archify vs Semantica: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of archify and Semantica — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
a
archify
tt-a1i
Agent skill for Claude, Codex, and opencode that turns a plain-English description into a polished, themeable architecture diagram in a single HTML file.
Key features
- Prompt-to-Diagram: Describe your system in English and get a polished technical diagram back.
- Multiple Diagram Types: Handles architecture, workflow, sequence, data-flow, lifecycle, CI/CD, and state-machine diagrams.
- Single-File HTML Output: Diagrams are self-contained HTML files you can open, share, or embed anywhere.
- Dark / Light Theme Toggle: One-click theme toggle inside the diagram, with the choice persisted across sessions.
- Multi-Format Export: Copy PNG to clipboard plus download as PNG, JPEG, WebP, or SVG at up to 4x source resolution.
- Semantic Tech Labels: Recognizes labels like aws.lambda, postgres, redis, github-actions, openai and maps them to the right visual category without a manual icon library.
- Multi-Agent Compatibility: Installs as a skill for Claude, Codex CLI, and opencode with a single command.
Best for
- System Design Sketches: Turn a rough design description into a shareable architecture diagram in minutes.
- Runbook & Incident Docs: Generate sequence and data-flow diagrams for runbooks and incident reviews on the fly.
- CI/CD Documentation: Draw pipeline and workflow diagrams from an English description of your build/deploy flow.
- Onboarding Materials: Produce lifecycle and request-chain diagrams to onboard new engineers to a service.
- Slide-Ready Visuals: Export high-resolution PNG or SVG diagrams straight from your agent for decks and blog posts.
S
Semantica
semantica-agi
Open-source, graph-native context and knowledge-graph infrastructure for accountable AI agents in regulated domains.
Key features
- Graph-Native Ingestion: Ingest enterprise data and extract entities, relationships, and provenance into a Context Graph plus a formal Knowledge Graph.
- Polyglot Graph Storage: Native support for both RDF and Labeled Property Graphs so you can pick the model that fits each domain.
- W3C Standards & Interoperability: SPARQL, OWL, and related standards keep the graph portable across tooling with zero vendor lock-in.
- Deterministic Reasoning: Rule-based and causal reasoning over the graph so agent decisions are reproducible, not black-box.
- Decision Provenance: Every decision is traceable back to the ingested evidence and the reasoning steps that produced it.
- Ontology & Knowledge Modeling: First-class tools for defining, evolving, and enforcing the domain ontology that governs agent context.
- Self-Hostable: Deploy the whole stack inside your own infrastructure — the code is MIT-licensed and open.
- Regulated-Domain Ready: Built for high-stakes, governed use cases where auditability and end-to-end traceability are mandatory.
Best for
- Auditable Agent Decisions: Build agents in finance, healthcare, or public sector where every decision must be traceable to source data.
- Enterprise Context Management: Turn scattered enterprise data into a queryable Context Graph the agent uses as its long-term memory.
- Causal Analysis: Run causal reasoning over the graph to explain outcomes, not just correlations.
- Ontology-Driven Extraction: Enforce a domain ontology so extracted entities and relationships remain consistent across sources.
- Regulated Deployment: Self-host in a compliance-bounded environment with zero third-party data egress.
- Knowledge Graph Bootstrapping: Ingest documents, databases, and events into a formal KG that agents and BI tools share.