Classic retrieval-augmented generation stitches together a vector store and an LLM call: embed the query, fetch nearest-neighbour chunks, paste them into a prompt. It works for static FAQ retrieval. It struggles with multi-hop reasoning, provenance, temporal correctness, and reactivity. Contexta keeps the embeddings, adds a graph substrate, and ships every recall as a Context Packet with citations, valid-time, and audit trail.
Vector RAG retrieves snippets. Contexta returns Context Packets with receipts.
| Feature | Contexta | Pinecone + LangChain |
|---|---|---|
| Graph-native memory (entities + edges) | ||
| Provenance / receipts on every fact | DIY | |
| Multi-hop reasoning across documents | ||
| Reactive triggers (Reflexes) on the corpus | ||
| Bi-temporal recall (asserted + valid time) | ||
| GDPR-grade surgical forget with cascade | Delete + re-embed | |
| Hosted vs. DIY | Hosted + self-host | DIY |
| Schema flexibility | Flat chunks | |
| Pure-text similarity retrieval |
Graph-native memory (entities + edges)
Provenance / receipts on every fact
Multi-hop reasoning across documents
Reactive triggers (Reflexes) on the corpus
Bi-temporal recall (asserted + valid time)
GDPR-grade surgical forget with cascade
Hosted vs. DIY
Schema flexibility
Pure-text similarity retrieval
Contexta uses embeddings under the hood and can sit beside or in front of an existing vector store. The upgrade is the substrate around them — graph, time, provenance, Reflexes — not a forced rip-and-replace of the index.
A Packet is structured: it carries entities, edges, citations, valid-time, confidence, and the policy that authorised the recall. A RAG context window is a flat string of chunks. The Packet is what makes the answer auditable.
Recall latency is comparable to a tuned pgvector setup for single-hop queries. Multi-hop and Reflex paths are where the architectural difference shows up — they are simply not expressible in pure-RAG.
Mem0, Letta and Zep give you storage. Contexta gives you a context layer.
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.