Artificial intelligence
Agentique AI platform: Flowise AI
Publiée le December 5, 2025
Publiée le December 5, 2025
Flowise AI is an open source platform dedicated to the creation and orchestration of agents based on language models. It is presented as a set of modular blocks for building agentic systems, ranging from simple flows to multi-role autonomous agents. What sets Flowise AI apart is its open approach, high flexibility and ability to be self-hosted. In September 2025, Workday announced the acquisition of Flowise AI, underlining the strategic importance of the solution for automating HR and financial processes. This chapter details how Flowise works, its main modules and its role in the generative AI landscape.
| Criteria | Dust.tt | StackAI | Flowise AI | Bardeen AI |
|---|---|---|---|---|
| Positioning | Enterprise-oriented intelligent agent platform (AI Operating System). | No-code / low-code to simplify the creation of AI automations. | Open-source platform for visually building complex agents. | Personal/team automation via browser extension & AI workflows. |
| Target audience | Medium-sized and large companies, product, data, support and sales teams. | Business teams looking for a simple, fast tool (finance, ops, HR). | Developers & technical teams looking for a high degree of customization. | Solopreneurs, sales people, marketers, productivity-oriented teams. |
| Agent creation | ✓ Multi-stage, contextualized agents connected to the entire internal stack. | ✓ Simple to moderate AI automations. | ✓ Advanced multi-agents via visual interface. | ✓ Simple agents, focused on web automation. |
| Connection to data | Strong: Notion, Slack, GitHub, internal documents, CRM, custom APIs. | Good: standard business connectors. | Very strong: 100+ sources, vector DB, RAG pipelines. | Average: mostly web data + Google Sheets + CRM. |
| AI models supported | OpenAI, Anthropic, Gemini, Mistral… (agnostic & scalable). | OpenAI mainly + some third-party models. | Virtually all models via open-source. | Built-in templates for common tasks. |
| Security & compliance | SOC2, GDPR, regional hosting, zero-retention model. Strong enterprise advantage. | SOC2, HIPAA, GDPR. | Variable depending on deployment (open-source). | Depends on browser & integrations (less enterprise-ready). |
| Customization | Very high (agents, custom tools, deep integrations). | Medium (workflow builder). | Very high (code, nodes, workflows). | Low to medium (mainly templates). |
| User experience | Pro & collaborative, designed for teams. | Very accessible & quick to learn. | More technical, dev. oriented. | Very simple, almost plug-and-play. |
| Core strength | Agents deeply integrated into the enterprise + agnostic model + security. | Speed to create automations without technical skills. | Extreme flexibility + open-source. | Fast, simple web automations. |
| Limitations | Requires a structured AI strategy. | Less flexible than Flowise/Dust. | More complex to master. | Not designed for complex or enterprise use cases. |
| Ideal use cases | Support, sales ops, knowledge management, cross-functional productivity. | Back-office, finance, support, simple automation. | Copilots, multi-agents, embedded AI in internal products. | Prospecting, scraping, browser automation. |
| Scalability | Very strong – designed to become the complete AI OS. | Good. | Very strong, depends on tech skills. | Moderate. |
| Rapid deployment | Fast once integrations are connected. | Very fast. | Fast but requires configuration. | Instantaneous. |
| Cost | Enterprise positioning. | Affordable for SMEs & business teams. | Variable (open source + cloud). | Affordable, freemium. |
| Slight advantage | ✓ |
Flowise defines itself as an open source “agentic systems development platform”. This means that any organization can deploy the platform on its own infrastructure or use it via the cloud. Its key principles are :
Modularity: Flowise provides modular blocks for building any agent system, from simple workflows to multi-agent systems. Developers can assemble and reuse these blocks to adapt agents to their needs.
Openness and extensibility: being open source, the platform benefits from an active community, enabling the integration of new models, connectors or functionalities via external contributions.
Flexible deployment: Flowise can be deployed on site or in the cloud. It is possible to use npm install to install it and start it locally with npx flowise start.
Transparency and auditability: the open source approach enables code inspection and security verification. Flowise includes traceability and observability tools to monitor workflow execution.
