Your agents are only as smart as their context.
One engine for agent memory, retrieval, knowledge and operational state - fresh, joined and fast. Stop stitching a vector DB, a graph DB, a search cluster and SQL behind every agent.
Under-informed agents fail in production.
The model is rarely the problem - the context is. When the data an agent needs is scattered, stale and slow, even a great model gives confident, wrong answers.
Memory in one store, embeddings in another, relationships in a third. Your agent stitches them at request time.
Context lags behind the operational data it describes, so the agent reasons over yesterday's truth.
Every hop to a separate system adds latency - and agents fail when retrieval misses the window.
Without durable, queryable memory, agents relearn the same context every session.
One engine. Every shape of context.
Agents need four things from their data layer - and OriginChainDB delivers all four from a single engine, joined at write time and queryable in one request.
Live operational records in full SQL, kept current by ingestion connectors - the ground truth your agent reasons over.
Explore SQLNavigate entities, paths and neighbourhoods. Give the agent the structure between facts, not just the facts.
Explore GraphHNSW / IVF / IVF-PQ vector memory that compounds across sessions - the agent remembers and gets sharper.
Explore VectorBM25 full-text and vector similarity in one query - keyword precision and semantic recall together.
Explore SearchFour databases is four sync pipelines. We're one.
The four-vendor stack doesn't just cost four bills - it costs the glue code and the consistency bugs between them. One engine means a vector and the entity it belongs to land in the same transaction, and the agent reads them together.
- 4 systems to run
- Sync pipeline + glue code
- Cross-store drift
- 4 bills
- 1 engine
- No pipeline to build
- Atomic, no drift
- 1 bill
Memory, knowledge and facts in one write.
An agent observes something, embeds it, and links it to the entities it touches. With four databases that's four writes and a consistency problem. With OriginChainDB it's one transaction across SQL, vector, graph and text - so the agent never reads half-updated context.
- ✓One transaction spanning every shape
- ✓No dual-write drift, no reconciliation job
- ✓Read-your-writes the moment context lands
Context that tracks reality in real time.
Pipe operational data in through connectors and push changes out through reactive /watch. The agent's context stays current with the systems it acts on - no nightly export, no staleness gap.
- ✓Ingestion connectors keep state fresh
- ✓Reactive /watch streams changes to your app
- ✓Retrieval hits live data, not a snapshot
Let the agent query every shape at once.
Natural-language /ask turns a question into a plan across SQL, vector, graph and full-text - cost-walker informed - so the agent retrieves the right context without you hand-writing four queries and merging the results.
- ✓One question -> one plan across all shapes
- ✓Hybrid retrieval, ranked and merged for you
- ✓EXPLAIN ANALYZE on every plan
Build agents that get smarter.
Give your agents one context engine - fresh, joined and fast - and stop running a database stack to feed them.