Tools · Vector-native memory layer
Engram Explained: Weaviate’s AI Memory Layer
Weaviate Engram is a managed memory and context service for AI agents — built on Weaviate’s vector database, it extracts, deduplicates and retrieves agent memories asynchronously, without bolting a separate memory store onto your stack. It reached general availability in Weaviate Cloud on June 15, 2026.
Engram pipeline
Definition
What is Engram?
Engram is Weaviate’s product for agent long-term memory — a managed service that turns raw agent events into structured, durable, scoped memories.
Not the neuroscience term: in biology an “engram” is the physical trace of a memory in the brain; Weaviate borrowed the name. The core proposition is a memory layer native to the vector infrastructure — not a separate memory API bolted onto arbitrary backends. It ships as a REST API and Python SDK inside Weaviate Cloud.
Disambiguation
How Engram differs from a vector database alone
A vector database stores embeddings; Engram adds the memory layer — what to remember, when to update, how to retrieve for agents.
Weaviate (the database) does storage and similarity search. Engram adds extraction, deduplication, reconciliation of changing facts, and agent-oriented memory operations on top — memory semantics, not just retrieval.
Mechanism
Engram architecture and how it works
Your app sends input; Engram returns a run_id and builds memory asynchronously: extract → transform → commit, retrieved back via hybrid search.
Send
Raw text, a conversation, or pre-extracted facts via API/SDK
Extract
Pipeline pulls individual facts from the input
Transform
Deduplicate and merge with existing memories
Commit
Persist clean memory state to the store
Retrieve
Vector, BM25 or hybrid search on next turns
Pipelines are fire-and-forget — the app keeps working while memory builds in the background; poll the run_id for status (Weaviate docs, 2026). → Writing memories · Memory retrieval
Features
Key features of Weaviate Engram
Automatic extraction
Async pipelines process raw input into structured memories — no preprocessing.
Dedupe & reconcile
Handles preference changes and time-evolving facts against what’s already known.
Scoped memory
Per-project, per-user (user_id) and custom-property isolation as a built-in primitive.
Topics & groups
Topics define what to extract (e.g. UserKnowledge); groups bundle topics with a pipeline.
Hybrid retrieval
Vector, BM25 and hybrid search inherited from the Weaviate database.
Templates
Personalization at GA; continual learning and multi-agent state templates following (Weaviate, June 2026).
Comparison
Engram vs Mem0 vs Zep vs Letta
Engram when Weaviate-native; Mem0 when framework-agnostic API; Zep when temporal graph; Letta when paging.
| Tool | Architecture | Best for | Managed / OSS |
|---|---|---|---|
| Engram | Vector-native layer on Weaviate | Weaviate-native stacks | Managed (Weaviate Cloud) |
| Mem0 | Vector API + optional graph | Framework-agnostic personalization | Both |
| Zep | Temporal knowledge graph | Time-changing facts | Both |
| Letta | Virtual context paging | Very long conversations | Both |
Use it when
When to use Engram
- Already on Weaviate
- Want vector search + memory on one platform
- Building an AI-native app with a memory layer in the stack
- Prefer dynamic memory over a static RAG corpus
Pricing: free tier of 1,000 pipeline runs/month; paid plans from $45/month (Weaviate GA announcement, June 15, 2026).
Skip it when
Limitations
- You need graph-temporal memory with fact validity windows → Zep
- You need virtual-context paging for very long conversations → Letta
- Non-Weaviate stack and you want minimal migration → Mem0 and alternatives
- Managed-only — no self-hosted Engram
- Newer community than some established tools
Implementation
How to add Engram to an AI agent
Four steps: create a Weaviate Cloud project → get an API key → send interactions via the Engram API → retrieve memories before each LLM call.
Pick a template (personalization is available at GA), send user interactions or application context with your scope parameters, and let the pipeline handle extraction in the background. Retrieval is a search call with vector, bm25 or hybrid type. Code-level steps are in Weaviate’s Engram quickstart; the generic write/retrieve pattern is in our guide.
Memory types
Engram and long-term memory
Engram implements long-term, semantic and episodic memory patterns — memories persist across sessions, scoped per user.
In the agent-memory taxonomy, Engram’s topics map to semantic memory (facts like UserKnowledge) and episodic memory (conversation summaries), persisted as durable long-term memory. Short-term/working memory stays in your agent’s context window.
Evaluation
Evaluating Engram for your use case
Run your own evaluation on domain queries: recall hit rate, retrieval latency and token cost per turn.
The best evaluation is always domain-specific — test Engram against your own queries and memory workloads. Measure precision, recall, latency and cost per conversation turn to validate fit.
FAQ
Frequently asked questions
What is Weaviate Engram?
Engram is Weaviate's managed memory and context service for AI agents — it extracts, deduplicates and stores memories asynchronously on the Weaviate vector database and serves them back through hybrid search. Generally available in Weaviate Cloud since June 15, 2026.
Is Engram the same as Weaviate?
No. Weaviate is the vector database (storage + similarity search). Engram is the memory service built on top — extraction, deduplication, scoping and agent-oriented memory operations. See vector databases for memory.
Engram vs Mem0 — which is better?
Engram if you're on Weaviate and want memory and retrieval on one platform. Mem0 if you want a framework-agnostic managed memory API independent of your vector DB. See Mem0 alternatives.
Do I need a separate vector database with Engram?
No — that is the point. Memories are stored and searched in Weaviate itself, so there is no parallel memory system to deploy, secure or operate.
Is Engram open source?
No. Engram is a proprietary managed service in Weaviate Cloud (the Weaviate database itself is open source). Free tier: 1,000 pipeline runs/month; paid plans from $45/month (Weaviate, June 2026).
Engram vs RAG — what is the difference?
RAG retrieves from a static document corpus. Engram maintains dynamic memory — facts extracted from interactions that update and reconcile over time. See memory vs RAG.
How does Engram fit in an AI-native tech stack?
It fills the memory-layer slot between the LLM and your data, alongside the model, tools and orchestration loop — on the same Weaviate platform as retrieval. See memory layer in the AI-native stack.