Is your feature request related to a problem?

Currently, FlowiseAI provides support for a variety of 3rd-party memory integrations, but lacks support for MemMachine. Users who use MemMachine as their memory backend must rely on external workarounds and cannot integrate directly into Flowise workflows.

Describe the solution you'd like

Currently, FlowiseAI provides support for a variety of 3rd-party memory integrations, but lacks support for MemMachine. Users who use MemMachine as their memory backend must rely on external workarounds and cannot integrate directly into Flowise workflows.

Integrate MemMachine as a new memory provider in FlowiseAI via a dedicated node following the established approach seen in 'packages/components/nodes/memory'. This feature would harness the MemMachine Platform (https://api.memmachine.ai/) for memory storage, comparable to how existing integrations (such as Mem0) use their own platforms. The implementation should use the officially supported Typescript MemMachine-Client REST API wrappers for robust and future-proof communication.

Add a dedicated, first-class integration for MemMachine as a configurable memory node under 'packages/components/nodes/memory'.

• Adapter should use the MemMachine-Client Typescript API wrappers and communicate with https://api.memmachine.ai/.
• Replicate the integration blueprint/models as seen with other providers (such as Mem0).
• Ensure documentation and configuration options are aligned with current standards.
• Consider providing simple usage examples and including test coverage.

Describe alternatives you've considered

No response

Additional context

References

• MemMachine Platform API
• Existing Flowise memory integrators (e.g., Mem0 implementation)

Additional Context

The proposed integration will close a gap for enterprise FlowiseAI users already running on MemMachine. Close parity with Mem0's integration is needed. See also: MemMachine Typescript client libraries for best practices.