Introducing Agent Memory JumpStart

Your AI agent forgets you.
Ours doesn’t.

Stop rebuilding agent memory with five different services. Give your AI agent durable, private, and relational memory with vector search, graph traversal, and LLM generation in one unified engine.

Run locally in 1s:
docker run -d -p 8080:8080 ghcr.io/synapcores/community
Engine status
All systems operational
< 50ms
Unified query latency
100%
Local & private CE
1 binary
Graph + vector + LLM
TypeScript + Py
Official typed SDKs
Interactive Sandbox

Watch the Memory Loop

Type a message to simulate how SynapCores acts as the complete, framework-free brain of an agent. It combines vector recall, graph traversal, tool routing, and generation in one query path.

No active session. Enter a query below to start the memory loop.

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Engine Diagnostics

Behind the Brain

Recall Memory (Vector Store)

Embeds the query and runs a vector search for past user context.

Retrieve Knowledge (GraphRAG)

Traverses knowledge graph relationships for structural context.

Semantic Tool Routing

Decides which tool to trigger based on semantic similarity of descriptions.

Generate Reply

Bundles all retrieved context and runs grounded generation.

Persist Turn

Writes the current transaction and user interaction back to the graph memory.

Architecture comparison

The Complexity Cost

Stitching together multiple point solutions creates latency, high bills, security risks, and endless debugging cycles. See how SynapCores collapses the stack.

Fragile Multi-Service Setup
Pinecone / Qdrant
Vector Embeddings
PostgreSQL / MongoDB
Relational/Document State
Redis / Memcached
Caching & Active Sessions
Neo4j / FalkorDB
Graph relationships
LangChain / LlamaIndex
Orchestration & Glue Code
Total Latency: ~400ms – 1.2s | Complex Network Overheads | Multi-SaaS Bill

The Glue-Code Tax

  • State Synchronization: Keeping vector search, relational DBs, and graph nodes synced requires complex, error-prone transaction glue.
  • High Bill & Latency: Multiple network hops between different SaaS endpoints add hundreds of milliseconds of latency and multiple API bills.
  • Security & Privacy: Pushing user interactions across five third-party cloud services violates strict privacy requirements.
Ready to simplify your stack?
Deep Collaboration

Design Partner Program

We are selecting exactly three technical teams to work directly with our core engineers. Bring your active agent memory or GraphRAG bottleneck; let's build the solution together.

Program Benefits

Co-Engineering Support

Direct access to SynapCores' core database engineering team. We help you design your schemas, optimize Cypher queries, and tune vector indexes.

Private Slack/Discord

A shared private channel for your engineering team to get sub-hour responses to bugs, performance questions, or deployment blockers.

Roadmap Priority

Your features move to the front of our backlog. Help shape the future of our vector subsystem, GraphRAG pipeline, and SDK surfaces.

Free Production License

Receive a free 12-month production license for SynapCores Enterprise Edition once your agent workflow transitions from pilot to live.

Program Requirements

01

Active Agent Project

You have an active AI agent, chatbot, copilot, or GraphRAG prototype currently under development or in early production.

02

Weekly Feedback Loop

Agree to a 30-minute weekly engineering sync for 4 weeks to share performance telemetry, API feedback, and UX friction points.

03

Case Study Permission

Willingness to co-publish a technical implementation note or anonymized case study detailing your architecture and outcomes.

Limited to 3 slots

Apply for Design Partner Track

Submit your technical details below. All fields are required.

Now accepting applications

The Agent Memory JumpStart

A founder-led two-to-four-week engineering sprint where we help your team build, deploy, and benchmark a persistent memory or private GraphRAG layer on SynapCores.

Track 01Free program

Design Partner

For early-stage teams with high-learning AI agent prototypes who will trade active product feedback and a case study for hands-on founder-led engineering.

Two-to-four weeks of direct integration support
Dedicated private Slack/Discord channel
Influence over our core product roadmap
Recommended
Track 02From $5,000

Paid Pilot

Fixed-fee two-to-four-week engagement for teams that want production-grade private memory, formal SLA-lite support, deployment review, and a written success report.

Complete memory schema design & audit
Custom SDK wrapper for your agent loop
Performance & latency benchmark report
30-day production support tail
Proof, not promises

Run the recipes. Verify yourself.

We don’t lead with logos because we’re early. We lead with the recipes — installable, copy-pastable, end-to-end examples that take five minutes to run on your machine. The recipe is the demo.

FAQ

Common questions

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