Lindy AI Memory vs n8n AI Agent Memory
Lindy AI 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.
Lindy AI Memory · n8n AI Agent Memory
Cost & capability
| Lindy AI Memory | n8n AI Agent Memory | |
|---|---|---|
| Capability band | competent | competent |
| Capability composite | 55 | 60 |
| Cost tier | premium | premium |
| Use cases | Long Running Session, Memory Augmented Chat, Scoped Agentic | Scoped Agentic, Memory Augmented Chat, Long Running Session |
Where they differ (11)
Rows where both sides have data and the values disagree — the shortlist of dimensions that actually distinguish these two systems.
| Lindy AI Memory | n8n AI Agent Memory | |
|---|---|---|
| Capability composite | 55 | 60 |
| Use cases | Long Running Session, Memory Augmented Chat, Scoped Agentic | Scoped Agentic, Memory Augmented Chat, Long Running Session |
| Type | Selective KV memory injected into prompt | Pluggable buffer + Postgres/Redis + vector |
| Created | 2023-01 (Lindy AI founded and launched January 2023 by Flo Crivello; YC W23) | 2019-10 (n8n platform launched Oct 2019; AI Agent node added in n8n 1.x era 2023-2024) |
| Pricing | Free plan; Plus $49.99/mo; Pro $99.99/mo; Max $199.99/mo; Enterprise custom with SSO/SCIM/audit logs | Free self-hosted Community Edition; Cloud: Starter €20/mo; Pro €50/mo; Business $800/mo (40k executions); Enterpris… |
| Funding | $49.9M total; Series B $35M Jan 2023; Battery Ventures key investor; YC W23 | $253.5M total; Series C $180M Oct 2025 led by Accel (NVentures/Nvidia participating); prior Series B €55M Mar 2025 … |
| Deployment | Managed cloud only (SaaS) | Self-hosted (Docker / npm); cloud via n8n.cloud SaaS |
| MCP | searched not found | via official adapter — n8n MCP node |
| OpenTelemetry | searched not found | via adapter — Langfuse / OTel community |
| Optimised for | selective high-signal memory injection | low-code workflow + pluggable memory |
| Anti-fit | not for code-first developers | not for code-first agent stacks (low-code workflow positioning) |
At a glance
| Lindy AI Memory | n8n AI Agent Memory | |
|---|---|---|
| Section | Framework-embedded memory | Framework-embedded memory |
| Tier | T1 | T1 |
| Type | Selective KV memory injected into prompt | Pluggable buffer + Postgres/Redis + vector |
| Created | 2023-01 (Lindy AI founded and launched January 2023 by Flo Crivello; YC W23) | 2019-10 (n8n platform launched Oct 2019; AI Agent node added in n8n 1.x era 2023-2024) |
| Pricing | Free plan; Plus $49.99/mo; Pro $99.99/mo; Max $199.99/mo; Enterprise custom with SSO/SCIM/audit logs | Free self-hosted Community Edition; Cloud: Starter €20/mo; Pro €50/mo; Business $800/mo (40k executions); Enterpris… |
| Funding | $49.9M total; Series B $35M Jan 2023; Battery Ventures key investor; YC W23 | $253.5M total; Series C $180M Oct 2025 led by Accel (NVentures/Nvidia participating); prior Series B €55M Mar 2025 … |
| Backend storage | searched not found | searched not found |
| Deployment | Managed cloud only (SaaS) | Self-hosted (Docker / npm); cloud via n8n.cloud SaaS |
| API surface | searched not found | searched not found |
| Embedding | searched not found | searched not found |
| Multi-tenancy | searched not found | searched not found |
| MCP | searched not found | via official adapter — n8n MCP node |
| A2A | searched not found | searched not found |
| OpenTelemetry | searched not found | via adapter — Langfuse / OTel community |
| Optimised for | selective high-signal memory injection | low-code workflow + pluggable memory |
| Anti-fit | not for code-first developers | not for code-first agent stacks (low-code workflow positioning) |
Taxonomy
| Axis | Lindy AI Memory | n8n AI Agent Memory |
|---|---|---|
| storage | kv | vector |
| retrieval | injection | similarity |
| persistence | cross-session | cross-session |
| update | extraction | append-only |
| unit | fact | episode |
| governance | user-controllable | inspectable |
| conflict | llm-arbitrate | append-only |
Pros & cons
Lindy AI Memory
Pros: Lifelong-agent positioning — memory isn't a feature but the product premise; most opinionated about memory of any framework.
Cons: Closed ecosystem; lock-in risk is highest of the framework-embedded options.
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.