote servers behind Caddy or Nginx with HTTPS, and Kubernetes for production-grade high availability. Your platform team owns certificates, renewals, access logs and network policy for whichever option you pick. This is a recurring task with a one-time setup, and it is often the first place a self-hosted pilot stalls. Confirm who on your side holds the proxy configuration and certificate lifecycle before you commit.
Python or Node.js skills in house
The running cost depends on who can debug and extend the code. Langflow customizes components in Python, and it installs as a Python package on Python 3.10 to 3.14 or as a Docker image. Flowise is a Node.js server with a React UI and a components package, installed with npm on Node.js 20 or later or run with Docker Compose. If your platform team already supports Python services, Langflow adds less new skill load. If your builders are JavaScript-first, the reverse holds. Price the training or contractor time for whichever stack you do not already run.
Upgrade and patch effort
Upgrades recur for as long as you run the platform. Langflow v1.12.6 was published on October 13, 2026, with weekly patch releases through September and October 2026, so your team should expect a steady flow of changes to test. Flowise's last release, flowise@3.1.4, came on July 29, 2026, and the repository is archived. Each upgrade on Langflow is a routine maintenance task. On Flowise, your team takes on every security fix itself, which is an ongoing staffing decision rather than a one-time project.
Carrying a Flowise fork
The Flowise source remains under Apache 2.0, and the maintainers encouraged teams to fork the repository for internal updates. A fork is paid for by your staff or a contractor: merging upstream changes, patching vulnerabilities, testing the node library and keeping dependencies current. Ask whether the fork cost is in your budget for three to five years, not just the first year. Migrating flows to Langflow is a one-time rebuild cost. Staying on a Flowise fork is a recurring cost with no vendor backstop.
Support and commercial parts
Flowise has a separate commercial license for its enterprise directory, so features such as identity management are not covered by the Apache 2.0 terms, and Flowise Cloud is a hosted alternative to self-hosting. Langflow's core is MIT licensed, and the README lists observability integrations you can run yourself. Whichever you choose, DeployOpen can plan, implement, harden and document the deployment under a fixed-scope agreement, and offer retainers for ongoing upgrades and support. Ask any vendor, including us, for the written scope, who owns patching, and what happens when a release is missed.
Where Langflow and Flowise projects usually run long
Langflow projects tend to slip at the move from a laptop install to production. Langflow uses SQLite by default, and its own docs say PostgreSQL is better suited for production because of concurrent users, data integrity and scalability, so the database switch belongs in the scope before anyone builds flows that depend on it. Authentication is the second decision. Official release packages and Docker images disable automatic login and require a superuser password set through LANGFLOW_SUPERUSER_PASSWORD, which means someone must plan how users, roles and secrets are handled before the first team logs in. Then add the HTTPS reverse proxy (the deployment docs cover Caddy or Nginx) and, if traces matter, the observability hookup to LangSmith or Langfuse, which the README lists. Confirm each of these with your own infrastructure team rather than assuming a default works for your network.
Flowise projects tend to slip for reasons that are about ownership, not installation. Many flows rely on custom components that import Node.js packages from the components package, so every dependency upgrade can break a flow that worked last quarter. The larger issue is the archive. The repository is read-only, the core team left on August 31, 2026, and no further releases, security fixes or issue triage will come from the FlowiseAI core team. Your team must decide whether to maintain an internal fork under the Apache 2.0 terms, follow a community-led fork, or migrate. Also check which code you actually use: the enterprise directory and files with explicit copyright notices, such as IdentityManager.ts, are under a commercial license, so features that depend on them need a license decision of their own.
A DeployOpen quote follows from a written scope, so the scope must name each of these items. For a Langflow build, expect it to list the database migration, the authentication and superuser setup, the reverse proxy and certificates, the observability integration, the target platform (Docker, a remote server or Kubernetes), and testing of each flow you carry over. For a Flowise rebuild, it should list an inventory of every custom component and its Node.js dependencies, the fork or migration decision, the rebuild of each flow in Langflow, and the handover to your own operators. Ask for the effort per item, what is one-time and what recurs (retained support, upgrades and testing are offered as retainers), and which items you own internally.
Questions buyers ask before choosing between Langflow and Flowise
Does Flowise still receive security fixes?
No. The FlowiseAI core team announced a code freeze on July 29, 2026, archived the repository to read-only on August 13, 2026, and ended core team presence on August 31, 2026. The last release, flowise@3.1.4, was published on July 29, 2026. Because the repository is archived, no further releases, security fixes or issue triage come from the core team. The source remains under Apache 2.0 for the open code, so an internal team or a community fork can patch it, but that work becomes your responsibility. Ask who will own vulnerability response for any Flowise instance you keep running, and check the Future of Flowise discussion before you plan.
Is Langflow safe to adopt now?
Langflow is a reasonable candidate for a new build, provided you configure it deliberately. It is MIT licensed, and its latest release on the releases page at the time of research was v1.12.6, published on October 13, 2026, with weekly patch releases through September and October 2026. Official packages and Docker images disable automatic login by default and require a superuser password set through LANGFLOW_SUPERUSER_PASSWORD. Its default database is SQLite, which the project's own docs say is less suited to production than PostgreSQL. Confirm the release cadence, your upgrade process and your authentication setup against your own risk criteria before committing.
