ai-job-search vs LoopX: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of ai-job-search and LoopX — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
a
ai-job-search
Mads Lorentzen
Open-source AI job application framework built on Claude Code — evaluate postings, tailor CVs, write cover letters, and prep interviews on your machine.
Key features
- /scrape Workflow: Pull job postings from configured sources into a structured queue on your machine.
- /apply Workflow: Tailor your CV and generate a cover letter for a specific posting via a drafter/reviewer agent pipeline.
- /interview Workflow: Prep for interviews with role- and company-specific question generation and answer drafts.
- Local-First Execution: Runs entirely on your machine — your profile and application drafts never leave your computer.
- Profile-Driven Personalization: Fork, fill in your profile once, and every application is grounded in your real experience.
- Language & Country Agnostic: Works for job searches in any language and any local job market.
Best for
- Full-Time Job Hunt: Automate the tailored-application pipeline for dozens of postings a week.
- Career Transitions: Reframe your existing profile for a new industry by editing prompts, not rewriting every CV.
- Interview Preparation: Generate role-specific mock questions and structured answers before phone screens.
- Contractor Pipeline: Contract and freelance workers use it to keep applications flowing across multiple platforms.
- Career Coach Tooling: Coaches fork the repo to run structured application workflows for clients.
L
LoopX
huangruiteng
Provider-neutral state kernel and local-first control plane for governing long-running AI agent loops across Codex, Claude Code, Cursor, and peer teams.
Key features
- Loop-Engineering State Kernel: A compact durable-state layer that keeps objectives, gates, todos, evidence, quotas, and handoffs consistent across many bounded turns.
- Runtime-Agnostic: Governs work executed by any coding-agent runtime — Codex, Claude Code, Cursor, or your own — without replacing them.
- Peer-Agent Model: Registered agents are peers; claims, leases, capabilities, and typed continuation decide who acts next, with no durable leader identity.
- Kanban-Style Control Plane: Cards carry identity, authority, evidence, and continuation; moves are validated operators (claim, gate, monitor, writeback).
- Local-First: The control plane runs locally by default — the public/private boundary is explicit, so private data and code stay on your machine.
- Auto-Wake and Quotas: Quota-aware auto-wake keeps agents progressing on long-running goals without a runaway scheduler.
- Evidence & Continuation: 200+ hour example loops preserve decision lineage, evidence branches, and invalid experiments across turns.
- Human-In-Command: Dangerous permissions, publishing, and production writes remain gated to the human owner — not autonomous.
Best for
- Multi-Day SWE Loops: Drive week-long engineering objectives across many bounded agent turns while keeping scope and review state intact.
- PR/Issue Automation: Preserve review state, evidence, and reviewer preferences across a PR that touches multiple turns and agents.
- Auto-ML Experiments: Keep hypotheses, matched evidence, invalid lineages, and promote/stop gates visible in a single graph over hundreds of hours.
- Multi-Agent Coordination: Coordinate a peer team of Codex + Claude Code + Cursor agents on the same objective with typed handoffs.
- Recurring Monitors: Run heartbeat or monitoring loops with owner-visible gates and evidence trails.
- Creator/Research Workflows: Give non-engineering owners a legible board of long-running work with human sign-off at each gate.