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Semantica

Graph-native infrastructure for context and accountable AI systems

★ 9,400 GitHub MIT knowledge-graphragllmagentssemantic-memory RAG & Knowledge

Overview

Semantica is graph-native infrastructure for building context-aware and accountable AI systems. Instead of stuffing everything into isolated vector stores, Semantica organizes knowledge as a persistent graph — entities, relationships, and provenance — so LLMs can ground answers in structured, auditable facts. It ships with a self-hosted server, a Python SDK, and a semantic memory layer designed for agents, RAG pipelines, and multi-step reasoning workflows. Key capabilities include a queryable knowledge graph with temporal facts, built-in memory for agents, retrieval that understands relationships (not just similarity), and full audit trails for every piece of context used in a generation. Everything runs in your own infrastructure via Docker, keeping your data private and your systems accountable.

Requirements

Min vCPU
2
Min RAM
4096 MB
Min Disk
10 GB
Rec vCPU
4
Rec RAM
8192 MB
Rec Disk
20 GB

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Docker Compose

# Generated by Run This Ai — docker-compose.yml
services:
  semantica:
    image: fadellvk/semantica:latest
    restart: unless-stopped
    ports:
      - 8080:8080
    volumes:
      - ./data/semantica:/data

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