How long does a migration from Flowise to Langflow take?
The duration depends on the scope, and no supplied figure establishes a typical timeline. The main drivers are the number of flows, how many custom or third-party nodes each flow uses, how many credentials and environment variables must be re-created, which vector stores and memory stores are attached, and how much testing your business owners require before cutover. Flows built only from standard components move faster than flows with custom Node.js code, which must be rewritten as Python components. Ask any implementation partner for a written, fixed-scope plan that lists flows, integrations and acceptance tests before agreeing on a schedule.
What drives the cost of moving from Flowise to Langflow?
Cost follows the same drivers as effort. You pay for discovery (inventory of flows and integrations), rebuilding flows and custom components, data and credential migration, hosting setup, security configuration, testing and documentation. Some of these costs are one-time, such as the rebuild. Others recur, such as hosting, patching and a support arrangement. Ask for a quote that separates these items, states what is in scope and out of scope, names who performs each task, and lists the acceptance criteria. DeployOpen does not publish a price list, so the quote follows from the written scope.
Can our engineers rebuild Flowise flows in Langflow themselves?
Yes, if your team has Python skills and time to learn the Langflow component model. Flowise nodes are written in TypeScript within a Node.js monorepo, while Langflow components are Python classes that you can customize, and the Langflow README describes Python-customizable components. Your team will need to map each Flowise chain, agent and retriever to Langflow equivalents, rebuild credentials, and test outputs against the originals. Start with one low-risk flow as a pilot. If your team lacks capacity or the flows depend on heavy custom nodes, consider a fixed-scope engagement for the rebuild while your staff review and operate the result.
How do we keep existing Flowise flows when we move?
Export each flow to JSON, inventory every node and credential, and treat the export as a starting point rather than a finished migration. Flowise stores flows and configuration in its database, which can be SQLite, MySQL, PostgreSQL or MariaDB according to the Flowise database configuration docs. Keep a copy of that database before any change. Then rebuild each flow in Langflow, compare outputs on a fixed set of test prompts, and record differences. Keep the old Flowise instance read-only until the new flows pass acceptance, and document where each credential now lives.
Which database should we choose for Langflow in production?
Choose PostgreSQL for production. Langflow uses SQLite by default, and its documentation says PostgreSQL is better suited for production because it supports concurrent users, data integrity and scalability. Plan backups, restore tests and connection limits for the database from the start, and confirm the configuration path in the Langflow custom database docs. If you already run a managed PostgreSQL service in your own infrastructure, reuse its operational practices for Langflow. SQLite remains suitable for a single-user evaluation environment.
Do we need DeployOpen or can an in-house engineer handle the move?
An in-house engineer can handle a small migration, but a team with several flows, custom nodes and compliance evidence needs should weigh a partner. DeployOpen plans, implements, customizes, integrates, hardens, backs up, upgrades, documents and supports self-hosted open-source platforms inside your own infrastructure, and most engagements are fixed-scope with scope, deliverables and timelines agreed in writing before work starts. Ongoing support and upgrades are available as retainers. DeployOpen does not issue audit reports or certifications, so independent assessors remain necessary for formal compliance. Ask for a written scope that matches your flows, environment and internal skills.
How DeployOpen helps teams choose, implement or migrate
DeployOpen plans, implements, integrates, hardens, documents and supports self-hosted open-source platforms inside your own cloud accounts, data centers or air-gapped networks. For Langflow vs Flowise, we start with the written scope of your flows, data stores and access model, then decide whether a rebuild, a fork or a fresh start fits your team.
How we scope
- We inventory your existing Flowise flows, custom nodes, connected databases and API consumers before any build work begins.
- Engagements are fixed-scope, so the written scope states which flows move to Langflow, which stay as they are and what is out of bounds.
- We confirm your licensing position, including whether any Flowise enterprise-licensed code paths are in use, and record it in the scope.
How we build
- We rebuild selected flows in Langflow, mapping Flowise nodes to Python-customizable components and documenting each gap we find.
- We configure PostgreSQL instead of the SQLite default, set superuser credentials through environment variables and deploy behind HTTPS.
- We test each rebuilt flow against the same inputs and outputs used in production, and record results before any cutover.
How we hand over
- You receive deployment documentation, configuration records, flow inventories and a mapping of Flowise components to Langflow equivalents.
- We walk your operators through upgrades, backups and access controls so your team can run the platform after handover.
- Where you keep a Flowise fork for internal use, we document the patching responsibilities that stay with your team.
How we support
- Ongoing support, upgrades and continuous testing are available as retainers, so a Langflow deployment keeps pace with its weekly release cadence.
- Retainer scope is agreed in writing, covering which Langflow versions you track, how upgrades are tested and who approves changes.
- Pricing follows from the written scope, and we do not publish a price list, so ask for a quote tied to your flows and environment.
