New — Native MCP + OpenClaw long-term memory

Your AI agent forgets you.
Ours doesn't.

SynapCores is an AI-native database that gives your agent durable, private memory — vector recall, graph relationships, and LLM reasoning answered in a single query, not stitched across five services.Self-hosted. One binary. Running in 30 seconds.

Open source · MIT reference agent6 on GitHubv1.8 ships native LLM inference — no daemon187 production-ready recipes

Fork the MIT reference agent on GitHub·github.com/SynapCores/synapcores-agent

agent_memory.cypher
-- Semantic recall · graph context · LLM ranking — one round trip.
MATCH (session:Episode {id: $session_id})
      -[:SIMILAR_TO > 0.80]->(past:Episode)
MATCH (past)-[:MENTIONS]->(ctx:Entity)
WITH  past, collect(ctx.name) AS context,
      llm_score("Relevance to the current question", past) AS fit
RETURN past.summary AS memory, context, fit AS relevance
ORDER BY relevance DESC LIMIT 5;

Vector search, graph traversal, and generation — one engine, one round-trip.

★ 5 LIVE DEMOS187 READY-TO-RUN RECIPES★ NATIVE MCP★ OPENCLAW MEMORY★ MACOS + LINUX + DOCKER★ OPEN COMMUNITY EDITION

MIT-licensed reference agent·Self-hosted — your data never leaves your box·No lock-in — single binary·Become a design partner and shape the roadmap →

One query. Three systems other databases need.

SynapCores unifies graph traversal, vector similarity, and LLM inference into a single execution engine.

Graph 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.

On 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.

Stack Comparison

StackSystemsRound-trips
Pinecone + Postgres + OpenAI rerank3 + your service4–5
Neo4j + external embedding + LLM3 + your service3–4
SynapCores11

Build agent memory in 5 emails.

One runnable SQL recipe per email. No LangChain. Free.

5-email course · free

Ship an agent with persistent memory in 5 emails

A 5-email course where you build a real agent with persistent memory — one runnable SQL recipe per email, no LangChain, no Pinecone, no Redis. Lesson 1 hits your inbox the second you sign up. After that: a new recipe every Monday. No spam.

Free forever. We'll never share your email.

What's in Community Edition

Community Edition is a complete AI-native engine. Sales is an upgrade path, not a front door.

FeatureCommunityEnterprise
Core SQL Engine
Vector Search (HNSW)
Graph Database (Cypher)
AI / LLM Integration
MCP Server (Model Context Protocol)
Multimedia (PDF/AV)
AutoML
Multi-node Clustering
Raft Replication
Fine-grained RBAC
Audit Logging (Scale)
SSO / SAML / LDAP
Immutable Tables

Where we're going: v1.6 (Q3 2026) targets binary wire protocol, shared buffer pool, and B-tree indexes for OLTP at PG scale.

Building production agents? Partner with us early.

We're taking on a small number of design partners shaping the SynapCores roadmap. You get direct access to the engineering team, priority on the features you need, and SLA-backed support. We get a real workload to build against.

Become a design partner →

Need clustering, RBAC, or production support?

The Enterprise Edition (EE) ships everything in CE plus mission-critical scale and security. Paid Enterprise Support is also available for CE deployments.

Scale

Multi-node clustering + Raft replication + CDC inbound from MySQL/Postgres binlog.

Security & Compliance

Fine-grained RBAC, SSO/SAML, LDAP, encryption-at-rest, audit logging, and immutable tables.

Performance

Binary wire protocol, shared buffer pool, row-store fast path (v1.6+).

Enterprise Support (also available for CE)

SLA-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.