Aliu-AiRobot/ESEILANE — 136 Stars

Topics

#ai #graph-database #graph-ql #graphrag #high-performance #knowledge-graph #langchain #llm #machine-learning #openai #python #rag #rust #typescript #vector-database

Language breakdown

No language data available.

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README

ESEILANE is a next-generation, high-performance Knowledge Graph engine purpose-built for Large Language Models (LLMs), GraphRAG, and AI-native applications. Leveraging sparse matrix algebra and linear algebra-based query execution, ESEILANE delivers sub-millisecond graph traversals at scale.

Why ESEILANE?

| Feature | Description | |---|---| | Ultra-Low Latency | Sub-millisecond response times powered by GraphBLAS sparse matrix algebra | | GraphRAG Native | First-class integration with LLMs — reduce hallucinations, improve AI accuracy | | Property Graph Model | Nodes and relationships with rich attributes, full OpenCypher support | | Horizontal Scale | Pay-as-you-grow distributed architecture with zero-overhead multi-tenancy | | Enterprise Ready | Role-based access control, audit logging, encryption at rest and in transit | | Rust Core | Next-gen engine rewritten in Rust for maximum performance and memory safety |

Quick Start

Docker (Recommended)

docker run -p 6379:6379 -p 3000:3000 eseilane/eseilane:latest

Python

pip install eseilane

from eseilane import ESEILANE
db = ESEILANE(host='localhost', port=6379) g = db.select_graph('KnowledgeBase')
g.query("""   CREATE (:Entity {name:'Artificial Intelligence'})-[:RELATED_TO]->(:Domain {name:'Machine Learning'}),          (:Entity {name:'GraphRAG'})-[:ENHANCES]->(:Entity {name:'Artificial Intelligence'}) """)
results = g.query("""   MATCH (e:Entity)-[:ENHANCES]->(ai:Entity)   WHERE ai.name = 'Artificial Intelligence'   RETURN e.name, ai.name """) for row in results.result_set:     print(f"{row[0]} enhances {row[1]}")

TypeScript

npm install eseilane-js

import { ESEILANE } from 'eseilane-js';
const db = new ESEILANE({ host: 'localhost', port: 6379 }); const graph = db.selectGraph('KnowledgeBase'); const result = await graph.query('MATCH (n:Entity) RETURN n.name LIMIT 10'); console.log(result.data);

Architecture

┌─────────────────────────────────────────────────────────────┐
│                        ESEILANE Core                        │
│                                                             │
│   ┌─────────────┐   ┌──────────────┐   ┌───────────────┐   │
│   │ Query Layer  │   │ Graph Engine  │   │ Storage Layer │   │
│   │ (Cypher /   │──▶│ (GraphBLAS   │──▶│ (Sparse       │   │
│   │  REST /     │   │  + Rust)     │   │  Matrix /     │   │
│   │  GraphQL)   │   └──────────────┘   │  RDB Hybrid)  │   │
│   └─────────────┘                      └───────────────┘   │
│                                                             │
│   ┌─────────────────────────────────────────────────────┐   │
│   │            LLM Integration Layer                    │   │
│   │  GraphRAG · Embeddings · Semantic Search · RAG      │   │
│   └─────────────────────────────────────────────────────┘   │
└─────────────────────────────────────────────────────────────┘

GraphRAG Integration

from eseilane.graphrag import GraphRAGClient
client = GraphRAGClient(dbhost="localhost", llmprovider="openai", model="gpt-4o")
client.ingest("""   ESEILANE is a high-performance knowledge graph engine.   It integrates with LLMs to power GraphRAG applications.   GraphRAG reduces hallucinations by grounding responses in structured knowledge. """)
response = client.query("How does ESEILANE reduce LLM hallucinations?") print(response.answer) print(response.graph_context)

Performance Benchmarks

| Operation | ESEILANE | Neo4j | ArangoDB | |---|---|---|---| | Node Lookup (1M nodes) | 0.8ms | 4.2ms | 3.1ms | | 3-hop Traversal | 2.1ms | 12.4ms | 9.8ms | | Cypher Query (complex) | 5.3ms | 28.7ms | 21.2ms | | Bulk Insert (1M edges) | 1.2s | 8.6s | 5.4s | | GraphRAG Context Build | 120ms | 890ms | 640ms |

Benchmarks run on AWS r6i.4xlarge. Results may vary by workload.

Roadmap

[x] Core graph engine (GraphBLAS)
[x] OpenCypher query support
[x] Python & JavaScript SDKs
[x] Docker & cloud deployment
[x] Multi-tenancy & RBAC
[ ] Rust engine GA (Q3 2026)
[ ] Native vector index (Q3 2026)
[ ] GraphQL API (Q4 2026)
[ ] Managed cloud — ESEILANE Cloud (Q4 2026)
[ ] Federated graphs (2027)

Documentation

| Guide | Description | |---|---| | Getting Started | Installation, configuration, and first queries | | API Reference | Complete REST API documentation | | Python SDK | Python client library reference | | TypeScript SDK | TypeScript/JavaScript client reference | | GraphRAG Guide | Build your first GraphRAG pipeline | | Deployment | Docker, Kubernetes, and cloud deployment |