SynapCores
Summary
Most AI agent memory stacks are three services duct-taped together — a vector store, a graph database, and a model API — each adding a round-trip and another failure surface. SynapCores collapses all three into a single query engine that runs in one binary.
The engine handles graph traversal, HNSW vector similarity, and in-database LLM inference inside a single MATCH statement, so the four-to-five round-trips that Pinecone plus Postgres plus an external reranker produce become one. The Community Edition ships with 161 ready-to-run recipes covering GraphRAG, fraud detection, document ingestion, and AutoML — each a runnable markdown file you can modify locally. The ceiling arrives at the infrastructure layer: multi-node clustering, Raft replication, and CDC ingest from MySQL or Postgres binlogs are paid-only features. Teams that outgrow a single host hit that wall before they hit a query performance problem. For single-host deployments, the binary wire protocol and B-tree indexes the vendor targets in a future release are not yet available.
Bottom line: Bet on SynapCores when you need graph plus vector plus in-database inference on a single node and want to avoid stitching three services together — but plan for a paid upgrade conversation the moment your architecture requires multi-node clustering or replication.
Pricing Plans
Free|Subscription- Price
- Free (Community Edition); Enterprise custom pricing
- Free Tier
- Community Edition limited to single-host deployment; no per-query or data limits documented.
Community Edition
Single-host deployment with all core features: SQL, vector, graph, embeddings, LLM inference, 161 recipes, local Llama 3.2 1B.
- Unlimited queries and data
- Single-host clustering
- Bundled Llama 3.2 1B GGUF
- HNSW vector indexing
- Cypher graph engine
- Filesystem Collections
Enterprise Edition
Multi-host clustering, fine-grained RBAC, SSO, change data capture, encryption-at-rest, SLA-backed support.
- Multi-node clustering
- Row-level RBAC and SSO
- Change Data Capture (CDC)
- AES-256-GCM encryption at rest
- SLA-backed support
View full pricing on synapcores.com →
Pricing may have changed since last verified. Check the official site for current plans.
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Pros
Sign in to edit- Graph traversal, vector similarity, and LLM inference execute inside a single query statement, so you eliminate the multi-service round-trips that add latency and failure points in stacks built on pgvector plus AGE plus an external model server.
- 161 ready-to-run recipes ship with the binary — each a self-contained markdown file with embedded SQL or Cypher — so you can validate a GraphRAG pipeline, fraud detection graph, or clinical similarity search against your own data before writing any application code.
- The Community Edition runs as a single binary on macOS, Linux, or Docker with no feature cap beyond single-host deployment, which means local-first and edge teams avoid cloud API costs and data leaving the host entirely.
- Native MCP server and OpenClaw long-term memory support are included in the Community Edition, so agents that use the Model Context Protocol can read and write persistent relational memory without an external memory service.
- Provider-agnostic local LLM inference is built into the engine, so teams absorbing high OpenAI API costs can shift inference to a local model without changing query structure or adding a separate model-serving layer.
Cons
Sign in to edit- Multi-node clustering and Raft replication are paid-only features. A single-host deployment that needs to scale horizontally hits this wall before it hits a query performance ceiling — at that point the team either pays for Enterprise Edition or re-architects around an external distributed store, which undoes the single-system advantage.
- The binary wire protocol and B-tree indexes required for OLTP-scale transactional workloads are not yet available per the vendor's roadmap. Teams running write-heavy transactional applications alongside their vector and graph queries cannot treat SynapCores as a Postgres replacement today — they end up running a second database for the transactional layer.
- Fine-grained RBAC, SSO/SAML/LDAP, audit logging, and immutable tables are all Enterprise-only. Security-conscious organizations in regulated industries that evaluate the Community Edition for a production deployment will discover the compliance features require a paid license before they finish the security review.
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About
- Platforms
- Linux, macOS, Windows (via binary or Docker)
- API Available
- Yes
- Self-Hosted
- Yes
- Last Updated
- 2026-06-09T10:35:48.741Z
Best For
Who it's for
- Teams building local-first AI applications without external API dependencies
- Organizations needing unified vector + relational + graph storage
- Edge and embedded deployments requiring a single-binary database
- Rapid prototyping of AI features with bundled recipes and LLM
- Data pipelines requiring document ingestion, embedding, and semantic search
What it does well
- Building AI agent memory and knowledge bases with persistent semantic search
- Document processing pipelines with automatic embedding and OCR
- Multi-modal RAG applications combining text, images, and video
- Graph-based entity relationship queries with vector similarity weighting
- Running local LLM inference at scale without cloud API costs
Integrations
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Frequently Asked Questions
- Is SynapCores free?
- SynapCores is a paid tool (Free (Community Edition); Enterprise custom pricing). No permanent free tier is offered.
- Is SynapCores open source?
- No — SynapCores is a closed-source tool. Source code is not publicly available.
- Does SynapCores have an API?
- Yes. SynapCores exposes a developer API. See the official documentation at https://synapcores.com for details.
- Can I self-host SynapCores?
- Yes. SynapCores supports self-hosting on your own infrastructure.
- What platforms does SynapCores support?
- SynapCores is available on: Linux, macOS, Windows (via binary or Docker).
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Curated lists that include this category
Stitching a vector store, a graph database, and a model server together means writing the glue code that routes between them, handling partial failures at each hop, and explaining to your team why three services are required for one logical query. SynapCores is a single-binary database engine that natively combines SQL, Cypher-based graph traversal, HNSW vector search, and LLM inference so all three workloads execute in one statement. The typical workflow is: install the binary (the vendor states 30 seconds on macOS, Linux, or Docker), connect via the existing API, and run any of the 161 bundled recipes to validate a use case before writing application code.
The core differentiator is the unified execution engine. Where a Postgres-based stack requires pgvector for embeddings, Apache AGE for graphs, and a separate model server — plus application code to stitch the results — SynapCores executes graph traversal, vector similarity, and model inference inside a single MATCH query. The docs describe response times from microseconds for pure graph queries to a few milliseconds when embedding or model inference is in the loop. This matters most for GraphRAG patterns, where entity resolution, semantic similarity, and LLM scoring would otherwise require sequential round-trips.
The Community Edition includes the core SQL engine, vector search, graph database, Cypher support, AI and LLM integration, MCP server, multimedia processing (PDF and AV), and AutoML with no stated feature caps beyond running on a single host. Multi-node clustering, Raft replication, fine-grained RBAC, SSO/SAML/LDAP, audit logging, and immutable tables are paid-only features gated behind the Enterprise Edition. Teams building local-first or edge-deployed AI applications get a full-featured engine at no cost; teams that need horizontal scale or enterprise security controls face an upgrade path. The binary wire protocol and B-tree indexes required for OLTP at Postgres scale are roadmap items, which means write-heavy transactional workloads are not the target today.