agent-manager vs Zero: Features, Pricing & Which Is Better (2026)

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

agent-manager

Yoan Wai

Free

Go TUI on top of tmux that spawns, tracks, and reviews Claude Code, Codex, Cursor, and other coding agents in one keypress.

One-keypress Spawn: Hit space on a group row, type the task, press enter — a new agent session starts immediately with the prompt embedded and the correct working directory set, without any form or naming step.
Answer In Place: On a session row, the same space key sends your reply into the agent's pane as a user message, so a blocked agent never costs you a tmux attach.
Multi-CLI Support: Tab cycles which CLI the next enter launches — claude, opencode, codex, grok, gemini, or anything you configured — so different tasks pick different agents from the same bar.
Git Worktree Spawn: alt+w spawns the agent into a fresh git worktree so parallel branches don't fight over the checkout.
Hook-based Status Detection: Six statuses (working, waiting, finished, errored, idle, dead) come from Claude Code's own hook events rather than guessing from pane output.
Foldable Project Tree: Groups are paths, not folders — backend/api/auth nests as deep as the work does, folded groups keep per-status counts, and the layout persists across restarts.
Whole-file Diff Reviewer: Review each agent's diff without leaving the list; line comments become follow-up messages to that agent.
Uses Your Local CLI As-is: Every session runs your installed CLI with your login, config, and MCP servers — nothing is re-wrapped or proxied.

Parallel Feature Work: Fan out five features across five agents in seconds and monitor them all in one folded tree without switching terminals.
Reviewing Blocked Agents: Answer a permission prompt or clarifying question directly from the list without tmux-attaching, so waiting agents never idle on you.
Multi-repo Development: Keep several projects open at once, spawn each new task into the correct project directory, and never lose track of which agent is where.
Mixed CLI Workflows: Use Claude Code for one task, Codex for another, and opencode for a third — all launched from the same TUI without switching contexts.
Diff-driven Code Review: Scan a whole-file diff, drop a line comment, and it becomes the next message to the agent, closing the review-fix loop inside the TUI.
Long-running Agent Fleets: Fold what you aren't watching, archive finished sessions, and let dozens of agents run without the terminal turning into a wall of processes.

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.