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

A side-by-side comparison of Henji and Zero — 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.

Vercel Labs

Free

An experimental graph-first programming language where agents edit a compiler-checked program graph instead of raw source text.

Graph as the Program: A compiler-owned semantic graph of symbols, calls, types, effects and node IDs is the source of truth, so agents reason over program structure rather than parsing and regenerating text.
Hash-Guarded Patches: Every edit carries an expected graph hash and expected field values, so a stale or conflicting patch is rejected before it reaches the store instead of silently corrupting the program.
Compiler in the Loop: Shape, type, stale-state and repository metadata checks run as part of applying a patch, collapsing the write-build-test-inspect cycle into a single checked operation.
Readable Text Projections: The graph renders to reviewable .0 source projections so humans can read diffs, audit what an agent changed and make rare manual edits.
Structured JSON Diagnostics: The compiler emits machine-readable diagnostics rather than prose error text, so agents can act on failures without parsing terminal output.
Explicit Effects via World: Side effects are passed through an explicit World capability parameter, making what a function can touch visible in its signature.
Runtime Constraints by Design: Targets token efficiency, low memory, fast startup, fast builds, low latency and zero dependencies rather than relaxing systems goals for agent ergonomics.
Query and Patch CLI: zero init, zero query, zero patch and zero run give agents a direct command surface over the graph, with agent skills carrying the graph discipline instead of rigid human prompts.

Reliable Agent Code Edits: Let a coding agent make semantic changes that are rejected outright if its view of the program is stale, instead of producing plausible-looking but broken text diffs.
Reducing Agent Token Spend: Query the specific symbols, types and nodes relevant to a task rather than feeding whole files into context on every turn.
Outcome-Driven Development: Describe a desired result in conversation — add auth, fix a failing route, build a CRM API — and review the resulting projection rather than writing the code.
Auditable AI-Written Code: Review what changed through readable .0 projections and graph hashes, keeping a human checkpoint over agent-authored programs.
Language and Tooling Research: Explore what a compiler and program representation look like when machine editors, not human typists, are the primary writers.
Sandboxed Experimentation: Prototype agent-driven codebases in an isolated environment where breaking changes and pre-1.0 churn are acceptable.