Nexusyn Engine MCP server
Commit Nov 1, 2026Provides AI agents with persistent, time-aware long-term memory using bi-temporal versioning and hybrid retrieval.
Also known as nexusyn/engine
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- Nov 1, 2026
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- Apache-2.0
What is Nexusyn Engine MCP?
Nexusyn Engine provides AI agents with persistent, time-aware long-term memory using bi-temporal versioning and hybrid retrieval. It exposes 3 tools, such as add_memory, search_memory, get_guideline.
Summary generated from the project's README
About Nexusyn Engine MCP server
Providing AI agents with persistent, time-aware long-term memory is the primary function of this engine. It solves the problem of agents forgetting information or retrieving outdated facts by using bi-temporal versioning, which stamps every fact with transaction and valid time. This allows new information to supersede old data automatically without losing historical context.
Developers can integrate this via a native MCP server running over HTTP, making it compatible with clients like Claude Code, Cursor, and Windsurf. The system uses hybrid retrieval, combining dense vector similarity through pgvector HNSW, BM25 full-text search, and entity graph expansion. It also features a cross-encoder reranking step to ensure high-precision results.
Users can deploy the stack locally using Docker Compose, which includes a PostgreSQL 18 database and a background worker for asynchronous tasks like entity extraction. The engine supports various model providers for embeddings and generation, including OpenAI, Anthropic, and Ollama. It also implements row-level security to ensure strict multi-tenant isolation.
What you can do with it
- 1Record evolving project decisionsAn agent can store a specific architectural change so it remains aware of the current standard.
- 2Query historical context with accuracyA developer can ask about past technical implementations and receive a synthesized answer with verifiable sources.
- 3Enforce organizational standardsAn AI assistant can retrieve mandatory project guidelines to ensure code follows specific rules.
- 4Correct outdated informationUsers can manually curate or fix existing memory records to prevent hallucinations.
Written from the project's README. Every use case is checked against a quote from it.
Nexusyn Engine MCP tools 3
Detected from the READMEadd_memorysearch_memoryget_guideline
| add_memory | — | |
| search_memory | — | |
| get_guideline | — |
Automated checks
Ran Nov 14, 2026Full report →Not verified yet. A server needs a score of 80, no serious finding, and enough data for us to check. How checks work · What to fix for more points
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- Open source license (OSI)Apache-2.0spdx.org/licenses/Apache-2.0.html ↗Pass
- Commit or release in the last yearLast commit or release 2026-11-01github.com/nexusyn/engine ↗Pass
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- No critical advisory in the package or its dependenciesNot checked
- No hidden instructions to the model3 tool descriptions and the README read; no override phrases, credential paths or hidden charactersPass
- Destructive tools are labelledNo tool named like delete, drop or execPass
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README
View on GitHubNexusyn Engine
Long-Term, Time-Aware Memory Engine for AI Agents
Stop building agents that forget. Give your LLMs persistent, grounded memory across sessions.
Quickstart • MCP Integration • Architecture • Benchmarks • Cloud vs Self-Hosted • Documentation
The Problem: Why Vector Databases Alone Aren't "Memory"
Most AI agent frameworks implement memory by taking the last conversation turn, calculating an embedding, and storing it in a vector database.
When the agent queries this "memory", it runs a simple nearest-neighbor search. This breaks down in production:
- Vectors don't understand time: If a user says "I live in Berlin" in January and "I moved to Madrid" in August, both vectors have identical semantic similarity to "Where do I live?". A pure vector search frequently returns the outdated fact.
- Context window bloat: Dumping raw matching chunks into the prompt forces the LLM to resolve contradictions, inflating token costs and increasing hallucination rates.
- No entity understanding: Vector similarity misses relational facts that don't share keywords (e.g. connecting a bug report to the architecture decision that caused it).
The Solution: How Nexusyn Works
Nexusyn is a dedicated data plane engine designed specifically for agentic long-term memory:
- 🕓 Bi-Temporal Versioning: Every fact is stamped with transaction time and valid time (
valid_from/valid_to). New statements supersede older ones automatically without deleting history. - 🔀 Hybrid Retrieval (RRF): Fuses dense vector similarity (
pgvectorHNSW) + BM25 full-text search + entity graph expansion + recency & date-anchor boosts using Reciprocal Rank Fusion. - 🎯 Reranking: High-precision cross-encoder re-scoring of candidate chunks before generation.
- 🛡️ Grounded Synthesis: Returns a synthesized, ready-to-use answer that cites verifiable source chunks — so the agent receives answers, not just raw text fragments.