Flowise offers three complementary visual builders, each corresponding to a different level of complexity:
Assistant: the simplest version for creating a conversational agent. The user can configure a chatbot that follows instructions, calls tools and retrieves information from files via retrieval-augmented generation.
Chatflow: designed to build more flexible LLM flows. Users can use advanced techniques such as Graph RAG or rerankers, giving finer control over how the agent retrieves and combines information.
Agentflow: a complete module for creating multi-agent systems with orchestration, coordination and management of complex tasks. It encompasses the other two modules and enables the orchestration of several agents, for example one agent preparing a report while another summarizes documents.
Flowise’s capabilities cover the entire agent lifecycle, from design to monitoring. The table below summarizes the main functional areas and their capabilities:
| Functional area | Flowise capabilities |
|---|---|
| Orchestration | Visual editor for combining open source and proprietary models, branching logic, loops, conditional execution. |
| Ingestion & Integration | Connection to over 100 data sources, integration of vector databases, long-term memory. |
| Monitoring & Observability | Execution logs, visual debugging, distribution of logs to external tools (Prometheus, OpenTelemetry). |
| Deployment | Options for self-hosting and deployment in air-gap environments; horizontal scaling through message queues and workers. |
| Data processing | Transformations, filters, aggregations, RAG pipelines to index data. |
| Memory & Planning | Integration of advanced memory mechanisms to optimize context management and plan agent actions. |
| MCP integration | MCP (Multi-Agent Control Protocol) support for orchestrating agent clients and servers, tool management, SSE streaming. |
| Security and control | Input moderation, output post-processing, secrets and permissions management (RBAC, SSO, encryption). |
| Scalability | Scalable vertically or horizontally to handle high loads and complex workflows. |
| Evaluations | Set of tools for testing agents with datasets, measuring performance and improving quality – docs.flowiseai.com. |
In addition to these capabilities, Flowise provides an API, SDKs in TypeScript and Python, and a CLI for integrating agents into external applications.
In September 2025, Workday announced the acquisition of Flowise AI to enrich its AI strategy. According to an Agilysis article, Flowise AI is a low-code tool designed to help non-technical professionals assemble agents like Lego pieces. Workday officials said the Flowise integration allows customers to build and deploy their own agents on Workday with simplicity and transparency.
The article points out that this integration enables users to :
Building multi-agent systems with distributed orchestration.
Create conversational assistants capable of using tools and augmented retrieval (AR) to answer questions.
Set up human-in-the-loop checkpoints to validate or reject agent actions.
Integrate applications via API, integrated widget or SDK.
This acquisition shows that Flowise is seen as a strategic component for automating complex business processes, particularly in the HR and finance sectors. Its open source and self-hosting capabilities reassure customers concerned about data security and sovereignty.
Open source and extensible: code transparency fosters trust and community collaboration. Developers can create new connectors, templates or specific functionalities.
Support for multiple models and sources: Flowise lets you choose from over 100 LLMs and vector databases, and connects to various sources thanks to its integration modules. This opens up a wide range of possible scenarios.
Visual builders adapted to complexity: the segmentation into Assistant, Chatflow and Agentflow enables both beginners and experts to build agents adapted to their needs.
Observability and human-in-the-loop: the platform provides execution traces, monitoring tools and the possibility of inserting human validations.
Self-hosting and scalability: the ability to deploy the solution on site and scale it horizontally is appreciated by large enterprises.
Learning curve: although Flowise offers a visual builder, creating complex workflows may require an understanding of orchestration and AI concepts. Non-technical users may be intimidated by the wealth of options.
A less mature ecosystem than commercial solutions: open source implies a pace of evolution that depends on the community. Some very specific integrations or functionalities may be missing compared to commercial platforms such as StackAI or Dust AI.
Business support: support depends in part on the community. Workday now offers support via its teams, but this can entail additional costs.
*Source: Palmer Research Lab
Flowise AI stands out for its open source approach, modularity and ability to orchestrate complex agents. The tool is aimed at developers and technical teams wishing to build flexible systems with total control over the infrastructure and models used. Workday’s acquisition confirms Flowise’s relevance for companies seeking to integrate AI into their processes while controlling data security and sovereignty. For organizations ready to invest in a scalable solution, Flowise represents a powerful competitor to Dust AI and no-code platforms.