A context window the size of your database.
Polygres turns Postgres into working memory for AI agents. Rows, relationships, and embeddings, retrievable in one hybrid query. Stop stuffing prompts. Start querying.
Product HuntAsk once. Retrieve everything.
pgGraph walks your relationships. pgContext runs ten kinds of search at once. Polygres fuses it all into one ranked answer, in the Postgres you already run.
Graph retrieval
Your foreign keys become a compiled graph. Multi-hop traversal, shortest paths, and relationship search over the tables you already have. The customer's orders, the ticket's history, all at memory-scan speed.
evokoa/pggraphFused search
Ten ways to search, one engine. They all run against your live tables at once, then a single fusion pass merges the results and re-scores every hit exactly. One query in, one ranked list out.
evokoa/pgcontextOne fused result
Graph hops and ten search methods don't return separate answers. Semantic hits seed the graph, pgGraph expands along the relationships, and everything gets re-ranked together. Rows, paths, and matches land in a single, token-ready context block.
Context assembly is one call.
Say what the agent needs. Semantic match, graph hops, scalar filters. Polygres hands back a ranked, token-ready context block. No vector store. No sync layer. No glue code.
Read the documentation# everything the agent needs, in one query
context = polygres.retrieve(
query="why did order #8231 fail?",
graph_hops=2, # orders → payments → disputes
filters={"status": "failed"},
limit_tokens=8_000,
)
agent.run(context) # grounded in your databaseStop rationing tokens.
Models cap out at a few million tokens. Your database doesn't. Retrieval turns every row into working memory, fetched the moment your agent needs it.
Give your agents the whole database.
Deploy a managed instance in seconds. Or point the open source SDK at your existing schema.
Start Building with Polygres
Get full access to relational table definitions, pgGraph traversals, and pgVector similarity search instantly.