- ⚡ MCP-Native: First-class Model Context Protocol server (
/v1/mcp) running over streamable HTTP. Connects directly to Claude Code, Cursor, Windsurf, or custom agent frameworks with zero glue code. - 🏢 Multi-Tenant with Row-Level Security (RLS): True isolation at the PostgreSQL layer. A tenant can never see or search another tenant's memories.
- ⚙️ Async Pipeline (River): Heavy background workloads (chunking, batch embedding, entity extraction, and wiki compilation) run asynchronously on a Postgres-backed queue.
Comparison
| Capability | Raw Vector DB | Simple Buffer / Window | Nexusyn Engine |
|---|---|---|---|
| Semantic Vector Search | ✅ | ❌ | ✅ |
| BM25 Keyword Search | ⚠️ (Requires hybrid setup) | ❌ | ✅ |
| Bi-Temporal Fact Superseding | ❌ | ❌ | ✅ |
| Entity Knowledge Graph (GraphRAG) | ❌ | ❌ | ✅ |
| Grounded Answers with Sources | ❌ (Raw chunks only) | ❌ | ✅ |
| Native MCP Server | ❌ | ❌ | ✅ (/v1/mcp) |
| Database Multi-Tenancy (RLS) | ⚠️ (Manual filters) | ❌ | ✅ (Engine-enforced) |
| Token Cost Efficiency | ⚠️ (Dumps all chunks) | ❌ (Huge prompts) | ✅ (Synthesized / Grounded) |
Benchmarks
Nexusyn is evaluated against standard public benchmarks for agent long-term memory under reproducible conditions:
| Benchmark | Nexusyn Score | Metric Focus |
|---|---|---|
| LongMemEval-S | 81.1% (284/350) | Multi-session recall, temporal updates, and preference tracking across long horizons |
| LoCoMo | 74.3% (1,476/1,986) | Complex multi-turn conversational reasoning, aggregation, and contradiction resolution |
Architecture
┌─────────────────────────┐
│ AI Agent / IDE │
│ (Claude Code, Cursor, …)│
└────────────┬────────────┘
HTTP │ MCP (/v1/mcp)
▼
┌────────────────────────────────────────────────────────────────────────┐
│ NEXUSYN ENGINE │
│ │
│ POST /v1/ingest POST /v1/query │
│ │ │ │
│ ▼ ▼ │
│ [ Deduplication ] [ Sub-query Gen ] │
│ │ │ │
│ [ Chunker ] [ Hybrid Retrieval ] │
│ │ ├── Vector (HNSW) │
│ ▼ ├── BM25 Full-Text │
│ [ River Queue (Async) ] ├── Entity Graph │
│ ├── Batch Embeddings └── Date Anchor Boost │
│ ├── Entity & Relation Extraction │ │
│ └── Wiki / Profile Compilation ▼ │
│ │ [ RRF Fusion ] │
│ ▼ │ │
│ [ PostgreSQL 18 (pgvector) ] [ Cross-Encoder Rerank]│
│ Row-Level Security (Multi-Tenant) │ │
│ ▼ │
│ [ Grounded Answer ] │
│ (Synthesized + Sources)│
└────────────────────────────────────────────────────────────────────────┘
Quickstart (Docker Compose)
Get the complete stack running locally (PostgreSQL 17+ with pgvector, the Nexusyn HTTP/MCP API on :8044, and the River background worker) in under 60 seconds:
# 1. Clone the repository
git clone https://github.com/nexusyn/engine.git
cd engine
# 2. Configure environment
cp .env.example .env
# Edit .env with your preferred model provider API keys
# (OpenAI, Anthropic, Gemini, Ollama, Jina, etc.)
# 3. Spin up the containers
docker compose up -d
# 4. Verify health
curl http://localhost:8044/health
# {"status":"ok","time":"..."}
First-Class MCP Integration
Nexusyn runs an HTTP Model Context Protocol (MCP) server at /v1/mcp. Any MCP-compliant client gets persistent long-term memory tools out of the box.
1. Claude Code
Connect Nexusyn to Claude Code with a single CLI command:
claude mcp add nexusyn --transport http \
"http://localhost:8044/v1/mcp" \
--header "Authorization: Bearer <YOUR_API_TOKEN>"
2. Cursor (~/.cursor/mcp.json)
Add Nexusyn to your Cursor global or project configuration:
{
"mcpServers": {
"nexusyn": {
"url": "http://localhost:8044/v1/mcp",
"headers": {
"Authorization": "Bearer <YOUR_API_TOKEN>"
}
}
}
}
3. Windsurf (~/.codeium/windsurf/mcp_config.json)
{
"mcpServers": {
"nexusyn": {
"serverUrl": "http://localhost:8044/v1/mcp",
"headers": {
"Authorization": "Bearer <YOUR_API_TOKEN>"
}
}
}
}
Available MCP Tools
add_memory— Record a decision, convention, lesson, or fact tagged byprojectandagent.search_memory— Query memories using hybrid search, returning grounded answers with sources.get_guideline— Retrieve organization/project-wide mandatory standards.update_memory/delete_memory— In-place curation and correction of existing records.
