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

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

Ego Lite

Citrolabs

Free

The fastest browser for AI agents to run browser automation, letting agents like Codex and Claude Code share your logged-in browser state.

Agent-Optimized Browser: A dedicated browser built for AI agents to run automation faster and more reliably than driving a general-purpose browser.
Shared Logged-In State: Agents like Codex and Claude Code use your existing authenticated sessions without prompting you to re-login inside a sandbox.
Non-Disruptive Coexistence: Runs alongside your primary browser so the agent's activity does not interfere with what you are doing.
Zero-Config Setup: Marketed as zero cost and zero configuration so developers can point an agent at Ego Lite in minutes.
Native macOS Builds: Distributed as DMGs for Apple Silicon and Intel Macs.
Open Source on GitHub: Hosted on GitHub under citrolabs/ego-lite so users can inspect and contribute.
Fast Automation Loop: Optimized for high-throughput agent-driven web tasks.
Trendshift-Recognized: Called out on Trendshift as a trending repository for agent tooling.

Claude Code Web Tasks: Let Claude Code fill forms, click buttons, and read pages on your logged-in accounts without you re-authenticating in a sandbox.
Codex Browser Automation: Give Codex a browser that already has your session cookies and 2FA state so end-to-end scripts just work.
Automated QA on Authenticated Apps: Run agent-driven regression checks against apps that require login.
Data Collection Behind Logins: Scrape and summarize dashboards or SaaS reports without re-implementing auth flows for the agent.
Personal Automation: Automate personal workflows like inbox triage or booking, using an agent that inherits your browser state.
Developer Tooling: Embed Ego Lite as the browser backend for internal agent tools that need shared session state.

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.