n8n AI Agent Memory
https://docs.n8n.io/advanced-ai/examples/understand-memory/
Low-code workflow platform. AI Agent node ships with pluggable memory sub-graph: Simple Memory for development; Postgres/Redis for production; vector store nodes (Qdrant, Pinecone, MongoDB Atlas) for semantic recall.
At a glance
- Type
- Pluggable buffer + Postgres/Redis + vector
- Tier
- T1
- Section
- Framework-embedded memory
- Created
- 2019-10 (n8n platform launched Oct 2019; AI Agent node added in n8n 1.x era 2023-2024)
- Latest release
- not applicable — not OSS
- License
- not applicable — not OSS
- GitHub
- not applicable — no GitHub repo
- Pricing
- Free self-hosted Community Edition; Cloud: Starter €20/mo; Pro €50/mo; Business $800/mo (40k executions); Enterpris…
- Funding
- $253.5M total; Series C $180M Oct 2025 led by Accel (NVentures/Nvidia participating); prior Series B €55M Mar 2025 …
Taxonomy
- storage
- vector
- retrieval
- similarity
- persistence
- cross-session
- update
- append-only
- unit
- episode
- governance
- inspectable
- conflict
- append-only
When to use
Optimised for: low-code workflow + pluggable memory
Anti-fit: not for code-first agent stacks (low-code workflow positioning)
Pros & cons
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.
Claims & capabilities
Free self-hosted (Community Edition); Cloud from €24/mo.
Technical surface
- API surface
- searched not found
- Backend storage
- searched not found
- Deployment
- Self-hosted (Docker / npm); cloud via n8n.cloud SaaS
- Embedding model
- searched not found
- Multi-tenancy
- searched not found
- MCP
- via official adapter — n8n MCP node
- A2A
- searched not found
- OpenTelemetry
- via adapter — Langfuse / OTel community
Compare n8n AI Agent Memory with…
Similar systems
Other framework-embedded memory in the catalog, ranked by inbound references.
- LangGraph Persistence T2
Distinct from LangMem. Built-in checkpointer saves graph state per super-step (short-term, thread-scoped). Store System adds long-term hierarchical key-value memory across threads with optional vector search + TTL. Postgres / Mongo / Redis stores for production.
- AutoGen Memory T2
ListMemory chronological context + teachable agents that vectorise corrections. Integrates with Mem0/Zep rather than building deep memory natively.
- CrewAI Memory T2
Memory subsystem inside the CrewAI orchestration framework; integrates with Mem0 for the long-term tier.
- AGiXT Adaptive Memory T2
Open-source AI automation platform. Routes between short-term and long-term memory adaptively across any LLM provider; plugin system for storage backends. Memory managed at the instruction-management layer — task context, instruction state, conversation history as unified agent state.
- Agno (Phidata) Memory T2
Agno (formerly Phidata). AgentStorage persists sessions to a DB; AgentMemory auto-classifies/store user preferences and conversation summaries. Single-line integrations with LanceDB, Pinecone, Weaviate, Qdrant.
- Botpress LLMz T1
Per-plan vector-DB storage quota + LLMz autonomous engine (in-session working memory) + Knowledge Base (semantic search over uploaded docs). Long-term user memory persists across sessions.
Related systems
References (3)
- MongoDB Atlas Vector Search integrates with — vector store nodes (Qdrant, Pinecone, MongoDB Atlas) for semantic recall
- Pinecone integrates with — vector store nodes (Qdrant, Pinecone, MongoDB Atlas) for semantic recall
- Qdrant integrates with — vector store nodes (Qdrant, Pinecone, MongoDB Atlas) for semantic recall