Flowise Memory vs n8n AI Agent Memory
Flowise Memory vs n8n AI Agent Memory: side-by-side comparison of two framework-embedded memory systems — architecture, taxonomy, license, pricing, MCP/A2A support, and direct edges.
Flowise Memory · n8n AI Agent Memory
Cost & capability
| Flowise Memory | n8n AI Agent Memory | |
|---|---|---|
| Capability band | competent | competent |
| Capability composite | 55 | 60 |
| Cost tier | mid | premium |
| Use cases | Memory Augmented Chat, Scoped Agentic | Scoped Agentic, Memory Augmented Chat, Long Running Session |
Where they differ (16)
Rows where both sides have data and the values disagree — the shortlist of dimensions that actually distinguish these two systems.
| Flowise Memory | n8n AI Agent Memory | |
|---|---|---|
| Capability composite | 55 | 60 |
| Cost tier | mid | premium |
| Use cases | Memory Augmented Chat, Scoped Agentic | Scoped Agentic, Memory Augmented Chat, Long Running Session |
| Type | Buffer + Buffer-Window + Conversation-Summary nodes | Pluggable buffer + Postgres/Redis + vector |
| Created | 2023 (Flowise launched 2023; YC S23) | 2019-10 (n8n platform launched Oct 2019; AI Agent node added in n8n 1.x era 2023-2024) |
| Pricing | Pre-acquisition: Free OSS; Starter $35/mo; Pro $65/mo; Enterprise custom. Post-Workday acquisition Aug 2025: pricin… | Free self-hosted Community Edition; Cloud: Starter €20/mo; Pro €50/mo; Business $800/mo (40k executions); Enterpris… |
| Funding | $500K total Seed (YC) · 2023-01 | $253.5M total; Series C $180M Oct 2025 led by Accel (NVentures/Nvidia participating); prior Series B €55M Mar 2025 … |
| Backend storage | pluggable | searched not found |
| Deployment | Both (OSS self-hosted; Flowise Cloud managed; air-gapped deployment supported) | Self-hosted (Docker / npm); cloud via n8n.cloud SaaS |
| API surface | REST, SDK: JS/TS | searched not found |
| Embedding | multiple supported | searched not found |
| Multi-tenancy | namespace | searched not found |
| MCP | via official adapter — Flowise MCP node | via official adapter — n8n MCP node |
| OpenTelemetry | searched not found | via adapter — Langfuse / OTel community |
| Optimised for | visual LangChain canvas + memory nodes | low-code workflow + pluggable memory |
| Anti-fit | not for production-grade SLA workloads | not for code-first agent stacks (low-code workflow positioning) |
At a glance
| Flowise Memory | n8n AI Agent Memory | |
|---|---|---|
| Section | Framework-embedded memory | Framework-embedded memory |
| Tier | T1 | T1 |
| Type | Buffer + Buffer-Window + Conversation-Summary nodes | Pluggable buffer + Postgres/Redis + vector |
| Created | 2023 (Flowise launched 2023; YC S23) | 2019-10 (n8n platform launched Oct 2019; AI Agent node added in n8n 1.x era 2023-2024) |
| Pricing | Pre-acquisition: Free OSS; Starter $35/mo; Pro $65/mo; Enterprise custom. Post-Workday acquisition Aug 2025: pricin… | Free self-hosted Community Edition; Cloud: Starter €20/mo; Pro €50/mo; Business $800/mo (40k executions); Enterpris… |
| Funding | $500K total Seed (YC) · 2023-01 | $253.5M total; Series C $180M Oct 2025 led by Accel (NVentures/Nvidia participating); prior Series B €55M Mar 2025 … |
| Backend storage | pluggable | searched not found |
| Deployment | Both (OSS self-hosted; Flowise Cloud managed; air-gapped deployment supported) | Self-hosted (Docker / npm); cloud via n8n.cloud SaaS |
| API surface | REST, SDK: JS/TS | searched not found |
| Embedding | multiple supported | searched not found |
| Multi-tenancy | namespace | searched not found |
| MCP | via official adapter — Flowise MCP node | via official adapter — n8n MCP node |
| A2A | searched not found | searched not found |
| OpenTelemetry | searched not found | via adapter — Langfuse / OTel community |
| Optimised for | visual LangChain canvas + memory nodes | low-code workflow + pluggable memory |
| Anti-fit | not for production-grade SLA workloads | not for code-first agent stacks (low-code workflow positioning) |
Taxonomy
| Axis | Flowise Memory | n8n AI Agent Memory |
|---|---|---|
| storage | vector | vector |
| retrieval | similarity | similarity |
| persistence | session | cross-session |
| update | append-only | append-only |
| unit | episode | episode |
| governance | inspectable | inspectable |
| conflict | append-only | append-only |
Pros & cons
Flowise Memory
Pros: Visual builder lowers the bar for non-engineers to design memory pipelines; LangChain-compatible nodes.
Cons: Memory is as good as the LangChain primitive underneath — no novel architecture; less appealing to engineers building from code.
n8n AI Agent Memory
Pros: n8n's workflow-orchestration backbone gives agents memory tied to durable workflow state — not just chat history.
Cons: Memory features are still maturing relative to dedicated memory layers; thin documentation on eviction policy.