Henji vs Semantica: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of Henji and Semantica — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

Henji

Freemium

Mac app that drafts chat and email replies in your own voice across Slack, LINE, Gmail, and Messages.

Voice Matching: Learns your usual tone and phrasing over time so replies read as you-ish rather than AI-ish.
Tone Modes: Switch between Polite, Casual, Team, and Friends styles so each reply fits the relationship and channel.
Multi-Channel Coverage: Works across Slack, LINE, Gmail, and Messages so chat and email replies are handled in one place.
Scribble-to-Reply: Type a short note or intent and Henji expands it into a complete, context-aware message.
Multilingual: Supports multiple languages including English and Japanese for replies.

Faster Messaging: Knocking out quick chat and email replies during a busy day without sounding robotic.
Difficult Replies: Politely declining requests or negotiating deadlines while keeping the tone warm.
Team Communication: Keeping internal Slack threads fast and to the point with a team-appropriate tone.
Cross-Language Correspondence: Drafting replies in English or Japanese for international contacts.

semantica-agi

Free

Open-source, graph-native context and knowledge-graph infrastructure for accountable AI agents in regulated domains.

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.

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.