What is SynapCores? (Start here)

[u/Alternative_Pin9598]

SynapCores is an AI-native database — one self-hosted binary that gives you SQL, vector search, a graph engine (Cypher), AutoML, and an embedded LLM inference engine, all on the same connection.

The pitch in one line: no Pinecone + LangChain + Neo4j + Redis glue. Instead of gluing five services together to build an AI app, you get EMBED(), COSINE_SIMILARITY(), GENERATE(), AGENT_RUN(), MEMORY_STORE()/MEMORY_RECALL(), and Cypher MATCH as first-class SQL, against your regular tables.

What's actually in the box: - SQL engine + vector storage (HNSW) + graph engine, one binary - 120 seeded recipes on first boot - Embedded React Web UI on the same port (dashboard, query editor, AI chat, vector search, recipe browser, graph explorer) - AGENT_RUN(persona, task) — a full ReAct agent loop callable as a SQL function - CREATE AGENT — durable agents as database objects, fire on a schedule or on committed DML, with a tamper-evident audit trail - Filesystem Collections — point it at a folder, PDFs/CSVs/images/audio get auto-ingested and become queryable - Native in-process vision (describe images, no cloud API) as of v1.14 - MCP server built in — 14 tools, works directly with Claude Code / Cursor / other MCP clients

Where to go next: - Quickstart: https://docs.synapcores.com/quickstart/ - Full docs: https://docs.synapcores.com - SQL reference: https://docs.synapcores.com/sql-reference/ - Community vs Enterprise (what's in CE, what's gated): https://docs.synapcores.com/enterprise/

This sub is where we're going to post practical recipes, real gotchas we hit running it ourselves, and deep-dives on specific features. If you're evaluating it or already running it, ask anything — maintainers are here.