REST API Usage
Two endpoints cover 90% of all integration needs:
1. Store a Memory (POST /v1/ingest)
curl -X POST http://localhost:8044/v1/ingest \
-H "Authorization: Bearer $TOKEN" \
-H "Content-Type: application/json" \
-d '{
"title": "Auth Architecture",
"content": "We migrated from session cookies to stateless JWT with Ed25519 token signing on 2026-08-15.",
"agent": "cursor",
"project": "mobile-app"
}'
Response:
{
"job_id": 482,
"status": "queued"
}
2. Query Grounded Memory (POST /v1/query)
curl -X POST http://localhost:8044/v1/query \
-H "Authorization: Bearer $TOKEN" \
-H "Content-Type: application/json" \
-d '{
"question": "What token signing algorithm do we use for authentication?",
"project": "mobile-app"
}'
Response:
{
"question": "What token signing algorithm do we use for authentication?",
"answer": "The authentication system uses stateless JWTs signed with the Ed25519 algorithm, migrated on August 15, 2026.",
"sources": [
{
"id": "chk_9a8b7c",
"title": "Auth Architecture",
"content": "We migrated from session cookies to stateless JWT with Ed25519 token signing on 2026-08-15.",
"score": 0.942
}
],
"usage": {
"model": "gpt-4o-mini",
"prompt_tokens": 312,
"completion_tokens": 38
}
}
Streaming: Real-time token streaming with Server-Sent Events (SSE) is available at
POST /v1/query/stream.
Pluggable Providers
Nexusyn uses an adapter architecture. Bring your own models for generation, embeddings, and reranking:
| Component | Supported Adapters |
|---|---|
| Generation / Answers | OpenAI, Anthropic Claude, Google Gemini, MiniMax, Ollama (local), OpenRouter, Voyage |
| Fact & Entity Extraction | OpenAI, Anthropic Claude, MiniMax, Gemini, Ollama |
| Embeddings | OpenAI (text-embedding-3-*), Jina (jina-embeddings-v5), Voyage AI, Ollama |
| Reranking | Jina Reranker v3, Cohere Rerank, Local Cross-Encoder |
Self-Hosted vs. Nexusyn Cloud
| Feature | Open-Source Engine | Nexusyn Cloud |
|---|---|---|
| License | Apache 2.0 | Hosted SaaS |
| Deployment | Self-hosted (Docker, Kubernetes, VPS) | Fully Managed |
| API & MCP Server | ✅ Full feature set | ✅ Global edge latency |
| Model Providers | Bring Your Own Keys (BYOK) | Pre-configured & Optimized |
| Web UI & Dashboard | Local CLI / API | Modern Web Console |
| 3D Graph Visualization | ❌ | ✅ Interactive 3D Explorer |
| Team Management & Billing | ❌ | ✅ Organization & Workspace controls |
| Maintenance & Scaling | Self-managed | 99.9% Uptime SLA & Auto-backups |
| Pricing | Free forever | Free tier available |
Development & Testing
# Build binary
go build -v ./...
# Run unit tests
go test -v ./internal/core/... ./internal/auth/... ./internal/dateutil/...
# Run security checks
gitleaks detect --source . --no-git
govulncheck ./...
Community & Security
- Bug Reports & Feature Requests: Please open an issue on GitHub Issues.
- Security Vulnerabilities: Review our Security Policy and report privately to security@nexusyn.ai.
- Website: https://nexusyn.ai
- Documentation: https://nexusyn.ai/docs
License & Trademark
- Source Code: Released under the Apache License, Version 2.0.
- Trademark: "Nexusyn" and associated marks are trademarks of RedFoxCode.
Nexusyn Engine MCP questions
What tools does Nexusyn Engine MCP provide?
It exposes 3 tools, including add_memory, search_memory, get_guideline.
Can I use my own LLM providers with Nexusyn?
Yes, it uses an adapter architecture that allows you to bring your own keys for generation, embeddings, and reranking from providers like OpenAI, Anthropic, and Ollama.
Does it support multi-tenancy?
Yes, it provides true isolation at the PostgreSQL layer using Row-Level Security (RLS) to ensure tenants cannot see each other's memories.