Memory libraries solve a real problem: "where do I put what the agent learned?" They each take a different shape — Mem0 extracts facts, Letta gives agents memory blocks, Zep packages chat history. Contexta sits one layer down: a graph-native, bi-temporal, reactive substrate with first-class provenance. The model is commodity. The harness is the moat. Contexta is the context layer inside that moat.
Mem0, Letta and Zep give you storage. Contexta gives you a context layer.
| Feature | Contexta | Memory library |
|---|---|---|
| Graph-native memory (entities + edges) | Partial | |
| Provenance / receipts on every fact | Implicit | |
| Reactive triggers (Reflexes) on the corpus | ||
| Bi-temporal recall | Some (Zep) | |
| Multi-hop reasoning across entities | ||
| GDPR-grade surgical forget with cascade | ||
| Multi-tenant namespaces + KMS profiles | Partial | |
| Framework-agnostic SDK | ||
| Hosted vs. DIY | Hosted + self-host | Hosted + self-host |
Graph-native memory (entities + edges)
Provenance / receipts on every fact
Reactive triggers (Reflexes) on the corpus
Bi-temporal recall
Multi-hop reasoning across entities
GDPR-grade surgical forget with cascade
Multi-tenant namespaces + KMS profiles
Framework-agnostic SDK
Hosted vs. DIY
Yes. Several teams keep an agent-local memory library and route durable, cross-agent knowledge through Contexta. Migration guides handle the gradual case explicitly.
Most migrations are a weekend. We expose source-specific importers for Mem0, Zep, Letta, LangMem, and custom Pinecone-RAG stacks. See /compare/migration.
Not a block primitive — Contexta exposes nodes and edges with bi-temporal validity. A memory block can be modelled as a node with structured content, but the graph is the unit of recall.
Vector RAG retrieves snippets. Contexta returns Context Packets with receipts.
Read →Neo4j stores graphs. Contexta turns graphs into context.
Read →Zapier and n8n react to flat events. Contexta reacts to graph state.
Read →LangGraph and CrewAI build the agent loop. Contexta is the context layer underneath.
Read →Drop your evaluation criteria — we will price honestly against your signal volume and ship you a Context Packet to inspect.