{"slug": "show-hn-synapcores-ai-native-database-vector-graph-sql-automl-llm", "title": "Show HN: SynapCores – AI-native database (vector, graph, SQL, AutoML, LLM)", "summary": "SynapCores has launched an AI-native database that unifies graph traversal, vector similarity, and LLM inference into a single execution engine, eliminating the need for multiple systems and round-trips. The free Community Edition installs as a single binary in about 30 seconds and includes vector search, graph database capabilities, AutoML, and MCP server support. The company positions the database as a replacement for stacks combining Pinecone, Postgres, Neo4j, and external AI services, with 148 ready-to-run recipes available for immediate use.", "body_md": "New — Native MCP + OpenClaw long-term memory\n\n# Graph + Vector + LLM + MCP.\n\nOne database. One query.\n\nFree, single-binary install. Production-ready Community Edition.\n\n~30 seconds to your first AI-augmented query.\n\n`curl -fsSL https://get.synapcores.com | sh`\n\n[Watch the 5 live demos](/demos)\n\n[All download options](/download)\n\n## One query. Three systems other databases need.\n\nSynapCores unifies graph traversal, vector similarity, and LLM inference into a single execution engine.\n\n```\n> ▋\n```\n\nSame engine, three workloads — graph, vectors, and ML — each in one statement. Compose all three in a single query, too.\n\nGraph traversal, **HNSW vector similarity**, and **in-database ML** aren't three services here — they're one execution engine. Each answers in a single statement, from **microseconds** for graph to a couple of milliseconds with embedding or model inference in the loop.\n\nOn Postgres that's pgvector + Apache AGE + a model server + your application code stitching them together. On SynapCores it's one query — and you can compose all three in a single `MATCH`\n\n.\n\n### Stack Comparison\n\n| Stack | Systems | Round-trips |\n|---|---|---|\n| Pinecone + Postgres + OpenAI rerank | 3 + your service | 4–5 |\n| Neo4j + external embedding + LLM | 3 + your service | 3–4 |\n| SynapCores | 1 | 1 |\n\n## 148 ready-to-run recipes. Pick one to try.\n\nEach recipe is a self-contained markdown file with embedded SQL or Cypher. Run them locally, modify them, ship them.\n\n[ graph 5 minHello GraphRAGadvanceddocs/bulkRecipes/recipes/graph/016_graphrag_qna.md](/recipes/graph/016_graphrag_qna)\n\nCombine vector similarity, graph traversal, and LLM scoring in one query.\n\n[ graph 7 minFraud Ring Detectionintermediatedocs/bulkRecipes/recipes/graph/006_fraud_ring_detection.md](/recipes/graph/006_fraud_ring_detection)\n\nDetect circular money laundering patterns up to 4 hops in a transaction graph.\n\n[ ml 5 minCustomer Churn MLbeginnerdocs/bulkRecipes/recipes/ml/002_customer_churn_prediction.md](/recipes/ml/002_customer_churn_prediction)\n\nAutoML training and inference in one SQL statement.\n\n[ graph 6 minKnowledge Graph from Earnings Callbeginnerdocs/bulkRecipes/recipes/graph/017_extract_earnings_call.md](/recipes/graph/017_extract_earnings_call)\n\nSend a CFO call transcript to /v2/graph/extract. Query entities seconds later.\n\n[ graph 10 minDrug Repurposingadvanceddocs/bulkRecipes/recipes/graph/025_drug_repurposing.md](/recipes/graph/025_drug_repurposing)\n\nWalk drug-target-disease chains. Use SIMILAR_TO on mechanism embeddings.\n\n[ graph 6 minSemantic Patient Cohortsbeginnerdocs/bulkRecipes/recipes/graph/029_clinical_patient_similarity.md](/recipes/graph/029_clinical_patient_similarity)\n\nFind clinical lookalikes by symptom embedding in real-time.\n\n## What's in Community Edition\n\nCommunity Edition is a complete AI-native engine. Sales is an upgrade path, not a front door.\n\n| Feature | Community | Enterprise |\n|---|---|---|\n| Core SQL Engine | ||\n| Vector Search (HNSW) | ||\n| Graph Database (Cypher) | ||\n| AI / LLM Integration | ||\n| MCP Server (Model Context Protocol) | ||\n| Multimedia (PDF/AV) | ||\n| AutoML | ||\n| Multi-node Clustering | ||\n| Raft Replication | ||\n| Fine-grained RBAC | ||\n| Audit Logging (Scale) | ||\n| SSO / SAML / LDAP | ||\n| Immutable Tables |\n\n**Where we're going:** v1.6 (Q3 2026) targets binary wire protocol, shared buffer pool, and B-tree indexes for OLTP at PG scale.\n\n## Need clustering, RBAC, or production support?\n\nThe Enterprise Edition (EE) ships everything in CE plus mission-critical scale and security. Paid Enterprise Support is also available for CE deployments.\n\n### Scale\n\nMulti-node clustering + Raft replication + CDC inbound from MySQL/Postgres binlog.\n\n### Security & Compliance\n\nFine-grained RBAC, SSO/SAML, LDAP, encryption-at-rest, audit logging, and immutable tables.\n\n### Performance\n\nBinary wire protocol, shared buffer pool, row-store fast path (v1.6+).\n\n### Enterprise Support (also available for CE)\n\nSLA-backed support for production CE deployments: prioritized bug fixes, custom feature work, roadmap input, and direct access to the engineering team. Buy support without buying EE.\n\n[Contact sales](/contact)\n\nOr start free with [Community Edition](/download)", "url": "https://wpnews.pro/news/show-hn-synapcores-ai-native-database-vector-graph-sql-automl-llm", "canonical_source": "https://synapcores.com", "published_at": "2026-05-25 16:28:38+00:00", "updated_at": "2026-05-25 16:37:14.572978+00:00", "lang": "en", "topics": ["ai-infrastructure", "ai-products", "ai-tools", "large-language-models", "machine-learning"], "entities": ["SynapCores", "Postgres", "Pinecone", "Neo4j", "OpenAI", "Apache AGE"], "alternates": {"html": "https://wpnews.pro/news/show-hn-synapcores-ai-native-database-vector-graph-sql-automl-llm", "markdown": "https://wpnews.pro/news/show-hn-synapcores-ai-native-database-vector-graph-sql-automl-llm.md", "text": "https://wpnews.pro/news/show-hn-synapcores-ai-native-database-vector-graph-sql-automl-llm.txt", "jsonld": "https://wpnews.pro/news/show-hn-synapcores-ai-native-database-vector-graph-sql-automl-llm.jsonld"}}