Nexusyn Engine MCP server
Commit 1 Nov 2026Memberikan memori jangka panjang yang persisten dan sadar waktu bagi AI agent menggunakan bi-temporal versioning dan hybrid retrieval.
Juga dikenal sebagai nexusyn/engine
- Stars
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- Tools
- 3
- Unduhan, 30 hari
- —
- Versi terbaru
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- Commit terakhir
- 1 Nov 2026
- Lisensi
- Apache-2.0
Apa itu Nexusyn Engine MCP?
Nexusyn Engine adalah MCP server. Memberikan memori jangka panjang yang persisten dan sadar waktu bagi AI agent menggunakan bi-temporal versioning dan hybrid retrieval. Server ini menyediakan 3 tool, seperti add_memory, search_memory, get_guideline.
Ringkasan dibuat dari README proyek
Tentang Nexusyn Engine MCP server
Fungsi utama dari engine ini adalah memberikan memori jangka panjang yang persisten dan sadar waktu kepada AI agent. Engine ini menyelesaikan masalah agent yang melupakan informasi atau mengambil fakta yang sudah kedaluwarsa dengan menggunakan bi-temporal versioning, yang memberikan stempel waktu transaksi dan waktu validitas pada setiap fakta. Hal ini memungkinkan informasi baru untuk menggantikan data lama secara otomatis tanpa kehilangan konteks historis.
Developer dapat mengintegrasikan ini melalui MCP server native yang berjalan di atas HTTP, sehingga kompatibel dengan client seperti Claude Code, Cursor, dan Windsurf. Sistem ini menggunakan hybrid retrieval, yang menggabungkan dense vector similarity melalui pgvector HNSW, BM25 full-text search, dan entity graph expansion. Sistem ini juga dilengkapi dengan langkah cross-encoder reranking untuk memastikan hasil dengan presisi tinggi.
User dapat men-deploy stack ini secara lokal menggunakan Docker Compose, yang sudah mencakup database PostgreSQL 18 dan background worker untuk tugas asinkron seperti entity extraction. Engine ini mendukung berbagai model provider untuk embeddings dan generation, termasuk OpenAI, Anthropic, dan Ollama. Selain itu, sistem ini mengimplementasikan row-level security untuk memastikan isolasi multi-tenant yang ketat.
Apa yang bisa Anda lakukan dengannya
- 1Mencatat perubahan keputusan proyekSeorang agent dapat menyimpan perubahan arsitektur spesifik agar tetap sadar akan standar yang berlaku saat ini.
- 2Melakukan query konteks historis dengan akuratSeorang developer dapat bertanya tentang implementasi teknis di masa lalu dan menerima jawaban terintegrasi dengan sumber yang dapat diverifikasi.
- 3Menegakkan standar organisasiAI assistant dapat mengambil panduan proyek yang wajib diikuti untuk memastikan kode mematuhi aturan tertentu.
- 4Memperbaiki informasi yang kedaluwarsaUser dapat melakukan kurasi manual atau memperbaiki catatan memori yang ada untuk mencegah halusinasi.
Ditulis berdasarkan README proyek. Setiap use case diperiksa terhadap kutipan dari README tersebut.
Tool Nexusyn Engine MCP 3
Terdeteksi dari READMEadd_memorysearch_memoryget_guideline
| add_memory | — | |
| search_memory | — | |
| get_guideline | — |
Pengecekan otomatis
Berjalan 14 Nov 2026Laporan lengkap →Belum diverifikasi. Sebuah server memerlukan skor 80, tidak ada temuan serius, dan data yang cukup untuk kami periksa. Cara kerja checks · Yang perlu diperbaiki untuk menambah poin
- Source repository terhubung dan dapat dijangkaunexusyn/engine is public and reachablegithub.com/nexusyn/engine ↗Lolos
- Nama paket mencantumkan repositori ini sebagai sumbernyaTidak diperiksa
- Provenance build (atestasi Sigstore)Tidak diperiksa
- Lisensi open source (OSI)Apache-2.0spdx.org/licenses/Apache-2.0.html ↗Lolos
- Ada commit atau release dalam satu tahun terakhirLast commit or release 2026-11-01github.com/nexusyn/engine ↗Lolos
- OpenSSF ScorecardTidak diperiksa
- Nama package tidak mirip dengan package populerTidak diperiksa
- Tidak ada advisory kritis pada paket atau dependensinyaTidak diperiksa
- Tidak ada instruksi tersembunyi untuk model3 tool descriptions and the README read; no override phrases, credential paths or hidden charactersLolos
- Tools yang bersifat destruktif telah diberi labelNo tool named like delete, drop or execLolos
- Endpoint remote merespons handshake MCPTidak diperiksa
README
Lihat di 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.
Pertanyaan tentang Nexusyn Engine MCP
Tool apa yang disediakan Nexusyn Engine MCP?
Server ini menyediakan 3 tool, termasuk add_memory, search_memory, get_guideline.
Bisakah saya menggunakan provider LLM saya sendiri dengan Nexusyn?
Ya, sistem ini menggunakan arsitektur adapter yang memungkinkan Anda menggunakan API key sendiri untuk proses generation, embeddings, dan reranking dari provider seperti OpenAI, Anthropic, dan Ollama.
Apakah mendukung multi-tenancy?
Ya, sistem ini menyediakan isolasi nyata pada layer PostgreSQL menggunakan Row-Level Security (RLS) untuk memastikan antar tenant tidak dapat melihat memori satu sama lain.